CN118596711B

CN118596711B - Consumable monitoring method, device, equipment and storage medium of printing equipment

Info

Publication number: CN118596711B
Application number: CN202410901576.9A
Authority: CN
Inventors: 叶宝兵; 袁姗
Original assignee: Shenzhen Senqi Printing Co ltd
Current assignee: Shenzhen Senqi Printing Co ltd
Priority date: 2024-07-05
Filing date: 2024-07-05
Publication date: 2025-07-15
Anticipated expiration: 2044-07-05
Also published as: CN118596711A

Abstract

The present invention relates to the field of data analysis technology, and discloses a consumables monitoring method, device, equipment and storage medium for printing equipment, which are used to improve the consumables monitoring accuracy of printing equipment. The method includes: dividing communication data to obtain multiple sub-data, implicitly encoding data to obtain multiple encoded data; identifying data signal interference to obtain interference signal data, cleaning data to obtain a target data set and performing abnormal state analysis to obtain a state analysis result, performing a first vector conversion on the state analysis result to obtain a first state vector; performing a second vector conversion on the consumables monitoring data to obtain a second state vector, performing vector fusion on the first state vector and the second state vector to obtain a target fusion vector, performing data format conversion to obtain target feature data, identifying the device and consumables status on the target feature data to obtain the target device and consumables status, and sending the target device and consumables status to a remote monitoring terminal.

Description

Consumable monitoring method, device, equipment and storage medium of printing equipment

Technical Field

The present invention relates to the field of data analysis technologies, and in particular, to a method, an apparatus, a device, and a storage medium for monitoring consumable materials of a printing apparatus.

Background

In modern industrial and commercial environments, printing equipment is an indispensable tool and is widely used in various printing tasks including book publishing, advertisement making, packaging printing, etc. To ensure efficient operation and print quality of printing equipment, the management of consumables (e.g., ink, paper, printheads, etc.) is critical. The traditional consumable monitoring method is usually a method based on schedule maintenance or experience, lacks of real-time performance and accuracy, and is easy to cause resource waste and production interruption. With the development of internet of things (IoT) technology, the degree of intellectualization of printing devices is continuously improved, and the devices can monitor various parameters including consumption conditions of consumables in real time. Accordingly, researchers have begun exploring data-based consumable monitoring methods to better manage and maintain printing equipment.

Existing data monitoring methods still face some challenges. For example, communication data transmitted from a printing apparatus contains a large amount of information, and an effective data processing and analysis method is required to extract useful information. That is, the accuracy of the existing scheme is low.

Disclosure of Invention

The invention provides a consumable monitoring method, device and equipment of printing equipment and a storage medium, which are used for improving consumable monitoring accuracy of the printing equipment.

The first aspect of the invention provides a consumable monitoring method of printing equipment, which comprises the following steps:

Constructing a communication network of preset target printing equipment to obtain a target communication network, accessing the target printing equipment into the target communication network, and acquiring data through consumable monitoring sensors in the target printing equipment to obtain consumable monitoring data corresponding to the target printing equipment;

collecting communication data sent by the target printing equipment through a preset signal collecting device, and carrying out signal band identification on the communication data to obtain a signal band corresponding to the communication data;

Carrying out data division on the communication data through the signal wave band to obtain a plurality of sub data, and carrying out data coding segment mapping on each sub data to obtain a data coding segment corresponding to each sub data;

Respectively carrying out data implicit coding on each sub data through a data coding section corresponding to each sub data to obtain a plurality of coded data;

Carrying out data signal interference identification on each coded data to obtain corresponding interference signal data, and carrying out data cleaning on each coded data through the interference signal data to obtain a target data set;

Inputting the target data set into a preset abnormal communication identification model to perform abnormal state analysis to obtain a state analysis result, and performing first vector conversion on the state analysis result to obtain a first state vector;

And performing second vector conversion on the consumable monitoring data to obtain a second state vector, and simultaneously, performing vector fusion on the first state vector and the second state vector to obtain a target fusion vector, performing data format conversion to obtain target feature data, performing equipment and consumable state identification on the target feature data to obtain target equipment and consumable states, and transmitting the target equipment and consumable states to a preset remote monitoring terminal.

With reference to the first aspect, in a first implementation manner of the first aspect of the present invention, the collecting, by a preset signal collecting device, communication data sent by the target printing device, and performing signal band identification on the communication data, to obtain a signal band corresponding to the communication data, includes:

Analyzing data interface parameters of the signal acquisition device to obtain first interface parameters corresponding to the signal acquisition device;

analyzing the data interface parameters of the target printing equipment to obtain second interface parameters corresponding to the target printing equipment;

Performing data transmission parameter matching on the first interface parameter and the second interface parameter to obtain a corresponding adaptive interface parameter;

Carrying out parameter correction on the first interface parameter and the second interface parameter based on the adaptive interface parameter to obtain a first target parameter corresponding to the first interface parameter and a second target parameter corresponding to the second interface parameter;

Carrying out interface parameter correction on the signal acquisition device through the first target parameter, carrying out interface parameter correction on the target printing equipment through the second target parameter, and acquiring communication data sent by the target printing equipment through the signal acquisition device;

and carrying out signal wave band identification on the communication data to obtain a signal wave band corresponding to the communication data.

With reference to the first implementation manner of the first aspect, in a second implementation manner of the first aspect of the present invention, the performing signal band identification on the communication data to obtain a signal band corresponding to the communication data includes:

Performing frequency domain conversion on the communication data to obtain a corresponding frequency domain data set;

Carrying out data frequency calculation on the frequency domain data set through a Fourier transform algorithm to obtain corresponding frequency data;

Based on the frequency data, extracting frequency spectrum characteristics of the frequency domain data to obtain a frequency spectrum characteristic set corresponding to the communication data, wherein the frequency spectrum characteristic set comprises modulation depth data and frequency offset data;

constructing a standard wave band data table based on the frequency data to obtain a corresponding standard wave band data table;

and carrying out standard wave band matching on the frequency spectrum characteristic set through the standard wave band data table to obtain a signal wave band corresponding to the communication data.

With reference to the second implementation manner of the first aspect, in a third implementation manner of the first aspect of the present invention, the performing data division on the communication data through the signal band to obtain a plurality of sub-data, and performing data coding segment mapping on each sub-data to obtain a data coding segment corresponding to each sub-data includes:

performing band type identification on the signal band to obtain a band type corresponding to the signal band;

dividing the wave band type into time window construction to obtain a plurality of different time window data;

Performing first data division on the communication data through a plurality of different time window data to obtain a plurality of candidate division data;

Frequency component identification is carried out on the frequency data to obtain at least one frequency component data;

Performing second data division on the plurality of candidate division data through at least one piece of frequency component data to obtain a plurality of sub-data;

and mapping the data coding segments of each piece of sub data to obtain the data coding segments corresponding to each piece of sub data.

With reference to the third implementation manner of the first aspect, in a fourth implementation manner of the first aspect of the present invention, the mapping the data coding segment of each sub-data to obtain a data coding segment corresponding to each sub-data includes:

dividing the data blocks of each sub data to obtain a data block set corresponding to each sub data;

Respectively carrying out data multi-layer fusion on the data block set corresponding to each sub data to obtain multi-layer fusion data corresponding to each sub data;

And mapping the data coding segments through the multi-layer fusion data corresponding to each sub data to obtain the data coding segments corresponding to each sub data.

With reference to the first aspect, in a fifth implementation manner of the first aspect of the present invention, the performing, by using a data coding segment corresponding to each sub-data, data implicit coding on each sub-data to obtain a plurality of coded data includes:

respectively carrying out data desensitization processing on each sub data through a data coding section corresponding to each sub data to obtain a plurality of desensitized data;

analyzing the data cut-off positions of each desensitized data to obtain a plurality of data cut-off positions;

carrying out data value truncation on each desensitization data through a plurality of data truncation positions to obtain a plurality of truncated data;

creating a data blurring mapping table, and carrying out data blurring processing on a plurality of truncated data according to the data blurring mapping table to obtain a plurality of encoded data.

With reference to the first aspect, in a sixth implementation manner of the first aspect of the present invention, the performing a second vector conversion on the consumable monitoring data to obtain a second state vector, and simultaneously performing vector fusion on the first state vector and the second state vector to obtain a target fusion vector and performing data format conversion to obtain target feature data, performing device and consumable state identification on the target feature data to obtain a target device and consumable state, and sending the target device and consumable state to a preset remote monitoring terminal, where the method includes:

Carrying out data weighted fusion on the consumable monitoring data to obtain the second state vector;

Carrying out semantic information analysis on the first state vector to obtain corresponding first semantic information;

Carrying out semantic information analysis on the second state vector to obtain corresponding second semantic information;

Performing first weight calculation on the first state vector through the first semantic information to obtain first weight data;

performing second weight calculation on the second state vector through the second semantic information to obtain second weight data;

vector fusion is carried out on the first state vector and the second state vector based on the first weight data and the second weight data, and the target fusion vector is obtained;

Based on a preset vector feature mapping table, vector feature mapping is carried out on the target fusion vector to obtain the target feature data;

And carrying out state relation matching on the target feature vector based on a preset feature state relation library to obtain corresponding target equipment and consumable states, and sending the target equipment and consumable states to the remote monitoring terminal.

A second aspect of the present invention provides a consumable monitoring device for a printing apparatus, the consumable monitoring device for a printing apparatus comprising:

the construction module is used for constructing a communication network of preset target printing equipment to obtain a target communication network, accessing the target printing equipment into the target communication network, and acquiring data through a consumable monitoring sensor in the target printing equipment to obtain consumable monitoring data corresponding to the target printing equipment;

The identification module is used for acquiring communication data sent by the target printing equipment through a preset signal acquisition device, and carrying out signal wave band identification on the communication data to obtain a signal wave band corresponding to the communication data;

The division module is used for carrying out data division on the communication data through the signal wave band to obtain a plurality of sub-data, and carrying out data coding segment mapping on each sub-data to obtain a data coding segment corresponding to each sub-data;

The coding module is used for respectively carrying out data implicit coding on each sub data through a data coding section corresponding to each sub data to obtain a plurality of coded data;

the cleaning module is used for carrying out data signal interference identification on each piece of encoded data to obtain corresponding interference signal data, and carrying out data cleaning on each piece of encoded data through the interference signal data to obtain a target data set;

the analysis module is used for inputting the target data set into a preset abnormal communication identification model to perform abnormal state analysis to obtain a state analysis result, and performing first vector conversion on the state analysis result to obtain a first state vector;

The conversion module is used for carrying out second vector conversion on the consumable monitoring data to obtain a second state vector, carrying out vector fusion on the first state vector and the second state vector to obtain a target fusion vector, carrying out data format conversion to obtain target feature data, carrying out equipment and consumable state identification on the target feature data to obtain target equipment and consumable states, and sending the target equipment and consumable states to a preset remote monitoring terminal.

The third aspect of the invention provides a consumable monitoring device of a printing device, which comprises a memory and at least one processor, wherein the memory stores instructions, and the at least one processor calls the instructions in the memory so that the consumable monitoring device of the printing device can execute the consumable monitoring method of the printing device.

A fourth aspect of the present invention provides a computer-readable storage medium having instructions stored therein that, when run on a computer, cause the computer to perform the above-described consumable monitoring method of a printing apparatus.

According to the technical scheme, communication data are subjected to data division to obtain a plurality of sub-data, data implicit coding is conducted to obtain a plurality of coded data, data signal interference identification is conducted to obtain interference signal data, data cleaning is conducted to obtain a target data set, abnormal state analysis is conducted to obtain a state analysis result, first vector conversion is conducted on the state analysis result to obtain a first state vector, second vector conversion is conducted on consumable monitoring data to obtain a second state vector, vector fusion is conducted on the first state vector and the second state vector to obtain a target fusion vector, data format conversion is conducted on the target fusion vector to obtain target feature data, equipment and consumable state identification is conducted on the target feature data to obtain target equipment and consumable state, and the target equipment and consumable state is sent to a remote monitoring terminal. According to the invention, the consumable state and performance of the printing equipment are monitored in real time, and any potential problem or abnormal situation can be immediately detected through the acquisition and analysis of the sensor data and the communication data. Through the accurate monitoring to the consumable consumption condition, can carry out predictive maintenance, discern in advance and solve the problem that leads to equipment trouble or shut down to reduce production interruption and cost of maintenance. Is beneficial to optimizing the use of consumable materials and avoiding unnecessary waste. Through real-time data, a production manager can better plan the supplement and replacement of consumable materials, and effective utilization of resources is ensured. The data-based method provides more accurate consumable consumption information, and can more accurately predict the service life and replacement time of consumable materials compared with the traditional schedule maintenance method, so that the equipment performance is improved. The monitoring and maintenance process is more automated and intelligent by utilizing data analysis and machine learning techniques. By accurately monitoring the equipment status and consumable consumption, the efficiency of the production process can be significantly improved. This helps reduce production time, improves yield to reduce manufacturing cost, and then improve printing equipment's consumable monitoring accuracy and efficiency.

Drawings

FIG. 1 is a schematic diagram of an embodiment of a consumable monitoring method for a printing apparatus according to an embodiment of the present invention;

FIG. 2 is a flow chart of signal band identification for communication data in an embodiment of the invention;

FIG. 3 is a flow chart of data division of communication data through signal bands in an embodiment of the present invention;

FIG. 4 is a flowchart of mapping data encoding segments for each sub-data according to an embodiment of the present invention;

FIG. 5 is a schematic view of an embodiment of a consumable monitoring device for a printing apparatus according to an embodiment of the present invention;

fig. 6 is a schematic diagram of an embodiment of a consumable monitoring device for a printing apparatus according to an embodiment of the present invention.

Detailed Description

The embodiment of the invention provides a consumable monitoring method, device and equipment of printing equipment and a storage medium, which are used for improving the consumable monitoring accuracy of the printing equipment.

The terms "first," "second," "third," "fourth" and the like in the description and in the claims and in the above drawings, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments described herein may be implemented in other sequences than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, article, or apparatus.

For ease of understanding, a specific flow of an embodiment of the present invention is described below with reference to fig. 1, and an embodiment of a consumable monitoring method for a printing apparatus according to an embodiment of the present invention includes:

S101, constructing a communication network of preset target printing equipment to obtain a target communication network, accessing the target printing equipment into the target communication network, and acquiring data through consumable monitoring sensors in the target printing equipment to obtain consumable monitoring data corresponding to the target printing equipment;

It can be understood that the execution body of the present invention may be a consumable monitoring device of a printing apparatus, and may also be a terminal or a server, which is not limited herein. The embodiment of the invention is described by taking a server as an execution main body as an example.

Specifically, the target printing devices are selected, and the devices should be provided with communication capability. Preset settings are made for these devices to ensure that they are compatible with the communication network. The topology of the communication network is determined, including the connection between the devices, the routing and the network topology. This helps to ensure efficient communication between the devices. An appropriate communication protocol is selected to ensure the security and reliability of data transmission. For example, the communication data may be encrypted using TLS/SSL or the like protocols, preventing data leakage and interference. Communication hardware, such as sensors, communication modules or network interfaces, are installed on the printing device so that the device can connect to a communication network. The device is configured with a network, including assigning IP addresses, setting network parameters, and the like. Connecting the device to the target communication network ensures that the device can join the network successfully. Once the target communication network is established and the device is successfully accessed, data acquisition is next performed by consumable monitoring sensors within the device. These sensors can monitor various parameters of the printing apparatus including consumable consumption, temperature, humidity, speed, etc. For example, consider a printing plant that uses multiple digital printers to produce various printed matter. Each printer is equipped with consumable monitoring sensors for monitoring ink and paper consumption. These sensors are connected to the central data management system of the plant via a target communication network. The network interface of the printer is configured to connect to a communication network of the factory. This involves assigning each printer a unique IP address and port number. Consumable monitoring sensors within the printer begin to monitor ink and paper consumption in real time. These sensors can measure parameters such as the weight of the ink bucket, the diameter of the paper roll, etc. The sensor converts the monitored data into a digital format and transmits the digital format to a central data management system via a communication network. The data may be a consumption record of the time stamp. The central data management system receives and stores the sensor data while analyzing the data.

S102, collecting communication data sent by target printing equipment through a preset signal collecting device, and carrying out signal band identification on the communication data to obtain a signal band corresponding to the communication data;

Specifically, a preset signal acquisition device is used for collecting communication data sent by the target printing equipment. The communication data contains information such as equipment status, consumable consumption, etc. And carrying out signal band identification on the communication data to determine the specific signal band to which the data belong. And analyzing the data interface parameters of the signal acquisition device to acquire the first interface parameters of the device. These parameters include communication rate, data format, communication protocol, etc. This is to ensure a matching of the data transmission parameters between the acquisition device and the printing apparatus. Meanwhile, data interface parameter analysis is performed on the target printing device to obtain second interface parameters of the device, wherein the parameters also comprise communication speed, data format, communication protocol and the like. This step is to learn about the communication characteristics of the device itself. And matching the first interface parameter of the signal acquisition device with the second interface parameter of the target printing equipment, and ensuring the compatibility of data transmission parameters between the first interface parameter and the second interface parameter. And respectively carrying out parameter correction on the first interface parameter and the second interface parameter based on the adaptive interface parameter. This involves adjusting the communication rate, data format, or other communication parameters to ensure accurate transmission of the data. And carrying out signal band identification on the communication data again by using the corrected interface parameters. This time, the identification of the signal band will be more accurate, as the interface parameters have been adjusted according to the target device. For example, assume that a printing company has multiple digital printers, each with a different communication protocol and data interface. The server monitors the ink consumption of each printer. The server is provided with a special signal acquisition device connected to each printer. The device periodically collects communication data sent by the printer, including ink consumption reports. And analyzing the data interface parameters of the signal acquisition device, and determining the first interface parameters of the signal acquisition device, including the communication rate and the data format. A data interface parameter analysis is performed for each printer to obtain its second interface parameter, which varies with the equipment model. By matching the first interface parameter and the second interface parameter, the compatibility of data transmission parameters between the signal acquisition device and the printer is ensured. And adjusting the first interface parameter and the second interface parameter according to the adaptive interface parameter. For example, if a printer requires a lower communication rate, the interface parameters will be adjusted accordingly. And carrying out signal band identification on the communication data again by using the corrected interface parameters. This may help the server accurately identify the signal band of the communication data, such as a particular signal band of the ink consumption report.

Wherein the collected communication data is frequency-domain converted to convert the time-domain data into frequency-domain data. This process typically involves the use of fourier transforms or similar techniques to analyze the components of the data at different frequencies. The frequency domain data set is processed by a fourier transform algorithm to calculate the frequency content of the data. This step helps to determine the different frequency components in the communication data. And carrying out frequency spectrum characteristic extraction based on the frequency data. This includes extracting spectral information about the signal from the frequency domain data, such as modulation depth data and frequency offset data. And constructing a standard band data table based on the frequency data. This table lists the frequency ranges and characteristics of the various standard signal bands. The standard band data table is used to map communication data to a specific signal band. And carrying out standard band matching on the frequency spectrum characteristic set by using a standard band data table so as to determine the signal band to which the communication data belong. This step compares the frequency and characteristics of the signal band to those of the standard band for matching. For example, assume that a printing company performs a print job using a digital printer. The server monitors the ink consumption of each printer and automatically orders more ink when needed. The signal acquisition device of the server collects communication data sent by the printer, including ink consumption reports. These data are first subjected to frequency domain conversion to convert time domain data into frequency domain data. The frequency domain data is analyzed by a fourier transform algorithm to calculate the frequency components of the data. This step helps to determine the frequency components of the communication data that are related to ink consumption. Based on the frequency data, the server extracts spectral features from the frequency domain data. This includes modulation depth data (describing the change in intensity of the signal) and frequency offset data (describing the offset of the signal frequency). The server builds a standard band data table listing the frequency ranges and characteristics of standard signal bands associated with ink consumption. Using the standard band data table, the server performs standard band matching on the spectral feature set collected from the printer. This step helps the server determine the signal band to which the communication data belongs, and thus knows the ink consumption.

S103, carrying out data division on communication data through a signal wave band to obtain a plurality of sub-data, and carrying out data coding segment mapping on each sub-data to obtain a data coding segment corresponding to each sub-data;

Specifically, the signal band in the collected communication data is identified. This step helps to determine the communication characteristics in the different bands so that the data can be better divided and understood. Based on the band type, the time window is divided for better analysis of the data. Different band types require different time windows to capture their characteristics. And carrying out first division on the communication data by using a plurality of different time window data to obtain a plurality of candidate division data. This step helps to divide the communication data into smaller portions for further processing. Frequency components in the communication data are identified. This may help determine frequency characteristics of the different portions, which are associated with different data encoding segments. The plurality of candidate division data is divided a second time using the at least one frequency component data. This step helps to divide the communication data more precisely and into smaller sub-data. And mapping the data coding segment of each piece of sub data to obtain the data coding segment corresponding to each piece of sub data. This step may map the communication data to the corresponding encoded segments based on its characteristics. For example, assume that a printing company performs a print job using digital printers, and wishes to monitor the print head status of each printer. The printhead status includes the ink ejection frequency, which directly affects print quality. The signal acquisition device of the server collects communication data sent by the printer, including a printhead status report. By identifying characteristics of different bands, such as different ink ejection frequencies, the communication data may be divided into different band types. Different band types require different time windows to capture their characteristics. For example, a shorter time window is required for high frequency ink ejection. And carrying out first division on the communication data by using different time window data to obtain a plurality of candidate division data. Each candidate partition data corresponds to a printhead state over a different time period. Frequency components in each candidate division data are identified to determine ink ejection frequency characteristics therein. The candidate division data is divided a second time using the identified frequency component data. This may help more accurately capture printhead status information related to different ink ejection frequencies. Each sub data is mapped to a corresponding data code segment. This step helps to organize and manage the printhead status information for subsequent analysis and monitoring.

Wherein each sub-data is divided into data blocks, which are divided into smaller sets of data blocks. This process helps to better manage and process large amounts of data making it easier to analyze and store. And carrying out data multi-layer fusion on the data block set corresponding to each sub data. The data in the set of data blocks is fused multiple times to generate a higher level data representation. This helps extract more information and features of the data. And mapping the data coding segments to corresponding data coding segments by mapping the multi-layer fusion data corresponding to each sub data. This step helps to sort and manage the data and associate it with a particular encoded segment. For example, assume a printing company performs a print job using digital printers and monitors the status of the printheads of each printer, including the ink ejection frequency. The data block division is performed for each sub-data (the print head status report of each printer). This may divide different time periods or specific events in the report into smaller data blocks, such as data blocks per hour or per ink ejection event. And carrying out data multi-layer fusion on each data block set. The data within each data block is fused to generate a higher level data representation, such as an average ink ejection frequency per hour. The fused data of the different data blocks are fused again to obtain a data representation over a longer time period, such as an average ink ejection frequency per day. And mapping the data coding segments to corresponding data coding segments by mapping the multi-layer fusion data corresponding to each sub data. This helps to organize and manage the data to associate with a particular code segment (e.g., a unique identification for each printer).

S104, respectively carrying out data implicit coding on each piece of sub data through a data coding section corresponding to each piece of sub data to obtain a plurality of coded data;

Specifically, each sub data is subjected to data implicit coding according to the corresponding data coding section. Implicit coding of data is a technique to convert raw data into another form in order to preserve key features while protecting the privacy of the data. This may be accomplished by encryption, hashing, or other encoding methods. And simultaneously, carrying out data desensitization processing on each piece of sub data. Data desensitization is a data protection technique that reduces the risk of data leakage by eliminating or replacing sensitive information. A data cut-off position analysis was performed for each desensitized data. This step helps determine where to truncate or cut the data for further processing. The truncation position is typically determined based on a particular rule or algorithm of the data. And according to the plurality of data truncation positions, performing data value truncation on each desensitization data. Some parts of the data are deleted or replaced with other values. And creating a data blurring mapping table which comprises data truncation positions and corresponding blurring rules. This mapping table is used to record the data truncation position and truncation rules so that the encoded data may be subsequently restored or processed. And carrying out data blurring processing on the plurality of truncated data according to the data blurring mapping table. This step may be a reverse operation, restoring truncated data to partially obscured or fully desensitized data according to a mapping table. For example, a printing company uses printing equipment to monitor consumable consumption of each printer, including ink consumption data. The server needs to transmit these data to the remote monitoring terminal for analysis, but it is not desirable to reveal specific data for each printer to protect its privacy. The ink consumption data of each printer is implicitly encoded, for example, the consumption is encrypted into a specific encoded form. Meanwhile, the ink consumption data is subjected to data desensitization processing, for example, a specific printer model and serial number are replaced with an anonymous number. Based on the characteristics of the desensitized data, the data truncation position is determined, e.g., where to truncate the fractional part of the consumption. The fractional part of the consumption data is truncated according to the truncation position to reserve the integer part, and the fractional part is replaced with "X". And creating a data blurring mapping table, and recording the data truncation position and the truncation rule. For example, the mapping table may indicate that it is truncated at the third decimal place and replaced with an "X". And restoring the truncated data into partially blurred data according to the data blurring mapping table so as to analyze on the remote monitoring terminal.

S105, carrying out data signal interference identification on each piece of encoded data to obtain corresponding interference signal data, and carrying out data cleaning on each piece of encoded data through the interference signal data to obtain a target data set;

Specifically, data signal interference identification is performed for each encoded data. In the data preprocessing stage, the server processes the encoded data transmitted from the printing device, removing the noise and outliers present. Next, an interference detection algorithm, such as frequency domain analysis, is employed to detect potential signal interference. This step helps identify problems in the data. Interference features are then extracted, including frequency, amplitude, time domain features, etc. of the interference. These characteristic information will be used for further interference recognition. Using a machine learning algorithm or rules engine, the server can categorize or flag the identified interference. Subsequently, interference signal data is acquired. This stage involves intercepting the data segments containing the interference to form interference signal data. At the same time, detailed information about the disturbance is recorded, including the type, time of day, duration, etc. of the disturbance for subsequent analysis and data cleaning. Each coded data is data cleaned by the interference signal data. By using the interference signal data, the server removes or corrects the portion of the encoded data that is subject to interference. This may be accomplished by interpolation, substitution or deletion of corrupted data, etc. In addition, the server may also attempt to recover the original data from the interfering signal data, using a difference, interpolation or reconstruction algorithm, selecting an appropriate method depending on the type and extent of the interference. Finally, the cleaned data is verified, and the integrity and the accuracy of the data are ensured. This may be done by data checksum, CRC (cyclic redundancy check) or like techniques to check the consistency of the data. Through the complete process, the server can cope with different types of signal interference while ensuring the accuracy of data, and ensure that a monitoring system obtains reliable consumable monitoring data.

S106, inputting the target data set into a preset abnormal communication identification model for abnormal state analysis to obtain a state analysis result, and performing first vector conversion on the state analysis result to obtain a first state vector;

specifically, the cleaned and processed target data set is ready for input. This data set contains monitoring data collected from the printing apparatus including information on consumable consumption, equipment performance parameters, etc. An abnormal communication recognition model capable of automatically detecting an abnormal communication mode of the apparatus is developed and trained in advance. This model may be built based on techniques such as machine learning, deep learning, or rule engines. The target data set is input into an abnormal communication identification model for analysis. The model evaluates the data to detect any abnormal communication patterns or abnormal events. The model identifies the abnormal situation by comparison with known normal communication patterns. Such abnormal conditions include equipment failure, consumable consumption abnormality, performance degradation, etc. The model outputs a state analysis result including detailed information of the type of abnormality, the time stamp of the abnormality, the cause of the abnormality, and the like. These results may help operators better understand the operating conditions of the equipment. The results of the state analysis are encoded into digital or symbolic form for further processing. The code may be a discrete label, for example, "1" indicates that the device is normal, "2" indicates that the consumable is consuming abnormally, "3" indicates that the performance is degraded, etc. Based on the encoding result, a first state vector is generated. This vector is a data structure containing state information for subsequent analysis and decision making. For example, assume a printing company uses a printing device monitoring system to track the operation of multiple printers. The system collects consumable consumption data and performance parameters of each printer and inputs the consumable consumption data and the performance parameters as target data sets into a preset abnormal communication identification model. The system organizes consumable consumption and performance parameters for each printer into a data set including number of printed pages per hour, ink consumption, paper consumption, etc. The server has developed an abnormal communication recognition model in previous studies, which is trained to recognize various abnormal modes of device communication, such as network disconnection, packet loss, etc. The data set is input into a model that analyzes the communication mode of each printer. For example, it detects that communication of a certain printer is frequently interrupted for a certain period of time, and then marks this as abnormal. The model may also analyze consumable data and if the ink consumption of a printer is abnormally high, it will also be identified as abnormal. The model outputs a state analysis result. The state analysis results are encoded into a digital representation, e.g., "1" for normal, "2" for communication anomalies, and "3" for consumable anomalies. These codes are combined into a first state vector so that the system can more easily perform subsequent analysis and alarm processing.

S107, performing second vector conversion on consumable monitoring data to obtain a second state vector, and simultaneously, performing vector fusion on the first state vector and the second state vector to obtain a target fusion vector, performing data format conversion to obtain target feature data, performing equipment and consumable state identification on the target feature data to obtain target equipment and consumable states, and sending the target equipment and consumable states to a preset remote monitoring terminal.

The data weighted fusion is performed on the first state vector and the second state vector to obtain the second state vector. The weighted fusion may assign weights based on the importance of the different parameters. And carrying out semantic information analysis on the first state vector and the second state vector, and extracting semantic information of each vector. This may involve natural language processing techniques or domain knowledge. Based on the semantic information, first weight data and second weight data are calculated. These weights reflect the importance of each vector and can be adjusted according to the specific situation. And vector fusion is carried out on the first state vector and the second state vector by using the first weight data and the second weight data, so as to obtain a target fusion vector. And converting the target fusion vector into target feature data by using a preset vector feature mapping table. This step may involve feature extraction and dimension reduction techniques to obtain a more informative data representation. And matching the target characteristic data with the state relation by using a preset characteristic state relation library. The library contains the correspondence between various states and features to quickly and accurately identify the states of the equipment and consumables. And determining the states of the equipment and the consumable according to the matching result. This includes various conditions such as normal operation of the apparatus, replacement of consumables, etc. And sending the state information of the target equipment and the consumable to a remote monitoring terminal through a preset communication module. This may be accomplished through a network connection, API call, or other communication means. For example, assume that a printing company manages its multiple printers using this consumable monitoring method. The monitoring system collects data on ink consumption, paper consumption, printing speed, etc. of each printer every hour and generates a first status vector based on the data. At the same time, the system also collects sensor data, such as device temperature, voltage, etc., and generates a second state vector. And vectorizing the sensor data, and then carrying out data weighted fusion on the first state vector and the second state vector to obtain the second state vector. Through semantic information analysis, the system knows that ink consumption has a greater impact on printer performance, and therefore assigns a higher weight to the first state vector. And fusing the two vectors by using the weights to obtain a target fusion vector. And converting the target fusion vector into target feature data by using a preset vector feature mapping table, wherein the data represents the overall state of the printer. The system matches the target feature data with the state relation library to determine the state of each printer. For example, if the target characteristic data for a certain printer indicates that ink is consumed too quickly, the status is identified as "ink replacement is required". The system uses the communication module to send the equipment and consumable state information to the remote monitoring terminal, so that operators can check and take necessary maintenance measures in real time.

In the embodiment of the invention, the communication data is subjected to data division to obtain a plurality of sub-data, the data is subjected to implicit coding to obtain a plurality of coded data, the data signal is subjected to interference recognition to obtain interference signal data, the data is cleaned to obtain a target data set, the abnormal state is analyzed to obtain a state analysis result, the state analysis result is subjected to first vector conversion to obtain a first state vector, the consumable monitoring data is subjected to second vector conversion to obtain a second state vector, the first state vector and the second state vector are subjected to vector fusion to obtain a target fusion vector, the data format conversion is performed to obtain target feature data, the target feature data is subjected to equipment and consumable state recognition to obtain target equipment and consumable states, and the target equipment and consumable states are sent to a remote monitoring terminal. According to the invention, the consumable state and performance of the printing equipment are monitored in real time, and any potential problem or abnormal situation can be immediately detected through the acquisition and analysis of the sensor data and the communication data. Through the accurate monitoring to the consumable consumption condition, can carry out predictive maintenance, discern in advance and solve the problem that leads to equipment trouble or shut down to reduce production interruption and cost of maintenance. Is beneficial to optimizing the use of consumable materials and avoiding unnecessary waste. Through real-time data, a production manager can better plan the supplement and replacement of consumable materials, and effective utilization of resources is ensured. The data-based method provides more accurate consumable consumption information, and can more accurately predict the service life and replacement time of consumable materials compared with the traditional schedule maintenance method, so that the equipment performance is improved. The monitoring and maintenance process is more automated and intelligent by utilizing data analysis and machine learning techniques. By accurately monitoring the equipment status and consumable consumption, the efficiency of the production process can be significantly improved. This helps reduce production time, improves yield to reduce manufacturing cost, and then improve printing equipment's consumable monitoring accuracy and efficiency.

In a specific embodiment, the process of executing step S102 may specifically include the following steps:

(1) Analyzing the data interface parameters of the signal acquisition device to obtain first interface parameters corresponding to the signal acquisition device;

(2) Analyzing the data interface parameters of the target printing equipment to obtain second interface parameters corresponding to the target printing equipment;

(3) Performing data transmission parameter matching on the first interface parameter and the second interface parameter to obtain a corresponding adaptive interface parameter;

(4) Respectively carrying out parameter correction on the first interface parameter and the second interface parameter based on the adaptive interface parameter to obtain a first target parameter corresponding to the first interface parameter and a second target parameter corresponding to the second interface parameter;

(5) Carrying out interface parameter correction on the signal acquisition device through the first target parameter, carrying out interface parameter correction on the target printing equipment through the second target parameter, and acquiring communication data sent by the target printing equipment through the signal acquisition device;

(6) And carrying out signal wave band identification on the communication data to obtain a signal wave band corresponding to the communication data.

Specifically, the signal acquisition device needs to establish communication connection with the target printing device to acquire the monitoring data of the device. Different printing devices have different communication interfaces and parameter requirements, so that parameter analysis and matching are required. The data of the interface parameters of the signal acquisition device are firstly acquired from the signal acquisition device, and the parameters describe the communication characteristics of the acquisition device, such as the speed of USB connection, communication protocol and the like. Next, its interface parameter data is obtained from the target printing device, which parameters describe the communication requirements of the device, such as supported protocols and rates. The two sets of parameter data are matched to determine an adaptation relationship between them. The process of matching may involve comparison of protocol compatibility, rate matching, and other communication parameters. Once the match is successful, the adaptation interface parameters are obtained, which describe how to configure the communication parameters of the signal acquisition device to match the target printing device. And correcting the interface parameters of the signal acquisition device and the target printing equipment by using the adaptive interface parameters. This ensures that the communication between the two is based on matching interface parameters, thereby reducing the risk of communication errors and data loss. Once the interface parameters are modified, the signal acquisition device can establish an effective communication connection with the target printing device and begin to acquire the monitoring data sent by the device. Such data may include information on ink consumption, printing speed, device status, etc., for subsequent data processing and analysis. After the monitoring data is collected, the signal wave band where the communication data is located can be determined by a signal wave band identification method. This facilitates sorting and analysis of the data to further understand the status and performance of the printing apparatus. For example, consider a printing company having multiple different models of printers. These printers use different communication protocols and rates. Through interface parameter analysis and matching, the signal acquisition device can establish correct communication connection with each printer, and accurate acquisition of data is ensured. Therefore, the server can monitor the state of each printer in real time, and timely take maintenance measures, so that the production efficiency and the resource utilization rate are improved. This process also helps to reduce errors and malfunctions and improves the reliability of the monitoring system.

In a specific embodiment, as shown in fig. 2, the process of performing the signal band identifying step on the communication data may specifically include the following steps:

s201, performing frequency domain conversion on communication data to obtain a corresponding frequency domain data set;

s202, carrying out data frequency calculation on the frequency domain data set through a Fourier transform algorithm to obtain corresponding frequency data;

s203, carrying out frequency spectrum feature extraction on the frequency domain data based on the frequency data to obtain a frequency spectrum feature set corresponding to the communication data, wherein the frequency spectrum feature set comprises modulation depth data and frequency offset data;

s204, constructing a standard wave band data table based on the frequency data to obtain a corresponding standard wave band data table;

S205, carrying out standard band matching on the frequency spectrum characteristic set through a standard band data table to obtain a signal band corresponding to communication data.

The communication data is subjected to frequency domain conversion to obtain a corresponding frequency domain data set. Raw signal data is extracted from the communication data and converted into a frequency domain data set using a fourier transform algorithm. Fourier transform is a key technique for converting a signal from the time domain to the frequency domain, which represents data as a combination of frequency components. The frequency domain data set is subjected to data frequency calculation by a fourier transform algorithm to obtain data related to the signal frequency, i.e. frequency data. Frequency data is a quantized representation of the signal frequency that plays a key role in subsequent analysis. Based on the frequency data, spectral feature extraction is performed, which is a key step in signal band identification. In this step, the extracted spectral features typically include modulation depth data and frequency offset data. The modulation depth data describes the modulation amplitude of the signal, and the frequency offset data describes the degree of offset of the signal frequency from the reference frequency. For better comparison with known standard signal bands, a standard band data table needs to be constructed. This table lists the frequency ranges and characterization of various signal bands, such as Wi-Fi bands or bluetooth bands. And carrying out standard band matching on the previously extracted spectrum feature set through a standard band data table. This matching process typically involves comparing the frequency range, modulation depth, and frequency offset characteristics to determine the particular signal band to which the communication data belongs. For example, assume that a printing company monitors printing equipment on its production line. By applying the signal band identification technology, the server can accurately identify the signal band where different devices are located from communication data transmitted by the printing device. For example, if a printer uses Wi-Fi communication, the monitoring method may determine that its communication data is in the Wi-Fi band of 2.4GHz or 5 GHz. This helps the server to know the communication status of the individual devices in real time in order to better manage and maintain them.

In a specific embodiment, as shown in fig. 3, the process of executing step S103 may specifically include the following steps:

S301, carrying out band type identification on a signal band to obtain a band type corresponding to the signal band;

S302, performing time window division construction on the band type to obtain a plurality of different time window data;

S303, carrying out first data division on communication data through a plurality of different time window data to obtain a plurality of candidate division data;

s304, frequency component identification is carried out on the frequency data, and at least one frequency component data is obtained;

S305, performing second data division on the plurality of candidate division data through at least one frequency component data to obtain a plurality of sub-data;

s306, mapping the data coding segments of each piece of sub data to obtain the data coding segments corresponding to each piece of sub data.

The type of the signal band is identified. The purpose is to determine the kind of signal band in which the communication data is located. Signal band type identification is achieved by analyzing characteristics of the frequency, modulation depth, frequency offset, etc. of the communication data. For example, assume that the server has a printing device whose communication data is contained in the Wi-Fi band of 2.4 GHz. By analyzing the characteristics of the communication data, the server determines the band type as Wi-Fi. Depending on the type of signal band identified, a time window needs to be divided in order to analyze the communication data in more detail. Communications in different bands require different time window sizes and positions. For example, wi-Fi communication requires a time window of 1 second, while bluetooth communication requires a time window of 0.5 seconds. These time windows are used to divide the communication data into different time periods for subsequent data processing. After the partitioning of the time window, a first data partitioning may begin. This step divides the communication data in each time window into a plurality of candidate division data, each of which represents a piece of communication data in one time period. This facilitates a finer granularity of analysis of the communication data. Frequency component identification is performed. This is an important step aimed at identifying frequency components in the communication signal. The frequency content of the communication signal can be determined by applying a frequency domain analysis technique such as fourier transform. For example, if the server identifies a particular frequency component in Wi-Fi communication, this component corresponds to the base frequency of the Wi-Fi signal. After identifying the at least one frequency component, a second sub-data division may be performed. This step further divides the candidate division data using the identified frequency component data, decomposing the communication data within each time window into smaller sub-data segments. These sub-data fragments will more specifically reflect different communication events or activities. And mapping the data coding segments of each piece of sub data. This step associates each sub-data with a particular data encoding segment for further analysis and identification. The data encoding segments are typically used to represent specific features or attributes of the data, which facilitate a better understanding of the communicated data.

In a specific embodiment, as shown in fig. 4, the process of performing the data encoding segment mapping step for each sub-data may specifically include the following steps:

s401, respectively dividing data blocks of each piece of sub data to obtain a data block set corresponding to each piece of sub data;

s402, respectively carrying out data multi-layer fusion on the data block set corresponding to each piece of sub data to obtain multi-layer fusion data corresponding to each piece of sub data;

s403, mapping the data coding segments through the multi-layer fusion data corresponding to each piece of sub data to obtain the data coding segments corresponding to each piece of sub data.

Specifically, for each sub data, data block division is performed. The goal of this step is to break each sub-data into smaller data blocks for further analysis and processing. The size of the data block can be adjusted according to specific requirements, and is generally determined according to the characteristics of the data and the application scene. For example, for a piece of audio data, it may be divided into several hundred milliseconds or seconds of audio pieces. And carrying out data multi-layer fusion on the data block set corresponding to each sub data. Multi-layer fusion is the process of merging or fusing multiple data blocks into one larger data unit. This process may involve different levels of data fusion, from simple block-level fusion to higher-level feature fusion. For example, if a server is processing a series of image data blocks, they may be combined into one complete image, while at a higher level their features may be fused to obtain more comprehensive information. And mapping the data coding segments through the multi-layer fusion data corresponding to each sub data. The goal of this step is to associate multiple layers of fused data with specific data encoding segments for further analysis, identification, or storage. A data encoding segment is typically a predefined block or set of features that are used to represent different aspects or attributes of data. For example, in image processing, the data encoding segments may represent different image regions or objects. For example, assume that the server is monitoring the performance of a high speed digital printer that prints newspapers at extremely high speeds. The server collects a series of print data via the sensor, producing a single data per second. The print data per second is divided into sub-data. In this case, the sub-data may be a snapshot of data per second, including status information of various parts on the printer, such as ink jet, paper transport system, printing speed, etc. For each sub-data, a data block division is performed. Taking the example of ink jet status information, it may be divided into a plurality of data blocks, each block representing a different area or cell of the ink jet. These data blocks contain information such as ink ejection conditions and ink flow rates. And carrying out data multi-layer fusion on the data block set corresponding to each sub data. In the case of ink jet status information, the information for the different data blocks may be consolidated into a more comprehensive status report, including a summary of the overall ink jet performance, such as ink uniformity, jetting pressure, etc. And mapping the data coding segments through the multi-layer fusion data corresponding to each sub data. The data encoding sections herein may represent different printer conditions such as normal operation, ink problems, paper jams, etc. By associating multiple layers of fused data with these data encoding segments, the server can recognize the status of the printer in real time and take timely action to ensure a high quality print job.

In a specific embodiment, the process of executing step S104 may specifically include the following steps:

(1) Respectively carrying out data desensitization processing on each piece of sub data through a data coding section corresponding to each piece of sub data to obtain a plurality of pieces of desensitized data;

(2) Analyzing the data cut-off position of each desensitized data to obtain a plurality of data cut-off positions;

(3) Carrying out data value truncation on each desensitization data through a plurality of data truncation positions to obtain a plurality of truncated data;

(4) Creating a data blurring mapping table, and carrying out data blurring processing on a plurality of truncated data according to the data blurring mapping table to obtain a plurality of encoded data.

Specifically, data desensitization processing is performed on each piece of sub data through a data coding section corresponding to each piece of sub data, so as to obtain a plurality of desensitized data. For each sub-data block, a data desensitization process is performed. The purpose of data desensitization is to hide or obscure sensitive information to protect privacy. Desensitization may take a variety of forms, such as substitution, noise addition, data blurring, etc. For example, if a sub-data block indicates ink consumption, a particular value may be replaced with a range of values, such as 100ml-200ml may be desensitized to 100ml-500ml. For each desensitized data block, a data truncation position analysis is performed. The purpose of this step is to determine which bits or values need to be reserved and which can be truncated. The truncated position analysis is based on the importance and context of the data. For example, for data representing temperature, the number of bits after a decimal point may be truncated because the accuracy after a decimal point is not critical information. And according to the result of the data cut-off position analysis, cutting off the data value of the desensitized data block. The data value is limited to a certain range or truncated to a value of a certain number of bits. For example, the temperature data is truncated from 27.356 ℃ to 27 ℃. A data obfuscation mapping table is created that will specify how to obfuscate different types of data blocks. The mapping table contains desensitization and truncation rules, and the mapping mode of the data values. For example, the mapping table may specify how to map temperature values to integers and map a particular range of ink consumption to range values. And carrying out data blurring processing on the truncated data block according to the rule of the data blurring mapping table. This step will generate blurred encoded data containing privacy preserving information. For example, the temperature of 27 ℃ is blurred to "25 ℃ to 30 ℃, and the ink consumption amount is blurred to" 100ml to 500ml ".

In a specific embodiment, the process of executing step S107 may specifically include the following steps:

(1) Carrying out data weighted fusion on consumable monitoring data to obtain a second state vector;

(2) Carrying out semantic information analysis on the first state vector to obtain corresponding first semantic information;

(3) Carrying out semantic information analysis on the second state vector to obtain corresponding second semantic information;

(4) Performing first weight calculation on the first state vector through the first semantic information to obtain first weight data;

(5) Performing second weight calculation on the second state vector through second semantic information to obtain second weight data;

(6) Vector fusion is carried out on the first state vector and the second state vector based on the first weight data and the second weight data, and a target fusion vector is obtained;

(7) Vector feature mapping is carried out on the target fusion vector based on a preset vector feature mapping table, so that target feature data are obtained;

(8) And carrying out state relation matching on the target feature vector based on a preset feature state relation library to obtain corresponding states of target equipment and consumable materials, and sending the states of the target equipment and the consumable materials to a remote monitoring terminal.

Specifically, the first state vector and the second state vector which are subjected to the processing are subjected to data weighted fusion. This step aims to combine the information of the two state vectors to obtain more comprehensive information. Fusion may employ various methods such as weighted averaging, weighted summation, and the like. Semantic information analysis is performed on the first state vector and the second state vector. This includes understanding the data in the state vector and their meaning. For example, for a first state vector, information about the performance of the printing device is contained, while a second state vector contains information about the consumable. Semantic information analysis helps determine the importance of each vector and how to perform the weight calculation. And carrying out first weight calculation on the first state vector based on the semantic information to obtain first weight data. And similarly, performing second weight calculation on the second state vector to obtain second weight data. Weight calculation involves a machine learning algorithm to determine the relative importance of each state vector. And carrying out vector fusion on the first state vector and the second state vector by using the first weight data and the second weight data. This may be achieved by weighted averaging or other linear combinations. The fused vector is the target fusion vector, and integrates the performance and consumable information of the printing equipment. And the quantity feature mapping table is used for carrying out feature mapping on the target fusion vector. This step may reduce the vector dimensions or map it to a higher level feature space for further analysis and processing. And carrying out state relation matching on the target feature vector based on a preset feature state relation library. This step involves comparing and matching the target feature data with known equipment and consumable conditions. The matching process may be implemented using fuzzy logic, a rules engine, or a machine learning algorithm. And sending the target equipment and consumable states obtained through matching to a preset remote monitoring terminal. This ensures real-time monitoring and remote management, as well as timely action to maintain and manage the printing equipment and consumables. For example, assume that a printing apparatus monitoring system needs to monitor ink consumption and printing speed of a printer. The first state vector contains printing speed information and the second state vector contains ink consumption information. By semantic information analysis, the system determines that the printing speed is more critical to the monitoring task. Thus, the weight of the first state vector is higher. Through vector fusion, feature mapping and state relation matching, the system can obtain the running state of the printer, such as normal, low ink, high speed and the like, and send the state information to a remote monitoring terminal for real-time monitoring and management.

The method for monitoring consumable parts of a printing apparatus according to an embodiment of the present invention is described above, and the consumable part monitoring device of a printing apparatus according to an embodiment of the present invention is described below, referring to fig. 5, where an embodiment of the consumable part monitoring device of a printing apparatus according to an embodiment of the present invention includes:

The construction module 501 is configured to perform communication network construction on a preset target printing device to obtain a target communication network, and simultaneously, access the target printing device to the target communication network, and perform data acquisition through a consumable monitoring sensor in the target printing device to obtain consumable monitoring data corresponding to the target printing device;

The identifying module 502 is configured to collect, by using a preset signal collecting device, communication data sent by the target printing device, and identify a signal band of the communication data, so as to obtain a signal band corresponding to the communication data;

a dividing module 503, configured to divide the communication data into a plurality of sub-data according to the signal band, and map a data coding segment of each sub-data to obtain a data coding segment corresponding to each sub-data;

the encoding module 504 is configured to perform data implicit encoding on each sub-data through a data encoding segment corresponding to each sub-data, so as to obtain a plurality of encoded data;

The cleaning module 505 is configured to perform data signal interference identification on each piece of encoded data to obtain corresponding interference signal data, and perform data cleaning on each piece of encoded data through the interference signal data to obtain a target data set;

the analysis module 506 is configured to input the target data set into a preset abnormal communication recognition model to perform abnormal state analysis, obtain a state analysis result, and perform first vector conversion on the state analysis result to obtain a first state vector;

the conversion module 507 is configured to perform a second vector conversion on the consumable monitoring data to obtain a second state vector, and simultaneously perform vector fusion on the first state vector and the second state vector to obtain a target fusion vector, perform data format conversion to obtain target feature data, perform device and consumable state identification on the target feature data to obtain a target device and consumable state, and send the target device and consumable state to a preset remote monitoring terminal.

The method comprises the steps of carrying out data division on communication data to obtain a plurality of sub-data through cooperation of the components, carrying out data implicit coding to obtain a plurality of coded data, carrying out data signal interference identification to obtain interference signal data, carrying out data cleaning to obtain a target data set, carrying out abnormal state analysis to obtain a state analysis result, carrying out first vector conversion on the state analysis result to obtain a first state vector, carrying out second vector conversion on consumable monitoring data to obtain a second state vector, carrying out vector fusion on the first state vector and the second state vector to obtain a target fusion vector, carrying out data format conversion to obtain target feature data, carrying out equipment and consumable state identification on the target feature data to obtain target equipment and consumable states, and sending the target equipment and consumable states to a remote monitoring terminal. According to the invention, the consumable state and performance of the printing equipment are monitored in real time, and any potential problem or abnormal situation can be immediately detected through the acquisition and analysis of the sensor data and the communication data. Through the accurate monitoring to the consumable consumption condition, can carry out predictive maintenance, discern in advance and solve the problem that leads to equipment trouble or shut down to reduce production interruption and cost of maintenance. Is beneficial to optimizing the use of consumable materials and avoiding unnecessary waste. Through real-time data, a production manager can better plan the supplement and replacement of consumable materials, and effective utilization of resources is ensured. The data-based method provides more accurate consumable consumption information, and can more accurately predict the service life and replacement time of consumable materials compared with the traditional schedule maintenance method, so that the equipment performance is improved. The monitoring and maintenance process is more automated and intelligent by utilizing data analysis and machine learning techniques. By accurately monitoring the equipment status and consumable consumption, the efficiency of the production process can be significantly improved. This helps reduce production time, improves yield to reduce manufacturing cost, and then improve printing equipment's consumable monitoring accuracy and efficiency.

Fig. 5 above describes the consumable monitoring device of the printing apparatus in the embodiment of the present invention in detail from the point of view of the modularized functional entity, and the consumable monitoring device of the printing apparatus in the embodiment of the present invention is described in detail from the point of view of hardware processing.

Fig. 6 is a schematic structural diagram of a consumable monitoring device of a printing apparatus according to an embodiment of the present invention, where the consumable monitoring device 600 of the printing apparatus may have a relatively large difference due to different configurations or performances, and may include one or more processors (centralprocessingunits, CPU) 610 (e.g., one or more processors) and a memory 620, and one or more storage mediums 630 (e.g., one or more mass storage devices) storing applications 633 or data 632. Wherein the memory 620 and the storage medium 630 may be transitory or persistent storage. The program stored on the storage medium 630 may include one or more modules (not shown), each of which may include a series of instruction operations in the consumable monitoring device 600 of the printing device. Still further, the processor 610 may be configured to communicate with the storage medium 630 to execute a series of instruction operations in the storage medium 630 on the consumable monitoring device 600 of the printing device.

The consumable monitoring device 600 of the printing device may also include one or more power supplies 640, one or more wired or wireless network interfaces 650, one or more input/output interfaces 660, and/or one or more operating systems 631, such as WindowsServe, macOSX, unix, linux, freeBSD, and the like. It will be appreciated by those skilled in the art that the consumable monitoring device structure of the printing device shown in FIG. 6 does not constitute a limitation of the consumable monitoring device of the printing device and may include more or fewer components than shown, or may combine certain components, or may be arranged in a different arrangement of components.

The invention also provides a consumable monitoring device of the printing device, which comprises a memory and a processor, wherein the memory stores computer readable instructions, and when the computer readable instructions are executed by the processor, the processor executes the steps of the consumable monitoring method of the printing device in the above embodiments.

The present invention also provides a computer readable storage medium, which may be a non-volatile computer readable storage medium, and may also be a volatile computer readable storage medium, where instructions are stored in the computer readable storage medium, when the instructions are executed on a computer, cause the computer to perform the steps of the consumable monitoring method of the printing apparatus.

It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.

The integrated units, if implemented in the form of software functional units and sold or passed as separate products, may be stored in a computer readable storage medium. Based on the understanding that the technical solution of the present invention may be embodied in essence or in a part contributing to the prior art or in whole or in part in the form of a software product stored in a storage medium, comprising instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. The storage medium includes a U disk, a removable hard disk, a read-only memory (ROM), a random access memory (randomacceSmemory, RAM), a magnetic disk or an optical disk, etc. which can store the program code.

While the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those skilled in the art that the foregoing embodiments may be modified or equivalents may be substituted for some of the features thereof, and that the modifications or substitutions do not depart from the spirit and scope of the embodiments of the invention.

Claims

1. A consumables monitoring method for a printing device, characterized in that the consumables monitoring method for the printing device comprises:

A communication network is constructed for a preset target printing device to obtain a target communication network. Meanwhile, the target printing device is connected to the target communication network, and data is collected through a consumable monitoring sensor in the target printing device to obtain consumable monitoring data corresponding to the target printing device.

The communication data sent by the target printing device is collected by a preset signal collection device, and the signal band of the communication data is identified to obtain the signal band corresponding to the communication data;

Dividing the communication data by the signal band to obtain a plurality of sub-data, and mapping each sub-data into a data coding segment to obtain a data coding segment corresponding to each sub-data;

Each of the sub-data is implicitly encoded using the data encoding segment corresponding to each of the sub-data to obtain a plurality of encoded data;

Performing data signal interference identification on each of the coded data to obtain corresponding interference signal data, and performing data cleaning on each of the coded data using the interference signal data to obtain a target data set;

Inputting the target data set into a preset abnormal communication recognition model to perform abnormal state analysis to obtain a state analysis result, and performing a first vector conversion on the state analysis result to obtain a first state vector;

Perform a second vector conversion on the consumable monitoring data to obtain a second state vector. At the same time, perform vector fusion on the first state vector and the second state vector to obtain a target fusion vector and perform data format conversion to obtain target feature data. Perform device and consumable status identification on the target feature data to obtain target device and consumable status, and send the target device and consumable status to a preset remote monitoring terminal. Specifically, it includes: performing data weighted fusion on the consumable monitoring data to obtain the second state vector; performing semantic information analysis on the first state vector to obtain corresponding first semantic information; performing semantic information analysis on the second state vector to obtain corresponding second semantic information; through the The first semantic information performs a first weight calculation on the first state vector to obtain first weight data; the second semantic information performs a second weight calculation on the second state vector to obtain second weight data; based on the first weight data and the second weight data, the first state vector and the second state vector are vector-fused to obtain the target fusion vector; based on a preset vector feature mapping table, vector feature mapping is performed on the target fusion vector to obtain the target feature data; based on a preset feature-state relationship library, state relationship matching is performed on the target feature vector to obtain the corresponding target device and consumables status, and the target device and consumables status are sent to the remote monitoring terminal.

2. The consumables monitoring method of a printing device according to claim 1, characterized in that the communication data sent by the target printing device is collected by a preset signal collection device, and the signal band of the communication data is identified to obtain the signal band corresponding to the communication data, including:

Performing data interface parameter analysis on the signal acquisition device to obtain first interface parameters corresponding to the signal acquisition device;

Performing data interface parameter analysis on the target printing device to obtain second interface parameters corresponding to the target printing device;

Performing data transmission parameter matching on the first interface parameter and the second interface parameter to obtain corresponding adaptation interface parameters;

Based on the adaptation interface parameters, the first interface parameters and the second interface parameters are respectively modified to obtain first target parameters corresponding to the first interface parameters and second target parameters corresponding to the second interface parameters;

Correcting the interface parameters of the signal acquisition device by using the first target parameter, and correcting the interface parameters of the target printing device by using the second target parameter, and collecting the communication data sent by the target printing device by using the signal acquisition device;

Perform signal band identification on the communication data to obtain a signal band corresponding to the communication data.

3. The consumables monitoring method of a printing device according to claim 2, characterized in that the step of performing signal band identification on the communication data to obtain the signal band corresponding to the communication data comprises:

Performing data frequency calculation on the frequency domain data set by using a Fourier transform algorithm to obtain corresponding frequency data;

Based on the frequency data, extracting spectrum features from the frequency domain data to obtain a spectrum feature set corresponding to the communication data, wherein the spectrum feature set includes modulation depth data and frequency offset data;

Constructing a standard band data table based on the frequency data to obtain a corresponding standard band data table;

The standard band data table is used to perform standard band matching on the spectrum feature set to obtain a signal band corresponding to the communication data.

4. The consumables monitoring method of a printing device according to claim 3, characterized in that the communication data is divided into data by the signal band to obtain a plurality of sub-data, and each sub-data is mapped into a data coding segment to obtain a data coding segment corresponding to each sub-data, comprising:

Dividing the band type into time windows to construct a plurality of different time window data;

Performing a first data division on the communication data using the plurality of different time window data to obtain a plurality of candidate division data;

Performing frequency component identification on the frequency data to obtain at least one frequency component data;

Performing a second data division on the plurality of candidate divided data by at least one of the frequency component data to obtain a plurality of the sub-data;

Data coding segment mapping is performed on each sub-data to obtain a data coding segment corresponding to each sub-data.

5. The consumables monitoring method of a printing device according to claim 4, characterized in that the step of mapping each sub-data into a data encoding segment to obtain a data encoding segment corresponding to each sub-data comprises:

Divide each of the sub-data into data blocks respectively to obtain a data block set corresponding to each of the sub-data;

Performing multi-layer data fusion on the data block sets corresponding to each of the sub-data respectively to obtain multi-layer fused data corresponding to each of the sub-data;

Data coding segments corresponding to each sub-data are mapped through the multi-layer fused data corresponding to each sub-data to obtain the data coding segments corresponding to each sub-data.

6. The consumables monitoring method of a printing device according to claim 1, characterized in that the data encoding segment corresponding to each sub-data is used to perform data implicit encoding on each sub-data to obtain a plurality of encoded data, including:

Performing data desensitization processing on each sub-data respectively through the data encoding segment corresponding to each sub-data to obtain multiple desensitized data;

Performing data truncation position analysis on each of the desensitized data to obtain multiple data truncation positions;

Performing data value truncation on each of the desensitized data through the multiple data truncation positions to obtain multiple truncated data;

A data fuzzification mapping table is created, and data fuzzification processing is performed on the plurality of truncated data according to the data fuzzification mapping table to obtain the plurality of encoded data.

7. A consumables monitoring device for a printing device, characterized in that the consumables monitoring device for the printing device comprises:

A construction module is used to construct a communication network for a preset target printing device to obtain a target communication network, and at the same time, connect the target printing device to the target communication network, and collect data through a consumable monitoring sensor in the target printing device to obtain consumable monitoring data corresponding to the target printing device;

An identification module, used for collecting the communication data sent by the target printing device through a preset signal collection device, and performing signal band identification on the communication data to obtain a signal band corresponding to the communication data;

A division module, used to divide the communication data according to the signal band to obtain a plurality of sub-data, and to map each sub-data to a data coding segment to obtain a data coding segment corresponding to each sub-data;

An encoding module, used for performing data implicit encoding on each sub-data through a data encoding segment corresponding to each sub-data to obtain a plurality of encoded data;

A cleaning module, used to identify data signal interference on each of the coded data to obtain corresponding interference signal data, and to clean each of the coded data using the interference signal data to obtain a target data set;

An analysis module, used for inputting the target data set into a preset abnormal communication recognition model to perform abnormal state analysis to obtain a state analysis result, and performing a first vector conversion on the state analysis result to obtain a first state vector;

A conversion module is used to perform a second vector conversion on the consumable monitoring data to obtain a second state vector, and at the same time, perform vector fusion on the first state vector and the second state vector to obtain a target fusion vector and perform data format conversion to obtain target feature data, and perform device and consumable status identification on the target feature data to obtain target device and consumable status, and send the target device and consumable status to a preset remote monitoring terminal; specifically including: performing data weighted fusion on the consumable monitoring data to obtain the second state vector; performing semantic information analysis on the first state vector to obtain corresponding first semantic information; performing semantic information analysis on the second state vector to obtain corresponding second semantic information; Perform a first weight calculation on the first state vector through the first semantic information to obtain first weight data; perform a second weight calculation on the second state vector through the second semantic information to obtain second weight data; perform vector fusion on the first state vector and the second state vector based on the first weight data and the second weight data to obtain the target fusion vector; perform vector feature mapping on the target fusion vector based on a preset vector feature mapping table to obtain the target feature data; perform state relationship matching on the target feature vector based on a preset feature state relationship library to obtain the corresponding target device and consumables status, and send the target device and consumables status to the remote monitoring terminal.

8. A consumables monitoring device for a printing device, characterized in that the consumables monitoring device for the printing device comprises: a memory and at least one processor, wherein instructions are stored in the memory;

The at least one processor calls the instructions in the memory to enable the consumables monitoring device of the printing device to execute the consumables monitoring method of the printing device according to any one of claims 1 to 6.

9. A computer-readable storage medium having instructions stored thereon, wherein when the instructions are executed by a processor, the consumables monitoring method for a printing device according to any one of claims 1 to 6 is implemented.