CN118410442B - Supply chain management method based on industrial Internet identification analysis - Google Patents
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Abstract
本发明公开了一种基于工业互联网标识解析的供应链管理方法，涉及供应链管理技术领域，包括部署供应链物品智能标识，供应链平台通过工业互联网收集供应链数据；根据供应链数据实时监测供应链网络中的异常，并对异常进行处理；结合供应链数据动态调整供应链网络。本发明有益效果为：本发明通过收集供应链网络数据构建决策树模型和异常评分函数对供应链网络中异常进行检测，有助于供应链网络实时监测，减少因异常造成的损失，同时实时对供应链网络进行动态调整，提升了供应链网络的灵活性和适应性，通过对供应链网络未来状态进行预测为供应链网络的未来发展提供了助力，有效地帮助供应链网络发展壮大。
The present invention discloses a supply chain management method based on industrial Internet identification resolution, which relates to the field of supply chain management technology, including deploying intelligent identification of supply chain items, and the supply chain platform collecting supply chain data through the industrial Internet; monitoring anomalies in the supply chain network in real time according to the supply chain data, and processing the anomalies; and dynamically adjusting the supply chain network in combination with the supply chain data. The beneficial effects of the present invention are as follows: the present invention collects supply chain network data to construct a decision tree model and anomaly scoring function to detect anomalies in the supply chain network, which helps to monitor the supply chain network in real time and reduce losses caused by anomalies. At the same time, the supply chain network is dynamically adjusted in real time, which improves the flexibility and adaptability of the supply chain network, and provides assistance for the future development of the supply chain network by predicting the future state of the supply chain network, effectively helping the supply chain network to grow and develop.
Description
Technical Field
The invention relates to the technical field of supply chain management, in particular to a supply chain management method based on industrial Internet identification analysis.
Background
With the rapid development and popularization of industrial internet, supply chain management is taken as an important component of links such as enterprise logistics, production, sales and the like, and is in front of unprecedented challenges and opportunities, traditional supply chain management depends on linear and static processes and information systems, and often because of problems such as information islands, data delay and low processing efficiency and the like, the requirements of modern enterprises on efficient, transparent and flexible management are difficult to meet, in recent years, supply chain management methods based on the industrial internet gradually become hot spots for research and application, real-time tracking and data collection of articles in a supply chain are realized through combination of intelligent identification and internet technology, new possibilities are provided for optimizing management of the supply chain, however, the prior art lacks comprehensive insight and dynamic adjustment capability of the whole supply chain network, and particularly cannot effectively cope with complex market demands and supply chain risks in aspects of identifying and processing abnormal events in the real-time monitoring supply chain and predicting supply chain states based on big data analysis.
Disclosure of Invention
The present invention has been made in view of the above-mentioned problems occurring in the conventional supply chain management method based on industrial internet identification resolution.
The problem to be solved by the present invention is therefore that the prior art lacks comprehensive insight and dynamic adjustment capabilities for the whole supply chain network, especially in the real-time monitoring of the identification, handling of abnormal events in the supply chain and the prediction of the supply chain status based on big data analysis, which is not effective for complex and varied market demands and supply chain risks.
The technical scheme includes that the supply chain management method based on industrial Internet identification analysis comprises the steps of deploying supply chain article intelligent identification, collecting supply chain data through an industrial Internet by a supply chain platform, monitoring anomalies in a supply chain network in real time according to the supply chain data, processing the anomalies, dynamically adjusting the supply chain network according to the supply chain data, predicting the future state of the supply chain, displaying the supply chain through the supply chain platform, collecting data by a database, storing the data, and implementing data security protection measures.
The supply chain management method based on industrial Internet identification analysis is characterized in that the supply chain platform is used for deploying the intelligent identification of the supply chain article, collecting supply chain data through the industrial Internet means deploying the intelligent identification on the supply chain article to store article information, the intelligent identification comprises an RFID tag, a two-dimensional code and an NFC tag, collecting data in the intelligent identification by utilizing a sensor and a reading device, and transmitting the data to the supply chain platform through the industrial Internet to carry out data cleaning, filtering and standardization.
As an optimal scheme of the supply chain management method based on industrial Internet identification analysis, the method for monitoring the abnormality in the supply chain network according to the supply chain data in real time comprises the following steps:
Deriving feature vectors from collected supply chain item information data :
,
Wherein the method comprises the steps ofAs a feature vector of the object set,For the flow rate of time t,For the stock level at time t,As the order quantity at time t,For the order delivery time, the order delivery time is,The quality of the item is scored,Scoring vendor reliability;
Calculating a supply chain comprehensive state value:
,
Wherein the method comprises the steps of Integrating state values for the supply chain;
Defining a decision tree feature judgment threshold value:
,
Wherein the method comprises the steps of A threshold value is determined for the characteristic of the time t,For the length of time to be considered,In order to take into account the number of time points,For the flow rate at the i-th point in time,For the inventory level at the i-th point in time,For the order quantity at the i-th point in time,For the order delivery time at the ith point in time,The item quality score for the ith time point,The supplier reliability score for the ith time point,And b is an adjustment parameter;
Constructing a decision tree model:
,
Wherein the method comprises the steps of In order to make a decision tree model,In order to input the value for the decision tree,For the number of feature vectors,The supply chain integrated status value for the jth eigenvector,For the weight coefficient of the j-th feature vector,A threshold is determined for the features of the jth feature vector,To indicate the function whenThe value is 1 when the time is taken, otherwise, the value is 0;
training a decision tree model by adopting regularization terms, and defining an optimization objective function as follows:
,
Wherein the method comprises the steps of In order to train the number of features,For the i-th feature to be a genuine tag,For the output of the ith decision tree in the decision tree model,As a function of the loss,For the regularization coefficient(s),Judging the L2 norm of the threshold for the ith feature;
Defining an anomaly scoring function based on the decision tree model as:
,
Wherein the method comprises the steps of For the purpose of supply chain anomaly scoring,In order to make a decision about the number of trees,And inputting the collected supply chain data into an anomaly scoring function for the weight of the ith decision tree to obtain a supply chain anomaly score.
As a preferable scheme of the supply chain management method based on industrial Internet identification analysis, the method for processing the anomalies refers to obtaining supply chain anomaly scoresPost and preset anomaly thresholdComparing and judging the state of the supply chain:
If it is <The method includes the steps that the operation state of a supply chain is good, collection and processing of supply chain data are kept, abnormal detection is conducted on the supply chain regularly, a periodic detection report is generated, and meanwhile parameters of a decision tree model and an abnormal scoring function are updated regularly;
If it is ≥And (3) indicating that the abnormality exists in the operation process of the supply chain, informing a worker to analyze the abnormality occurrence position according to the collected supply chain data, checking the abnormality of the supply chain, if the worker analyzes that the supply chain is not abnormal, feeding back a misjudgment notification to a supply chain platform, marking the supply chain data as misjudgment data, training a decision tree model and an abnormality grading function again by the supply chain platform, and verifying the decision tree model and the abnormality grading function by using the misjudgment data after the training is finished until an abnormality analysis result is correct.
As an optimal scheme of the supply chain management method based on industrial Internet identification analysis, the method comprises the steps of dynamically adjusting a supply chain network in combination with supply chain data, wherein the step of dynamically adjusting the supply chain network in real time through the collected supply chain data is as follows:
,
Wherein the method comprises the steps of The policy value is adjusted for the supply chain at time t,AndIn order to adjust the parameters of the device,For a weight value of the flow velocity at time t,For the weight of time torder quantity and order delivery time, iteratively calculating untilAnd when the value reaches the maximum, taking all the input parameters at the moment as adjustment values to carry out supply chain network adjustment.
The supply chain management method based on industrial Internet identification analysis is characterized in that the predicting the future state of the supply chain refers to predicting the future state of the supply chain after the abnormality detection and dynamic adjustment of the supply chain network:
,
Wherein the method comprises the steps of Is time ofIs used to determine the supply chain feature vector of (1),Is time ofIs characterized in that,AndIs characterized byIs used for the weight of the (c),、AndIn order to adjust the parameters of the shape of the prediction function,As a function of the normalization,As a total number of features,For time delay, get timeAfter the supply chain feature vectors of (a), combining the supply chain data in the feature vectors to form a timeIs provided for the supply chain status data of (a).
The invention relates to a supply chain management method based on industrial Internet identification analysis, which comprises the following steps that a supply chain platform displays collected and analyzed data to supply chain network participants through a visual control interaction panel, wherein the supply chain platform provides interactive data service for the supply chain network participants and allows supply chain inquiry and management through the industrial Internet, and the supply chain management method comprises the steps of providing a visual control interaction panel to the supply chain network participants, and providing a visual control interaction interface to the supply chain network participants.
The method for managing the supply chain based on the industrial Internet identification analysis is characterized in that the database collects data to store and implements data security protection measures, namely the database stores the collected supply chain network data, a decision tree model, an exception evaluation function, an exception analysis result, an exception handling measure process, a supply chain dynamic adjustment measure and a future state prediction result, the database sets security access authority and access passwords for the stored data, and keeps monitoring the stored data when the data is accessed, and the database scans the accessed data to generate detection records to synchronously store after the data access is finished.
The computer equipment comprises a memory and a processor, wherein the memory stores a computer program, and the computer equipment is characterized in that the processor executes the computer program to realize the steps of the supply chain management method based on industrial Internet identification analysis.
A computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor performs the steps of the supply chain management method based on industrial internet identification resolution as described above.
The method has the beneficial effects that the method detects the abnormality in the supply chain network by collecting the supply chain network data to construct the decision tree model and the abnormality scoring function, is beneficial to the real-time monitoring of the supply chain network, reduces the loss caused by the abnormality, dynamically adjusts the supply chain network in real time, improves the flexibility and adaptability of the supply chain network, provides assistance for the future development of the supply chain network by predicting the future state of the supply chain network, and effectively helps the development of the supply chain network to be strong.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a supply chain management method based on industrial Internet identification resolution.
FIG. 2 is a schematic diagram of a supply chain platform collecting data.
FIG. 3 is a schematic diagram of an abnormality detection structure of a supply chain platform.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention is not limited to the specific embodiments disclosed below.
Further, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic can be included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
Example 1
Referring to fig. 1 to 3, in a first embodiment of the present invention, a supply chain management method based on industrial internet identification analysis is provided, and the supply chain management method based on industrial internet identification analysis includes the following steps:
S1, deploying intelligent identification of supply chain articles, and collecting supply chain data by a supply chain platform through an industrial Internet;
Specifically, the intelligent identification of the supply chain object is deployed, the supply chain platform collects supply chain data through the industrial Internet, the intelligent identification is deployed on the supply chain object to store object information, the intelligent identification comprises an RFID tag, a two-dimensional code and an NFC tag, the data in the intelligent identification is collected by using a sensor and reading equipment, and the data is transmitted to the supply chain platform through the industrial Internet to carry out data cleaning, filtering and standardization.
The RFID, the two-dimensional code or the NFC label is deployed on the article, so that quick identification and information acquisition of the article can be realized, error rate and time consumption of traditional manual input information are greatly reduced, in addition, the intelligent identification also supports real-time tracking, abnormal conditions in a supply chain, such as delay, loss or theft, can be found timely, data in the intelligent identification are collected through the sensor and the reading equipment, efficient data transmission is performed by utilizing an industrial Internet technology, the process not only ensures the instantaneity and the integrity of the data, but also provides possibility for supply chain management across regions, enterprises can easily manage supply chain nodes distributed in different regions through high-speed and stable transmission of the industrial Internet, and collected data is subjected to cleaning, filtering and standardized processing on a supply chain platform, so that the quality and consistency of the data are ensured.
S2, monitoring the abnormality in the supply chain network in real time according to the supply chain data, and processing the abnormality;
specifically, monitoring anomalies in a supply chain network in real time based on supply chain data includes:
Deriving feature vectors from collected supply chain item information data :
,
Wherein the method comprises the steps ofAs a feature vector of the object set,For the flow rate of time t,For the stock level at time t,As the order quantity at time t,For the order delivery time, the order delivery time is,The quality of the item is scored,Providing a supplier reliability score that can effectively reflect supply chain status by forming supply chain item information data into feature vectors;
Calculating a supply chain comprehensive state value:
,
Wherein the method comprises the steps of Comprehensively obtaining a supply chain comprehensive state value through collected supply chain network data;
Defining a decision tree feature judgment threshold value:
,
Wherein the method comprises the steps of A threshold value is determined for the characteristic of the time t,For the length of time to be considered,In order to take into account the number of time points,For the flow rate at the i-th point in time,For the inventory level at the i-th point in time,For the order quantity at the i-th point in time,For the order delivery time at the ith point in time,The item quality score for the ith time point,The supplier reliability score for the ith time point,And b is an adjustment parameter;
Constructing a decision tree model:
,
Wherein the method comprises the steps of In order to make a decision tree model,In order to input the value for the decision tree,For the number of feature vectors,The supply chain integrated status value for the jth eigenvector,For the weight coefficient of the j-th feature vector,A threshold is determined for the features of the jth feature vector,To indicate the function whenThe value is 1, otherwise, the value is 0;
training a decision tree model by adopting regularization terms, and defining an optimization objective function as follows:
,
Wherein the method comprises the steps of In order to train the number of features,For the i-th feature to be a genuine tag,For the output of the ith decision tree in the decision tree model,As a function of the loss,For the regularization coefficient(s),Judging the L2 norm of the threshold for the ith feature;
Defining an anomaly scoring function based on the decision tree model as:
,
Wherein the method comprises the steps of For the purpose of supply chain anomaly scoring,In order to make a decision about the number of trees,And inputting the collected supply chain data into an anomaly scoring function for the weight of the ith decision tree to obtain a supply chain anomaly score.
By collecting and analyzing the supply chain data in real time, the invention can timely find and process abnormal conditions in the supply chain, such as logistics delay, excessive or insufficient inventory, abnormal order processing and the like, thereby improving the operation efficiency and response speed of the supply chain. The method is favorable for identifying key influencing factors in the supply chain and providing support for decision making, the abnormal state in the supply chain can be automatically identified by constructing the decision tree model, and the abnormal state can be accurately judged according to rules obtained by historical data learning, the method improves the accuracy and the efficiency of abnormality detection, and by introducing regularization terms in the training process of the decision tree model, the method can effectively avoid model overfitting and ensure better generalization capability of the model, which means that the model not only has good performance on training data, but also has stronger prediction capability on unknown data.
Further, processing anomalies refers to obtaining supply chain anomaly scoresPost and preset anomaly thresholdComparing and judging the state of the supply chain:
If it is <The method includes the steps that the operation state of a supply chain is good, collection and processing of supply chain data are kept, abnormal detection is conducted on the supply chain regularly, a periodic detection report is generated, and meanwhile parameters of a decision tree model and an abnormal scoring function are updated regularly;
If it is ≥And (3) indicating that the abnormality exists in the operation process of the supply chain, informing a worker to analyze the abnormality occurrence position according to the collected supply chain data, checking the abnormality of the supply chain, if the worker analyzes that the supply chain is not abnormal, feeding back a misjudgment notification to a supply chain platform, marking the supply chain data as misjudgment data, training a decision tree model and an abnormality grading function again by the supply chain platform, and verifying the decision tree model and the abnormality grading function by using the misjudgment data after the training is finished until an abnormality analysis result is correct.
S3, dynamically adjusting a supply chain network in combination with the supply chain data, predicting the future state of the supply chain and displaying the future state through a supply chain platform;
Specifically, dynamically adjusting the supply chain network in conjunction with the supply chain data refers to dynamically adjusting the supply chain network in real time through the collected supply chain data:
,
Wherein the method comprises the steps of The policy value is adjusted for the supply chain at time t,AndIn order to adjust the parameters of the device,For a weight value of the flow velocity at time t,For the weight of time torder quantity and order delivery time, iteratively calculating untilAnd when the value reaches the maximum, taking all the input parameters at the moment as adjustment values to carry out supply chain network adjustment.
The method has the beneficial effects that the transparency of the supply chain is improved, the decision maker is promoted to deeply understand the health condition of the supply chain, improvement measures are formulated according to analysis and suggestion in the report, and when the abnormal score of the supply chain reaches or exceeds the preset threshold value, an abnormal processing mechanism is triggered, so that a worker is required to analyze and check the abnormal condition in detail. If the data is confirmed to be misjudged, the related data is marked as misjudged data and fed back to a supply chain platform, the process is not only conducive to reducing the future misjudgment rate, but also provides important information for continuous optimization of the decision tree model and the abnormal scoring function, the collection and analysis of the misjudged data are crucial for improving the accuracy and robustness of the model, and the model training and verification are carried out by periodically updating the parameters of the decision tree model and the abnormal scoring function and using the misjudged data.
Further, predicting the future state of the supply chain refers to predicting the future state of the supply chain network after anomaly detection and dynamic adjustment of the supply chain network:
,
Wherein the method comprises the steps of Is time ofIs used to determine the supply chain feature vector of (1),Is time ofIs characterized in that,AndIs characterized byIs used for the weight of the (c),、AndIn order to adjust the parameters of the shape of the prediction function,As a function of the normalization,As a total number of features,For time delay, get timeAfter the supply chain feature vectors of (a), combining the supply chain data in the feature vectors to form a timeIs provided for the supply chain status data of (a).
By precisely defining and quantifying the key features of the supply chain, the current state of the supply chain can be more fully understood, this step helps to identify which factors have a significant impact on the supply chain performance, thereby providing support for management decisions, assigning weights to different supply chain features, meaning that their importance can be adjusted according to their actual degree of impact on the supply chain state, this approach ensures that predictive models can focus on those key features, improving the accuracy and reliability of predictions, the normalization processing is an important step of data preprocessing, ensures that data of different orders can be compared and analyzed under the same standard, is particularly important for comprehensive analysis and prediction model construction by using a plurality of features of different dimensions, inputs normalized supply chain feature vectors into a prediction model, and can predict future states of a supply chain based on current and historical data, and the step has important significance for identifying potential supply chain risks in advance, optimizing resource allocation and improving supply chain strategies.
Further, the presentation by the supply chain platform means that the supply chain platform presents the collected and analyzed data to the supply chain network participants through the visual control interactive panel, including suppliers, sellers, logistics providers, and supply chain platform staff, and the supply chain platform provides interactive data services for the supply chain network participants and allows for supply chain querying and management through the industrial internet.
The system and the method have the advantages that the real-time monitoring and analysis of the state of the whole supply chain can be realized through the collection and analysis of data from suppliers, sellers and logistics providers by the supply chain platform, the timeliness and the accuracy of decisions are improved, potential risks and problems can be timely found and dealt with, the interactive data service provided by the supply chain platform enables all participants of the supply chain network to inquire, analyze and manage the supply chain data according to own requirements, the provision of the service enhances the collaboration and communication of all the participants in the supply chain network, the supply chain management and operation are optimized together, the supply chain platform allows users to remotely inquire and manage the supply chain by industrial internet technology, the flexibility and the efficiency of the supply chain management are greatly improved, the system and the method are particularly important for the supply chain network which runs across regions, the timeliness of information circulation and management decisions can be ensured, the transparency of the supply chain can be improved through the analysis of the data to the supply chain network participants by the visual control interaction panel, and each participant in the supply chain can participate in the supply chain management more actively, and the transparency and the establishment of the close cooperative relationship can be improved.
S4, collecting data by the database for storage and implementing data security protection measures.
Specifically, the database collects data and stores the collected supply chain network data, the decision tree model, the abnormality evaluation function, the abnormality analysis result, the abnormality processing measure process, the supply chain dynamic adjustment measure and the future state prediction result, the database sets the security access authority and the access password for the stored data, and keeps monitoring the stored data when the data is accessed, and the database scans the accessed data to generate detection records and synchronously stores the detection records after the data access is finished.
The centralized storage facilitates the retrieval, updating and backup of data by intensively storing all key information in the database, provides a solid data basis for supply chain management, sets safe access authority and passwords as basic measures for protecting the database from unauthorized access, ensures that only users with corresponding authority can access sensitive data by carrying out identity verification and authorization on the users, thereby protecting the safety of supply chain information, monitors the access of data stored in the database in real time, can timely find and respond to potential data leakage or misuse behaviors, is helpful for strengthening data safety management, preventing the data from being accessed by unauthorized persons or programs, and can further strengthen the safety of the data by scanning and generating detection records after the data is accessed, thereby not only tracking the history of the data access, but also detecting whether the data is tampered or damaged in the access process and ensuring that the integrity and the accuracy of the data are protected.
Example 2
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several 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 (RAM, random Access Memory), a magnetic disk, an optical disk, or other various media capable of storing program codes.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium include an electrical connection (an electronic device) having one or more wires, a portable computer diskette (a magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of techniques known in the art, discrete logic circuits with logic gates for implementing logic functions on data signals, application specific integrated circuits with appropriate combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.
Claims (9)
1. A supply chain management method based on industrial Internet identification analysis is characterized by comprising the following steps of,
Deploying a supply chain article intelligent identifier, and collecting supply chain data by a supply chain platform through an industrial Internet;
monitoring anomalies in the supply chain network in real time according to the supply chain data, and processing the anomalies;
Dynamically adjusting a supply chain network in combination with supply chain data, predicting the future state of the supply chain and displaying the future state through a supply chain platform;
the database collects data for storage and implements data security protection measures;
the monitoring of anomalies in the supply chain network in real time from supply chain data includes:
Deriving feature vectors from collected supply chain item information data :
;
Wherein the method comprises the steps ofAs a feature vector of the object set,For the flow rate of time t,For the stock level at time t,As the order quantity at time t,For the order delivery time, the order delivery time is,The quality of the item is scored,Scoring vendor reliability;
Calculating a supply chain comprehensive state value:
;
Wherein the method comprises the steps of Integrating state values for the supply chain;
Defining a decision tree feature judgment threshold value:
;
Wherein the method comprises the steps of A threshold value is determined for the characteristic of the time t,For the length of time to be considered,In order to take into account the number of time points,For the flow rate at the i-th point in time,For the inventory level at the i-th point in time,For the order quantity at the i-th point in time,For the order delivery time at the ith point in time,The item quality score for the ith time point,The supplier reliability score for the ith time point,And b is an adjustment parameter;
Constructing a decision tree model:
;
Wherein the method comprises the steps of In order to make a decision tree model,In order to input the value for the decision tree,For the number of feature vectors,The supply chain integrated status value for the jth eigenvector,For the weight coefficient of the j-th feature vector,A threshold is determined for the features of the jth feature vector,To indicate the function whenThe value is 1 when the time is taken, otherwise, the value is 0;
training a decision tree model by adopting regularization terms, and defining an optimization objective function as follows:
;
Wherein the method comprises the steps of In order to train the number of features,For the i-th feature to be a genuine tag,For the output of the ith decision tree in the decision tree model,As a function of the loss,For the regularization coefficient(s),Judging the L2 norm of the threshold for the ith feature;
Defining an anomaly scoring function based on the decision tree model as:
;
Wherein the method comprises the steps of For the purpose of supply chain anomaly scoring,In order to make a decision about the number of trees,The weight of the ith decision tree;
Inputting the collected supply chain data into an anomaly scoring function to obtain a supply chain anomaly score;
the exception processing refers to obtaining a supply chain exception score Post and preset anomaly thresholdAnd comparing and judging the state of the supply chain.
2. The supply chain management method based on industrial Internet identification analysis of claim 1, wherein the step of deploying the intelligent supply chain object identification is characterized in that the step of collecting supply chain data through the industrial Internet is to deploy the intelligent supply chain object identification on the supply chain object to store object information, wherein the intelligent supply chain object identification comprises an RFID tag, a two-dimensional code and an NFC tag, the data in the intelligent supply chain object identification is collected by utilizing a sensor and a reading device, and the data is transmitted to the supply chain platform through the industrial Internet to carry out data cleaning, filtering and standardization.
3. The method for managing a supply chain based on industrial Internet identification resolution as set forth in claim 2, wherein said processing of anomalies is obtaining supply chain anomaly scoresPost and preset anomaly thresholdComparing and judging the state of the supply chain:
If it is <The method includes the steps that the operation state of a supply chain is good, collection and processing of supply chain data are kept, abnormal detection is conducted on the supply chain regularly, a periodic detection report is generated, and meanwhile parameters of a decision tree model and an abnormal scoring function are updated regularly;
If it is ≥And (3) indicating that the abnormality exists in the operation process of the supply chain, informing a worker to analyze the abnormality occurrence position according to the collected supply chain data, checking the abnormality of the supply chain, if the worker analyzes that the supply chain is not abnormal, feeding back a misjudgment notification to a supply chain platform, marking the supply chain data as misjudgment data, training a decision tree model and an abnormality grading function again by the supply chain platform, and verifying the decision tree model and the abnormality grading function by using the misjudgment data after the training is finished until an abnormality analysis result is correct.
4. The method for managing a supply chain based on industrial Internet identification resolution as set forth in claim 3, wherein said dynamically adjusting the supply chain network in combination with the supply chain data is a real-time dynamic adjustment of the supply chain network by the collected supply chain data:
;
Wherein the method comprises the steps of The policy value is adjusted for the supply chain at time t,AndIn order to adjust the parameters of the device,For a weight value of the flow velocity at time t,Weights for time tAcorder quantity and order delivery time;
Iterative computation until And when the value reaches the maximum, taking all the input parameters at the moment as adjustment values to carry out supply chain network adjustment.
5. The method for managing a supply chain based on industrial Internet identification resolution as set forth in claim 4, wherein said predicting the future state of the supply chain is to predict the future state of the supply chain after performing anomaly detection and dynamic adjustment of the supply chain network:
;
Wherein the method comprises the steps of Is time ofIs used to determine the supply chain feature vector of (1),Is time ofIs characterized in that,AndIs characterized byIs used for the weight of the (c),、AndIn order to adjust the parameters of the shape of the prediction function,As a function of the normalization,As a total number of features,Is a time delay;
Time of acquisition After the supply chain feature vectors of (a), combining the supply chain data in the feature vectors to form a timeIs provided for the supply chain status data of (a).
6. The method for managing a supply chain based on industrial Internet identification resolution as set forth in claim 5, wherein the step of displaying by the supply chain platform means that the supply chain platform displays the collected and analyzed data to the supply chain network participants through a visual control interaction panel, wherein the visual control interaction panel comprises suppliers, sellers, logistics providers and supply chain platform staff, and the supply chain platform provides interactive data services for the supply chain network participants and allows for inquiring and managing the supply chain by the industrial Internet.
7. The method for managing a supply chain based on industrial Internet identification analysis according to claim 6, wherein the step of collecting data by the database and implementing data security protection measures is that the database stores collected supply chain network data, a decision tree model, an exception evaluation function, an exception analysis result, an exception handling measure process, supply chain dynamic adjustment measures and a future state prediction result, the database sets security access authority and access passwords for the stored data and keeps monitoring the stored data when the data is accessed, and the database scans the accessed data to generate detection records and synchronously stores the detection records after the data access is finished.
8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method according to any one of claims 1 to 7 when the computer program is executed.
9. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method according to any one of claims 1 to 7.
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