CN119676278A - One-stop multi-source data acquisition and monitoring systems and equipment for industrial use - Google Patents
One-stop multi-source data acquisition and monitoring systems and equipment for industrial use Download PDFInfo
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Abstract
The application relates to the technical field of data processing and discloses an industrial one-stop multi-source data acquisition and monitoring system and equipment, wherein the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring multi-source industrial monitoring data and generating data flow input nodes; the system comprises a data flow model, a data preprocessing module, a data conversion module, a task scheduling module and an abnormality detection module, wherein the data flow model is used for generating a data flow model, the data preprocessing module is used for processing input data in the data flow model to generate a normalized data flow, the data conversion module is used for dynamically converting data nodes in the normalized data flow according to different data protocols to generate a compatible data flow, the task scheduling module is used for optimizing the data flow according to the compatible data flow and combining node priority and a dynamic scheduling strategy in the data flow model and distributing the data flow to a target node, and the abnormality detection module is used for analyzing abnormal states of the optimized data flow and judging whether the data flow is abnormal according to preset rules. The technical scheme in the disclosure can promote the compatibility and reliability of the monitoring system.
Description
Technical Field
The application relates to the technical field of data processing, in particular to an industrial one-stop multi-source data acquisition and monitoring system and equipment.
Background
In the context of rapid global economic development, industrial automation and intelligent manufacturing have become important trends to promote the upgrading and transformation of various industries. Along with the continuous maturity of the internet of things technology, the demands of enterprises for multi-source data acquisition, real-time analysis and intelligent monitoring present a rapidly growing situation. In particular, in the intelligent fields of smart water affairs, smart cities, smart energy sources, smart construction, etc., enterprises desire to improve production efficiency, optimize resource allocation, and further reduce operation costs to enhance market competitiveness by more efficient data management means.
In the prior art, an industrial data acquisition and monitoring system has basic environmental parameter monitoring functions, such as acquisition and uploading of temperature, humidity, pressure and other data, and supports integration of various communication protocols. However, these systems exhibit large limitations in terms of compatibility and functional scalability in complex scenarios, and are difficult to accommodate the dynamic processing requirements of multi-source data. In addition, the existing system often depends on static rule configuration in task scheduling and data conversion, so that flexibility is insufficient in practical application, and particularly poor performance is caused in the scenes of multi-protocol data interaction and complex task distribution.
In summary, the existing industrial monitoring system has significant shortcomings in compatibility, reliability and intelligent data processing, and is difficult to meet the actual requirements of efficient and diversified data management in an intelligent service scene.
Disclosure of Invention
The application provides an industrial one-stop type multi-source data acquisition and monitoring system and industrial one-stop type multi-source data acquisition and monitoring equipment, and aims to solve the technical problems of the existing industrial monitoring system in aspects of compatibility, reliability and intelligence.
Additional aspects and advantages of the disclosure will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the disclosure.
According to a first aspect of the disclosure, a system for collecting and monitoring industrial one-stop multi-source data is provided, which comprises a data collecting module, a data preprocessing module, a task scheduling module and an abnormality detecting module, wherein the data collecting module is used for accessing external equipment through various data interfaces to obtain multi-source industrial monitoring data and generating data stream input nodes so as to construct a data stream model based on a directed acyclic graph, the data preprocessing module is used for formatting, detecting abnormal values and carrying out complement processing on the input data in the data stream model so as to generate a normalized data stream, the data converting module is used for dynamically converting the data nodes in the normalized data stream according to different data protocols so as to generate compatible data streams, the task scheduling module is used for carrying out optimization processing on the data streams according to the compatible data streams and combining node priority and dynamic scheduling strategies in the data stream model, and distributing the data streams to target nodes, and the abnormality detecting module is used for carrying out abnormal state analysis on the optimized data streams, judging whether abnormality exists according to preset rules and generating alarm signals and triggering alarm devices under abnormal conditions.
According to a second aspect of the present disclosure, there is provided an industrial one-stop multi-source data acquisition and monitoring device configured with the industrial one-stop multi-source data acquisition and monitoring system according to the above embodiment, the device including a power supply, a housing including a plurality of external device connection interfaces, a main control unit for providing calculation and software service deployment functions required for system operation, a communication component connected with the main control unit for establishing various types of communication connections with external data sources through various communication means, a sensor component including an environmental monitoring sensor and a device status sensor for acquiring temperature and humidity, air pressure, air concentration, and device operation parameters in real time, and a display and interaction component including a touch screen and a front end interface for displaying monitoring data and system status in real time and providing a user operation interface for parameter configuration and task management.
According to the technical scheme, the novel intelligent control system has at least one of the following advantages and positive effects:
the industrial one-stop multi-source data acquisition and monitoring system comprises a data acquisition module, a data preprocessing module, a data conversion module, a task scheduling module and an abnormality detection module. Wherein:
The data acquisition module is connected with external equipment through various data interfaces, supports acquisition of multi-source industrial monitoring data, and constructs a data flow model based on the directed acyclic graph, so that the adaptation capability of the system to various data sources is improved, and a normalized data basis is provided for subsequent processing.
The data preprocessing module performs formatting, outlier detection and complement processing on input data in the data stream model, so that the influence of data redundancy and deficiency is eliminated, and the data stream is more normalized. The method not only improves the effectiveness and consistency of the data, but also provides high-quality input for subsequent data conversion and task scheduling.
The data conversion module utilizes a dynamic conversion mechanism to carry out protocol adaptation on the data nodes in the normalized data stream according to different data protocols, so as to generate compatible data streams. The module improves the flexibility and expansibility of the system in a multi-protocol communication environment, and can adapt to the requirements of different industrial application scenes.
And the task scheduling module optimizes the compatible data stream and distributes the compatible data stream to the target node by combining the node priority and the dynamic scheduling strategy in the data stream model. The efficiency of task execution and the reasonable allocation capability of resources are effectively improved, and orderly transmission and efficient use of the data stream in a complex environment are ensured.
Finally, the abnormality detection module judges abnormality according to a preset rule and generates an alarm signal by carrying out state analysis on the optimized data stream. The module can respond to abnormal conditions in the system in real time, and the reliability of the data monitoring process and the safety of the system operation are ensured. On the basis of realizing the cooperative work of the modules, the technical scheme remarkably enhances the compatibility, the reliability and the intelligent data processing capability of the system, and meets the diversified requirements in the industrial monitoring scene.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a block diagram of a one-stop multi-source data acquisition and monitoring system for industrial use in accordance with one embodiment of the present disclosure;
FIG. 2 is a block diagram of a system for one-stop multi-source data acquisition and monitoring for industrial use in accordance with another embodiment of the present disclosure;
FIG. 3 is a schematic diagram of a workflow of a one-stop multi-source data acquisition and monitoring system for industrial use in accordance with an embodiment of the present disclosure;
FIG. 4 is a schematic diagram of a software service architecture of an industrial one-stop multi-source data acquisition and monitoring system according to an embodiment of the present disclosure;
FIG. 5 is a user operation page diagram of an industrial one-stop multi-source data acquisition and monitoring system according to an embodiment of the present disclosure;
Fig. 6 is a schematic structural diagram of a one-station multi-source data acquisition and monitoring device for industrial use in an embodiment of the present disclosure.
In the drawings, the same or corresponding reference numerals indicate the same or corresponding parts.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the present specification. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present description as detailed in the accompanying claims.
The terminology used in the description presented herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the description. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any or all possible combinations of one or more of the associated listed items.
It should be understood that although the terms first, second, third, etc. may be used in this specification to describe various information, these information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, the first information may also be referred to as second information, and similarly, the second information may also be referred to as first information, without departing from the scope of the present description. The term "if" as used herein may be interpreted as "at..once" or "when..once" or "in response to a determination", depending on the context.
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein, but rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the disclosed aspects may be practiced without one or more of the specific details, or with other methods, components, devices, steps, etc. In other instances, well-known methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of the disclosure.
Moreover, the drawings are only schematic illustrations and are not necessarily drawn to scale. The block diagrams depicted in the figures are merely functional entities and do not necessarily correspond to physically separate entities. That is, the functional entities may be implemented in software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
In an example embodiment of the present disclosure, an industrial one-stop multi-source data acquisition and monitoring system is first provided. Fig. 1 schematically illustrates a block diagram of an industrial one-stop multi-source data acquisition and monitoring system in accordance with an embodiment of the present disclosure. Referring to fig. 1, the industrial one-stop multi-source data acquisition and monitoring system comprises a data acquisition module 1, a data preprocessing module 2, a data conversion module 3, a task scheduling module 4 and an abnormality detection module 5. The system comprises a data acquisition module 1, a data preprocessing module 2, a data conversion module 3, a task scheduling module 4 and an abnormality detection module 5, wherein the data acquisition module 1 can be used for accessing external equipment through various data interfaces to acquire multi-source industrial monitoring data and generate data stream input nodes to construct a directed acyclic graph-based data stream model, the data preprocessing module 2 can be used for formatting, detecting abnormal values and carrying out complement processing on the input data in the data stream model to generate normalized data streams, the data conversion module 3 can be used for carrying out dynamic conversion on the data nodes in the normalized data streams according to different data protocols to generate compatible data streams, the task scheduling module 4 can be used for carrying out optimization processing on the data streams according to the compatible data streams and combining node priority and dynamic scheduling strategies in the data stream model and distributing the data streams to a target node, and the abnormality detection module 5 can be used for carrying out abnormal state analysis on the optimized data streams, judging whether abnormal conditions exist or not according to preset rules and generating alarm signals and triggering alarm devices under abnormal conditions.
In the operation process of the one-station multi-source data acquisition and monitoring system in the embodiment, the data acquisition module 1 is firstly connected with external equipment through various data interfaces to acquire multi-source industrial monitoring data in real time and generate data stream input nodes, so that a data stream model based on a directed acyclic graph is constructed, and an organized data basis is provided for subsequent processing. Then, the input data in the data flow model is transmitted to the data preprocessing module 2 for processing, redundant, inconsistent or missing data is corrected into a normalized data flow by formatting, outlier detection and complementation processing, the integrity and consistency of the data are ensured, and a foundation is laid for further conversion of the data. Then, the data conversion module 3 dynamically converts the data nodes in the normalized data stream according to a plurality of preset communication protocol rules to generate compatible data streams which can adapt to different communication protocols, thereby meeting the communication requirements under the multi-protocol environment and realizing flexible data transmission. On the basis, the task scheduling module 4 performs optimization processing on the data stream according to the compatible data stream and combining node priority information and a dynamic scheduling strategy in a data stream model, and distributes the data stream to a target node so as to realize efficient allocation of tasks and reasonable utilization of resources. Finally, the optimized data stream is input to an abnormality detection module 5 for abnormality analysis, the module judges whether the data stream has an abnormality through a preset rule, generates an alarm signal when the abnormality is detected, and triggers an alarm device to realize timely response, thereby effectively guaranteeing the stable operation of the system and the reliability of data monitoring. The whole process completes the whole process monitoring and management from data acquisition to anomaly detection through orderly cooperative operation among modules.
Next, with reference to fig. 2, the functional principles of the data acquisition module 1, the data preprocessing module 2, the data conversion module 3, the task scheduling module 4, and the anomaly detection module 5 in the industrial one-stop multi-source data acquisition and monitoring system will be described in detail in other embodiments of the present disclosure.
The data acquisition module 1 can be used for accessing external equipment through various data interfaces, acquiring multi-source industrial monitoring data and generating data flow input nodes so as to construct a data flow model based on a directed acyclic graph. The external device may represent an industrial device capable of being connected to the data acquisition module 1 through various data interfaces, including, but not limited to, a sensor, a controller, a measuring instrument, and other industrial devices supporting data output, and may, of course, also be a third party system and a third party database. The industrial monitoring data may represent a data set generated by an external device and received by the data acquisition module 1 for reflecting the operating state of the industrial system or environmental parameters, including, but not limited to, temperature, humidity, pressure, flow, current, voltage, and other measured values of physical or chemical parameters. The data flow model based on the directed acyclic graph can represent a data organization model generated by the data acquisition module 1, the model represents the logic structure of a data flow in the form of the directed acyclic graph (DIRECTED ACYCLIC GRAPH, DAG), wherein each node in the graph corresponds to a single task or data unit of data acquisition, and the directed edges in the graph represent the data dependency relationship among the nodes, and the model has the characteristic of no loop, ensures that a data processing flow is executed according to a predefined sequence, simultaneously supports parallel processing of the data flow among multiple nodes, and improves the processing efficiency of the system and the flexibility of task allocation.
Referring to fig. 2, in some embodiments, the data acquisition module includes a multi-channel interface unit 11, a data synchronization unit 12, a data mapping unit 13, and a node generation unit 14. Wherein:
The multi-channel interface unit 11 may be used to access an external device through a target data interface, collect multi-source industrial monitoring data, and generate a corresponding data stream input node The target data interface may include one or more of a serial port, an ethernet interface, and an I2C bus.
The data synchronization unit 12 may be configured to receive the original data stream in the data stream input node and generate a consistent directed acyclic graph through time stamp alignment and sequence correctionWherein V represents a set of nodes in the data flow model and E represents a set of directed edges in the data flow model. Specifically, the time stamp alignment is based on the following formula:
Wherein, Representing nodesAnd nodeIs used for the time offset of (a),AndRepresenting nodes respectivelyAndIs used for the time stamp of (a),Is an allowable time deviation threshold. When the time offset satisfies the above condition, the data nodes are considered aligned.
Sequence correction adjusts the order of the data streams by defining sequence consistency constraints as follows:
Wherein, Representing nodesAnd nodeTime-sequential relationship between. Nodes in a data stream followOrdering the values of (2) to ensure that the time-dependent relationships of all edges in the data flow model are in order. Through the time stamp pair Ji Heshun sequence correction formula, the logic consistency and the time sequence accuracy of data transmission are ensured.
The data mapping unit 13 may be configured to determine, based on a set of predecessor nodes for each node in the synchronous data streamCalculating node data input statesAnd by processing functionsGenerating output data of a nodeThe calculated relationship is:
,
Wherein, Representing a precursor nodeTo the input nodeIs used for the weight of the (c),Representing the identity of the current node,Representing the identity of the precursor node,Representing an activation function, optionally a ReLU or Sigmoid function, etc., for introducing a nonlinear mapping to enhance the representation capability of the model on the data,Representing nodesThe weight matrix of the processing function is processed,Representing nodesIs included in the offset vector of (a). Therefore, the dependency relation mapping between the data nodes can be realized, and the logic integrity and accuracy of the data flow in the model are ensured.
The node generating unit 14 may be configured to map the node data stream output by the data mapping unit to a standardized input node set, and form an initial path in the directed acyclic graph structure.
The data preprocessing module 2 may be configured to sequentially perform formatting, outlier detection and complement processing on the input data in the data stream model, so as to generate a normalized data stream. The data preprocessing module 2 is used for unifying data from different sources into a standard format by formatting the input data, identifying and processing abnormal data deviating from a normal range by an abnormal value detection method to reject or mark interference data, and filling and repairing vacant information existing in the data by a missing value complement method to generate a normalized data stream.
In some embodiments, referring to fig. 2, the data preprocessing module includes a field mapping unit 21, an outlier detection unit 22, and a missing value padding unit 23. Wherein:
The field mapping unit 21 may be configured to map the field according to a field mapping rule Fields the source data in the input dataMapping to a target format fieldTo perform unified conversion and formatting of data fields.
The outlier detection unit 22 may be configured to perform outlier detection on the result of the formatting process by an outlier detection method based on a standard score, and to reject outlier data. The calculation process of the outlier detection method can be expressed as:
where x represents the current value, and, The mean value of the sample is represented,The standard deviation of the sample is represented, Z represents the standardized deviation value of the data point, and is used for measuring the deviation degree of the current value x and the sample mean mu, and the standard deviation sigma of the sample is taken as a unit to measure. When |Z| exceeds a predetermined threshold, the data point is determined to be an outlier.
The missing value filling unit 23 may be configured to fill the data after the outlier detection by a linear interpolation algorithm to generate the normalized data stream. The calculation process of the linear interpolation algorithm can be expressed as follows:
Wherein, A data point representing the current missing, the value of which is obtained by interpolation,Representing the missing valueThe data value of the previous point in time,Representing the missing valueThe data value at the latter point in time.
The data conversion module 3 may be configured to dynamically convert the data nodes in the normalized data stream according to different data protocols, so as to generate a compatible data stream. The data protocol may represent a standardized communication specification for defining the format, semantics, and rules of data during transmission, including, but not limited to, modbus TCP protocol, OPC-UA protocol, HTTP protocol, MQTT protocol, socket protocol, and Web Service protocol, among other data communication protocols suitable for use in an industrial environment. The compatible data stream can represent the data stream generated after being processed by the data conversion module 3, each data node of the data stream is converted into a format and a structure which meet the requirements of a target data protocol, the compatible data stream can adapt to the communication requirements of different protocols, seamless transmission of data among heterogeneous devices or systems is ensured, meanwhile, data interaction in a multi-protocol environment is supported, and the adaptability and the expansibility of the system are improved.
In some embodiments, the data nodes in the normalized data stream are dynamically converted according to different data protocols to generate compatible data streams, which specifically includes the following technical steps:
Firstly, carrying out protocol matching on data nodes in the normalized data stream, and determining a target protocol type corresponding to each data node by referring to a preset protocol mapping rule table, wherein the target protocol type comprises but is not limited to MQTT, socket and HTTP. Specifically, the data conversion module determines the target protocol type matched with the data node according to a preset protocol mapping rule table by reading the metadata attribute of each data node. The protocol mapping rule table stores a mapping relationship between a source data type and a target protocol type. In the protocol matching process, the most suitable data communication protocol is dynamically selected according to the attribute (such as data format and communication interface type) of the node, and an explicit rule basis is provided for the subsequent analysis and conversion steps.
And then, according to the type of the target protocol, analyzing the content of the data node, and remapping the analyzed fields to a standard field set of the target protocol. Specifically, according to the target protocol type, the content of the data node is subjected to structural analysis. The parsing process includes reading the field structure of the data node, extracting key field information (e.g., timestamp, device ID, data value, etc.), and remapping the parsed fields according to the standard field set of the target protocol. The standard field set is defined according to the format requirements of the target protocol, including but not limited to a protocol header field, a data payload field, and a check field. For example, when mapping data nodes to the HTTP protocol, data values are mapped to payload fields and time stamps are mapped to URL parameters to ensure that the generated data nodes conform to the semantics and format specifications of the target protocol.
And finally, recombining the mapping field according to the encapsulation rule corresponding to the target protocol type, and generating the compatible data stream matched with the target protocol type by adding protocol header information and updating verification information. Specifically, the fields after mapping are subjected to repackaging processing according to the encapsulation rules corresponding to the target protocol types. The encapsulating step includes reorganizing field contents in a standard format of the target protocol, adding protocol header information to identify the data source and transmission path, and updating the check field to ensure data integrity. The update of the check field is calculated according to the check rules of the target protocol, e.g. generating a new check value based on a CRC check or MD5 hash value. The finally packaged data nodes are recombined into compatible data streams conforming to the target protocol format, and different communication protocols can be adapted to realize data transmission, so that the communication requirements under the multi-protocol environment are met.
The task scheduling module can be used for carrying out optimization processing on the data stream and distributing the data stream to a target node according to the compatible data stream and combining the node priority and the dynamic scheduling strategy in the data stream model. The node priority may represent an importance ranking basis of the task scheduling module for executing an order and resource allocation on each data node in the data flow model. The dynamic scheduling policy may represent a rule set for dynamically adjusting the processing sequence and resource allocation of the data stream by the task scheduling module according to the node priority and the real-time operating environment in the data stream model. The dynamic scheduling strategy comprises, but is not limited to, rules of scheduling nodes with lower priority values preferentially to maximize resource utilization, waiting for resource release through a task queuing mechanism under the condition of insufficient resources, and distributing tasks on the premise of ensuring completion of predecessor nodes by combining with the dependency relationship of the tasks.
In some embodiments, referring to fig. 2, the task scheduling module may include a priority calculating unit 41, a dynamic ordering unit 42, and a data stream allocating unit 43. Wherein:
The priority calculating unit 41 may be configured to combine the resource consumption of the nodes according to the node information in the compatible data stream And delay timeAccording to a priority calculation formula:
Determining priority of nodes Wherein, the method comprises the steps of, wherein,AndWeight factors of resource consumption and delay requirement are used for balancing the influence of the resource consumption and the delay requirement on the priority, and the value range satisfies. Resource consumption weighting factorThe system resource occupation measuring method can be used for measuring occupation conditions of tasks on system resources, including but not limited to utilization rate of CPU, GPU and other computing units in the task execution process, bandwidth occupation conditions in data transmission and the like. Time delay demand weight factorMay be used to measure the timeliness of task execution including, but not limited to, task response time, data transfer delay, etc. The unit extracts resource consumption parameters from the data flow model by analyzing the task requirements of each nodeAnd delay demand parameter. Resource consumption parametersRepresenting the computing resources required by the node to execute the task, such as CPU utilization rate, memory occupancy rate, etc., delay requirement parametersIndicating how sensitive the node task execution is to time, such as data transmission delays or task completion time limits.
The dynamic ordering unit 42 may be configured to dynamically order the nodes according to the result of the priority calculating unit, delay and schedule the nodes with low priority, and process the nodes with high priority preferentially. The unit first extracts the priority values of all nodes and sorts the nodes in order from low to high. Nodes with low priority are regarded as priority processing nodes, and nodes with high priority delay scheduling. In the ordering process, the unit also combines the node dependency relationship in the data flow model to ensure that the predecessor node completes tasks before the successor node so as to maintain the logic consistency of the data flow. Under the condition of resource allocation conflict, the dynamic ordering unit dynamically adjusts the scheduling sequence of the nodes according to the current system resource state so as to realize efficient arrangement of tasks and reasonable utilization of resources.
The data stream allocation unit 43 may be configured to distribute the optimized data stream to a corresponding target node according to the scheduling result of the dynamic ordering unit, and adjust an allocation policy to optimize the data stream transmission efficiency when the resource is insufficient. Specifically, the unit first extracts a dynamically ordered sequence of nodes and associates it with a target node in the data stream to determine a distribution path for the data stream. During the distribution process, the data stream distribution unit adjusts the distribution strategy according to the resource state of the target node, including bandwidth distribution, computing resources, storage resources and the like. In addition, when the resources are insufficient, the unit temporarily stores the tasks to be distributed by adopting a dynamic queue mechanism, and redistributes the data flow according to the priority order after the resources are released, so as to ensure the continuity and the transmission efficiency of task distribution. Through the process, the data stream distribution unit can realize high-efficiency transmission of the data stream and load balancing of the target node, so that the overall operation performance of the system is optimized.
The anomaly detection module 5 is used for performing anomaly state analysis on the optimized data stream, judging whether the anomaly exists according to a preset rule, generating an alarm signal under the anomaly condition, and triggering an alarm device. In some embodiments, referring to fig. 2, the abnormality detection module may include an alarm unit 51 and a data trend analysis unit 52. Wherein:
the alarm unit 51 may be configured to monitor the target parameter in the data stream in real time, and determine whether the data exceeds the normal range through the early warning threshold, so as to generate a corresponding alarm signal and trigger the alarm device.
The data trend analysis unit 52 may be configured to analyze the historical monitoring data stream and the real-time monitoring data stream using the long-short term memory network model, and to mark the abnormal nodes according to the analysis result. The long-term and short-term memory network model is a deep learning model based on a recurrent neural network and can be specially used for processing and analyzing time series data.
In some embodiments, the long-term and short-term memory network model is used for analyzing the historical monitoring data flow and the real-time monitoring data flow, and the abnormal nodes are marked according to the analysis result, and the method specifically comprises the following steps:
Normalizing the historical monitoring data stream and the real-time monitoring data stream, and constructing a time sequence matrix based on the normalization result:
Wherein, Represent the firstThe time of each data nodeIs used for the monitoring of the value of (a),Representing the total number of data nodes,Representing the length of the time series.
Matrix the time seriesInputting the long-term and short-term memory network model, calculating the state vector of each time step through a network hidden layer, wherein the calculation formula of the hidden layer state is as follows:
Wherein, The hidden layer state representing the current time step,AndRepresenting a matrix of weights of the network,The offset vector is represented as such,The activation function is represented as a function of the activation,Representing time stepsCorresponding input data vectors, i.e. historical monitoring data flow and real-time monitoring data flow at timeThe observed value of the time of day,Representing a previous time stepIs a hidden layer state of (c).
Calculating the abnormal score of each data node according to the hidden layer state vector, wherein the score calculation formula is as follows:
Wherein, Represent the firstThe anomaly score for the individual node is determined,Mean value representing hidden layer state, when scoringAnd when the preset threshold value is exceeded, marking the corresponding data node as an abnormal node.
In some embodiments, referring to fig. 2, the industrial one-stop multi-source data acquisition and monitoring system further includes a fault detection module 6, which can be used to monitor the system operation state in real time, perform fault detection on the operation state data based on the support vector machine and the isolated forest algorithm, and switch to the backup system through the fault isolation mechanism when the fault is detected.
Specifically, the fault detection module 6 monitors the system operation state in real time, and collects the multidimensional operation state data set of the systemAcquisition timeThe key parameters of the moment include, but are not limited to, CPU utilization, memory occupancy, network delay, power consumption, etc. The module samples the parameters at fixed time intervals by a data acquisition unit to form a time sequence matrixWhereinRepresent the firstThe sequence value of the individual operating state parameters,Is oneIs a matrix of the (c) in the matrix,The number of state parameters is indicated,Indicating the number of time steps. By the matrix, the real-time performance and the integrity of the system running state data are ensured, and high-quality input is provided for subsequent fault detection.
The fault detection module firstly utilizes a Support Vector Machine (SVM) to carry out classification detection on the running state data, and builds a classification model to distinguish a normal state from an abnormal state. The optimization objective of the SVM model is:
Wherein, Normal vector representing classification hyperplane, defining direction of classification boundary; A bias term representing a classification boundary for adjusting a boundary position; Represent the first A relaxation variable for each sample for allowing classification errors; Is a regularization parameter used to balance the trade-off between model complexity and classification error. In the optimization model, the kernel function is used for Implementing a nonlinear mapping in whichRepresenting a feature mapping function, mapping the input data to a high-dimensional feature space. The gaussian kernel function is in the form of:
Wherein, AndTwo data points are provided for each,For the kernel width parameter, the degree of nonlinearity of the mapping is controlled. The module generates a classification model through the training set, and inputs the real-time input data in the detection stageInput to the model, output the classification resultWhereinIndicating whether the current state is normal or abnormal.
Based on the SVM classification detection, the module further utilizes an isolated forest algorithm (iForest) to mine the anomaly data. The isolated forest constructs an isolated tree by randomly dividing a sample feature space, and measures the isolation depth of the sample. The specific process of the isolated forest treatment is as follows:
first, from an operational state dataset Random decimated subsetsWhereinRepresenting the number of orphaned trees.
Then randomly select featuresAnd a segmentation thresholdConstructing a binary splitting rule: entering the left subtree, otherwise entering the right subtree, wherein, Representing the sample at the featureThe value of the above-mentioned value,Is a randomly selected threshold.
Finally, calculate the average isolation depth of each sampleAnd calculates an anomaly score according to the following formula: Wherein, For the sampleIs used for the abnormal scoring of (a),For the average isolation depth to be the same,Representing sample sizeIs used to determine the standard average isolation depth of (1), for normalizing the score. When (when)When approaching 1, sampleIs considered an outlier.
When the module judges that the running state of the system is abnormal through the detection algorithm, the fault isolation mechanism is immediately started to switch to the backup system. The method comprises the steps of firstly evaluating the resource state of a backup system, including the availability of a CPU, a memory and a bandwidth, confirming that the backup system can support the current task demand, then executing data synchronization, copying key parameters of the current task to the backup system through real-time mirror image, ensuring seamless connection of the task after switching, finally switching the task to the backup system, and stopping fault operation of a main system. The fault isolation mechanism realizes high reliability and stability of system operation through quick response, and task interruption and data loss are avoided.
In some embodiments, the workflow in the industrial one-stop multi-source data acquisition and monitoring system is shown in fig. 3, the system is firstly initialized when started, and the system accesses the external device through various data interfaces by starting the monitoring serial port sensor to acquire data from each monitoring device. In the process, after the monitoring serial port sensor receives the transmitted data, the sensor data is acquired and transmitted to the data processing module in a polling reading mode. The module firstly cleans the data to ensure that the data meets the format requirement and complete abnormal value detection. The processed data are normalized according to a preset standard, and then an alarm mechanism judging link is carried out to judge whether the data reach the early warning condition or not.
When the data reach the preset early warning condition, the system can immediately trigger an alarm, an alarm signal is sent out through the loudspeaker alarm equipment, and real-time alarm information is displayed on the LCD display screen. At the same time, the system starts a video monitoring control panel to acquire real-time video stream and display the real-time video stream on a monitoring interface. If the intrusion behavior is detected on the monitoring picture, the system immediately starts intrusion detection and judges whether illegal intrusion behavior exists or not. If the system confirms that the intrusion occurs, an alarm mechanism is automatically triggered, and corresponding safety response is carried out. In the process, the system also starts the front-end web interface configuration, and dynamically adjusts the parameter configuration of the data acquisition service according to the task requirements. The system sets the priority of real-time data acquisition and task scheduling according to the difference of the current tasks, and ensures the efficiency and the precision of data processing. After the data is processed, the data is sent to the target node as required to complete the final data processing task.
In some embodiments, a software service architecture in an industrial one-stop multi-source data acquisition and monitoring system is shown in FIG. 4. Specifically, the system first accesses data from a variety of data sources, including third party databases, sensors, third party systems, and the like. The system can perform data interaction with external equipment through various access protocols, such as database types of Oracle, SQL SERVER, mySQL, REDIS, postgreSQL and the like, and communication protocols of HTTP, MQTT, socket, web Service, modbus TCP and the like, so that comprehensive collection and summarization of data are realized. After data acquisition, the system performs formatting, cleaning, conversion and other processes on the data through the data processing unit. In the process, the data are integrated through the connector module, and enter a data storage stage after being cleaned and formatted. At this stage, the system ensures that the data is effectively stored through mass storage and metadata management techniques, and can be efficiently queried and analyzed. All processed data will be organized into standardized data streams, ensuring their reliability and efficiency in subsequent processing.
The task configuration module is used for flexibly configuring each task in the system, and a user can define different tasks according to requirements and adjust priority. The task scheduling depends on a dynamic scheduling strategy of the system, and the execution sequence of the tasks is optimized through a scheduling algorithm so as to realize optimal resource utilization efficiency and data flow rate. In the data transmission process, the system adjusts the allocation and processing strategy of the data stream in real time according to the priority of the task and the allocation condition of the resource. The data security module provides a guarantee for the operation of the system and ensures the safe transmission and storage of data. The system encrypts sensitive data by using an encryption technology and protects personal privacy data by using a desensitization technology. Meanwhile, through strict authority management, unauthorized users are prevented from accessing data, and the integrity and the safety of the data are ensured.
The system also comprises a real-time monitoring and management function which can monitor important indexes such as task execution, system state, data flow and the like. The monitoring module tracks the circulation condition of the data in real time, tracks and analyzes each stage of task execution, and ensures the stability of the system in the running process. Finally, the data sharing and synchronizing module supports a plurality of synchronizing modes, including real-time synchronization, incremental synchronization and bidirectional synchronization, and ensures the consistency of data between the inside and the outside of the system. The system can select a proper synchronization mode according to the requirements, so that synchronous updating of each data node is realized, and timeliness and reliability of data are ensured.
In some embodiments, a user operation page in an industrial one-stop multi-source data acquisition and monitoring system is shown in fig. 5. Specifically, the user interface provides an integrated data processing platform, wherein a user can select different data input and output modes through the left panel. The user can select a data output mode such as xml output and JSONOutput, excel output, and connect the data output mode with each device or data source through a drag operation, so as to facilitate data acquisition, processing and export. The overall process of the data stream includes data acquisition, data processing and final output. The data is firstly collected through the connected production equipment and Siemens equipment, and enters a data stream processing unit. After the data stream is processed by a filter, character code conversion and the like, a cleaned data stream is generated, and the data is used for subsequent processing links, including abnormal value removal operation. The user can export the data stream from the Microsoft Excel output node, and the data processing result is displayed in real time. In addition, the interface may also present each node and connection path associated with the data stream, allowing the user to conveniently manage and control the path and configuration of the data stream through simple graphical operations. The operation interface is convenient for a user to flexibly configure and monitor in real time in the whole data acquisition and processing process, and ensures effective acquisition, conversion and accurate output of data.
In some embodiments, the software service architecture adopts Java to write a back end, uses Vue to construct a front end interface, and aims to realize efficient data integration and configuration management. Specifically, the back-end service is developed based on a Spring Boot framework and is responsible for data processing, task scheduling and service logic realization. The back-end service has high-efficiency data interaction capability with various databases, large data platforms, internet of things protocols and text files, and flexible expansion capability and high efficiency of the system when processing mass data are ensured. By means of plug-in design, the back-end system follows the opening and closing principle, can be conveniently expanded, and an existing core module does not need to be modified.
The front end interface is constructed by Vue. Js, and a user-friendly operation interface is provided, so that a user can rapidly configure a data source, a target database and a processing flow through simple drag operation without writing codes, and the use threshold of the user is greatly reduced. In the aspect of data storage, the system uses PostgreSQL as a main database solution, can process complex SQL queries and advanced data operations, and ensures the safety, reliability and high efficiency of data. Meanwhile, the system uses MinIO as an object storage service, and ensures that the management of mass data has high availability and expandability by supporting the parallel processing of multiple threads and multiple nodes, data redundancy and automatic fault recovery. In the aspect of communication, the system adopts a communication mode of combining HTTP and WebSocket, wherein HTTP is used for common request and response, and WebSocket is used for realizing data bidirectional real-time transmission between components, so that the efficiency of user interaction and the system performance are improved. In order to ensure high availability and load balance of the system, front-end deployment and request management perform reverse proxy through Nginx, and meanwhile, the performance and stability of the system are further improved by combining an efficient caching mechanism and a dynamic and static separation configuration mode.
The system deployment uses a Docker containerization technique, all components are encapsulated by Docker, and orchestration and management is performed using Docker-Compose. Therefore, a user can quickly start or stop service through a simple Docker command, flexible environment configuration and resource isolation are realized, and efficient management of system components and resources thereof is facilitated. In the aspect of the design of a data processing engine, the concept of streaming computation and task scheduling is consulted, and the configuration of complex data integration tasks is simplified through a graphical drag type operation interface. The core module comprises a data flow engine, a task scheduler and a data cleaning processing module, can support the whole process from multi-source data acquisition to target database storage, and ensures seamless integration of different data sources and protocols. Through the designs, the system can efficiently process various data types and sources, ensure smooth transmission and processing of data streams, and provide convenient and reliable data management services for users.
In some embodiments, referring to fig. 2, the industrial one-stop multi-source data collection and monitoring system further includes a dynamic threshold adjustment module 7, which can be used to dynamically update the early warning threshold of each monitoring parameter by using a bayesian optimization algorithm according to the multi-source industrial monitoring data collected in real time and the statistical result of the historical data.
Specifically, the dynamic threshold adjustment module 7 first performs statistical analysis on the multi-source industrial monitoring data and the historical data collected in real time. Let the monitoring parameter set beWhereinRepresent the firstAnd monitoring the parameters. The module collects current monitoring data in real time through the data acquisition unitIn combination with historical monitoring dataCalculating statistical features, including meansStandard deviation ofDistribution density function. The calculation formula for statistical analysis can be expressed as:
Wherein, Represent the firstHistorical average values of the monitoring parameters reflect central trends of the data; Represent the first Standard deviation of each monitoring parameter reflects the discrete degree of the data; is the number of historical data points; Represent the first The first monitoring parameter in the historical dataA value; a probability density function representing the monitored parameter, describing the distribution characteristics of the data.
The dynamic threshold adjustment module builds a Bayesian optimization model based on the statistical analysis result and the real-time data so as to dynamically adjust the early warning threshold of the monitoring parameter. Setting monitoring parametersIs of the threshold value ofThe goal of bayesian optimization is to maximize the monitoring accuracy of the system and the sensitivity of anomaly detection. The objective function of the optimization problem is:
Wherein, Representing a threshold valueThe corresponding monitoring effect is achieved by the method,Representing a loss function for measuring data pointsExceeding a threshold valueA penalty in the time of the day,Representing data pointsIs described, describing the distribution of the data,A candidate value representing the current monitoring threshold.
The loss function is defined as:
Wherein, Representing a monitored data point; representing an early warning threshold; representing the deviation value of the data point when it exceeds the threshold.
Bayesian optimization model estimates objective functions based on Gaussian Process Regression (GPR)Posterior distribution of (c):
Wherein, Representing a sampled dataset comprising historical and real-time data; mean value representing posterior distribution for estimation The effect of (3); representing the variance of the posterior distribution, which is used to measure the uncertainty of the estimate.
The module dynamically updates the pre-warning threshold for each monitored parameter, employing the desired improvement (Expected Improvement, EI) criteria:
Wherein, Representing the desired improvement value for measuring the selection thresholdIs a potential optimization benefit of (1); a posterior mean value representing the current threshold; An objective function value representing a current optimal threshold; representing uncertainty of the current threshold; representing a normalized score reflecting Exceeding the limitIs to be used as a potential for a vehicle; A cumulative distribution function representing a standard normal distribution; a probability density function representing a standard normal distribution.
By maximisingThe module determines a new optimal thresholdAnd the early warning threshold value of the monitoring parameter is updated in real time, and the change of the data is dynamically adapted, so that the accuracy of anomaly detection and the monitoring efficiency of the system are improved.
In some embodiments, the workflow of the industrial one-stop multi-source data acquisition and monitoring system can comprise that firstly, the system is initialized by starting a main control unit, and an operating system and a necessary driving program are loaded to ensure the normal operation of the equipment. And then, configuring network connection and initializing a 4G module to ensure that the equipment can access the network and ensure that the subsequent data transmission is smooth. At this time, the touch screen can automatically acquire and display the IP address of the terminal, so that the Web page can be conveniently accessed through the IP, and in addition, the environment detection sensor, the loudspeaker and the camera of the terminal can be automatically initialized, so that the environment monitoring and the data acquisition are ready. Next, the system deploys a data integration service, including installing a dock and java environment on the host system. The database, minIO and the nmginx service are started by the docker containerization technology, the front-end page is proxied by the nmginx, and simultaneously the Java back-end service is started, and the back-end service is responsible for receiving the configuration request of the user and managing the data stream. The front-end Vue application interacts with the back-end service through the Web interface to provide a user configuration interface, so that the user can conveniently configure the data stream. In order to ensure that the system can be automatically started after the system is started, systemd services are added, and the software services are set to be powered on and started automatically. In the task configuration stage, a user accesses a Web interface of the data integration service through a browser input address, and enters a task configuration interface after login authentication. The user can select the required data source, target database and data processing flow through the intuitive drag interface. The system supports various data source types, including databases, large data platforms, internet of things equipment and text files, and ensures that different service requirements can be flexibly met. In the data acquisition process, the back-end service acquires the data of the required equipment or database at fixed time according to the task configured by the user. The data is first stored locally as a primary backup to ensure the reliability and integrity of the data, and then the data is transferred in the form of a data stream to the next data processing step for entry into the process flow.
And in the data processing and pushing stage, collected data can be cleaned through a data processing plug-in unit configured on the Web page, abnormal values and repeated data are removed, and the data quality is ensured. After the data is standardized, the data from different sources is converted into a unified format, and the field type parameters are explicitly output. The processed data can be pushed to a configured target database, and meanwhile, the data can be pushed to a designated target system or an external API according to configuration, so that multi-platform data sharing is realized, and different service requirements are met. In the real-time monitoring and alarming links, the system can monitor data change in real time, and when abnormal conditions are detected, the system can trigger an alarming mechanism to respond. In addition, the system can monitor the picture of the camera, when the picture changes, the USB camera can record an abnormal event and send out an audible alarm through the loudspeaker to remind a worker of paying attention. The data integration service can update the Web interface in real time, display the current monitoring data, task state and system log, and facilitate the user to track the system operation condition. The user can check the historical data on the Web interface, and perform task management and configuration adjustment so as to ensure that the system continuously and stably operates and meet the service requirements. In the task scheduling and management stage, the back-end service periodically schedules the configured data acquisition task, so that the automation and high efficiency of the system operation are ensured. The system also supports the suspension, starting and deleting of tasks, and a user can adjust the task configuration at any time so as to optimize the data integration workflow according to the actual business requirements.
According to the industrial one-stop multi-source data acquisition and monitoring system in the embodiment, the accuracy and the system reliability of data acquisition, monitoring and fault diagnosis are effectively improved through a multi-level data processing and optimizing mechanism. The data acquisition module is accessed to external equipment through various interfaces to acquire and generate data flow input nodes, so that a data flow model based on a directed acyclic graph is constructed, and the flexibility and the expansibility of data processing are ensured. In the data preprocessing stage, the system can perform formatting, outlier detection and completion processing on input data, so as to generate normalized data flow, and provide standardized input for subsequent data processing. In the data conversion process, the system can convert nodes of different data protocols according to the target protocol types through dynamic protocol conversion, so that the compatibility of data streams is ensured. The mechanism not only improves the adaptability of the system to various devices and protocols, but also optimizes the processing efficiency of the data stream. In the task scheduling process, the system optimizes the data flow according to the node priority and the dynamic scheduling strategy in the data flow model, and distributes the data flow to the target node, so that the efficiency and the accuracy of data flow transmission are further improved. Through the anomaly detection module, the system can conduct real-time anomaly state analysis on the optimized data stream, generate alarm signals in time and trigger an alarm device when anomalies occur, and effectively guarantee stability and safety of the system. The technology of data trend analysis and node anomaly marking by using the long-short-term memory network model further enhances the recognition capability of the system to complex data modes and ensures accurate detection of potential anomalies. In addition, the dynamic threshold adjustment module dynamically adjusts the early warning threshold of the monitoring parameter by using a Bayesian optimization algorithm based on the data acquired in real time and the historical statistical result, so that the early warning mechanism can be automatically optimized according to the data change, and the intelligent level of early warning is improved. The fault detection module monitors the running state of the system in real time by combining the support vector machine with the isolated forest algorithm and rapidly switches to the backup system through the fault isolation mechanism when a fault occurs, so that the high availability and the disaster recovery capability of the system are ensured. Through the combination of the technical means, the high-efficiency integration of data acquisition, processing, storage and monitoring is realized, the automation level, reliability and flexibility of the system are obviously improved, the ever-increasing multi-source data fusion and real-time processing requirements in industrial monitoring are met, and the method has important application value.
In addition, the embodiment of the disclosure further provides industrial one-stop multi-source data acquisition and monitoring equipment, which is provided with the industrial one-stop multi-source data acquisition and monitoring system in the embodiment, and comprises a power supply, a shell, a main control unit, a communication component, a sensor component, a display and interaction component and a display and interaction component, wherein the shell comprises a plurality of external equipment connection interfaces, the main control unit is used for providing calculation and software service deployment functions required by system operation, the communication component is connected with the main control unit and used for establishing various communication connections with external data sources through various communication modes, the sensor component comprises an environment monitoring sensor and an equipment state sensor and is used for acquiring temperature and humidity, air pressure, air concentration and equipment operation parameters in real time, and the display and interaction component comprises a touch screen and a front end interface and is used for displaying monitoring data and system states in real time and providing a user operation interface for parameter configuration and task management.
The main control unit adopts a Raspberry group 5 main board, a 64-bit ARM architecture, a Raspberry Pi OS system is installed, a CPU is four-core ARM Cortex-A76@2.4 GHz, and the main control unit is responsible for overall control and data processing of the system and software service deployment functions. The communication module comprises an 802.11ac dual-frequency Wi-Fi and an extended EC200A-CN 4G module, and is connected with the main control unit by adopting a USB standard interface to support data transmission in various network environments. The sensor module comprises a temperature and humidity sensor, a gas sensor and the like, an STM32F030 main board is adopted, an SHT20 temperature and humidity chip is adopted as a chip, the sensor is connected with the main control unit through a USB port, and serial port communication is adopted for sensor communication to monitor environmental data in real time. The display screen adopts MHS 3528.5 inch touch screen, the resolution ratio is 340 x 480, SPI input is provided for touch information processing, the touch information processing is carried out with the Pin Pin of the main control unit, the screen is positioned on the front panel of the equipment, and the user operation and information display are facilitated. The remote monitoring and alarming module adopts a USB drive-free camera and a loudspeaker, is connected with the main control unit through a standard USB interface, and the camera acquires video information in real time, and transmits video streams to the intrusion detection service after acquiring the video information, and alarms through the loudspeaker when detecting picture differences. The power supply supports 5V/5A DC power input, is compatible with a Type-C protocol, and provides stable power supply for the main control unit by using a Type-C interface.
Further, referring to FIG. 6, the industrial one-stop multi-source data acquisition and monitoring device further comprises a software deployment (supporting platform service, touch screen program, motion detection program) 61, a temperature and humidity sensor interface 62, a USB expansion board reserved interface 63, an alarm speaker interface 64, a camera interface 65, a gigabit Ethernet interface 66, a 5V/5A Type-C power interface 67, a 2X micro HDMI two-way 4Kp60 interface 68, and a 3.5 inch touch screen 69.
In addition, the industrial one-stop type multi-source data acquisition and monitoring equipment in the embodiment of the disclosure ensures the rapid deployment and flexible application of the equipment through the integrated modularized design, so that the equipment can be conveniently connected with data sources such as sensors and databases of different types, and the diversified data acquisition requirements are met. Meanwhile, the low-power consumption characteristic of the raspberry group 5 and the optimized power management strategy are adopted, so that the overall power consumption of the system is effectively reduced, the operation cost is reduced, the energy efficiency of equipment is improved, and the lower energy consumption in the long-time operation process is ensured. The device supports various data sources and protocols, does not need to frequently switch different tools or platforms to collect and process data, improves the data integration efficiency, and simplifies the workflow. The characteristics enable a user to efficiently complete data processing tasks without complex configuration in the using process. In addition, the equipment has the multifunctional applications of intrusion detection alarm, remote real-time monitoring and the like, and the comprehensive performance and reliability of the equipment can be ensured under the flexible cooperation of hardware and software. In terms of compatibility, the device particularly supports a mainstream domestic database and an operating system, ensures that the system can stably operate in different technical environments, provides safer and more reliable selection, and promotes the development of autonomous controllable technical ecology. Meanwhile, the design of the equipment allows for the operation experience of the user, and the front-end application interface constructed through the Vue enables the user to perform task configuration through drag operation without writing codes, so that the operation threshold is greatly reduced, and the usability of the system is enhanced. In addition, the lightweight design of the device ensures flexible deployment in narrow areas and edge computing applications, meeting application requirements in different environments.
Furthermore, the above-described figures are only schematic illustrations of processes included in the method according to the exemplary embodiments of the present disclosure, and are not intended to be limiting. It will be readily appreciated that the processes shown in the above figures do not indicate or limit the temporal order of these processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, for example, among a plurality of modules.
From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, and includes several instructions to cause a computing device (may be a personal computer, a server, a touch terminal, or a network device, etc.) to perform the method according to the embodiments of the present disclosure.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This disclosure is intended to cover any adaptations, uses, or adaptations of the disclosure following the general principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It is to be understood that the present disclosure is not limited to the precise arrangements and instrumentalities shown in the drawings, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
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