CN116232854A

CN116232854A - CPS node fault identification method and system based on logic fault probe

Info

Publication number: CN116232854A
Application number: CN202310210815.1A
Authority: CN
Inventors: 何斌; 王怡翔; 李刚; 王志鹏; 陆萍; 周艳敏; 程斌; 朱忠攀; 蒋烁; 张朋朋; 李鑫; 朱晗
Original assignee: Tongji University
Current assignee: Tongji University
Priority date: 2023-03-07
Filing date: 2023-03-07
Publication date: 2023-06-06

Abstract

The application discloses a CPS node fault identification method and system based on a logic fault probe, wherein in the method, for each CPS node in a CPS node cluster, sampling is carried out based on a signal sampling module configured for the CPS node so as to determine corresponding CPS node state data; wherein the CPS node state information includes node modality state; based on node identification information of each CPS node, respectively identifying the sampled CPS node state data; and performing fault analysis according to the identified CPS node state data to determine a fault state result of each CPS node in the CPS node cluster. Therefore, a set of complete and feasible CPS logic fault probe technology is provided, the fault characteristics of each node in the CPS system can be automatically identified and positioned, and the automatic monitoring of unmanned system faults is realized.

Description

CPS node fault identification method and system based on logic fault probe

Technical Field

The application belongs to the technical field of the Internet of things, and particularly relates to a CPS node fault identification method, electronic equipment and a storage medium.

Background

The Cyber-Physical Systems (CPS) is a computer system based on algorithmic control or monitoring. In the information physical system, the physical world and the virtual world are tightly interwoven together, can operate on different space and time scales, show a plurality of different behavior modes, interact with the external environment, and form the combination and coordination of the physical elements and the computing elements at a higher level. The CPS is essentially that the state of the physical world is monitored and calculated through the virtual world, a closed loop with automatic data flow is constructed, interaction between the physical world and the virtual world is formed, and finally a control strategy for the physical entity is generated.

A failure refers to a deviation of certain characteristics or parameters of the system from normal, thereby causing the system to fail to perform its intended function. Faults interfere with and prevent the operation of normal systems and must be diagnosed as early as possible in order to avoid serious consequences. Fault detection refers to detecting whether a system has failed. The existing fault detection means are mainly divided into three types, namely analysis model-based, qualitative experience-based and data-driven.

Along with the gradual expansion of the CPS application field, each node in the CPS application environment needs to be monitored and maintained, the traditional fault identification and positioning are mainly performed on the manual site operation, and a unified automatic monitoring tool is lacking, so that the automatic identification and positioning of the fault characteristics of each node in the information flow of the multi-unmanned system are key technical problems to be solved in the CPS field at present.

In view of the above problems, currently, no preferred technical solution is proposed.

Disclosure of Invention

The embodiment of the application provides a CPS node fault identification method, electronic equipment and storage medium, which are used for at least solving one of the technical problems.

In a first aspect, an embodiment of the present application provides a method for identifying a fault of a CPS node, including: for each CPS node in a CPS node cluster, sampling based on a signal sampling module configured for the CPS node to determine corresponding CPS node state data; wherein the CPS node state information includes node modality state; based on node identification information of each CPS node, respectively identifying the sampled CPS node state data; and performing fault analysis according to the identified CPS node state data to determine a fault state result of each CPS node in the CPS node cluster.

In a second aspect, embodiments of the present application provide an electronic device, including: the system comprises at least one processor and a memory communicatively coupled to the at least one processor, wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the steps of the method described above.

In a third aspect, embodiments of the present application provide a storage medium having stored therein one or more programs including execution instructions that are readable and executable by an electronic device (including, but not limited to, a computer, a server, or a network device, etc.) for performing the steps of the methods described herein.

In a fourth aspect, embodiments of the present application also provide a computer program product comprising a computer program stored on a storage medium, the computer program comprising program instructions which, when executed by a computer, cause the computer to perform the steps of the above-described method.

The beneficial effects of this application embodiment lie in:

the distributed signal sampling module is utilized to collect and identify the state data of each node in the CPS node cluster, and further, through carrying out fault analysis on the node state data, a set of complete and feasible CPS logic fault probe technology is provided, automatic monitoring on unmanned system faults is realized, and fault characteristics of each node in the CPS system can be automatically identified and positioned.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 illustrates a flowchart of an example of a CPS node failure identification method according to an embodiment of the present application;

FIG. 2 illustrates a flowchart of one example of a process of determining CPS node status data according to an embodiment of the present application;

FIG. 3 illustrates a schematic diagram of a CPS node system based on CPS logical fault probe technology according to an example of an embodiment of the present application;

FIG. 4 is a flowchart illustrating an example of a node failure recognition method according to the CPS node system of FIG. 3;

FIG. 5 shows a schematic diagram of an example of a generation process of a multiple unmanned system fault test data set according to S440 in FIG. 4;

fig. 6 is a schematic structural diagram of an embodiment of an electronic device of the present application.

Detailed Description

For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.

It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be combined with each other.

The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.

In this application, "module," "system," and the like refer to a related entity, either hardware, a combination of hardware and software, or software in execution, as applied to a computer. In particular, for example, an element may be, but is not limited to being, a process running on a processor, an object, an executable, a thread of execution, a program, and/or a computer. Also, the application or script running on the server, the server may be an element. One or more elements may be in processes and/or threads of execution, and elements may be localized on one computer and/or distributed between two or more computers, and may be run by various computer readable media. The elements may also communicate by way of local and/or remote processes in accordance with a signal having one or more data packets, e.g., a signal from one data packet interacting with another element in a local system, distributed system, and/or across a network of the internet with other systems by way of the signal.

Finally, it is also noted that, in this document, the terms "comprises," comprising, "and" includes not only those elements but also other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.

Fig. 1 shows a flowchart of an example of a CPS node failure recognition method according to an embodiment of the application.

As shown in fig. 1, in S110, for each CPS node in the CPS node cluster, sampling is performed based on a signal sampling module configured for the CPS node to determine corresponding CPS node state data. Here, CPS node state information includes node modal states, such as node motion gestures, node accelerations, etc., to enable collection of modal data for each node in the unmanned system.

In some examples of embodiments of the present application, the signal sampling module may include various types of modal sampling units, and may include one or more of the following: acceleration sensor, gyroscope, pressure sensor to realize the information acquisition to all kinds of modal parameter. Optionally, the signal sampling module may further include one or more of: the system comprises a laser radar, a depth sensor and a temperature sensor, so as to realize information acquisition of various system working environment parameters.

In S120, the sampled respective CPS node status data are individually identified based on the node identification information of the respective CPS nodes.

In some examples of embodiments of the present application, each CPS node in the unmanned system has unique corresponding node identification information, and a corresponding identifier is added to the sampled CPS node status data after the signal sampling module completes the sampling operation. In other examples of embodiments of the present application, identification of deployed nodes may be accomplished using a distributed probe number of a signal sampling module or its communication address.

In some service scenarios, each node in the CPS node cluster does not have unified information identification distinction originally, and at this time, the identification of different nodes can be realized by using the communication address of the signal sampling module. Specifically, each signal sampling module or node maintains a node sampling module configuration table with the same content, and a plurality of node identification information and corresponding module communication addresses in the CPS system are recorded in the table. After the state data of the CPS nodes are obtained through the signal sampling module, the module communication address of the signal acquisition module corresponding to the CPS node state data is determined according to the sampled CPS node state data. And then, determining node identification information of CPS nodes according to the module communication address and a preset node sampling module configuration table, and further identifying corresponding CPS node state data according to the determined node identification information of the CPS nodes.

In S130, a failure analysis is performed according to the identified respective CPS node status data to determine a failure status result for each CPS node in the CPS node cluster. Here, various preset fault analysis algorithms may be used to process node status data in the CPS system, so as to obtain fault status results of each CPS node in the unmanned system.

In the embodiment of the application, by deploying the corresponding sampling probes for each node in the CPS node system, the state data of each node can be collected in real time, and the system integration of the physical world and the virtual world is realized by the whole-link online monitoring, diagnosis and control framework of the system fault.

In some examples of embodiments of the present application, after determining the failure state results for each CPS node in the cluster, the determined failure state results for each CPS node in the CPS node cluster may also be stored, such that the stored failure state results can be used to trace back the state of the CPS node at the corresponding historical moment. Therefore, the comprehensive backtracking of the states of each CPS node is realized by generating the fault test data set of the multi-unmanned system.

With regard to S130 described above, in some embodiments, a bayesian network may be constructed based on the identified respective CPS node state data, wherein conditional probability information for each CPS node is defined in the bayesian network. Furthermore, known fault information of each CPS node is obtained, the Bayesian network is updated based on the known fault information, and fault diagnosis information of each CPS node is calculated based on the updated Bayesian network. Therefore, the state data of each CPS node collected by the logic fault probe is interleaved and analyzed through the Bayesian network, and the accuracy of the identified and positioned node fault can be effectively ensured.

In some examples of embodiments of the present application, after determining the failure state results of each CPS node in the cluster, the determined failure state results of each CPS node may be categorized according to a preset failure state category. Illustratively, the fault state categories include node bias faults and node efficiency loss faults, so that classification of the node fault states is realized, and comprehensive understanding and analysis of system faults by management staff can be facilitated.

In an additional or alternative embodiment, after determining the failure status results of each node in the CPS system, the node association features between each CPS node in the CPS node cluster may also be obtained, and then, based on the cross convergence mapping algorithm and the node association features, the failure status results of each CPS node may be analyzed to construct a node failure map. By way of example, a fault diagram can be constructed based on a cross convergence mapping algorithm according to obvious nonlinear and weak coupling characteristics among all nodes in a cluster system, so that the causal relationship between all nodes and fault diagnosis information in one direction or two directions can be analyzed, and a richer reference dimension can be provided for management staff to system fault analysis.

In some cases, the sampled node state data may be interacted between each node in the CPS system, and the state data of each node may be updated and calibrated according to the state data of other nodes.

Fig. 2 shows a flowchart of an example of a process of determining CPS node status data according to an embodiment of the application.

As shown in fig. 2, in S210, first CPS node state data is determined based on a first signal sampling module configured by the first CPS node. Here, the first CPS node may be any node in the CPS node system, and should not be limited herein.

In S220, corresponding second CPS node status data is received from a second signal sampling module configured by at least one second CPS node, respectively. Here, the second CPS node may be any node or a designated node in the CPS node system other than the first CPS node. In some embodiments, the second CPS node may be a node that is either positionally adjacent to the first CPS node or has an association on a particular service.

In S230, the first CPS node status data is calibrated based on the relative orientation information of the first CPS node and each of the second CPS nodes and the corresponding second CPS node status data. It will be appreciated that the physical relative orientation between the various nodes in the unmanned system is fixed and measurable, and when there is no match between the differences in the node state data and the fixed physical relative orientation, the state data can be calibrated to ensure the accuracy of the determined state data.

In some additional or alternative embodiments, the currently determined state data may also be calibrated based on historical state data of the node to further improve the accuracy of the currently determined state data. Specifically, third CPS node state data is determined based on a third signal sampling module configured by the third CPS node. Furthermore, a historical node state data set of the third CPS node is obtained, and the third CPS node state data is updated according to the historical node state data set.

Fig. 3 shows a schematic structural diagram of a CPS node system based on the CPS logic failure probe technique according to an example of the embodiment of the present application.

As shown in fig. 3, the CPS node system based on the CPS logic failure probe technology may include a multi-unmanned system composed of a plurality of CPS nodes, a logic failure probe module, and a monitoring platform. In the logic fault probe module, scene perception of node states is achieved based on distributed signal sampling modules deployed for each node. Finally, the node fault information can be presented to the manager in real time through the monitoring platform through the display terminal or the monitoring host.

Aiming at different typical scenes, the CPS node system can extract fault characteristics of each node of the CPS system information flow, and the faults of multiple unmanned systems in the CPS system are identified and positioned by using methods such as Bayesian network, cross convergence mapping and the like.

Fig. 4 shows an operation flowchart of an example of a node failure recognition method according to the CPS node system in fig. 3.

As shown in fig. 4, in S410, the distributed wireless status scene sensing system monitors and records the status of each information flow node of the CPS in real time, and transmits the status to the uploading cloud server through wireless device aggregation.

In S420, the information collected by each information flow node is decomposed by the fieldbus.

In S430, the fault identification and location of the CPS system is performed by using the method of bayesian network, cross convergence mapping, etc. through the logical fault probe.

In S440, the CPS data storage stores the fault identification and localization information, generating a multiple unmanned system fault test dataset. Therefore, the omnibearing backtracking of the states of the nodes of each information flow of CPS is realized.

Fig. 5 shows a schematic diagram of an example of a generation process of a multi-unmanned system failure test data set according to S440 in fig. 4.

As shown in fig. 5, a signal sampling device is placed at each information flow node of the multi-unmanned system, in which a single device and surrounding neighboring devices can measure relative position, speed and acceleration with each other, and the system and the environmental conditions interacting with the system are known and analyzed by the distributed wireless state scene perception system.

In some examples of embodiments of the present application, information collected by the fieldbus pair of information flow nodes is aggregated and decomposed into smaller fault information, and fault diagnosis information is generated concurrently using coordinated operations of a multi-core processor (spatial parallelism) or a multi-threading technology (temporal parallelism), including both cases of offset faults and efficiency losses.

Specifically, the calculation formula of the relationship between the fault information and the environmental conditions of the system interaction is as follows:

wherein Ω _i As an efficiency matrix, x _ｉ (t) is a fault state input, f _ｉ And (t) is the offset fault input quantity.

Further, according to the information collected by the distributed CPS signal sampling module, the physical model of the multi-unmanned system equipment and the probability information of the fault condition after the equipment is extracted are called from the CPS data storage module. Specifically, the CPS signal processing module performs fault diagnosis and storage of extraction equipment according to Bayesian network, cross convergence mapping and other algorithms, and provides performance supervision and distributed monitoring and maintenance in the whole interaction process.

In one example of the embodiment of the present application, when performing fault diagnosis by using a bayesian algorithm, information of each detected information flow node (and, CPS node) is connected to each other to form a bayesian network, uncorrelated and/or redundant variables in the bayesian network structure of each information flow node are removed, according to the input known fault of the diagnosis device and the bayesian network from which the uncorrelated and/or redundant variables of each information flow node have been removed, adjacent and/or correlated fault diagnosis information and known fault diagnosis information in the bayesian network of each information flow node are perfected, the conditional probability information of each information flow node is updated, and according to the conditional probability information, the maximum likelihood fault diagnosis information corresponding to each signal flow node is calculated and the effect of the signal flow node in the whole interaction process is evaluated.

In another example of the embodiment of the application, according to obvious nonlinear and weak coupling characteristics of each component part of the multi-unmanned system, a fault diagram is constructed based on a cross convergence mapping algorithm, and a one-way or two-way causal relationship between each information flow node and fault diagnosis information is analyzed.

Fig. 5 shows a timing diagram of an example of a fault identification method of a CPS node according to an embodiment of the application.

As shown in fig. 5, after receiving input of a single-node multi-mode sensor, the CPS node performs preprocessing and initialization judgment on multi-mode data, detects the data through a fault recognition algorithm after the preprocessing of the data is completed, realizes positioning and recognition of faults, and extracts fault information recognized and positioned by a logic fault probe, including cleaning, deleting, formatting and setting a threshold value on the data, generating a multi-unmanned system fault test data set, and constructing a fault diagram according to the recognized faults.

According to the embodiment of the application, an online monitoring, diagnosing and controlling framework of the whole link is constructed, the purpose of realizing system integration of the physical world and the virtual world is achieved, and a multi-unmanned system with high flexibility, adaptability and robustness is built. In addition, CPS data is widely shared on various layers of a multi-layer system architecture, a cloud platform plays a virtual role in CPS, and a field bus and a network entity serve as physical peers to integrate a virtual world and a physical entity. In addition, the cloud platform reserves the expansibility of the system, centralizes production scheduling resources, and has the advantages of high automation degree of CPS node state update and the like.

It should be noted that, for simplicity of description, the foregoing method embodiments are all illustrated as a series of acts, but it should be understood by those skilled in the art that the present application is not limited by the order of acts described, as some steps may be performed in other orders or concurrently in accordance with the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily required in the present application. In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to related descriptions of other embodiments.

In some embodiments, embodiments of the present application provide a non-transitory computer readable storage medium having stored therein one or more programs including execution instructions that can be read and executed by an electronic device (including, but not limited to, a computer, a server, or a network device, etc.) for performing the CPS node failure identification method described herein above.

In some embodiments, embodiments of the present application also provide a computer program product comprising a computer program stored on a non-volatile computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, cause the computer to perform the above-described CPS node failure identification method.

In some embodiments, embodiments of the present application further provide an electronic device, including: the system comprises at least one processor and a memory communicatively coupled to the at least one processor, wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a CPS node failure identification method.

Fig. 6 is a schematic hardware structure of an electronic device for performing a fault identification method of a CPS node according to another embodiment of the application, as shown in fig. 6, the device includes:

one or more processors 610, and a memory 620, one processor 610 being illustrated in fig. 6.

The apparatus for performing the CPS node failure recognition method may further include: an input device 630 and an output device 640.

The processor 610, memory 620, input devices 630, and output devices 640 may be connected by a bus or other means, for example in fig. 6.

The memory 620 is used as a non-volatile computer readable storage medium, and may be used to store a non-volatile software program, a non-volatile computer executable program, and modules, such as program instructions/modules corresponding to the CPS node fault identification method in the embodiments of the present application. The processor 610 executes various functional applications of the server and data processing, i.e., implements the CPS node failure recognition method of the above-described method embodiments, by running nonvolatile software programs, instructions, and modules stored in the memory 620.

Memory 620 may include a storage program area that may store an operating system, at least one application program required for functionality, and a storage data area; the storage data area may store data created according to the use of the voice interaction device, etc. In addition, memory 620 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, memory 620 optionally includes memory remotely located relative to processor 610, which may be connected to the voice interaction device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The input device 630 may receive input numeric or character information and generate signals related to user settings and function control of the voice interaction device. The output device 640 may include a display device such as a display screen.

The one or more modules are stored in the memory 620, which when executed by the one or more processors 610, perform the CPS node failure identification method of any of the method embodiments described above.

The product can execute the method provided by the embodiment of the application, and has the corresponding functional modules and beneficial effects of the execution method. Technical details not described in detail in this embodiment may be found in the methods provided in the embodiments of the present application.

The electronic device of the embodiments of the present application exist in a variety of forms including, but not limited to:

(1) Mobile communication devices, which are characterized by mobile communication functionality and are aimed at providing voice, data communication. Such terminals include smart phones, multimedia phones, functional phones, low-end phones, and the like.

(2) Ultra mobile personal computer equipment, which belongs to the category of personal computers, has the functions of calculation and processing and generally has the characteristic of mobile internet surfing. Such terminals include PDA, MID, and UMPC devices, etc.

(3) Portable entertainment devices such devices can display and play multimedia content. The device comprises an audio player, a video player, a palm game machine, an electronic book, an intelligent toy and a portable vehicle navigation device.

(4) Other on-board electronic devices with data interaction functions, such as on-board devices mounted on vehicles.

The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

From the above description of embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus a general purpose hardware platform, or may be implemented by hardware. Based on such understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the related art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform the method described in the respective embodiments or some parts of the embodiments.

Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and are not limiting thereof; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions.

Claims

1. A CPS node fault identification method comprising:

for each CPS node in a CPS node cluster, sampling based on a signal sampling module configured for the CPS node to determine corresponding CPS node state data; wherein the CPS node state information includes node modality state;

based on node identification information of each CPS node, respectively identifying the sampled CPS node state data;

and performing fault analysis according to the identified CPS node state data to determine a fault state result of each CPS node in the CPS node cluster.

2. The method as recited in claim 1, wherein after performing a failure analysis based on the identified respective CPS node status data to determine a failure status result for each CPS node in the CPS node cluster, the method further comprises:

the determined fault state results for each CPS node in the CPS node cluster are stored such that the stored fault state results can be used to trace back the state of the CPS node at the corresponding historical moment.

3. The method as recited in claim 1, wherein for each CPS node in the CPS node cluster, sampling based on a signal sampling module configured for the CPS node to determine corresponding CPS node state data, comprises:

determining first CPS node state data based on a first signal sampling module configured by the first CPS node;

the second signal sampling module is configured from at least one second CPS node and respectively receives corresponding second CPS node state data;

and calibrating the first CPS node state data based on the relative azimuth information of the first CPS node and each second CPS node and the corresponding second CPS node state data.

4. A method as recited in claim 1 or 3, wherein the sampling is performed for each CPS node in the CPS node cluster based on a signal sampling module configured for the CPS node to determine corresponding CPS node state data, the method further comprising:

determining third CPS node state data based on a third signal sampling module configured by the third CPS node;

and acquiring a historical node state data set of the third CPS node, and updating the third CPS node state data according to the historical node state data set.

5. The method as recited in claim 1, wherein said performing a failure analysis based on the identified respective CPS node status data to determine a failure status result for each CPS node in the CPS node cluster comprises:

constructing a bayesian network based on the identified respective CPS node state data; wherein conditional probability information for each CPS node is defined in the Bayesian network;

acquiring known fault information of each CPS node, and updating the Bayesian network based on the known fault information;

calculating fault diagnosis information of each CPS node based on the updated Bayesian network;

and determining a fault state result of each CPS node in the CPS node cluster according to the fault diagnosis information of each CPS node.

6. The method as recited in claim 5, wherein after determining a failure state result for each CPS node in the CPS node cluster based on the failure diagnosis information of the individual CPS nodes, the method further comprises:

acquiring node association characteristics among CPS nodes in the CPS node cluster;

and analyzing the fault state results of each CPS node based on a cross convergence mapping algorithm and the node association characteristics to construct a node fault graph.

7. The method as recited in claim 1, wherein after performing a failure analysis based on the identified respective CPS node status data to determine a failure status result for each CPS node in the CPS node cluster, the method further comprises:

classifying the determined fault state results of each CPS node according to the preset fault state category; wherein the fault status categories include node bias faults and node efficiency loss faults.

8. The method according to claim 1, wherein the identifying the sampled respective CPS node status data based on the node identification information of the respective CPS nodes, respectively, comprises:

determining module communication addresses of signal acquisition modules corresponding to the CPS node state data according to the sampled CPS node state data;

determining node identification information of the CPS node according to the module communication address and a preset node sampling module configuration table; the node sampling module configuration table comprises a plurality of node identification information and corresponding module communication addresses;

and identifying the corresponding CPS node state data according to the determined node identification information of the CPS node.

9. An electronic device, comprising: at least one processor, and a memory communicatively coupled to the at least one processor, wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the steps of the method of any one of claims 1-8.

10. A storage medium having stored thereon a computer program, which when executed by a processor performs the steps of the method according to any of claims 1-8.