Control system network performance testing method based on field application conditions

Abstract

The application provides a control system network performance test method based on field application conditions, comprising: determining the dimension of control system network performance influence evaluation; building a control system network architecture simulating field application; determining the quasi-state operation model of the control system under network performance changes from the two dimensions of network delay and bandwidth limitation; wherein the quasi-state operation model is used for evaluating the network performance of the control system. Thus, the influence of network performance on the running state of the control system can be evaluated based on the two dimensions of network delay and bandwidth limitation, and through test verification, data statistics and analysis, etc., the optimal solution of network delay and bandwidth limitation that can meet the safe operation of the control system is found, effective guidance and technical support for the field application of the control system are realized, and the safe and stable operation of the system is facilitated.

Description

Technical Field

[0001] This invention relates to the field of network testing technology, and more specifically, to a method for testing the network performance of a control system based on field application conditions. Background Technology

[0002] Industrial control systems are used in complex environments with multi-layered communication networks and diverse communication media. For example, in oilfield projects, networks are primarily based on fiber optics or long-distance satellite transmission. These unique field conditions lead to network latency and bandwidth limitations in the control system network.

[0003] Currently, no mainstream evaluation system provides an effective method for assessing the network performance of control systems based on network latency and bandwidth limitations, thus failing to guide and provide optimal solutions. Therefore, it is necessary to test the network performance of control systems based on network latency and bandwidth limitations, seeking the optimal solution for reliable system operation under typical application environments.

[0004] Current mainstream communication network performance testing technologies are primarily based on positive evaluation, which involves testing network performance using certain methods or techniques. This includes testing specific metrics such as network latency and bandwidth limits, or focusing on improving existing network performance testing methods. However, the research content and problems addressed by these technologies are largely irrelevant to control system applications, and they cannot obtain the optimal solution for understanding the impact of network latency and bandwidth limitations on control system network performance. Summary of the Invention

[0005] In view of the shortcomings of the prior art, the purpose of this invention is to provide a method for testing the network performance of control systems based on field application conditions.

[0006] Firstly, this application provides a method for testing the network performance of a control system based on field application conditions, including:

[0007] Step 1: Determine the dimensions for evaluating the impact of control system network performance;

[0008] Step 2: Build the network architecture of the control system for simulating field applications;

[0009] Step 3: Determine the quasi-state operation model that affects the control system under network performance changes from two dimensions: network latency and bandwidth limitation; wherein, the quasi-state operation model is used to evaluate the network performance of the control system.

[0010] Optionally, step 3 includes:

[0011] Step 3.1: From the perspective of network latency, determine the first quasi-state operation model that affects the network performance of the control system, and determine the weights of different quasi-state operations;

[0012] Step 3.2: From the perspective of bandwidth constraints, determine the second quasi-state operation model that affects the network performance of the control system, and determine the weights of different quasi-state operations.

[0013] Optionally, step 3.1 includes:

[0014] Step 3.1.1: Use open-source software to simulate the communication network and fix the network latency at 30ms;

[0015] Step 3.1.2: Using a single operator station, simulate various operations performed manually on-site in the control system, and set up various components for the control system software application;

[0016] Step 3.1.3: Record the performance response of the control system under different manual operations, and determine the affected operation mode and the control system software components involved under the current network performance conditions based on the response results;

[0017] Step 3.1.4: Based on the degree of influence, assign weights to each quasi-state operation to determine the first preliminary model of the fuzzy state operation;

[0018] Step 3.1.5: Use open-source software to simulate different levels of network latency in the communication network and conduct regression experiments to obtain experimental data corresponding to the network latency;

[0019] Step 3.1.6: Based on the experimental data corresponding to network latency, analyze the accuracy of the operation model, adjust the weights of each quasi-state operation, and obtain the first quasi-state operation model.

[0020] Optionally, step 3.2 includes:

[0021] Step 3.2.1: Establish and configure a test environment that simulates large-scale network applications in the field;

[0022] Step 3.2.2: Under the current network performance conditions, gradually increase the number of CCR operator stations to simulate various operations used manually in the control system field;

[0023] Step 3.2.3: Record the system network traffic data under the current configuration conditions, and determine the operation methods and the number of CCR operation stations that affect network data traffic;

[0024] Step 3.2.4: Determine the mathematical relationships between the factors affecting the network data flow of the control system, and determine the second preliminary model for fuzzy state operations;

[0025] Step 3.2.5: Conduct a regression experiment under a preset bandwidth network communication environment to obtain experimental data corresponding to the preset bandwidth network environment;

[0026] Step 3.2.6: Based on the experimental data corresponding to the preset bandwidth network environment, analyze the accuracy of the operation model, adjust the weights of each quasi-state operation, and obtain the second quasi-state operation model.

[0027] Optionally, step 3 further includes:

[0028] Step 3.3: Conduct combined condition experiments on the first quasi-state operation model;

[0029] Step 3.4: Conduct combined condition experiments on the second quasi-state operation model.

[0030] Optionally, step 3.3 includes:

[0031] Step 3.3.1: Use open-source software to simulate different levels of network latency in the communication network;

[0032] Step 3.3.2: Under different network delay conditions, perform manual operation of the control system based on the first quasi-state model to test the impact on the working state of the control system and obtain the first test data;

[0033] Step 3.3.3: Statistical analysis of the first test data to obtain the optimal solution for the control system to operate normally without being affected under the network latency dimension.

[0034] Optionally, step 3.4 includes:

[0035] Step 3.4.1: Change the configuration of the management switch in the test network to artificially limit the bandwidth in order to simulate the rate that needs to be limited;

[0036] Step 3.4.2: Under network bandwidth constraints, based on the second quasi-state operation model, carry out changes in the configuration of the control system operator station and simulate accurate manual operation to test the impact on the working state of the control system and obtain the second test data;

[0037] Step 3.4.3: Verify the mathematical relationships involved in Step 3.3 using the second test data;

[0038] Step 3.4.4: Analyze the second test data to obtain the optimal solution for the control system to operate normally without being affected under the bandwidth constraint dimension.

[0039] Optionally, it also includes:

[0040] Based on the optimal solution corresponding to the first test data and / or the second test data, guide the field application of the control system;

[0041] Based on feedback data from field applications, the parameters of the first quasi-state operation model and / or the second quasi-state operation model are adjusted.

[0042] Secondly, embodiments of this application provide a control system network performance testing device based on field application conditions, comprising: a processor and a memory, wherein the memory stores executable program instructions, and when the processor calls the program instructions in the memory, the processor is used to:

[0043] Perform the steps of the control system network performance testing method based on field application conditions as described in any one of the first aspects.

[0044] Thirdly, embodiments of this application provide a computer-readable storage medium for storing a program, characterized in that, when the program is executed, it implements the steps of the control system network performance testing method based on field application conditions as described in the first aspect.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] This invention provides a network performance testing model for control systems based on field application conditions. Through model system testing, the impact of network performance on the operating status of the control system is evaluated from two dimensions: network latency and bandwidth limitations. Through testing verification, data statistics and analysis, the optimal solution for network latency and bandwidth limitations that can meet the safe operation of the control system is found. The measured data and conclusions provide effective guidance for the field application of the control system. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort. Other features, objects, and advantages of the present invention will become more apparent by reading the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0048] Figure 1 A schematic diagram of the architecture of a control system network performance test model based on field application conditions provided in an embodiment of this application;

[0049] Figure 2 This is a schematic diagram of the network architecture used in simulating a real-world testing environment in an embodiment of this application;

[0050] Figure 3 A flowchart illustrating the control system network performance testing method based on field application conditions provided in this application embodiment;

[0051] Figure 4This is a schematic diagram of the structure of a quasi-state operation model for evaluating the performance impact of a control system based on network delay dimension, as described in an embodiment of this application.

[0052] Figure 5 This is a schematic diagram of the quasi-state operation model for evaluating the performance impact of a control system based on bandwidth limitation, as described in an embodiment of this application.

[0053] Figure 6 This is a schematic diagram of the network traffic data results of the control system based on the quasi-state operation model under a 100M bandwidth condition, according to an embodiment of this application. Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0055] It should be noted that when a component is said to be "fixed" to another component, it can be directly on the other component or it can be in a middle component. When a component is said to be "connected" to another component, it can be directly connected to the other component or it may be in a middle component.

[0056] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the specification of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0057] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a particular order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented, for example, in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0058] The technical solutions of the present invention and how they solve the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0059] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0060] This application aims to provide a method for testing the network performance of control systems under field application conditions. It evaluates the impact of network performance on the operating state of the control system from two dimensions: network latency and bandwidth limitations, obtaining the minimum allowable network latency and minimum network bandwidth data that do not affect the operating state of the control system. The method in this embodiment can be applied to industrial control systems, such as distributed control systems (DCS) and programmable logic controllers (PLCs), for network performance evaluation under field application conditions, and is used to determine the parameter requirements for network environment performance for the safe operation of the control system.

[0061] Figure 1 This is a schematic diagram of the architecture of a control system network performance test model based on field application conditions, provided in an embodiment of this application. Figure 2 This is a schematic diagram of the network architecture used in simulating a real-world testing environment in an embodiment of this application. For example... Figure 1 As shown, different levels of network communication latency in control systems are simulated using both open-source software and self-made equipment. Network bandwidth is limited by modifying switch configurations, thus building a network environment for control systems based on field applications. Figure 2 As shown, under a single specific experimental condition, generalized operations were performed on the control system using artificial means. The resulting experimental data was normalized to identify quasi-state operations that could affect network performance. Subsequently, quasi-state operations were performed under combined conditions, and the resulting experimental data were analyzed to determine the parameter requirements for network environment performance for the safe operation of the control system.

[0062] Figure 3 A flowchart illustrating the control system network performance testing method based on field application conditions provided in this application embodiment is shown below. Figure 3 As shown, the method in this embodiment may include:

[0063] Step S1: Determine the dimensions for evaluating the impact of the control system network performance.

[0064] In this embodiment, the network performance of the control system is evaluated from two dimensions: network latency and bandwidth limitation.

[0065] Step S2: Build the network architecture of the control system for simulating field applications.

[0066] Step S3: Determine the quasi-state operation model that affects the field manual use of the control system under network performance changes from two dimensions: network latency and bandwidth limitation.

[0067] For example, step S3 above may include:

[0068] Step S3.1: From the perspective of network delay, determine the first quasi-state operation model that affects the network performance of the control system, and determine the weights of different quasi-state operations;

[0069] Step S3.2: From the perspective of bandwidth constraints, determine the second quasi-state operation model that affects the network performance of the control system, and determine the weights of different quasi-state operations.

[0070] In this embodiment, simulation experiments under specific conditions are used to determine the first quasi-state operation model affecting the network performance of the control system in terms of network latency, and to determine the weights of different quasi-state operations. Similarly, simulation experiments under specific conditions are used to determine the second quasi-state operation model affecting the network performance of the control system in terms of bandwidth limitation, and to determine the weights of different quasi-state operations.

[0071] Optionally, step S3.1 may include the following steps:

[0072] Step S3.1.1: Use open-source software to simulate the communication network and fix the network delay at 30ms.

[0073] Step S3.1.2: Using a single operator station, simulate various operations performed manually on-site in the control system, and set up various components for the control system software application.

[0074] Step S3.1.3: Record the performance response of the control system under different manual operations, and determine the affected operation mode and the control system software components involved under the current network performance conditions based on the response results.

[0075] Step S3.1.4: Based on the degree of influence, assign weights to each quasi-state operation to determine the first preliminary model of the fuzzy state operation.

[0076] Step S3.1.5: Use open-source software to simulate different levels of network latency in the communication network and conduct regression experiments to obtain experimental data corresponding to the network latency. For example, latency scenarios of 5ms, 20ms, and 60ms can be simulated.

[0077] Step S3.1.6: Based on the experimental data corresponding to network latency, analyze the accuracy of the operation model, adjust the weights of each quasi-state operation, and determine the first quasi-state operation model.

[0078] For example, Figure 4 This is a schematic diagram of the structure of a quasi-state operation model for evaluating the performance impact of a control system based on network delay dimension, as described in an embodiment of this application.

[0079] Optionally, step S3.2 may include the following steps:

[0080] Step S3.2.1: Establish and configure a test environment that simulates a large-scale network application in the field. For example, a specific 100M bandwidth network communication environment can be used.

[0081] Step S3.2.2: Under the current network performance conditions, gradually increase the number of CCR (Central Control Room) operator stations to simulate various operations performed manually by personnel in the field of the control system. For example, this can involve operations related to various components of the control system software application.

[0082] Step S3.2.3: Record the system network traffic data under the current configuration conditions, and determine the operation mode and the number of CCR operation stations that affect the network data traffic.

[0083] Step S3.2.4: Determine the mathematical relationships between the factors that affect the network data flow of the control system, and determine the second preliminary model of fuzzy state operation.

[0084] Step S3.2.5: Conduct a regression experiment under a preset bandwidth network communication environment to obtain the experimental data corresponding to the preset bandwidth network environment. For example, a regression experiment can be conducted under a 100M bandwidth network communication environment.

[0085] Step S3.2.6: Based on the experimental data corresponding to the preset bandwidth network environment, analyze the accuracy of the operation model, adjust the weights of each quasi-state operation, and determine the second quasi-state operation model.

[0086] For example, Figure 5 This is a schematic diagram of the quasi-state operation model for evaluating the performance impact of a control system based on bandwidth limitation, as described in an embodiment of this application.

[0087] Furthermore, it may also include step S3.3: conducting combined condition experiments on the first quasi-state operation model.

[0088] For example, step S3.3 may include:

[0089] Step S3.3.1: Use open source software to simulate different levels of network latency (5ms, 10ms, 20ms, 30ms, 40ms, 50ms, 60ms).

[0090] Step S3.3.2: Under different network delay conditions, perform manual operation of the control system based on the first quasi-state model to test the impact on the working state of the control system and obtain the first test data.

[0091] Step S3.3.3: Statistical analysis of the first test data to obtain the optimal solution for the control system to operate normally without being affected under the network delay dimension.

[0092] Furthermore, it may also include step S3.4: conducting combined condition experiments on the second quasi-state operation model.

[0093] For example, step S3.4 may include:

[0094] Step S3.4.1: Change the configuration of the management switch in the test network to artificially limit the bandwidth (64Kbps, 128Kbps, 256Kbps, 512Kbps, 10Mbps, 100Mbps, 1000Mbps) to simulate the rate that needs to be limited.

[0095] Step S3.4.2: Under network bandwidth constraints, based on the second quasi-state operation model, carry out changes in the configuration of the control system operator station, simulate accurate manual operation, test the impact on the working state of the control system, and obtain the second test data.

[0096] Step S3.4.3: Verify the mathematical relationships involved in step S3.3 using the second test data.

[0097] Step S3.4.4: Analyze the second test data to obtain the optimal solution for the control system to operate normally without being affected under the bandwidth constraint dimension.

[0098] Specifically, as shown in Tables 1 and 2, Table 1 presents the results of verifying the impact of network delay on the control system performance under the quasi-state operation model in the embodiments of this application. Table 2 presents the optimal solution results obtained from the embodiments of this application where network performance does not affect the normal operation of the control system.

[0099] Table 1

[0100]

[0101] Figure 6 This is a schematic diagram of the network traffic data results of the control system based on the quasi-state operation model under a 100M bandwidth condition, according to an embodiment of this application.

[0102] Table 2

[0103]

[0104] Furthermore, the method in this application embodiment may also include step S4: after the experiment ends, the optimal solution corresponding to the obtained first test data and second test data is used to guide the field application of the control system.

[0105] Furthermore, the method in this embodiment may further include step S5: collecting on-site feedback data and correcting the first quasi-state operation model and the second quasi-state operation model obtained in steps S3.1 and S3.2. Through regression experiments and iterative optimization, the accuracy of the model is continuously improved, and the parameter requirements of the feedback control system for the network environment performance are updated in a timely manner.

[0106] The method in this embodiment evaluates the impact of network performance on the operating state of a control system from two dimensions: network latency and bandwidth limitations. It obtains the minimum allowable network latency and minimum network bandwidth data that do not affect the operating state of the control system. Based on this, it finds the optimal solution for network latency and bandwidth limitations that can meet the safe operation of the control system, providing effective guidance and technical support for field applications of the control system, and contributing to the safe and stable operation of the system.

[0107] It should be noted that those skilled in the art will understand that various aspects of the present invention can be implemented as systems, methods, or program products. Therefore, various aspects of the present invention can be specifically implemented in the following forms: a completely hardware implementation, a completely software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, collectively referred to herein as a "circuit," "module," or "platform."

[0108] Furthermore, embodiments of this application also provide a computer-readable storage medium storing computer-executable instructions, wherein when at least one processor of a user device executes the computer-executable instructions, the user device performs the various possible methods described above.

[0109] Computer-readable media include computer storage media and communication media, wherein communication media include any medium that facilitates the transfer of computer programs from one place to another. Storage media can be any available medium accessible to a general-purpose or special-purpose computer. An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and storage medium can reside in an ASIC. Alternatively, the ASIC can reside in a user device. Of course, the processor and storage medium can also exist as discrete components in a communication device.

[0110] This application also provides a program product including a computer program stored in a readable storage medium. At least one processor of the server can read the computer program from the readable storage medium, and the at least one processor executes the computer program to cause the server to implement any of the methods described in the embodiments of the present invention.

[0111] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks. It can be a portable compact disc read-only memory (CD-ROM) containing program code and can run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto. In this document, a readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0112] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0113] Computer-readable storage media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable storage medium may also be any readable medium other than a readable storage medium that can transmit, propagate, or transfer a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0114] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0115] The various embodiments described in this specification are presented in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0116] The specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art can make various modifications or variations within the scope of the claims, which do not affect the essence of the present invention.

Claims

1. A method for testing the network performance of a control system based on field application conditions, characterized in that, include: Step 1: Determine the dimensions for evaluating the impact of control system network performance; Step 2: Build the network architecture of the control system for simulating field applications; Step 3: Determine the quasi-state operation model of the control system under network performance changes from two dimensions: network latency and bandwidth limitations; Step 3 includes: Step 3.1: From the perspective of network latency, determine the first quasi-state operation model that affects the network performance of the control system, and determine the weights of different quasi-state operations; Step 3.2: From the perspective of bandwidth constraints, determine the second quasi-state operation model that affects the network performance of the control system, and determine the weights of different quasi-state operations; The quasi-state operation refers to the generalized operation performed by the control system under a single specific experimental condition, and the resulting experimental data is normalized to obtain operational data that can affect network performance. Step 3.1 includes: Step 3.1.1: Use open-source software to simulate the communication network and fix the network latency at 30ms; Step 3.1.2: Using a single operator station, simulate various operations performed manually on-site in the control system, and set up various components for the control system software application; Step 3.1.3: Record the performance response of the control system under different manual operations, and determine the affected operation mode and the control system software components involved under the current network performance conditions based on the response results; Step 3.1.4: Based on the degree of influence, assign weights to each quasi-state operation to determine the first preliminary model of the fuzzy state operation. The fuzzy state operation corresponds to the quasi-state operation and refers to the generalized operation manually used by the control system. Step 3.1.5: Use open-source software to simulate different levels of network latency in the communication network and conduct regression experiments to obtain experimental data corresponding to the network latency; Step 3.1.6: Based on the experimental data corresponding to network latency, analyze the accuracy of the first preliminary model, adjust the weights of each quasi-state operation, and obtain the first quasi-state operation model; the accuracy of the first preliminary model refers to the confidence level of the output result of the first preliminary model; Step 3.2 includes: Step 3.2.1: Establish and configure a test environment that simulates large-scale network applications in the field; Step 3.2.2: Under the current network performance conditions, gradually increase the number of CCR operator stations to simulate various operations used manually in the control system field; Step 3.2.3: Record the system network traffic data under the current configuration conditions, and determine the operation methods and the number of CCR operation stations that affect network data traffic; Step 3.2.4: Determine the mathematical relationships between the factors affecting the network data flow of the control system, and determine the second preliminary model of fuzzy state operation. The fuzzy state operation corresponds to the quasi-state operation and refers to the generalized operation manually used by the control system. The mathematical relationships between the factors affecting the network data flow of the control system are as follows: the number of operator stations is directly proportional to the amount of network data, and the number of concurrent operations is directly proportional to the amount of network data. Step 3.2.5: Conduct a regression experiment under a preset bandwidth network communication environment to obtain experimental data corresponding to the preset bandwidth network environment; Step 3.2.6: Based on the experimental data corresponding to the preset bandwidth network environment, analyze the accuracy of the second preliminary model, adjust the weights of each quasi-state operation, and obtain the second quasi-state operation model; the accuracy of the second preliminary model refers to the confidence level of the output results of the second preliminary model.

2. The method for testing the network performance of a control system based on field application conditions according to claim 1, characterized in that, Step 3 also includes: Step 3.3: Conduct combined condition experiments on the first quasi-state operation model; Step 3.4: Conduct combined condition experiments on the second quasi-state operation model.

3. The method for testing the network performance of a control system based on field application conditions according to claim 2, characterized in that, Step 3.3 includes: Step 3.3.1: Use open-source software to simulate different levels of network latency in the communication network; Step 3.3.2: Under different network delay conditions, perform manual operation of the control system based on the first quasi-state operation model to test the impact on the working state of the control system and obtain the first test data; Step 3.3.3: Statistical analysis of the first test data to obtain the optimal solution for the control system to operate normally without being affected under the network latency dimension.

4. The method for testing the network performance of a control system based on field application conditions according to claim 2, characterized in that, Step 3.4 includes: Step 3.4.1: Change the configuration of the management switch in the test network to artificially limit the bandwidth in order to simulate the rate that needs to be limited; Step 3.4.2: Under network bandwidth constraints, based on the second quasi-state operation model, carry out changes in the configuration of the control system operator station and simulate accurate manual operation to test the impact on the working state of the control system and obtain the second test data; Step 3.4.3: Verify the mathematical relationships involved in Step 3.4.2 using the second test data; Step 3.4.4: Analyze the second test data to obtain the optimal solution for the control system to operate normally without being affected under the bandwidth constraint dimension.

5. The method for testing the network performance of a control system based on field application conditions according to claim 3 or 4, characterized in that, Also includes: Based on the optimal solution corresponding to the first test data and / or the second test data, guide the field application of the control system; Based on feedback data from field applications, the parameters of the first quasi-state operation model and / or the second quasi-state operation model are adjusted.

6. A control system network performance testing device based on field application conditions, characterized in that, include: A processor and a memory, wherein the memory stores executable program instructions, and when the processor invokes the program instructions in the memory, the processor is used to: Perform the steps of the control system network performance testing method based on field application conditions as described in any one of claims 1 to 5.

7. A computer-readable storage medium for storing a program, characterized in that, When the program is executed, it implements the steps of the control system network performance testing method based on field application conditions as described in any one of claims 1 to 5.

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