A modeling method, system, and terminal equipment for power communication systems

Abstract

This invention discloses a modeling method, system, and terminal equipment for a power communication system, relating to the field of power communication technology. The method includes the following steps: Step 1: Establishing power communication device nodes; creating device textures of a preset area at the locations of the power communication device nodes; and establishing edge structures of a preset length between related power communication device nodes to form a preliminary model of the power communication system; Step 2: Marking the power communication devices at both ends of the edge structure as associated device groups; obtaining the association adjustment value of the associated device groups; and adjusting the length of the edge structure of the associated device groups according to the association adjustment value at each period node based on a preset period; Step 3: Obtaining the icon value of the power communication device at each period node; and adjusting the area of ​​the device texture of the power communication device according to the icon value.

Description

Technical Field

[0001] This invention relates to the field of power communication technology, and more specifically, to a power communication system modeling method, system, and terminal equipment. Background Technology

[0002] With the continuous development of smart grids and power information technology, the scale of power communication networks continues to expand, with wide coverage. Power communication systems now contain numerous and diverse devices, carrying a wide variety of services and experiencing rapid growth in information volume. This has led to problems such as complex network structures and increased maintenance difficulties. Current power communication system models can only indicate which power communication devices exist and the relationships between them, but they cannot directly reflect the degree of correlation and influence between these devices. Therefore, when anomalies occur in the power communication system, it is difficult to quickly and specifically identify and resolve the abnormal issues. Summary of the Invention

[0003] In view of the shortcomings of the existing technology, the purpose of this invention is to provide a modeling method, system and terminal equipment for power communication systems.

[0004] To achieve the above objectives, the present invention provides the following technical solution:

[0005] A modeling method for a power communication system includes the following steps:

[0006] Step 1: Establish power communication equipment nodes, create equipment textures of a preset area at the locations of the power communication equipment nodes, and create edge structures of a preset length between related power communication equipment nodes to form a preliminary model of the power communication system;

[0007] Step 2: Mark the power communication devices at both ends of the edge structure as associated device groups, obtain the association adjustment value of the associated device groups, and adjust the edge structure length of the associated device groups according to the association adjustment value at each period node based on the preset period.

[0008] Step 3: At each periodic node, obtain the icon value of the power communication equipment and adjust the equipment texture area of ​​the power communication equipment according to the icon value.

[0009] Furthermore, a power communication system modeling system includes a model initialization module and a model optimization module;

[0010] The initial model building module is used to obtain the attribute information of the power communication equipment, establish power communication equipment nodes based on the attribute information, establish equipment textures of a preset area at the location of the power communication equipment nodes, and establish edge structures of a preset length between the power communication equipment nodes based on the relationship between the power communication equipment, thereby forming an initial model of the power communication system.

[0011] The model optimization module marks the power communication equipment at both ends of the edge structure as associated equipment groups, obtains the association adjustment value of the associated equipment groups, marks the preset length of the edge structure of the associated equipment groups as YSB, and uses the formula HMR=YSB+SWD×a1 to obtain the optimized length HMR of the edge structure, where a1 is the association adjustment value coefficient, and adjusts the edge structure of the associated equipment groups to the optimized length.

[0012] Based on a preset period, at each period node, the icon value TB of the power communication device in the current period is obtained, the icon value TY of the power communication device in the previous period is obtained, and the icon value of the power communication device in the previous period is obtained.

[0013] The device texture area of ​​TB is marked as BG, based on BG. TY Texture area adjustment is used to adjust the equipment texture of power communication equipment.

[0014] Furthermore, the associated adjustment value for the associated device group is obtained in the following manner:

[0015] Both power communication devices within the associated device group are marked as associated devices. Based on a preset period, at each period node, the operating data of the associated devices in the current period is obtained, the device type of the associated devices is determined, and the operating data is used as the data for the operating analysis model of that device type to obtain the operating evaluation of the associated devices. Then, the total number of evaluation fluctuations and the average low-stability number difference of one of the associated devices are obtained and marked as A = [A1, A2], where A1 is the total number of evaluation fluctuations and A2 is the average low-stability number difference. Then, the total number of evaluation fluctuations and the average low-stability number difference of the other associated device are obtained and marked as B = [B1, B2], where B1 is the total number of evaluation fluctuations and B2 is the average low-stability number difference. The cosine similarity algorithm is used to obtain the association adjustment value of the associated device group and it is marked as SWD.

[0016] Furthermore, the total number of evaluation fluctuations and the average low-stability number difference of the associated equipment were obtained in the following way:

[0017] Obtain the m operational evaluation values ​​PZi of the associated devices before the current system time, where i is the number of the operational evaluation value, i = 1, 2, ..., m. Sort all operational evaluation values ​​in order of their numbers. Set high operational evaluation values ​​and low operational evaluation values. When an operational evaluation value is greater than or equal to the high operational evaluation value, mark it as a high stability evaluation value type. When an operational evaluation value is less than or equal to the low operational evaluation value, mark it as a low stability evaluation value type. When an operational evaluation value is between the high and low operational evaluation values, mark it as a normal evaluation value type. Compare the types of adjacent operational evaluation values ​​after sorting. When adjacent operational evaluation values ​​have the same type, no corresponding processing is performed. When adjacent operational evaluation values ​​have different types, the evaluation fluctuation count is increased by one. Sum all evaluation fluctuation counts to obtain the total number of evaluation fluctuations.

[0018] Sort all operational evaluations of low stability rating types in order of their numbers. Calculate the difference between the numbers of two adjacent operational evaluations after sorting to obtain the low stability number difference. Sum all low stability number differences and take the average to obtain the average low stability number difference.

[0019] Furthermore, the icon value of the power communication equipment is obtained through the following steps:

[0020] The power communication equipment is marked as the central communication equipment, and the other power communication equipment that has an edge structure relationship with the central communication equipment is marked as the associated communication equipment. The edge structure length between the central communication equipment and the associated communication equipment is obtained, and an edge structure length threshold is set. When the edge structure length is less than the edge structure length threshold, the corresponding associated communication equipment is marked as the influencing communication equipment. When the edge structure length is greater than or equal to the edge structure length threshold, no corresponding processing is performed.

[0021] Obtain the number of affected communication devices and label them as EST; obtain the number of associated communication devices and label them as BNL; obtain the association adjustment value between the central communication device and the other affected communication devices; sum all association adjustment values ​​and take the average to obtain the association adjustment mean, which is labeled as FSA. Then, use the formula... The icon value TB of the central communication device is obtained, where b1 is the coefficient of the number of communication devices, b2 is the coefficient of the number of associated communication devices, and b3 is the coefficient of the average value of the associated adjustment. The icon value of the central communication device is the icon value of the power communication device.

[0022] Compared with the prior art, the present invention has the following beneficial effects:

[0023] The method of this invention constructs a power communication system model based on the device relationships in the power communication system, and optimizes and adjusts the power communication system model based on the correlation between various power devices. This allows the power communication system model to intuitively and accurately reflect the influence relationships between various devices in the current power communication system model. When an anomaly occurs in the power communication system, the devices can be quickly analyzed through the power communication system model. By setting up a model optimization module, the influence between power communication devices and the influence of power communication devices on other devices can be quickly and accurately correlated in the power communication system model. Attached Figure Description

[0024] Figure 1 This is a flowchart of a power communication system modeling method according to the present invention. Detailed Implementation

[0025] Example 1

[0026] Reference Figure 1 A modeling method for a power communication system includes the following steps:

[0027] Step 1: Obtain the attribute information of the power communication equipment, establish power communication equipment nodes based on the attribute information, create equipment textures of a preset area at the location of the power communication equipment nodes, and establish edge structures of a preset length between the related power communication equipment nodes based on the relationships between the power communication equipment, thus forming a preliminary model of the power communication system.

[0028] Step 2: Mark the power communication devices at both ends of the edge structure as associated device groups, obtain the association adjustment value of the associated device groups, and adjust the edge structure length of the associated device groups according to the association adjustment value at each period node based on the preset period.

[0029] Step 3: At each periodic node, obtain the icon value of the power communication equipment and adjust the equipment texture area of ​​the power communication equipment according to the icon value.

[0030] The method of this invention constructs a power communication system model based on the device relationships in the power communication system, and optimizes and adjusts the power communication system model based on the correlation between various power devices. This allows the power communication system model to intuitively and accurately reflect the influence relationships of each device in the current power communication system model. When an anomaly occurs in the power communication system, the devices can be quickly analyzed through the power communication system model.

[0031] Example 2

[0032] A modeling system for power communication systems includes a model initialization module and a model optimization module.

[0033] The initial model building module is used to obtain the attribute information of power communication equipment, establish power communication equipment nodes based on the attribute information, create equipment textures of a preset area at the location of the power communication equipment nodes, and establish edge structures of a preset length between the power communication equipment nodes based on the relationship between the power communication equipment, thus forming the initial model of the power communication system.

[0034] The preset area of ​​the device texture and the preset length of the edge structure are default settings. You can adjust the default settings as needed. The area of ​​the device texture affects the size of the device texture in the model, and the length of the edge structure affects the distance between two device textures in the model.

[0035] The attribute information of power communication equipment includes, but is not limited to, power equipment ID, power equipment model, and power equipment IP.

[0036] The relationships between power communication equipment include supply and demand relationships, service relationships, and subordinate relationships. For example, electrical equipment such as generators, transformers, circuit breakers, reactors, and contactors have supply and demand relationships, service relationships, and subordinate relationships.

[0037] Edge structure indicates that there are supply and demand relationships, service relationships, and subordinate relationships between two power communication devices.

[0038] Building upon the initial model building module, the ability to acquire and process attribute information can be further enhanced:

[0039] Automated data acquisition: Utilizing Internet of Things (IoT) technology, real-time attribute information of power communication equipment, such as temperature, humidity, voltage, and current, is automatically collected through sensors, RFID tags, and other devices, improving data acquisition efficiency and accuracy.

[0040] Big Data and AI Analytics: Utilizing big data analytics techniques to mine historical data and identify equipment failure warning patterns; combining machine learning algorithms to predict the lifespan and maintenance needs of power and communication equipment, providing forward-looking data support for the model.

[0041] Standardization and compatibility: Establish unified attribute information standards and interface protocols to ensure that different manufacturers and different types of power communication equipment can be seamlessly integrated into the model, thereby improving the model's versatility and scalability.

[0042] Refined equipment node construction:

[0043] 3D Modeling: Using 3D modeling technology, not only are equipment textures created, but also 3D models of the equipment are created, including details such as internal structure, heat dissipation channels, and connection ports, which facilitates virtual inspection and fault simulation.

[0044] Dynamic attribute update: Device nodes should support a dynamic attribute update mechanism to automatically adjust the device status in the model based on real-time monitoring data, such as color changes reflecting temperature levels and flashing indicating fault alarms.

[0045] Interactive Interface: The user interface is designed to be intuitive and easy to use, allowing operators to easily manage device nodes through drag-and-drop, zoom and other operations, improving the operability of the model and the user experience.

[0046] Optimize edge structure establishment and relationship management:

[0047] Intelligent correlation analysis: Utilizing graph databases and complex network analysis techniques, it automatically identifies physical connections, logical dependencies, signal flow, and other relationships between power communication equipment, reducing human input errors and improving model accuracy.

[0048] Dynamic path planning: Integrating dynamic path planning algorithms into the edge structure, it automatically adjusts data transmission paths based on factors such as network traffic and device load, optimizes network performance, and reduces congestion and latency.

[0049] Fault isolation and recovery: Establish a fault propagation model to simulate the impact of equipment failure on the system, quickly locate the fault point and plan recovery strategies to improve the reliability and resilience of the system.

[0050] Integrated simulation and testing functions

[0051] System simulation: Integrate power system simulation software to conduct simulation tests on the preliminary model, verify the effectiveness of network layout, equipment configuration, and protection strategies, and identify and resolve potential problems in advance.

[0052] Performance testing: Conduct performance tests to simulate scenarios such as extreme weather and large-scale failures, evaluate the system's load-bearing capacity, recovery speed, and stability, and ensure that the system meets operational requirements within its design life.

[0053] Security audit: Conduct regular security audits to check for vulnerabilities, weaknesses, and compliance issues in the model, and enhance system security by using encryption technology, access control, and other means.

[0054] The model optimization module marks the power communication devices at both ends of the edge structure as associated device groups and obtains the associated adjustment values ​​of the associated device groups.

[0055] The associated adjustment value for the associated device group is obtained in the following way:

[0056] Both power communication devices within the associated device group are marked as associated devices. Based on a preset period, at each period node, the operating data of the associated devices in the current period is obtained, the device type of the associated devices is determined, and the operating data is used as the data for the operating analysis model of that device type to obtain the operating evaluation of the associated devices. Then, the total number of evaluation fluctuations and the average low stability number difference of one of the associated devices are obtained and marked as A = [A1, A2], where A1 is the total number of evaluation fluctuations and A2 is the average low stability number difference. Then, the total number of evaluation fluctuations and the average low stability number difference of the other associated device are obtained and marked as B = [B1, B2], where B1 is the total number of evaluation fluctuations and B2 is the average low stability number difference. The cosine similarity algorithm is used to obtain the association adjustment value of the associated device group and marked as SWD, where the value of SWD ranges from -1 to 1.

[0057] The preset length of the edge structure of the associated device group is marked as YSB. The optimized length HMR of the edge structure is obtained using the formula HMR = YSB + SWD × a1, where a1 is the association adjustment coefficient, and the value of a1 is 3.5. The edge structure of the associated device group is then adjusted to the optimized length. The longer the edge structure, the greater the mutual influence between the two power communication devices within the associated device group. That is, if one power communication device malfunctions, the other power communication device is more likely to experience problems.

[0058] Example: Two power communication devices within an associated device group are labeled as power communication device a and power communication device b, respectively. Power communication device a has a total of 5 rating fluctuations and an average low-stability number difference of 3, so A = [5, 3]. Power communication device b has a total of 4 rating fluctuations and an average low-stability number difference of 4, so B = [4, 4]. What is the associated adjustment value for the associated device group?

[0059] The operational analysis model is obtained through the following method: Multiple sets of operational data are acquired. These data can be pre-set experimental data or real data, all from similar types of equipment. This operational data is used as training data for the neural network model. Operational ratings are assigned to the operational data, and the operational analysis model is obtained through iterative training using training and validation sets. The ratio of the training and validation sets can be set, but is not limited to, 1:3 or 2:3. A higher operational rating indicates more stable operation of the associated equipment, while a lower rating indicates less stable operation. For example, voltage fluctuations, voltage sags, and harmonics in associated equipment can all cause a decrease in the operational rating.

[0060] The total number of evaluation fluctuations and the average low-stability number difference of related equipment were obtained through the following method:

[0061] Obtain the m operational evaluation values ​​PZ i of the associated devices before the current system time, where i is the number of the operational evaluation value, i = 1, 2, ..., m. Sort all operational evaluation values ​​in order of their numbers. Set high and low operational evaluation values, where the high operational evaluation value is greater than the low operational evaluation value. Both high and low operational evaluation values ​​are preset values ​​of the system and can be adjusted according to needs. When an operational evaluation value is greater than or equal to the high operational evaluation value, it is marked as a high-stability evaluation value type. When an operational evaluation value is less than or equal to the low operational evaluation value, it is marked as a low-stability evaluation value type. When an operational evaluation value is between the high and low operational evaluation values, it is marked as a normal evaluation value type. Compare the types of adjacent operational evaluation values ​​after sorting. When adjacent operational evaluation values ​​have the same type, no corresponding processing is performed. When adjacent operational evaluation values ​​have different types, the evaluation fluctuation count is increased by one. Sum all evaluation fluctuation counts to obtain the total number of evaluation fluctuations.

[0062] Sort all operational evaluations of low stability rating types in order of their numbers. Calculate the difference between the numbers of two adjacent operational evaluations after sorting to obtain the low stability number difference. Sum all low stability number differences and take the average to obtain the average low stability number difference.

[0063] Based on a preset period, at each period node, the icon value TB of the power communication device in the current period is obtained, the icon value TY of the power communication device in the previous period is obtained, and the icon value of the power communication device in the previous period is obtained.

[0064] The device texture area of ​​TB is marked as BG, based on BG. TY Texture area is used to adjust the device texture of the power communication equipment. The larger the texture area of ​​the power communication equipment, the greater the degree and scope of its influence on other related power communication equipment. In other words, when this power communication equipment malfunctions, other related power communication equipment are more likely to experience problems.

[0065] The icon value of power communication equipment is obtained through the following steps:

[0066] The power communication equipment is marked as the central communication equipment, and the other power communication equipment that has an edge structure relationship with the central communication equipment is marked as associated communication equipment. The edge structure length between the central communication equipment and the associated communication equipment is obtained, and an edge structure length threshold is set. The edge structure length threshold is a system preset threshold that can be modified according to actual needs. When the edge structure length is less than the edge structure length threshold, the corresponding associated communication equipment is marked as an influencing communication equipment. When the edge structure length is greater than or equal to the edge structure length threshold, no corresponding processing is performed.

[0067] Obtain the number of affected communication devices and label them as EST; obtain the number of associated communication devices and label them as BNL; obtain the association adjustment value between the central communication device and the other affected communication devices; sum all association adjustment values ​​and take the average to obtain the association adjustment mean, which is labeled as FSA. Then, use the formula... The icon value TB of the central communication device is obtained, where b1 is the coefficient of the number of communication devices, b2 is the coefficient of the number of associated communication devices, and b3 is the coefficient of the average value of the association adjustment. The value of b1 is 0.69, the value of b2 is 0.68, and the value of b3 is 5.77. The icon value of the central communication device is the icon value of the power communication device.

[0068] Setting up a model optimization module can quickly and accurately correlate the influence between power communication devices and the influence of power communication devices on other devices in the power communication system model.

[0069] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0070] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0071] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0072] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0073] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0074] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0075] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0076] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A modeling method for a power communication system, characterized in that, Includes the following steps: Step 1: Establish power communication equipment nodes, create equipment textures of a preset area at the locations of the power communication equipment nodes, and create edge structures of a preset length between related power communication equipment nodes to form a preliminary model of the power communication system; Step 2: Mark the power communication devices at both ends of the edge structure as associated device groups, obtain the association adjustment value of the associated device groups, and adjust the edge structure length of the associated device groups according to the association adjustment value at each period node based on the preset period. The associated adjustment value for the associated device group is obtained in the following way: Both power communication devices within the associated device group are marked as associated devices. Based on a preset period, at each period node, the operating data of the associated devices in the current period is obtained, the device type of the associated devices is determined, and the operating data is used as the data for the operating analysis model of that device type to obtain the operating evaluation of the associated devices. Then, the total number of evaluation fluctuations and the difference between the average low-stability number of one of the associated devices are obtained and marked as... A1 represents the total number of rating fluctuations, and A2 represents the average low-stability number difference. Then, the total number of rating fluctuations and the average low-stability number difference of another related device are obtained and marked as follows. B1 represents the total number of evaluation fluctuations, and B2 represents the average low-stability number difference. The cosine similarity algorithm is used to obtain the associated adjustment value of the associated equipment group and it is marked as SWD. Step 3: At each periodic node, obtain the icon value of the power communication equipment, and adjust the equipment texture area of ​​the power communication equipment according to the icon value; The icon value of power communication equipment is obtained through the following steps: The power communication equipment is marked as the central communication equipment, and the other power communication equipment that has an edge structure relationship with the central communication equipment is marked as the associated communication equipment. The edge structure length between the central communication equipment and the associated communication equipment is obtained, and an edge structure length threshold is set. When the edge structure length is less than the edge structure length threshold, the corresponding associated communication equipment is marked as the influencing communication equipment. When the edge structure length is greater than or equal to the edge structure length threshold, no corresponding processing is performed. Obtain the number of affected communication devices and label them as EST; obtain the number of associated communication devices and label them as BNL; obtain the association adjustment value between the central communication device and the other affected communication devices; sum all association adjustment values ​​and take the average to obtain the association adjustment mean, which is labeled as FSA. Then, use the formula... The icon value TB of the central communication device is obtained, where b1 is the coefficient of the number of communication devices, b2 is the coefficient of the number of associated communication devices, and b3 is the coefficient of the average value of the associated adjustment. The icon value of the central communication device is the icon value of the power communication device.

2. A power communication system modeling system, applied to the power communication system modeling method described in claim 1, characterized in that, Includes a model initialization module and a model optimization module; The initial model building module is used to obtain the attribute information of the power communication equipment, establish power communication equipment nodes based on the attribute information, establish equipment textures of a preset area at the location of the power communication equipment nodes, and establish edge structures of a preset length between the power communication equipment nodes based on the relationship between the power communication equipment, thereby forming an initial model of the power communication system. The model optimization module marks the power communication devices at both ends of the edge structure as associated device groups, obtains the association adjustment value of the associated device groups, marks the preset length of the edge structure of the associated device groups as YSB, and uses the formula... The optimized length HMR of the edge structure is obtained, where a1 is the correlation adjustment value coefficient, and the edge structure of the associated device group is adjusted to the optimized length. Based on a preset period, at each period node, the icon value TB of the power communication device in the current period is obtained, the icon value TY of the power communication device in the previous period is obtained, and the device texture area of ​​the power communication device in the previous period is obtained and marked as BG. Texture area adjustment is used to adjust the equipment texture of power communication equipment.

3. The power communication system modeling system according to claim 2, characterized in that, The total number of evaluation fluctuations and the average low-stability number difference of related equipment were obtained through the following method: Obtain the m operational evaluation values ​​PZi of the associated devices before the current system time, where i is the number of the operational evaluation value, i=1, 2, ..., m. Sort all operational evaluation values ​​in order of their numbers. Set high operational evaluation values ​​and low operational evaluation values. When an operational evaluation value is greater than or equal to the high operational evaluation value, mark it as a high stability evaluation value type. When an operational evaluation value is less than or equal to the low operational evaluation value, mark it as a low stability evaluation value type. When an operational evaluation value is between the high and low operational evaluation values, mark it as a normal evaluation value type. Compare the types of adjacent operational evaluation values ​​after sorting. When adjacent operational evaluation values ​​have the same type, no corresponding processing is performed. When adjacent operational evaluation values ​​have different types, the evaluation fluctuation count is increased by one. Sum all evaluation fluctuation counts to obtain the total number of evaluation fluctuations. Sort all operational evaluations of low stability rating types in order of their numbers. Calculate the difference between the numbers of two adjacent operational evaluations after sorting to obtain the low stability number difference. Sum all low stability number differences and take the average to obtain the average low stability number difference.

4. A power communication system modeling terminal device, applied to the power communication system modeling method described in claim 1, characterized in that, Applied to performing the method of claim 1.

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