Electric power communication system modeling method and system and terminal equipment

Abstract

The invention discloses an electric power communication system modeling method and system and terminal equipment, and relates to the technical field of electric power communication, and the method comprises the following steps: 1, establishing electric power communication equipment nodes, and establishing an equipment map with a preset area at the positions of the electric power communication equipment nodes, establishing an edge structure with a preset length between the electric power communication equipment nodes with the relationship to form a preliminary model of the electric power communication system; 2, marking the electric power communication equipment at the two ends of the edge structure as an associated equipment group, obtaining an associated adjustment value of the associated equipment group, and adjusting the length of the edge structure of the associated equipment group according to the associated adjustment value every time a period node is reached based on a preset period; and step 3, obtaining the icon value of the electric power communication equipment every time the period node is reached, and adjusting the equipment mapping area of the electric power communication equipment according to the icon value.

Description

Technical Field

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

[0002] With the continuous development of smart grid and power informatization, the scale of power communication network continues to expand, the network coverage is wide, the equipment in the power communication system is numerous and complex, the types of services carried are complex and diverse, and the amount of information is growing rapidly, resulting in problems such as complex network structure and increased difficulty in operation and maintenance. The current power communication system model can only feedback which power communication equipment exists in the power communication system and reflect the relationship between power communication equipment, but cannot intuitively feedback the relationship and influence between power communication equipment. When the power communication system is abnormal, it is impossible to quickly and specifically discover and solve the abnormal problems of power communication equipment. Summary of the invention

[0003] In view of the deficiencies in the prior art, the object of the present invention is to provide a power communication system modeling method, system and terminal device.

[0004] To achieve the above object, the present invention provides the following technical solutions:

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

[0006] Step 1: Establish power communication equipment nodes, establish equipment maps of preset areas at the power communication equipment node locations, establish edge structures of preset lengths between related power communication equipment nodes, and 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 an associated device group, obtain the associated adjustment value of the associated device group, and adjust the edge structure length of the associated device group according to the associated adjustment value at each cycle node based on a preset cycle;

[0008] Step 3: At each cycle node, the icon value of the power communication device is obtained, and the device map area of ​​the power communication device is adjusted according to the icon value.

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

[0010] The model initialization module is used to obtain attribute information of power communication equipment, establish power communication equipment nodes according to the attribute information, establish a device map of a preset area at the power communication equipment node position, and establish an edge structure of a preset length between power communication equipment nodes with relationships according to the relationships between the power communication equipment, so as to form a preliminary model of the power communication system;

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

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

[0013] TB device map area, marked as BG, based on BG TY Map area, adjust the equipment map of power communication equipment.

[0014] Furthermore, the associated adjustment value of the associated device group is obtained by:

[0015] The two power communication devices in the associated device group are marked as associated devices. Based on the preset cycle, at each cycle node, the operation data of the associated devices in the current cycle is obtained, the device type of the associated devices is obtained, and the operation data is used as the data of the operation analysis model of the equipment type to obtain the operation evaluation of the associated devices. Then, the total number of evaluation fluctuations and the average low stable number difference of one of the associated devices are obtained and marked as A=[A1,A2], A1 is the total number of evaluation fluctuations, A2 is the average low stable number difference, and then the total number of evaluation fluctuations and the average low stable number difference of another associated device are obtained and marked as B=[B1,B2], B1 is the total number of evaluation fluctuations, B2 is the average low stable number difference, and the cosine similarity algorithm is used to obtain the association adjustment value of the associated device group and marked as SWD.

[0016] Furthermore, the total number of fluctuations in the evaluation of the associated equipment and the difference between the average low stability number are obtained by the following method:

[0017] Obtain m operation evaluation values ​​PZi of the associated equipment before the current time of the system, where i is the number of the operation evaluation value, i=1, 2, ..., m, sort all the operation evaluation values ​​in order of the numbers, set the high operation evaluation value and the low operation evaluation value, and when the operation evaluation value is greater than or equal to the high operation evaluation value, mark the operation evaluation value as a high stable evaluation type; when the operation evaluation value is less than or equal to the low operation evaluation value, mark the operation evaluation value as a low stable evaluation type; when the operation evaluation value is between the high operation evaluation value and the low operation evaluation value, mark the operation evaluation value as a common evaluation type, and compare the types of the two adjacent operation evaluation values ​​after sorting. When the types of the adjacent operation evaluation values ​​are the same, no corresponding processing is performed; when the types of the two adjacent operation evaluation values ​​are different, the number of evaluation fluctuations is increased by one, and all the number of evaluation fluctuations are summed up to obtain the total number of evaluation fluctuations;

[0018] Sort the operation evaluation values ​​of all low stability evaluation types in order of number, calculate the difference between the numbers of two adjacent operation evaluation values ​​after sorting to obtain the low stability number difference, sum up all the 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 device is obtained by the following steps:

[0020] Mark the power communication device as the central communication device, mark the remaining power communication devices that have an edge structure relationship with the central communication device as associated communication devices, obtain the edge structure lengths of the central communication device and the associated communication devices, set an edge structure length threshold, and when the edge structure length is less than the edge structure length threshold, mark the corresponding associated communication device as an influencing communication device, and when the edge structure length is greater than or equal to the edge structure length threshold, do not perform corresponding processing;

[0021] Get the number of influencing communication devices and mark it as EST, get the number of associated communication devices and mark it as BNL, get the associated adjustment value of the central communication device and the other influencing communication devices, sum up all the associated adjustment values ​​and take the average, get the associated adjustment mean and mark it as FSA, use the formula The icon value TB of the central communication device is obtained, wherein b1 is the coefficient of the number of influencing communication devices, b2 is the coefficient of the number of associated communication devices, and b3 is the associated adjustment mean coefficient. 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 the present invention constructs a power communication system model based on the equipment relationship in the power communication system, and optimizes and adjusts the power communication system model based on the association between each power equipment, so that the power communication system model can intuitively and accurately feedback the influence relationship of each equipment in the current power communication system model. When an abnormality occurs in the power communication system, the equipment can be quickly analyzed through the power communication system model, and a model optimization module can be set to quickly and accurately associate the influence between power communication equipment in the power communication system model, as well as the influence of power communication equipment on other equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 The present invention is a flowchart of a method for modeling a power communication system. DETAILED DESCRIPTION

[0025] Example 1

[0026] Reference Figure 1 , a power communication system modeling method, comprising the following steps:

[0027] Step 1: Acquire the attribute information of the power communication equipment, establish the power communication equipment node according to the attribute information, establish a device map of a preset area at the power communication equipment node position, and establish an edge structure of a preset length between the power communication equipment nodes with relationships according to the relationships between the power communication equipment, so as to form a preliminary model of the power communication system;

[0028] Step 2: Mark the power communication devices at both ends of the edge structure as an associated device group, obtain the associated adjustment value of the associated device group, and adjust the edge structure length of the associated device group according to the associated adjustment value at each cycle node based on a preset cycle;

[0029] Step 3: At each cycle node, the icon value of the power communication device is obtained, and the device map area of ​​the power communication device is adjusted according to the icon value.

[0030] The method of the present invention constructs a power communication system model based on the equipment relationship in the power communication system, and optimizes and adjusts the power communication system model based on the relationship between each power equipment, so that the power communication system model can intuitively and accurately feedback the influence relationship of each equipment in the current power communication system model. When an abnormality occurs in the power communication system, the equipment can be quickly analyzed through the power communication system model.

[0031] Example 2

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

[0033] The model initialization module is used to obtain the attribute information of the power communication equipment, establish the power communication equipment nodes according to the attribute information, establish the equipment map of the preset area at the power communication equipment node position, and establish the edge structure of the preset length between the power communication equipment nodes with the relationship between the power communication equipment to form a preliminary model of the power communication system.

[0034] The preset area of ​​the device map and the preset length of the edge structure are the default settings. You can adjust the default settings according to your needs. The area of ​​the device map affects the size of the device map in the model, and the length of the edge structure affects the distance between two device maps in the model.

[0035] The attribute information of the power communication equipment includes but is not limited to the 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 affiliation relationships. For example, there are supply and demand relationships, service relationships, and affiliation relationships between electrical equipment such as generators, transformers, circuit breakers, reactors, and contactors.

[0037] The edge structure indicates that there is a supply-demand relationship, service relationship, affiliation relationship, etc. between two power communication devices.

[0038] Based on the initial model building module, the ability to obtain and process attribute information can also be enhanced:

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

[0040] Big data and AI analysis: Use big data analysis technology to mine historical data and identify equipment failure warning patterns; combine machine learning algorithms to predict the life and maintenance requirements of power 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 power communication equipment from different manufacturers and different types can be seamlessly integrated into the model, thereby improving the versatility and scalability of the model.

[0042] Refined device node construction:

[0043] 3D modeling: Using 3D modeling technology, not only equipment mapping is established, but also a 3D model of the equipment is created, including details such as internal structure, heat dissipation channels, connection ports, etc., to facilitate virtual inspection and fault simulation.

[0044] Dynamic attribute update: Device nodes should support dynamic attribute update mechanism to automatically adjust the device status in the model according to real-time monitoring data, such as color changes to reflect temperature, flashing to indicate fault alarm, etc.

[0045] Interactive interface: An intuitive and easy-to-use user interface is designed to allow operators to easily manage device nodes through operations such as dragging, dropping, and scaling, improving the operability of the model and user experience.

[0046] Optimize edge structure establishment and relationship management:

[0047] Intelligent association analysis: Utilize graph database and complex network analysis technology to automatically identify the physical connection, logical dependency, signal flow and other relationships between power communication equipment, reduce manual input errors and improve model accuracy.

[0048] Dynamic path planning: Integrate dynamic path planning algorithms into the edge structure to automatically adjust data transmission paths based on factors such as network traffic and device load, optimize network performance, and reduce 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 a recovery strategy to improve the reliability and resilience of the system.

[0050] Integrated simulation and testing functions

[0051] System simulation: Integrate power system simulation software to simulate and test preliminary models, verify the effectiveness of network layout, equipment configuration, and protection strategies, and discover and resolve potential problems in advance.

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

[0053] Security Audit: Conduct security audits regularly to check for loopholes, weaknesses, and compliance issues in the model, and use encryption technology, access control, and other means to enhance system security.

[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 of the associated device group is obtained in the following way:

[0056] The two power communication devices in the associated device group are marked as associated devices. Based on the preset cycle, at each cycle node, the operation data of the associated device in the current cycle is obtained, the device type of the associated device is obtained, and the operation data is used as the data of the operation analysis model of the device type to obtain the operation evaluation of the associated device. Then, the total number of evaluation fluctuations and the average low stable number difference of one of the associated devices are obtained and marked as A=[A1,A2], A1 is the total number of evaluation fluctuations, A2 is the average low stable number difference, and then the total number of evaluation fluctuations and the average low stable number difference of another associated device are obtained and marked as B=[B1,B2], B1 is the total number of evaluation fluctuations, B2 is the average low stable number difference, and the associated adjustment value of the associated device group is obtained by using the cosine similarity algorithm and marked as SWD, and the value range of SWD is between -1 and 1.

[0057] The preset length of the edge structure of the associated device group is marked as YSB, and the optimized length HMR of the edge structure is obtained using the formula HMR = YSB + SWD × a1, where a1 is the associated adjustment value coefficient, and the value of a1 is 3.5, and the edge structure of the associated device group is adjusted to the optimized length. The longer the length of the edge structure, the greater the degree of influence between the two power communication devices in the associated device group, that is, when one of the power communication devices is abnormal, the other power communication device is more likely to have problems.

[0058] Example: Two power communication devices in the associated device group are marked as power communication device a and power communication device b, where the total number of evaluation fluctuations of power communication device a is 5, and the average low stable number difference of power communication device a is 3, then A = [5, 3], the total number of evaluation fluctuations of power communication device b is 4, and the average low stable number difference of power communication device b is 4, then B = [4, 4], then the associated adjustment value of the associated device group is

[0059] The operation analysis model is obtained in the following way: obtain multiple sets of operation data, which can be experimental preset data or real data. The above operation data are all operation data of the same type of equipment, use the operation data as training data of the neural network model, assign operation evaluation to the operation data, and iteratively train the training data through the training set and the validation set to obtain the operation analysis model. The setting ratio of the training set to the validation set includes but is not limited to 1:3 and 2:3. Among them, the larger the value of the operation evaluation value, the more stable the operation of the associated equipment, and the smaller the value of the operation evaluation value, the more unstable the operation of the associated equipment. For example, voltage fluctuations, voltage sags, harmonics, etc. of the associated equipment will cause the value of the operation evaluation value to decrease.

[0060] The total number of fluctuations in the evaluation of the associated equipment and the difference between the average low stability number are obtained by the following method:

[0061] Obtain m operation evaluation values ​​PZ i of the associated equipment before the current time of the system, where i is the number of the operation evaluation value, i=1, 2, ..., m, sort all the operation evaluation values ​​in order of the numbers, set the high operation evaluation value and the low operation evaluation value, wherein the high operation evaluation value is greater than the low operation evaluation value, and both the high operation evaluation value and the low operation evaluation value are system preset values, which can be adjusted according to needs, when the operation evaluation value is greater than or equal to the high operation evaluation value, the operation evaluation value is marked as a high stable evaluation type, when the operation evaluation value is less than or equal to the low operation evaluation value, the operation evaluation value is marked as a low stable evaluation type, when the operation evaluation value is between the high operation evaluation value and the low operation evaluation value, the operation evaluation value is marked as a common evaluation type, and the types of the two adjacent operation evaluation values ​​after sorting are compared, when the types of the adjacent operation evaluation values ​​are the same, no corresponding processing is performed, when the types of the two adjacent operation evaluation values ​​are different, the number of evaluation fluctuations is increased by one, and all the number of evaluation fluctuations are summed to obtain the total number of evaluation fluctuations.

[0062] Sort the operation evaluation values ​​of all low stability evaluation types in order of number, calculate the difference between the numbers of two adjacent operation evaluation values ​​after sorting to obtain the low stability number difference, sum up all the low stability number differences and take the average to obtain the average low stability number difference.

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

[0064] TB device map area, marked as BG, based on BG TY Map area, adjust the device map of the power communication device. The larger the map area of ​​the power communication device, the greater the degree and scope of the impact of the power communication device on other related power communication devices, that is, when the power communication device is abnormal, other related power communication devices are more likely to have problems.

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

[0066] The power communication device is marked as the central communication device, and the remaining power communication devices that have an edge structure relationship with the central communication device are marked as associated communication devices. The edge structure lengths of the central communication device and the associated communication devices are obtained, and an edge structure length threshold is set. The edge structure length threshold is a threshold preset by the system and can be modified according to actual needs. When the edge structure length is less than the edge structure length threshold, the corresponding associated communication device is marked as an influencing communication device. When the edge structure length is greater than or equal to the edge structure length threshold, no corresponding processing is performed.

[0067] Get the number of influencing communication devices and mark it as EST, get the number of associated communication devices and mark it as BNL, get the associated adjustment value of the central communication device and the other influencing communication devices, sum up all the associated adjustment values ​​and take the average, get the associated adjustment mean and mark it as FSA, use the formula The icon value TB of the central communication equipment is obtained, where b1 is the coefficient affecting the number of communication equipment, b2 is the coefficient of the number of associated communication equipment, and b3 is the associated adjustment mean coefficient. 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 equipment is the icon value of the power communication equipment.

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

[0069] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0070] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of 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, the process or function described in the embodiment of the present application is generated in whole or in part. 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 computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). 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 contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0071] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean 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 the present application.

[0072] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example 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 performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

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

[0074] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0075] If the functions are implemented in the form of 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 the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0076] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for modeling a power communication system, characterized in that: The steps include: Step 1: Establish power communication equipment nodes, establish equipment maps of preset areas at the power communication equipment node locations, establish edge structures of preset lengths between related power communication equipment nodes, and form a preliminary model of the power communication system; Step 2: Mark the power communication devices at both ends of the edge structure as an associated device group, obtain the associated adjustment value of the associated device group, and adjust the edge structure length of the associated device group according to the associated adjustment value at each cycle node based on a preset cycle; Step 3: At each cycle node, the icon value of the power communication device is obtained, and the device map area of ​​the power communication device is adjusted according to the icon value.

2. A power communication system modeling system, applied to a power communication system modeling method according to claim 1, characterized in that: Including model initialization module and model optimization module; The model initialization module is used to obtain attribute information of power communication equipment, establish power communication equipment nodes according to the attribute information, establish a device map of a preset area at the power communication equipment node position, and establish an edge structure of a preset length between power communication equipment nodes with relationships according to the relationships between the power communication equipment, so as to form a preliminary model of the power communication system; The model optimization module marks the power communication devices at both ends of the edge structure as an associated device group, obtains the associated adjustment value of the associated device group, marks the preset length of the edge structure of the associated device group as YSB, and uses the formula HMR=YSB+SWD×a1 to obtain the optimized length HMR of the edge structure, where a1 is the associated adjustment value coefficient, and adjusts the edge structure of the associated device group to the optimized length; Based on the preset cycle, at each cycle node, obtain the icon value TB of the power communication device in the current cycle, obtain the icon value TY of the power communication device in the previous cycle, obtain the device map area of ​​the power communication device in the previous cycle, and mark it as BG. Map area, adjust the equipment map of power communication equipment.

3. A power communication system modeling system according to claim 2, characterized in that: The associated adjustment value of the associated device group is obtained in the following way: The two power communication devices in the associated device group are marked as associated devices. Based on the preset cycle, at each cycle node, the operation data of the associated device in the current cycle is obtained, the device type of the associated device is obtained, and the operation data is used as the data of the operation analysis model of the equipment type to obtain the operation evaluation of the associated device. Then, the total number of evaluation fluctuations and the average low stable number difference of one of the associated devices are obtained and marked as A=[A1,A2], A1 is the total number of evaluation fluctuations, A2 is the average low stable number difference, and then the total number of evaluation fluctuations and the average low stable number difference of another associated device are obtained and marked as B=[B1,B2], B1 is the total number of evaluation fluctuations, B2 is the average low stable number difference, and the cosine similarity algorithm is used to obtain the association adjustment value of the associated device group and marked as SWD.

4. A power communication system modeling system according to claim 3, characterized in that: The total number of fluctuations in the evaluation of the associated equipment and the difference between the average low stability number are obtained by the following method: Obtain m operation evaluation values ​​PZi of the associated equipment before the current time of the system, where i is the number of the operation evaluation value, i=1, 2, ..., m, sort all the operation evaluation values ​​in order of the numbers, set the high operation evaluation value and the low operation evaluation value, and when the operation evaluation value is greater than or equal to the high operation evaluation value, mark the operation evaluation value as a high stable evaluation type; when the operation evaluation value is less than or equal to the low operation evaluation value, mark the operation evaluation value as a low stable evaluation type; when the operation evaluation value is between the high operation evaluation value and the low operation evaluation value, mark the operation evaluation value as a common evaluation type, and compare the types of the two adjacent operation evaluation values ​​after sorting. When the types of the adjacent operation evaluation values ​​are the same, no corresponding processing is performed; when the types of the two adjacent operation evaluation values ​​are different, the number of evaluation fluctuations is increased by one, and all the number of evaluation fluctuations are summed up to obtain the total number of evaluation fluctuations; Sort the operation evaluation values ​​of all low stability evaluation types in order of number, calculate the difference between the numbers of two adjacent operation evaluation values ​​after sorting to obtain the low stability number difference, sum up all the low stability number differences and take the average to obtain the average low stability number difference.

5. A power communication system modeling system according to claim 4, characterized in that: The icon value of the power communication equipment is obtained by the following steps: Mark the power communication device as the central communication device, mark the remaining power communication devices that have an edge structure relationship with the central communication device as associated communication devices, obtain the edge structure lengths of the central communication device and the associated communication devices, set an edge structure length threshold, and when the edge structure length is less than the edge structure length threshold, mark the corresponding associated communication device as an influencing communication device, and when the edge structure length is greater than or equal to the edge structure length threshold, do not perform corresponding processing; Get the number of influencing communication devices and mark it as EST, get the number of associated communication devices and mark it as BNL, get the associated adjustment value of the central communication device and the other influencing communication devices, sum up all the associated adjustment values ​​and take the average, get the associated adjustment mean and mark it as FSA, use the formula The icon value TB of the central communication device is obtained, wherein b1 is the coefficient of the number of influencing communication devices, b2 is the coefficient of the number of associated communication devices, and b3 is the associated adjustment mean coefficient. The icon value of the central communication device is the icon value of the power communication device.

6. A power communication system modeling terminal device, applied to a power communication system modeling method according to claim 1, characterized in that: Applicable to executing the method described in claim 1.

Images