Rapid convergence method for simulation analysis of electric power communication network

Abstract

The invention relates to the technical field of communication simulation, and discloses a power communication network simulation analysis rapid convergence method, which comprises the steps of calculating a first computing power demand, calculating a first difference value and outputting a first convergence execution set. According to the method, the first convergence set and the first computing power demand are determined, and the difference value between the computing power state and the first computing power demand is compared, so that screening of the simulation convergence conditions of the power communication network is realized, the first computing power demand is calculated according to different simulation purposes in the simulation task set, convergence requirements of different simulation tasks are accurately matched, and the simulation efficiency of the power communication network is improved. After the real-time computing power state of the communication network is obtained, the difference value of the two computing power states is calculated, if the difference value does not meet the threshold value, obtaining and calculation are repeated, it is ensured that the computing power condition is continuously tracked, when the difference value meets the threshold value, the next step is executed, a foundation is laid for subsequent generation of more adaptive convergence conditions, the problem of low simulation efficiency caused by computing power mismatching can be avoided, and the simulation efficiency is improved. The simulation process is optimized, and the overall simulation efficiency is improved.

Description

Technical Field

[0001] This application relates to the field of communication simulation technology, and specifically to a fast convergence method for power communication network simulation analysis. Background Art

[0002] In the field of power communication network simulation analysis, accurate and efficient simulation is crucial for ensuring the stable operation of power communication systems and the corresponding power grids. However, the inventor found in the research on existing power communication network simulation analysis that the conditions for determining whether the simulation converges are usually statically set, which makes the computing power requirements for determining whether the simulation converges fixed. However, the actual power communication network environment is complex and changeable, and the computing power allocated to simulation analysis is not fixed. When the actual computing power is lower than the fixed computing power requirements, there will be a problem of insufficient computing power for simulation analysis, which will not only lead to slow simulation speed, but also may make the simulation results inaccurate and unable to provide effective support for the planning, operation and maintenance of power communication networks in a timely manner.

[0003] Chinese Patent No. CN114036779A discloses a power grid multi-time interval synchronous simulation method, device, medium and equipment, which improves the efficiency of power grid simulation. However, if it is migrated, there will be obvious deficiencies in power communication network simulation analysis. Specifically, the invention does not fully consider the unique network characteristics and complex dynamic changes of power communication networks. Under the requirements of large data volume and high real-time of power communication networks, the calculation overhead is large, seriously affecting the simulation speed. At the same time, it lacks a fast response mechanism for the dynamic changes of power communication networks and cannot flexibly adjust the convergence conditions according to the changes in computing power.

[0004] In summary, there is an urgent need for a new technical solution for fast convergence of power communication network simulation analysis to solve the above problems. Summary of the Invention

[0005] The purpose of this application is to provide a fast convergence method for power communication network simulation analysis to solve the technical problems proposed in the above background art.

[0006] To achieve the above purpose, this application discloses the following technical solutions: A fast convergence method for power communication network simulation analysis, the method includes the following steps:

[0007] S1: Obtain the simulation task set of the communication network, determine the first convergence set based on the simulation task set, and calculate the first computing power requirement based on the first convergence set; wherein, each simulation task and the corresponding simulation purpose are stored in the simulation task set, the simulation purpose corresponds to the first convergence condition, each of the first convergence conditions corresponding to different computing power requirements is stored in the first convergence set, and the first computing power requirement is the sum of the computing power requirements corresponding to each first convergence condition;

[0008] S2: Obtain the computing power status of the communication network, calculate the first difference between the first computing power requirement and the computing power status, and execute S3 when the first difference is greater than or equal to a preset first difference threshold; otherwise, repeat the execution of S1 - S2; wherein, the computing power status is the computing power value that the communication network can use for simulation analysis;

[0009] S3: Generate corresponding second convergence conditions based on each of the simulation purposes and the corresponding first convergence conditions, perform corresponding matching on the simulation tasks and the second convergence conditions to obtain a first convergence execution set and output it; wherein, the computing power requirement of the second convergence condition is less than the computing power requirement of its corresponding first convergence condition, and the first convergence execution set is used to determine whether the simulation converges based on the second convergence condition therein.

[0010] Preferably, the method further includes the following steps:

[0011] S4: Calculate the sum of the computing power requirements corresponding to each of the second convergence conditions to obtain a second computing power requirement, calculate the second difference between the second computing power requirement and the computing power status, and execute S5 when the second difference is greater than or equal to the first difference threshold;

[0012] S5: Generate corresponding third convergence conditions based on the first difference, the second difference, and the second convergence conditions, perform corresponding matching on the simulation tasks and the third convergence conditions to obtain a second convergence execution set and output it; wherein, the second convergence execution set is used to determine whether the simulation converges based on the third convergence condition therein.

[0013] Preferably, in S3, when generating corresponding second convergence conditions based on each of the simulation purposes and the corresponding first convergence conditions, determine the weights of timeliness, accuracy, and loss degree corresponding to the simulation purpose, and perform weighted adjustment on the corresponding parameters in the first convergence condition based on the weights of timeliness, accuracy, and loss degree to obtain the second convergence condition.

[0014] Preferably, the timeliness is calculated based on the ratio of the estimated completion time to the expected completion time of the simulation task, and the larger the ratio, the higher the timeliness; the accuracy is calculated based on the error rate of comparing the simulation result with the actual situation, and the lower the error rate, the higher the accuracy; the loss degree is calculated based on evaluating the loss caused by resource consumption and data loss during the simulation process, and the smaller the loss, the lower the loss degree.

[0015] Preferably, in step S5, generating corresponding third convergence conditions based on the first difference, the second difference, and the second convergence conditions includes:

[0016] In the second convergence condition, the weight of the corresponding timeliness is ω1, the weight of the accuracy is ω2, and the weight of the loss degree is ω3;

[0017] Calculate the adjustment factor and where Δ1 is the first difference and Δ2 is the second difference;

[0018] Calculate the weight of the corresponding timeliness in the third convergence condition The weight of the accuracy The weight of the loss degree

[0019] Preferably, in step S5, based on the first difference, the second difference, and the second convergence condition to generate the corresponding third convergence condition, it further includes:

[0020] After calculating the weights corresponding to the third convergence condition, adjust the key parameters in the second convergence condition to obtain the key parameters of the third convergence condition. This adjustment is calculated based on the adjustment coefficient, and the adjustment coefficient

[0021] Preferably, after obtaining the first convergence execution set and / or the second convergence execution set, the following steps are further included:

[0022] S6: Monitor the process of simulating the execution of the first convergence execution set and the second convergence execution set, and record the computing power usage, convergence time, and accuracy data of the simulation results during the simulation process.

[0023] Preferably, based on the computing power usage, convergence time, and accuracy data of the simulation results during the simulation process, establish a simulation analysis database, which is used to optimize the convergence conditions and computing power allocation in the simulation task.

[0024] Preferably, update the simulation analysis database regularly, and enter the new simulation task data and the optimized convergence conditions and computing power allocation strategies into the simulation analysis database.

[0025] Preferably, during the execution of the simulation task, detect the change value of the computing power state of the communication network. When the change value is greater than or equal to the preset change degree threshold, immediately pause the current simulation task and re-execute S1-S3.

[0026] Beneficial effects: The rapid convergence method for power communication network simulation analysis of this application uses the obtained simulation task set of the communication network to determine the first convergence set and the first computing power requirement, and realizes the screening of the simulation convergence conditions of the power communication network by comparing the difference between the computing power state and the first computing power requirement. By determining the corresponding first convergence conditions according to different simulation purposes in the simulation task set, and then calculating the first computing power requirement, it realizes the precise matching of the convergence requirements of different simulation tasks. After obtaining the real-time computing power state of the communication network, calculate the difference between the two. If the difference does not meet the threshold, repeat the acquisition and calculation to ensure continuous tracking of the computing power situation. When the difference meets the threshold, enter the next step, which lays a foundation for generating more adaptable convergence conditions in the follow-up, can avoid the problem of low simulation efficiency caused by mismatched computing power, optimize the simulation process, and improve the overall simulation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those skilled in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0028] Figure 1 It is a flowchart of the rapid convergence method for power communication network simulation analysis provided by the embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0029] The following will clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, rather than all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0030] In this article, the term "including" is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. Without further limitations, the elements defined by the statement "including..." do not exclude the existence of additional identical elements in the process, method, article or device including the said elements.

[0031] This embodiment discloses a rapid convergence method for power communication network simulation analysis as Figure 1 shown, and the method includes the following steps:

[0032] S1: Obtain the simulation task set of the communication network, determine the first convergence set based on this simulation task set, and calculate the first computing power requirement based on this first convergence set; wherein, each simulation task and its corresponding simulation purpose are stored in the simulation task set, the simulation purpose corresponds to the first convergence condition, each first convergence condition is stored in the first convergence set, the first convergence condition corresponds to different computing power requirements, and the first computing power requirement is the sum of the computing power requirements corresponding to each first convergence condition;

[0033] S2: Obtain the computing power status of the communication network, calculate the first difference between the first computing power requirement and the computing power status, and execute S3 when the first difference is greater than or equal to the preset first difference threshold, otherwise repeat the execution of S1 - S2; wherein, the computing power status is the computing power value that the communication network can use for simulation analysis;

[0034] S3: Generate the corresponding second convergence condition based on each simulation purpose and its corresponding first convergence condition, perform corresponding matching on the simulation task and the second convergence condition to obtain the first convergence execution set and output it; wherein, the computing power requirement of the second convergence condition is less than that of its corresponding first convergence condition, and the first convergence execution set is used to determine whether the simulation converges based on the second convergence condition therein.

[0035] Through the above, this embodiment uses the method of obtaining the simulation task set of the communication network to determine the first convergence set and the first computing power requirement, and comparing the difference between the computing power status and the first computing power requirement, realizes the screening of the simulation convergence conditions of the power communication network. By determining the corresponding first convergence conditions according to different simulation purposes in the simulation task set, and then calculating the first computing power requirement, it realizes the precise matching of the convergence requirements of different simulation tasks. After obtaining the real-time computing power status of the communication network, calculate the difference between the two. If the difference does not meet the threshold, repeat the acquisition and calculation to ensure continuous tracking of the computing power situation. When the difference meets the threshold, enter the next step, laying a foundation for generating more adaptable convergence conditions in the future, which can avoid the problem of low simulation efficiency caused by computing power mismatch, optimize the simulation process, and improve the overall simulation efficiency.

[0036] Specifically, the method further includes the following steps:

[0037] S4: Calculate the sum of the computing power requirements corresponding to each second convergence condition to obtain the second computing power requirement, calculate the second difference between the second computing power requirement and the computing power status, and execute S5 when the second difference is greater than or equal to the first difference threshold;

[0038] S5: Generate the corresponding third convergence condition based on the first difference, the second difference, and the second convergence condition, perform corresponding matching on the simulation task and the third convergence condition to obtain the second convergence execution set and output it; wherein, the second convergence execution set is used to determine whether the simulation converges based on the third convergence condition therein.

[0039] It should be noted that based on using the first difference and the second difference to update the second convergence condition, it can be ensured that the computing power requirement meets the first difference threshold. Therefore, there is no need to analyze the computing power requirement for the third convergence condition anymore.

[0040] Through the above, in this embodiment, by calculating the second computing power requirement corresponding to the second convergence condition and comparing the second difference between it and the computing power state, the further optimization of the convergence condition is realized. Calculating the second computing power requirement enables a more accurate assessment of the computing power required for the adjusted convergence condition. Comparing the differences again can determine whether the current computing power still meets the requirements after adjustment. If the second difference is greater than the first difference threshold, perform subsequent steps to generate the third convergence condition, which makes the adjustment of the convergence condition more refined. Generating the third convergence condition based on the first and second differences and the second convergence condition realizes dynamic adaptation to the change of computing power, ensures the adaptability of the convergence condition to the actual computing power during the simulation process, effectively avoids simulation stagnation or inaccuracy caused by insufficient computing power, and further improves the reliability and speed of the simulation.

[0041] Specifically, in S3, when generating the corresponding second convergence condition based on each simulation purpose and the corresponding first convergence condition, determine the weights of timeliness, accuracy, and loss degree corresponding to the simulation purpose, and perform weighted adjustment on the corresponding parameters in the first convergence condition based on the weights of timeliness, accuracy, and loss degree to obtain the second convergence condition.

[0042] Through the above, in this embodiment, when generating the second convergence condition, by using the weights of timeliness, accuracy, and loss degree determined according to the simulation purpose, the targeted adjustment of the first convergence condition is realized. Different simulation purposes have different emphases on timeliness, accuracy, and loss degree. By determining the corresponding weights and performing weighted adjustment on the first convergence condition parameters, the special requirements of different simulation tasks can be met. For example, for a simulation task with high real-time requirements, increase the weight of timeliness and give priority to meeting the time requirements on the premise of ensuring a certain degree of accuracy and loss degree. This method can flexibly optimize the convergence condition according to actual needs, avoid the disadvantages brought by using a fixed convergence condition, make the simulation results more suitable for the actual application scenario, and improve the usability and effectiveness of the simulation results.

[0043] Specifically, the timeliness is calculated based on the ratio of the estimated completion time to the expected completion time of the simulation task. The larger the ratio, the higher the timeliness; the accuracy is calculated based on the error rate of comparing the simulation results with the actual situation. The lower the error rate, the higher the accuracy; the loss degree is calculated based on evaluating the loss size caused by resource consumption and data loss during the simulation process. The smaller the loss, the lower the loss degree.

[0044] With the above, in this embodiment, by clarifying the specific calculation methods of timeliness, precision, and loss degree, a quantitative evaluation of the simulation quality is achieved. The timeliness is evaluated by calculating the ratio of the expected completion time to the desired completion time of the simulation task, which can intuitively reflect whether the simulation is completed on time; the precision is calculated by comparing the error rate between the simulation result and the actual situation, which can accurately measure the accuracy of the simulation result; the loss degree is evaluated based on the resource consumption and data loss during the simulation process, which can comprehensively consider the simulation cost, thereby providing an objective basis for determining the weights and adjusting the convergence conditions, making the adjustment of the convergence conditions more scientific and reasonable, helping to find the best balance among timeliness, precision, and loss in different simulation scenarios, and improving the quality and reliability of the simulation analysis.

[0045] Specifically, in step S5, generating the corresponding third convergence condition based on the first difference, the second difference, and the second convergence condition includes:

[0046] In the second convergence condition, the weight of the corresponding timeliness is ω1, the weight of the precision is ω2, and the weight of the loss degree is ω3;

[0047] Calculate the adjustment factor and where Δ1 is the first difference and Δ2 is the second difference;

[0048] Calculate the weight of the corresponding timeliness in the third convergence condition The weight of the precision The weight of the loss degree

[0049] With the above, in this embodiment, the adjustment factor is calculated using the first difference and the second difference, and based on this, the weights of timeliness, precision, and loss degree are adjusted to generate the third convergence condition, realizing the dynamic optimization of the convergence condition. By incorporating the first and second differences into the weight calculation, the weights are adaptively adjusted according to the actual changes in computing power. When the first difference is large, the weight of timeliness is appropriately reduced, and the weights of precision and loss degree are increased, paying more attention to the simulation quality when computing power is tight; conversely, the weights are reasonably allocated according to the difference ratio. This way of dynamically adjusting the weights realizes the optimization of the simulation process under different computing power conditions, improves the accuracy and stability of the simulation results, and at the same time ensures the efficient operation of the simulation under limited computing power.

[0050] Specifically, in step S5, generating the corresponding third convergence condition based on the first difference, the second difference, and the second convergence condition further includes:

[0051] After calculating the weights corresponding to the third convergence condition, the key parameters in the second convergence condition are adjusted to obtain the key parameters of the third convergence condition, and this adjustment is calculated based on the adjustment coefficient. The adjustment coefficient

[0052]

[0053] In a simple example, the key parameter in the second convergence condition is x2, and the key parameter x3 for obtaining the third convergence condition after adjustment is x3 = x2 * k.

[0054] Through the above, after obtaining the weight of the third convergence condition in this embodiment, the key parameter of the second convergence condition is adjusted by using the adjustment coefficient, realizing the refined optimization of the convergence condition. The key parameter of the third convergence condition is calculated based on the adjustment coefficient, so that the convergence condition not only adapts to the change of computing power in terms of weight, but also can be accurately adapted at the key parameter level. The adjustment coefficient is related to the first and second differences, ensuring that the parameter adjustment is closely combined with the computing power situation. For example, when the computing power is severely insufficient, the key parameter is adjusted more significantly to ensure that the simulation can proceed smoothly under low computing power. This optimized adjustment of the key parameter further improves the accuracy and effectiveness of the convergence condition, helps to improve the accuracy and efficiency of the simulation, and better meets the complex requirements of the power communication network simulation.

[0055] Specifically, after obtaining the first convergence execution set and / or the second convergence execution set, the following steps are further included:

[0056] S6: Monitor the process of simulating the execution of the first convergence execution set and the second convergence execution set, and record the computing power usage, convergence time, and accuracy data of the simulation results during the simulation process.

[0057] Through the above, in this embodiment, after obtaining the first and second convergence execution sets, the existing technology of monitoring the simulation process is used to record the computing power usage, convergence time, and accuracy data of the simulation results, realizing the comprehensive evaluation of the simulation process. Recording the computing power usage can understand the consumption of resources by the simulation task, facilitating subsequent optimization of computing power allocation; recording the convergence time can directly reflect the simulation efficiency, providing a reference in the time dimension for adjusting the convergence condition; recording the accuracy data of the simulation results helps to judge the reliability of the simulation results, thus providing rich information for subsequent analysis and optimization. By deeply studying these data, problems existing in the simulation process can be found, providing a strong basis for further optimizing the simulation method and convergence condition.

[0058] Specifically, based on the computing power usage, convergence time, and accuracy data of the simulation results during the simulation process, a simulation analysis database is established, and this simulation analysis database is used to optimize the convergence condition and computing power allocation in the simulation task.

[0059] With the above, this embodiment combines existing database technologies to establish a simulation analysis database based on the data recorded during the simulation process, achieving optimization of the convergence conditions and computing power allocation for simulation tasks. The computing power usage, convergence time, and accuracy data are stored in the database. Through the analysis of historical data, the relationships between different simulation tasks, convergence conditions, and computing power allocation can be summarized. For example, it is found that a certain type of simulation task has the highest computing power utilization efficiency under specific convergence conditions, and the optimized settings can be directly referred to when encountering similar tasks in the future. Based on this, experience can be continuously accumulated to make the convergence conditions and computing power allocation more reasonable, improve the simulation efficiency and accuracy, reduce unnecessary resource waste, and promote the continuous optimization of power communication network simulation technology.

[0060] Specifically, the simulation analysis database is updated regularly, and the new simulation task data and the optimized convergence conditions and computing power allocation strategies are entered into the simulation analysis database.

[0061] With the above, this embodiment realizes the continuous optimization of the database and the iterative improvement of the simulation technology by regularly updating the simulation analysis database and entering the new simulation task data and the optimized convergence conditions and computing power allocation strategies into it. As new simulation tasks are carried out, new data and optimization ideas will be generated. Timely updating the database can ensure its timeliness and accuracy. The new data can supplement and improve the understanding of different simulation tasks, and the optimized strategies can further improve the simulation effect. For example, a new topology of the power communication network brings new simulation requirements. After updating the database, subsequent simulations can refer to the new data and strategies to improve the simulation ability for complex networks and ensure that the simulation technology keeps up with the changes in actual requirements.

[0062] Specifically, during the execution of the simulation task, the change value of the computing power state of the communication network is detected. When the change value is greater than or equal to the preset change degree threshold, the current simulation task is immediately paused, and S1 - S3 are executed again.

[0063] With the above, this embodiment realizes the timely response to the dynamic change of computing power. The computing power state of the power communication network may change at any time, and real-time detection of the change value can capture these changes in a timely manner. When the change is large, re-executing steps such as obtaining the simulation task set, determining the convergence set, and generating convergence conditions can re-plan the simulation process according to the new computing power situation. It avoids inaccurate results or simulation stagnation caused by continuing the simulation when the computing power is insufficient or changes greatly, ensures the stability and reliability of the simulation process, enables the simulation to always be carried out under appropriate conditions, and improves the effectiveness of the simulation.

[0064] In summary, a fast convergence method for power communication network simulation analysis in this embodiment uses the obtained simulation task set of the communication network to determine the first convergence set and the first computing power requirement, and realizes the screening of the simulation convergence conditions of the power communication network by comparing the difference between the computing power state and the first computing power requirement. By determining the corresponding first convergence conditions according to different simulation purposes in the simulation task set, and then calculating the first computing power requirement, it realizes the precise matching of the convergence requirements of different simulation tasks. After obtaining the real-time computing power state of the communication network, calculate the difference between the two. If the difference does not meet the threshold, repeat the acquisition and calculation to ensure continuous tracking of the computing power situation. When the difference meets the threshold, enter the next step, laying a foundation for generating more adaptable convergence conditions in the future, which can avoid the problem of low simulation efficiency caused by mismatched computing power, optimize the simulation process, and improve the overall simulation efficiency.

[0065] In the embodiments provided in this application, it should be understood that the embodiments described here can be implemented in hardware, software, firmware, middleware, code, or any appropriate combination thereof. For hardware implementation, the processor can be implemented in one or more of the following units: application specific integrated circuit (ASIC), digital signal processor (DSP), digital signal processing device (DSPD), programmable logic device (PLD), field programmable gate array (FPGA), processor, controller, microcontroller, microprocessor, or other electronic units designed to implement the functions described here, or a combination thereof. For software implementation, part or all of the processes of the embodiments can be completed by a computer program instructing the relevant hardware. When implemented, the above program can be stored in a computer-readable storage medium or transmitted as one or more instructions or codes on a computer-readable storage medium. The computer-readable storage medium includes computer storage media and communication media, where the communication media includes any medium that facilitates the transmission of a computer program from one place to another. The storage medium can be any available medium that can be accessed by a computer. The computer-readable storage medium can include, but is not limited to, RAM, ROM, EEPROM, CD-ROM or other optical disc storage, magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer.

[0066] Finally, it should be noted that the above are only the preferred embodiments of this application and are not used to limit this application. Although this application has been described in detail with reference to the foregoing embodiments, for those skilled in the art, they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of this application shall be included in the protection scope of this application.

Claims

1. A fast convergence method for simulation analysis of power communication network, characterized in that: The method comprises the following steps: S1: Obtain a simulation task set of the communication network, determine a first convergence set based on the simulation task set, and calculate a first computing power requirement based on the first convergence set; wherein the simulation task set stores various simulation tasks and corresponding simulation purposes, the simulation purpose corresponds to a first convergence condition, the first convergence set stores various first convergence conditions determined based on the simulation purpose, the first convergence conditions correspond to different computing power requirements, and the first computing power requirement is the sum of the computing power requirements corresponding to various first convergence conditions; S2: Obtain the computing power status of the communication network, calculate a first difference between the first computing power requirement and the computing power status, and execute S3 when the first difference is greater than or equal to a preset first difference threshold, otherwise repeat S1 to S2; wherein the computing power status is a computing power value that the communication network can use for simulation analysis; S3: Generate a corresponding second convergence condition based on each of the simulation objectives and the corresponding first convergence condition, and obtain and output a first convergence execution set after matching the simulation task with the second convergence condition; wherein the computing power requirement of the second convergence condition is less than the computing power requirement of the corresponding first convergence condition, and the first convergence execution set is used to determine whether the simulation converges based on the second convergence condition.

2. The rapid convergence method for simulation analysis of electric power communication network according to claim 1, characterized in that: This method also The following steps are involved: S4: Calculate the sum of the computing power requirements corresponding to each of the second convergence conditions to obtain a second computing power requirement, calculate a second difference between the second computing power requirement and the computing power state, and execute S5 when the second difference is greater than or equal to the first difference threshold; S5: Generate a corresponding third convergence condition based on the first difference, the second difference and the second convergence condition, match the simulation task and the third convergence condition accordingly to obtain a second convergence execution set and output it; wherein the second convergence execution set is used to determine whether the simulation converges based on the third convergence condition.

3. The rapid convergence method for simulation analysis of electric power communication network according to claim 2, characterized in that: In S3, when generating the corresponding second convergence condition based on each of the simulation objectives and the corresponding first convergence condition, the corresponding timeliness weight, precision weight and loss weight are determined based on the simulation objective, and the corresponding parameters in the first convergence condition are weightedly adjusted based on the timeliness weight, the precision weight and the loss weight to obtain the second convergence condition.

4. The rapid convergence method for simulation analysis of electric power communication network according to claim 3 is characterized in that: The timeliness is calculated based on the ratio of the estimated completion time of the simulation task to the expected completion time. The larger the ratio, the higher the timeliness; the accuracy is calculated based on the error rate between the simulation results and the actual situation. The lower the error rate, the higher the accuracy; the loss degree is calculated based on the loss caused by resource consumption and data loss during the evaluation simulation process. The smaller the loss, the lower the loss degree.

5. The rapid convergence method for simulation analysis of electric power communication network according to claim 3, characterized in that: In step S5, generating a corresponding third convergence condition based on the first difference, the second difference and the second convergence condition includes: In the second convergence condition, the corresponding weight of timeliness is ω1, the weight of accuracy is ω2, and the weight of loss is ω3; Calculate the adjustment factor and Wherein, Δ1 is the first difference, and Δ2 is the second difference; Calculate the weight of the timeliness corresponding to the third convergence condition The weight of accuracy The weight of the loss 6. The rapid convergence method for simulation analysis of electric power communication network according to claim 5, characterized in that: In step S5, generating a corresponding third convergence condition based on the first difference, the second difference and the second convergence condition further includes: After calculating the weight corresponding to the third convergence condition, adjusting the key parameters in the second convergence condition to obtain the key parameters of the third convergence condition, the adjustment is calculated based on the adjustment coefficient, and the adjustment coefficient 7. The rapid convergence method for simulation analysis of electric power communication network according to claim 2, characterized in that: After obtaining the first convergent execution set and / or the second convergent execution set, the following steps are further included: S6: Monitor the process of simulating the execution of the first converged execution set and the second converged execution set, and record the computing power usage, convergence time and accuracy data of the simulation results during the simulation process.

8. The rapid convergence method for simulation analysis of electric power communication network according to claim 7, characterized in that: Based on the computing power usage, convergence time and accuracy data of simulation results during the simulation process, a simulation analysis database is established, which is used to optimize the convergence conditions and computing power allocation in the simulation task.

9. The rapid convergence method for simulation analysis of electric power communication network according to claim 8, characterized in that: The simulation analysis database is updated regularly, and new simulation task data and optimized convergence conditions and computing power allocation strategies are entered into the simulation analysis database.

10. The rapid convergence method for simulation analysis of electric power communication network according to claim 1, characterized in that: During the execution of the simulation task, the change value of the computing power state of the communication network is detected. When the change value is greater than or equal to the preset change degree threshold, the current simulation task is immediately suspended and S1 to S3 are re-executed.

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