An edge decision-making power distribution method based on smart meters

Abstract

The present application provides an edge decision-making power distribution method based on smart meters, which relates to the technical field of power distribution and includes: monitoring the data of the access circuit and sending edge power distribution decision signals to multiple edge power consumption terminals; continuously collecting power consumption characteristic data for edge power distribution decision-making; the smart meter monitors and receives the edge power distribution decision results sent by any one of the edge power consumption terminals, or selects the historical power consumption characteristic data sent and recorded by any one of the edge power consumption terminals for power consumption characteristic data prediction and central power distribution decision-making; performing balanced power distribution analysis to distribute power to multiple edge power consumption terminals; and continuing to perform balanced power distribution analysis. Through the present application, the technical problem of time delay caused by multiple edge power consumption terminals sequentially sending power distribution requirements and decision-making information in the prior art can be solved, the technical goal of parallel sending by multiple edge power consumption terminals can be achieved, and the technical effect of improving the real-time performance of power distribution can be achieved.

Description

Technical Field

[0001] This application relates to the field of power distribution technology, and particularly to an edge decision-making power distribution method based on smart meters. Background Art

[0002] In modern power systems, with the rapid growth of electricity demand and the diversification of electrical equipment, the traditional power distribution mode is facing more and more challenges. Especially in the context of the gradual popularization of edge computing and smart meters, how to achieve efficient and balanced power distribution in a distributed environment has become an urgent problem to be solved.

[0003] Currently, multiple existing edge power consumption terminals often need to send power distribution requirements and decision-making information sequentially. Although this sequential sending mode can meet the requirements in some simple scenarios, in a complex multi-edge environment, the delay caused by sequential sending will seriously affect the overall performance of the system. When each edge power consumption terminal generates and sends its power distribution decision, it must wait for the response of the previous power consumption terminal, resulting in delays in the overall decision-making process. In scenarios with high real-time requirements, such as coping with sudden power consumption fluctuations or rapidly changing power demands, the response speed of the existing system is insufficient to ensure the rationality and timeliness of power distribution, thus causing power outages or power waste.

[0004] In summary, there is a technical problem in the prior art that due to multiple edge power consumption terminals sending power distribution requirements and decision-making information sequentially, significant delays occur in the system when facing a complex multi-edge environment, further affecting the real-time performance and overall performance of power distribution. Summary of the Invention

[0005] The purpose of this application is to provide an edge decision-making power distribution method based on smart meters to solve the technical problem in the prior art that due to multiple edge power consumption terminals sending power distribution requirements and decision-making information sequentially, significant delays occur in the system when facing a complex multi-edge environment, further affecting the real-time performance and overall performance of power distribution.

[0006] In view of the above problems, this application provides an edge decision-making power distribution method based on smart meters.

[0007] The present application provides an edge decision power distribution method based on an intelligent electricity meter, including: monitoring the access circuit data through an access monitor in the intelligent electricity meter, and after a preset fluctuation event occurs, sending an edge power distribution decision signal to multiple edge power consumption ends, and obtaining multiple signal sending times, wherein the multiple edge power consumption ends regularly transmit power consumption characteristic data to the intelligent electricity meter; the multiple edge power consumption ends receive the edge power distribution decision signal, continuously collect the power consumption characteristic data within a preset number of time frames, perform edge power distribution decision, and after any one of the edge power consumption ends completes the edge power distribution decision, send the first edge power distribution decision result to the intelligent electricity meter, and obtain the first decision sending time; the intelligent electricity meter continuously monitors and receives the edge power distribution decision results sent by any one of the edge power consumption ends according to a preset monitoring time. When the first decision sending time and the corresponding first signal sending time are less than or equal to the preset monitoring time, it receives the first edge power distribution decision result and continues to monitor and receive. Otherwise, the intelligent electricity meter randomly selects the historical power consumption characteristic data sent and recorded by any one of the edge power consumption ends to perform power consumption characteristic data prediction and central power distribution decision, and obtains the first central power distribution decision result; the intelligent electricity meter performs balanced power distribution analysis according to the first edge power distribution decision result or the first central power distribution decision result, obtains a first balanced power distribution analysis result including multiple first power distribution parameters, and distributes power to the multiple edge power consumption ends; continues to monitor and obtain the second edge power distribution decision result or process and obtain the second central power distribution decision result according to twice the preset monitoring time, and combines the first edge power distribution decision result or the first central power distribution decision result to perform balanced power distribution analysis until balanced power distribution analysis is performed based on the power distribution decision results of the multiple edge power consumption ends, or the preset fluctuation event ends.

[0008] One or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0009] Through the access monitor in the smart meter, the access circuit data is monitored. After a preset fluctuation event occurs, an edge power distribution decision signal is sent to multiple edge power consumption ends, and multiple signal sending times are obtained. Among them, the multiple edge power consumption ends regularly transmit power consumption characteristic data to the smart meter; the multiple edge power consumption ends receive the edge power distribution decision signal, continuously collect the power consumption characteristic data within the number of preset time frames, perform edge power distribution decision. After any one of the edge power consumption ends completes the edge power distribution decision, the first edge power distribution decision result is sent to the smart meter, and the first decision sending time is obtained; the smart meter continuously monitors and receives the edge power distribution decision results sent by any one of the edge power consumption ends according to the preset monitoring time. When the first decision sending time and the corresponding first signal sending time are less than or equal to the preset monitoring time, the first edge power distribution decision result is received, and the monitoring and receiving continue. Otherwise, the smart meter randomly selects the historical power consumption characteristic data sent and recorded by any one of the edge power consumption ends to perform power consumption characteristic data prediction and central power distribution decision, and obtains the first central power distribution decision result; the smart meter performs balanced power distribution analysis according to the first edge power distribution decision result or the first central power distribution decision result, obtains the first balanced power distribution analysis result including multiple first power distribution parameters, and distributes power to the multiple edge power consumption ends; continues to monitor and obtain the second edge power distribution decision result or process and obtain the second central power distribution decision result according to twice the preset monitoring time, and combines the first edge power distribution decision result or the first central power distribution decision result to perform balanced power distribution analysis until the balanced power distribution analysis is performed based on the power distribution decision results of the multiple edge power consumption ends, or the preset fluctuation event ends, realizing the technical goal of parallelly sending power distribution requirements and decision-making information by multiple edge power consumption ends, and achieving the technical effect of improving the real-time performance of power distribution and the overall system performance.

[0010] The above description is only an overview of the technical solution of the present application. In order to be able to understand the technical means of the present application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of the present application more obvious and understandable, the specific embodiments of the present application are hereinafter specifically exemplified. It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become easily understood through the following description. Brief Description of the Drawings

[0011] In order to more clearly illustrate the technical solutions in the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings described below are only exemplary. For those of ordinary skill in the art, other drawings can be obtained according to the provided drawings without creative efforts.

[0012] Figure 1 Schematic flow diagram of an edge decision power distribution method based on an intelligent electricity meter in this application;

[0013] Figure 2 Schematic flow diagram of making edge power distribution decisions in an edge decision power distribution method based on an intelligent electricity meter in this application. Detailed implementation manners

[0014] By providing an edge decision power distribution method based on an intelligent electricity meter, this application solves the technical problem in the prior art that due to multiple edge power consumption terminals sequentially sending power distribution requirements and decision-making information, the system generates significant time delays when facing a complex multi-edge environment, further affecting the real-time performance and overall performance of power distribution. The technical goal of enabling multiple edge power consumption terminals to send power distribution requirements and decision-making information in parallel is achieved, and the technical effect of improving the real-time performance of power distribution and the overall system performance is achieved.

[0015] Next, the technical solutions in this application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments of this application. It should be understood that this application is not limited by the exemplary embodiments described here. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of this application. Additionally, it should be noted that for the sake of description, only parts related to this application are shown in the accompanying drawings rather than all.

[0016] Please refer to the attached Figure 1 , this application provides an edge decision power distribution method based on an intelligent electricity meter, which specifically includes the following steps:

[0017] Step 1: Monitor the access circuit data through the access monitor in the intelligent electricity meter. After a preset fluctuation event occurs, send an edge power distribution decision signal to multiple edge power consumption terminals, and obtain multiple signal sending times. Among them, the multiple edge power consumption terminals regularly transmit power consumption characteristic data to the intelligent electricity meter.

[0018] Specifically, through the access monitor in the smart meter, data such as voltage and current in the access circuit, i.e., access circuit data, can be monitored in real time. When the access circuit data fluctuates beyond the preset range, a preset fluctuation event is identified and immediate action is taken. At this time, the smart meter sends an edge power distribution decision signal to multiple edge power consumption ends, and then adjusts the power consumption according to the current power situation to relieve the grid pressure. The edge power consumption ends may include residential areas, industrial facilities, etc. Record the time when the edge power distribution decision signal is sent for subsequent analysis and optimization. At the same time, the edge power consumption ends will regularly feedback the power consumption characteristic data to the smart meter to ensure continuous monitoring and timely adjustment of power distribution, so as to maintain the stable and efficient operation of the power grid, effectively respond to power fluctuation events, optimize the use of power resources, and ensure the reliability of power supply. For example, in a power distribution station, by monitoring the voltage and current to be distributed, when preset fluctuations such as reaching the fluctuation threshold occur in the voltage and current, it indicates that the current power distribution cannot meet the use of multiple edge power consumption ends, and edge power distribution is required to decide to reduce the power consumption of the edge power consumption ends. For example, the edge power consumption ends are multiple households or multiple factory workshops, etc. At the same time, send a signal and obtain the multiple signal sending times.

[0019] Step 2: The multiple edge power consumption ends receive the edge power distribution decision signal, continuously collect the power consumption characteristic data within the number of preset time frames, make an edge power distribution decision, and after any one of the edge power consumption ends completes the edge power distribution decision, send the first edge power distribution decision result to the smart meter and obtain the first decision sending time.

[0020] Specifically, when multiple edge power-consuming terminals receive the edge power distribution decision signal, they continuously collect power consumption feature data within a preset number of time frames to analyze the amplitude at which power attenuation can be performed while maintaining normal operation, which is used as the acceptable power distribution attenuation coefficient. For example, the power consumption feature data includes parameters such as voltage and current that can reflect the power consumption situation, such as the power consumption of households and the production power consumption of workshops. Each edge power-consuming terminal independently makes edge power distribution decisions to ensure the balance between the power consumption load and the power grid supply. When any one of the edge power-consuming terminals completes the edge power distribution decision, the power distribution attenuation coefficient is used as the edge power distribution decision result. Among them, the first one to complete the edge power distribution decision will send the generated first edge power distribution decision result to the smart meter to ensure that the power grid can obtain the latest status of each edge power-consuming terminal in real time. Subsequently, the smart meter will record the sending time of the first edge power distribution decision result for analysis and optimization in subsequent processing to achieve efficient allocation of power resources and stable operation of the power grid. The preset number of time frames refers to the preset number of time intervals, which is used to define the duration or frequency of data collection or processing. In the context of edge power-consuming terminals, the preset number of time frames represents the collection period or time segment of power consumption feature data, that is, within the preset time frames, power consumption feature data is continuously or regularly collected. The setting of the preset number of time frames ensures that the system can obtain sufficient and representative data to support accurate power distribution decisions.

[0021] Step 3: The smart meter continuously monitors and receives the edge power distribution decision result sent by any one of the edge power-consuming terminals according to the preset monitoring time. When the first decision sending time and the corresponding first signal sending time are less than or equal to the preset monitoring time, it receives the first edge power distribution decision result and continues to monitor and receive. Otherwise, the smart meter randomly selects the historical power consumption feature data sent and recorded by any one of the edge power-consuming terminals to perform power consumption feature data prediction and central power distribution decision, and obtains the first central power distribution decision result.

[0022] Specifically, within the preset monitoring time, the smart meter continuously monitors the edge power distribution decision results sent from any one of the edge power consumption terminals. When the first decision sending time and the corresponding first signal sending time are within the preset monitoring time, the smart meter will receive the first edge power distribution decision result and continue to monitor to ensure the timeliness and accuracy of real-time data. However, if the first edge power distribution decision result is not received within the preset time, the smart meter will randomly select and use the recorded historical power consumption feature data from any one of the edge power consumption terminals to predict the power consumption feature data and make a central power distribution decision based on the selection result, thereby generating the first central power distribution decision result to ensure the continuity and stability of power distribution. Among them, the computing power of the edge power consumption terminal is weak, the communication may be unstable, and the time from receiving the signal to completing the decision processing and sending is long. However, the smart meter needs to perform power distribution processing quickly. Therefore, a preset monitoring time is set for detection. When the first decision sending time and the corresponding first signal sending time are less than or equal to the preset monitoring time, it is considered that the edge decision processing time meets the requirements, and subsequent power distribution is carried out according to the sent first edge power distribution decision result. If it is greater than the preset monitoring time, it is considered untimely. The smart meter randomly selects the historical power consumption feature data sent to and recorded by the smart meter within the historical time of an edge power consumption terminal for power consumption feature data prediction and central power distribution decision, improving the power distribution response speed and avoiding unreasonable power distribution and power shortage in some edge power consumption terminals, resulting in them stopping working.

[0023] Step Four: The smart meter performs balanced power distribution analysis based on the first edge power distribution decision result or the first central power distribution decision result to obtain a first balanced power distribution analysis result including multiple first power distribution parameters, and distributes power to the multiple edge power consumption terminals.

[0024] Specifically, after obtaining the power distribution attenuation coefficient in the first edge power distribution decision result or the first central power distribution decision result, the smart meter performs balanced power distribution analysis, that is, determines the reasonable distribution of power resources among multiple edge power consumption terminals according to the current power demand and supply situation, generates multiple first power distribution parameters, which reflect the amount of power that each power consumption terminal should be allocated under the current situation. For example, based on the generative adversarial network, multiple power distribution attenuation coefficients of multiple edge power consumption terminals are generated to obtain the power distribution decision result distribution, and then power distribution is carried out. Then, the actual power distribution operation is performed according to the first power distribution parameters to ensure that each edge power consumption terminal can obtain the required power, while maintaining the stability and efficiency of the entire power grid, so that the power resources can be optimally configured, avoiding the situation of power overload or resource waste, and ensuring the reliability and continuity of power supply.

[0025] Step 5: Continue to monitor and obtain the second edge power distribution decision result or process and obtain the second central power distribution decision result according to twice the preset monitoring time, and conduct balanced power distribution analysis in combination with the first edge power distribution decision result or the first central power distribution decision result until balanced power distribution analysis is conducted based on the power distribution decision results of multiple edge power consumption terminals or the preset fluctuation event ends.

[0026] Specifically, after the power distribution analysis is initially completed, continue to monitor according to twice the preset monitoring time to obtain the second edge power distribution decision result or process and obtain the second central power distribution decision result. During the initial power distribution analysis, the power distribution is based on multiple power distribution attenuation coefficients generated by the generative adversarial network, which may not be accurate enough for adaptation. Continue to monitor the edge power distribution decision result sent by the second completed edge power consumption terminal within the next preset monitoring time. By combining the newly obtained decision result with the previous first edge or central power distribution decision result, conduct balanced power distribution analysis again. The strategy of gradually expanding the monitoring time allows for flexible adjustment of the power distribution plan in a continuously changing power consumption environment, so as to more accurately respond to the actual power demand. If not received, randomly select any edge power consumption terminal other than the first edge power consumption terminal, conduct central power distribution decision based on the recorded historical data, generate an adversarial network based on the power distribution attenuation coefficients in the two power distribution decision results, and continue to repeat the steps until the final balanced power distribution analysis can be completed based on the power distribution decision results of multiple edge power consumption terminals, at which time the accuracy and adaptability are the highest, or until the preset fluctuation event ends, ensuring that during the entire fluctuation period, the power resources are always reasonably allocated, avoiding power supply interruptions or overloads, and ensuring the stable operation of the power grid and the power consumption safety of users.

[0027] The described edge decision power distribution method based on smart meters can achieve the technical goal of multiple edge power consumption terminals sending power distribution requirements and decision information in parallel, and achieve the technical effect of improving the real-time performance of power distribution and the overall system performance.

[0028] Furthermore, this application also includes:

[0029] Collect the access circuit data through the access monitor in the smart meter, where the access circuit data includes the access circuit voltage and the access circuit current; determine whether the access circuit data meets the fluctuation event threshold. If so, a preset fluctuation event occurs; if not, no preset fluctuation event occurs; after a preset fluctuation event occurs, send an edge power distribution decision signal to multiple edge power consumption terminals and obtain the signal sending times of multiple successful transmissions, where the multiple edge power consumption terminals regularly transmit power consumption characteristic data to the smart meter.

[0030] Specifically, through the access monitor in the smart meter, the access circuit data is collected to ensure real-time monitoring and data collection of the power grid. The access monitor in the smart meter can continuously record and analyze the access circuit data to form a dynamic database. For example, the access monitor is a sensor module built into the smart meter, which is used to monitor and collect key data in the access circuit, such as voltage and current. Among them, the access circuit data mainly includes the access circuit voltage and the access circuit current, and the access circuit voltage and the access circuit current reflect the working state of the circuit. In addition, the changes in voltage and current can provide information about power demand, power supply stability, equipment health status, etc. Therefore, real-time monitoring of the access circuit voltage and the access circuit current can foresee potential fault risks.

[0031] Next, the access circuit data is analyzed to determine whether the access circuit data meets the fluctuation event threshold. The fluctuation event threshold is a preset standard used to judge whether the fluctuations of the access circuit voltage and the access circuit current exceed the normal range. The fluctuation event threshold is set based on long-term historical data analysis and the experience of power grid operation to ensure that the fluctuation event threshold can effectively identify abnormal situations that may pose a threat to the stability of the power grid. If the access circuit data does not meet the fluctuation event threshold, indicating that no preset fluctuation event has occurred, normal monitoring continues; but if the access circuit data meets the fluctuation event threshold, indicating that a fluctuation event has occurred, countermeasures are triggered to ensure the continuity and stability of power supply and avoid equipment damage or power outages caused by abnormal fluctuations in voltage or current.

[0032] Then, after a preset fluctuation event occurs, an edge power distribution decision signal is immediately sent to multiple edge power consumption ends, and then the power distribution is adjusted in a timely manner to cope with sudden load changes in the power grid. Among them, the edge power distribution decision signal is a signal used to quickly respond and adjust the power distribution strategy when abnormal fluctuations occur in the power grid. Edge power distribution refers to the power distribution management for power consumption equipment or users located at the end or edge of the power grid, such as edge power consumption ends where users include residential areas, small industrial parks, commercial buildings, etc. The edge power distribution decision signal is a control signal sent when the smart meter or the central control system detects potential power supply problems or load imbalances in the power grid, used to guide the edge power consumption ends to adjust their power consumption behaviors, thereby maintaining the stability and reliability of the power grid. When a preset fluctuation event occurs, the edge power consumption ends adjust their power consumption demands according to the edge power distribution decision signal, reduce the pressure on the power grid, and then can effectively relieve the power grid load without affecting the overall power supply quality, avoiding power supply shortages or equipment failures caused by excessive power consumption.

[0033] Finally, the edge power consumption terminals regularly transmit power consumption characteristic data to the smart meter to continuously monitor and update the operating status of the power grid. The power consumption characteristic data includes information such as the real-time power consumption, power consumption patterns, and equipment operating status of each edge power consumption terminal. By regularly transmitting the power consumption characteristic data, a comprehensive understanding of the entire power grid can be maintained, and the operating model of the power grid can be updated in real time. This can not only promptly detect and solve potential power supply problems but also make more accurate power distribution decisions based on real-time data.

[0034] The access circuit data is collected through the access monitor in the smart meter, and the access circuit data is judged to identify preset fluctuation events. Subsequently, when a fluctuation event occurs, an edge power distribution decision signal is sent to multiple edge power consumption terminals, and the sending time of the edge power distribution decision signal is obtained. Finally, through the entire process of regularly transmitting the power consumption characteristic data from the edge power consumption terminals to the smart meter, through real-time monitoring, data analysis, and decision feedback, the efficient utilization of electricity and the stable operation of the power grid are ensured, providing reliable power services for users.

[0035] Furthermore, as Figure 2 shown, this application also includes:

[0036] After receiving the edge power distribution decision signal, the multiple edge power consumption terminals continuously collect power consumption characteristic data within a preset number of time frames, obtaining multiple edge power consumption characteristic data; within the multiple edge power consumption terminals, respectively, based on the multiple edge power consumption characteristic data, a power distribution attenuation coefficient decision is made; after the power distribution attenuation coefficient decision is completed at any one edge power consumption terminal, the first power distribution attenuation coefficient of the first edge power consumption terminal is obtained, which is used as the first edge power distribution decision result and sent to the smart meter, and the first decision sending time when the sending is successful is obtained.

[0037] Specifically, after receiving the edge power distribution decision signal, the multiple edge power consumption terminals will immediately start a process, that is, continuously collect power consumption characteristic data within a preset time frame. When a preset fluctuation event occurs in the power grid, the power consumption situation of each edge power consumption terminal can be quickly understood to enable more accurate distribution of power resources. The power consumption characteristic data includes various parameters such as voltage, current, and power factor, reflecting the real-time load, demand changes, and power quality of the power consumption terminal. By collecting data for multiple time frames within a preset time, a more comprehensive and accurate set of power consumption characteristic data can be obtained, providing a reliable basis for subsequent power distribution decisions.

[0038] Next, within each edge power consumption terminal, based on the corresponding power consumption characteristic data, a decision on the power distribution attenuation coefficient is made. The power distribution attenuation coefficient is a parameter that reflects the matching degree between the current power consumption state of the power consumption terminal and the power supply capacity of the power grid. By calculating the power distribution attenuation coefficient, it is then determined whether it is necessary to cut or adjust the power supply to the edge power consumption terminal under the current circumstances, so as to accurately match the power supply capacity of the power grid with the power consumption demand of users, thereby avoiding power waste or insufficient power supply caused by the imbalance between supply and demand. By each edge power consumption terminal making an independent decision on the power distribution attenuation coefficient according to its own edge power consumption characteristic data, the response speed and flexibility of the entire system are improved.

[0039] Next, after the decision on the power distribution attenuation coefficient is completed at any one edge power consumption terminal, a specific power distribution attenuation coefficient is generated at the corresponding edge power consumption terminal as the power distribution decision result, that is, the first power distribution attenuation coefficient of the first edge power consumption terminal, as the first edge power distribution decision result. And the first edge power distribution decision result is sent to the smart meter, which can obtain the latest decision information of each edge power consumption terminal in real time, ensuring that the overall regulation of the power grid can promptly reflect the state changes of each edge power consumption terminal. In addition, the time when the first edge power distribution decision result is sent will also be recorded, that is, the first decision sending time is used as important data for subsequent analysis and optimization.

[0040] Through distributed decision-making and real-time feedback, it is ensured that the power system can quickly respond to fluctuation events, achieve the optimal allocation of power resources, not only improve the stability of the power grid, but also ensure the reliability and efficiency of user power consumption.

[0041] Furthermore, this application also includes:

[0042] Within the multiple edge power consumption terminals, according to the power consumption characteristic data within the historical time, a plurality of sample power consumption characteristic data sets are collected according to the number of preset time frames, and according to the proportion of power distribution attenuation that can be performed under each sample power consumption characteristic data, a plurality of sample power distribution attenuation coefficient sets are obtained by marking; using the plurality of sample power consumption characteristic data sets and the plurality of sample power distribution attenuation coefficient sets, a plurality of edge power distribution attenuation decision-makers are respectively trained and configured within the multiple edge power consumption terminals; based on the plurality of edge power distribution attenuation decision-makers, a decision on the power distribution attenuation coefficient is made for the plurality of edge power consumption characteristic data.

[0043] Specifically, in multiple edge power consumption terminals, based on the power consumption characteristic data in the historical time, multiple sample power consumption characteristic data sets are collected according to the preset number of time frames. By analyzing the power consumption characteristic data in different time periods, common power consumption patterns and abnormal situations are identified to obtain a sample power consumption characteristic data set. The sample power consumption characteristic data set includes not only daily power consumption data, but also covers the changes in power consumption under different environmental conditions, such as seasonal fluctuations or power consumption peaks caused by special events. By collecting sample power consumption characteristic data sets according to the preset number of time frames, the power consumption characteristics can be analyzed more accurately to ensure that the collected data can truly reflect the power consumption status of each edge power consumption terminal under different circumstances, providing strong support for subsequent decision-making.

[0044] Next, according to the proportion of power distribution attenuation that can be performed under each sample power consumption characteristic data, multiple sets of sample power distribution attenuation coefficients were labeled and obtained, and the power consumption characteristic data in the historical time period was converted into reference coefficients that can be used for actual decision-making. Power distribution attenuation reflects the degree of power regulation of the system under a specific power consumption state, and thus more clearly understands the effective adjustment of power distribution to maintain the stability of the power grid and the efficiency of power consumption under different power consumption conditions.

[0045] Subsequently, multiple sample power consumption feature data sets and multiple sample power distribution attenuation coefficient sets are used to train multiple edge power distribution attenuation decision makers respectively. Through machine learning, different power consumption modes can be automatically identified and adapted, so that power distribution decisions can be made more flexibly and efficiently in actual operation. The edge power distribution attenuation decision maker at each edge power consumption end continuously optimizes the decision model in the edge power distribution attenuation decision maker during the training process, so that the edge power distribution attenuation decision maker can respond quickly and make reasonable power adjustments under different power consumption environments, and thus can cope with various complex power consumption situations, whether it is a sudden load increase or a long-term change in power consumption, it can ensure the reasonable allocation of power resources. Then, multiple edge power distribution attenuation decision makers are configured in each edge power consumption end.

[0046] Next, based on multiple edge distribution attenuation decision makers, distribution attenuation coefficient decisions are made for multiple edge power consumption feature data. This means that in actual operation, when new power consumption feature data is detected, the edge distribution attenuation decision maker calculates the distribution attenuation coefficient that best suits the current situation, and adjusts power consumption based on the distribution attenuation coefficient. This not only improves the resilience of the power grid, but also ensures that power supply can meet actual demand under any circumstances, avoiding resource waste or insufficient power supply due to improper power allocation, and achieving optimal allocation of power resources and efficient operation of the power grid.

[0047] The combination of machine learning and real-time decision-making ensures that the power grid can operate efficiently and stably under various complex situations and achieves the rational allocation of power resources.

[0048] Furthermore, this application also includes:

[0049] The smart meter continuously monitors and receives the edge power distribution decision results sent by any one of the edge power consumption terminals according to a preset monitoring time. When the first decision sending time and the corresponding first signal sending time are less than or equal to the preset monitoring time, the first edge power distribution decision result is received within the preset monitoring time; when the first decision sending time and the corresponding first signal sending time are greater than the preset monitoring time, the first edge power distribution decision result is not received within the preset monitoring time. The smart meter randomly selects the historical power consumption feature data sent and recorded by any one of the edge power consumption terminals to obtain the first historical power consumption feature data, and performs power consumption feature data prediction and central power distribution decision to obtain the first central power distribution decision result.

[0050] Specifically, the smart meter continuously monitors and receives the edge power distribution decision results sent by any one of the edge power consumption terminals without interruption according to a preset monitoring time. The preset monitoring time refers to the time for waiting for the decision data transmission of the edge power consumption terminal within a set time window, ensuring timely acquisition of the latest status information of the edge side and providing data support for the stable operation of the power grid. Through continuous monitoring, the smart meter can dynamically update the power supply distribution strategy to ensure a rapid response in case of power grid fluctuations or sudden changes in power consumption demand.

[0051] Next, when the first decision sending time and the corresponding first signal sending time are less than or equal to the preset monitoring time, the smart meter will receive the first edge power distribution decision result within the preset monitoring time, that is, if the edge power consumption terminal can respond and send its decision result in time within the preset monitoring time, the smart meter can obtain and process this decision result within this preset monitoring time, ensuring the timeliness of this decision result, so as to provide a basis for subsequent power distribution.

[0052] However, when the first decision sending time and the corresponding first signal sending time are greater than the preset monitoring time, the smart meter fails to receive the first edge power distribution decision result within the preset monitoring time, and thus cannot rely on real-time data for decision-making. Therefore, the smart meter randomly selects the historical power consumption feature data sent and recorded by any one of the edge power consumption terminals and obtains the first historical power consumption feature data. Even if the real-time data is missing, it can still make judgments and predictions through the historical power consumption feature data to ensure that the power distribution is not severely affected due to data delay. By analyzing the historical power consumption feature data, the current demand can be predicted based on past power consumption patterns, so as to formulate a reasonable central power distribution decision.

[0053] Finally, the smart meter predicts power consumption feature data based on the selected first historical power consumption feature data, and makes a central power distribution decision based on the prediction of the power consumption feature data, obtaining the first central power distribution decision result. Thus, even in the absence of real-time data, a relatively reasonable power distribution decision can still be formulated through prediction algorithms and historical data. The result of the first central power distribution decision will be used as the basis for the current power distribution, ensuring the stability and efficiency of the power grid in case of data delay or loss, and avoiding improper power distribution caused by data absence.

[0054] By ensuring the efficient operation of the power grid in various situations, it guarantees the timely satisfaction of power consumption demands and the rational allocation of power resources.

[0055] Furthermore, this application also includes:

[0056] According to the power consumption feature data of the multiple edge power consumption terminals recorded in the smart meter, collect multiple sample historical power consumption feature data sets, and obtain the power consumption feature data after each sample historical power consumption feature data, as multiple sample predicted power consumption feature data sets; use the multiple sample historical power consumption feature data sets and multiple sample predicted power consumption feature data sets to train multiple power consumption feature data prediction branches, obtain a power consumption feature data predictor, and configure it in the smart meter; input the first historical power consumption feature data into the power consumption feature data prediction branch corresponding to the edge power consumption terminal in the power consumption feature data predictor to predict and obtain the first predicted power consumption feature data; according to the power consumption feature data of the multiple edge power consumption terminals recorded in the smart meter, mark the power distribution attenuation coefficient, train multiple central power distribution attenuation decision makers and configure them in the smart meter, and perform central power distribution prediction on the first predicted power consumption feature data to obtain the first central power distribution decision result.

[0057] Specifically, according to the power consumption feature data of the multiple edge power consumption terminals recorded in the smart meter, collect multiple sample historical power consumption feature data sets, and further obtain the power consumption feature data after each sample historical power consumption feature data to form multiple sample predicted power consumption feature data sets, that is, by analyzing historical data and corresponding future data, establish a sample predicted power consumption feature data set that can reflect the power consumption trend. The sample predicted power consumption feature data set contains the power consumption changes in different time periods and different power consumption environments, which can provide extensive reference information to help understand and predict future power consumption patterns, and further expand and optimize the prediction ability, providing more accurate support for the subsequent decision-making process.

[0058] Next, using multiple sets of sample historical electricity consumption feature data and corresponding multiple sets of sample predicted electricity consumption feature data, train the corresponding multiple electricity consumption feature data prediction branches. Finally, integrate the multiple electricity consumption feature data prediction branches to obtain a complete electricity consumption feature data predictor, and configure the electricity consumption feature data predictor in the smart meter. Training multiple electricity consumption feature data prediction branches can master the variation law of electricity consumption features by learning the relationship between historical data and future data. When future electricity consumption situations occur, accurate predictions can be made through the electricity consumption feature data predictor, ensuring that the electricity consumption feature data predictor can provide high-precision prediction results under different edge electricity consumption terminals. After embedding the electricity consumption feature data predictor into the smart meter, electricity consumption predictions can be made at any time according to the actual situation.

[0059] Next, when prediction is needed, input the first historical electricity consumption feature data into the electricity consumption feature data predictor, specifically into the corresponding edge electricity consumption feature data prediction branch, so as to obtain the first predicted electricity consumption feature data, which can pre-judge future electricity consumption demands and make regulation decisions in advance when necessary, avoiding power supply imbalance caused by sudden increases or decreases in electricity consumption.

[0060] Finally, based on the electricity consumption feature data of multiple edge electricity consumption terminals recorded in the smart meter, label the power distribution attenuation coefficient, and use the labeled power distribution attenuation coefficient to train multiple central power distribution attenuation decision-making devices. Finally, configure the multiple central power distribution attenuation decision-making devices in the smart meter. Use the multiple central power distribution attenuation decision-making devices to conduct central power distribution prediction on the first predicted electricity consumption feature data, so as to obtain the first central power distribution decision result, ensuring that power resources can be reasonably allocated according to the predicted demands. By labeling and training multiple central power distribution attenuation decision-making devices, complex and changeable electricity consumption environments can be effectively dealt with, ensuring the efficient operation of the power grid and the stability of power supply.

[0061] Through the learning of historical data and real-time prediction, it is ensured that the power grid can flexibly respond to various electricity consumption scenarios and achieve the best power resource management.

[0062] Furthermore, this application also includes:

[0063] According to the marginal power distribution decision data of the multiple marginal power consumption terminals when a preset fluctuation event occurs within the historical time, collect the sample first power distribution decision result set of the first marginal power consumption terminal and the sample power distribution decision result distribution set, where each sample power distribution decision result distribution includes the power distribution decision results of the multiple marginal power consumption terminals; use the sample first power distribution decision result set and the sample power distribution decision result distribution set to train the first power distribution decision distribution restorer; use the first power distribution decision distribution restorer to perform power distribution decision distribution restoration on the first marginal power distribution decision result or the first central power distribution decision result to obtain a power distribution decision result distribution, where the power distribution decision result distribution includes the power distribution decision results of multiple marginal power consumption terminals, and each power distribution decision result includes a power distribution attenuation coefficient; according to the smart meter, collect the current access circuit data, combine it with the standard access circuit data, calculate the access attenuation amount, and calculate the multiple maximum power distribution attenuation amounts of the multiple marginal power consumption terminals according to the multiple power distribution attenuation coefficients and the rated power distribution data of the multiple marginal power consumption terminals; when the sum of the multiple maximum power distribution attenuation amounts is greater than or equal to the access attenuation amount, perform balanced power distribution according to the multiple power distribution attenuation coefficients to obtain a first balanced power distribution analysis result including multiple first power distribution parameters, where the ratio of the actual power distribution attenuation coefficient to the power distribution attenuation coefficient in each first power distribution parameter is the same, and the actual power distribution attenuation coefficient is greater than the corresponding power distribution attenuation coefficient; when the sum of the multiple power distribution attenuation amounts is less than the access attenuation amount, preferentially distribute power to the marginal power consumption terminals with small power distribution attenuation coefficients according to the multiple power distribution attenuation coefficients to obtain a first balanced power distribution analysis result including multiple first power distribution parameters, where the actual power distribution attenuation coefficient in each first power distribution parameter is greater than or equal to the corresponding power distribution attenuation coefficient.

[0064] Specifically, when a preset fluctuation event occurs within the historical time, collect the marginal power distribution decision data of multiple marginal power consumption terminals. The marginal power distribution decision data reflects the changes in the power demand response of each marginal power consumption terminal when the preset fluctuation event occurs. Among them, the sample first power distribution decision result set of the first marginal power consumption terminal extracted from the marginal power distribution decision data contains all the power distribution decision results made by this marginal power consumption terminal when a preset fluctuation event occurs at different historical times, providing a reference for subsequent analysis. At the same time, collect the sample power distribution decision result distribution set. The sample power distribution decision result distribution set includes the power distribution decision results of multiple marginal power consumption terminals, that is, each sample power distribution decision result distribution is the overall performance of the power distribution decisions of multiple marginal power consumption terminals under the preset fluctuation event, and thus can identify the behavior patterns of different marginal power consumption terminals when dealing with the preset fluctuation event, providing a rich historical basis for the subsequent optimization of the power distribution strategy, and helping to improve the response ability and decision-making accuracy of the system in future similar situations.

[0065] Next, using the collected sample first power distribution decision result set and the sample power distribution decision result distribution set, a first power distribution decision distribution restorer is trained. The role of the first power distribution decision distribution restorer is to be able to reconstruct and simulate these decision distributions in practical applications by learning the distribution patterns of the first power distribution decision result set and the sample power distribution decision result distribution set. By learning historical decision data, it can infer how the power distribution decisions among different edge power consumption ends are distributed under similar conditions, enabling the first power distribution decision distribution restorer to quickly and accurately predict the possible future power distribution decision distributions when facing new data, thus providing support for making more precise power distribution decisions.

[0066] Then, the first power distribution decision distribution restorer restores the power distribution decision for the first edge power distribution decision or the first central power distribution decision, enabling a more detailed analysis and simulation of the power distribution decision results for each edge power consumption end, so as to ensure that the power distribution can accurately reflect the actual power consumption needs and power supply capabilities of each edge power consumption end, thereby obtaining the power distribution decision result distribution. The power distribution decision result distribution includes the power distribution decision results of multiple edge power consumption ends, and each power distribution decision result contains the corresponding power distribution attenuation coefficient, which can better manage power resources and ensure that the power distribution remains balanced and stable under any fluctuation conditions.

[0067] Next, based on the current access circuit data collected by the smart meter, combined with the standard access circuit data, the access attenuation amount is calculated, and then the attenuation impact on the current circuit during actual operation is evaluated. Then, using multiple power distribution attenuation coefficients and the rated power distribution data of multiple edge power consumption ends, the multiple maximum power distribution attenuation amounts of the edge power consumption ends are calculated, that is, by comparing the actual data with the standard data, the most reasonable power distribution method for each edge power consumption end in the current environment is determined, ensuring that power resources can be effectively utilized, reducing waste and maintaining the stability of the power grid.

[0068] Then, when the sum of multiple maximum power distribution attenuation amounts is greater than or equal to the access attenuation amount, balanced power distribution is performed according to multiple power distribution attenuation coefficients to obtain a first balanced power distribution analysis result including multiple first power distribution parameters. By comparing the ratio of the actual power distribution attenuation coefficient to the power distribution attenuation coefficient, that is, the ratio of the actual power distribution attenuation coefficient in each edge power consumption end to the power distribution attenuation coefficient generated by the decision is the same. For example, it is 0.5. For example, the power distribution attenuation coefficients generated by the decision for edge power consumption ends A and B are 20% and 30%, and the actual power distribution attenuation coefficients are 10% and 15%. Ensuring that the actual power distribution attenuation coefficient is always greater than the corresponding power distribution attenuation coefficient helps to reasonably allocate power when resources are abundant, ensuring that each edge power consumption end can obtain sufficient power supply while maintaining the overall balance of the system. For example, calculate the total ratio of the sum of multiple maximum power distribution attenuation amounts to the access attenuation amount, then multiply this total ratio by the maximum power distribution attenuation amount of each edge power consumption end to obtain the actual power distribution attenuation amount, and divide it by the rated power distribution data of each edge power consumption end to obtain the actual power distribution attenuation coefficient.

[0069] However, if the sum of multiple power distribution attenuation amounts is less than the access attenuation amount, it indicates that the power distribution of some edge power distribution ends may not be satisfied. Then, according to multiple power distribution attenuation coefficients, priority power distribution is performed on the edge power consumption ends with smaller power distribution attenuation coefficients, and power distribution is directly performed according to the power distribution attenuation coefficients to obtain a first balanced power distribution analysis result including multiple first power distribution parameters. For example, the edge power consumption ends with smaller power distribution attenuation coefficients are the edge power consumption ends with relatively tight production or power consumption demands. Furthermore, when power resources are relatively limited, priority is given to ensuring power supply to the edge power consumption ends with higher or more critical power demands, thereby ensuring the effective utilization of power resources and avoiding power shortages caused by uneven resource allocation. The actual power distribution attenuation coefficient within each first power distribution parameter is greater than or equal to the corresponding power distribution attenuation coefficient, so as to ensure that power distribution can still meet key demands when resources are scarce.

[0070] By calculating the attenuation amount and the maximum power distribution attenuation amount, the power distribution strategy is flexibly adjusted to ensure that the power grid can operate in a balanced and stable manner whether resources are abundant or scarce, ultimately achieving efficient power resource management.

[0071] Furthermore, this application also includes:

[0072] Based on the generative adversarial network, construct the first power distribution decision distribution restorer, and the first power distribution decision distribution restorer includes a generation unit and an adversarial unit; use the sample first power distribution decision result set and the sample power distribution decision result distribution set to supervise and train the generation unit and the adversarial unit until the training converges to obtain the trained first power distribution decision distribution restorer.

[0073] Specifically, a generative adversarial network is a machine learning framework. Based on the generative adversarial network, a model of the first distribution decision distribution restorer is constructed, including a generation unit and an adversarial unit. The task of the generation unit is to generate a distribution of distribution decisions similar to real data, while the adversarial unit attempts to distinguish between the generated data and the real data. Through adversarial training, the generation unit continuously improves its output and finally produces results very close to the real data. In the first distribution decision distribution restorer, the collaborative work of the generation unit and the adversarial unit enables the system to simulate and reconstruct complex distribution decision distributions, thereby providing support for subsequent power distribution.

[0074] Next, using the set of sample first distribution decision results and the set of sample distribution decision result distributions, the generation unit and the adversarial unit are supervised and trained. The generation unit and the adversarial unit can continuously learn and improve. The generation unit attempts to generate a new distribution of distribution decisions, while the adversarial unit evaluates the authenticity of the new distribution of distribution decisions. In continuous iterations, the output of the generation unit gets closer and closer to the real distribution of distribution decisions. As the training progresses, the confrontation between the generation unit and the adversarial unit gradually tends to balance, and the training process finally converges. Training convergence means that the generation unit has been able to generate data highly similar to the real distribution of distribution decisions, and the adversarial unit can no longer easily distinguish between the generated data and the real data. At this time, the trained first distribution decision distribution restorer already has the ability to restore and simulate the distribution decision distribution in practical applications. The first distribution decision distribution restorer can, when faced with new data, generate a distribution of distribution decisions that conforms to the actual situation according to the patterns learned during the training process, providing accurate predictions and support for the scheduling and distribution of the power system.

[0075] By fully training the model, it can provide strong prediction and distribution support for the power system, ensuring the efficient use of power resources and the stable operation of the system.

[0076] In summary, the edge decision power distribution method based on smart meters provided in this application has the following technical effects:

[0077] Through the access monitor in the smart meter, the access circuit data is monitored. After a preset fluctuation event occurs, an edge power distribution decision signal is sent to multiple edge power consumption terminals, and multiple signal sending times are obtained. Among them, the multiple edge power consumption terminals regularly transmit power consumption feature data to the smart meter; the multiple edge power consumption terminals receive the edge power distribution decision signal, continuously collect power consumption feature data within the number of preset time frames, perform edge power distribution decision. After any one of the edge power consumption terminals completes the edge power distribution decision, the first edge power distribution decision result is sent to the smart meter, and the first decision sending time is obtained; the smart meter continuously monitors and receives the edge power distribution decision results sent by any one of the edge power consumption terminals according to the preset monitoring time. When the first decision sending time and the corresponding first signal sending time are less than or equal to the preset monitoring time, the first edge power distribution decision result is received, and the monitoring and receiving continue. Otherwise, the smart meter randomly selects the historical power consumption feature data sent and recorded by any one of the edge power consumption terminals to perform power consumption feature data prediction and central power distribution decision, and obtains the first central power distribution decision result; the smart meter performs balanced power distribution analysis according to the first edge power distribution decision result or the first central power distribution decision result, obtains the first balanced power distribution analysis result including multiple first power distribution parameters, and distributes power to the multiple edge power consumption terminals; continues to monitor and obtain the second edge power distribution decision result or process and obtain the second central power distribution decision result according to twice the preset monitoring time, and combines the first edge power distribution decision result or the first central power distribution decision result to perform balanced power distribution analysis until the balanced power distribution analysis is performed based on the power distribution decision results of the multiple edge power consumption terminals, or the preset fluctuation event ends, realizing the technical goal of parallelly sending power distribution requirements and decision-making information by multiple edge power consumption terminals, and achieving the technical effect of improving the real-time performance of power distribution and the overall system performance.

[0078] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0079] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is also intended to include these changes and variations.

Claims

1. An edge decision-making power distribution method based on smart meters, characterized in that: The method comprises: The access circuit data is monitored by an access monitor in the smart meter. After a preset fluctuation event occurs, an edge power distribution decision signal is sent to multiple edge power consumption terminals, and multiple signal sending times are obtained, wherein the multiple edge power consumption terminals transmit power consumption characteristic data to the smart meter at regular intervals. The multiple edge power consumption terminals receive the edge power distribution decision signal, continuously collect power consumption feature data within a preset number of time frames, make edge power distribution decisions, and after any edge power consumption terminal completes the edge power distribution decision, send a first edge power distribution decision result to the smart meter, and obtain a first decision sending time; The smart meter continuously monitors and receives the edge power distribution decision result sent by any edge power consumption terminal according to the preset monitoring time, and receives the first edge power distribution decision result when the first decision sending time and the corresponding first signal sending time are less than or equal to the preset monitoring time, and continues to monitor and receive, otherwise, the smart meter randomly selects any historical power consumption feature data sent and recorded by any edge power consumption terminal to perform power consumption feature data prediction and central power distribution decision, and obtains the first central power distribution decision result; The smart electric meter performs a balanced power distribution analysis according to the first edge power distribution decision result or the first central power distribution decision result, obtains a first balanced power distribution analysis result including a plurality of first power distribution parameters, and distributes power to the plurality of edge power consumption terminals; Continue to monitor and obtain the second edge power distribution decision result or process and obtain the second central power distribution decision result at twice the preset monitoring time, and perform balanced power distribution analysis in combination with the first edge power distribution decision result or the first central power distribution decision result, until a balanced power distribution analysis is performed based on the power distribution decision results of the multiple edge power consumption ends, or the preset fluctuation event ends.

2. The edge decision-making power distribution method based on smart meters according to claim 1 is characterized in that: The access monitor in the smart meter monitors the access circuit data. After a preset fluctuation event occurs, an edge power distribution decision signal is sent to multiple edge power consumption terminals, and multiple signal sending times are obtained, including: Collecting access circuit data through an access monitor in the smart meter, wherein the access circuit data includes an access circuit voltage and an access circuit current; Determine whether the access circuit data meets the fluctuation event threshold, if yes, a preset fluctuation event occurs, if no, a preset fluctuation event does not occur; After a preset fluctuation event occurs, an edge power distribution decision signal is sent to multiple edge power consumption terminals, and multiple signal sending times of successful sending are obtained, wherein the multiple edge power consumption terminals periodically transmit power consumption characteristic data to the smart meter.

3. The edge decision-making power distribution method based on smart meters according to claim 1 is characterized in that: The plurality of edge power consumption terminals receive the edge power distribution decision signal, continuously collect power consumption characteristic data within a preset number of time frames, and make edge power distribution decisions, including: After receiving the edge power distribution decision signal, the plurality of edge power consumption terminals continuously collect power consumption characteristic data within a preset number of time frames to obtain a plurality of edge power consumption characteristic data; In the plurality of edge power consumption terminals, respectively, a power distribution attenuation coefficient decision is made according to the plurality of edge power consumption characteristic data; After any edge power user completes the power distribution attenuation coefficient decision, the first power distribution attenuation coefficient of the first edge power user is obtained, which is sent to the smart meter as the first edge power distribution decision result, and the first decision sending time of successful sending is obtained.

4. The edge decision-making power distribution method based on smart meters according to claim 3 is characterized in that: In the plurality of edge power consumption terminals, respectively, according to the plurality of edge power consumption characteristic data, a power distribution attenuation coefficient decision is made, including: In the plurality of edge power consumption terminals, according to the power consumption characteristic data in the historical time, a plurality of sample power consumption characteristic data sets are collected according to the preset time frame number, and according to the proportion of power distribution attenuation that can be performed under each sample power consumption characteristic data, a plurality of sample power distribution attenuation coefficient sets are obtained by marking; Using the plurality of sample power consumption feature data sets and the plurality of sample power distribution attenuation coefficient sets, respectively training a plurality of edge power distribution attenuation decision makers, which are configured in the plurality of edge power consumption terminals; Based on the multiple edge power distribution attenuation decision makers, power distribution attenuation coefficient decisions are made on the multiple edge power consumption feature data.

5. The edge decision-making power distribution method based on smart meters according to claim 1 is characterized in that: include: The smart meter continuously monitors and receives the edge power distribution decision result sent by any edge power consumption terminal according to the preset monitoring time, and receives the first edge power distribution decision result within the preset monitoring time when the first decision sending time and the corresponding first signal sending time are less than or equal to the preset monitoring time; When the first decision sending time and the corresponding first signal sending time are greater than the preset monitoring time, and the first edge power distribution decision result is not received within the preset monitoring time, the smart meter randomly selects any historical power consumption characteristic data sent and recorded by the edge power consumption terminal, obtains the first historical power consumption characteristic data, performs power consumption characteristic data prediction and central power distribution decision, and obtains the first central power distribution decision result.

6. The edge decision-making power distribution method based on smart meters according to claim 5 is characterized in that: Conduct power consumption characteristic data prediction and central power distribution decision-making to obtain the first central power distribution decision result, including: According to the power consumption characteristic data of the plurality of edge power consumption terminals recorded in the smart meter, a plurality of sample historical power consumption characteristic data sets are collected, and the power consumption characteristic data after each sample historical power consumption characteristic data is obtained as a plurality of sample predicted power consumption characteristic data sets; Using the plurality of sample historical power consumption feature data sets and the plurality of sample predicted power consumption feature data sets, training a plurality of power consumption feature data prediction branches, obtaining a power consumption feature data predictor, and configuring the predictor in the smart meter; Inputting the first historical power consumption characteristic data into the power consumption characteristic data prediction branch corresponding to the edge power consumption terminal in the power consumption characteristic data predictor to predict and obtain first predicted power consumption characteristic data; According to the power consumption characteristic data of the multiple edge power consumption terminals recorded in the smart meter, the distribution attenuation coefficient is marked, multiple central distribution attenuation decision makers are trained and configured in the smart meter, and central distribution prediction is performed on the first predicted power consumption characteristic data to obtain a first central distribution decision result.

7. The edge decision-making power distribution method based on smart meters according to claim 1 is characterized in that: The smart meter performs balanced power distribution analysis according to the first edge power distribution decision result or the first central power distribution decision result to obtain a first balanced power distribution analysis result including a plurality of first power distribution parameters, including: According to the edge power distribution decision data of the plurality of edge power terminals when a preset fluctuation event occurs within a historical time, a sample first power distribution decision result set of the first edge power terminal and a sample power distribution decision result distribution set are collected, wherein each sample power distribution decision result distribution includes the power distribution decision results of the plurality of edge power terminals; Using the sample first power distribution decision result set and the sample power distribution decision result distribution set to train a first power distribution decision distribution restorer; The first power distribution decision distribution restorer is used to restore the first edge power distribution decision result or the first central power distribution decision result to obtain a power distribution decision result distribution, wherein the power distribution decision result distribution includes power distribution decision results of multiple edge power consumption ends, wherein each power distribution decision result includes a distribution attenuation coefficient; According to the smart meter, current access circuit data is collected and obtained, and access attenuation is calculated in combination with standard access circuit data, and multiple maximum distribution attenuation amounts of the multiple edge power terminals are calculated according to multiple distribution attenuation coefficients and rated distribution data of multiple edge power terminals; When the sum of the multiple maximum power distribution attenuations is greater than or equal to the access attenuation, balanced power distribution is performed according to the multiple power distribution attenuation coefficients to obtain a first balanced power distribution analysis result including multiple first power distribution parameters, wherein the ratio of the actual power distribution attenuation coefficient in each first power distribution parameter is the same as the power distribution attenuation coefficient, and the actual power distribution attenuation coefficient is greater than the corresponding power distribution attenuation coefficient; When the sum of the multiple distribution attenuations is less than the access attenuation, priority is given to edge power consumption terminals with small distribution attenuation coefficients according to the multiple distribution attenuation coefficients to obtain a first balanced distribution analysis result including multiple first distribution parameters, wherein the actual distribution attenuation coefficient within each first distribution parameter is greater than or equal to the corresponding distribution attenuation coefficient.

8. The edge decision-making power distribution method based on smart meters according to claim 7 is characterized in that: The sample first power distribution decision result set and the sample power distribution decision result distribution set are used to train a first power distribution decision distribution restorer, including: Based on the generative adversarial network, construct the first power distribution decision distribution reducer, wherein the first power distribution decision distribution reducer includes a generation unit and an adversarial unit; The sample first power distribution decision result set and the sample power distribution decision result distribution set are used to perform supervised training on the generation unit and the adversarial unit until the training converges, thereby obtaining the trained first power distribution decision distribution restorer.

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