An electric energy meter intelligent data processing method, system and terminal

Abstract

The application provides an electric energy meter intelligent data processing method, system and terminal, relates to the technical field of electric energy meter intelligent data processing, and comprises the following steps: acquiring real-time data of an electric energy meter; cleaning and formatting the real-time data to determine basic data; analyzing the basic data to generate normal classification data and early warning information; analyzing the normal classification data and early warning information to generate a structured data packet; and uploading the structured data packet to a master station. The application has the effect of improving the real-time data analysis and early warning of an electric energy meter.

Description

Technical Field

[0001] This application relates to the technical field of smart data processing for electricity meters, and in particular to a smart data processing method, system and terminal for electricity meters. Background Technology

[0002] Smart data processing for electricity meters refers to the process of collecting, analyzing, classifying, and identifying anomalies in the large amounts of complex data generated by electricity meters. The purpose is to ensure the power grid's requirements for real-time, accurate, and automated data processing.

[0003] In related technologies, intelligent data processing is usually performed by the master station to analyze and process the data. For example, the terminal first completes the collection of the original data of the electricity meter, and then transmits the original data to the master station according to a fixed transmission protocol. The master station analyzes and issues warnings, and then returns the analysis and warning results to the terminal through the original transmission link.

[0004] Regarding the aforementioned technologies, the terminal sends the collected electricity meter data to the master station, which then analyzes and issues warnings before returning the results to the terminal. This process results in low real-time performance of the electricity meter's data analysis and warnings, leaving room for improvement. Summary of the Invention

[0005] To improve the real-time performance of electricity meter data analysis and early warning, this application provides an intelligent data processing method, system, and terminal for electricity meters.

[0006] In a first aspect, this application provides a smart data processing method for electricity meters, employing the following technical solution:

[0007] A smart data processing method for electricity meters, comprising:

[0008] Obtain real-time data from the electricity meter;

[0009] Real-time data is cleaned and formatted to determine the underlying data;

[0010] The basic data is analyzed to generate normal classification data and early warning information;

[0011] Analyze normal classification data and early warning information to generate structured data packets;

[0012] Upload the structured data packet to the main site.

[0013] By adopting the above technical solution, real-time data is cleaned and formatted to determine basic data. After analyzing the basic data, normal classification data and early warning information are determined. After analyzing the normal classification data and early warning information, structured data packets are generated and uploaded to the main station. Thus, after analyzing and issuing early warnings for meter data locally, only the generated structured data packets need to be uploaded to the main station, without the main station needing to analyze the data or return the data. This enables local real-time analysis and processing of meter data, improving the real-time performance of meter data analysis and early warning.

[0014] Optionally, the steps for analyzing the basic data to generate normal classification data and early warning information include:

[0015] Extract the trend characteristics of data changes from the basic data to generate trend analysis results;

[0016] The trend analysis result is determined to be either a preset result with no abnormal trend or a preset result with abnormal trend.

[0017] If the result shows no abnormal trend, the preset local terminal will be determined as the best processing terminal.

[0018] If there is an abnormal trend, the best processing terminal will be determined within the preset terminal group based on the trend analysis results.

[0019] Based on preset threshold judgment rules and preset power consumption pattern rules, the optimal processing terminal is controlled to analyze basic data to generate normal classification data and early warning information.

[0020] By adopting the above technical solution, trend analysis results are generated after extracting the data change trend characteristics from the basic data. When no abnormal trend result is determined, the local terminal is directly identified as the best processing terminal. When an abnormal trend result is determined, the best processing terminal is determined within the terminal unit based on the trend analysis results. Then, based on threshold judgment rules and electricity consumption pattern rules, the best processing terminal is controlled to analyze the basic data and generate normal classification data and early warning information, thereby realizing local real-time analysis and processing of meter data, improving the real-time performance of meter data analysis and early warning, and reducing the data transmission volume and computing load of the main station.

[0021] Optionally, the steps to extract data change trend features from the base data to generate trend analysis results include:

[0022] Calculate the mean of the baseline data to generate the deviation rate benchmark data;

[0023] Calculate the difference between the base data and the deviation rate benchmark data to generate the deviation rate change value;

[0024] Calculate the quotient of the deviation rate change value and the deviation rate baseline data to generate the trend change deviation rate;

[0025] Determine whether the trend change deviation rate is greater than the preset baseline deviation rate;

[0026] If the result is not greater than the specified value, then the result without abnormal trends is defined as the trend analysis result.

[0027] If the value is greater than the threshold, then the result with an abnormal trend will be defined as a trend analysis result.

[0028] By adopting the above technical solution, after calculating the mean of the basic data, a deviation rate benchmark data is generated. Then, after calculating the difference between the basic data and the deviation rate benchmark data, a deviation rate change value is generated. Finally, after calculating the quotient of the deviation rate change value and the deviation rate benchmark data, a trend change deviation rate is generated. If the trend change deviation rate is determined to be no greater than the basic deviation rate, the result without abnormal trends is defined as a trend analysis result. If it is greater than the basic deviation rate, the result with abnormal trends is defined as a trend analysis result. Based on the trend analysis results, the optimal processing terminal is determined, thereby realizing local real-time analysis and processing of meter data.

[0029] Optionally, the step of determining the optimal processing terminal within a preset terminal group based on trend analysis results includes:

[0030] The number of abnormal terminals within the terminal unit is determined based on the trend analysis results;

[0031] Determine if the number of abnormal terminals exceeds the preset standard number;

[0032] If the value is greater than 1, the local terminal will be determined as the best processing terminal.

[0033] If it is not greater than, then obtain the resource usage parameters, real-time parameters and processing accuracy of the terminals in the terminal group;

[0034] Analyze resource usage parameters, real-time parameters, and processing accuracy to determine the optimal processing terminal.

[0035] By adopting the above technical solution, the number of abnormal terminals in the terminal unit is determined based on the trend analysis results. When the number of abnormal terminals is greater than the standard number, the local terminal is directly determined as the best processing terminal. If it is not greater than the standard number, the best processing terminal is determined after analyzing the resource usage parameters, real-time parameters, and processing accuracy. Thus, the basic data is analyzed and warned on the best processing terminal, thereby realizing local real-time analysis and processing of meter data and improving the real-time performance of meter data analysis and warning.

[0036] Optionally, the steps of analyzing resource consumption parameters, real-time parameters, and processing accuracy to determine the optimal processing terminal include:

[0037] The cumulative runtime, CPU full load threshold, maximum task queue length, and task queue length are determined based on resource usage parameters.

[0038] Calculate the quotient of cumulative runtime and preset sampling period to generate CPU utilization;

[0039] Analyze CPU utilization, CPU full load threshold, maximum task queue length, and task queue length to determine resource availability;

[0040] The local send timestamp, local receive timestamp, processing start timestamp, and processing end timestamp are determined based on real-time parameters.

[0041] The local sending timestamp, local receiving timestamp, and preset number of communication delay samples are analyzed to determine the average communication delay.

[0042] The processing start timestamp, processing end timestamp, and preset number of time-consuming samples are analyzed to determine the average processing time.

[0043] The average communication delay and average processing time are weighted and summed according to the preset real-time weight parameters to determine the real-time score.

[0044] The terminal adaptation score is determined by weighting and summing the processing accuracy, resource availability, and real-time performance scores based on preset impact factor parameters.

[0045] The terminal adaptation scores are sorted to determine the maximum adaptation score, and the terminal in the terminal group that corresponds to the maximum adaptation score is determined as the best processing terminal.

[0046] By adopting the above technical solution, the cumulative running time, CPU full load threshold, maximum task queue length, and task queue length are determined based on resource usage parameters. This allows for the generation of CPU utilization after calculating the quotient of the cumulative running time and sampling period. Furthermore, resource availability is determined after analyzing the CPU utilization, CPU full load threshold, maximum task queue length, and task queue length. Local send timestamp, local receive timestamp, processing start timestamp, and processing end timestamp are determined based on real-time parameters. The average communication delay is determined after analyzing the local send timestamp, local receive timestamp, and the number of communication delay samples. Finally, the processing start timestamp and processing end timestamp are analyzed. After analyzing the timestamps and the number of time-consuming samples, the average processing time is determined. Then, based on the real-time weight parameters, the average communication delay and the average processing time are weighted and summed to determine the real-time score. Then, based on the influencing factor parameters, the processing accuracy, resource availability, and real-time score are weighted and summed to determine the terminal adaptation score. After sorting the terminal adaptation scores, the maximum adaptation score is determined. The terminal with the maximum adaptation score in the terminal group is then determined as the optimal processing terminal. In this way, the terminal with the highest adaptation degree in the terminal group is found to analyze and issue early warnings for the electricity meter data, thereby realizing local real-time analysis and processing of electricity meter data and improving the real-time performance of electricity meter data analysis and early warning.

[0047] Optionally, the steps of controlling the optimal processing terminal to analyze basic data and generate normal classification data and early warning information according to preset threshold judgment rules and preset power consumption pattern rules include:

[0048] Determine the threshold parameters according to the threshold judgment rules;

[0049] Determine if the base data is greater than the threshold parameter;

[0050] If the value is greater than the specified value, an early warning message will be generated.

[0051] If it is not greater than, the basic data will be analyzed according to the preset time period classification rules to determine the time period data;

[0052] The time-segmented data and the preset standard electricity consumption curves are analyzed to determine the curve similarity.

[0053] Determine whether the curve similarity is less than the preset benchmark similarity;

[0054] If it is not less than, then normal classification data will be generated according to the electricity consumption pattern rules and time-of-use data;

[0055] If the value is less than the specified value, an early warning message will be generated.

[0056] By adopting the above technical solution, a threshold parameter is determined according to the threshold judgment rule. When the basic data is determined to be greater than the threshold parameter, an early warning message is directly generated. If it is not greater than the threshold parameter, the basic data is analyzed according to the time period classification rule to determine the time period data. Then, the similarity between the time period data and the standard electricity consumption curve is determined. When the curve similarity is determined to be not less than the benchmark similarity, normal classification data is generated according to the electricity consumption pattern rule and the time period data. If it is less than the benchmark similarity, an early warning message is directly generated. The normal classification data and the early warning message are then generated into a structured data packet for subsequent transmission to the main station. This enables local real-time analysis and processing of meter data, improving the real-time performance of meter data analysis and early warning.

[0057] Optionally, the steps of analyzing time-of-use data and preset standard electricity consumption curves to determine curve similarity include:

[0058] Determine the current voltage and current values ​​of the preset sampling points based on the time-segmented data;

[0059] Calculate the product of the current voltage and current values ​​to generate the current power value;

[0060] Determine the maximum standard power and the power consumption values ​​at the sampling points based on the standard power consumption curve;

[0061] The power consumption value, maximum standard power, current power value, and preset number of sampling points are analyzed to determine the curve similarity.

[0062] By adopting the above technical solution, the current voltage and current values ​​of the sampling points are determined based on the time-segmented data. The current power value is then generated by multiplying the current voltage and current values. The maximum standard power and the power consumption value of the sampling points are determined based on the standard power consumption curve. The similarity of the curve is determined by analyzing the power consumption value, the maximum standard power, the current power value, and the number of sampling points. This determines the degree of similarity between the time-segmented data and the standard power consumption curve, and further determines whether the power consumption behavior and scenario are correct, so as to facilitate the subsequent generation of normal classification data and early warning information.

[0063] Secondly, this application provides an intelligent data processing system for electricity meters, which adopts the following technical solution:

[0064] A smart data processing system for electricity meters, comprising:

[0065] The acquisition module is used to acquire real-time data from the electricity meter.

[0066] A memory for storing a program for a smart data processing method for an energy meter as described in any of the preceding claims;

[0067] The processor and the program in the memory can be loaded and executed by the processor to implement a smart data processing method for electricity meters as described in any of the above.

[0068] By adopting the above technical solution, a program for a smart data processing method for electricity meters, stored in memory, is loaded and executed by a processor. This program controls the acquisition module to acquire a series of data related to smart data processing. After cleaning and formatting the real-time data, basic data is determined. Then, after analyzing the basic data, normal classification data and early warning information are generated. After further analysis of the normal classification data and early warning information, structured data packets are generated and uploaded to the main station. Thus, after analyzing and issuing early warnings for electricity meter data locally, only the generated structured data packets need to be uploaded to the main station. The main station does not need to analyze the data or return the data, thereby realizing local real-time analysis and processing of electricity meter data and improving the real-time performance of electricity meter data analysis and early warning.

[0069] Thirdly, this application provides a smart terminal, which adopts the following technical solution:

[0070] A smart terminal includes a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executed as described in any of the preceding claims for a smart data processing method for an electricity meter.

[0071] By adopting the above technical solution, a program for a smart data processing method for electricity meters, stored in memory, is loaded and executed by a processor. This program controls the acquisition module to acquire a series of data related to smart data processing. After cleaning and formatting the real-time data, basic data is determined. Then, after analyzing the basic data, normal classification data and early warning information are generated. After further analysis of the normal classification data and early warning information, structured data packets are generated and uploaded to the main station. Thus, after analyzing and issuing early warnings for electricity meter data locally, only the generated structured data packets need to be uploaded to the main station. The main station does not need to analyze the data or return the data, thereby realizing local real-time analysis and processing of electricity meter data and improving the real-time performance of electricity meter data analysis and early warning.

[0072] In summary, this application includes at least one of the following beneficial technical effects:

[0073] 1. After cleaning and formatting real-time data, basic data is determined. After analyzing the basic data, normal classification data and early warning information are determined. After analyzing the normal classification data and early warning information, structured data packets are generated and then uploaded to the main station. Thus, after analyzing and issuing early warnings for meter data locally, only the generated structured data packets need to be uploaded to the main station, without the main station needing to analyze the data or return the data. This enables local real-time analysis and processing of meter data, improving the real-time performance of meter data analysis and early warning.

[0074] 2. After extracting the trend characteristics of data changes from the basic data, trend analysis results are generated. When no abnormal trend is found, the local terminal is directly identified as the best processing terminal. When an abnormal trend is found, the best processing terminal is determined within the terminal unit based on the trend analysis results. Then, based on threshold judgment rules and electricity consumption pattern rules, the best processing terminal is controlled to analyze the basic data and generate normal classification data and early warning information. The best processing terminal is determined based on the trend analysis results, thereby realizing local real-time analysis and processing of meter data.

[0075] 3. By determining the number of abnormal terminals within the terminal unit based on trend analysis results, if the number of abnormal terminals is greater than the standard number, the local terminal is directly identified as the best processing terminal; if it is not greater than the standard number, the best processing terminal is determined after analyzing resource usage parameters, real-time parameters, and processing accuracy. Thus, basic data is analyzed and alerted on the best processing terminal, thereby realizing local real-time analysis and processing of meter data and improving the real-time performance of meter data analysis and alerts. Attached Figure Description

[0076] Figure 1 This is a flowchart of a smart data processing method for an electricity meter according to an embodiment of this application.

[0077] Figure 2 This is a flowchart of the steps in this application embodiment to analyze basic data to generate normal classification data and early warning information.

[0078] Figure 3 This is a flowchart of the steps in this application embodiment to extract data change trend features from basic data to generate trend analysis results.

[0079] Figure 4 This is a flowchart of the steps in this application embodiment to determine the optimal processing terminal within a preset terminal group based on trend analysis results.

[0080] Figure 5 This is a flowchart illustrating the steps in this application embodiment to analyze resource usage parameters, real-time parameters, and processing accuracy to determine the optimal processing terminal.

[0081] Figure 6 This is a flowchart illustrating the steps in this application embodiment to control the optimal processing terminal to analyze basic data and generate normal classification data and early warning information based on preset threshold judgment rules and preset power consumption pattern rules.

[0082] Figure 7 This is a flowchart illustrating the steps in this application embodiment to analyze time-segmented data and preset standard electricity consumption curves to determine the curve similarity. Detailed Implementation

[0083] To make the purpose, technical solution, and advantages of this application clearer, the following description is provided in conjunction with the appendix. Figures 1 to 7 The present application will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the application.

[0084] This application discloses a smart data processing method for electricity meters, specifically disclosing an electricity meter and a processing terminal. The processing terminal is communicatively connected to both the electricity meter and a master station to achieve data interaction and control. After receiving real-time data from the electricity meter, the processing terminal cleans and formats the real-time data to determine the basic data. After analyzing the basic data, it determines normal classification data and early warning information. After analyzing the normal classification data and early warning information, it generates a structured data packet, which is then uploaded to the master station. Thus, after analyzing and issuing early warnings locally, the generated structured data packet only needs to be uploaded to the master station, eliminating the need for the master station to analyze and return the data. This enables local real-time analysis and processing of electricity meter data, improving the real-time performance of electricity meter data analysis and early warning.

[0085] Reference Figure 1 This application discloses a smart data processing method for electricity meters, including the following steps:

[0086] Step S100: Obtain real-time data from the electricity meter.

[0087] Among them, real-time electricity meter data refers to the core parameters of electricity consumption status recorded by the meter in the current or very short period of time. Data acquisition commands are sent to the meter via an RS-485 hardware interface, and the meter receives and returns the corresponding data, such as the meter's real-time voltage and current. When voltage and current increase, power consumption also increases, indicating an increase in current electricity load. This is the core basis for overload warnings, thus providing data support for the subsequent generation of warning information.

[0088] Step S101: Clean and format the real-time data to determine the underlying data.

[0089] Among them, basic data refers to data that, after being cleaned and formatted, can accurately reflect the current operating status of the electricity meter and can be directly used for analysis. The processing terminal uses a moving average filter to denoise the real-time data and converts the processed data into a standard format, such as unifying values ​​of different precisions to a specified number of decimal places, thereby providing data support for subsequent terminal analysis and processing of the data.

[0090] Step S102: Analyze the basic data to generate normal classification data and early warning information.

[0091] Among them, normal classification data refers to classification data that meets the threshold range and the current electricity consumption scenario. It is obtained by the optimal processing terminal after classifying the basic data through threshold judgment rules, time-segmentation rules, and electricity consumption pattern rules. For specific methods, please refer to [link / reference]. Figure 2 The steps, such as collecting household electricity consumption data on weekdays, such as a power consumption of 2.5kW, accounting for 50% of the daily consumption, are classified as normal peak power data for households, thus providing data support for the subsequent generation of structured data packets.

[0092] Warning information refers to the generated alerts indicating anomalies. These are generated by the optimal processing terminal after verifying basic data using threshold judgment rules or electricity consumption pattern rules. For specific methods, please refer to [link / reference]. Figure 2 The process involves steps such as determining if a household's off-peak power is 4.2kW, exceeding the power threshold of 2kW, and generating an early warning message about the electricity consumption pattern, thereby providing data support for the subsequent generation of structured data packets.

[0093] Step S103: Analyze the normal classification data and early warning information to generate a structured data package.

[0094] Structured data packets refer to text data blocks generated according to a fixed format. The optimal processing terminal organizes normal classification data and early warning information according to a fixed format. By generating formatted data packets, the transmission efficiency and data integrity between the processing terminal and the main station can be effectively improved, thereby realizing data synchronization between the main station and the local terminal.

[0095] Step S104: Upload the structured data packet to the main station.

[0096] After generating structured data packets, the optimal processing terminal uploads the structured data packets to the main station, thereby reducing the amount of data transmitted and the computational load on the main station, while improving the timeliness and accuracy of anomaly warnings.

[0097] Reference Figure 2 The steps for analyzing basic data to generate normal classification data and early warning information include:

[0098] Step S200: Extract the data change trend characteristics from the basic data to generate trend analysis results.

[0099] The trend analysis results refer to the results of whether there are any abnormal trends in the data changes in the basic data, including results with and without abnormal trends. These results are obtained by the processing terminal extracting the data change trend characteristics from the basic data. Specific methods are detailed in [reference needed]. Figure 3 The steps are as follows. By analyzing the trend analysis results, the optimal processing terminal for basic data can be determined, thereby enabling local real-time analysis and processing of electricity meters.

[0100] Step S201: Determine whether the trend analysis result is a preset result of no abnormal trend or a preset result of abnormal trend.

[0101] Among them, the result of no abnormal trend means that the current electricity consumption data has not shown any sudden changes or deviations, and is stored by the operator in the processing terminal.

[0102] An abnormal trend result means that the current electricity consumption data is significantly contrary to the recent electricity consumption pattern, and there is a sudden change in the electricity consumption data, which is stored by the operator in the processing terminal.

[0103] By determining whether the trend analysis results are without abnormal trends or have abnormal trends, it is possible to identify whether the basic data has abnormal trends, thereby determining the optimal processing terminal and enabling local real-time analysis and processing of the electricity meter.

[0104] Step S2011: If there is no abnormal trend result, then the preset local terminal is determined as the best processing terminal.

[0105] If the result shows no abnormal trend, it indicates that there has been no sudden change or deviation in the current electricity consumption data. The local terminal is directly identified as the best processing terminal. By identifying the best processing terminal, normal classification data and early warning information are generated after analyzing the basic data, thereby realizing the local real-time analysis and processing of the electricity meter and reducing the computing load of the main station.

[0106] The optimal processing terminal refers to the terminal with the highest adaptability, which is determined by comprehensively considering the terminal's processing accuracy, real-time performance, and resource availability. In this step, the optimal processing terminal is the local terminal.

[0107] Step S2012: If there is an abnormal trend result, determine the best processing terminal within the preset terminal group based on the trend analysis result.

[0108] If the result shows an abnormal trend, it indicates that the current electricity consumption data significantly deviates from recent electricity consumption patterns, indicating a sudden change in electricity consumption. Based on the trend analysis results, the optimal processing terminal is determined within the terminal units. The specific method is described in [reference needed]. Figure 4 The steps involve determining the optimal processing terminal to analyze basic data and generate normal classification data and early warning information, thereby enabling local real-time analysis and processing of electricity meters, improving the timeliness of early warnings, and reducing the computational load on the main station.

[0109] A terminal unit refers to a collaborative network consisting of multiple adjacent electricity meter terminals, which is pre-configured by operators. By setting up terminal units, abnormal data can be processed locally in a collaborative manner, thereby improving the real-time performance of abnormal warnings.

[0110] Step S202: Based on the preset threshold judgment rules and preset power consumption pattern rules, the optimal processing terminal is controlled to analyze the basic data to generate normal classification data and early warning information.

[0111] Among them, the threshold judgment rule refers to setting a numerical boundary for various types of electricity consumption data. This boundary is set in advance by the operator and stored in the processing terminal. For example, the current threshold is 40A. When the real-time current exceeds 40A, it indicates overcurrent and triggers an early warning.

[0112] Electricity consumption mode rules refer to behavioral standards set according to the electricity consumption characteristics of specific electricity consumption scenarios. These rules are set in advance by operators and stored in the processing terminal. For example, if the total power during a business shutdown period in a certain commercial electricity consumption scenario should be 1kW-2kW, but the measured total power during that period is 3.5kW, it indicates that the commercial electricity consumption mode is abnormal, thus triggering an early warning.

[0113] By identifying and invoking threshold judgment rules and electricity consumption pattern rules, the optimal processing terminal is controlled to generate normal classification data and early warning information after analyzing basic data. For specific methods, please refer to [link / reference needed]. Figure 6 This process enables local real-time analysis and processing of meter data, allowing the meter to respond promptly to warning information and thus improving the real-time performance of meter data analysis and warnings.

[0114] Reference Figure 3 The steps for extracting trend characteristics from basic data to generate trend analysis results include:

[0115] Step S300: Calculate the mean of the baseline data to generate the deviation rate benchmark data.

[0116] Among them, the deviation rate benchmark data refers to the average value of the most recent n data points in the basic data. It is obtained by the processing terminal summing the most recent n data points in the basic data and then dividing it by n. By generating the deviation rate benchmark data, the overall trend of data change in the recent period can be determined, thereby providing data support for calculating the trend change deviation rate.

[0117] Step S301: Calculate the difference between the base data and the deviation rate benchmark data to generate the deviation rate change value.

[0118] Among them, the deviation rate change value refers to the absolute deviation between the current data point and the deviation rate benchmark data. It is obtained by the processing terminal by subtracting the basic data and the deviation rate benchmark data and taking the absolute value. By generating the deviation rate change value, the deviation range between the current data point and the deviation rate benchmark data can be determined. The larger the value, the greater the trend change deviation rate, thus providing data support for calculating the trend change deviation rate.

[0119] Step S302: Calculate the quotient of the deviation rate change value and the deviation rate benchmark data to generate the trend change deviation rate.

[0120] Among them, the trend change deviation rate refers to the degree of deviation of the current data point from the overall trend of the recent n data points. It is obtained by the processing terminal by dividing the deviation rate change value and the deviation rate benchmark data. By generating the trend change deviation rate, it can be determined that the smaller the trend change deviation rate, the smaller the probability of a data mutation, so as to facilitate the subsequent determination of trend analysis results.

[0121] Step S303: Determine whether the trend change deviation rate is greater than the preset baseline deviation rate.

[0122] The baseline deviation rate is a standard threshold used to determine whether there is an abnormal trend in the data, and it is set in advance by the operator. By judging whether the trend change deviation rate is greater than the baseline deviation rate, it is determined whether there is an abnormal trend in the basic data, and then different optimal processing terminals are determined.

[0123] Step S3031: If it is not greater than, then the result without abnormal trend is defined as the trend analysis result.

[0124] If the trend change deviation rate is not greater than the baseline deviation rate, it indicates that the current data has no abnormal trend. Therefore, the result without abnormal trend can be defined as the trend analysis result, so as to determine the best processing terminal in the future.

[0125] Step S3032: If it is greater than, then the result with abnormal trend is defined as the trend analysis result.

[0126] If the trend change deviation rate is greater than the baseline deviation rate, it indicates that the current data has an abnormal trend. Therefore, the result with an abnormal trend can be defined as the trend analysis result, so as to determine the best processing terminal in the future.

[0127] Reference Figure 4 The steps for determining the optimal processing terminal within a pre-set terminal group based on trend analysis results include:

[0128] Step S400: Determine the number of abnormal terminals in the terminal unit based on the trend analysis results.

[0129] The number of abnormal terminals refers to the number of terminals within the terminal unit that show abnormal trends. This number is obtained by the processing terminal after statistically analyzing the trend results of each terminal within the terminal unit. By counting the number of abnormal terminals, it can be determined that when only one processing terminal is connected to a meter showing an abnormal trend, finding the optimal processing terminal can improve the accuracy of data processing. However, when there are many abnormal terminals, if all processing is handled by a single processing terminal, the terminal will experience CPU overload due to simultaneously analyzing data from multiple terminals, resulting in decreased processing efficiency.

[0130] Step S401: Determine whether the number of abnormal terminals is greater than the preset standard number.

[0131] The standard number refers to the number of terminals in the terminal unit that have abnormal trend results, which is set in advance by the operator. By judging whether the number of abnormal terminals is greater than the standard number, the optimal processing terminal is determined, thereby realizing local real-time analysis and processing of meter data.

[0132] Step S4011: If it is greater than, then the local terminal is determined as the best processing terminal.

[0133] If the number of abnormal terminals exceeds the standard number, the local terminal is directly identified as the best processing terminal. The number of abnormal terminals indicates that when there are many abnormal terminals, each terminal only processes its own data, effectively reducing inter-terminal communication and interaction, and lowering the risk of data loss.

[0134] Step S4012: If not greater than, obtain the resource usage parameters, real-time parameters and processing accuracy of the terminals in the terminal group.

[0135] If the number of abnormal terminals is not greater than the standard number, it means that there is only one terminal with abnormal trend results in the terminal group. In this case, the resource usage parameters, real-time parameters and processing accuracy are directly obtained to find the best processing terminal for processing, thereby improving the accuracy of data processing.

[0136] Resource usage parameters refer to the status indicators of how much the terminal hardware and software tasks within the terminal unit are occupied by current services during operation. These include cumulative running time, CPU full load threshold, maximum task queue length, and task queue length, which are obtained in real time from the system interface. The cumulative running time is strongly correlated with CPU utilization; a longer cumulative running time indicates a higher CPU utilization. A longer task queue length indicates a smaller proportion of tasks that can be accommodated, thus providing data support for subsequent calculations of terminal adaptation scores.

[0137] Real-time parameters are indicators that measure data interaction speed and data processing efficiency. These include local send timestamp, local receive timestamp, processing start timestamp, and processing end timestamp, which are directly read from the terminal's built-in clock module. The local receive timestamp and local send timestamp are strongly correlated with data interaction speed; the smaller the difference, the faster the data interaction. Similarly, the processing start timestamp and processing end timestamp are strongly correlated with data processing efficiency; the smaller the difference, the higher the terminal's data processing efficiency. These parameters provide data support for subsequent calculations of the terminal adaptation score.

[0138] Processing accuracy refers to the degree of accuracy of data processing by each terminal in the terminal unit. It is obtained by dividing the number of times the data is processed correctly by the total number of times the data is processed. The higher the processing accuracy, the more reliable the analysis and output results of the power consumption data of the terminal are, so as to determine the best processing terminal in the future.

[0139] Step S40121: Analyze resource usage parameters, real-time parameters, and processing accuracy to determine the optimal processing terminal.

[0140] After determining the processing accuracy, the optimal processing terminal is determined by analyzing resource usage parameters, real-time parameters, and processing accuracy. The specific method is described in [reference needed]. Figure 5 The steps involve identifying the optimal processing terminal, thereby selecting the terminal with the best processing capacity, the most abundant resources, and the most timely response, thus improving the accuracy of data processing and the timeliness of anomaly warnings.

[0141] Reference Figure 5 The steps to determine the optimal processing terminal by analyzing resource consumption parameters, real-time parameters, and processing accuracy include:

[0142] Step S500: Determine the cumulative running time, CPU full load threshold, maximum task queue length, and task queue length based on resource usage parameters.

[0143] The cumulative running time refers to the cumulative CPU running time within the sampling period. It is identified and retrieved by the processing terminal from the resource usage parameters. By obtaining the cumulative running time, it can be determined that the higher the cumulative running time, the higher the CPU utilization rate and the less remaining resources the terminal has, thus providing data support for subsequent calculation of CPU utilization rate.

[0144] The CPU full load threshold is a benchmark value used to measure whether the CPU is overloaded. It is identified and retrieved by the processing terminal from the resource usage parameters. The CPU full load threshold directly determines the terminal's hardware computing power capacity. The higher the CPU full load threshold, the stronger the terminal's computing power, thus providing data support for subsequent calculations of CPU utilization.

[0145] The maximum task queue length refers to the maximum number of tasks that a single task queue can hold. It is identified and called by the processing terminal from the resource usage parameters. The maximum task queue length can be determined by obtaining the maximum task queue length. When the maximum task queue length is larger, the terminal memory capacity is larger and the task scheduling algorithm is more efficient, so as to facilitate the subsequent determination of resource availability.

[0146] The task queue length refers to the number of tasks waiting to be executed in the task queue at the current moment. It is identified and called by the processing terminal from the resource usage parameters. By obtaining the task queue length, the current task processing pressure of the terminal can be determined. The smaller the task queue length, the lower the task pressure of the terminal, thus providing data support for subsequent determination of resource availability.

[0147] Step S501: Calculate the quotient of the cumulative running time and the preset sampling period to generate the CPU utilization rate.

[0148] CPU utilization refers to the percentage of time the CPU is used to execute tasks within a sampling period, which is obtained by quoting the cumulative running time and the sampling period from the processing terminal. By calculating CPU utilization, the current computing power load of the terminal can be determined. The higher the CPU utilization, the less available resources the terminal has, providing data support for subsequent determination of resource availability.

[0149] Step S502: Analyze CPU utilization, CPU full load threshold, maximum task queue length, and task queue length to determine resource availability.

[0150] Resource availability refers to a comprehensive indicator of the idle level of the terminal's current computing resources and task scheduling resources. It is obtained by analyzing the processing terminal's CPU utilization, CPU full load threshold, maximum task queue length, and task queue length, and can be expressed as follows: ,in Indicates resource availability. This indicates the CPU full load threshold. Indicates CPU utilization. Indicates the maximum task queue length. This indicates the length of the task queue. The lower the CPU utilization and the shorter the task queue length, the higher the resource availability. By determining the resource availability, the terminal adaptation score can be determined, which will help determine the best processing terminal in the future.

[0151] Step S503: Determine the local send timestamp, local receive timestamp, processing start timestamp, and processing end timestamp based on the real-time parameters.

[0152] The local transmission timestamp refers to the local time recorded in real time when the current processing terminal sends data packets to other processing terminals in the terminal group. It is identified and called by the processing terminal from the real-time parameters. By calling the local transmission timestamp, the starting reference of the communication delay can be determined, providing data support for the subsequent determination of the average communication delay.

[0153] The local receive timestamp refers to the local time recorded in real time when the current processing terminal receives the response data packet returned by other processing terminals in the terminal group. It is identified and called by the processing terminal from the real-time parameters. By calling the local receive timestamp, the time when the processing terminal received the data can be determined, so as to determine the average communication delay in the future.

[0154] The processing start timestamp refers to the local time recorded in real time when the current processing terminal starts the data processing process. It is identified and called by the processing terminal from the real-time parameters. By calling the processing start timestamp, the starting point of data processing time can be determined, providing data support for the subsequent determination of average communication latency.

[0155] The processing end timestamp refers to the local time recorded in real time when the current processing terminal completes the data processing flow. It is identified and retrieved by the processing terminal from the real-time parameters. By calling the processing end timestamp, the end time of the data processing flow can be determined, thereby providing data support for subsequently determining the average processing time.

[0156] Step S504: Analyze the local sending timestamp, local receiving timestamp, and preset number of communication delay samples to determine the average communication delay.

[0157] The number of communication delay samples refers to the total number of single communication delay data collected, which is set in advance by the operator. Collecting samples multiple times can reduce the interference of single outliers on the results and make the average communication delay more accurate.

[0158] Average communication latency refers to the average time for a single data interaction between a processing terminal and other processing terminals within a terminal group. It is obtained by analyzing the local send timestamp, local receive timestamp, and the number of communication latency samples, and can be expressed as: ,in Indicates average communication delay. Indicates the number of communication delay samples. The sample number representing the delay of a single communication session. Indicates the first Local sending timestamp of the sample Indicates the first The local reception timestamp of the sample can be used to determine the average communication delay. The smaller the average communication delay, the faster the communication speed and the higher the real-time score. This provides data support for the subsequent determination of the real-time score.

[0159] Step S505: Analyze the processing start timestamp, processing end timestamp, and preset number of time-consuming samples to determine the average processing time.

[0160] The time-consuming sample number refers to the total number of data points that take time to process in a single data collection session. This number is set in advance by the operator. By collecting samples multiple times, the interference of single outliers on the results can be reduced, making the average processing time more accurate.

[0161] Average processing time refers to the average time it takes for a processing terminal to complete data processing. It is obtained by analyzing the processing start timestamp, processing end timestamp, and the number of time samples taken. It can be expressed as: ,in, This represents the number of samples that took time to complete. The sample number represents the processing time for a single operation. Indicates the first The timestamp at which the processing of this sample began. Indicates the first The processing end timestamp of the sample. This represents the average processing time. By determining the average processing time, we can see that the smaller the average processing time, the faster the data processing speed and the higher the real-time score, thus providing data support for the subsequent determination of the real-time score.

[0162] Step S506: The average communication delay and average processing time are weighted and summed according to the preset real-time weight parameters to determine the real-time score.

[0163] The real-time weight parameter refers to the coefficients that determine the degree of influence of average communication delay and average processing time on the real-time score. These parameters are set in advance by the operator. For example, there are weights for communication delay and processing time. Communication delay directly affects response timeliness and is more susceptible to environmental interference. Therefore, the weight for communication delay is set to 0.6, and the weight for processing time is set to 0.4. By setting the real-time weight parameter, the communication and processing requirements can be balanced to determine the real-time score.

[0164] Real-time score is a quantitative indicator of the speed of the processing terminal in data interaction and data processing. It is obtained by weighting and summing the average communication delay and average processing time according to the real-time weight parameters. By determining the real-time score, we can reflect how quickly the processing terminal responds to data requests and completes data processing. The higher the real-time score, the faster the terminal's data interaction and data processing speed, thus providing data support for the subsequent determination of the terminal adaptation score.

[0165] Step S507: Based on the preset impact factor parameters, the processing accuracy, resource availability, and real-time performance scores are weighted and summed to determine the terminal adaptation score.

[0166] Among them, the influencing factor parameter refers to the coefficients of the degree of influence of processing accuracy, resource availability, and real-time performance score on the terminal adaptation score. These parameters are set in advance by the operators. For example, there are weights for processing accuracy, resource availability, and real-time performance. Processing accuracy directly determines the reliability of the processing results, so the weight for processing accuracy is set to 0.4. Real-time performance score directly affects response efficiency, so the weight for real-time performance is set to 0.35. The weight for resource availability is set to 0.25. By setting different weights, it is possible to avoid ignoring the core processing accuracy and real-time performance due to excessive focus on resources, thereby providing data support for determining the terminal adaptation score.

[0167] Terminal adaptation score refers to the comprehensive quantitative score of processing accuracy, resource availability and real-time performance. It is obtained by weighting and summing the processing accuracy, resource availability and real-time performance scores of the processing terminal according to the influencing factor parameters. By determining the terminal adaptation score, the comprehensive data processing capability of each terminal in the terminal group can be determined. The higher the terminal adaptation score, the stronger the comprehensive capability, so as to determine the best processing terminal in the future.

[0168] Step S508: Sort the terminal adaptation scores to determine the maximum adaptation score, and determine the terminal in the terminal group that corresponds to the maximum adaptation score as the best processing terminal.

[0169] The maximum adaptation score refers to the highest score obtained by sorting the terminal adaptation scores of all processing terminals in the terminal unit. By determining the maximum adaptation score, the processing terminal with the strongest comprehensive data processing capability in the terminal unit can be identified and designated as the optimal processing terminal, so as to facilitate subsequent local real-time analysis and processing of meter data.

[0170] Reference Figure 6 The steps for controlling the optimal processing terminal to analyze basic data and generate normal classification data and early warning information based on preset threshold judgment rules and preset power consumption pattern rules include:

[0171] Step S600: Determine the threshold parameter according to the threshold judgment rule.

[0172] Among them, the threshold parameter refers to the critical value set to determine whether a certain electrical parameter is normal. It is identified and called by the optimal processing terminal from the threshold judgment rules. For example, the voltage threshold is 242V. When the voltage exceeds 242V, it indicates that the meter is overvoltage. By calling the threshold parameter, it can be determined whether the electrical parameter is within the normal range, so as to generate normal classification data and early warning information in the future.

[0173] Step S601: Determine whether the basic data is greater than the threshold parameter.

[0174] The optimal processing terminal determines whether the basic data is greater than the threshold parameter, thereby determining whether the basic data is within the threshold range, so as to facilitate the subsequent generation of normal classification data and early warning information.

[0175] Step S6011: If the value is greater than the specified value, then generate a warning message.

[0176] If the value is greater than the threshold parameter, it means that a certain electrical parameter has exceeded the set critical value and is not within the normal range, thereby generating an early warning message. This avoids response delays caused by analysis after transmission to the main station, and improves the real-time performance of abnormal early warnings.

[0177] Step S6012: If it is not greater than, then analyze the basic data according to the preset time period classification rules to determine the time period data.

[0178] If the value is not greater than the threshold parameter, it means that a certain electrical parameter is within the set critical value and meets the threshold judgment rule. Therefore, the basic data is analyzed according to the time period classification rule to determine the time period data. By determining the time period data, the basic data is classified according to different time periods, and the differences in electricity consumption in different time periods can be determined, thus providing data support for the subsequent determination of curve similarity.

[0179] Time period classification rules refer to the rules for dividing a 24-hour day into different electricity consumption periods based on the characteristics of the power grid load and users' electricity consumption habits. These rules are set in advance by operators, such as peak period 8:00-22:00 and valley period 22:00-8:00 the next day. By setting time period classification rules, the best processing terminal can automatically identify the time period to which the basic data belongs, thereby realizing time period management and facilitating the subsequent generation of normal classified data.

[0180] Time-segmented data refers to data formed by classifying and statistically analyzing basic data according to time-segmentation rules. It is obtained by the optimal processing terminal after analyzing the basic data according to the time-segmentation rules. By determining time-segmented data, the differences in electricity consumption in different time periods can be determined, so as to facilitate the subsequent determination of curve similarity.

[0181] Step S60121: Analyze the time-segmented data and the preset standard electricity consumption curve to determine the curve similarity.

[0182] The standard power consumption curve refers to a reference curve of standard power consumption over time, generated based on long-term power consumption data statistics. It is obtained by operators through statistical analysis of long-term power consumption data. By determining the standard power consumption curve, it is possible to judge whether there are any abnormalities in real-time power consumption, avoid misjudgment based on a single threshold, and facilitate the subsequent generation of normal classification data or early warning information.

[0183] Curve similarity refers to the degree of similarity between time-of-use data and the standard electricity consumption curve in terms of numerical fluctuation patterns. It is obtained by analyzing the time-of-use data and the standard electricity consumption data using the optimal processing terminal. For specific methods, please refer to [link / reference needed]. Figure 7 By determining the curve similarity, the similarity between the time-segmented data and the standard electricity consumption curve can be determined, thereby determining whether the electricity consumption behavior and scenario are correct, and providing data support for the subsequent generation of normal classification data or early warning information.

[0184] Step S60122: Determine whether the curve similarity is less than the preset benchmark similarity.

[0185] Among them, the benchmark similarity refers to the benchmark threshold for measuring the consistency between the time-segmented data and the standard electricity consumption curve. It is set in advance by the operator. The optimal processing terminal judges whether the curve similarity is less than the benchmark similarity, thereby determining the degree of deviation between the time-segmented data and the standard electricity consumption curve, which facilitates the subsequent generation of normal classification data or early warning information.

[0186] Step S601221: If it is not less than, then generate normal classification data according to the electricity consumption pattern rules and time-of-use data.

[0187] If the similarity is not less than the benchmark, it indicates that the time-segmented data is highly similar to the standard electricity consumption curve and belongs to normal data. Therefore, the best processing terminal generates normal classification data according to the electricity consumption pattern rules and time-segmented data to facilitate the subsequent generation of structured data packets.

[0188] Step S601222: If it is less than, then generate a warning message.

[0189] If the similarity is less than the benchmark, it indicates that the time-period data deviates significantly from the standard electricity consumption curve and does not conform to normal electricity consumption behavior or scenarios, thereby generating early warning information and improving the timeliness of local early warnings from the electricity meter.

[0190] Reference Figure 7 The steps for analyzing time-segmented data and preset standard electricity consumption curves to determine curve similarity include:

[0191] Step S700: Determine the current voltage and current values ​​of the preset sampling points based on the time-segmented data.

[0192] Among them, the preset sampling points refer to the fixed time nodes used to acquire voltage and current data. These points are set in advance by the operators. By setting the sampling points, random data acquisition is avoided, ensuring that there is complete data for each time period, thereby providing data support for subsequent calculation of curve similarity.

[0193] The current voltage value refers to the actual voltage value at the sampling point. It is identified and retrieved by the optimal processing terminal from the time-segmented data based on the preset sampling point, so as to determine the current power value later.

[0194] The current current value refers to the actual current value at the sampling point. It is identified and retrieved by the optimal processing terminal from the time-segmented data based on the preset sampling point, so as to determine the current power value later.

[0195] Step S701: Calculate the product of the current voltage value and the current current value to generate the current power value.

[0196] The current power value refers to the rate at which electrical energy is consumed at the sampling point. It is obtained by calculating the product of the current voltage value and the current current value by the optimal processing terminal, so as to facilitate the subsequent determination of curve similarity.

[0197] Step S702: Determine the maximum standard power and the power consumption value of the sampling point based on the standard power consumption curve.

[0198] Among them, the maximum standard power refers to the normal power peak of the current power consumption scenario. It is identified and retrieved from the standard power consumption curve by the best processing terminal. By determining the maximum standard power, the maximum power of the current power consumption scenario can be determined, thereby determining the maximum reasonable deviation of a single sampling point, and thus providing data support for the subsequent determination of curve similarity.

[0199] The power consumption value refers to the power value at the sampling point. It is identified and retrieved from the standard power consumption curve by the optimal processing terminal. By determining the power consumption value, the typical power during the time period mentioned at the sampling point can be determined, so as to facilitate the subsequent determination of curve similarity.

[0200] Step S703: Analyze the power consumption value, maximum standard power, current power value and preset number of sampling points to determine the curve similarity.

[0201] The number of sampling points refers to the total number of data collected after dividing a period of time into fixed time intervals. It is set in advance by the operator. The larger the number of sampling points, the more subtle the power consumption fluctuations can be captured, but the calculation time will also increase slightly. Therefore, the number of sampling points is set to 24 to balance accuracy and efficiency, so as to facilitate the subsequent determination of curve similarity.

[0202] The optimal processing terminal analyzes the power consumption value, maximum standard power, current power value, and number of sampling points to obtain the curve similarity, which can be expressed as: ,in Indicates curve similarity. Indicates the first The current power value of each sampling point Indicates the first The power consumption value at each sampling point Indicates the number of sampling points. The maximum standard power is represented by the difference between the current power value and the power consumption value, and the absolute value of the difference can be used to determine the actual deviation of the current value from the standard curve. The product of the number of sampling points and the maximum standard power represents the upper limit of the total deviation of all sampling points, thereby determining the curve similarity, so as to facilitate the subsequent generation of normal classification data and early warning information.

[0203] Based on the same inventive concept, embodiments of this application provide an intelligent data processing system for electricity meters, including:

[0204] The acquisition module is used to acquire real-time data from the electricity meter, the number of resource usage adoptions, real-time parameters, and processing accuracy.

[0205] A memory for storing a program for a smart data processing method for electricity meters;

[0206] The processor can load and execute programs in memory to implement a smart data processing method for electricity meters.

[0207] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0208] This application provides a computer-readable storage medium storing a computer program that can be loaded by a processor and executed as a smart data processing method for an electricity meter.

[0209] Computer storage media include, for example, USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media that can store program code.

[0210] Based on the same inventive concept, embodiments of this application provide a smart terminal, including a memory and a processor, wherein the memory stores a computer program that can be loaded and executed by the processor to perform a smart data processing method for an electricity meter.

[0211] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0212] The above are all preferred embodiments of this application and are not intended to limit the scope of protection of this application. Any feature disclosed in this specification (including the abstract and drawings) may be replaced by other equivalent or similar features unless specifically stated otherwise. That is, unless specifically stated otherwise, each feature is only one example of a series of equivalent or similar features.

Claims

1. A smart data processing method for electricity meters, characterized in that, include: Obtain real-time data from the electricity meter; Real-time data is cleaned and formatted to determine the underlying data; The basic data is analyzed to generate normal classification data and early warning information; Analyze normal classification data and early warning information to generate structured data packets; Upload the structured data packet to the main site; The steps for analyzing basic data to generate normal classification data and early warning information include: Extract the trend characteristics of data changes from the basic data to generate trend analysis results; The trend analysis result is determined to be either a preset result with no abnormal trend or a preset result with abnormal trend. If the result shows no abnormal trend, the preset local terminal will be determined as the best processing terminal. If there is an abnormal trend, the best processing terminal will be determined within the preset terminal group based on the trend analysis results. Based on preset threshold judgment rules and preset power consumption pattern rules, the optimal processing terminal is controlled to analyze basic data to generate normal classification data and early warning information. The steps for determining the optimal processing terminal within a pre-set terminal group based on trend analysis results include: The number of abnormal terminals within the terminal unit is determined based on the trend analysis results; Determine if the number of abnormal terminals exceeds the preset standard number; If the value is greater than 1, the local terminal will be determined as the best processing terminal. If it is not greater than, then obtain the resource usage parameters, real-time parameters and processing accuracy of the terminals in the terminal group; Analyze resource usage parameters, real-time parameters, and processing accuracy to determine the optimal processing terminal; The steps to analyze resource consumption parameters, real-time parameters, and processing accuracy to determine the optimal processing terminal include: The cumulative runtime, CPU full load threshold, maximum task queue length, and task queue length are determined based on resource usage parameters. Calculate the quotient of cumulative runtime and preset sampling period to generate CPU utilization; Analyze CPU utilization, CPU full load threshold, maximum task queue length, and task queue length to determine resource availability; The local send timestamp, local receive timestamp, processing start timestamp, and processing end timestamp are determined based on real-time parameters. The local sending timestamp, local receiving timestamp, and preset number of communication delay samples are analyzed to determine the average communication delay. The processing start timestamp, processing end timestamp, and preset number of time-consuming samples are analyzed to determine the average processing time. The average communication delay and average processing time are weighted and summed according to the preset real-time weight parameters to determine the real-time score. The terminal adaptation score is determined by weighting and summing the processing accuracy, resource availability, and real-time performance scores based on preset impact factor parameters. The terminal adaptation scores are sorted to determine the maximum adaptation score, and the terminal in the terminal group that corresponds to the maximum adaptation score is determined as the best processing terminal.

2. The intelligent data processing method for an electricity meter according to claim 1, characterized in that, The steps for extracting trend characteristics from basic data to generate trend analysis results include: Calculate the mean of the baseline data to generate the deviation rate benchmark data; Calculate the difference between the base data and the deviation rate benchmark data to generate the deviation rate change value; Calculate the quotient of the deviation rate change value and the deviation rate baseline data to generate the trend change deviation rate; Determine whether the trend change deviation rate is greater than the preset baseline deviation rate; If the result is not greater than the specified value, then the result without abnormal trends is defined as the trend analysis result. If the value is greater than the threshold, then the result with an abnormal trend will be defined as a trend analysis result.

3. The intelligent data processing method for an electricity meter according to claim 1, characterized in that, The steps for controlling the optimal processing terminal to analyze basic data and generate normal classification data and early warning information based on preset threshold judgment rules and preset power consumption pattern rules include: Determine the threshold parameters according to the threshold judgment rules; Determine whether the base data is greater than the threshold parameter; If the value is greater than the specified value, an early warning message will be generated. If it is not greater than, the basic data will be analyzed according to the preset time period classification rules to determine the time period data; The time-segmented data and the preset standard electricity consumption curves are analyzed to determine the curve similarity. Determine whether the curve similarity is less than the preset benchmark similarity; If it is not less than, then normal classification data will be generated according to the electricity consumption pattern rules and time-of-use data; If the value is less than the specified value, an early warning message will be generated.

4. The intelligent data processing method for an electricity meter according to claim 3, characterized in that, The steps for analyzing time-of-use data and preset standard electricity consumption curves to determine curve similarity include: Determine the current voltage and current values ​​of the preset sampling points based on the time-segmented data; Calculate the product of the current voltage and current current values ​​to generate the current power value; Determine the maximum standard power and the power consumption values ​​at the sampling points based on the standard power consumption curve; The power consumption value, maximum standard power, current power value, and preset number of sampling points are analyzed to determine the curve similarity.

5. An intelligent data processing system for electricity meters, characterized in that, include: The acquisition module is used to acquire real-time data from the electricity meter, resource usage parameters, real-time parameters, and processing accuracy. A memory for storing a program for a smart data processing method for an electricity meter as described in any one of claims 1 to 4; The processor and the program in the memory can be loaded and executed by the processor to implement the smart data processing method for electricity meters as described in any one of claims 1 to 4.

6. A smart terminal, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executed as described in any one of claims 1 to 4 for a smart data processing method for an electricity meter.

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