Intelligent data processing method, system and terminal of electric energy meter

Abstract

The invention provides an electric energy meter intelligent data processing method, system and terminal, and relates to the technical field of electric energy meter intelligent data processing. 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 the early warning information to generate a structured data packet; and uploading the structured data packet to a master station. The method has the effect of improving the real-time performance of data analysis and early warning of the electric energy meter.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent data processing of electric energy meters, in particular to an intelligent data processing method, system and terminal of electric energy meters. BACKGROUND

[0002] Intelligent data processing of electric energy meters refers to a process of collecting, analyzing, classifying and identifying abnormalities of a large amount of complex data generated by electric energy meters, aiming to ensure the real-time, accuracy and automation requirements of data processing of power grids.

[0003] In related technologies, intelligent data processing is usually analyzed and processed by a master station, for example, first, a terminal completes the collection of original data of electric meters, then the original data is transmitted to the master station according to a fixed transmission protocol, the master station analyzes and issues an early warning, and then the master station returns the analysis and early warning results to the terminal through the original transmission link.

[0004] For the related technologies in the above, the terminal sends the collected electric energy meter data to the master station, the master station analyzes and issues an early warning, and then the analysis and early warning results are returned to the terminal, which results in low real-time performance of data analysis and early warning of electric energy meters, and there is still room for improvement. SUMMARY

[0005] In order to improve the real-time performance of data analysis and early warning of electric energy meters, the present application provides an intelligent data processing method, system and terminal of electric energy meters.

[0006] In a first aspect, the present application provides an intelligent data processing method of electric energy meters, which adopts the following technical solution: An intelligent data processing method of electric energy meters, comprising: acquiring real-time data of electric meters; 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; uploading the structured data packet to a master station.

[0007] By adopting the above technical solution, the real-time data is cleaned and formatted to determine the basic data, the basic data is analyzed to determine the normal classification data and early warning information, the normal classification data and early warning information are analyzed to generate the structured data packet, and the structured data packet is uploaded to the master station, so that after analyzing and issuing an early warning of the electric meter data locally, only the generated structured data packet needs to be uploaded to the master station, without the master station analyzing and returning the data, thereby realizing local real-time analysis and processing of the electric meter data and improving the real-time performance of data analysis and early warning of electric meters.

[0008] Optionally, the step of analyzing the basic data to generate normal classification data and early warning information comprises: extracting data change trend features in the basic data to generate a trend analysis result; judging whether the trend analysis result is a preset non-anomalous trend result or a preset anomalous trend result; if the trend analysis result is the non-anomalous trend result, determining a preset local terminal as the optimal processing terminal; if the trend analysis result is the anomalous trend result, determining the optimal processing terminal in a preset terminal group according to the trend analysis result; controlling the optimal processing terminal to analyze the basic data according to preset threshold value judgment rules and preset power consumption mode rules to generate the normal classification data and the early warning information.

[0009] By using the above technical solution, after extracting the data change trend features in the basic data to generate the trend analysis result, if the trend analysis result is the non-anomalous trend result, the local terminal is directly determined as the optimal processing terminal; if the trend analysis result is the anomalous trend result, the optimal processing terminal is determined in the terminal group according to the trend analysis result, so that the optimal processing terminal analyzes the basic data according to the threshold value judgment rules and the power consumption mode rules to generate the normal classification data and the early warning information, thereby realizing local real-time analysis and processing of the electric meter data, and improving the real-time performance of the electric meter data analysis and early warning, and reducing the main station data transmission amount and the operation load.

[0010] Optionally, the step of extracting the data change trend features in the basic data to generate the trend analysis result comprises: calculating a mean value of the basic data to generate a deviation rate reference data; calculating a difference value between the basic data and the deviation rate reference data to generate a deviation rate change value; calculating a quotient of the deviation rate change value and the deviation rate reference data to generate a trend change deviation rate; judging whether the trend change deviation rate is greater than a preset basic deviation rate; if not, defining the non-anomalous trend result as the trend analysis result; if yes, defining the anomalous trend result as the trend analysis result.

[0011] By adopting the technical scheme, the deviation rate reference data is generated after the mean value of the basic data is calculated, the deviation rate change value is generated after the difference between the basic data and the deviation rate reference data is calculated, the trend change deviation rate is generated after the quotient of the deviation rate change value and the deviation rate reference data is calculated, the abnormal trend result is defined as the trend analysis result when the trend change deviation rate is not greater than the basic deviation rate, and the abnormal trend result is defined as the trend analysis result when the trend change deviation rate is greater than the basic deviation rate, so that the best processing terminal is determined according to the trend analysis result, and the local real-time analysis and processing of the electric meter data are realized.

[0012] Optionally, the step of determining the best processing terminal in the terminal group according to the trend analysis result comprises: determining the number of abnormal terminals in the terminal group according to the trend analysis result; judging whether the number of abnormal terminals is greater than a preset standard number; if yes, determining the local terminal as the best processing terminal; if no, obtaining resource occupation parameters, real-time parameters and processing accuracy of the terminals in the terminal group; analyzing the resource occupation parameters, the real-time parameters and the processing accuracy to determine the best processing terminal.

[0013] By adopting the technical scheme, the number of abnormal terminals in the terminal group is determined according to the trend analysis result, the local terminal is directly determined as the best processing terminal when the number of abnormal terminals is greater than the standard number, the resource occupation parameters, the real-time parameters and the processing accuracy are analyzed to determine the best processing terminal when the number of abnormal terminals is not greater than the standard number, the basic data is analyzed and warned on the best processing terminal, and the local real-time analysis and processing of the electric meter data are realized, and the real-time analysis and warning of the electric meter data are improved.

[0014] Optionally, the step of analyzing the resource occupation parameters, the real-time parameters and the processing accuracy to determine the best processing terminal comprises: determining cumulative running time, CPU full load threshold, maximum task queue length and task queue length according to the resource occupation parameters; calculating the quotient of the cumulative running time and a preset sampling period to generate CPU occupation rate; analyzing the CPU occupation rate, the CPU full load threshold, the maximum task queue length and the task queue length to determine resource availability; determining local sending time stamp, local receiving time stamp, processing start time stamp and processing end time stamp according to the real-time parameters; analyzing the local sending time stamp, the local receiving time stamp and a preset communication delay sample number to determine average communication delay; analyzing the processing start timestamp, the processing end timestamp and the preset number of time consumption samples to determine an average processing time consumption; performing weighted summation on the average communication delay and the average processing time consumption according to a preset real-time weight parameter to determine a real-time score; performing weighted summation on the processing accuracy, the resource availability and the real-time score according to a preset influence factor parameter to determine a terminal adaptation score; sorting the terminal adaptation scores to determine a maximum adaptation score, and determining a terminal corresponding to the maximum adaptation score in the terminal group as the best processing terminal.

[0015] By adopting the technical solutions, the cumulative running time, the CPU full load threshold, the maximum task queue length and the task queue length are determined according to the resource occupation parameter, so as to generate the CPU occupation rate after calculating the quotient of the cumulative running time and the sampling period, and then the resource availability is determined after analyzing the CPU occupation rate, the CPU full load threshold, the maximum task queue length and the task queue length. The local sending timestamp, the local receiving timestamp, the processing start timestamp and the processing end timestamp are determined according to the real-time parameter, so as to determine the average communication delay after analyzing the local sending timestamp, the local receiving timestamp and the communication delay sample number, and to determine the average processing time consumption after analyzing the processing start timestamp, the processing end timestamp and the time consumption sample number. Then, the real-time score is determined after performing weighted summation on the average communication delay and the average processing time consumption according to the real-time weight parameter, and then the terminal adaptation score is determined after performing weighted summation on the processing accuracy, the resource availability and the real-time score according to the influence factor parameter. The maximum adaptation score is determined after sorting the terminal adaptation scores, so as to determine the best processing terminal corresponding to the maximum adaptation score in the terminal group. Thus, the highest adaptation terminal in the terminal group is found to analyze and warn the electricity meter data, and the local real-time analysis and processing of the electricity meter data are realized, and the real-time analysis and warning of the electricity meter data are improved.

[0016] Optionally, the step of controlling the best processing terminal to analyze the basic data to generate normal classification data and warning information according to the preset threshold judgment rule and the preset power consumption mode rule comprises: determining a threshold parameter according to the threshold judgment rule; judging whether the basic data is greater than the threshold parameter; if yes, generating the warning information; if no, analyzing the basic data according to a preset time period classification rule to determine time period data; analyzing the time period data and a preset standard power consumption curve to determine a curve similarity; judging whether the curve similarity is less than a preset reference similarity; If not less than, normal classification data is generated according to the power consumption mode rule and the time period data; If less than, the early warning information is generated.

[0017] By adopting the technical solution, the threshold parameter is determined according to the threshold judgment rule, when the basic data is greater than the threshold parameter, the early warning information is directly generated, if not greater than the threshold parameter, the time period data is determined after the basic data is analyzed according to the time period classification rule, then the curve similarity is determined after the time period data and the standard power consumption curve are analyzed, when the curve similarity is not less than the reference similarity, the normal classification data is generated according to the power consumption mode rule and the time period data, if less than, the early warning information is directly generated, then the normal classification data and the early warning information are generated into a structured data packet, so as to be sent to the master station in the subsequent, and the local real-time analysis and processing of the electric meter data are realized, and the real-time of the electric meter data analysis and early warning is improved.

[0018] Optionally, the step of analyzing the time period data and the preset standard power consumption curve to determine the curve similarity comprises: determining the current voltage value and the current current value of the preset sampling point according to the time period data; calculating the product of the current voltage value and the current current value to generate the current power value; determining the maximum standard power and the power consumption value of the sampling point according to the standard power consumption curve; analyzing the power consumption value, the maximum standard power, the current power value and the preset sampling point number to determine the curve similarity.

[0019] By adopting the technical solution, the current voltage value and the current current value of the sampling point are determined according to the time period data, then the product of the current voltage value and the current current value is calculated to generate the current power value, the maximum standard power and the power consumption value of the sampling point are determined according to the standard power consumption curve, then the curve similarity is determined after the power consumption value, the maximum standard power, the current power value and the sampling point number are analyzed, so as to determine the similarity degree of the time period data and the standard power consumption curve, and then determine whether the power consumption behavior and the scene are correct, so as to generate the normal classification data and the early warning information in the subsequent.

[0020] In the second aspect, the present application provides an electric energy meter intelligent data processing system, which adopts the following technical solution: An electric energy meter intelligent data processing system comprises: an acquisition module for acquiring real-time electric meter data; a memory for storing the program of the electric energy meter intelligent data processing method according to any one of the above; a processor, the program in the memory can be loaded and executed by the processor, and the electric energy meter intelligent data processing method according to any one of the above is realized.

[0021] By adopting the technical scheme, the processor is used to load and execute the program of the power meter intelligent data processing method stored in the memory, the acquisition module is controlled to acquire a series of data related to intelligent data processing, so as to determine the basic data after the real-time data is cleaned and formatted, then generate normal classification data and early warning information after the basic data is analyzed, then generate a structured data packet after the normal classification data and early warning information are analyzed, and upload the structured data packet to the master station, so that after the power meter data is analyzed and warned locally, only the generated structured data packet needs to be uploaded to the master station, the master station does not need to analyze the data and return the data, and then the local real-time analysis and processing of the power meter data are realized, and the real-time performance of power meter data analysis and early warning is improved.

[0022] In a third aspect, the application provides an intelligent terminal, which adopts the technical scheme as follows: An intelligent terminal, comprising a memory and a processor, the memory storing a computer program capable of being loaded and executed by the processor to implement the power meter intelligent data processing method according to any one of the above.

[0023] By adopting the technical scheme, the processor is used to load and execute the program of the power meter intelligent data processing method stored in the memory, the acquisition module is controlled to acquire a series of data related to intelligent data processing, so as to determine the basic data after the real-time data is cleaned and formatted, then generate normal classification data and early warning information after the basic data is analyzed, then generate a structured data packet after the normal classification data and early warning information are analyzed, and upload the structured data packet to the master station, so that after the power meter data is analyzed and warned locally, only the generated structured data packet needs to be uploaded to the master station, the master station does not need to analyze the data and return the data, and then the local real-time analysis and processing of the power meter data are realized, and the real-time performance of power meter data analysis and early warning is improved.

[0024] In summary, the application has at least one of the following beneficial technical effects: 1. By cleaning and formatting the real-time data to determine the basic data, then determining the normal classification data and early warning information after analyzing the basic data, then generating a structured data packet after analyzing the normal classification data and early warning information, and then uploading the structured data packet to the master station, so that after the power meter data is analyzed and warned locally, only the generated structured data packet needs to be uploaded to the master station, the master station does not need to analyze the data and return the data, and then the local real-time analysis and processing of the power meter data are realized, and the real-time performance of power meter data analysis and early warning is improved. 2. The trend analysis result is generated by extracting the data change trend characteristics in the basic data. When the result is determined to be no abnormal trend, the local terminal is directly determined to be the best processing terminal. When the result is determined to be abnormal trend, the best processing terminal is determined in the terminal group according to the trend analysis result, so as to control the best processing terminal to generate normal classification data and early warning information after analyzing the basic data according to the threshold judgment rule and the power consumption mode rule, so as to determine the best processing terminal according to the trend analysis result, and then realize the local real-time analysis and processing of the electric meter data. 3. The number of abnormal terminals in the terminal group is determined according to the trend analysis result. When the number of abnormal terminals is greater than the standard number, the local terminal is directly determined to be the best processing terminal. If the number of abnormal terminals is not greater than the standard number, the best processing terminal is determined after analyzing the resource occupation parameter, the real-time parameter and the processing accuracy, so as to analyze and early warn the basic data on the best processing terminal, and then realize the local real-time analysis and processing of the electric meter data, and improve the real-time analysis and early warning of the electric meter data. BRIEF DESCRIPTION OF DRAWINGS

[0025] Figure 1 It is a flowchart of an electric energy meter intelligent data processing method in the embodiment of the present application.

[0026] Figure 2 It is a flowchart of the step of analyzing the basic data to generate normal classification data and early warning information in the embodiment of the present application.

[0027] Figure 3 It is a flowchart of the step of extracting the data change trend characteristics in the basic data to generate the trend analysis result in the embodiment of the present application.

[0028] Figure 4 It is a flowchart of the step of determining the best processing terminal in the preset terminal group according to the trend analysis result in the embodiment of the present application.

[0029] Figure 5 It is a flowchart of the step of analyzing the resource occupation parameter, the real-time parameter and the processing accuracy to determine the best processing terminal in the embodiment of the present application.

[0030] Figure 6 It is a flowchart of the step of controlling the best processing terminal to analyze the basic data to generate normal classification data and early warning information according to the preset threshold judgment rule and the preset power consumption mode rule in the embodiment of the present application.

[0031] Figure 7 It is a flowchart of the step of analyzing the time period data and the preset standard power consumption curve to determine the curve similarity in the embodiment of the present application. DETAILED DESCRIPTION

[0032] For the purpose, technical solutions and advantages of the present application to be more clearly and intelligibly understood, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application and not to limit the present application. Figures 1 to 7 The present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application and not to limit the present application.

[0033] The present application discloses an intelligent data processing method for an electric energy meter, specifically discloses an electric meter and a processing terminal, and the processing terminal is in communication connection with the electric meter and a master station to realize data interaction and control. After the processing terminal receives real-time data of the electric meter, the real-time data is cleaned and formatted to determine basic data, so that normal classification data and warning information are determined after analyzing the basic data, and a structured data packet is generated after analyzing the normal classification data and the warning information, and then the structured data packet is uploaded to the master station, so that after analyzing and warning the electric meter data locally, only the generated structured data packet needs to be uploaded to the master station, and the master station does not need to analyze and return data, thereby realizing local real-time analysis and processing of the electric meter data and improving the real-time performance of electric meter data analysis and warning.

[0034] With reference to Figure 1 The present application discloses an intelligent data processing method for an electric energy meter, specifically discloses an electric meter and a processing terminal, and the processing terminal is in communication connection with the electric meter and a master station to realize data interaction and control. After the processing terminal receives real-time data of the electric meter, the real-time data is cleaned and formatted to determine basic data, so that normal classification data and warning information are determined after analyzing the basic data, and a structured data packet is generated after analyzing the normal classification data and the warning information, and then the structured data packet is uploaded to the master station, so that after analyzing and warning the electric meter data locally, only the generated structured data packet needs to be uploaded to the master station, and the master station does not need to analyze and return data, thereby realizing local real-time analysis and processing of the electric meter data and improving the real-time performance of electric meter data analysis and warning. Step S100: acquiring real-time data of an electric meter.

[0035] The real-time data of the electric meter refers to core parameters of the power consumption state recorded by the electric meter in the current or very short time, and the acquisition instruction is sent by communication with the electric meter through a hardware interface RS-485, and the corresponding data is returned after being received by the electric meter to obtain the real-time voltage and current of the electric meter. When the voltage and current increase, the power will also increase, indicating that the current power load increases, which is the core basis for overload warning, thereby providing data support for subsequent generation of warning information Step S101: cleaning and formatting the real-time data to determine the basic data.

[0036] The basic data refers to data that can accurately reflect the current running state of the electric meter and can be directly used for analysis after cleaning and formatting. The real-time data is denoised by using a sliding average filter by the processing terminal, and the processed data is uniformly converted into a standard format to obtain, for example, different precision values are uniformly converted into a specified number of decimal places, thereby providing data support for subsequent terminal analysis and processing of the data.

[0037] Step S102: analyzing the basic data to generate normal classification data and warning information.

[0038] The normal classification data refers to classification data conforming to the threshold range and the current power consumption scenario, which is classified by the best processing terminal after the basic data is checked by the threshold judgment rule, the time period rule and the power consumption mode rule. For details, refer to the steps of Figure 2 For example, the power consumption data of a household user on a weekday is 2.5 kW, accounting for 50% of the daily consumption, which is classified as household peak segment normal power data, thereby providing data support for subsequent generation of structured data packets.

[0039] The early warning information refers to generated abnormal prompt information, which is generated by the best processing terminal after the basic data is checked by the threshold judgment rule or the power consumption mode rule. For details, refer to the steps of Figure 2 For example, the household valley segment power is 4.2 kW, which exceeds the power threshold of 2 kW, and generates power consumption mode early warning information, thereby providing data support for subsequent generation of structured data packets.

[0040] Step S103: Analyzing the normal classification data and the early warning information to generate a structured data packet.

[0041] The structured data packet refers to a text data block generated in a fixed format, which is obtained by the best processing terminal arranging the normal classification data and the early warning information in a fixed format. By generating a formatted data packet, the transmission efficiency and data integrity of the processing terminal and the master station can be effectively improved, thereby realizing data synchronization between the master station and the local terminal.

[0042] Step S104: uploading the structured data packet to the master station.

[0043] After generating the structured data packet, the best processing terminal uploads the structured data packet to the master station, thereby reducing the data transmission amount and computational load of the master station, and improving the timeliness and data accuracy of abnormal early warning.

[0044] Referring to Figure 2 , the steps of analyzing the basic data to generate normal classification data and early warning information include: Step S200: extracting the data trend feature in the basic data to generate a trend analysis result.

[0045] The trend analysis result refers to the result of whether the data trend in the basic data has an abnormal trend, including a no-abnormal trend result and an abnormal trend result, which is obtained by the processing terminal extracting the data trend feature in the basic data. For details, refer to the steps of Figure 3 By analyzing the trend analysis result, the best processing terminal of the basic data can be determined, thereby realizing local real-time analysis and processing of the electric meter.

[0046] Step S201: judging whether the trend analysis result is a preset non-anomaly trend result or a preset anomaly trend result.

[0047] The non-anomaly trend result means that the current power consumption data does not have mutation and deviation, and is stored in the processing terminal by an operator.

[0048] The anomaly trend result means that the current power consumption data is obviously inconsistent with the recent power consumption rule, and there is mutation in the power consumption data, and is stored in the processing terminal by the operator.

[0049] By judging whether the trend analysis result is the non-anomaly trend result or the anomaly trend result, whether the basic data has an anomaly trend can be determined, thereby the best processing terminal is determined, and local real-time analysis and processing of the electric meter are realized.

[0050] Step S2011: if the non-anomaly trend result is obtained, a preset local terminal is determined as the best processing terminal.

[0051] If the non-anomaly trend result is obtained, it means that the current power consumption data does not have mutation and deviation, and the local terminal is directly determined as the best processing terminal. By determining the best processing terminal, normal classification data and early warning information are generated after the basic data is analyzed, local real-time analysis and processing of the electric meter are realized, and the operation load of the main station is reduced.

[0052] The best processing terminal means a terminal with the highest adaptation degree determined by comprehensively considering the processing accuracy, real-time performance and resource availability of the terminal, and the best processing terminal in this step is the local terminal.

[0053] Step S2012: if the anomaly trend result is obtained, the best processing terminal is determined in a preset terminal group according to the trend analysis result.

[0054] If the anomaly trend result is obtained, it means that the current power consumption data is obviously inconsistent with the recent power consumption rule, and there is mutation in the power consumption data, thereby the best processing terminal is determined in the terminal group according to the trend analysis result, and the specific method is referred to the step of Figure 4 By determining the best processing terminal, normal classification data and early warning information are generated after the basic data is analyzed, local real-time analysis and processing of the electric meter are realized, the timeliness of early warning is improved, and the operation load of the main station is reduced.

[0055] The terminal group means a cooperative network composed of multiple adjacent electric meter terminals, and is obtained by being set in advance by an operator. By setting the terminal group, local cooperative processing of anomaly data can be realized, thereby the real-time performance of anomaly early warning is improved.

[0056] Step S202: According to the preset threshold judgment rule and the preset power consumption mode rule, the best processing terminal controls the analysis of the basic data to generate normal classification data and early warning information.

[0057] The threshold judgment rule refers to setting a numerical boundary for each type of power consumption data, which is set by the operator in advance and stored in the processing terminal. For example, the current threshold is 40A. When the real-time current exceeds 40A, it means that the power consumption is too large, thereby triggering the early warning.

[0058] The power consumption mode rule refers to the behavior standard set according to the power consumption characteristics of a specific power consumption scene, which is set by the operator in advance and stored in the processing terminal. For example, the total power of a certain commercial power consumption scene during the off-peak period should be 1kW-2kW, but the measured total power during this period is 3.5kW, which means that the commercial power consumption mode is abnormal, thereby triggering the early warning.

[0059] By identifying and calling the threshold judgment rule and the power consumption mode rule, the best processing terminal controls the analysis of the basic data to generate normal classification data and early warning information. For specific methods, refer to the steps of Figure 6 , thereby realizing local real-time analysis and processing of the meter data, enabling the meter to respond to early warning information in a timely manner, and further improving the real-time analysis and early warning of meter data.

[0060] Referring to Figure 3 , the steps of extracting the data trend characteristics in the basic data to generate trend analysis results include: Step S300: Calculate the mean of the basic data to generate the deviation rate reference data.

[0061] The deviation rate reference data refers to the average value of the last n data points in the basic data, which is obtained by summing the last n data points in the basic data by the processing terminal. By generating the deviation rate reference data, the overall trend of the data in the near future can be determined, thereby providing data support for calculating the trend change deviation rate.

[0062] Step S301: Calculate the difference between the basic data and the deviation rate reference data to generate the deviation rate change value.

[0063] The deviation rate change value refers to the absolute deviation between the current data point and the deviation rate reference data, which is obtained by taking the absolute value of the difference between the basic data and the deviation rate reference data by the processing terminal. By generating the deviation rate change value, the deviation amplitude between the current data point and the deviation rate reference data can be determined. The larger the value, the larger the trend change deviation rate, thereby providing data support for calculating the trend change deviation rate.

[0064] Step S302: Calculate the quotient of the deviation rate change value and the deviation rate reference data to generate the trend change deviation rate.

[0065] The trend change deviation rate refers to a deviation degree of the current data point relative to a whole trend of the recent n data points, and is obtained by processing the terminal by multiplying 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 is, the smaller the probability of data mutation is, so as to facilitate subsequent determination of the trend analysis result.

[0066] Step S303: determining whether the trend change deviation rate is greater than a preset basic deviation rate.

[0067] The basic deviation rate refers to a standard threshold value for determining whether the data has an abnormal trend, and is obtained by being preset by an operator. By determining whether the trend change deviation rate is greater than the basic deviation rate, it can be determined whether the basic data has an abnormal trend, and then different optimal processing terminals are determined.

[0068] Step S3031: if not, defining the no-abnormal-trend result as the trend analysis result.

[0069] If the trend change deviation rate is not greater than the basic deviation rate, it indicates that the current data does not have an abnormal trend, and therefore the no-abnormal-trend result can be defined as the trend analysis result, so as to facilitate subsequent determination of the optimal processing terminal.

[0070] Step S3032: if greater, defining the abnormal-trend result as the trend analysis result.

[0071] If the trend change deviation rate is greater than the basic deviation rate, it indicates that the current data has an abnormal trend, and therefore the abnormal-trend result can be defined as the trend analysis result, so as to facilitate subsequent determination of the optimal processing terminal.

[0072] Referring to Figure 4 , the step of determining the optimal processing terminal in the preset terminal group according to the trend analysis result comprises: Step S400: determining the number of abnormal terminals in the terminal group according to the trend analysis result.

[0073] The number of abnormal terminals refers to the number of terminals with the abnormal-trend result in the terminal group, and is obtained by processing the terminal by counting the trend analysis results of each terminal in the terminal group. By the number of abnormal terminals, it can be determined that when only the watt-hour meter connected to one processing terminal has an abnormal trend, the data processing accuracy can be improved by finding an optimal processing terminal for processing. When the number of abnormal terminals is large, if one processing terminal is still used for processing, the processing terminal will be overloaded due to simultaneous analysis of multiple terminal data, and the processing efficiency will be reduced.

[0074] Step S401: determining whether the number of abnormal terminals is greater than a preset standard number.

[0075] Wherein, the number of standards refers to the number of terminals in the terminal group with abnormal trend results is 1, which is obtained by the operator in advance, and the best processing terminal is determined by judging whether the number of abnormal terminals is greater than the number of standards, and then the local real-time analysis and processing of the meter data are realized.

[0076] Step S4011: If greater, the local terminal is determined as the best processing terminal.

[0077] Wherein, if the number of abnormal terminals is greater than the number of standards, the local terminal is directly determined as the best processing terminal. Through the number of abnormal terminals, when the number of abnormal terminals is large, each terminal only processes its own data, which can effectively reduce the communication interaction between terminals and reduce the risk of data loss.

[0078] Step S4012: If not greater, the resource occupation parameter, real-time parameter and processing accuracy of the terminal in the terminal group are obtained.

[0079] Wherein, if the number of abnormal terminals is not greater than the number of standards, it means that the terminal in the terminal group with abnormal trend results only has one, and then the resource occupation parameter, real-time parameter and processing accuracy are directly obtained, so as to find a best processing terminal for processing, and then improve the accuracy of data processing.

[0080] The resource occupation parameter refers to the state index of the terminal hardware and software task in the terminal group occupied by the current business in the running process, including cumulative running time, CPU full load threshold, maximum task queue length and task queue length, which is obtained by the system interface in real time. Among them, the cumulative running time is strongly related to the CPU occupation rate, the greater the cumulative running time, the higher the CPU occupation rate, and the greater the task queue length, indicating that the proportion of the remaining accommodated tasks is smaller, thereby providing data support for subsequent calculation of terminal adaptation score.

[0081] The real-time parameter refers to the index for measuring data interaction speed and data processing efficiency, including local sending timestamp, local receiving timestamp, processing start timestamp and processing end timestamp, which is directly read by the terminal built-in clock module. Among them, the local receiving timestamp and the local sending timestamp are strongly related to the data interaction speed, the smaller the difference, the faster the data interaction speed, and the processing start timestamp and the processing end timestamp are strongly related to the data processing efficiency, the smaller the difference, the higher the data processing efficiency of the terminal, thereby providing data support for subsequent calculation of terminal adaptation score.

[0082] The processing accuracy refers to the accuracy of processing data by each terminal in the terminal group, and is obtained by multiplying the processing correct times by the processing total times. When the processing accuracy is higher, it indicates that the analysis of the power consumption data by the terminal and the processing result of the output are more reliable, so as to determine the optimal processing terminal subsequently.

[0083] Step S40121: analyzing the resource occupation parameter, the real-time parameter and the processing accuracy to determine the optimal processing terminal.

[0084] After determining the processing accuracy, the optimal processing terminal is determined by analyzing the resource occupation parameter, the real-time parameter and the processing accuracy by the processing terminal. For details, refer to the steps of Figure 5 By determining the optimal processing terminal, the terminal with the optimal processing capacity, the most sufficient resources and the most timely response is screened out, and the correctness of data processing and the timeliness of abnormal early warning are improved.

[0085] For details, refer to Figure 5 The step of analyzing the resource occupation parameter, the real-time parameter and the processing accuracy to determine the optimal processing terminal comprises: Step S500: determining the cumulative running time, the CPU full load threshold, the maximum task queue length and the task queue length according to the resource occupation parameter.

[0086] The cumulative running time refers to the cumulative running time of the CPU in the sampling period, which is identified and called by the processing terminal from the resource occupation parameter. By obtaining the cumulative running time, it can be determined that the higher the cumulative running time is, the higher the CPU occupation rate is, and the less the remaining resources of the terminal are, thereby providing data support for subsequent calculation of the CPU occupation rate.

[0087] The CPU full load threshold refers to the benchmark value for measuring whether the CPU is overloaded, which is identified and called by the processing terminal from the resource occupation parameter. The CPU full load threshold directly determines the hardware computing power bearing capacity of the terminal. The greater the CPU full load threshold is, the stronger the computing power of the terminal is, thereby providing data support for subsequent calculation of the CPU occupation rate.

[0088] The maximum task queue length refers to the maximum number of tasks that can be accommodated by a single task queue, which is identified and called by the processing terminal from the resource occupation parameter. By obtaining the maximum task queue length, it can be determined that the greater the maximum task queue length is, the greater the memory capacity of the terminal is, and the more efficient the task scheduling algorithm is, so as to determine the resource availability subsequently.

[0089] The task queue length refers to the number of tasks waiting for execution in the task queue at the current time, which is called by the processing terminal from the resource occupation parameter. By obtaining the task queue length, the current task processing pressure of the terminal can be determined. The smaller the task queue length, the smaller the task pressure of the terminal, thereby providing data support for subsequent determination of resource availability.

[0090] Step S501: Calculate the quotient of the cumulative running time and the preset sampling period to generate the CPU occupation rate.

[0091] The CPU occupation rate refers to the proportion of time that the CPU is used to execute tasks within a sampling period, which is obtained by dividing the cumulative running time by the sampling period by the processing terminal. By calculating the CPU occupation rate, the current algorithm busy degree of the terminal can be determined. The higher the CPU occupation rate, the less the available remaining resources of the terminal, thereby providing data support for subsequent determination of resource availability.

[0092] Step S502: Analyze the CPU occupation rate, CPU full load threshold, maximum task queue length, and task queue length to determine the resource availability.

[0093] The resource availability refers to the comprehensive index of the idle degree of the current computing resources and task scheduling resources of the terminal, which is obtained by analyzing the CPU occupation rate, CPU full load threshold, maximum task queue length, and task queue length by the processing terminal, and can be represented as , wherein represents the resource availability, represents the CPU full load threshold, represents the CPU occupation rate, represents the maximum task queue length, represents the task queue length, the smaller the CPU occupation rate and the task queue length, the higher the resource availability, and by determining the resource availability, the terminal adaptation score can be determined to facilitate subsequent determination of the best processing terminal.

[0094] Step S503: Determine the local sending timestamp, local receiving timestamp, processing start timestamp, and processing end timestamp according to the real-time parameter.

[0095] The local sending timestamp refers to the local time recorded in real time by the current processing terminal when sending data packets with other processing terminals in the terminal group, which is called by the processing terminal from the real-time parameter. By calling the local sending timestamp, the starting reference of the communication delay can be determined, thereby providing data support for subsequent determination of the average communication delay.

[0096] 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 retrieved 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.

[0097] 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 the average communication latency.

[0098] 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.

[0099] Step S504: Analyze the local sending timestamp, local receiving timestamp, and preset number of communication delay samples to determine the average communication delay.

[0100] 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.

[0101] 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 The 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.

[0102] Step S505: Analyze the processing start timestamp, processing end timestamp, and preset number of time-consuming samples to determine the average processing time.

[0103] The number of time-consuming samples refers to the total number of single data processing time-consuming data collected, which is set in advance by an operator, and multiple sample collection can reduce the interference of single abnormal value on the result, so that the average processing time is more accurate.

[0104] The average processing time refers to the average time for the processing terminal to complete data processing, which is obtained by analyzing the processing start timestamp, processing end timestamp and time-consuming sample number of the processing terminal, and can be represented as , wherein represents the number of time-consuming samples, represents the sample number of single processing time-consuming, represents the processing start timestamp of the first sample, represents the processing start timestamp of the first sample, represents the processing end timestamp of the first sample, represents the processing end timestamp of the first sample, represents the average processing time, and the smaller the average processing time, the faster the data processing speed, and the higher the real-time score, thereby providing data support for subsequent determination of the real-time score.

[0105] Step S506: The average communication delay and the average processing time are weighted and summed according to the preset real-time weight parameter to determine the real-time score.

[0106] The real-time weight parameter refers to the coefficient of the influence degree of the average communication delay and the average processing time on the real-time score, which is set in advance by an operator, for example, the communication delay weight and the processing time weight, wherein the communication delay directly affects the response timeliness, and the communication is more affected by the environment, therefore the communication delay weight is set to 0.6, and the processing time weight is set to 0.4, by setting the real-time weight parameter, the communication and processing demand can be balanced, so as to determine the real-time score.

[0107] The real-time score refers to the quantitative index of the speed of the processing terminal in the data interaction and data processing link, which is obtained by weighting and summing the average communication delay and the average processing time according to the real-time weight parameter of the processing terminal, and the faster the speed of the processing terminal in responding to data request and completing data processing, the higher the real-time score, thereby providing data support for subsequent determination of the terminal adaptation score.

[0108] Step S507: The processing accuracy, resource availability and real-time score are weighted and summed according to the preset influence factor parameter to determine the terminal adaptation score.

[0109] The influence factor parameter refers to the coefficient of the processing accuracy, resource availability and real-time score affecting the terminal adaptation score, which is set by the operator in advance, for example, processing accuracy weight, resource availability weight and real-time weight. The processing accuracy directly determines the reliability of the processing result, so the processing accuracy weight is set to 0.4. The real-time score directly affects the response efficiency, so the real-time weight is set to 0.35, and the resource availability weight is set to 0.25. By setting different weights, the processing accuracy and real-time performance of the core can be avoided due to excessive attention to resources, thereby providing data support for subsequent determination of the terminal adaptation score.

[0110] The terminal adaptation score refers to a score value that quantifies the processing accuracy, resource availability and real-time score, which is obtained by weighting and summing the processing accuracy, resource availability and real-time score according to the influence factor parameter by the processing terminal. By determining the terminal adaptation score, the comprehensive ability of each terminal in the terminal group for data processing can be determined. The higher the terminal adaptation score, the stronger the comprehensive ability, so as to determine the best processing terminal subsequently.

[0111] Step S508: Sort the terminal adaptation score to determine the maximum adaptation score, and determine the terminal corresponding to the maximum adaptation score in the terminal group as the best processing terminal.

[0112] The maximum adaptation score refers to the highest score value obtained by sorting the terminal adaptation scores of all processing terminals in the terminal group. By sorting the terminal adaptation score, the processing terminal with the strongest data processing comprehensive ability in the terminal group can be determined, and the processing terminal is determined as the best processing terminal, so as to facilitate subsequent local real-time analysis and processing of the meter data.

[0113] Referring to Figure 6 , the step of controlling the best processing terminal to analyze the basic data according to the preset threshold judgment rule and the preset power consumption mode rule to generate normal classification data and warning information includes: Step S600: Determine the threshold parameter according to the threshold judgment rule.

[0114] The threshold parameter refers to a critical value set for judging whether a certain electrical parameter is normal, which is identified and called by the best processing terminal from the threshold judgment rule. 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 warning information subsequently.

[0115] Step S601: Determine whether the basic data is greater than the threshold parameter.

[0116] Wherein, whether the basic data is greater than the threshold parameter is judged by the best processing terminal, so as to determine whether the basic data is within the threshold range, so as to generate normal classification data and early warning information subsequently.

[0117] Step S6011: if greater, generate early warning information.

[0118] Wherein, if greater than the threshold parameter, it means that the certain electrical parameter exceeds the set critical value and is not within the normal range, so as to generate early warning information, thereby avoiding the response delay caused by transmission to the main station for analysis, and thereby improving the real-time of abnormal early warning.

[0119] Step S6012: if not greater, analyze the basic data according to the preset time period classification rule to determine the time period data.

[0120] Wherein, if not greater than the threshold parameter, it means that the certain electrical parameter is within the set critical value and meets the threshold judgment rule, so as to determine the time period data after analyzing the basic data according to the time period classification rule, and by determining the time period data, the basic data is classified according to different time periods, the power consumption difference of different time periods can be determined, and data support is provided for subsequent determination of curve similarity.

[0121] The time period classification rule refers to the rule of dividing 24 hours a day into different power consumption time periods according to the power grid power consumption load characteristics and user power consumption habits, which is obtained by being set in advance by the operator, for example, peak period 8:00-22:00, valley period 22:00-8:00 the next day, by setting the time period classification rule, the best processing terminal can automatically identify the time period to which the basic data belongs, thereby realizing time period management, so as to generate normal classification data subsequently.

[0122] The time period data refers to the data formed by classifying and counting the basic data according to the time period by the time period classification rule, which is obtained by analyzing the basic data according to the time period classification rule by the best processing terminal, and by determining the time period data, the power consumption difference of different time periods can be determined, so as to determine the curve similarity subsequently.

[0123] Step S60121: analyze the time period data and the preset standard power consumption curve to determine the curve similarity.

[0124] Wherein, the standard power consumption curve refers to the reference curve of the standard power consumption power changing with time based on long-term power consumption data statistics, which is obtained by statistical analysis of long-term power consumption data by the operator, by determining the standard power consumption curve, whether the real-time power consumption is abnormal can be judged, and single threshold misjudgment can be avoided, so as to generate normal classification data or early warning information subsequently.

[0125] The curve similarity refers to the similarity degree of the sub-period data and the standard power consumption curve in the numerical fluctuation rule, which is obtained by the best processing terminal after analyzing the sub-period data and the standard power consumption data. For details, refer to the steps of Figure 7 By determining the curve similarity, the similarity degree of the sub-period data and the standard power consumption curve can be determined, so as to determine whether the power consumption behavior and scene are correct, and then provide data support for subsequent generation of normal classification data or early warning information.

[0126] Step S60122: Determine whether the curve similarity is less than the preset reference similarity.

[0127] The reference similarity refers to a reference threshold for measuring the consistency of the sub-period data and the standard power consumption curve form, which is obtained by the operator in advance. The best processing terminal determines whether the curve similarity is less than the reference similarity, so as to determine the deviation degree of the sub-period data and the standard power consumption curve, and then facilitate the subsequent generation of normal classification data or early warning information.

[0128] Step S601221: If not, generate normal classification data according to the power consumption mode rule and the sub-period data.

[0129] If the reference similarity is not less than the reference similarity, it means that the sub-period data and the standard power consumption curve are highly similar, which belongs to normal data. Therefore, the best processing terminal generates normal classification data according to the power consumption mode rule and the sub-period data, so as to facilitate the subsequent generation of structured data packets.

[0130] Step S601222: If less than, generate early warning information.

[0131] If the reference similarity is less than the reference similarity, it means that the sub-period data and the standard power consumption curve have a large deviation, which does not conform to the normal power consumption behavior or scene, so as to generate early warning information, and then improve the timeliness of the meter local early warning.

[0132] Refer to Figure 7 , the steps of analyzing the sub-period data and the preset standard power consumption curve to determine the curve similarity include: Step S700: Determine the current voltage value and the current current value of the preset sampling point according to the sub-period data.

[0133] The preset sampling point refers to a fixed time node for obtaining voltage and current data, which is obtained by the operator in advance. By setting the sampling point, random data acquisition is avoided, complete data is ensured for each period, and data support is provided for subsequent calculation of curve similarity.

[0134] The current voltage value refers to the actual voltage value at the sampling point time, which is identified and called by the best processing terminal in the sub-period data according to the preset sampling point, so as to facilitate the subsequent determination of the current power value.

[0135] 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.

[0136] Step S701: Calculate the product of the current voltage value and the current current value to generate the current power value.

[0137] 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.

[0138] Step S702: Determine the maximum standard power and the power consumption value of the sampling point based on the standard power consumption curve.

[0139] 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.

[0140] 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.

[0141] Step S703: Analyze the power consumption value, maximum standard power, current power value and preset number of sampling points to determine the curve similarity.

[0142] 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.

[0143] 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 represents the maximum standard power, the actual deviation of the current from the standard curve can be determined by taking the absolute value of the difference between the current power value and the power value, and 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 generate normal classification data and early warning information subsequently.

[0144] Based on the same inventive concept, the embodiment of the present application provides an intelligent data processing system of an electric energy meter, comprising: The acquisition module is configured to acquire real-time data of the electric meter, resource occupation data, real-time parameters, and processing accuracy. The memory is configured to store a program of the intelligent data processing method of the electric energy meter. The processor is capable of loading and executing the program in the memory, and implementing the intelligent data processing method of the electric energy meter.

[0145] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above functional modules is exemplified, and in actual application, the above functions can be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. The specific working process of the above-described system, device and unit can refer to the corresponding process in the foregoing method embodiments, which will not be described here.

[0146] The embodiment of the present application provides a computer readable storage medium, which stores a computer program capable of being loaded and executed by a processor to implement an intelligent data processing method of an electric energy meter.

[0147] The computer storage medium includes, for example, a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0148] Based on the same inventive concept, the embodiment of the present application provides an intelligent terminal, comprising a memory and a processor, and the memory stores a computer program capable of being loaded and executed by the processor to implement an intelligent data processing method of an electric energy meter.

[0149] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above functional modules is exemplified, and in actual application, the above functions can be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. The specific working process of the above-described system, device and unit can refer to the corresponding process in the foregoing method embodiments, which will not be described here.

[0150] The above are only preferred embodiments of the present application and are not intended to limit the protection scope of the present application. Any one of the features disclosed in the specification (including the abstract and the drawings) can be replaced by other equivalent or similar features, unless specifically stated otherwise. That is, each feature is only an example of a series of equivalent or similar features, unless specifically stated otherwise.

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.

2. The intelligent data processing method for an electricity meter according to claim 1, characterized in that, 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.

3. The intelligent data processing method for an electricity meter according to claim 2, 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.

4. The intelligent data processing method for an electricity meter according to claim 2, characterized in that, 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.

5. The intelligent data processing method for an electricity meter according to claim 4, characterized in that, 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.

6. The intelligent data processing method for an electricity meter according to claim 2, 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.

7. The intelligent data processing method for an electricity meter according to claim 6, 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.

8. A smart data processing system for electricity meters, characterized in that, include: The acquisition module is used to acquire real-time data from the electricity meter. 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 7; 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 7.

9. 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 7 for a smart data processing method for an electricity meter.

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