A method and system for controlling electric energy meter operation

Abstract

The present invention provides an electric energy meter operation control method and system, which relates to the field of smart grid technology. The method includes: receiving a monitoring instruction, the monitoring instruction includes monitoring various working physical data of the electric energy meter, and the working physical data includes voltage, current, power factor, active power and reactive power; generating a corresponding data acquisition plan according to the monitoring instruction; allocating the data acquisition plan to the corresponding operator and operation terminal; acquiring the working physical data of the electric energy meter monitored by the monitoring terminal in real time, including voltage fluctuation, current change and power factor stability, and each time a monitoring cycle is completed, the monitoring terminal automatically calculates the change gradient of the working physical data. The present invention realizes the intelligent management of the electric energy meter by real-time monitoring, analyzing the working physical data of the electric energy meter and feedback control, thereby improving the stability and efficiency of the power system.

Description

Technical Field

[0001] The present invention relates to the field of smart grid technology, and in particular to an electric energy meter operation control method and system. Background Art

[0002] Some traditional methods use manual meter reading, which has a slow data collection and processing speed and cannot meet the demand for real-time data. Inefficient data processing may lead to control lags. Manual meter reading is easily affected by human factors, such as transcription errors, omissions, or inaccurate data entry. For control machines, inaccurate data may lead to wrong decisions and control instructions, which in turn affects the stability and efficiency of the entire system.

[0003] Therefore, if the real-time monitoring data of the energy meter cannot be provided, this limits the ability to respond to emergencies or make precise adjustments. In addition, if it is difficult to adapt to changes in the scale and demand of the power system, the scalability and flexibility of the control machine are limited, and relying on manual meter reading not only increases labor costs, but is also prone to human errors. For the control machine, this means that additional verification and error correction mechanisms are required to ensure the reliability of the data, which increases the complexity of the system and operating costs. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide an electric energy meter operation control method and system to achieve efficient and accurate management of the electric energy meter.

[0005] In order to solve the above technical problems, the technical solution of the present invention is as follows:

[0006] In a first aspect, a method for controlling an electric energy meter operation is provided, the method comprising:

[0007] Receiving monitoring instructions, the monitoring instructions include monitoring various working physical data of the electric energy meter, the working physical data include voltage, current, power factor, active power and reactive power;

[0008] Generate corresponding data collection plans according to monitoring instructions; assign data collection plans to corresponding operators and operation terminals;

[0009] Acquire the working physical data of the electric energy meter monitored by the monitoring terminal in real time, including voltage fluctuation, current change and power factor stability. After each monitoring cycle, the monitoring terminal automatically calculates the change gradient of the working physical data.

[0010] Perform data analysis based on the change gradient of the working physical data, including statistical distribution and trend analysis, and detect whether there are abnormal values ​​in the change gradient based on the preset threshold to obtain the analysis results;

[0011] Based on the analysis results, feedback control is performed on the electric energy meter to adjust the working status in real time.

[0012] Furthermore, according to the monitoring instructions, a corresponding data collection plan is generated; and the data collection plan is allocated to corresponding operators and operation terminals, including:

[0013] Receive and parse monitoring instructions, extract key information, including the monitored electric energy meter number, the collected working physical data type, and the monitoring period and frequency;

[0014] Based on the key information, initialize the parameters of group incremental learning, including the number of individuals, number of iterations, learning rate, inertia weight, and set the optimization goal of the data collection plan;

[0015] The optimization goal of the data collection plan is used as the evaluation function of group incremental learning to evaluate the quality of each individual, and the initialized individuals are iteratively optimized. In each iteration, the position of the individual is updated. When the preset number of iterations is reached, the final individual is used as the data collection plan.

[0016] According to the data collection plan, a task allocation plan is generated, including the operators for each task, the portable operation terminal used, and the execution time.

[0017] Furthermore, the calculation formula of the evaluation function is:

[0018] ;

[0019] in, represents the evaluation function; Represents the total acquisition time weighting coefficient; Indicates the total number of tasks; Represents an index variable; Indicates The estimated acquisition time for each task; Indicates The priority coefficient of each task; Indicates the maximum single acquisition time weighting factor; Indicates the basic weighting coefficient of data coverage; Indicates the number of data types covered; Indicates the total number of data types that should be covered; represents the adjustment factor; Indicates the penalty weighting coefficient for uncovered data types; An index representing a data type; Indicates The number of electric energy meters that have collected this data type; Indicates the number of electric energy meters collected; It represents the weighted coefficient of workload balance of operators; Indicates the total number of operators; Indicates the index of the operator; Indicates The workload of each operator; Indicates the average workload of all operators; Represents the weighted coefficient of the maximum operator workload.

[0020] Furthermore, the working physical data of the electric energy meter monitored by the monitoring terminal is obtained in real time, including voltage fluctuation, current change and power factor stability. After each monitoring cycle is completed, the monitoring terminal automatically calculates the change gradient of the working physical data, including:

[0021] Send monitoring instructions to the monitoring terminal, including the start time and end time of the monitoring period, and receive the working physical data of the electric energy meter sent by the monitoring terminal in real time during the monitoring period;

[0022] Check whether the current time reaches the end time of the monitoring cycle, and at the end of the monitoring cycle, check whether the working physical data set of the electric energy meter has been received;

[0023] The working physical data set of the electric energy meter is processed, and the change gradient of each data point relative to the previous data point is calculated to realize the continuous monitoring of the working physical data of the electric energy meter and the calculation of the change gradient.

[0024] Furthermore, data analysis is performed based on the change gradient of the working physical data, including statistical distribution and trend analysis, and based on a preset threshold, whether there are abnormal values ​​in the change gradient is detected to obtain analysis results, including:

[0025] The change gradient sequence of the working physical data of the electric energy meter received from the monitoring terminal, and the abnormal value detection threshold is set according to the historical data;

[0026] Calculate the mean and standard deviation statistics of the gradient sequence, and fit the gradient sequence using a normal distribution;

[0027] Use difference quantification indicators to evaluate the difference between the fitted distribution and the actual data, and perform time series analysis on the change gradient sequence to identify trends and periodicity in the data to obtain time series analysis results;

[0028] According to the results of time series analysis, a trend model is constructed, and the trend model is used to predict the change gradient to obtain the predicted value at each time point;

[0029] The difference quantification index is used to measure the difference between the trend model prediction value and the actual change gradient value as a trend analysis index, and the actual change gradient value is compared with the preset threshold value, and the points exceeding the threshold value are marked as outliers to obtain the anomaly detection results;

[0030] Integrate statistical distribution analysis, trend analysis and anomaly detection results to generate analysis results including outlier lists, statistics, and trend model parameter information.

[0031] Furthermore, the calculation formula of the difference quantitative index is:

[0032] ;

[0033] in, It represents the quantitative index of difference; represents the global adjustment coefficient; , represents the weight coefficient; Represents the total number of data points; Represents an index variable; Indicates The actual change gradient value at each time point; Indicates The predicted value at each time point; represents a data point; Represents data points in the actual data distribution The probability of Represents the data points in the fitted distribution probability.

[0034] Furthermore, the calculation formula for the change gradient of each data point relative to the previous data point is:

[0035] ;

[0036] in, Indicates The gradient of change of a data point relative to the previous data point; Indicates The measured value of the physical quantity at each data point; Indicates The measured value of the physical quantity at each data point; , , , , Indicates adjustment parameters; Indicates the maximum value of the physical quantity in the data set; represents the angular frequency; Indicates The measurement time of each data point; Indicates −Measurement time of 1 data point; Indicates phase; Represents the time interval between adjacent data points; Indicates the maximum value of the time interval in the data set; Represents the index of the data point; represents the time sensitivity coefficient; Indicates the magnification factor.

[0037] Furthermore, according to the analysis results, feedback control is performed on the electric energy meter to adjust the working state in real time, including:

[0038] According to the analysis results, evaluate whether the current working state of the electric energy meter is stable and whether it is within the preset normal range, and obtain the evaluation result;

[0039] If the evaluation result shows that the working state of the electric energy meter is abnormal, an abnormality detection procedure is performed. During the abnormality detection process, the adjusted control parameters and the corresponding adjustment amounts, including the voltage setting value and the current distribution ratio, are calculated to obtain feedback control instructions;

[0040] According to the feedback control instructions, corresponding adjustment operations are performed, including adjusting voltage output and optimizing current distribution.

[0041] In a second aspect, an electric energy meter operation control system includes:

[0042] Data acquisition module, used to collect electricity consumption data of electric energy meters in real time;

[0043] The central processing module is used to receive and process the data from the data acquisition module to obtain processed data;

[0044] A data management module, used for storing processed data;

[0045] Intelligent control module, used to monitor power usage in real time, and remotely control appliances, perform data analysis, and provide fault warnings;

[0046] Data analysis and optimization module, used to analyze the data of the data management module and determine the power consumption pattern and peak period;

[0047] The alarm and notification module is used to monitor abnormal conditions in power usage, including sudden high power consumption and equipment failure.

[0048] According to a third aspect, a computing device includes:

[0049] one or more processors;

[0050] The storage device is used to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the method described.

[0051] In a fourth aspect, a computer-readable storage medium stores a program, and when the program is executed by a processor, the method described is implemented.

[0052] The above solution of the present invention includes at least the following beneficial effects:

[0053] This method can comprehensively monitor the key working physical data of the electric energy meter, such as voltage, current, power factor, active power and reactive power, to ensure the integrity and accuracy of the data. The corresponding data collection plan is generated according to the monitoring instructions and assigned to the corresponding operators and operating terminals. This flexibility makes the data collection process more efficient and targeted, and can adapt to the electric energy meter monitoring tasks of different scenarios and needs. The working physical data of the electric energy meter at the monitoring terminal can be obtained in real time, and the data change gradient after each monitoring cycle can be calculated. This real-time and gradient analysis helps to promptly detect minor changes in the working status of the electric energy meter, providing strong support for preventing potential problems.

[0054] By performing statistical distribution and trend analysis on the gradient of changes in the working physical data, and combining it with the preset threshold to detect abnormal values, it is possible to deeply mine the potential information in the data, accurately identify abnormal conditions in the working state of the electric energy meter, and provide an important basis for timely response and processing. According to the data analysis results, this method can perform intelligent feedback control on the electric energy meter and adjust its working state in real time. This intelligent control method not only improves the working efficiency and stability of the electric energy meter, but also helps to extend the service life of the equipment and reduce maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a flow chart of an electric energy meter operation control method provided by an embodiment of the present invention.

[0056] Figure 2 It is a schematic diagram of an electric energy meter operation control system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0057] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0058] like Figure 1As shown, an embodiment of the present invention provides an electric energy meter operation control method, the method comprising the following steps:

[0059] Step 11, receiving a monitoring instruction, the monitoring instruction includes monitoring various working physical data of the electric energy meter, the working physical data includes voltage, current, power factor, active power and reactive power;

[0060] Step 12: Generate a corresponding data collection plan according to the monitoring instruction; distribute the data collection plan to the corresponding operator and operation terminal;

[0061] Step 13, obtaining the working physical data of the electric energy meter monitored by the monitoring terminal in real time, including voltage fluctuation, current change and power factor stability. After each monitoring cycle is completed, the monitoring terminal automatically calculates the change gradient of the working physical data;

[0062] Step 14, performing data analysis according to the change gradient of the working physical data, including statistical distribution and trend analysis, and detecting whether there are abnormal values ​​in the change gradient according to a preset threshold value to obtain analysis results;

[0063] Step 15: Based on the analysis results, feedback control is performed on the electric energy meter to adjust the working state in real time.

[0064] In the embodiment of the present invention, by receiving monitoring instructions containing detailed working physical data, it is possible to guide operators and working terminals to perform efficient and targeted data collection. This not only reduces the blindness of data collection, but also improves the efficiency and accuracy of data processing. The working physical data of the electric energy meter is obtained in real time, including key information such as voltage fluctuations, current changes, and power factor stability, ensuring real-time monitoring of the working status of the electric energy meter. At the same time, the data change gradient is automatically calculated after each monitoring cycle, further improving the accuracy and response speed of monitoring.

[0065] Using the gradient of working physical data changes to conduct in-depth data analysis, including statistical distribution and trend analysis, enables the system to more accurately grasp the working rules of the energy meter. In addition, by detecting abnormal values ​​through preset thresholds, potential faults or abnormal conditions can be identified in a timely manner, providing strong support for preventive maintenance. According to the data analysis results, the energy meter can be intelligently feedback controlled to adjust its working state in real time. This intelligent adjustment method not only ensures the stable operation of the energy meter, but also optimizes energy efficiency and reduces unnecessary energy consumption and cost expenditures.

[0066] In a preferred embodiment of the present invention, the above step 11, receiving a monitoring instruction, the monitoring instruction includes monitoring various working physical data of the electric energy meter, the working physical data includes voltage, current, power factor, active power and reactive power, and may include:

[0067] Build a communication interface that can receive external commands. It can be a network interface based on protocols such as TCP / IP, HTTP, MQTT, or a serial communication interface such as RS-232, RS-485, etc. Continuously monitor the above communication interface and wait for monitoring commands sent by the external system. When the command is received, decode the command through the preset parsing rules to extract the working physical data items that need to be monitored, such as voltage, current, power factor, active power and reactive power. According to the parsed monitoring requirements, determine the frequency, accuracy and duration of data collection. Connect to the data acquisition device of the energy meter, such as the smart meter data collector or the remote terminal unit (RTU), to ensure that it can work according to the collection strategy. Send a specific collection command to the energy meter or data acquisition device, which contains the identifier of the data item to be collected.

[0068] Wait for the energy meter or data acquisition device to respond and receive the returned working physical data. Verify the received data to ensure its integrity and accuracy. If the data is abnormal or missing, perform corresponding error processing or request retransmission. If any abnormality is found during the data collection process, such as data outside the preset range or equipment failure, record the abnormal information and promptly notify relevant personnel through the preset notification mechanism (such as SMS, email or APP push).

[0069] Assume that a power company needs to conduct real-time monitoring of the electricity meters under its jurisdiction to ensure the stable operation of the power grid and the safety of electricity use.

[0070] The central control system of the power company sends monitoring instructions to the electric energy meter management system through the TCP / IP network interface. After the electric energy meter management system listens to the instructions, it decodes the instructions using the preset protocol format and determines that the data items to be monitored are voltage, current, power factor, active power and reactive power. According to the analysis results, the electric energy meter management system determines to collect data every 5 minutes, and the accuracy is set to two decimal places. The intelligent data collector connected to the electric energy meter is configured to ensure that it can collect data according to the above strategy. At the beginning of each collection cycle, the electric energy meter management system sends a collection command to the intelligent data collector. After receiving the command, the intelligent data collector reads the current voltage, current, power factor, active power and reactive power data from the connected electric energy meter and sends the data back to the electric energy meter management system. After receiving the data, the electric energy meter management system stores it in the local database. After receiving the data each time, the electric energy meter management system will check whether it is within a reasonable range. If the voltage data collected at a certain time is found to be abnormally high or the current data is zero, the system will record these abnormal conditions and notify the power company's operation and maintenance personnel via SMS for inspection and processing.

[0071] In a preferred embodiment of the present invention, the above step 12, generating a corresponding data collection plan according to the monitoring instruction; and assigning the data collection plan to the corresponding operator and operation terminal, may include:

[0072] Step 121, receiving and parsing the monitoring instruction, extracting key information, including the monitored electric energy meter number, the collected working physical data type, and the monitoring period and frequency;

[0073] Step 122, based on the key information, initialize the parameters of group incremental learning, including the number of individuals, number of iterations, learning rate, inertia weight, and set the optimization target of the data collection plan;

[0074] Step 123, using the optimization target of the data collection plan as the evaluation function of group incremental learning to evaluate the quality of each individual, and iteratively optimizing the initialized individuals. In each iteration, the position of the individuals is updated. When the preset number of iterations is reached, the final individual is used as the data collection plan;

[0075] Step 124, generating a task allocation plan based on the data collection plan, including the operator for each task, the portable operation terminal to be used, and the execution time.

[0076] In an embodiment of the present invention, in step 121, a monitoring instruction is received from a user, and then the instruction is parsed to extract key information. The key information includes the unique number of the electric energy meter (used to identify the specific monitoring object), the type of physical data to be collected (such as voltage, current, power factor, etc.), the specific period of monitoring, and the frequency of data collection. In step 122, group incremental learning is an optimization algorithm that simulates the group behavior of nature (such as ant colonies, bird flocks, etc.). In this step, the relevant parameters of group incremental learning are initialized according to the key information extracted in step 121. These parameters include:

[0077] Number of individuals: represents the number of individuals in a group.

[0078] Iterations: The maximum number of iterations the algorithm runs.

[0079] Learning rate: affects the rate at which individuals learn both individually and globally.

[0080] Inertia weight: affects how well an individual maintains its current speed.

[0081] The optimization goal is to minimize the data collection cost, maximize the data collection efficiency, etc. In step 123, the group incremental learning algorithm will be used to optimize the data collection plan. Through multiple iterations, the algorithm will gradually converge to an optimized data collection plan. After obtaining the optimized data collection plan, in step 124, the system generates a specific task allocation plan based on the plan. This includes assigning operators and portable operation terminals to each data collection task and determining the execution time.

[0082] Assume that there is a monitoring instruction that requires monitoring of the electric energy meter numbered A123, and collecting voltage data once an hour during the two periods of 8:00-10:00 and 14:00-16:00 every day. In step 121, the key information extracted includes the electric energy meter number A123, the data type is voltage, the monitoring period is 8:00-10:00 and 14:00-16:00, and the frequency is once an hour. In step 122, the group incremental learning parameters are initialized, for example, the number of individuals is set to 30, the number of iterations is 100, the learning rate is 2, and the inertia weight is 0.9. The optimization goal is set to minimize the data collection cost. In step 123, the data collection plan is iteratively optimized by the group incremental learning algorithm until the number of iterations is reached to obtain the final data collection plan. In step 124, a task allocation plan is generated based on the optimized data collection plan. For example, operator No. 1 is assigned to use portable terminal A to collect data from 8:00 to 10:00, and operator No. 2 is assigned to use portable terminal B to collect data from 14:00 to 16:00.

[0083] Step 121 can quickly and accurately extract key information by receiving and parsing monitoring instructions, provide a clear direction for subsequent data collection work, and reduce the possibility of information misunderstanding or omission.

[0084] Steps 122 and 123 use a group incremental learning algorithm to optimize the data collection plan, which can not only intelligently adjust the collection strategy according to actual needs, but also achieve the final task allocation when resources are limited, thereby improving the efficiency of resource use. The application of the group incremental learning method enables the system to flexibly adjust the data collection plan according to different monitoring instructions, better adapt to various complex and changeable actual situations, and improve the flexibility and adaptability of the system. The task allocation plan generated by step 124 clearly specifies the operator of each task, the portable operation terminal used, and the execution time, which helps to reduce confusion and delays in task allocation and ensure that data collection work can be completed on time and efficiently.

[0085] In another preferred embodiment of the present invention, the calculation formula of the evaluation function is:

[0086] ;

[0087] in, represents the evaluation function; Represents the total acquisition time weighting coefficient; Indicates the total number of tasks; Represents an index variable; Indicates The estimated acquisition time for each task; Indicates The priority coefficient of each task; Indicates the maximum single acquisition time weighting factor; Indicates the basic weighting coefficient of data coverage; Indicates the number of data types covered; Indicates the total number of data types that should be covered; represents the adjustment factor; Indicates the penalty weighting coefficient for uncovered data types; An index representing a data type; Indicates The number of electric energy meters that have collected this data type; Indicates the number of electric energy meters collected; It represents the weighted coefficient of workload balance of operators; Indicates the total number of operators; Indicates the index of the operator; Indicates The workload of each operator; Indicates the average workload of all operators; Represents the weighted coefficient of the maximum operator workload.

[0088] In an embodiment of the present invention, It is the weighted coefficient of the total acquisition time, reflecting the influence of the total acquisition time on the evaluation function. It is the weighting factor of the maximum single acquisition time, emphasizing the importance of the single longest acquisition time. It is the basic weighting coefficient of data coverage, which indicates the contribution of the number of covered data types to the evaluation function. is the penalty weighting coefficient for uncovered data types, which imposes a penalty on uncovered data types. It is the weighted coefficient of the workload balance of operators, taking into account the balance of workload. It is the weighted coefficient of the maximum operator’s workload, focusing on the impact of the maximum workload. is the total number of tasks, that is, the number of electric energy meter tasks that need to collect data. , set the estimated collection time and priority coefficient The total number of data types that should be covered , such as voltage, current and other different types of physical data. Number of data types covered , which is initialized to zero and increases as the acquisition progresses. , determine the number of electric energy meters of this type that should be collected and the number of collected . Total number of operators , which is the number of people involved in data collection. , assign tasks , which is the number of tasks that the person is responsible for collecting. The average number of tasks for all operators , which is used to evaluate the balance of workload. After the parameters are initialized, various evaluation indicators are calculated step by step according to the formula of the evaluation function. Traverse all tasks and calculate the sum of the product of the estimated collection time and the priority coefficient of each task, that is, Find the task with the longest estimated acquisition time among all tasks and record its time as the maximum value. Calculate the ratio of the number of covered data types to the total number of data types that should be covered, and multiply it by the adjustment factor. , and obtain the basic contribution of data coverage.

[0089] Traverse all data types and calculate the sum of the penalty items for uncovered types, that is, . Calculate the variance of the workload of all operators. First, square the difference between the workload of each operator and the average workload, then sum and divide by the total number of operators, and finally take the square root. Find the workload of the operator with the largest workload as the maximum workload. Substitute the calculated evaluation index values ​​into the evaluation function formula, perform weighted summation, and obtain the final evaluation function value. .

[0090] By optimizing the weighting factor of the total acquisition time and the maximum single acquisition time ( and ), can motivate to reduce the overall and individual task acquisition time, thereby improving the overall efficiency of data acquisition. Task priority coefficient ( ) enables the evaluation function to be flexibly adjusted according to the urgency or importance of different tasks, ensuring that high-priority tasks are given priority. By adjusting the basic weighting coefficient of data coverage ( ) and the penalty weighting factor for uncovered data types ( ), can balance the impact of covered data types and uncovered data types, and encourage more comprehensive coverage of all necessary data types, thereby improving data integrity and availability. Weighted coefficient of operator workload balance ( ) and the maximum operator workload weighting factor ( ), which helps to achieve a balanced distribution of tasks among operators and avoid overloading of individual operators, thereby improving overall work efficiency and employee satisfaction. The evaluation function quantifies multiple complex decision factors into a specific value, making the comparison between different plans more intuitive and objective, and enhancing the transparency and persuasiveness of the decision-making process. The results of the evaluation function can be used as feedback to guide the continuous optimization and iteration of the data collection plan. By continuous trial and error and adjustment of parameters, the final data collection strategy can be gradually approached.

[0091] In a preferred embodiment of the present invention, the above step 13, real-time acquisition of the working physical data of the electric energy meter monitored by the monitoring terminal, including voltage fluctuation, current change and power factor stability, each time a monitoring cycle is completed, the monitoring terminal automatically calculates the working physical data change gradient, which may include:

[0092] Step 131, sending a monitoring instruction to the monitoring terminal, including the start time and end time of the monitoring period, and receiving the working physical data of the electric energy meter sent by the monitoring terminal in real time during the monitoring period;

[0093] Step 132, checking whether the current time reaches the end time of the monitoring cycle, and at the end of the monitoring cycle, checking whether the working physical data set of the electric energy meter has been received;

[0094] Step 133, processing the working physical data set of the electric energy meter, calculating the change gradient of each data point relative to the previous data point, and realizing continuous monitoring of the working physical data of the electric energy meter and calculation of the change gradient.

[0095] In the embodiment of the present invention, a monitoring instruction is sent to determine the start time and the end time of the monitoring period.

[0096] Send a monitoring instruction including the start time and end time to the monitoring terminal. During the time period of the start time and end time, receive the working physical data of the electric energy meter sent by the monitoring terminal in real time. These data include voltage, current and power factor.

[0097] Step 132, continuously compare the current time with the end time. When the current time = the end time, confirm whether a complete set of working physical data of the electric energy meter has been received.

[0098] Step 133, perform necessary preprocessing on the received working physical data set of the electric energy meter, such as filtering, denoising, etc. Calculate the change gradient:

[0099] For example, the voltage gradient ,in, is the time interval for data sampling; It is at the moment The measured voltage value; It's time Measured voltage value.

[0100] Current gradient ,in, It is at the moment The measured current value; Indicates time Measured current value.

[0101] Power factor change gradient , It is at the moment The measured power factor value; It is at the moment Measured power factor value. Data processing and change gradient calculation are performed continuously throughout the monitoring period.

[0102] By real-time monitoring of the working physical data of the energy meter, including voltage fluctuations, current changes, and power factor stability, the system can promptly detect abnormal conditions such as voltage sags, current overloads, or power factor instability, thereby triggering an early warning mechanism to ensure the safe operation of the power system. Calculating the gradient of the working physical data helps analyze the dynamic changes in the working state of the energy meter. This analysis can provide more in-depth information on the operation of the power system, helping staff understand the real-time status of the power grid, predict potential problems, and take timely measures to prevent failures.

[0103] By continuously monitoring electricity meter data and calculating change gradients, energy managers can more effectively distribute and dispatch energy, optimize energy utilization efficiency, reduce energy waste, and thus save costs.

[0104] Through real-time analysis of the data from the electric energy meter, factors that may cause instability in the power system, such as voltage fluctuations or unstable power factor, can be discovered and dealt with in a timely manner, thereby enhancing the stability and reliability of the entire power system. The real-time acquisition of the working physical data of the electric energy meter and the analysis results of the change gradient can provide strong support for the decision-making of the power company, such as equipment maintenance plans, energy procurement strategies, etc., to improve operational efficiency and customer satisfaction. The monitoring terminal automatically calculates the change gradient of the working physical data, which reduces the workload of manual processing and analysis of data, improves work efficiency, and also reduces the possibility of human error.

[0105] In a preferred embodiment of the present invention, the above step 14 performs data analysis according to the change gradient of the working physical data, including statistical distribution and trend analysis, and detects whether there are abnormal values ​​in the change gradient according to a preset threshold value to obtain the analysis result, which may include:

[0106] Step 141, receiving a change gradient sequence of the working physical data of the electric energy meter from the monitoring terminal, and setting an abnormal value detection threshold according to historical data;

[0107] Step 142, calculating the mean and standard deviation statistics of the change gradient sequence, and fitting the change gradient sequence using a normal distribution;

[0108] Step 143, using a difference quantification index to evaluate the difference between the fitted distribution and the actual data, and performing a time series analysis on the change gradient sequence to identify trends and periodicity in the data, and obtaining a time series analysis result;

[0109] Step 144, constructing a trend model based on the time series analysis results, and using the trend model to predict the change gradient to obtain a predicted value at each time point;

[0110] Step 145, using a difference quantification index to measure the difference between the trend model prediction value and the actual change gradient value as a trend analysis index, and comparing the actual change gradient value with a preset threshold, marking the point exceeding the threshold as an abnormal value, and obtaining an abnormality detection result;

[0111] Step 146, integrating the statistical distribution analysis, trend analysis and anomaly detection results to generate an analysis result including an outlier list, statistics, and trend model parameter information.

[0112] In the embodiment of the present invention, the change gradient sequence of the working physical data of the electric energy meter is obtained from the monitoring terminal { , , }. According to historical data, an outlier detection threshold is set for each change gradient.

[0113] Step 142, for each gradient sequence, calculate its mean and standard deviation For example, for the voltage gradient, the mean and standard deviation are:

[0114] ;in, Indicates the mean value of voltage; Indicates the total number of data points; Indicates at time The voltage gradient of Represents an index variable.

[0115] ;in, Represents the standard deviation of the voltage. Use normal distribution to fit the gradient sequence, assuming Normal distribution ( , ).

[0116] Step 143, apply the Kolmogorov-Smirnov test to compare the difference between the actual data distribution and the fitted normal distribution. This test returns a statistic and the corresponding If If the value is greater than the significance level (for example, 0.05), then you cannot reject the hypothesis that the actual data come from the fitted normal distribution.

[0117] if The larger the value, the better the fitting effect. The value is small, indicating that the distribution of the actual data is different from the normal distribution, and the fitting effect is not good. Similarly, the change gradient sequence of the preprocessed working physical data of the electric energy meter is obtained from the monitoring terminal. The autocorrelation function of the change gradient sequence is calculated to evaluate the correlation between different time points in the sequence. The autocorrelation function of the change gradient sequence is ;in, Indicates that in the lag period The autocorrelation coefficient of Indicates at a point in time The changing gradient sequence value of Represents the mean of the gradient sequence; Indicates at a point in time forward The data value at a time; Indicates the length of the gradient sequence; represents the lag period; Represents an index variable. It can help identify periodic patterns in the data, because periodic data will show correlation at different lags. The partial autocorrelation function of the gradient sequence is calculated to exclude the influence of other lags and directly examine the correlation between a specific lag and the current value. The calculation formula of the partial autocorrelation function of the gradient sequence is ;in, Indicates that in the lag period The partial autocorrelation coefficient of . Indicates at a point in time The value of the changing gradient sequence; Represents the mean of the gradient sequence; Indicates the length of the time series; Indicates at a point in time forward The data value at time. Autocorrelation function It helps to identify lagged relationships in time series, especially for autoregressive models. By observing the autocorrelation function and Graphs or values ​​of the data can identify trends (such as persistent positive or negative correlations) and periodicity (such as autocorrelation functions). Peaks that appear regularly in the graph).

[0118] Step 144, constructing a trend model, such as a linear trend model, based on the time series analysis results.

[0119] Step 145, use a difference quantification indicator (such as mean square error MSE) to measure the difference between the trend model prediction value and the actual change gradient value: ;in, Represents the mean square error of the voltage change gradient prediction; Indicates the total number of data points; Indicates at a point in time The actual observed value of the voltage gradient; Represents an index variable; Indicates at a point in time The model predicts the value of the voltage change gradient. The actual change gradient value is compared with the preset threshold, and the points exceeding the threshold are marked as outliers. For example, for the voltage change gradient, the outlier can be defined as:

[0120] Outliers ,in, Indicates the voltage anomaly detection threshold.

[0121] Step 146 , integrating the statistical distribution analysis, trend analysis and anomaly detection results, and generating an analysis result including an outlier list, statistics (such as mean, standard deviation), and trend model parameter information.

[0122] Assuming there is a set of data on voltage gradient change, the following results are obtained:

[0123] Mean =0.05, standard deviation =0.1.

[0124] The normal distribution fits well, and the Kolmogorov-Smirnov test is Value greater than 0.05.

[0125] The time series analysis shows that there is a clear upward trend, and the parameters of the linear trend model are 0.03 and 0.002. The mean square error is 0.01, indicating that the trend model has a good prediction effect.

[0126] Outlier detection found that at time point =50 and =100, the voltage change gradient exceeds the preset threshold and is marked as an abnormal value.

[0127] Through outlier detection, abnormal data caused by equipment failure, data transmission errors, etc. can be identified and eliminated, thereby improving the overall data quality. Statistical distribution analysis and time series analysis provide a means of in-depth understanding of data, helping users better grasp the changing laws and potential patterns of data.

[0128] Based on accurate data analysis and trend prediction, it can provide enterprises or institutions with more powerful decision-making support, such as equipment maintenance plans, energy management strategies, etc. Through the prediction of trend models, potential problems or anomalies can be discovered in time, so that measures can be taken in advance to avoid system performance degradation or failure. The entire analysis process can be integrated into the automation system to achieve real-time data analysis and automatic abnormal alarm, improving the intelligence level of the system.

[0129] In another preferred embodiment of the present invention, the calculation formula of the difference quantitative index is:

[0130] ;

[0131] in, It represents the quantitative index of difference; represents the global adjustment coefficient; , represents the weight coefficient; Represents the total number of data points; Represents an index variable; Indicates The actual change gradient value at each time point; Indicates The predicted value at each time point; represents a data point; Represents data points in the actual data distribution The probability of Represents the data points in the fitted distribution probability.

[0132] In the embodiment of the present invention, it is ensured that there is an actual change gradient value sequence and the predicted value sequence at the corresponding time point ,in is the index variable, from 1 to , is the total number of data points. Enter the actual gradient value sequence , the predicted value sequence , the total number of data points . Determine the global adjustment coefficient , weight coefficient and These parameters can be set according to actual conditions or experience to adjust the relative importance of different parts of the difference quantification index. Enter the global adjustment coefficient , weight coefficient and For each time point , calculate the actual change gradient value With the predicted value The square error between Then, calculate the average of the squared errors at all time points, i.e. . Outputs the mean of the squared error terms.

[0133] First, calculate each data point according to the actual data distribution and the fitted distribution Probability in two distributions and Then, for each data point , calculate the probability distribution difference term Finally, the probability distribution difference terms of all data points are summed up, that is, .

[0134] Multiply the average of the squared error terms by the weight coefficient , multiply the sum of the probability distribution difference terms by the weight coefficient These two weighted terms are then added together and multiplied by the global adjustment factor , and obtain the final difference quantitative index .

[0135] Difference Quantification Index It can comprehensively consider the deviation between the actual data and the predicted data (reflected by the squared error term) and the difference between the actual data distribution and the fitted distribution (reflected by the probability distribution difference term). This comprehensive evaluation capability makes It can more comprehensively reflect the differences between data and provide analysts with more accurate data difference measurements. and weight coefficient , quantitative index of difference It is highly flexible. These coefficients can be adjusted according to actual needs to highlight or balance the differences in different aspects. For example, if you are more concerned about the accuracy of the predicted value, you can increase If you are more concerned about the fit of the data distribution, you can increase The value of the difference. It is sensitive to small changes in the data. Since the calculation of the squared error term and the probability distribution difference term involves the specific value of each data point, even if the data changes, The value will also react accordingly. This makes It can detect abnormal situations in a timely manner during data monitoring and analysis. The squared error term reflects the average degree of deviation between the predicted value and the actual value, while the probability distribution difference term reveals the similarity or difference between the actual data distribution and the fitted distribution. This interpretability helps analysts understand the nature and source of data differences and make more reasonable decisions.

[0136] In another preferred embodiment of the present invention, the calculation formula for the change gradient of each data point relative to the previous data point is:

[0137] ;

[0138] in, Indicates The gradient of change of a data point relative to the previous data point; Indicates The measured value of the physical quantity at each data point; Indicates The measured value of the physical quantity at each data point; , , , , Indicates adjustment parameters; Indicates the maximum value of the physical quantity in the data set; represents the angular frequency; Indicates The measurement time of each data point; Indicates −Measurement time of 1 data point; Indicates phase; Represents the time interval between adjacent data points; Indicates the maximum value of the time interval in the data set; Represents the index of the data point; represents the time sensitivity coefficient; Indicates the magnification factor.

[0139] In the embodiment of the present invention, it is ensured that the physical quantity measurement value sequence of the existing data points , and the measurement time series of the corresponding data points .in, is the index of the data point. Input physical quantity measurement value sequence , measuring time series . Determine the tuning parameters , , , , , angular frequency , Phase , and the maximum value of the physical quantity in the data set The maximum value of the time interval .enter , , , , , , , , For each data point , calculate the difference between the measured values ​​of physical quantities The difference between the measured time . Note that for the first data point ( =1), since there is no previous data point, its gradient can be set to 0. Output physical quantity measurement value difference sequence, measurement time difference sequence. For each data point , calculate the molecular part , and obtain the molecular partial sequence of the gradient change. For each data point , first calculate the time interval between adjacent data points Then, calculate , and get the denominator sequence of the gradient change. For each data point , divide the numerator of the change gradient by the denominator to get the change gradient of the corresponding data point relative to the previous data point .

[0140] The formula includes multiple adjustment parameters , , , , ,These parameters can be flexibly adjusted according to the actual data characteristics and analysis requirements, so as to better adapt to data changes in different scenarios.

[0141] Through the magnification factor and time sensitivity coefficient The formula can nonlinearly adjust the impact of the measured value of the physical quantity of the data point and the time interval on the change gradient. This nonlinear adaptability enables the model to more accurately capture the complex change patterns in the data. The term takes into account possible periodic change factors. This is particularly useful for analyzing data sets with periodic characteristics (such as seasonal changes, periodic fluctuations, etc.), and can more accurately reflect the relative changes between data points.

[0142] In a preferred embodiment of the present invention, the above step 15, based on the analysis result, feedback control is performed on the electric energy meter to adjust the working state in real time, which may include:

[0143] Step 151, based on the analysis result, evaluating whether the current working state of the electric energy meter is stable and within a preset normal range, and obtaining an evaluation result;

[0144] Step 152, if the evaluation result shows that the working state of the electric energy meter is abnormal, an abnormality detection procedure is performed. During the abnormality detection process, the adjusted control parameters and the corresponding adjustment amounts, including the voltage setting value and the current distribution ratio, are calculated to obtain a feedback control instruction;

[0145] Step 153, performing corresponding adjustment operations according to the feedback control instruction, including adjusting voltage output and optimizing current distribution.

[0146] In the embodiment of the present invention, the statistics (such as mean, standard deviation) and trend analysis indicators in the analysis results are compared with the preset normal range. By comparing the trend and periodic components in the time series analysis results, it is determined whether the working state of the electric energy meter is stable. If all indicators are within the normal range and the working state is stable, the evaluation result is normal; otherwise, the evaluation result is abnormal.

[0147] Step 152: When the evaluation result is abnormal, start the abnormality detection program. Suppose the original voltage setting value is , the adjusted voltage setting value is expressed as ′= + ,in, is the voltage adjustment amount. Assume that the original current distribution ratio is , the adjusted current distribution ratio is expressed as ′= + ,in The calculated adjusted voltage setting value is ′ and current distribution ratio ′ as feedback control instruction.

[0148] Step 153, according to the feedback control instruction ′, adjust the voltage output of the electric energy meter in real time. ′, optimize the current distribution of the electric energy meter in real time.

[0149] Assume that the original voltage setting value of the energy meter is 220V, and the original current distribution ratio is 50:50 (distribution between the two circuits). Through data analysis, it is found that the working state of the energy meter is abnormal and needs to be adjusted.

[0150] The evaluation found that the working state of the energy meter was unstable and the voltage fluctuated greatly. After calculation, it was determined that the voltage needed to be increased by 5V and the current distribution ratio needed to be adjusted to 60:40. Therefore, the feedback control instruction was generated ′=225V and According to the feedback control instruction, the electric energy meter adjusts the voltage output to 225V in real time and optimizes the current distribution ratio to 60:40.

[0151] By monitoring the working status of the energy meter in real time and adjusting it when it is abnormal, the stability of the system can be significantly improved. When the working status of the energy meter is detected to be unstable or out of the normal range, timely adjustment of voltage output and current distribution can prevent potential failures or performance degradation and ensure the continuous and stable operation of the energy meter.

[0152] Timely adjustment of the working parameters of the energy meter can avoid the equipment from operating under unfavorable conditions for a long time, thereby reducing the wear and aging of the equipment. By optimizing the current distribution, the overload of some parts can be reduced, thereby extending the service life of the energy meter and its related components. Optimizing the voltage setting value and the current distribution ratio can improve the efficiency of energy use. Reasonable voltage and current settings can reduce energy waste, reduce line losses, and improve the overall efficiency of the energy meter. This not only helps to save energy and reduce emissions, but also saves electricity bills for users. Abnormal working status of the energy meter may cause safety hazards such as electrical fires. Through real-time feedback control, these abnormal conditions can be discovered and handled in time, thereby reducing the risk of electrical accidents and enhancing the safety of the system. The stable operation of the energy meter is directly related to the user's electricity experience. By adjusting the working status in real time, problems such as power outages and voltage fluctuations can be reduced, thereby providing a more stable and reliable power supply and improving user satisfaction. This feedback control system is an important part of the smart grid. Through data analysis and automatic adjustment, the intelligent management of the energy meter can be realized, and the adaptive ability and response speed of the power grid can be improved.

[0153] like Figure 2 As shown, an embodiment of the present invention further provides an electric energy meter operation control system 20, comprising:

[0154] The data collection module 21 is used to collect the electricity consumption data of the electric energy meter in real time;

[0155] The central processing module 22 is used to receive and process the data from the data acquisition module to obtain processed data;

[0156] A data management module 23, used for storing processed data;

[0157] Intelligent control module 24, used to monitor the power usage in real time, and remotely control electrical appliances, perform data analysis and provide fault warnings;

[0158] The data analysis and optimization module 25 is used to analyze the data of the data management module and determine the power consumption mode and peak time period;

[0159] The alarm and notification module 26 is used to monitor abnormal conditions in the use of electric energy, including sudden high power consumption and equipment failure.

[0160] It should be noted that the system is a system corresponding to the above method, and all implementation methods in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0161] The embodiment of the present invention further provides a computing device, comprising: a processor, a memory storing a computer program, wherein when the computer program is executed by the processor, the method described above is executed. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0162] The embodiment of the present invention also provides a computer-readable storage medium storing instructions, which, when executed on a computer, enable the computer to execute the method described above. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0163] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for controlling an electric energy meter operation, characterized in that: The method comprises: Receiving monitoring instructions, the monitoring instructions include monitoring various working physical data of the electric energy meter, the working physical data include voltage, current, power factor, active power and reactive power; Generate corresponding data collection plans according to monitoring instructions; assign data collection plans to corresponding operators and operation terminals; Acquire the working physical data of the electric energy meter monitored by the monitoring terminal in real time, including voltage fluctuation, current change and power factor stability. After each monitoring cycle, the monitoring terminal automatically calculates the change gradient of the working physical data. Perform data analysis based on the change gradient of the working physical data, including statistical distribution and trend analysis, and detect whether there are abnormal values ​​in the change gradient based on the preset threshold to obtain the analysis results; According to the analysis results, feedback control is performed on the electric energy meter to adjust the working status in real time; Data analysis is performed based on the change gradient of the working physical data, including statistical distribution and trend analysis. Based on the preset threshold, it is detected whether there are abnormal values ​​in the change gradient, and the analysis results are obtained, including: The change gradient sequence of the working physical data of the electric energy meter received from the monitoring terminal, and the abnormal value detection threshold is set according to the historical data; Calculate the mean and standard deviation statistics of the gradient sequence, and fit the gradient sequence using a normal distribution; Use difference quantification indicators to evaluate the difference between the fitted distribution and the actual data, and perform time series analysis on the change gradient sequence to identify trends and periodicity in the data to obtain time series analysis results; According to the results of time series analysis, a trend model is constructed, and the trend model is used to predict the change gradient to obtain the predicted value at each time point; The difference quantification index is used to measure the difference between the trend model prediction value and the actual change gradient value as a trend analysis index, and the actual change gradient value is compared with the preset threshold value, and the points exceeding the threshold value are marked as outliers to obtain the anomaly detection results; Integrate statistical distribution analysis, trend analysis and anomaly detection results to generate analysis results including outlier lists, statistics, and trend model parameter information; The calculation formula of the difference quantitative index is: ； in, It represents the quantitative index of difference; represents the global adjustment coefficient; , represents the weight coefficient; Represents the total number of data points; Represents an index variable; Indicates The actual change gradient value at each time point; Indicates The predicted value at each time point; represents a data point; Represents data points in the actual data distribution The probability of Represents the data points in the fitted distribution The probability of The formula for calculating the gradient of each data point relative to the previous data point is: ； in, Indicates The gradient of change of a data point relative to the previous data point; Indicates The measured value of the physical quantity at each data point; Indicates The measured value of the physical quantity at each data point; , , , , Indicates adjustment parameters; Indicates the maximum value of the physical quantity in the data set; represents the angular frequency; Indicates The measurement time of each data point; Indicates −Measurement time of 1 data point; Indicates phase; Represents the time interval between adjacent data points; Indicates the maximum value of the time interval in the data set; Represents the index of the data point; represents the time sensitivity coefficient; Indicates the magnification factor.

2. The electric energy meter operation control method according to claim 1, characterized in that: Generate corresponding data collection plan according to monitoring instructions; Allocate data collection plans to corresponding operators and terminals, including: Receive and parse monitoring instructions, extract key information, including the monitored electric energy meter number, the collected working physical data type, and the monitoring period and frequency; Based on the key information, initialize the parameters of group incremental learning, including the number of individuals, number of iterations, learning rate, inertia weight, and set the optimization goal of the data collection plan; The optimization goal of the data collection plan is used as the evaluation function of group incremental learning to evaluate the quality of each individual, and the initialized individuals are iteratively optimized. In each iteration, the position of the individual is updated. When the preset number of iterations is reached, the final individual is used as the data collection plan. According to the data collection plan, a task allocation plan is generated, including the operators for each task, the portable operation terminal used, and the execution time.

3. The electric energy meter operation control method according to claim 2, characterized in that: The calculation formula of the evaluation function is: ； in, represents the evaluation function; Represents the total acquisition time weighting coefficient; Indicates the total number of tasks; Represents an index variable; Indicates The estimated acquisition time for each task; Indicates The priority coefficient of each task; Indicates the maximum single acquisition time weighting factor; Indicates the basic weighting coefficient of data coverage; Indicates the number of data types covered; Indicates the total number of data types that should be covered; represents the adjustment factor; Indicates the penalty weighting coefficient for uncovered data types; An index representing a data type; Indicates The number of electric energy meters that have collected this data type; Indicates the number of electric energy meters collected; It represents the weighted coefficient of workload balance of operators; Indicates the total number of operators; Indicates the index of the operator; Indicates The workload of each operator; Indicates the average workload of all operators; Represents the weighted coefficient of the maximum operator workload.

4. The electric energy meter operation control method according to claim 3, characterized in that: The monitoring terminal obtains the working physical data of the electric energy meter monitored by the monitoring terminal in real time, including voltage fluctuation, current change and power factor stability. After each monitoring cycle, the monitoring terminal automatically calculates the change gradient of the working physical data, including: Send monitoring instructions to the monitoring terminal, including the start time and end time of the monitoring period, and receive the working physical data of the electric energy meter sent by the monitoring terminal in real time during the monitoring period; Check whether the current time reaches the end time of the monitoring cycle, and at the end of the monitoring cycle, check whether the working physical data set of the electric energy meter has been received; The working physical data set of the electric energy meter is processed, and the change gradient of each data point relative to the previous data point is calculated to realize the continuous monitoring of the working physical data of the electric energy meter and the calculation of the change gradient.

5. The electric energy meter operation control method according to claim 4, characterized in that: According to the analysis results, the electric energy meter is feedback controlled and the working status is adjusted in real time, including: According to the analysis results, evaluate whether the current working state of the electric energy meter is stable and within the preset normal range, and obtain the evaluation result; If the evaluation result shows that the working state of the electric energy meter is abnormal, an abnormality detection procedure is performed. During the abnormality detection process, the adjusted control parameters and the corresponding adjustment amounts, including the voltage setting value and the current distribution ratio, are calculated to obtain feedback control instructions; According to the feedback control instructions, corresponding adjustment operations are performed, including adjusting voltage output and optimizing current distribution.

6. An electric energy meter operation control system, the system implementing the method according to any one of claims 1 to 5, characterized in that: include: Data acquisition module, used to collect electricity consumption data of electric energy meters in real time; The central processing module is used to receive and process the data from the data acquisition module to obtain processed data; A data management module, used for storing processed data; Intelligent control module, used to monitor power usage in real time, and remotely control appliances, perform data analysis, and provide fault warnings; Data analysis and optimization module, used to analyze the data from the data management module and determine the power consumption pattern and peak period; The alarm and notification module is used to monitor abnormal conditions in power usage, including sudden high power consumption and equipment failure.

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