Low-power-consumption management method and system of electric energy meter

Abstract

The invention belongs to the technical field of electric energy metering, and discloses a low-power-consumption management method and system for an electric energy meter. Comprising the steps of collecting a real-time load; acquiring a historical load, and calculating an average load in combination with the real-time load; dynamically adjusting the data frequency based on the average load; dynamically adjusting a transmission strategy according to the historical load and the data frequency; acquiring electric power data, compressing the electric power data, and transmitting the compressed electric power data based on the transmission strategy; the method can effectively adapt to real-time load change, optimize data transmission time and reduce transmission load, so that power consumption of the electric energy meter is reduced, operation efficiency is improved, the service life is prolonged, and the requirements of an intelligent power grid for sustainable development and high performance are met.

Description

Technical Field

[0001] The present invention relates to the technical field of electric energy metering, and more specifically, to a low power consumption management method and system for an electric energy meter. Background Art

[0002] With the development of smart grids, the functions of electricity meters have expanded from traditional metering to intelligent monitoring, data acquisition, and communication transmission. However, in practical applications, electricity meters face challenges such as high power consumption, long-term operation requirements, and sustainable development pressure. In order to improve the reliability of electricity meters, extend their service life, and respond to the needs of green development, a low-power consumption management method is urgently needed to achieve a balance between high performance and low energy consumption, thereby improving the operating efficiency and economy of smart grids.

[0003] The patent with announcement number CN112098713B discloses an electric energy meter operation control method; including: 1) starting the electric energy meter; 2) judging whether to power on: after the electric energy meter is started, the MCU of the management core judges whether a power-off signal is received, if yes, enters step 3), if not, enters step 4); 3) the electric energy meter enters a low power consumption state, if a wake-up signal is received, enters step 5), if no wake-up signal is received, returns to step 2); 4) the MCU of the management core runs the operating system version, and returns to step 2 after running for a certain period of time); 5) the MCU of the management core runs the bare metal version, and returns to step 2 after running for a certain period of time); this invention can run dual systems through the same MCU core, can run at a low speed when power is off, reduce the working current under the condition of backup power supply, and meet the requirements of low power operation.

[0004] However, although the above-mentioned technology can realize low-power management of electricity meters, it mainly relies on power-off information monitoring and low-power state switching, that is, it relies too much on fixed logical processes for low-power management, lacks dynamic and flexible refined management, and fails to adaptively optimize and adjust data collection, processing and transmission based on actual operating conditions, making it difficult to achieve accurate adaptation to complex scenarios and dynamic loads.

[0005] In view of this, the present invention proposes a low power consumption management method and system for an electric energy meter to solve the above problems. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art and achieve the above-mentioned purpose, the present invention provides the following technical solution: a low power consumption management method of an electric energy meter, comprising:

[0007] Collect real-time load;

[0008] Get historical load and calculate average load based on real-time load;

[0009] Dynamically adjust data frequency based on average load;

[0010] Dynamically adjust transmission strategies based on historical load and data frequency;

[0011] The power data is acquired, compressed, and transmitted based on a transmission strategy.

[0012] Furthermore, the real-time load is the load power collected in real time, and the load power is the rate at which the load equipment in the power system consumes electric energy; the real-time load collection method is: obtaining the real-time voltage, real-time current and power factor, and calculating the real-time load; the expression of the real-time load is: ; In the formula, For real-time load, is the real-time voltage, is the real-time current, is the power factor;

[0013] The historical load is the load power collected at historical moments;

[0014] The method for calculating the average load includes:

[0015] Set the selection duration and obtain the current time, which is the time corresponding to the real-time load collection; subtract the selection duration from the current time to obtain the selection time; construct the calculation period based on the selection time and the current time, and the range of the calculation period is ,in To select the time, is the current moment; obtain the load power corresponding to the calculation period in the historical load and mark it as the calculation load; count the number of calculation loads and add one, marked as the load number; add each calculation load and real-time load in turn, and then divide it by the load number to obtain the average load.

[0016] Furthermore, the step of setting the selection duration includes:

[0017] Step A1: Preset the selected duration range , Split ratio and the number of iterations ;

[0018] Step A2: According to the division ratio , from the selected duration range Split into two new points and , , ;

[0019] Step A3: According to the points and At the current moment, the corresponding computing load is selected from the historical load; The filtered computing load is taken as the first set, the standard deviation corresponding to the first set is calculated and marked as the first standard deviation; The filtered computing loads are taken as the second set, and the standard deviation corresponding to the second set is calculated and marked as the second standard deviation;

[0020] Step A4: With the first standard deviation as the first data, and the second standard deviation as the second data; input the first data and the second data into the trained matching analysis model respectively to predict the corresponding matching degree; mark the matching degree corresponding to the first data as the first matching degree, and mark the matching degree corresponding to the second data as the second matching degree; the matching analysis model is a deep learning network model;

[0021] Step A5: If the first matching degree is greater than or equal to the second matching degree, the minimum value of the updated selected duration range is the minimum value of the selected duration range before the update, and the maximum value is the point corresponding to the second matching degree; if the first matching degree is less than the second matching degree, the minimum value of the updated selected duration range is the point corresponding to the first matching degree, and the maximum value is the maximum value of the selected duration range before the update;

[0022] Step A6: Preset a width threshold, subtract the minimum value from the maximum value in the updated selected duration range to obtain the interval width; compare the interval width with the width threshold; if the interval width is greater than or equal to the width threshold, divide the updated selected duration range into two groups according to the segmentation ratio. , re-dividing two new points and , and return to step A4; if the interval width is less than the width threshold, the average of the maximum and minimum values ​​in the updated selected duration range is used as the selected duration.

[0023] Further, the data frequency includes sampling frequency, processing frequency and transmission frequency;

[0024] Methods for dynamically adjusting data frequency include:

[0025] Defining the search space , Radius Threshold and iteration threshold ; Search space The method for constructing n frequency sets is as follows: obtaining a frequency range, which includes a sampling frequency range, a processing frequency range, and a transmission frequency range; randomly selecting a value from each range in the frequency range to construct a set of frequency sets, and constructing n frequency sets in total, each of which is different; setting numerical labels that increase in sequence for the n frequency sets and marking them as set labels, and the range of the set labels is ;

[0026] Set the initial aperture center and the initial aperture radius , and set the number of iterations ;in, , ; Determine the excellence function and the cyclic process; The cyclic process is: generate m candidate solutions within the aperture range and reduce the aperture radius , ; Calculate the excellence of each candidate solution, move the aperture center to the candidate solution with the greatest excellence; repeat the cycle, and each time the cycle is repeated, set the number of iterations ; until or When , the repeated cycle process is stopped, and the candidate solution corresponding to the aperture center is obtained and marked as the best solution; according to the frequency set corresponding to the set label corresponding to the best solution, the data frequency is dynamically adjusted.

[0027] Furthermore, the expression of the excellence function is: ; In the formula, For excellence, is the degree of adaptation; the method for obtaining the degree of adaptation is to obtain the data frequency in the corresponding frequency set according to the set label corresponding to the candidate solution and mark it as the candidate frequency; the candidate frequency and the average load are used as evaluation data; the evaluation data is input into the trained adaptation analysis model to predict the corresponding degree of adaptation; the adaptation analysis model is a deep learning network model;

[0028] The expression of the candidate solution is: ; In the formula, is the i-th candidate solution, is the aperture center corresponding to the tth repetition of the cycle, is the aperture radius corresponding to the tth repetition of the cycle, is the random coefficient, , ;

[0029] The expression for reducing the aperture radius is: ; In the formula, is the reduced aperture radius, is the shrinkage coefficient, .

[0030] Further, the dynamically adjusting the transmission strategy includes determining a transmission period and evaluating a transmission frequency;

[0031] The method for determining the transmission period comprises:

[0032] Based on the historical load, the load power corresponding to the future time is predicted and marked as the future load. The future time includes the time from the current time to the end of the day. The load power in the future load corresponds to the time in the future time. Preset low load segment and medium load segment ; According to the low load segment and medium load segment , judge the power of each load in the future load, if , then the corresponding future moment is marked as a low-load moment. is the load power in the future load; if , then the corresponding future moment is marked as the medium load moment; if , then the corresponding future moment is not marked;

[0033] Sort the future moments from early to late to generate a moment sorting table; mark the consecutive low-load moments in the moment sorting table as low-load periods, and mark the consecutive medium-load moments in the moment sorting table as medium-load periods; mark the latest low-load moment in each low-load period as the latest moment, and compare each latest moment; mark the low-load period corresponding to the latest latest moment as a transmission period; if there is no low-load period in the moment sorting table, mark the latest medium-load moment in each medium-load moment as the latest moment, and mark the medium-load period corresponding to the latest latest moment as a transmission period.

[0034] Furthermore, the method for predicting the load power corresponding to the future moment includes:

[0035] The historical load and real-time load are used as prediction data, and the prediction data is input into the trained load prediction model to predict the load power corresponding to the future moment; the load prediction model is an RNN neural network model, and the training process of the load prediction model includes:

[0036] Pre-continuous acquisition load power and build a load training set. , To predict the load power quantity in the data; based on the load training set, train a load prediction model for predicting the load power at future moments;

[0037] Preset the sliding step size L and the sliding window length E; convert the load power in the load training set into multiple training samples using the sliding window method, and each training sample includes E load powers; use each training sample as the input of the load prediction model, and predict the load power after the sliding step size L as the output, and use the actual last L load powers corresponding to each training sample as the prediction target. Use the mean absolute percentage error MAPE to evaluate the model accuracy of the prediction result. When the calculated MAPE is less than the preset MAPE, the load prediction model training corresponding to the load power is completed; generate a load prediction model that predicts the load power at future moments based on the load power.

[0038] Furthermore, the method for evaluating the transmission frequency includes:

[0039] Mark the latest moment corresponding to the transmission period as the end moment, and mark all moments from the current moment to the end moment as the analysis period; calculate the average load corresponding to each moment in the analysis period according to the load power corresponding to each moment in the analysis period and the historical load; calculate the transmission frequency corresponding to each moment in the analysis period according to the average load corresponding to each moment in the analysis period; obtain the historical end moment, which is the moment corresponding to the last time the electric energy meter ended data transmission; construct an estimation period according to the end moment and the historical end moment, and the range of the estimation period is ,in For the end of history, is the end moment; the transmission frequencies corresponding to each moment in the estimation period are added up in sequence to obtain the total transmission frequency; the number of moments in the transmission period is counted, the total transmission frequency is divided by the number of moments, and the transmission frequency corresponding to each moment in the transmission period is obtained.

[0040] Furthermore, the power data is all data collected by the power meter during the estimation period;

[0041] The step of compressing the power data comprises:

[0042] Step B1: Count the number of each value in the power data and use it as the frequency of occurrence; use each value as a node, and use the frequency of occurrence of each value as the node frequency of the corresponding node; sort all nodes from small to large according to the node frequency, and generate a node sorting table;

[0043] Step B2: According to the positive order of the node sorting table, obtain the two nodes at the front, mark them as child nodes, and delete the child nodes from the node sorting table;

[0044] Step B3: merge the two child nodes into a parent node and add it to the node sorting table. The node frequency of the parent node is the sum of the node frequencies of the two child nodes.

[0045] Step B4: looping steps B2 to B3 until only one node is left in the node sorting table, the loop ends, and the remaining node in the node sorting table is used as the root node; the root node is used as the starting point and extended downward, and two child nodes corresponding to each node are obtained in turn, until the child nodes corresponding to all nodes are obtained, a node tree is formed, and step B5 is entered;

[0046] Step B5: Starting from the root node of the node tree, follow the path of the node tree, and when you encounter a node on the left, record the code , when encountering a node on the right, record the code ,coding With encoding is a binary value; until the path of the node tree ends, the recorded codes are combined according to the code recording order as the compressed code corresponding to the value of the last node;

[0047] Step B6: Loop step B5 until the compression code corresponding to each value in the power data is obtained, and the compressed data is used together with the node tree; wherein the order of each compression code in the compressed data is consistent with the order of each value in the power data.

[0048] A low power consumption management system for an electric energy meter, implementing the low power consumption management method for an electric energy meter, comprising:

[0049] Load collection module, used to collect real-time load;

[0050] The load calculation module is used to obtain historical loads and calculate the average load in combination with the real-time load;

[0051] Frequency adjustment module, dynamically adjusts data frequency based on average load;

[0052] The policy adjustment module dynamically adjusts the transmission policy based on historical load and data frequency;

[0053] The data compression module is used to obtain power data, compress the power data, and transmit the compressed power data based on the transmission strategy.

[0054] The technical effects and advantages of the low power consumption management method and system of an electric energy meter of the present invention are as follows:

[0055] By collecting and analyzing real-time load and historical load, the average load can be accurately calculated; based on the average load, a heuristic algorithm is used to dynamically adjust the data frequency; combined with load prediction and data compression technology, the transmission strategy is dynamically adjusted to transmit compressed data, thereby achieving refined management and control of the electric energy meter; it can effectively adapt to real-time load changes, optimize data transmission time, and reduce transmission load, thereby reducing the power consumption of the electric energy meter, improving operating efficiency and service life, meeting the smart grid's needs for sustainable development and high performance, and providing an innovative solution for the balance between low power consumption and high performance of the electric energy meter. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 This is a schematic diagram of a low power consumption management system for an electric energy meter according to Embodiment 1 of the present invention;

[0057] Figure 2 This is a schematic diagram of a node tree according to Embodiment 1 of the present invention;

[0058] Figure 3 This is a flow chart of a low power consumption management method for an electric energy meter according to Embodiment 2 of the present invention. DETAILED DESCRIPTION

[0059] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0060] Example 1

[0061] See also Figure 1 As shown, the low power consumption management system of an electric energy meter described in this embodiment includes and; each module is connected by wire and / or wireless means to realize data transmission between modules;

[0062] The load collection module is used to collect real-time load.

[0063] The real-time load is the load power collected in real time. The load power is the rate at which the load equipment in the power system consumes electric energy. The real-time load collection method is: obtain the real-time voltage, real-time current and power factor, and calculate the real-time load. The expression of the real-time load is: ; In the formula, For real-time load, is the real-time voltage, is the real-time current, is the power factor; the real-time voltage, real-time current and power factor are obtained through the electric energy meter.

[0064] The load calculation module is used to obtain historical loads and calculate the average load in combination with real-time loads.

[0065] The historical load is the load power collected at historical moments. The methods for calculating the average load include:

[0066] Set the selection duration and obtain the current time, which is the time corresponding to the real-time load collection; subtract the selection duration from the current time to obtain the selection time; construct the calculation period based on the selection time and the current time, and the range of the calculation period is ,in To select the time, is the current moment; obtain the load power corresponding to the calculation period in the historical load and mark it as the calculation load; count the number of calculation loads and add one, marked as the load number; add each calculation load and real-time load in turn, and then divide it by the load number to obtain the average load.

[0067] The steps to set the selection duration include:

[0068] Step A1: Preset the selected duration range , Split ratio and the number of iterations ; This embodiment is preferred The duration range, segmentation ratio and number of iterations are selected and set by those skilled in the art according to actual conditions;

[0069] Step A2: According to the division ratio , from the selected duration range Split into two new points and , , ;

[0070] Step A3: According to the points and At the current moment, the corresponding computing load is selected from the historical load; The filtered computing load is taken as the first set, the standard deviation corresponding to the first set is calculated and marked as the first standard deviation; The filtered computing loads are taken as the second set, and the standard deviation corresponding to the second set is calculated and marked as the second standard deviation;

[0071] Step A4: With the first standard deviation as the first data, and the second standard deviation as the second data; input the first data and the second data into the trained matching analysis model respectively to predict the corresponding matching degree; mark the matching degree corresponding to the first data as the first matching degree, and mark the matching degree corresponding to the second data as the second matching degree;

[0072] Step A5: If the first matching degree is greater than or equal to the second matching degree, the minimum value of the updated selected duration range is the minimum value of the selected duration range before the update, and the maximum value is the point corresponding to the second matching degree; if the first matching degree is less than the second matching degree, the minimum value of the updated selected duration range is the point corresponding to the first matching degree, and the maximum value is the maximum value of the selected duration range before the update; illustratively, the selected duration range Passing point and After updating, if the first matching degree is greater than or equal to the second matching degree, the selected duration range is updated to , if the first matching degree is less than the second matching degree, then update the selected duration range to ;

[0073] Step A6: Preset a width threshold. The width threshold is preset by a person skilled in the art according to actual conditions. The maximum value in the updated selected duration range is subtracted from the minimum value to obtain the interval width. The interval width is compared with the width threshold. If the interval width is greater than or equal to the width threshold, the updated selected duration range is divided according to the segmentation ratio. , re-dividing two new points and , and return to step A4; if the interval width is less than the width threshold, the average of the maximum and minimum values ​​in the updated selected duration range is used as the selected duration.

[0074] In the above step A4, the training process of the matching analysis model includes:

[0075] The first data and the second data are marked as research data, Z groups of research data are collected in advance, and corresponding matching degrees are set for the Z groups of research data, where Z is an integer greater than 1; the matching degrees corresponding to the research data are collected by a technician in the historical operation process of the electric energy meter, and each group of research data is analyzed in turn based on actual experience, the matching degree corresponding to each group of research data is determined, and the corresponding matching degrees are set for the Z groups of research data in turn;

[0076] The research data and the corresponding matching degree are converted into a corresponding set of feature vectors; each set of feature vectors is used as the input of the matching analysis model, and the matching analysis model uses a set of predicted matching degrees corresponding to each set of research data as output, and the actual matching degree corresponding to each set of research data as the prediction target, and the actual matching degree is the pre-set matching degree corresponding to the research data; the training goal is to minimize the sum of the prediction errors of all research data; where the calculation formula of the prediction error is: ,in is the prediction error, K is the group number of the eigenvector corresponding to the research data, is the predicted matching degree corresponding to the Kth group of research data, is the actual matching degree corresponding to the Kth group of research data; the matching analysis model is trained until the sum of the prediction errors reaches convergence and the training is stopped.

[0077] The above matching analysis model is specifically a deep neural network model; it includes an input layer, a hidden layer and an output layer; each hidden layer includes multiple neurons, each neuron is connected to the neurons in the next layer, and the connection contains weights, which determine the importance and influence of data transmission in the neural network; an activation function is applied to each neuron between the hidden layer and the output layer, and the activation function introduces nonlinearity, allowing the network to learn more complex patterns and features.

[0078] The frequency adjustment module dynamically adjusts the data frequency based on the average load.

[0079] Data frequency includes sampling frequency, processing frequency and transmission frequency; sampling frequency is the number of times the electric energy meter collects data in unit time, processing frequency is the number of times the electric energy meter processes data in unit time, and transmission frequency is the number of times the electric energy meter transmits data in unit time; the unit of unit time is consistent with the unit at the current moment.

[0080] Methods for dynamically adjusting data frequency include:

[0081] Defining the search space , Radius Threshold and iteration threshold ; Radius threshold and iteration threshold It is pre-set by technicians in this field according to the actual situation; the search space The method for constructing the n frequency sets is as follows: according to the technical parameters of the electric energy meter, a frequency range is obtained, the frequency range includes a sampling frequency range, a processing frequency range and a transmission frequency range, a value is randomly selected from each range in the frequency range, and a set of frequency sets is constructed, and a total of n frequency sets are constructed, and each frequency set is different; a digital label that increases in sequence is set for the n frequency sets, and is marked as a set label, and the range of the set label is ;

[0082] Set the initial aperture center and the initial aperture radius , and set the number of iterations ;in, , ; Determine the excellence function and the cyclic process; The cyclic process is: generate m candidate solutions within the aperture range and reduce the aperture radius , ; Calculate the excellence of each candidate solution, move the aperture center to the candidate solution with the greatest excellence; repeat the cycle, and each time the cycle is repeated, set the number of iterations ; until or When , the repeated cycle process is stopped, and the candidate solution corresponding to the aperture center is obtained and marked as the best solution; according to the frequency set corresponding to the set label corresponding to the best solution, the data frequency is dynamically adjusted.

[0083] The expression of the excellence function is: ; In the formula, For excellence, is the degree of adaptation; the method for obtaining the degree of adaptation is to obtain the data frequency in the corresponding frequency set according to the set label corresponding to the candidate solution, and mark it as the candidate frequency; the candidate frequency and the average load are used as evaluation data; the evaluation data is input into the trained adaptation analysis model to predict the corresponding degree of adaptation; the training process of the adaptation analysis model is consistent with the training process of the matching analysis model, and both are deep learning network models.

[0084] The expression of the candidate solution is: ; In the formula, is the i-th candidate solution, is the aperture center corresponding to the tth repetition of the cycle, is the aperture radius corresponding to the tth repetition of the cycle, is the random coefficient, , .

[0085] The expression for reducing the aperture radius is: ; In the formula, is the reduced aperture radius, is the shrinkage coefficient, .

[0086] It should be noted that in order to reduce the power consumption of the electricity meter, the data transmission of the electricity meter should be carried out when the load power is low, that is, the electricity meter will cache the collected and processed data first to delay data transmission; therefore, the purpose of dynamically adjusting the transmission frequency is to first calculate the transmission frequency corresponding to each moment, which will help the subsequent analysis of the actual transmission frequency of the electricity meter during the data transmission process.

[0087] The policy adjustment module dynamically adjusts the transmission policy based on historical load and data frequency.

[0088] Dynamically adjusting the transmission strategy includes determining the transmission period and evaluating the transmission frequency;

[0089] Methods for determining the transmission period include:

[0090] Based on the historical load, the load power corresponding to the future time is predicted and marked as the future load. The future time includes the time from the current time to the end of the day. The load power in the future load corresponds to the time in the future time. Preset low load segment and medium load segment , low load segment and medium load segment It is pre-set by technicians in the field according to the actual situation; and medium load segment , judge the power of each load in the future load, if , then the corresponding future moment is marked as a low-load moment. is the load power in the future load; if , then the corresponding future moment is marked as the medium load moment; if , then the corresponding future moment is not marked;

[0091] Sort the future moments from early to late to generate a moment sorting table; mark the consecutive low-load moments in the moment sorting table as low-load periods, and mark the consecutive medium-load moments in the moment sorting table as medium-load periods; mark the latest low-load moment in each low-load period as the latest moment, and compare each latest moment; mark the low-load period corresponding to the latest latest moment as a transmission period; if there is no low-load period in the moment sorting table, mark the latest medium-load moment in each medium-load moment as the latest moment, and mark the medium-load period corresponding to the latest latest moment as a transmission period.

[0092] Methods for predicting the load power corresponding to the future time include:

[0093] The historical load and real-time load are used as prediction data, and the prediction data is input into the trained load prediction model to predict the load power corresponding to the future moment; the load prediction model is an RNN neural network model, and the training process of the load prediction model includes:

[0094] Pre-continuous acquisition load power and build a load training set. , To predict the load power quantity in the data; based on the load training set, train a load prediction model for predicting the load power at future moments;

[0095] The sliding step length L and the sliding window length E are preset according to the actual experience of technicians in this field; the load power in the load training set is converted into multiple training samples using the sliding window method, and each training sample includes E load powers; each training sample is used as the input of the load prediction model, and the load power after the predicted sliding step length L is used as the output, and the actual last L load powers corresponding to each training sample are used as the prediction target. The prediction result is evaluated for model accuracy using the mean absolute percentage error MAPE. When the calculated MAPE is less than the preset MAPE, the load prediction model training corresponding to the load power is completed; wherein, the calculation formula of the mean absolute percentage error MAPE is: ; In the formula, For the The predicted target corresponding to the predicted load power is For the The predicted load power, The load power is the amount of the predicted load power; a load prediction model is generated to predict the load power at a future moment according to the load power.

[0096] Exemplarily, the load training set A includes 10 load powers. , For the Load power, , define the length of the sliding window as 3, the sliding step L as 1, use the sliding window to construct 7 training samples, each training sample contains 3 consecutive load powers, and the next load power of the 3 consecutive load powers is used as the prediction target; for example: training sample , training samples The corresponding prediction target is ; Training samples , training samples The corresponding prediction target is ; And so on, it is used to train the load prediction model.

[0097] Methods for assessing transmission frequency include:

[0098] Mark the latest moment corresponding to the transmission period as the end moment, and mark all moments from the current moment to the end moment as the analysis period; calculate the average load corresponding to each moment in the analysis period according to the load power corresponding to each moment in the analysis period and the historical load; calculate the transmission frequency corresponding to each moment in the analysis period according to the average load corresponding to each moment in the analysis period; obtain the historical end moment, which is the moment corresponding to the last time the electric energy meter ended data transmission; construct an estimation period according to the end moment and the historical end moment, and the range of the estimation period is ,in For the end of history, is the end moment; the transmission frequencies corresponding to each moment in the estimation period are added up in sequence to obtain the total transmission frequency; the number of moments in the transmission period is counted, the total transmission frequency is divided by the number of moments, and the transmission frequency corresponding to each moment in the transmission period is obtained.

[0099] The data compression module is used to obtain power data, compress the power data, and transmit the compressed power data based on the transmission strategy.

[0100] Electricity data refers to all data collected by the electric energy meter during the estimation period, such as power consumption data (such as active electric energy, reactive electric energy, etc.), power quality data (such as voltage, current, frequency, etc.), and electric energy load data (such as active power, reactive power, peak load, etc.).

[0101] The steps of compressing power data include:

[0102] Step B1: Count the number of each value in the power data and use it as the frequency of occurrence; use each value as a node, and use the frequency of occurrence of each value as the node frequency of the corresponding node; sort all nodes from small to large according to the node frequency, and generate a node sorting table;

[0103] Step B2: According to the positive order of the node sorting table, obtain the two nodes at the front, mark them as child nodes, and delete the child nodes from the node sorting table;

[0104] Step B3: merge the two child nodes into a parent node and add it to the node sorting table. The node frequency of the parent node is the sum of the node frequencies of the two child nodes.

[0105] Step B4: looping steps B2 to B3 until only one node is left in the node sorting table, the loop ends, and the remaining node in the node sorting table is used as the root node; the root node is used as the starting point and extended downward, and two child nodes corresponding to each node are obtained in turn, until the child nodes corresponding to all nodes are obtained, a node tree is formed, and step B5 is entered;

[0106] Step B5: Starting from the root node of the node tree, follow the path of the node tree, and when you encounter a node on the left, record the code , when encountering a node on the right, record the code ,coding With encoding is a binary value; until the path of the node tree ends, the recorded codes are combined according to the code recording order as the compressed code corresponding to the value of the last node;

[0107] Step B6: Loop step B5 until the compression code corresponding to each value in the power data is obtained, and the compressed data is used together with the node tree; wherein the order of each compression code in the compressed data is consistent with the order of each value in the power data.

[0108] For example, the power data includes 1, 2, 3, 3, where the frequency of 1 is 1, the frequency of 2 is 1, and the frequency of 3 is 2; therefore, 1 and 2 are first used as child nodes, the corresponding parent node is obtained, and it is marked as the first parent node, and then the first parent node and 3 are used as child nodes, the corresponding parent node is obtained, and it is marked as the second parent node. Since the second parent node is the only node left in the node sorting table, the second parent node is the root node; the node tree corresponding to the power data is as follows Figure 2 As shown; the compression code of 1 is The compression code of 2 is , 3 is compressed as .

[0109] This embodiment collects and analyzes real-time load and historical load to accurately calculate the average load; based on the average load, a heuristic algorithm is used to dynamically adjust the data frequency; combined with load prediction and data compression technology, the transmission strategy is dynamically adjusted to transmit compressed data, thereby achieving refined management and control of the electric energy meter; it can effectively adapt to real-time load changes, optimize data transmission time, and reduce transmission load, thereby reducing the power consumption of the electric energy meter, improving operating efficiency and service life, meeting the smart grid's needs for sustainable development and high performance, and providing an innovative solution for the balance between low power consumption and high performance of the electric energy meter.

[0110] Example 2

[0111] See also Figure 3 As shown, the part not described in detail in this embodiment is described in Example 1, and a low power consumption management method of an electric energy meter is provided, the method comprising:

[0112] Collect real-time load;

[0113] Get historical load and calculate average load based on real-time load;

[0114] Dynamically adjust data frequency based on average load;

[0115] Dynamically adjust transmission strategies based on historical load and data frequency;

[0116] The power data is acquired, compressed, and transmitted based on a transmission strategy.

[0117] Example 3

[0118] The present application also provides an electronic device. The electronic device may include one or more processors and one or more memories. The memories store computer-readable codes, and when the computer-readable codes are executed by the one or more processors, the low-power consumption management method of an electric energy meter as described above may be executed.

[0119] The method or system according to the implementation mode of the present application can also be implemented with the help of the architecture of the electronic device shown in the present application. The electronic device may include a bus, one or more CPUs, ROM, RAM, a communication port connected to a network, input / output, a hard disk, etc. A storage device in the electronic device, such as a ROM or a hard disk, can store a low-power consumption management method for an electric energy meter provided in the present application. Furthermore, the electronic device may also include a user interface. Of course, the architecture shown in the present application is only exemplary. When implementing different devices, one or more components in the electronic device shown in the present application may be omitted according to actual needs.

[0120] Example 4

[0121] One embodiment of the present application discloses a computer-readable storage medium. Computer-readable instructions are stored on the computer-readable storage medium. When the computer-readable instructions are executed by a processor, a low-power management method for an electric energy meter according to an embodiment of the present application described with reference to the above figures can be executed. The storage medium includes, but is not limited to, for example, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and cache memory (cache), etc. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0122] In addition, according to the implementation of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the present application provides a non-transitory machine-readable storage medium, which stores machine-readable instructions, and the machine-readable instructions can be executed by a processor to execute instructions corresponding to the method steps provided by the present application, for example: a low power consumption management method for an electric energy meter. When the computer program is executed by a central processing unit (CPU), the above functions defined in the method of the present application are executed.

[0123] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

[0124] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A low power consumption management method for an electric energy meter, characterized in that: include: Collect real-time load; Get historical load and calculate average load based on real-time load; Dynamically adjust data frequency based on average load; Dynamically adjust transmission strategies based on historical load and data frequency; The power data is acquired, compressed, and transmitted based on a transmission strategy.

2. The low power consumption management method of an electric energy meter according to claim 1, characterized in that: The real-time load is the load power collected in real time, and the load power is the rate at which the load equipment in the power system consumes electric energy; the real-time load collection method is: obtain the real-time voltage, real-time current and power factor, and calculate the real-time load; the expression of the real-time load is: ; In the formula, For real-time load, is the real-time voltage, is the real-time current, is the power factor; The historical load is the load power collected at historical moments; The method for calculating the average load includes: Set the selection duration and obtain the current time, which is the time corresponding to the real-time load collection; subtract the selection duration from the current time to obtain the selection time; construct the calculation period based on the selection time and the current time, and the range of the calculation period is ,in To select the time, is the current moment; obtain the load power corresponding to the calculation period in the historical load and mark it as the calculation load; count the number of calculation loads and add one, marked as the load number; add each calculation load and real-time load in turn, and then divide it by the load number to obtain the average load.

3. The low power consumption management method of an electric energy meter according to claim 2, characterized in that: The step of setting the selection duration includes: Step A1: Preset the selected duration range , Split ratio and the number of iterations ; Step A2: According to the division ratio , from the selected duration range Split into two new points and , , ; Step A3: According to the points and At the current moment, the corresponding computing load is selected from the historical load; The filtered computing load is taken as the first set, the standard deviation corresponding to the first set is calculated and marked as the first standard deviation; The filtered computing loads are taken as the second set, and the standard deviation corresponding to the second set is calculated and marked as the second standard deviation; Step A4: With the first standard deviation as the first data, and the second standard deviation as the second data; input the first data and the second data into the trained matching analysis model respectively to predict the corresponding matching degree; mark the matching degree corresponding to the first data as the first matching degree, and mark the matching degree corresponding to the second data as the second matching degree; the matching analysis model is a deep learning network model; Step A5: If the first matching degree is greater than or equal to the second matching degree, the minimum value of the updated selected duration range is the minimum value of the selected duration range before the update, and the maximum value is the point corresponding to the second matching degree; if the first matching degree is less than the second matching degree, the minimum value of the updated selected duration range is the point corresponding to the first matching degree, and the maximum value is the maximum value of the selected duration range before the update; Step A6: Preset a width threshold, subtract the minimum value from the maximum value in the updated selected duration range to obtain the interval width; compare the interval width with the width threshold; if the interval width is greater than or equal to the width threshold, divide the updated selected duration range into two groups according to the segmentation ratio. , re-dividing two new points and , and return to step A4; if the interval width is less than the width threshold, the average of the maximum and minimum values ​​in the updated selected duration range is used as the selected duration.

4. The low power consumption management method of an electric energy meter according to claim 3, characterized in that: The data frequency includes sampling frequency, processing frequency and transmission frequency; Methods for dynamically adjusting data frequency include: Defining the search space , Radius Threshold and iteration threshold ; Search space The method for constructing n frequency sets is as follows: obtaining a frequency range, which includes a sampling frequency range, a processing frequency range, and a transmission frequency range; randomly selecting a value from each range in the frequency range to construct a set of frequency sets, and constructing n frequency sets in total, each of which is different; setting numerical labels that increase in sequence for the n frequency sets and marking them as set labels, and the range of the set labels is ; Set the initial aperture center and the initial aperture radius , and set the number of iterations ;in, , ; Determine the excellence function and the cyclic process; The cyclic process is: generate m candidate solutions within the aperture range and reduce the aperture radius , ; Calculate the excellence of each candidate solution, move the aperture center to the candidate solution with the greatest excellence; repeat the cycle, and each time the cycle is repeated, set the number of iterations ; until or When , the repeated cycle process is stopped, and the candidate solution corresponding to the aperture center is obtained and marked as the best solution; according to the frequency set corresponding to the set label corresponding to the best solution, the data frequency is dynamically adjusted.

5. The low power consumption management method of an electric energy meter according to claim 4, characterized in that: The expression of the excellence function is: ; In the formula, For excellence, is the degree of adaptation; the method for obtaining the degree of adaptation is to obtain the data frequency in the corresponding frequency set according to the set label corresponding to the candidate solution and mark it as the candidate frequency; the candidate frequency and the average load are used as evaluation data; the evaluation data is input into the trained adaptation analysis model to predict the corresponding degree of adaptation; the adaptation analysis model is a deep learning network model; The expression of the candidate solution is: ; In the formula, is the i-th candidate solution, is the aperture center corresponding to the tth repetition of the cycle, is the aperture radius corresponding to the tth repetition of the cycle, is the random coefficient, , ; The expression for reducing the aperture radius is: ; In the formula, is the reduced aperture radius, is the shrinkage coefficient, .

6. The low power consumption management method of an electric energy meter according to claim 5, characterized in that: The dynamically adjusting the transmission strategy includes determining the transmission period and evaluating the transmission frequency; The method for determining the transmission period comprises: Based on the historical load, the load power corresponding to the future time is predicted and marked as the future load. The future time includes the time from the current time to the end of the day. The load power in the future load corresponds to the time in the future time. Preset low load segment and medium load segment ; According to the low load segment and medium load segment , judge the power of each load in the future load, if , then the corresponding future moment is marked as a low-load moment. is the load power in the future load; if , then the corresponding future moment is marked as the medium load moment; if , then the corresponding future moment is not marked; Sort the future moments from early to late to generate a moment sorting table; mark the consecutive low-load moments in the moment sorting table as low-load periods, and mark the consecutive medium-load moments in the moment sorting table as medium-load periods; mark the latest low-load moment in each low-load period as the latest moment, and compare each latest moment; mark the low-load period corresponding to the latest latest moment as a transmission period; if there is no low-load period in the moment sorting table, mark the latest medium-load moment in each medium-load moment as the latest moment, and mark the medium-load period corresponding to the latest latest moment as a transmission period.

7. A low power consumption management method for an electric energy meter according to claim 6, characterized in that: The method for predicting the load power corresponding to the future moment includes: The historical load and real-time load are used as prediction data, and the prediction data is input into the trained load prediction model to predict the load power corresponding to the future moment; the load prediction model is an RNN neural network model, and the training process of the load prediction model includes: Pre-continuous acquisition load power and build a load training set. , To predict the load power quantity in the data; based on the load training set, train a load prediction model for predicting the load power at future moments; Preset the sliding step size L and the sliding window length E; convert the load power in the load training set into multiple training samples using the sliding window method, and each training sample includes E load powers; use each training sample as the input of the load prediction model, and predict the load power after the sliding step size L as the output, and use the actual last L load powers corresponding to each training sample as the prediction target. Use the mean absolute percentage error MAPE to evaluate the model accuracy of the prediction result. When the calculated MAPE is less than the preset MAPE, the load prediction model training corresponding to the load power is completed; generate a load prediction model that predicts the load power at future moments based on the load power.

8. The low power consumption management method of an electric energy meter according to claim 7, characterized in that: The method for evaluating transmission frequency comprises: Mark the latest moment corresponding to the transmission period as the end moment, and mark all moments from the current moment to the end moment as the analysis period; calculate the average load corresponding to each moment in the analysis period according to the load power corresponding to each moment in the analysis period and the historical load; calculate the transmission frequency corresponding to each moment in the analysis period according to the average load corresponding to each moment in the analysis period; obtain the historical end moment, which is the moment corresponding to the last time the electric energy meter ended data transmission; construct an estimation period based on the end moment and the historical end moment, and the range of the estimation period is ,in For the end of history, is the end moment; the transmission frequencies corresponding to each moment in the estimation period are added up in sequence to obtain the total transmission frequency; the number of moments in the transmission period is counted, the total transmission frequency is divided by the number of moments, and the transmission frequency corresponding to each moment in the transmission period is obtained.

9. The low power consumption management method of an electric energy meter according to claim 8, characterized in that: The power data refers to all data collected by the electric energy meter during the estimation period; The step of compressing the power data comprises: Step B1: Count the number of each value in the power data and use it as the frequency of occurrence; use each value as a node, and use the frequency of occurrence of each value as the node frequency of the corresponding node; sort all nodes from small to large according to the node frequency, and generate a node sorting table; Step B2: According to the positive order of the node sorting table, obtain the two nodes at the front, mark them as child nodes, and delete the child nodes from the node sorting table; Step B3: merge the two child nodes into a parent node and add it to the node sorting table. The node frequency of the parent node is the sum of the node frequencies of the two child nodes. Step B4: looping steps B2 to B3 until only one node is left in the node sorting table, the loop ends, and the remaining node in the node sorting table is used as the root node; the root node is used as the starting point and extended downward, and two child nodes corresponding to each node are obtained in turn, until the child nodes corresponding to all nodes are obtained, a node tree is formed, and step B5 is entered; Step B5: Starting from the root node of the node tree, follow the path of the node tree, and when you encounter a node on the left, record the code , when encountering a node on the right, record the code ,coding With encoding is a binary value; until the path of the node tree ends, the recorded codes are combined according to the code recording order as the compressed code corresponding to the value of the last node; Step B6: Loop step B5 until the compression code corresponding to each value in the power data is obtained, and the compressed data is used together with the node tree; wherein the order of each compression code in the compressed data is consistent with the order of each value in the power data.

10. A low power consumption management system for an electric energy meter, implementing a low power consumption management method for an electric energy meter as claimed in any one of claims 1 to 9, characterized in that: include: Load collection module, used to collect real-time load; The load calculation module is used to obtain historical loads and calculate the average load in combination with the real-time load; Frequency adjustment module, dynamically adjusts data frequency based on average load; The policy adjustment module dynamically adjusts the transmission policy based on historical load and data frequency; The data compression module is used to obtain power data, compress the power data, and transmit the compressed power data based on the transmission strategy.

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