Electricity Bill Abnormality Error Handling Method and System Based on Intelligent Verification

Abstract

The present invention discloses a method and system for handling electricity bill anomalies and errors based on intelligent verification, which relates to the technical field of electricity billing and includes: obtaining the first electricity bill anomaly data, performing anomaly detection on electricity bill calculation based on the neural network algorithm, where the first electricity bill anomaly data includes electricity price characteristics, power factor adjustment standard characteristics, and fixed ratio characteristics; when the electricity bill calculation is abnormal, correcting the electricity quantity based on power flow calculation and energy balance algorithm, retrieving the user's historical electricity price information and current electricity price information for consistency verification, judging and updating the user's electricity price, and recalculating the electricity bill; based on visualization technology, the calculation result of electricity bill correction is updated and displayed with three-color marking in real time, obtaining the contribution degree of input parameters to the electricity bill based on the local sensitivity method, and sorting the input parameters according to the contribution degree; through the precise correction of key parameters for electricity bill calculation, the present invention effectively improves the efficiency and accuracy of handling electricity bill errors, reduces the work pressure of grass-roots units, and improves customer satisfaction.

Description

Technical Field

[0001] The present invention relates to the technical field of electricity billing, and more specifically, to a method and system for processing electricity bill anomalies and errors based on intelligent verification. Background Art

[0002] With the development of social economy and the progress of technology, the power system has become an indispensable part of the modern social infrastructure. The relationship between power consumption and production is becoming increasingly complex, and power companies conduct precise electricity consumption monitoring and cost settlement through smart meters and automated metering systems. However, with the replacement of power equipment and the continuous development of smart meter technology, there are still certain errors and anomalies in electricity bill metering, which affect the fairness and transparency between power suppliers and users.

[0003] For example, a method, device, equipment, and storage medium for detecting basic electricity bill anomalies disclosed in the invention patent announcement with the publication number: CN117786568A, includes: retrieving electricity consumption data of at least one target user that meets preset conditions from the marketing system database; processing the electricity consumption data according to preset anomaly detection conditions to determine whether there are anomalies in the electricity consumption data; if so, an anomaly prompt is given. The present invention effectively reduces the error rate of basic electricity bill review and improves the efficiency and accuracy of basic electricity bill verification.

[0004] For example, a method for determining the type of electricity meter anomaly, its device, electronic equipment, and storage medium disclosed in the invention patent announcement with the publication number: CN118940199B, includes: determining the electricity meter to be monitored based on the meter replacement service work order, and obtaining the attribute data of the electricity meter to be monitored; extracting the metering data of the electricity meter to be monitored from the metering database, and preprocessing the metering data to obtain the preprocessed metering data; performing anomaly determination on the preprocessed metering data based on a pre-constructed anomaly determination rule to obtain the anomaly determination result of the electricity meter to be monitored; in the case where the electricity meter to be monitored has an anomaly, generating an anomaly alarm message based on the anomaly determination result and sending the anomaly alarm message to the operation and maintenance side. The present invention solves the technical problem in the related art that when locating an abnormal electricity meter through the electricity bill error of the electricity meter and manually checking the abnormal electricity meter to determine the type of anomaly to conduct a check on the electricity meter anomaly, the checking efficiency is relatively low.

[0005] In the above disclosed technical solutions, there are at least the following technical problems:

[0006] In the power industry, errors in the parameters of power user files may lead to inaccurate electricity bill calculations, resulting in electricity bill losses for power grid companies and users, and even causing customer complaints. Existing methods for correcting electricity bill calculations often rely on manual review and manual correction, which are not only cumbersome but also prone to omissions or errors. Moreover, user satisfaction is not high. In order to improve the efficiency and accuracy of correcting electricity bill errors, there is an urgent need for an intelligent and efficient processing method.

[0007] In response to the above problems, the present invention proposes a solution. Summary of the Invention

[0008] To overcome the above-mentioned defects of the prior art, embodiments of the present invention provide a method and system for processing electricity bill anomalies and errors based on intelligent verification, which realizes the precise correction of key parameters in electricity bill calculations, reproduces the entire process of past electricity bill calculations, and outputs the correct electricity bill calculation process formula. It effectively improves the efficiency and accuracy of electricity bill error processing, reduces the work pressure of grass-roots units, and at the same time improves customer satisfaction, having significant economic and social benefits.

[0009] To achieve the above object, the present invention provides the following technical solutions:

[0010] A method and system for processing electricity bill anomalies and errors based on intelligent verification, including the following steps: obtaining the first electricity bill anomaly data, performing anomaly detection on electricity bill calculations based on a neural network algorithm, where the first electricity bill anomaly data includes electricity price characteristics, power factor adjustment standard characteristics, and fixed ratio characteristics; when the electricity bill calculation is abnormal, correcting the electricity quantity based on a power flow calculation method and an energy balance algorithm; performing consistency verification by retrieving the user's historical electricity price information and the current electricity price information, judging and updating the user's electricity price, and recalculating the electricity bill according to the corrected electricity quantity and electricity price; based on visualization technology, performing real-time update of three-color marking display on the electricity bill correction calculation result, obtaining the input parameters of the electricity bill, obtaining the contribution degree of the input parameters to the electricity bill based on the local sensitivity method, and sorting the input parameters according to the contribution degree for importance.

[0011] In a preferred embodiment, the obtaining of the first electricity charge anomaly data and the abnormal detection of the electricity charge calculation based on the neural network algorithm are specifically as follows: Obtain historical electricity charge anomaly data, which includes historical electricity price characteristics, historical power factor adjustment standard characteristics, and historical fixed ratio characteristics; Calculate the average value of all data in the historical electricity charge anomaly data; Based on the clustering algorithm, set the average value of all data as the initial clustering center respectively, and according to the cosine similarity between the historical electricity charge anomaly data and the initial clustering center, assign each data point to the initial clustering center to form a cluster; Recalculate the clustering center of each cluster and continue the assignment until the iteration ends; Based on the neural network algorithm, construct an abnormal detection model with the processed data; Input the first electricity charge anomaly data into the abnormal detection model, obtain the outputs of several abnormal detection models and calculate the standard deviation based on statistical methods; If the standard deviation exceeds the preset discrete degree value, there is an electricity charge anomaly situation.

[0012] In a preferred embodiment, when the electricity charge calculation is abnormal, the electricity quantity is corrected based on the power flow calculation method and the energy balance algorithm, specifically as follows: Obtain the corrected indication and the electricity meter multiplier through actual detection and measurement of the electricity meter; Obtain the topological structure of the power grid, and analyze the topological structure of the power grid based on the power flow calculation method to obtain the power data of each node of the power grid, where the power data includes current, input electricity quantity, output electricity quantity, and power flow direction; Based on the current, obtain the line loss of the transmission line according to the power loss formula, and quantify the line loss to obtain the corrected line electricity quantity; Obtain the input electricity quantity and the output electricity quantity, and compare them. If there is a deviation between the input electricity quantity and the output electricity quantity, combine the electricity quantity difference and the line loss electricity quantity, and correct the supplementary electricity quantity based on the electricity quantity balance equation; Based on the corrected indication, electricity meter multiplier, line electricity quantity, and supplementary electricity quantity, recalculate the actual electricity quantity of the user based on the billing electricity quantity calculation model.

[0013] In a preferred embodiment, the combination of the electricity quantity difference and the line loss electricity quantity to correct the supplementary electricity quantity based on the electricity quantity balance equation is specifically as follows: Define the supplementary electricity quantity objective function by minimizing the difference between the actual measured electricity quantity and the standard electricity quantity, and based on the law of conservation of energy, construct the electricity quantity balance equation by combining the input electricity quantity, output electricity quantity, and line loss electricity quantity; Solve the supplementary electricity quantity objective function based on the least squares method, and continuously update the supplementary electricity quantity by calculating the difference between the actual measured electricity quantity and the corrected supplementary electricity quantity until the objective function reaches the minimum value, stop the iteration, and the optimal solution obtained is the corrected supplementary electricity quantity.

[0014] In a preferred embodiment, the consistency verification is performed by retrieving the user's historical electricity price information and the current electricity price information, the user's electricity price is judged and updated, and the electricity bill is recalculated according to the corrected electricity quantity and electricity price. Specifically: obtain the user's electricity price information, where the user's electricity price information includes electricity consumption category, user classification, voltage level, and time-of-use electricity price information; retrieve the user's historical electricity price information, perform consistency verification with the current user's electricity price information, and judge whether the user's electricity price information has been changed; if the verification result is inconsistent, update the changed electricity price information of the user, and correct the changed electricity price of the user according to the current electricity price standard; calculate the kilowatt-hour electricity bill, basic electricity bill, power factor adjustment electricity bill, and other electricity bills respectively according to the corrected electricity price and electricity quantity, and regenerate the electricity bill calculation formula.

[0015] In a preferred embodiment, the real-time update of the three-color marking display of the electricity bill correction calculation result is based on visualization technology. Specifically: guide the user to use and clearly display the calculation result through red, yellow, and blue colors; the blue marking indicates the changeable parameters, which are used as the input values of the electricity bill calculation formula; the red marking indicates the corrected parameters, which are displayed side by side with the original data for the user to compare and view conveniently; the yellow color represents the output result value, which is displayed side by side with the original data to accurately display the change of the associated result; according to the electricity bill composition structure, the electricity bill composition structure includes kilowatt-hour electricity bill, basic electricity bill, power factor adjustment electricity bill, and other electricity bills, and the calculation process after correcting the parameters is listed in detail by category.

[0016] In a preferred embodiment, the input parameters of the electricity bill are obtained, the contribution degree of the input parameters to the electricity bill is obtained based on the local sensitivity method, and the input parameters are sorted according to the contribution degree. Specifically: obtain the input parameters that affect the electricity bill, where the input parameters include electricity price, electricity quantity measurement, basic electricity bill, and power factor adjustment electricity bill; perform local sensitivity analysis on all the parameters in the input parameters, and calculate the between-group variance and total variance of all the parameters; compare and analyze the between-group variance and total variance, and according to the comparison result, obtain the variance contribution degree of all the parameters in the input parameters; sort the input parameters of the electricity bill according to the variance contribution degree of all the parameters from large to small.

[0017] In a preferred embodiment, the electricity price feature is obtained in the following specific method: based on time series analysis, set the time window of the electricity price data; for each time point, take the data within the time window before and after this time point, and calculate the arithmetic mean of these data as the smoothed value of the current point; obtain the actual electricity price, and calculate the deviation value between the actual electricity price and the smoothed value; take the standard deviation of the smoothed value as the reference value, and if the deviation value exceeds three times the standard deviation of the smoothed value, then quantify this deviation value as the electricity price feature.

[0018] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0019] 1. Using neural networks and clustering algorithms for anomaly detection can accurately identify the root causes of abnormal electricity charges. By cleaning and processing historical data and analyzing real-time electricity charge data, abnormal situations in electricity charge fluctuations can be detected in a timely manner, reducing unnecessary expenses and optimizing electricity usage strategies.

[0020] 2. By comprehensively applying intelligent computing models, data verification, energy balance algorithms, and least squares optimization methods, the anomaly detection results in electricity charge calculation are accurately corrected. Its advantages include: ensuring the accuracy of electricity quantity data and avoiding electricity charge errors caused by measurement deviations; realizing electricity charge calculation consistent with users' actual electricity consumption behaviors by correcting parameters such as meter multipliers, voltages, and currents; optimizing the supplementary electricity quantity using the least squares method to ensure the stability of power grid operation and the energy balance of the power system; at the same time, ensuring the accurate application of electricity price standards, avoiding inconsistencies caused by changes in electricity price policies or user classifications, and improving the accuracy and fairness of electricity charge settlement.

[0021] 3. Combining visualization technology and multivariable sensitivity analysis, the corrected electricity charge calculation results are updated and displayed in real time, making the electricity charge calculation process more transparent and intuitive, facilitating quick verification by business personnel and understanding by customers. By using three-color marking (red, yellow, blue) to clearly mark input parameters, corrected data, and output results, it helps users quickly identify changes and optimize decisions. At the same time, based on sensitivity analysis, the importance of each input parameter is ranked, helping users prioritize adjusting the parameters that have the greatest impact on electricity charges, thereby improving decision-making efficiency, reducing unnecessary adjustments, and ultimately achieving the accuracy and optimization of electricity charge calculation. Description of the Drawings

[0022] Figure 1 It is a schematic flowchart of the method for processing electricity charge anomalies and errors based on intelligent verification provided by an embodiment of the present application.

[0023] Figure 2 It is a schematic structural diagram of the system for processing electricity charge anomalies and errors based on intelligent verification provided by an embodiment of the present application. Detailed Embodiments

[0024] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0025] Embodiment 1, Figure 1 It is a schematic flowchart of the method for processing electricity charge anomalies and errors based on intelligent verification provided by an embodiment of the present application, including the following steps:

[0026] S1. Obtain the first electricity charge anomaly data, and perform anomaly detection on the electricity charge calculation based on the neural network algorithm. Specifically:

[0027] Obtain the first electricity charge anomaly data and historical electricity charge anomaly data. The first electricity charge anomaly data includes electricity price characteristics, power factor adjustment standard characteristics, and fixed ratio characteristics. The historical electricity charge anomaly data includes historical electricity price characteristics, historical power factor adjustment standard characteristics, and historical fixed ratio characteristics;

[0028] Perform data cleaning and data processing on the historical electricity charge anomaly data. Specifically:

[0029] Calculate the average value of all data in the historical electricity charge anomaly data respectively;

[0030] Based on the clustering algorithm, set the average value of all data as the initial clustering center respectively. According to the cosine similarity between the historical electricity charge anomaly data and the initial clustering center, assign each data point to the initial clustering center to form a cluster;

[0031] Recalculate the clustering center of each cluster and continue the assignment until the iteration ends;

[0032] Divide the processed data into a training set and a test set. Based on the neural network algorithm, continuously train the model through the training set and optimize it with the test set to build an anomaly detection model;

[0033] Input the first electricity charge anomaly data into the anomaly detection model to obtain the output of the anomaly detection model;

[0034] Calculate the standard deviation of the outputs of several anomaly detection models based on statistical methods;

[0035] If the standard deviation exceeds the preset dispersion value, there is an electricity charge anomaly.

[0036] The electricity price characteristic refers to the electricity price anomaly, which leads to inaccurate electricity charge calculation. Analyzing the electricity charge anomaly by obtaining the electricity price characteristic has the following advantages:

[0037] Discover problems in a timely manner: The electricity price characteristic can quickly reveal the abnormal fluctuations of the electricity charge, helping users or enterprises discover the reasons for the electricity charge anomaly in a timely manner and avoid unnecessary expenses.

[0038] Accurately identify the reasons: By comparing the data of normal electricity prices and abnormal electricity prices, the root causes of price fluctuations can be found, such as changes in the supply and demand of the electricity market, adjustments in the pricing strategies of power companies, or equipment failures.

[0039] Optimize the electricity consumption strategy: By understanding the fluctuation law of the electricity price, users can reasonably plan the electricity consumption time and load, avoid using electricity during peak electricity price periods, thereby saving electricity charges, and even conducting demand response management.

[0040] The method for obtaining the electricity price characteristics is as follows:

[0041] Based on time series analysis, set the time window for electricity price data;

[0042] For each time point, take the data within the time window before and after this time point, and calculate the arithmetic mean of these data as the smoothed value of the current point;

[0043] Obtain the actual electricity price, and calculate the deviation value between the actual electricity price and the smoothed value;

[0044] Take the standard deviation of the smoothed value as the reference value. If the deviation value exceeds three times the standard deviation of the smoothed value, then quantify this deviation value as the electricity price characteristic.

[0045] The specific calculation formula for the smoothed value is as follows:

[0046]

[0047] The specific calculation formula for the electricity price characteristic is as follows:

[0048]

[0049] In the formula, is the smoothed value at time point , is the window size, is the th electricity price at the time point, where = 1, 2, 3,..., R, and R is an integer, is the electricity price characteristic, is the actual electricity price at time point .

[0050] The power adjustment standard characteristic refers to the error generated during the power adjustment (load adjustment) process. The power adjustment standard characteristic generally compares the error between the actual load and the theoretical load. Analyzing the electricity bill anomalies by obtaining the power adjustment standard characteristic has the following advantages:

[0051] Accurately identify the abnormal source: The power adjustment standard characteristic can help detect irregular fluctuations or abnormal loads in power use, and thus help locate the root cause of electricity bill anomalies. This can reduce the wrong analysis caused by interference from other factors.

[0052] Early warning: By real-time monitoring of the power adjustment standard characteristic, early warning can be carried out before problems occur, helping enterprises or households to timely discover possible equipment failures or improper power use behaviors in power use, and avoid excessive electricity bill expenditure.

[0053] Help formulate energy-saving measures: The characteristics of the power adjustment standard can provide data support for the changes in power load, so as to analyze which equipment or areas have unreasonable energy consumption, which helps to further optimize the power consumption structure and formulate energy-saving and emission-reduction plans.

[0054] Improve the accuracy of electricity bills: Through in-depth analysis of the characteristics of the power adjustment standard, the trend and pattern of power consumption can be better understood, ensuring the accuracy of electricity bill calculation and avoiding abnormal electricity bills caused by meter failures or measurement errors.

[0055] The specific method for obtaining the characteristics of the power adjustment standard is as follows:

[0056] Obtain historical load data and initial load data at the current node, and calculate the average value of the historical load data based on statistical methods;

[0057] Obtain the actual load data after power adjustment, and conduct data analysis on the actual load data and the average value of the historical load data to obtain the characteristics of the power adjustment standard.

[0058] The specific calculation formula for the characteristics of the power adjustment standard is as follows:

[0059]

[0060] In the formula, is the characteristic of the power adjustment standard, is the actual load data, is the initial load data.

[0061] The fixed ratio characteristic refers to the abnormal ratio of the change in electricity consumption compared with historical data within a specific time range. This kind of abnormality usually reflects the abnormal fluctuation of the electricity bill, which may be caused by equipment failures, changes in usage habits or electricity theft.

[0062] The specific method for obtaining the fixed ratio characteristic is as follows:

[0063] Obtain the abnormal electricity quantity within the time to be evaluated, and calculate the fixed ratio characteristic by calculating the abnormal electricity quantity within the time to be evaluated and the preset safe electricity quantity threshold. The preset safe electricity quantity threshold is the normal electricity consumption threshold quantified by statistically calculating and calculating the average value of a large amount of historical electricity data of users.

[0064] The specific calculation formula for the fixed ratio characteristic is as follows:

[0065]

[0066] In the formula, is the fixed ratio characteristic, is the actual electricity quantity at the current node, is the historical electricity quantity.

[0067] The abnormal detection model has the following specific calculation formula:

[0068]

[0069] In the formula, is the abnormal detection value, is the electricity price feature, is the power factor adjustment standard feature, is the fixed ratio feature, , , are the weight coefficients.

[0070] The standard deviation of the outputs of several abnormal detection models is calculated based on statistical methods, and the specific calculation formula is as follows:

[0071]

[0072] In the formula, is the standard deviation of the outputs of the abnormal detection models, is the th abnormal detection value, is the th average value of the abnormal detection values, where = 1, 2, 3,..., R, R is an integer, and N is the total number of abnormal detection values.

[0073] It should be noted that the abnormal detection model can be understood as taking the abnormal impacts of electricity price features, power factor adjustment standard features, and fixed ratio features as input items, and based on the neural network algorithm, outputting the abnormal degree detection value of the electricity bill calculation.

[0074] S2. When the electricity bill calculation is abnormal, the electricity quantity is corrected based on the power flow calculation method and the energy balance algorithm, specifically as follows:

[0075] The corrected indication and the electricity meter multiplier are obtained through actual detection and measurement of the electricity meter;

[0076] The topological structure of the power grid is obtained, and based on the power flow calculation method, the power grid topological structure is analyzed to obtain the power data of each node of the power grid. The power data includes current, input electricity quantity, output electricity quantity, and power flow direction;

[0077] The line loss of the transmission line is obtained according to the current based on the power loss formula, and the line loss is quantified to obtain the corrected line electricity quantity;

[0078] The input electricity quantity and the output electricity quantity are obtained and compared. If there is a deviation between the input electricity quantity and the output electricity quantity, the supplementary electricity quantity is corrected based on the electricity quantity balance equation by combining the electricity quantity difference and the line loss electricity quantity;

[0079] Recalculate the actual electricity consumption of the user based on the corrected reading, electricity meter multiplier, line electricity consumption, and refund and supplement electricity consumption using the electricity consumption billing calculation model.

[0080] Based on the electricity quantity difference and line loss electricity quantity, correct the refund and supplement electricity quantity according to the electricity quantity balance equation, specifically as follows:

[0081] Define the refund and supplement electricity quantity objective function by minimizing the difference between the actually measured electricity quantity and the standard electricity quantity, and construct the electricity quantity balance equation based on the law of conservation of energy, combining the input electricity quantity, output electricity quantity, and line loss electricity quantity;

[0082] Solve the refund and supplement electricity quantity objective function based on the least squares method. By calculating the difference between the actually measured electricity quantity and the corrected refund and supplement electricity quantity, continuously update the refund and supplement electricity quantity until the objective function reaches the minimum value, stop the iteration, and the optimal solution obtained is the corrected refund and supplement electricity quantity.

[0083] The refund and supplement electricity quantity objective function has the following specific calculation formula:

[0084]

[0085] The electricity quantity balance equation has the following specific calculation formula:

[0086]

[0087] The electricity consumption billing calculation model has the following specific calculation formula:

[0088]

[0089] In the formula, is the corrected refund and supplement electricity quantity, is the actual electricity quantity at the th measurement point, is the standard electricity quantity, is the number of measurement points, where = 1, 2, 3,..., R, and R is an integer, is the input electricity quantity, is the output electricity quantity, is the line loss electricity quantity, is the actual electricity quantity, is the corrected reading, is the corrected electricity meter multiplier, is the corrected line electricity consumption, is the corrected refund and supplement electricity quantity.

[0090] It should be noted that the power flow calculation method is a very important part of power system analysis. It calculates parameters such as current, voltage, and power in the power grid through a mathematical model to analyze the operating state of the power system, and then helps us estimate the power losses of each node and transmission line. The power balance equation The goal of this equation is to ensure that the power input and output of the power grid are consistent while considering losses. If there is a difference between the measured power and the theoretical power, the supplementary power can be adjusted to correct it. The least squares method is a commonly used optimization method for minimizing the difference between actual measurement data and predicted data. It can be used to adjust the supplementary power data to make it more consistent with the energy balance relationship of the power grid.

[0091] S3. By retrieving the user's historical electricity price information and current electricity price information for consistency verification, judging and updating the user's electricity price, and recalculating the electricity bill according to the corrected electricity quantity and electricity price, specifically:

[0092] Obtain the user's electricity price information, which includes electricity consumption category, user classification, voltage level, and time-of-use electricity price information;

[0093] Retrieve the user's historical electricity price information and conduct consistency verification with the current user's electricity price information to judge whether the user's electricity price information has been changed;

[0094] If the verification result is inconsistent, update the user's changed electricity price information and correct the user's changed electricity price according to the current electricity price standard;

[0095] According to the corrected electricity price and electricity quantity, calculate the kilowatt-hour electricity bill, basic electricity bill, power factor adjustment electricity bill, and other electricity bills respectively, and regenerate the electricity bill calculation formula.

[0096] The electricity bill calculation formula, the specific calculation formula is as follows:

[0097]

[0098] In the formula, is the corrected electricity bill, is the actual electricity quantity, is the corrected electricity price, is the maximum demand, is the power factor adjustment coefficient, is the other electricity bill.

[0099] It should be noted that, is the kilowatt-hour electricity bill, is the basic electricity bill, For the power adjustment electricity charge, the type of electricity consumption determines the demand characteristics of electricity users. For example, residential electricity consumption, commercial electricity consumption, or industrial electricity consumption, etc. The electricity prices for different types of users may vary. For example, industrial users may enjoy preferential electricity prices, while residential users may adopt tiered electricity prices. The type of electricity consumption is an important basis for electricity price reform. User classification: Based on the different natures of users, the electricity price standards for different types of users by power companies are also different. Large customers usually customize electricity prices according to contracts, while ordinary users are charged according to standard electricity prices. Voltage level: The voltage level represents the scale and demand of users' electricity consumption. Generally speaking, users with larger electricity consumption will use higher voltage levels (such as high-voltage electricity). Users with different voltage levels will apply different electricity prices. For example, the use of high-voltage electricity is often accompanied by a lower unit electricity price because power companies can reduce substation and transmission costs. Time-of-use electricity price information charges according to different time periods (such as peak hours, normal hours, valley hours) to balance the grid load and encourage users to use electricity during off-peak hours. Whether a user implements time-of-use electricity price will affect the calculation method of its electricity price. Users' electricity consumption behaviors and electricity demands may change over time. For example, a certain user may originally be a residential user but later become a commercial user, and the electricity demand and electricity price will change. Therefore, by retrieving and verifying historical electricity price information, it can be ensured that the user's electricity price matches its current electricity consumption situation.

[0100] Among them, in order to ensure the accuracy of electricity price correction, it is also necessary to verify the corrected electricity price: Check the user's electricity charge against historical records: Compare the calculation result of the corrected electricity price with the user's historical electricity bill to ensure consistency. If there are significant differences, further check the process of electricity price correction. User feedback and confirmation: Under permitted conditions, a new electricity price plan can be provided to the user, solicit their feedback and conduct confirmation. If the user has objections, adjust in a timely manner.

[0101] S4, based on visualization technology, real-time updates the three-color annotation display of the electricity charge correction calculation result, obtains the input parameters of the electricity charge, gets the contribution degree of the input parameters to the electricity charge based on the local sensitivity method, and sorts the input parameters according to the contribution degree for importance.

[0102] Adopting three-color annotation and detailed calculation process display makes the electricity charge calculation result and parameter changes clear at a glance, facilitating business personnel to verify and check and customers to understand. By analyzing the changes of multiple input parameters, it is identified which parameters have the greatest impact on the electricity charge calculation and which parameter changes have a smaller amplitude on the electricity charge result change. This can help users optimize decisions, prioritize adjusting the parameters with greater impact on the electricity charge, and reduce unnecessary adjustments.

[0103] Based on visualization technology, real-time updates the three-color annotation display of the electricity charge correction calculation result, specifically as follows:

[0104] Guide users to use through red, yellow, and blue colors and clearly display the calculation results;

[0105] The blue marking indicates the changeable parameters, which are used as the input values for the electricity charge calculation formula;

[0106] The red marking indicates the corrected parameters, which are displayed side by side with the original data for easy comparison and viewing by users;

[0107] The yellow color represents the output result value, which is displayed side by side with the original data to accurately display the change of the associated result;

[0108] Meanwhile, according to the electricity charge composition structure, which includes the kilowatt-hour charge, the basic charge, the power factor adjustment charge, and other charges, the calculation process after correcting the parameters is listed in detail by category, and it supports verification and checking by business personnel.

[0109] It should be noted that users can clearly see the electricity charge calculation process, the comparison of the corrected data, and the final calculation result in an intuitive and easy-to-operate interface. At the same time, they can also efficiently modify and verify the parameters. The color markings (red, yellow, blue) provide a clear visual hierarchy, which helps users quickly locate and understand each part of the content. The original data can be understood as the original parameter data without correction.

[0110] Obtain the input parameters for the electricity charge, and based on the local sensitivity method, obtain the contribution degree of the input parameters to the electricity charge, and sort the input parameters according to the contribution degree. Specifically:

[0111] Obtain the input parameters that affect the electricity charge, and the input parameters include the electricity price, the electricity consumption, the basic charge, and the power factor adjustment charge;

[0112] For all parameters in the input parameters, based on the local sensitivity analysis, calculate the between-group variance and the total variance of all parameters;

[0113] Compare and analyze the between-group variance and the total variance, and according to the comparison result, obtain the variance contribution degree of all parameters in the input parameters;

[0114] Sort the input parameters of the electricity charge according to the variance contribution degree of all parameters from large to small;

[0115] The greater the variance contribution degree, the greater the importance of the change of this parameter to the electricity charge.

[0116] It should be noted that the local sensitivity analysis can be understood as analyzing the impact of the change of each input parameter within a certain specific range on the electricity charge. That is, only one input parameter is changed each time, and its impact on the electricity charge output is analyzed.

[0117] Embodiment 2 Figure 2Schematic diagram of the electricity bill exception error handling system based on intelligent verification provided by the embodiments of the present application, including an electricity bill exception detection module, an electricity quantity calculation and correction module, a electricity price and electricity bill calculation and correction module, and a visualization and importance analysis module, with connections between the modules:

[0118] The electricity bill exception detection module is used to obtain the first electricity bill exception data and perform exception detection on the electricity bill calculation based on the neural network algorithm. The first electricity bill exception data includes electricity price characteristics, power factor adjustment standard characteristics, and fixed ratio characteristics;

[0119] The electricity quantity calculation and correction module is used to correct the electricity quantity based on the power flow calculation method and the energy balance algorithm when the electricity bill calculation is abnormal;

[0120] The electricity price and electricity bill calculation and correction module is used to perform consistency verification by retrieving the user's historical electricity price information and the current electricity price information, judge and update the user's electricity price, and recalculate the electricity bill according to the corrected electricity quantity and electricity price;

[0121] The visualization and importance analysis module is used to perform real-time update of three-color annotation display on the electricity bill correction calculation result based on visualization technology, obtain the input parameters of the electricity bill, obtain the contribution degree of the input parameters to the electricity bill based on the local sensitivity method, and perform importance ranking on the input parameters according to the contribution degree.

[0122] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to obtain a formula that is closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0123] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product.

[0124] Those of ordinary skill in the art can realize that the modules and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0125] In addition, in each embodiment of the present application, the functional modules can be integrated into one processing module, or each module can exist physically alone, or two or more modules can be integrated into one module.

[0126] As described above, it is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the said claims.

[0127] Finally: The above is only the preferred embodiment of the present invention and is not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. The method for handling abnormal electricity charges based on intelligent verification is characterized in that: The steps include: Acquire first data of abnormal electricity charges, and perform abnormal detection on electricity charge calculation based on a neural network algorithm, wherein the first data of abnormal electricity charges includes electricity price characteristics, power regulation standard characteristics, and fixed ratio characteristics; The force adjustment standard characteristic refers to the error produced by the force adjustment process; The fixed ratio feature refers to the abnormal ratio of the change in power consumption compared with historical data within a specific time range; When the electricity fee calculation is abnormal, the electricity quantity is corrected based on the power flow calculation method and energy balance algorithm; By retrieving the user's historical electricity price information and performing consistency verification with the current electricity price information, the user's electricity price is determined and updated, and the electricity fee is recalculated based on the revised electricity volume and electricity price; Based on visualization technology, the three-color annotation display of the electricity charge correction calculation results is updated in real time, and the input parameters of the electricity charge are obtained. Based on the local sensitivity method, the contribution of the input parameters to the electricity charge is obtained, and the input parameters are ranked in importance according to the contribution.

2. The method for handling abnormal electricity charges based on intelligent verification according to claim 1, characterized in that: The obtaining of the first data of abnormal electricity charges and performing abnormality detection on the electricity charge calculation based on the neural network algorithm is specifically as follows: Acquire historical electricity fee anomaly data, wherein the historical electricity fee anomaly data includes historical electricity price characteristics, historical power regulation standard characteristics, and historical fixed ratio characteristics; All data in the historical electricity fee abnormal data are averaged separately; Based on the clustering algorithm, the average of all data is set as the initial cluster center. According to the cosine similarity between the historical electricity price anomaly data and the initial cluster center, each data point is assigned to the initial cluster center to form a cluster. Recalculate the cluster center of each cluster and continue to assign until the iteration ends; The processed data is used to build an anomaly detection model based on a neural network algorithm; Inputting the first data of abnormal electricity charges into an abnormality detection model, obtaining outputs of several abnormality detection models and calculating standard deviations based on statistical methods; If the standard deviation exceeds the preset dispersion value, there is an abnormal electricity charge.

3. The method for handling abnormal electricity charges based on intelligent verification according to claim 1, characterized in that: When the electricity fee calculation is abnormal, the electricity quantity is corrected based on the power flow calculation method and the energy balance algorithm, specifically: By actually testing and measuring the electric meter, the corrected reading and meter multiplication factor are obtained; Obtain the topological structure of the power grid, and analyze the topological structure of the power grid based on the power flow calculation method to obtain power data of each node in the power grid, wherein the power data includes current, input power, output power and power flow direction; The line loss of the transmission line is obtained according to the power loss formula based on the current, and the line loss is quantified to obtain the corrected line power; Obtain the input power and output power and compare them. If there is a deviation between the input power and the output power, the power withdrawal and subsidy will be corrected based on the power balance equation in combination with the power difference and line loss. The corrected indication, meter multiple, line power and refunded power are used to recalculate the user's actual power consumption based on the billing power calculation model.

4. The method for handling abnormal electricity charges based on intelligent verification according to claim 3 is characterized in that: The combined power difference and line loss power are used to correct the refund power based on the power balance equation, specifically: The objective function of the electricity withdrawal is defined by minimizing the difference between the actual measured electricity and the standard electricity, and the electricity balance equation is constructed based on the law of conservation of energy by combining the input electricity, output electricity and line loss electricity. The objective function of the power withdrawal and subsidy is solved based on the least squares method. The power withdrawal and subsidy amount is continuously updated by calculating the difference between the actual measured power and the corrected power withdrawal and subsidy amount until the objective function reaches the minimum value. The iteration is stopped and the optimal solution is the corrected power withdrawal and subsidy amount.

5. The method for handling abnormal electricity charges based on intelligent verification according to claim 1, characterized in that: The method of retrieving the user's historical electricity price information and performing consistency verification with the current electricity price information, determining and updating the user's electricity price, and recalculating the electricity fee based on the revised electricity quantity and electricity price is as follows: Obtaining user electricity price information, the user electricity price information including electricity consumption category, user classification, voltage level and time-of-use electricity price information; Retrieve the user's historical electricity price information, perform consistency verification with the current user electricity price information, and determine whether the user's electricity price information needs to be changed; If the verification results are inconsistent, the electricity price information selected by the user will be updated, and the electricity price selected by the user will be corrected according to the current electricity price standard; According to the revised electricity price and electricity consumption, the electricity fee, basic electricity fee, power regulation electricity fee and other electricity fees are calculated respectively, and the electricity fee calculation formula is regenerated.

6. The method for handling abnormal electricity charges based on intelligent verification according to claim 1, characterized in that: The visualization technology is used to update the three-color annotation display of the electricity fee correction calculation results in real time, specifically: Use red, yellow, and blue to guide users and clearly display calculation results; The parameters marked in blue can be changed and serve as input values ​​for the electricity fee calculation formula; Corrected parameters are marked in red and displayed side by side with the original data for easy comparison by users; Yellow represents the output result value, which is displayed side by side with the original data to accurately show the changes in the associated results; According to the electricity fee composition structure, the electricity fee composition structure includes electricity fee, basic electricity fee, power regulation electricity fee, and other electricity fees, and the calculation process after the correction parameters are listed in detail.

7. The method for handling abnormal electricity charges based on intelligent verification according to claim 1, characterized in that: The input parameters of the electricity charge are obtained, and the contribution of the input parameters to the electricity charge is obtained based on the local sensitivity method. The input parameters are ranked in importance according to the contribution, specifically: Acquire input parameters affecting electricity charges, the input parameters including electricity price, electricity quantity, basic electricity charge and power regulation electricity charge; For all parameters in the input parameters, the between-group variance and total variance of all parameters were calculated based on local sensitivity analysis; Compare and analyze the inter-group variance and the total variance, and obtain the variance contribution of all parameters in the input parameters based on the comparison results; The input parameters of electricity charges are ranked in order of importance according to the variance contribution of all parameters from large to small.

8. The method for handling abnormal electricity charges based on intelligent verification according to claim 2, characterized in that: The specific method for obtaining the electricity price characteristics is as follows: Based on time series analysis, set the time window for electricity price data; For each time point, take the data in the time window before and after the time point, calculate the arithmetic mean of these data as the smoothing value of the current point; Obtain the actual electricity price and calculate the deviation between the actual electricity price and the smoothed value; The standard deviation of the smoothed value is used as a reference value. If the deviation value exceeds three times the standard deviation of the smoothed value, the deviation value is quantified as an electricity price feature.

9. The method for handling abnormal electricity charges based on intelligent verification according to claim 8, characterized in that: The specific calculation formula of the smoothing value is as follows: The specific calculation formula of the electricity price characteristics is as follows: In the formula, For time point The smoothed value of is the window size, For the The electricity price at a point in time, where =1,2,3,...,R, R is an integer, is the electricity price characteristic, For time point actual electricity price.

10. A system using the method for handling abnormal electricity charges based on intelligent verification as claimed in any one of claims 1 to 9, characterized in that: It includes an electricity fee anomaly detection module, an electricity fee calculation and correction module, an electricity price and electricity fee calculation and correction module, and a visualization and importance analysis module. There are connections between the modules: An electricity charge anomaly detection module, used to obtain first data of electricity charge anomaly, and perform anomaly detection on electricity charge calculation based on a neural network algorithm, wherein the first data of electricity charge anomaly includes electricity price characteristics, power regulation standard characteristics, and fixed ratio characteristics; The power calculation correction module is used to correct the power based on the power flow calculation method and energy balance algorithm when the electricity fee calculation is abnormal; The electricity price and electricity fee calculation and correction module is used to retrieve the user's historical electricity price information and verify the consistency with the current electricity price information, determine and update the user's electricity price, and recalculate the electricity fee based on the corrected electricity quantity and electricity price; The visualization and importance analysis module is used to update the three-color annotation display of the electricity charge correction calculation results in real time based on visualization technology, obtain the input parameters of the electricity charge, obtain the contribution of the input parameters to the electricity charge based on the local sensitivity method, and sort the importance of the input parameters according to the contribution.

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