An electricity fee calculation system for fitting electric quantity calculation

Abstract

The present application discloses an electricity fee calculation system for fitting electricity calculation, which relates to the technical field of electricity fee calculation, and includes: a fitting electricity calculation module, a fitting electricity mathematical model analysis module, a data monitoring module, a mode calculation module and a fitting electricity mathematical model logic design module connected in sequence. It is used to realize the calculation of missing metering values ​​under abnormal conditions, which is more reliable and efficient than manual experience values.

Description

Technical Field

[0001] The present application relates to the technical field of electricity fee calculation, and in particular to an electricity fee calculation system for fitting electric quantity calculation. Background Art

[0002] Abnormal electricity data refers to metering anomalies caused by factors such as failure of electric energy metering device components or artificial external forces. Specific manifestations include: user electricity data is inconsistent with actual electricity consumption, power and power factor and other electric energy meter display data do not match the nature and pattern of electricity consumption, and the load curve is contrary to the seasonal electricity consumption of production and life. In terms of its nature, the causes of abnormal electricity can be divided into two types: missed electricity caused by non-human factors, and abnormal electricity caused by human malicious inaccuracy or non-measurement. Both belong to the situation where the electric energy metering device cannot accurately measure. The electricity during this period can only be calculated and fitted into the system through calculation. This part of the non-metered electricity is called fitted electricity.

[0003] Since the metering and billing of electricity production is dynamic and real-time, when the measured electricity data is abnormal, it is necessary to fit and supplement the electricity in time. The current manual deduction method cannot achieve the calculation speed of hourly charges for trading users, which is easy to cause human errors and reduce customer conviction. Summary of the invention

[0004] The present application provides an electricity fee calculation system for fitting electricity quantity calculation, which is used to realize the calculation of missing measurement values ​​under abnormal conditions, and is more reliable and efficient than manual experience values.

[0005] The present application provides an electricity fee calculation system for fitting electricity calculation, comprising: a fitting electricity calculation module, a fitting electricity mathematical model analysis module, a data monitoring module, a mode calculation module and a fitting electricity mathematical model logic design module connected in sequence;

[0006] The fitting power estimation module is used to obtain the power consumption data of the target power meter, and determine the fitting total power of the target power meter according to the meter reading cutoff number, the metering multiple and the preset metering error correction coefficient in the power consumption data;

[0007] The fitted electricity mathematical model analysis module is used to compare and analyze the electricity consumption data with the historical electricity consumption data in a preset database, determine the abnormal data in the electricity consumption data, and calculate the fitted electricity in the time period corresponding to the abnormal data in combination with the fitted total electricity;

[0008] The data monitoring module is used to determine the abnormal information corresponding to the abnormal data, the theoretical line loss rate and the influencing factors of the power consumption data and their quantitative influence degree;

[0009] The mode calculation module is used to determine the abnormal location corresponding to the abnormal data, the calculation parameters corresponding to the optimal fitting quantity, and the abnormal suspected household corresponding to the abnormal power;

[0010] The fitted electric quantity mathematical model logic design module is used to record the logic model of the electric quantity data.

[0011] Optionally, the fitted power estimation module comprises: an uncertainty factor estimation unit and a big data theoretical calculation unit connected to each other;

[0012] The uncertainty factor estimation unit is used to extract the meter reading cutoff number, the metering multiple and the metering error correction coefficient from the electricity consumption data, and determine the fitted total electricity in combination with a preset fitted electricity calculation formula;

[0013] The big data theoretical calculation unit is used to record the electricity consumption data and store the historical electricity consumption data.

[0014] Optionally, the data monitoring module includes a data anomaly monitoring unit and a line loss rate mathematical model unit which are connected to each other;

[0015] The abnormality monitoring unit is used to determine the abnormality information corresponding to the abnormal data;

[0016] The line loss rate mathematical model unit is used to determine the theoretical line loss rate according to the line information and substation information corresponding to the target electric meter; and to determine the influencing factors of the electricity consumption data and their quantitative influence degree according to the theoretical line loss rate and a pre-constructed typical value query table.

[0017] Optionally, the mode calculation module includes an electric quantity abnormal position positioning unit, a fitting electric quantity calculation parameter selection unit and an online data analysis unit which are interconnected;

[0018] The power abnormality location positioning unit is used to determine the abnormality location corresponding to the abnormal data;

[0019] The fitting power quantity calculation parameter selection unit is used to determine the calculation parameters corresponding to the optimal fitting quantity;

[0020] The online data analysis unit is used to determine the abnormal suspected households corresponding to the abnormal power volume.

[0021] Optionally, the data anomaly monitoring unit includes a fluctuation monitoring subunit, a discrete monitoring subunit and an anomaly monitoring subunit that are interconnected.

[0022] Optionally, the line loss rate mathematical model unit includes a theoretical line loss rate determination subunit and a factor quantification analysis subunit that are interconnected;

[0023] The theoretical line loss rate determination subunit is used to determine the theoretical line loss rate according to the line information and the substation information corresponding to the target electric meter;

[0024] The factor quantification analysis subunit is used to determine the influencing factors of the power consumption data and their quantitative influence degrees according to the theoretical line loss rate and a pre-constructed typical value query table.

[0025] Optionally, the fitting power calculation formula is specifically:

[0026] W=Pt=KP'K=KJ m β,

[0027] Where K is the measurement error correction coefficient, P' is the power displayed by the wrong connection line, and P is the actual active power. t is the time when abnormal power occurs, J m is the meter reading cutoff number, and β is the metering multiple.

[0028] Optionally, the online data analysis unit is specifically used for:

[0029] The abnormal suspected household corresponding to the abnormal electricity quantity is determined by one or more of the cover opening record trace method, the data analysis and comparison method, and the line loss and electricity quantity difference method.

[0030] Optionally, the power abnormality location locating unit is specifically used for:

[0031] The abnormal location corresponding to the abnormal data is determined by a search method that uses the reverse direction of the power supply flow of the power grid as a positioning method.

[0032] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

[0033] The present application provides an electricity fee calculation system for fitting electricity calculation, comprising: a fitting electricity calculation module, a fitting electricity mathematical model analysis module, a data monitoring module, a mode calculation module and a fitting electricity mathematical model logic design module connected in sequence; the fitting electricity calculation module is used to obtain the electricity consumption data of the target electricity meter, and determine the fitting total electricity of the target electricity meter according to the meter reading cutoff number, the metering multiple and the preset metering error correction coefficient in the electricity consumption data; the fitting electricity mathematical model analysis module is used to perform a horizontal reference analysis on the electricity consumption data and the historical electricity consumption data in a preset database, determine the abnormal data in the electricity consumption data, and calculate the fitting electricity in the time period corresponding to the abnormal data in combination with the fitting total electricity; the data monitoring module is used to determine the abnormal information corresponding to the abnormal data, the theoretical line loss rate and the influencing factors of the electricity consumption data and their quantitative influence degree; the mode calculation module is used to determine the abnormal location corresponding to the abnormal data, the calculation parameters corresponding to the optimal fitting amount, and the abnormal suspected households corresponding to the abnormal electricity; the fitting electricity mathematical model logic design module is used to record the logic model of the electricity consumption data. This enables the calculation of missing measurement values ​​under abnormal circumstances, which is more reliable and efficient than manual experience values. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 Flowchart of the present invention application.

[0035] in:

[0036] 1 is the fitted electricity estimation module, 101 is the uncertainty factor estimation unit, 102 is the big data theoretical calculation unit, 2 is the fitted electricity mathematical model analysis module, 3 is the data monitoring module, 301 is the data anomaly monitoring unit, 302 is the line loss rate mathematical model unit, 4 is the pattern calculation module, 401 is the electricity anomaly position positioning unit, 402 is the fitted electricity calculation parameter selection unit, 403 is the online data analysis unit, and 5 is the fitted electricity mathematical model logic design module. DETAILED DESCRIPTION

[0037] The embodiment of the present application provides an electricity fee calculation system for fitting electricity quantity calculation, which is used to realize the calculation of missing measurement values ​​under abnormal conditions, and is more reliable and efficient than manual experience values.

[0038] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0039] In the description of the present application, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present application. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.

[0040] Unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0041] See also Figure 1 , the present application provides a schematic diagram of the composition of an electricity fee calculation system for fitting electricity calculation, including: a fitting electricity calculation module 1, a fitting electricity mathematical model analysis module 2, a data monitoring module 3, a mode calculation module 4 and a fitting electricity mathematical model logic design module 5 connected in sequence;

[0042] The fitting power estimation module 1 is used to obtain the power consumption data of the target power meter, and determine the fitting total power of the target power meter according to the meter reading cut-off number, the metering multiple and the preset metering error correction coefficient in the power consumption data;

[0043] The fitted electricity mathematical model analysis module 2 is used to compare and analyze the electricity consumption data with the historical electricity consumption data in a preset database, determine the abnormal data in the electricity consumption data, and calculate the fitted electricity in the time period corresponding to the abnormal data in combination with the fitted total electricity;

[0044] The data monitoring module 3 is used to determine the abnormal information corresponding to the abnormal data, the theoretical line loss rate and the influencing factors of the power consumption data and their quantitative influence degree;

[0045] The mode calculation module 4 is used to determine the abnormal location corresponding to the abnormal data, the calculation parameters corresponding to the optimal fitting quantity, and the abnormal suspected household corresponding to the abnormal power quantity;

[0046] The fitted electric quantity mathematical model logic design module 5 is used to record the logic model of the electric quantity data.

[0047] In an embodiment of the present invention, the fitted electricity mathematical model analysis module 2 adopts a fitted electricity mathematical model based on the conservation of electricity energy. The application of big data theory is helpful to realize multi-dimensional analysis, and through the scientific application and precise positioning of the data processing system, it can realize rapid response to abnormalities and calculation in seconds. The advantages of big data theory are obvious, mainly including: First, through the supervision of electricity consumption information and electricity consumption behavior of massive users, the purpose of all-round coverage monitoring, timely discovery and rapid processing is achieved; second, through system calculation, horizontal reference analysis of data is realized to eliminate "noise" items, so as to achieve the purpose of accurately targeting problems; third, through mathematical model calculation, the fitted electricity of the missing metering period is accurately calculated.

[0048] In an optional embodiment, the fitting power calculation formula is specifically:

[0049] W=Pt=KP'K=KJ m β,

[0050] Among them, K is the measurement error correction coefficient, P' is the power displayed by the wrong connection line, P is the actual active power, t is the time when the abnormal power occurs, J m is the meter reading cutoff number, and β is the metering multiple.

[0051] In an optional embodiment, the fitting power estimation module 1 includes: an uncertainty factor estimation unit 101 and a big data theoretical calculation unit 102 which are connected to each other;

[0052] The uncertainty factor estimation unit 101 is used to extract the meter reading cutoff number, the metering multiple and the metering error correction coefficient from the electricity consumption data, and determine the fitted total electricity in combination with a preset fitted electricity calculation formula;

[0053] The big data theoretical calculation unit 102 is used to record the power consumption data and store the historical power consumption data.

[0054] In an optional embodiment, the data monitoring module 3 includes a data anomaly monitoring unit 301 and a line loss rate mathematical model unit 302 which are connected to each other;

[0055] The abnormality monitoring unit 301 is used to determine the abnormality information corresponding to the abnormal data;

[0056] The line loss rate mathematical model unit 302 is used to determine the theoretical line loss rate according to the line information and substation information corresponding to the target electric meter; and to determine the influencing factors of the power consumption data and their quantitative influence degree according to the theoretical line loss rate and a pre-constructed typical value query table.

[0057] The key to fitting power calculation is to obtain the mutation signal of power anomaly. According to the inevitable abnormal phenomenon of power anomaly, the fitting power calculation is realized by tracking the abnormal signal, which can narrow the search scope, improve work efficiency, and consider the influence weight of the power anomaly suspicion coefficient to establish the user power evaluation index system. The main function is to capture the characteristic quantities such as line loss, three-phase imbalance rate, power factor, etc. of the metering automation system backflow.

[0058] In an embodiment of the present invention, the data anomaly monitoring unit 301 includes a fluctuation monitoring subunit, a discrete monitoring subunit and an abnormality monitoring subunit, wherein the fluctuation monitoring subunit is used to detect power consumption anomalies and metering anomalies; and then the discrete monitoring subunit is used to assist in the qualitative characterization of the above abnormal situations; the abnormality monitoring subunit is used to accurately analyze deviation events.

[0059] The line loss rate mathematical model unit 302 includes two sub-units: establishing a theoretical line loss rate mathematical model and quantitatively analyzing the factors affecting line loss changes. The theoretical line loss rate is calculated using typical lines and typical substations, and the technical factors affecting the theoretical line loss rate are analyzed. A typical value query table for lines and substations that conform to actual characteristics is constructed, and this is used as a threshold for initiating an abnormal power processing work order and included in the monitoring scope.

[0060] In a specific implementation, the working process of the line loss rate mathematical model unit 302 is to first establish a theoretical line loss rate mathematical model, and then quantitatively analyze the factors affecting the line loss change.

[0061] The mathematical model of theoretical line loss rate calculates the theoretical line loss rate based on typical lines and typical substations, analyzes the technical factors that affect the theoretical line loss rate, constructs a typical value query table for lines and substations that conform to actual characteristics, and uses this as the threshold for initiating the abnormal power processing work order and includes it in the monitoring scope. The quantitative analysis of the factors affecting line loss changes is to analyze the difference between the degree of influence of each variable and the theoretical line loss rate based on specific data, and establish an information base of influence quantities with different spacings and different influencing factor dimensions.

[0062] In an optional embodiment, the mode calculation module 4 includes an electric quantity abnormal position positioning unit 401, a fitting electric quantity calculation parameter selection unit 402 and an online data analysis unit 403 which are connected to each other;

[0063] The power abnormality location locating unit 401 is used to determine the abnormality location corresponding to the abnormal data;

[0064] The fitting power calculation parameter selection unit 402 is used to determine the calculation parameters corresponding to the optimal fitting quantity;

[0065] The online data analysis unit 403 is used to determine the abnormal suspected households corresponding to the abnormal power consumption.

[0066] In an optional embodiment, the power abnormality location locating unit 402 is specifically used for:

[0067] The abnormal location corresponding to the abnormal data is determined by a search method that uses the reverse direction of the power supply flow of the power grid as a positioning method.

[0068] In an optional embodiment, the online data analysis unit 403 is specifically used to:

[0069] The abnormal suspected household corresponding to the abnormal electricity quantity is determined by one or more of the cover opening record trace method, the data analysis and comparison method, and the line loss and electricity quantity difference method.

[0070] In an embodiment of the present invention, the potential abnormality position in the power abnormality position positioning unit 401 adopts a search method based on the reverse direction of the power supply flow of the power grid. Generally, the power supply of the power grid is in the form of power flow from the power supply center to each user in the power grid. When a power abnormality occurs at any node in the power grid, the line loss of the corresponding power grid line increases. Following this principle, the power abnormality position is located by a search method based on the reverse direction of the power supply flow of the power grid. The user corresponding to the line loss node is determined to be the power abnormality position. Assuming that the power abnormality has occurred, the Bayesian formula can be used to calculate the possibility of a certain cause, and then the occurrence point of the power abnormality can be determined.

[0071] The fitting power calculation parameter selection unit 402 uses one or more of the calculation parameters of the metering terminal, meter events, comprehensive power supply operation data, abnormal detection results of abnormal power of the user, abnormal power amount calculation results and abnormal power node positioning. When it is determined that the power user has abnormal power, collect field data, count the abnormal power of the user, and use the metering terminal, meter events, comprehensive power supply operation data, abnormal detection results of abnormal power of the user, abnormal power amount calculation results and abnormal power node positioning results to realize fitting power calculation, and let all power values ​​be (x1, y1), ..., (x n ,y n ), and the fitted charge e1=y1-y(x1) is optimal.

[0072] The analysis methods in the online data analysis unit 403 include a cover-opening record trace method, a data analysis comparison method, and a line loss and electricity difference method. After the system senses the high-loss line electricity consumption data, it determines the users suspected of having abnormal electricity through comprehensive analysis such as the cover-opening record trace method, the data analysis comparison method, and the line loss and electricity difference method. It also responds to the online real-time call for the forward and reverse electricity, voltage, current and other data of all users on the line, as well as the cover-opening record and other information to conduct a multi-dimensional comprehensive analysis of the suspected users of abnormal electricity, so as to achieve accurate identification.

[0073] Among them, the cover-opening record trace method refers to: measuring the line user's electric energy meter cover-opening record to show the user's cover-opening occurrence time, end time and corresponding time stop code. When the user's monthly average power after opening the cover is less than 50% of the monthly average power before opening the cover, the user's power consumption is judged to be abnormal. The use of the cover-opening record trace method can not only quickly identify users with abnormal meter power consumption, but also provide accurate time and fitting power basis.

[0074] The data analysis and comparison method refers to: exporting key data of the line smart meter, performing online data comparison, screening current and voltage anomalies and reverse users, and analyzing and determining the type of power anomaly.

[0075] The line loss and electricity difference method refers to: analyzing and comparing the changes in line loss data, locking abnormal lines, correlating and analyzing the daily line loss and daily electricity consumption of users, analyzing abnormal time periods, screening zero-degree users and users with sudden electricity changes, and improving the effectiveness of fitted electricity calculations.

[0076] In addition, in the embodiment of the present invention, the electric quantity mathematical model fitting logic module 5 performs logic design for the electric quantity mathematical model fitting, specifically:

[0077] Based on the big data analysis method, the logic design model of the fitted electricity mathematical model is adopted. Model 5 adopts the electricity of the same period. Through the two analysis methods of comparison judgment and line loss abnormality judgment, based on the principle of electricity transmission conservation and energy will not change suddenly, when the historical data is sufficient and the data quality is guaranteed, the linear theory and the minimum residual square method based on machine learning are used to optimize the exhaustive and regression models, and the input / output electricity of the user metering system data of relative energy conservation is calculated to truly reflect the missing electric energy; the electricity fitting data is composed of w n (1+x n ) calculation, from the electrical principle, the sufficient and necessary condition for the correct power fitting data is that the power input energy is equal to the sum of the user's power consumption plus the system network loss, that is:

[0078]

[0079] In the formula, w0 and w i x0 and x1 are the electric energy values ​​of the total electric energy meter and the user's sub-meter respectively; i The measurement errors of the corresponding energy values ​​of the total meter and each sub-meter respectively; and They are the corresponding power factors of the total meter and each sub-meter respectively; is the system network loss, which is a function of multiple variables.

[0080] Ignoring the error of the electric energy meter and the network loss, the formula for determining the normal fitting meter electric energy can be described as:

[0081]

[0082] Optimal solution Δw 0i The combination with the smallest corresponding value

[0083] If a set of data can be found to make the above equation true, then all the electric energy data corresponding to this data set can theoretically fit the correctly fitted meter electric energy.

[0084] Build the minimum residual model:

[0085] In all possible energy meters, use W = Pt = KP'K = KJ m βSolve the equations and calculate the measurement error of the electric energy meter.

[0086] Find Δw using the formula for determining the energy of the normal fitting meter 0i The optimal solution is the combination with the smallest corresponding value. When energy is conserved, the optimal solution is the fitted electric quantity at that time.

[0087] Then, based on the minimum residual model, the gradient descent algorithm is used to find the optimal solution:

[0088] In the process of finding the optimal solution, the gradient descent algorithm is needed. Using the idea of ​​iteration, the value of θ is continuously updated, and eventually θ will shrink to a certain value, that is:

[0089]

[0090] In the formula, a is the step size, that is, the learning rate.

[0091] A schematic diagram of the composition of an electricity fee calculation system for fitting electricity calculation provided by the present invention comprises: a fitting electricity calculation module 1, a fitting electricity mathematical model analysis module 2, a data monitoring module 3, a mode calculation module 4 and a fitting electricity mathematical model logic design module 5 connected in sequence; the fitting electricity calculation module 1 is used to obtain the electricity consumption data of the target electricity meter, and determine the fitting total electricity of the target electricity meter according to the meter reading cut-off number, the metering multiple and the preset metering error correction coefficient in the electricity consumption data; the fitting electricity mathematical model analysis module 2 is used to perform a horizontal reference analysis on the electricity consumption data and the historical electricity consumption data in the preset database, determine the abnormal data in the electricity consumption data, and calculate the fitting electricity in the time period corresponding to the abnormal data in combination with the fitting total electricity; the data monitoring module 3 is used to determine the abnormal information corresponding to the abnormal data, the theoretical line loss rate and the influencing factors of the electricity consumption data and their quantitative influence degree; the mode calculation module 4 is used to determine the abnormal location corresponding to the abnormal data, the calculation parameters corresponding to the optimal fitting amount, and the abnormal suspected households corresponding to the abnormal electricity; the fitting electricity mathematical model logic design module 5 is used to record the logic model of the electricity consumption data. This enables the calculation of missing measurement values ​​under abnormal circumstances, which is more reliable and efficient than manual experience values.

[0092] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. An electricity fee calculation system for fitting electric quantity calculation, characterized in that: include: A fitted power estimation module, a fitted power mathematical model analysis module, a data monitoring module, a mode calculation module and a fitted power mathematical model logic design module connected in sequence; The fitting power estimation module is used to obtain the power consumption data of the target power meter, and determine the fitting total power of the target power meter according to the meter reading cutoff number, the metering multiple and the preset metering error correction coefficient in the power consumption data; The fitted electricity mathematical model analysis module is used to compare and analyze the electricity consumption data with the historical electricity consumption data in a preset database, determine the abnormal data in the electricity consumption data, and calculate the fitted electricity in the time period corresponding to the abnormal data in combination with the fitted total electricity; The data monitoring module is used to determine the abnormal information corresponding to the abnormal data, the theoretical line loss rate and the influencing factors of the power consumption data and their quantitative influence degree; The mode calculation module is used to determine the abnormal location corresponding to the abnormal data, the calculation parameters corresponding to the optimal fitting quantity, and the abnormal suspected household corresponding to the abnormal power; The fitted electric quantity mathematical model logic design module is used to record the logic model of the electric quantity data; The fitted power estimation module includes: an uncertainty factor estimation unit and a big data theoretical calculation unit connected to each other; The uncertainty factor estimation unit is used to extract the meter reading cutoff number, the metering multiple and the metering error correction coefficient from the electricity consumption data, and determine the fitted total electricity in combination with a preset fitted electricity calculation formula; The big data theoretical calculation unit is used to record the power consumption data and store the historical power consumption data; The fitted electric quantity mathematical model analysis module adopts a fitted electric quantity mathematical model based on the conservation of electric quantity energy; The data monitoring module includes a data anomaly monitoring unit and a line loss rate mathematical model unit which are interconnected; The abnormality monitoring unit is used to determine the abnormality information corresponding to the abnormal data; The line loss rate mathematical model unit is used to determine the theoretical line loss rate according to the line information and the substation information corresponding to the target electric meter; and determine the influencing factors of the electric consumption data and their quantitative influence degree according to the theoretical line loss rate and the pre-constructed typical value query table; The mode calculation module includes an electric quantity abnormal position positioning unit, a fitting electric quantity calculation parameter selection unit and an online data analysis unit which are connected to each other; The power abnormality location positioning unit is used to determine the abnormality location corresponding to the abnormal data; The fitting power quantity calculation parameter selection unit is used to determine the calculation parameters corresponding to the optimal fitting quantity; The online data analysis unit is used to determine the abnormal suspected households corresponding to the abnormal power volume.

2. The electricity fee calculation system for fitting electric quantity calculation according to claim 1, characterized in that: The data anomaly monitoring unit includes a fluctuation monitoring subunit, a discrete monitoring subunit and an anomaly monitoring subunit which are interconnected.

3. The electricity fee calculation system for fitting electric quantity calculation according to claim 1, characterized in that: The line loss rate mathematical model unit includes a theoretical line loss rate determination subunit and a factor quantitative analysis subunit that are interconnected; The theoretical line loss rate determination subunit is used to determine the theoretical line loss rate according to the line information and the substation information corresponding to the target electric meter; The factor quantification analysis subunit is used to determine the influencing factors of the power consumption data and their quantitative influence degrees according to the theoretical line loss rate and a pre-constructed typical value query table.

4. The electricity fee calculation system for fitting electric quantity calculation according to claim 1, characterized in that: The fitting power calculation formula is specifically: W=Pt=KP'K=KJ m β, Among them, K is the measurement error correction coefficient, P' is the power displayed by the wrong connection line, P is the actual active power, t is the time when the abnormal power occurs, J m is the meter reading cutoff number, and β is the metering multiple.

5. The electricity fee calculation system for fitting electric quantity calculation according to claim 1, characterized in that: The online data analysis unit is specifically used for: The abnormal suspected household corresponding to the abnormal electricity quantity is determined by one or more of the cover opening record trace method, the data analysis and comparison method, and the line loss and electricity quantity difference method.

6. The electricity fee calculation system for fitting electric quantity calculation according to claim 1, characterized in that: The power abnormality location positioning unit is specifically used for: The abnormal location corresponding to the abnormal data is determined by a search method that uses the reverse direction of the power supply flow of the power grid as a positioning method.

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