Ammeter misalignment detection method and system based on high-frequency sampling data of electricity utilization acquisition system

Abstract

The invention relates to the technical field of electrical variable measurement, and discloses an electricity meter misalignment detection method and system based on high-frequency sampling data of an electricity utilization acquisition system, and the method comprises the steps: obtaining high-frequency acquisition data; executing voltage range verification, electric quantity abnormity screening and time continuity verification; performing calculation to realize single-phase meter division phase-returning identification; establishing a linear regression model between the general table and the in-phase sub-table; regularization constraint is introduced to solve each sub-table correction coefficient; and carrying out threshold value judgment and result output. In the prior art, an electric meter abnormity identification method depends on manual sampling inspection, and especially under the condition that branch meter phase access is not clear, high-precision misalignment detection is difficult to realize. Due to the fact that a high-frequency data unified time reference construction mechanism and an automatic phase-returning recognition mechanism based on voltage correlation are introduced, fine modeling and stable solving of metering deviation of all sub-meters are achieved, and the accuracy of ammeter misalignment detection is improved.

Description

Technical Field

[0001] This invention relates to the field of electrical variable measurement technology, and in particular to a method and system for detecting meter inaccuracies based on high-frequency sampling data from an electricity consumption acquisition system. Background Technology

[0002] Currently, in electricity information collection systems, the assessment of meter operating status mainly relies on periodic meter reading data or low-frequency statistical data. Identification of meter inaccuracies often relies on manual sampling, experience-based judgment, or simple total / minimum electricity consumption comparisons. These methods have significant shortcomings in practical applications. For example, in situations with frequent load fluctuations in distribution areas, random user electricity consumption behavior, and missing or altered phase information for sub-meter access, traditional statistical analysis methods based on low-frequency data struggle to accurately reflect the fine-grained characteristics of electricity consumption changes and cannot effectively distinguish the relationship between metering errors and load fluctuations. Furthermore, when there is strong correlation between multiple sub-meters or when data contains noise, missing data, or time asynchrony issues, existing methods struggle to construct stable and reliable analytical models, leading to significant fluctuations in inaccurate meter identification results and a high misjudgment rate.

[0003] Furthermore, existing technologies typically lack the ability to automatically identify meter relocation issues, often relying on manual ledgers or on-site verification. In actual operation, if phase wiring adjustments or data labeling errors occur, it can easily lead to distortion of the analytical basis, further affecting the accuracy of meter malfunction assessments. Especially in high-density distribution areas or scenarios with distributed load access, existing technologies cannot fully meet the needs for refined, automated, and quantifiable assessment of metering deviations.

[0004] Therefore, there is an urgent need for a meter misalignment detection method that can still achieve automatic phase resetting of sub-meters, stable solution of metering deviation, and quantitative determination of misalignment ratio even when the sub-meter phase is unclear, the data is subject to noise interference, and the load fluctuates significantly, so as to improve the accuracy, stability and engineering application feasibility of meter anomaly identification. Summary of the Invention

[0005] To address the aforementioned technical shortcomings, the purpose of this invention is to propose a method for detecting meter inaccuracies based on high-frequency sampling data from an electricity consumption acquisition system. This method aims to solve the technical problem of existing methods that rely on manual sampling of meters for anomaly identification, especially when the phase connection of individual meters is unclear, making it difficult to achieve high-precision inaccuracy detection.

[0006] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: The present invention provides a method for detecting meter inaccuracy based on high-frequency sampling data of an electricity consumption acquisition system.

[0007] The method for detecting meter inaccuracies based on high-frequency sampling data from an electricity consumption data acquisition system includes: Step S10: Obtain high-frequency acquisition data of the master table and each sub-table within the target area. Based on the high-frequency acquisition data, use the time synchronization correction and window mapping construction mechanism to execute the original detection dataset construction task and output the synchronized original detection dataset. Step S20: Based on the synchronous original detection dataset, a multi-dimensional validity verification mechanism is used to perform anomaly screening tasks and output a valid detection dataset; Step S30: Based on the effective detection dataset, a voltage correlation phase recognition mechanism is used to perform the phase separation and preprocessing task, and the phase separation detection dataset is output. Step S40: Based on the phase detection dataset, the correction coefficient solution task is performed using a sliding window sample construction and regularized linear regression solution mechanism, and the correction coefficient set of each sub-table is output; Step S50: Calculate the misalignment ratio, determine the threshold, and report the results based on the correction coefficient set of each sub-meter, and output the meter misalignment detection result set.

[0008] Preferably, in step S10, the steps of acquiring high-frequency acquisition data of the master table and each sub-table within the target area, performing the original detection dataset construction task based on the high-frequency acquisition data using a time synchronization correction and window mapping mechanism, and outputting the synchronized original detection dataset specifically include: Step S101: Establish communication connection with the main meter and each sub-meter via RS-485 bus through the preset misalignment detection module, and read high-frequency acquisition data. The high-frequency acquisition data includes the meter address, phase type, multiplier parameter and sampling time stamp information of each meter. Step S102: Based on the high-frequency acquisition data, obtain the total meter power data, total meter voltage data, each sub-meter power data and each sub-meter voltage data according to the preset high-frequency time granularity to form the original reading dataset; Step S103: Check the consistency between the copying time of each sub-table in the original copying dataset and the standard time slice of the main table. When there is a time deviation, establish a time deviation correction model based on historical copying patterns. The time deviation correction model uses linear interpolation or time period power allocation to correct the original copying dataset to a unified time window, thus obtaining a synchronized original detection dataset.

[0009] Preferably, step S20, which involves performing anomaly screening based on the synchronous original detection dataset using a multi-dimensional validity verification mechanism and outputting a valid detection dataset, specifically includes: Step S201: Read the voltage data from the master table and sub-tables at each time point in the synchronous raw detection dataset, perform range constraint verification on the voltage data, and when any voltage value in the voltage data exceeds the preset normal range, mark the corresponding time point data as invalid data and remove it. Step S202: Read the power data of the total table and sub-tables at each time point in the synchronous raw detection dataset, and perform continuity verification on the power data of adjacent frozen time points. When power data mutation, missing, reverse anomaly or discontinuous time points are detected, the corresponding window data is removed. Step S203: Filter out data in the synchronous raw detection dataset whose power change is lower than a preset power change threshold; Step S204: Output the synchronized raw detection dataset after performing abnormal data screening as the valid detection dataset.

[0010] Preferably, in step S202, a continuity check is performed on the electricity data at adjacent freezing time points. Specifically, the absolute value of the deviation between the time interval between two adjacent freezing time points and the preset sampling period is used as the criterion. When the following conditions are met: When this happens, the corresponding interval is determined to be a non-continuous data interval; among which, Indicates the first A freeze point in time, Indicates the first +1 freeze time point, Indicates the preset sampling period. This represents the continuity tolerance threshold.

[0011] Preferably, step S30, which involves performing a phase-separation preprocessing task based on the effective detection dataset using a voltage-related phase recognition mechanism and outputting the phase-separated detection dataset, specifically includes: Step S301: Extract the total three-phase voltage sequence from the valid detection dataset And the voltage sequence of the single-phase sub-meter to be identified Construct candidate phase recognition data sets; Step S302: Calculate the voltage sequence With the total three-phase voltage sequence Pearson correlation coefficient between Pearson correlation coefficient satisfy: ; in, This represents the voltage sequence corresponding to any candidate phase in the total table, where the candidate phases include the total three-phase voltage sequence. Any one of them; Indicates the number of sample points involved in the correlation calculation; This represents the voltage value of the k-th voltage sequence of the single-phase sub-meter to be identified at the k-th sampling time; This represents the average voltage value of the single-phase submeter to be identified within the sample interval; This represents the average voltage value of the voltage sequence corresponding to the candidate phase in the total table within the sample interval; Step S303: Select the candidate phase with the largest Pearson correlation coefficient as the assigned phase of the corresponding single-phase sub-meter, and retain only the total meter power data and the corresponding sub-meter power data under the assigned phase to form a phase detection dataset.

[0012] Preferably, step S40, which involves using a sliding window sample construction and regularized linear regression solution mechanism based on the phase detection dataset to perform the correction coefficient calculation task and output the correction coefficient sets for each sub-table, specifically includes: Step S401: Establish a linear regression model between the total meter charge and the charge of the same sub-meter for each phase in the phase detection dataset. Let the total meter charge be Y, and the vector of charge of the same sub-meter be X=[X1,X2,…,X…]. n ], where n is the length of the same sub-meter power vector; X n For the nth in-phase sub-meter, the electrical quantity value within the corresponding sampling window is defined; a linear relationship model is established: Where e represents the comprehensive error term; This is the correction coefficient corresponding to the nth sub-meter, used to characterize the proportional relationship between the electricity consumption of this sub-meter and the total electricity consumption; Based on a linear relationship model, a sliding window strategy is used to construct M sets of samples, forming an overdetermined system of equations, where M is greater than n; Step S402: Perform normalization on each column of the sample input matrix in the overdetermined system of equations, outputting a normalized matrix; and construct normal equations with regularization terms based on the normalized matrix: ;in, This represents a sample input matrix consisting of M groups of sliding window samples, where each row of the sample input matrix corresponds to the power observation value of each sub-table under a sampling window; Representation matrix The transpose of the matrix; This represents the regularization parameter, and λ ≥ 0; Represents the identity matrix; This represents the normalized coefficient vector obtained by solving the normalization matrix; This represents the total meter reading vector corresponding to the sample input matrix; Step S403: Solve the normal equation with regularization terms using Gaussian elimination to obtain the normalized set of correction coefficients for each sub-table.

[0013] Preferably, step S50, which involves calculating the misalignment ratio, determining the threshold, and reporting the results based on the correction coefficient sets of each sub-meter, and outputting the meter misalignment detection result set, specifically includes: Step S501: Obtain the correction coefficient of the i-th sub-meter from the meter misalignment detection result set. According to the table correction coefficient Calculate the inaccuracy ratio of the corresponding sub-table. , ; when When the corresponding submeter is in normal condition, it is determined that the meter reading is normal. when At that time, it was determined that the corresponding sub-table was moving too slowly; when At that time, it was determined that the corresponding sub-table was moving too fast; Step S502: Adjust the misalignment ratio Compared with the preset misalignment threshold Compare, when satisfied When this happens, the corresponding sub-meter is marked as a faulty meter, and a list of faulty meters and suggested correction coefficients are generated. Step S503: Upload the list of inaccurate meters, suggested correction coefficient information and inaccuracy ratio to the operation and management terminal through the preset remote communication unit, and output the meter inaccuracy detection result set.

[0014] This invention also provides a meter misalignment detection system based on high-frequency sampling data from an electricity consumption acquisition system, comprising: The data construction module is used to acquire high-frequency collected data from the master table and each sub-table within the target area. Based on the high-frequency collected data, it uses a time synchronization correction and window mapping construction mechanism to execute the original detection dataset construction task and outputs a synchronized original detection dataset. The validity screening module is used to perform anomaly screening tasks based on the synchronous raw detection dataset using a multi-dimensional validity verification mechanism, and output a valid detection dataset. The phase recognition module is used to perform a phase-separation preprocessing task based on the effective detection dataset using a voltage-related phase recognition mechanism, and outputs a phase-separated detection dataset. The coefficient calculation module is used to perform the task of calculating correction coefficients based on the phase detection dataset by using a sliding window sample construction and regularized linear regression solution mechanism, and outputs the correction coefficient set of each sub-table; The result determination module is used to calculate the misalignment ratio, determine the threshold, and report the results based on the correction coefficient set of each sub-meter, and output the meter misalignment detection result set.

[0015] The present invention also provides a meter misalignment detection device based on high-frequency sampling data of an electricity consumption acquisition system, comprising: a memory, a processor, and a meter misalignment detection program based on high-frequency sampling data of an electricity consumption acquisition system stored in the memory and executable on the processor. When the meter misalignment detection program based on high-frequency sampling data of an electricity consumption acquisition system is executed by the processor, a meter misalignment detection method based on high-frequency sampling data of an electricity consumption acquisition system is implemented.

[0016] The present invention also provides a computer program product, including a meter misalignment detection program based on high-frequency sampling data of an electricity consumption acquisition system. When the meter misalignment detection program based on high-frequency sampling data of an electricity consumption acquisition system is executed by a processor, it implements the meter misalignment detection method based on high-frequency sampling data of an electricity consumption acquisition system.

[0017] The beneficial effects of this invention are as follows: By introducing an automatic phase reversion identification mechanism based on voltage correlation, this invention can achieve accurate phase reversion of single-phase meters without manual calibration in complex transformer substation environments where the phase access of sub-meters is unclear or there are wiring changes. This effectively avoids the statistical errors and model deviations caused by phase misjudgment in traditional methods, and improves the accuracy and consistency of subsequent inaccuracy detection calculations from the source.

[0018] This invention constructs a sliding window sample matrix based on high-frequency sampling data and combines it with a regularized linear regression solution mechanism. Even under conditions of strong correlation among multiple sub-meters, data noise interference, and insufficient samples, it can still stably solve the correction coefficients of each sub-meter, thereby achieving a quantitative assessment of the meter misalignment ratio. This avoids the problems of unstable regression solution and large fluctuations in results in traditional methods, and improves the robustness and engineering applicability of meter misalignment detection. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating the first embodiment of a method for detecting meter inaccuracies based on high-frequency sampling data from an electricity consumption acquisition system according to the present invention.

[0021] Figure 2 This is a schematic diagram of a device for detecting meter inaccuracies based on high-frequency sampling data from an electricity consumption acquisition system, according to the present invention. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] Example 1: As Figure 1The diagram shown is a flowchart of the first embodiment of the meter misalignment detection method based on high-frequency sampling data of an electricity consumption acquisition system according to the present invention. The first embodiment of the meter misalignment detection method based on high-frequency sampling data of an electricity consumption acquisition system according to the present invention is presented.

[0024] In the first embodiment, the meter misalignment detection method based on high-frequency sampling data from the electricity consumption acquisition system includes: Step S10: Obtain high-frequency acquisition data of the master table and each sub-table within the target area. Based on the high-frequency acquisition data, use the time synchronization correction and window mapping construction mechanism to execute the original detection dataset construction task and output the synchronized original detection dataset. Step S20: Based on the synchronous original detection dataset, a multi-dimensional validity verification mechanism is used to perform anomaly screening tasks and output a valid detection dataset; Step S30: Based on the effective detection dataset, a voltage correlation phase recognition mechanism is used to perform the phase separation and preprocessing task, and the phase separation detection dataset is output. Step S40: Based on the phase detection dataset, the correction coefficient solution task is performed using a sliding window sample construction and regularized linear regression solution mechanism, and the correction coefficient set of each sub-table is output; Step S50: Calculate the misalignment ratio, determine the threshold, and report the results based on the correction coefficient set of each sub-meter, and output the meter misalignment detection result set.

[0025] It should be noted that this invention can be deployed inside a meter box, including a main meter (assessment meter), a misalignment detection module, and multiple branch meters. The main meter is installed at the inlet of the meter box, and the branch meters are installed at the outlets. The misalignment detection module communicates with each meter via an RS-485 bus, collecting electricity data at high frequency. The detection module has a built-in remote communication unit, which uploads the misalignment detection results to the main station system or maintenance terminal via HPLC power line carrier. The misalignment detection module is based on a processor unit (ARM Cortex-M), connected to a memory unit (Flash / RAM), an RTC clock module, an RS-485 communication interface, a remote communication module, and a power supply module. The processor performs data acquisition, algorithm calculation, and result output; the memory stores meter parameters, historical data, and algorithm programs; the RS-485 interface communicates with each meter via the bus; the remote communication module uses HPLC technology to realize power line carrier data transmission; and the power supply module draws power from the meter box and supplies power to each unit.

[0026] It should be noted that the algorithm execution process includes: after starting, high-frequency acquisition of meter data is performed via RS-485 bus at 30-minute intervals; the validity of the acquired data is verified for voltage, power consumption, and time; phase identification is performed based on voltage correlation; a linear regression model is constructed using a sliding window strategy; regularization coefficients are solved using ridge regression and Gaussian elimination; inaccuracy is determined by threshold comparison; finally, the results are stored locally and uploaded remotely via HPLC to enter the next cycle.

[0027] It should be understood that the effects achieved by this invention include: 1. Edge computing and fast response: The detection module is deployed at the meter box, realizing localized data acquisition and calculation without relying on the main station system, and can achieve near real-time inaccuracy detection (e.g., outputting results once a day or hour). 2. High recognition accuracy: Utilizing 15-minute or minute-level high-frequency data, a sufficiently large overdetermined system of equations is constructed, significantly improving the accuracy of coefficient solving compared to the traditional daily frozen data scheme. 3. Strong robustness: The introduction of a regularization term effectively solves the multicollinearity problem caused by similar electricity consumption behavior of sub-meters, preventing model overfitting. 4. Phase adaptation: An innovative phase recognition step based on voltage correlation is added before inaccuracy calculation, ensuring the physical consistency of total and sub-meter electricity consumption matching. 5. Flexible deployment: The module operates independently of the meter, accessed via RS-485 bus, without requiring modification of existing meters, suitable for new construction and renovation projects. 6. Embedded friendly: The core algorithm is matrix operation and Gaussian elimination, with controllable computational complexity, suitable for running in resource-constrained embedded modules. 7. High maintainability: The module has status indicator lights and remote communication functions, allowing maintenance personnel to monitor the module's operating status and test results in real time.

[0028] Example 2: Furthermore, the present invention provides a meter misalignment detection system based on high-frequency sampling data from an electricity consumption data acquisition system, employing a meter misalignment detection method based on high-frequency sampling data from an electricity consumption data acquisition system as described in the above embodiments. This system can solve the technical problem of meter misalignment detection based on high-frequency sampling data from an electricity consumption data acquisition system. The beneficial effects of the meter misalignment detection system based on high-frequency sampling data from an electricity consumption data acquisition system provided by the present invention are the same as those of the meter misalignment detection method based on high-frequency sampling data from an electricity consumption data acquisition system provided in the above embodiments. Other technical features of the meter misalignment detection system based on high-frequency sampling data from an electricity consumption data acquisition system are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0029] Example 3: This invention provides a meter misalignment detection device based on high-frequency sampling data from an electricity consumption acquisition system. Please refer to... Figure 2A meter misalignment detection device based on high-frequency sampling data from an electricity consumption data acquisition system includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, enable the at least one processor to perform the meter misalignment detection method based on high-frequency sampling data from an electricity consumption data acquisition system as described in Embodiment 1 above. The meter misalignment detection device based on high-frequency sampling data from an electricity consumption data acquisition system in this embodiment of the invention may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), and vehicle terminals (e.g., vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. This meter misalignment detection device based on high-frequency sampling data from an electricity consumption data acquisition system is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the invention. A meter misalignment detection device based on high-frequency sampling data from an electricity consumption data acquisition system may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory 1002 or a program loaded from a storage device 1003 into a random access memory 1004. The random access memory 1004 also stores various programs and data required for the operation of the meter misalignment detection device based on high-frequency sampling data from an electricity consumption data acquisition system. The processing unit 1001, the read-only memory 1002, and the random access memory 1004 are interconnected via a bus 1005. An I / O interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. Communication device 1009 allows a meter misalignment detection device based on high-frequency sampling data from an electricity consumption acquisition system to wirelessly or wiredly communicate with other devices to exchange data. While the figure shows a meter misalignment detection device based on high-frequency sampling data from an electricity consumption acquisition system with various systems, it should be understood that implementing or having all of the systems shown is not required. More or fewer systems may be implemented alternatively.

[0030] Example 4: This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method for detecting meter inaccuracies based on high-frequency sampling data from an electricity consumption data acquisition system. The computer program product provided by this invention can solve the technical problem of detecting meter inaccuracies based on high-frequency sampling data from an electricity consumption data acquisition system. Compared with the prior art, the beneficial effects of the computer program product provided by this invention are the same as those of the above-described method for detecting meter inaccuracies based on high-frequency sampling data from an electricity consumption data acquisition system, and will not be repeated here.

[0031] In particular, according to the embodiments disclosed in this invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from read-only memory 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this invention.

[0032] It should be understood that the various parts disclosed in this invention can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics may be combined in any suitable manner in one or more embodiments or examples.

[0033] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for detecting meter inaccuracy based on high-frequency sampling data from an electricity consumption acquisition system, characterized in that, The methods include: Step S10: Obtain high-frequency acquisition data of the master table and each sub-table within the target area. Based on the high-frequency acquisition data, use the time synchronization correction and window mapping construction mechanism to execute the original detection dataset construction task and output the synchronized original detection dataset. Step S20: Based on the synchronous original detection dataset, a multi-dimensional validity verification mechanism is used to perform anomaly screening tasks and output a valid detection dataset; Step S30: Based on the effective detection dataset, a voltage correlation phase recognition mechanism is used to perform the phase separation and preprocessing task, and the phase separation detection dataset is output. Step S40: Based on the phase detection dataset, the correction coefficient solution task is performed using a sliding window sample construction and regularized linear regression solution mechanism, and the correction coefficient set of each sub-table is output; Step S50: Calculate the misalignment ratio, determine the threshold, and report the results based on the correction coefficient set of each sub-meter, and output the meter misalignment detection result set.

2. The method for detecting meter inaccuracy based on high-frequency sampling data from an electricity consumption acquisition system as described in claim 1, characterized in that, In step S10, the high-frequency acquisition data of the master table and each sub-table within the target area are obtained. Based on the high-frequency acquisition data, the original detection dataset construction task is performed using a time synchronization correction and window mapping mechanism, and the steps to output the synchronized original detection dataset are specifically included: Step S101: Establish communication connection with the main meter and each sub-meter via RS-485 bus through the preset misalignment detection module, and read high-frequency acquisition data. The high-frequency acquisition data includes the meter address, phase type, multiplier parameter and sampling time stamp information of each meter. Step S102: Based on the high-frequency acquisition data, obtain the total meter power data, total meter voltage data, each sub-meter power data and each sub-meter voltage data according to the preset high-frequency time granularity to form the original reading dataset; Step S103: Check the consistency between the copying time of each sub-table in the original copying dataset and the standard time slice of the main table. When there is a time deviation, establish a time deviation correction model based on historical copying patterns. The time deviation correction model uses linear interpolation or time period power allocation to correct the original copying dataset to a unified time window, thus obtaining a synchronized original detection dataset.

3. The method for detecting meter inaccuracy based on high-frequency sampling data from an electricity consumption acquisition system as described in claim 1, characterized in that, Step S20, which involves performing anomaly screening based on the synchronous original detection dataset using a multi-dimensional validity verification mechanism and outputting a valid detection dataset, specifically includes: Step S201: Read the voltage data from the master table and sub-tables at each time point in the synchronous raw detection dataset, perform range constraint verification on the voltage data, and when any voltage value in the voltage data exceeds the preset normal range, mark the corresponding time point data as invalid data and remove it. Step S202: Read the power data of the total table and sub-tables at each time point in the synchronous raw detection dataset, and perform continuity verification on the power data of adjacent frozen time points. When power data mutation, missing, reverse anomaly or discontinuous time points are detected, the corresponding window data is removed. Step S203: Filter out data in the synchronous raw detection dataset whose power change is lower than a preset power change threshold; Step S204: Output the synchronized raw detection dataset after performing abnormal data screening as the valid detection dataset.

4. The method for detecting meter inaccuracy based on high-frequency sampling data from an electricity consumption acquisition system as described in claim 3, characterized in that, In step S202, a continuity check is performed on the electricity data at adjacent freezing time points. Specifically, the absolute value of the deviation between the time interval between two adjacent freezing time points and the preset sampling period is used as the criterion. When the following conditions are met: When this happens, the corresponding interval is determined to be a non-continuous data interval; among which, Indicates the first A freeze point in time, Indicates the first +1 freeze time point, Indicates the preset sampling period. This represents the continuity tolerance threshold.

5. The method for detecting meter inaccuracy based on high-frequency sampling data from an electricity consumption acquisition system as described in claim 1, characterized in that, Step S30, which involves performing a phase-separation preprocessing task based on the effective detection dataset using a voltage-related phase recognition mechanism and outputting the phase-separation detection dataset, specifically includes: Step S301: Extract the total three-phase voltage sequence from the valid detection dataset And the voltage sequence of the single-phase sub-meter to be identified Construct candidate phase recognition data sets; Step S302: Calculate the voltage sequence With the total three-phase voltage sequence Pearson correlation coefficient between Pearson correlation coefficient satisfy: ； in, This represents the voltage sequence corresponding to any candidate phase in the total table, where the candidate phases include the total three-phase voltage sequence. Any one of them; Indicates the number of sample points involved in the correlation calculation; This represents the voltage value of the k-th voltage sequence of the single-phase sub-meter to be identified at the k-th sampling time; This represents the average voltage value of the single-phase submeter to be identified within the sample interval; This represents the average voltage value of the voltage sequence corresponding to the candidate phase in the total table within the sample interval; Step S303: Select the candidate phase with the largest Pearson correlation coefficient as the assigned phase of the corresponding single-phase sub-meter, and retain only the total meter power data and the corresponding sub-meter power data under the assigned phase to form a phase detection dataset.

6. The method for detecting meter inaccuracy based on high-frequency sampling data of an electricity consumption acquisition system as described in claim 1, characterized in that, Step S40, which involves using a sliding window sample construction and regularized linear regression solution mechanism based on the phase detection dataset to perform the correction coefficient calculation task and output the correction coefficient sets for each sub-table, specifically includes: Step S401: Establish a linear regression model between the total meter charge and the charge of the same sub-meter for each phase in the phase detection dataset. Let the total meter charge be Y, and the vector of charge of the same sub-meter be X=[X1,X2,…,X…]. n ], where n is the length of the same sub-meter power vector; X n For the nth in-phase sub-meter, the electrical quantity value within the corresponding sampling window is defined; a linear relationship model is established: Where e represents the comprehensive error term; This is the correction coefficient corresponding to the nth sub-meter, used to characterize the proportional relationship between the electricity consumption of this sub-meter and the total electricity consumption; Based on the linear relationship model, a sliding window strategy is used to construct M sets of samples, forming an overdetermined system of equations, where M is greater than n; Step S402: Perform normalization on each column of the sample input matrix in the overdetermined system of equations, outputting a normalized matrix; and construct normal equations with regularization terms based on the normalized matrix: ;in, This represents a sample input matrix consisting of M groups of sliding window samples, where each row of the sample input matrix corresponds to the power observation value of each sub-table under a sampling window; Representation matrix The transpose of the matrix; This represents the regularization parameter, and λ≥0; Represents the identity matrix; This represents the normalized coefficient vector obtained by solving the normalization matrix; This represents the total meter reading vector corresponding to the sample input matrix; Step S403: Solve the normal equation with regularization terms using Gaussian elimination to obtain the normalized set of correction coefficients for each sub-table.

7. The method for detecting meter inaccuracy based on high-frequency sampling data from an electricity consumption acquisition system as described in claim 1, characterized in that, Step S50, which involves calculating the misalignment ratio, determining the threshold, and reporting the results based on the correction coefficient set for each sub-meter, and outputting the meter misalignment detection result set, specifically includes: Step S501: Obtain the correction coefficient of the i-th sub-meter from the meter misalignment detection result set. According to the table correction coefficient Calculate the inaccuracy ratio of the corresponding sub-table. , ; when When the corresponding submeter is in normal condition, it is determined that the meter reading is normal. when At that time, it was determined that the corresponding sub-table was moving too slowly; when At that time, it was determined that the corresponding sub-table was moving too fast; Step S502: Adjust the misalignment ratio Compared with the preset misalignment threshold Compare, when satisfied When this happens, the corresponding sub-meter is marked as a faulty meter, and a list of faulty meters and suggested correction coefficients are generated. Step S503: Upload the list of inaccurate meters, suggested correction coefficient information and inaccuracy ratio to the operation and management terminal through the preset remote communication unit, and output the meter inaccuracy detection result set.

8. A meter misalignment detection system based on high-frequency sampling data of an electricity consumption acquisition system, applied to the meter misalignment detection method based on high-frequency sampling data of an electricity consumption acquisition system as described in any one of claims 1 to 7, characterized in that, The meter misalignment detection system based on high-frequency sampling data from the electricity consumption acquisition system includes: The data construction module is used to acquire high-frequency collected data from the master table and each sub-table within the target area. Based on the high-frequency collected data, it uses a time synchronization correction and window mapping construction mechanism to execute the original detection dataset construction task and outputs a synchronized original detection dataset. The validity screening module is used to perform anomaly screening tasks based on the synchronous raw detection dataset using a multi-dimensional validity verification mechanism, and output a valid detection dataset. The phase recognition module is used to perform a phase-separation preprocessing task based on the effective detection dataset using a voltage-related phase recognition mechanism, and outputs a phase-separated detection dataset. The coefficient calculation module is used to perform the task of calculating correction coefficients based on the phase detection dataset by using a sliding window sample construction and regularized linear regression solution mechanism, and outputs the correction coefficient set of each sub-table; The result determination module is used to calculate the misalignment ratio, determine the threshold, and report the results based on the correction coefficient set of each sub-meter, and output the meter misalignment detection result set.

9. A meter misalignment detection device based on high-frequency sampling data from an electricity consumption acquisition system, characterized in that, The meter misalignment detection device based on high-frequency sampling data of the electricity consumption acquisition system includes: a memory, a processor, and a meter misalignment detection program based on high-frequency sampling data of the electricity consumption acquisition system stored in the memory and executable on the processor. When the meter misalignment detection program based on high-frequency sampling data of the electricity consumption acquisition system is executed by the processor, it implements the meter misalignment detection method based on high-frequency sampling data of the electricity consumption acquisition system according to any one of claims 1 to 7.

10. A computer program product, characterized in that, The computer program product includes a meter misalignment detection program based on high-frequency sampling data from an electricity consumption acquisition system. When the meter misalignment detection program based on high-frequency sampling data from an electricity consumption acquisition system is executed by a processor, it implements a meter misalignment detection method based on high-frequency sampling data from an electricity consumption acquisition system as described in any one of claims 1 to 7.

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