Dynamic returning, supplementing and restoring method and system for electric quantity data and medium

Abstract

The invention discloses a method and system for dynamically returning, supplementing and restoring electric quantity data and a medium, and relates to the technical field of electrical measurement, and the method comprises the steps: building the direct connection between an electric power medium station and a first data source end and a second data source end; introducing a bidirectional asymmetric return and compensation protocol, deploying a return and compensation decision maker in a power grid middle station, performing data interaction with the first data source end and the second data source end, executing a return and compensation decision under a double-end decision, and determining electric quantity return and compensation restoration data; and according to the electric quantity returning, supplementing and restoring data, executing returning, supplementing and restoring management of power grid electric power measurement. According to the invention, the technical problems of bidirectional metering deviation and poor accuracy and adaptability of back-and-compensation processing caused by the fact that the existing electric quantity back-and-compensation method cannot effectively deal with electric energy quality fluctuation and metering information loss are solved; the technical effects of accurate quantification and responsibility division of two types of metering deviations and improvement of the accuracy and adaptability of return and supplement processing are achieved.

Description

Technical Field

[0001] This invention relates to the field of electrical measurement technology, specifically to a method, system, and medium for dynamic data recovery and restoration. Background Technology

[0002] In existing power metering systems, energy measurement typically relies on basic quantities such as voltage and current for energy accumulation. However, power quality issues such as harmonic pollution, voltage sags, three-phase imbalance, and transient disturbances are prevalent in power grid operation. These factors can cause waveform distortion, measurement deviations, and loss of effective information in the metering link, making traditional compensation methods based on energy differences unable to accurately reflect actual electricity consumption. Simultaneously, user-side load characteristics can also negatively impact power grid operation quality, resulting in bidirectional and dynamic metering errors. Existing technologies primarily rely on unidirectional error correction or fixed compensation rules, lacking quantitative analysis of the impact of power quality differences, failing to identify the sources of metering loss at the information level, and unable to achieve dynamic allocation of responsibility between the load side and the grid side. Summary of the Invention

[0003] This application provides a dynamic power data refund / replacement method, system, and medium to solve the technical problem that existing power data refund / replacement methods cannot effectively cope with bidirectional metering deviations caused by power quality fluctuations and loss of metering information, and that the accuracy and adaptability of refund / replacement processing are poor.

[0004] The first aspect of this application provides a dynamic refund / replacement method for electricity data. The method includes: establishing a direct connection between a power grid platform and a first data source and a second data source; introducing a bidirectional asymmetric refund / replacement protocol, deploying a refund / replacement decision-maker in the power grid platform, and executing a refund / replacement decision under a dual-end decision through data interaction with the first and second data sources to determine the electricity refund / replacement restoration data. The dual-end decision includes a refund / replacement decision based on quality level differentiation according to the power quality spectrum and a refund / replacement decision based on metering information loss and distortion according to information enthalpy quantification; and performing refund / replacement restoration management of power grid metering based on the electricity refund / replacement restoration data.

[0005] A second aspect of this application provides a dynamic power data refund / replacement / restoration system, the system comprising: a direct connection structure establishment module for establishing a direct connection between a power grid platform and a first data source and a second data source; a dual-end refund / replacement decision module for introducing a bidirectional asymmetric refund / replacement protocol, deploying a refund / replacement decision-maker in the power grid platform, and executing a refund / replacement decision under dual-end decision through data interaction with the first data source and the second data source to determine the power data refund / replacement / restoration data, wherein the dual-end decision includes a refund / replacement decision based on the quality level difference of the power quality spectrum and a refund / replacement decision based on the loss and distortion of metering information based on information enthalpy quantification; and a refund / replacement / restoration management module for performing power grid metering refund / replacement / restoration management according to the power data refund / replacement / restoration.

[0006] A third aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method of the first aspect.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages: This application provides a dynamic refund / recovery method, system, and medium for electricity data, relating to the field of electrical measurement technology. It constructs a dual-end dynamic refund / recovery system based on power quality spectrum and information enthalpy quantification, and deploys a refund / recovery decision-maker in the power grid central platform. Through data interaction with the first and second data sources, it executes dual-end decisions based on power quality spectrum and information enthalpy quantification to accurately determine the refund / recovery data. This solves the technical problem that existing electricity refund / recovery methods cannot effectively cope with bidirectional measurement deviations caused by power quality fluctuations and loss of measurement information, resulting in poor accuracy and adaptability of refund / recovery processing. It achieves the technical effect of accurately quantifying and assigning responsibility for two types of measurement deviations by constructing a dual-end decision-making mechanism based on power quality spectrum and information enthalpy quantification, thereby improving the accuracy and adaptability of refund / recovery processing. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying 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.

[0009] Figure 1 This is a schematic flowchart of a method for dynamically compensating for and restoring electricity data, provided in an embodiment of this application. Figure 2 This is a schematic diagram of a dynamic power data recovery system provided in an embodiment of this application.

[0010] Explanation of reference numerals in the attached diagram: Direct connection structure establishment module 11, dual-end refund / replacement decision module 12, refund / replacement restoration management module 13. Detailed Implementation

[0011] This application provides a dynamic power data refund / replacement method, system, and medium to solve the technical problem that existing power data refund / replacement methods cannot effectively cope with bidirectional metering deviations caused by power quality fluctuations and loss of metering information, and that the accuracy and adaptability of refund / replacement processing are poor.

[0012] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0013] It should be noted that the terms "first," "second," etc., in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules not explicitly listed or inherent to such processes, methods, products, or devices.

[0014] Example 1, as Figure 1 As shown, this application provides a method for dynamic compensation and restoration of electricity data, the method comprising: P10: Establish direct connections between the power middle platform and the first and second data sources.

[0015] Furthermore, step P10 in this embodiment of the application also includes: P11: The first data source is a smart terminal deployed at a key node on the load side, and the second data source is a sensor array deployed in the power grid metering loop; wherein, the smart terminal is used to analyze the quality spectrum of the incoming power in real time and generate an additional quality fraction.

[0016] It should be understood that the first step is to establish a direct data path between the power data processing platform and multi-source metering data. This would enable the platform to acquire electricity measurement information from both the user side and the grid side on the same time scale, thus ensuring that subsequent compensation and refund analyses have an accurate, synchronous, and verifiable data foundation. In other words, as the core data processing and decision-making platform, the power data processing platform needs to establish stable and efficient connections with the two key data sources to achieve real-time monitoring and analysis of electricity data.

[0017] Specifically, intelligent terminals are deployed at key nodes on the load side, i.e., the user's electricity consumption side, serving as the primary data source. These intelligent terminals possess local signal acquisition and edge analysis capabilities, enabling real-time analysis of the electrical waveforms entering the user's side, including parameters such as instantaneous voltage and current values, harmonic content, voltage sags and swells, and three-phase imbalance. After capturing this measurement data, the intelligent terminal converts the waveform characteristics into a quality spectrum. The quality spectrum is a set of multi-dimensional features characterizing the health of the electrical waveform, such as the amplitude variations of each harmonic and the degree to which the voltage waveform deviates from the standard sine wave. After generating the quality spectrum, the intelligent terminal compares it to the standard power supply quality range to further generate an additional quality score, quantifying the quality level and deviation of the input electrical energy. This additional quality score will subsequently serve as an important criterion for judging the quality of the electrical energy and will be directly used in decision-making regarding power quality adjustments.

[0018] Meanwhile, the second data source is deployed within the power grid metering loop, employing a sensor array as the data acquisition method. This sensor array, composed of multiple high-precision sensors, can monitor key parameters such as current and voltage in the power grid metering loop in real time, ensuring the accuracy and reliability of the metering data. Unlike traditional single-point metering, the sensor array can provide the operating status at different locations within the metering link, enabling the power platform to grasp the overall energy transmission situation from the grid side to the user side, including whether disturbances, noise coupling, or signal distortion occur in the metering link. This provides more comprehensive data support for power quality assessment.

[0019] By using the intelligent terminal on the load side and the sensor array in the power grid metering loop as the first and second data sources, respectively, and establishing a direct connection with the power data center, comprehensive coverage from power quality monitoring to metering data acquisition can be achieved. This direct connection mode reduces the latency and potential errors caused by additional aggregation nodes, helps to achieve high-precision time synchronization, and ensures data integrity and consistency, providing accurate input for subsequent bidirectional compensation algorithms.

[0020] P20: Introducing a bidirectional asymmetric refund / refund protocol, deploying a refund / refund decision-maker in the power grid middle platform, and executing refund / refund decisions under the bidirectional decision-making through data interaction with the first data source end and the second data source end to determine the power refund / refund restoration data. The bidirectional decision-making includes refund / refund decisions based on the quality level differentiation of power quality spectrum and refund / refund decisions based on the loss and distortion of metering information based on information enthalpy quantification.

[0021] Specifically, a compensation and refund decision-making system that distinguishes responsible parties can be built within the power data center to achieve dynamic restoration and compensation for electricity metering deviations and power quality issues. Firstly, a two-way asymmetric compensation and refund protocol is introduced to clarify which party should bear the responsibility for compensation and refund when damage, distortion, or contamination occurs in the power transmission and metering link. For example, when the power quality provided by the grid deteriorates, causing distortion or deviation from the normal quality range in the actual power waveform received by the user-side equipment, the protocol stipulates that the grid side should compensate the user side. Conversely, if the user-side load generates strong harmonics, impulsive loads, or other interference, which contaminates the grid-side metering link in reverse, causing a deterioration in power quality or metering errors on the grid side, then the user side should bear the responsibility for compensation and refund. This asymmetric structure ensures that the determination of the responsible party is clear and enforceable.

[0022] To ensure the dynamic implementation of the aforementioned agreement in actual operation, a power supply refund / refund decision-maker was deployed in the power grid middleware platform. This decision-maker continuously acquires the power quality status on the user side and the operational status of the metering link on the grid side through real-time data interaction with the first and second data sources. Based on the synchronized data stream, it performs a dual-end decision, that is, it comprehensively judges whether the power needs to be refunded / refunded, the amount of refund / refund, and the responsible party for refund / refund from both the perspective of power quality on the user side and the perspective of the integrity of metering information on the grid side.

[0023] The dual-end decision-making process comprises two key components. First, it involves differentiated compensation / reduction decisions based on power quality spectrum (HMS) levels. The HMS reflects the deviation of actual power supply quality from the normal standard. Therefore, the decision-maker calculates the HMS level based on parameters such as harmonic components, waveform distortion index, and sag / boost conditions within the HMS. Different HMS levels correspond to different compensation / reduction rules. For example, the more severe the quality degradation, the higher the compensation / reduction ratio, thus compensating for low-quality power received by the user.

[0024] Secondly, the decision-making process for compensation of lost or distorted metering information is based on enthalpy quantification. Enthalpy is an indicator of information integrity and accuracy, reflecting the amount of signal information in the power waveform that can be effectively identified and utilized by the metering link. In the power grid, an ideal sinusoidal signal has the highest enthalpy, while distorted signals lead to a decrease in enthalpy. The decision-maker quantifies the degree of metering information loss based on the waveform characteristics provided by the grid-side sensor array and converts the decrease in enthalpy into a compensation factor to compensate for metering deviations caused by metering link distortion. For example, when metering information is distorted due to grid disturbances, the grid side needs to provide corresponding compensation to the user side to compensate for losses caused by metering errors.

[0025] By superimposing and cross-verifying the above two types of decisions, the power refund / refund decision-maker can generate the final power refund / refund restoration data, thereby achieving overall correction of energy deviation, quality deviation, and information deviation.

[0026] Furthermore, in the data interaction with the first data source, step P20 of this application embodiment also includes: P21: Based on the quality impairment event, define a first quality loss class based on the load side and a second quality loss class based on the grid side; P22: Based on the smart terminal, perform real-time incoming power analysis to determine the quality spectrum sequence, wherein the additional quality fraction of the first quality loss class is a positive value of the sequence, and the additional quality fraction of the second quality loss class is a negative value of the sequence; P23: Transmit the quality spectrum sequence to the power platform.

[0027] Optionally, the data interaction process with the primary data source, namely the load-side intelligent terminal, can be further refined. During implementation, based on quality impairment events such as voltage sags, harmonic pollution, phase imbalance, and transient interruptions, two categories of quality losses are clearly distinguished: the first category is the primary quality loss, characterizing user-side damage caused by a decline in grid power supply quality; the second category reflects the reverse impact on the grid side caused by harmonics or impulse disturbances introduced by user-side loads. This classification method allows for a clear definition of the source and responsibility for quality impairments, laying the foundation for determining the direction of subsequent compensation and remediation.

[0028] Next, when the intelligent terminal continuously analyzes the real-time incoming power, it maps the aforementioned quality impairment events into a quality spectrum sequence in chronological order. The quality spectrum sequence is a collection of power quality characteristics, reflecting the real-time state of power quality. In this sequence, the additional quality score for the first quality loss category is defined as a positive value, representing the increased losses on the user side due to grid quality issues; while the additional quality score for the second quality loss category is defined as a negative value, representing the increased losses on the grid side due to user load characteristics. Through this positive-negative distinction, the same quality spectrum sequence can simultaneously cover quality impacts in two directions, enabling subsequent decision-makers to perform superimposed analysis and directional judgment of quality deviations within a unified dimension.

[0029] After the mass spectrum sequence is generated, the intelligent terminal transmits the completed mass spectrum sequence to the power platform in the form of a continuous data stream. Since the mass spectrum sequence contains event-level quantization information with high temporal resolution, the platform can directly use it to dynamically calculate the compensation coefficient, identify the mass fluctuation trend, and cross-verify it with the data provided by the grid-side sensor array, thereby ensuring the accuracy, real-time performance, and traceability of compensation decisions.

[0030] Furthermore, based on the quality level differentiation decision of the power quality spectrum, step P20 in this application embodiment also includes: P24: The first branch of the refund / refund decision-maker receives the quality spectrum sequence and calls the first protocol part of the bidirectional asymmetric refund / refund protocol to perform protocol rule matching and decision-making on the sequence nodes of the quality spectrum sequence one by one to determine the first refund / refund data; wherein, the first protocol part defines the differentiated tariff rates for different quality electricity, including the load-side refund / refund direction and the grid-side refund / refund direction.

[0031] Specifically, in order to refine the refund and subsidy process for different quality levels of electricity and ensure that the refund and subsidy judgment has a clear basis for implementation, the specific implementation process of the refund and subsidy decision based on the quality level differentiation of the electricity quality spectrum can be further refined.

[0032] During implementation, the first branch of the power quality decision-making unit first receives the quality spectrum sequence uploaded by the smart terminal. The quality spectrum sequence records the characteristic quantities of various quality impairment events and their corresponding additional quality scores in chronological order, thus reflecting the power quality fluctuations experienced by the user during operation. After receiving the sequence, the first branch analyzes each node point by point according to the chronological order of the sequence nodes. Each sequence node is a single event point or characteristic point that constitutes the quality spectrum sequence, and each node contains basic information for decision-making, such as event type, event intensity, and additional quality score.

[0033] During the parsing process, the first branch will sequentially call the first protocol part of the bidirectional asymmetric compensation protocol. This protocol part predefines the differentiated rate ranges corresponding to different power quality levels; the greater the deviation of the power quality from the standard, the higher the compensation amount. Simultaneously, the protocol clearly distinguishes between the load-side compensation direction and the grid-side compensation direction. For example, when the additional quality score corresponding to the sequence node is positive, it indicates that the user side has suffered quality damage from the grid, and according to the protocol, compensation should be provided to the user from the grid side. When the additional quality score corresponding to the sequence node is negative, it indicates that the user load has polluted the grid, and the protocol stipulates that compensation should be provided from the user side to the grid side.

[0034] The first branch of the compensation decision-maker performs protocol rule matching and decision-making for each node in the quality spectrum sequence. Specifically, it analyzes each node in the sequence, which represents the state of power quality at different points in time. Then, based on the damage level contained in the node, it maps the node's additional quality score to the rate range defined by the protocol. For example, mild harmonic fluctuations may correspond to a low-level compensation range, while severe voltage sags may correspond to a high-level compensation range. By sequentially matching each node with the differentiated rates stipulated in the protocol, the first branch can calculate the specific compensation amount for each node, ultimately forming continuous first compensation data in the time dimension. The first compensation data directly reflects the compensation responsibilities and corresponding compensation magnitudes of the user side and the grid side in the quality dimension, and can serve as an important input for subsequent comprehensive compensation results.

[0035] Furthermore, prior to the decision on loss or distortion of measurement information based on information enthalpy quantification, step P20 in this embodiment of the application further includes: P25: Determine the first quantization relationship, wherein the first quantization relationship is the correlation between the waveform purity, signal-to-noise ratio, timing accuracy and information enthalpy of the electrical signal; P26: Quantize the metering loop of the physical power grid into a non-ideal information channel; P27: Based on the first quantization relationship and the non-ideal information channel, construct the power grid information channel.

[0036] In one possible embodiment of this application, in order for the power metering decision-maker to further quantify the loss and distortion of metering information beyond the quality dimension, it is necessary to model the information characteristics of the power metering channel before performing distortion decision based on information enthalpy quantization.

[0037] In the implementation process, it is first necessary to establish a quantitative relationship between power waveform characteristics and information enthalpy. This embodiment considers the electrical signals involved in the electricity metering process as carriers of effective metering information, and the amount of effective information used for metering is characterized by information enthalpy. The higher the information enthalpy, the more useful information in the waveform that can be accurately measured and effectively identified. Therefore, it is first necessary to determine the first quantitative relationship, namely the correlation between the waveform purity, signal-to-noise ratio, timing accuracy, and information enthalpy of the electrical signal. Waveform purity reflects the degree to which the electrical signal approximates a standard sine wave; signal-to-noise ratio measures the ratio of useful components to noise components in the waveform; timing accuracy measures the stability and consistency of voltage and current sampling points on the time coordinate. All three indicators directly affect the amount of effective information the metering system can use to identify and process electrical energy. Therefore, through theoretical derivation and experimental calibration, they are mapped together to the information enthalpy value. For example, by analyzing metering data under different waveform purity, signal-to-noise ratio, and timing accuracy conditions, the specific degree of their influence on information enthalpy can be determined, and corresponding quantitative formulas or models can be formed.

[0038] Next, a quantitative analysis of the metering loop in the physical power grid is conducted. The metering loop extends from the grid inlet to the meter chip; any distortion or loss in this process can lead to a decrease in the accuracy of metering information. Quantifying the metering loop of the physical power grid as a non-ideal information channel means acknowledging and quantifying various interference factors present in the actual power grid, such as harmonics, noise, and voltage dips. These factors lead to a decrease in information enthalpy, thus affecting the accuracy and integrity of metering information. Specific operations include monitoring key nodes in the metering loop, collecting relevant data, analyzing this data, identifying the main interference factors and their impact on metering information, and establishing mathematical models to quantify the degree of influence of these interference factors on information enthalpy. For example, high-precision sensors can be installed at different locations in the metering loop to monitor changes in parameters such as voltage and current in real time, analyze the relationship between these parameter changes and interference factors, and thus quantify the impact of interference factors on information enthalpy.

[0039] Finally, based on the first quantization relation and the characteristics of non-ideal information channels, a power grid information channel model is constructed. This model comprehensively considers various physical characteristics in the power transmission process and their impact on metering information, and can more accurately reflect the loss and distortion of metering information in the actual power grid. For example, parameters such as waveform purity, signal-to-noise ratio, and timing accuracy determined in the first quantization relation can be combined with interference factors in non-ideal information channels. Through mathematical modeling and simulation analysis, a power grid information channel model that reflects changes in actual power grid metering information can be constructed. With this combination, the power grid central station can use this channel model to calculate the loss of metering information in segments, and in subsequent steps, map the decrease in information enthalpy to the distortion compensation amount that needs to be replenished.

[0040] Furthermore, based on the decision-making process for lost or distorted measurement information using information enthalpy quantification, step P20 in this embodiment of the application also includes: P28: Perform full waveform sampling of electrical data from the metering loop and transmit it back to the second branch of the compensation decision-maker; P29: Perform spectral entropy and approximate entropy analysis on the electrical data to determine the information enthalpy value, wherein the spectral entropy is a waveform frequency domain feature quantization, and the approximate entropy is based on time domain complexity quantization; P210: Perform Granger causality analysis on the information enthalpy value to determine the information enthalpy disturbance, wherein the information enthalpy disturbance is a load-side disturbance or a grid-side disturbance.

[0041] Optionally, after the construction of the power grid information channel is completed, in order to realize the compensation and refund of lost or distorted metering information based on information enthalpy quantification, the compensation and refund decision-maker needs to identify the source of disturbance and assign the responsibility for compensation and refund based on the actual metering data.

[0042] First, the voltage and current signals in the metering loop are sampled in their entirety, and the sampling results are transmitted back to the second branch of the compensation decision-maker in real time. Full waveform sampling means not only acquiring traditional low-dimensional metering data such as RMS and average values, but also obtaining the complete time-domain shape of the energy waveform and its frequency components, thus ensuring a sufficient data foundation for subsequent enthalpy calculations. The sampling range of the full waveform data spans the entire metering path from the grid inlet to the meter chip, enabling the second branch to comprehensively grasp the waveform evolution in the metering link, including real waveform distortion caused by harmonic coupling, noise superposition, and voltage waveform stretching or compression.

[0043] Next, the second branch performs spectral entropy and approximate entropy analysis on the returned full waveform data to calculate the corresponding information enthalpy. Spectral entropy is a quantitative analysis based on the frequency domain characteristics of the waveform. Through frequency domain analysis methods such as Fourier transform, the electrical data is converted from the time domain to the frequency domain, and its frequency distribution characteristics are analyzed to calculate the spectral entropy value. Spectral entropy reflects the complexity and information content of electrical data in the frequency domain. If the waveform is close to an ideal sine wave, its energy is concentrated in the fundamental wave, resulting in a lower spectral entropy. If it is contaminated by harmonics or interference, causing energy to diffuse among multiple frequency components, the spectral entropy increases significantly. Approximate entropy is a quantitative analysis based on time domain complexity. By analyzing the variation and complexity of electrical data in the time domain, the approximate entropy value can be calculated. Approximate entropy reflects the regularity and information content of electrical data in the time domain. For example, if pulse disturbances, transient rises and falls, or abrupt changes occur in the time domain, the approximate entropy will fluctuate significantly. Combining the analysis results of spectral entropy and approximate entropy, the information enthalpy value of the electrical data can be comprehensively determined, that is, the effective amount of information carried in electrical energy that can be accurately measured.

[0044] Finally, Granger causality analysis is performed on the determined information enthalpy value to identify the source direction of the information enthalpy disturbance. Granger causality analysis is a statistical analysis method used to determine the causal relationship between time series, that is, to determine whether the change in one series statistically causes the change in another series. By using the user-side waveform series and the grid-side waveform series as inputs for causality analysis, it can be determined whether the decrease in information enthalpy is triggered by a disturbance on the load side or caused by fluctuations on the grid side. For example, when a strong harmonic injection occurs on the load side, the load-side sequence will have a significant causal contribution to the decrease in information enthalpy; conversely, when a sag or frequency disturbance occurs on the grid side, the grid-side sequence will show a dominant causal relationship to the change in information enthalpy. Based on the results of this causality analysis, the compensation decision-maker can identify the source of the information enthalpy disturbance and attribute it to either a load-side disturbance or a grid-side disturbance, thereby clarifying the responsible party for subsequent distortion compensation calculations.

[0045] Furthermore, after determining the information enthalpy perturbation method, step P20 in this embodiment of the application further includes: P211: Using the aforementioned information enthalpy value as a benchmark, perform counterfactual causal reasoning based on the electrical characteristics of the metering loop to reconstruct the normal waveform pattern under undisturbed conditions; P212: Using the normal waveform pattern as a reference, call the second protocol part of the bidirectional asymmetric compensation protocol to calculate the effective energy difference carried by the waveform distortion, as the second compensation data, wherein the second protocol part defines information enthalpy conservation; wherein the calculation method based on the second protocol part includes: a. Define a nulling quantum, wherein the nulling quantum is the smallest nulling unit; b. Using the normal waveform mode as a reference, quantize the information enthalpy difference based on the information enthalpy value, and calculate the number of quantumes based on the nulling quantum; c. Determine the effective energy difference based on the number of quantumes according to the nulling protocol rules of the information enthalpy perturbation party.

[0046] It should be understood that after identifying the source of the information enthalpy disturbance, the refund / refund decision-maker needs to further clarify the scale of the metering deviation caused by the disturbance and convert the effective power loss corresponding to the decrease in information enthalpy into actionable refund / refund data.

[0047] First, using the information enthalpy value as a benchmark, and combining the physical characteristics of the power waveform in the metering loop with the identified disturbance direction, waveform reconstruction based on counterfactual causal reasoning is performed. This process aims to infer the normal waveform pattern that the metering loop should have under undisturbed conditions by analyzing current electrical data and known power characteristics. For example, electrical data in the metering loop, including key parameters such as voltage and current, is first collected. Then, counterfactual causal reasoning is performed, utilizing known power characteristics, such as the impedance characteristics of the power grid and the dynamic characteristics of the load, combined with the current electrical data, to simulate the operating state of the power grid under undisturbed conditions through mathematical modeling and simulation analysis, thereby inferring the normal waveform pattern under undisturbed conditions. This pattern not only includes the ideal waveform shape but also its corresponding information enthalpy benchmark value, which can provide a reference for subsequent compensation calculations.

[0048] Next, using the reconstructed normal waveform as a reference, the second protocol part of the bidirectional asymmetric compensation protocol is invoked to calculate the effective energy difference caused by waveform distortion, thus forming the second compensation data. The second protocol part is based on the principle of information enthalpy conservation. That is, in an ideal lossless metering system, if there is no distortion in the metering link, the information enthalpy carried by the waveform should remain unchanged. Therefore, any decrease in information enthalpy can be mapped to an effective energy loss that should be compensated. For example: First, define the offset quantum. The offset quantum is the smallest offset unit obtained after discretizing the information enthalpy difference value. It is used to unify the energy deviation caused by complex waveform distortion in metering, so that the final offset value has linear superposition and operational feasibility. The size of the quantum can be configured according to the metering accuracy level, power grid operating characteristics, or regulatory standards.

[0049] Then, using the normal waveform pattern as a reference, the information enthalpy difference between the actual waveform and the normal pattern is quantized. The information enthalpy difference can be understood as the amount of effective measurement information lost due to noise, harmonics, disturbances, or decreased sampling accuracy. This difference can be calculated by comparing the current waveform with the normal waveform pattern. This difference reflects the effective energy loss or increase caused by disturbances. Next, the information enthalpy difference is quantized into the number of de-compensation quanta. The greater the decrease in information enthalpy, the greater the number of de-compensation quanta required. Therefore, the specific number of quanta can be determined by dividing the information enthalpy difference by the de-compensation quanta.

[0050] Finally, based on the direction of the disturbance, and according to the rules of the second protocol section, the quantum quantity is converted into an effective energy difference. For example, when the disturbance originates from the grid side, it indicates that an anomaly on the grid side has caused a decrease in metering information; in this case, compensation should be returned from the grid side to the user side. When the disturbance originates from the load side, it indicates that interference introduced by the user load has caused a decrease in information enthalpy; in this case, compensation should be returned from the user side to the grid side. By multiplying the quantum quantity by the energy conversion coefficient corresponding to the compensation direction, the final effective energy compensation value can be obtained. This difference will be used as the second compensation data for subsequent compensation operations.

[0051] Furthermore, after executing the withdrawal / repair decision under the dual-end decision-making process, step P20 of this application embodiment also includes: P213: Verify the first refund data and the second refund data. If they match, generate the power refund restoration data. P214: If they do not match, divide the consistent refund data into consistent and inconsistent refund data. Average the inconsistent refund data and integrate them to generate the power refund restoration data.

[0052] Specifically, after completing the differentiated refund / refund decision based on power quality spectrum and the distortion refund / refund decision based on information enthalpy quantification, it is necessary to further verify the consistency of the two types of decision results to ensure the reliability of the final refund / refund result.

[0053] First, the first setback / refund data (derived from the quality level-based setback / refund decision based on the power quality spectrum) and the second setback / refund data (derived from the metering information loss and distortion decision based on information enthalpy quantification) are cross-verified. The purpose of this cross-verification is to check whether the setback / refund data obtained from the two different decision paths are consistent. Specifically, the first and second setback / refund data are compared and analyzed to check for matching in terms of values, directions, etc. When the two types of setback / refund data are consistent in key dimensions such as the determination of the responsible party, the time period of the setback / refund, and the magnitude of the setback / refund, it indicates a high degree of consistency between the quality dimension and the information dimension in their judgment of the same event. The setback / refund decision-maker can directly output this consistent result as the power setback / refund restoration data, ensuring the accuracy and certainty of the setback / refund behavior.

[0054] When the cross-verification results show inconsistencies between the first and second refund / refund data, the decision-maker will perform a difference decomposition on the two types of refund / refund data to ensure a reasonable balance between different dimensions in the final refund / refund result. First, it distinguishes between consistent and inconsistent refund / refund portions: the consistent portion is where the two types of data are completely consistent in terms of responsibility direction and refund / refund magnitude, and can be used directly; the inconsistent portion is where the two types of data differ in magnitude, direction, or time distribution, and requires further processing. When processing the inconsistent portion, a mean-smoothing method can be used to numerically smooth the refund / refund amounts given by different calculation mechanisms, using this as the final refund / refund data for the inconsistent portion. This method balances the differences between the two decision paths, ensuring that the final electricity refund / refund restoration data considers both power quality issues and the impact of metering information loss and distortion. Finally, the consistent refund / refund portion and the mean-smoothed inconsistent refund / refund portion are integrated to generate complete electricity refund / refund restoration data for subsequent electricity refund / refund operations, effectively avoiding the problems of bias expansion or outlier dominance.

[0055] P30: Based on the power metering refund / refund and restoration data, perform power metering refund / refund and restoration management.

[0056] Optionally, after completing the two-way decision-making, mutual verification and integration of the refund and compensation data, the obtained electricity refund and compensation restoration data can be applied to the power grid's metering management process to realize the correction of metering deviations, quantitative compensation for the impact of power quality, and dynamic adjustment of metering accounts.

[0057] When implementing refund / refund / restoration management, the power platform first corrects the metering results on both the user side and the grid side based on the electricity refund / refund / restoration data. This includes adjusting the electricity data within the billing cycle, recording the refund / refund direction, and clarifying the responsibility for metering deviations. For situations requiring compensation, the system automatically adjusts the electricity receivable / payable or corresponding fees on the user side or grid side according to the refund / refund direction, thereby achieving dynamic compensation for additional energy differences caused by power quality fluctuations or lost metering information. Refund / refund operations include user-side refunds / refunds: for user-side losses caused by grid quality issues, compensation is provided to users according to the refund / refund data, including adjusting user electricity bills, refunding overcharged electricity fees, or compensating for equipment damage caused by power quality issues; and grid-side refunds / refunds: for grid-side losses caused by user load characteristics, corresponding fees are charged to users according to the refund / refund data, including adjusting user electricity bill structures and increasing compensation fees for grid losses caused by user-side interference.

[0058] After the refund / refund operation is executed, the relevant data in the power grid metering system should be updated in a timely manner. This includes updating the metering data and updating the electricity data after the refund / refund to the power grid metering system to ensure that the data in the system reflects the latest refund / refund status. The refund / refund operation should also be recorded, with detailed records of the process and results of each refund / refund operation, including the reason for the refund / refund, the amount of the refund / refund, and the users or power grid nodes involved, to ensure the transparency and traceability of the refund / refund operation.

[0059] In summary, the embodiments of this application have at least the following technical effects: This application accurately reflects the impact of events such as harmonic pollution and voltage dips on metering results by differentially identifying and quantifying power quality impairments; it accurately measures the loss of effective metering information by identifying information loss and waveform distortion in the metering link through information enthalpy calculation; it clarifies and rationalizes the direction of refunds and compensations by automatically tracing disturbances on the power supply side or load side and completing the division of responsibilities; it improves the accuracy and stability of refund and compensation data by cross-verifying and integrating refund and compensation results in the quality dimension and information dimension; and it makes power metering results more consistent with reality and improves the fairness and transparency of metering management by constructing a complete dynamic refund and compensation restoration system.

[0060] The technology achieves the technical effect of accurately quantifying and assigning responsibility for two types of measurement deviations by constructing a dual-end decision-making mechanism based on power quality spectrum and information enthalpy quantification, thereby improving the accuracy and adaptability of refund and compensation processing.

[0061] Example 2, based on the same inventive concept as the dynamic compensation and restoration method for power data in the aforementioned examples, such as... Figure 2 As shown, this application provides a dynamic compensation and restoration system for electricity data. The system and method embodiments in this application are based on the same inventive concept. The system includes: The direct connection structure establishment module 11 is used to establish a direct connection between the power platform and the first data source end and the second data source end.

[0062] The dual-end refund / refund decision module 12 is used to introduce a bidirectional asymmetric refund / refund protocol, deploy a refund / refund decision-maker in the power grid middle platform, and execute refund / refund decisions under dual-end decision-making through data interaction with the first data source end and the second data source end to determine the power refund / refund restoration data. The dual-end decision-making includes refund / refund decisions based on the quality level differentiation of power quality spectrum and refund / refund decisions based on the loss and distortion of metering information based on information enthalpy quantification.

[0063] The refund / reduction management module 13 is used to perform refund / reduction management of power grid metering based on the power refund / reduction data.

[0064] Furthermore, the direct connection structure establishment module 11 is also used to perform the following steps: The first data source is a smart terminal deployed at a key node on the load side, and the second data source is a sensor array deployed in the power grid metering loop; wherein, the smart terminal is used to analyze the quality spectrum of the incoming power in real time and generate an additional quality fraction.

[0065] Furthermore, the dual-end refund / replacement decision module 12 is also used to perform the following steps: Based on the quality impairment events, a first quality loss class based on the load side and a second quality loss class based on the grid side are defined; real-time inflow power analysis is performed using the smart terminal to determine the quality spectrum sequence, wherein the additional quality fraction of the first quality loss class is a positive value of the sequence, and the additional quality fraction of the second quality loss class is a negative value of the sequence; the quality spectrum sequence is transmitted to the power platform.

[0066] Furthermore, the dual-end refund / replacement decision module 12 is also used to perform the following steps: The first branch of the refund / refund decision-maker receives the quality spectrum sequence and calls the first protocol part of the bidirectional asymmetric refund / refund protocol to perform protocol rule matching and decision-making on the sequence nodes of the quality spectrum sequence one by one to determine the first refund / refund data; wherein, the first protocol part defines the differentiated tariff rates for different quality electricity, including the load-side refund / refund direction and the grid-side refund / refund direction.

[0067] Furthermore, the dual-end refund / replacement decision module 12 is also used to perform the following steps: A first quantization relationship is determined, wherein the first quantization relationship is the correlation between the waveform purity, signal-to-noise ratio, timing accuracy and information enthalpy of the electrical signal; for the metering loop of the physical power grid, it is quantized into a non-ideal information channel; based on the first quantization relationship and the non-ideal information channel, a power grid information channel is constructed.

[0068] Furthermore, the dual-end refund / replacement decision module 12 is also used to perform the following steps: The metering circuit performs full waveform sampling of electrical data and transmits it back to the second branch of the compensation decision-maker; spectral entropy and approximate entropy analysis are performed on the electrical data to determine the information enthalpy value, wherein the spectral entropy is a waveform frequency domain feature quantization, and the approximate entropy is based on time domain complex quantization; Granger causality analysis is performed on the information enthalpy value to determine the information enthalpy disturbance, wherein the information enthalpy disturbance is a load-side disturbance or a grid-side disturbance.

[0069] Furthermore, the dual-end refund / replacement decision module 12 is also used to perform the following steps: Based on the information enthalpy value, counterfactual causal reasoning based on the electrical characteristics of the metering loop is performed to reconstruct the normal waveform pattern under undisturbed conditions. Using the normal waveform pattern as a reference, the second protocol part of the bidirectional asymmetric compensation protocol is invoked to calculate the effective energy difference carried by the waveform distortion, which is used as the second compensation data. The second protocol part defines information enthalpy conservation. The calculation method based on the second protocol part includes: setting a compensation quantum, where the compensation quantum is the smallest compensation unit; quantizing the information enthalpy difference based on the information enthalpy value using the normal waveform pattern as a reference, and calculating the quantum quantity based on the compensation quantum; and determining the effective energy difference based on the quantum quantity according to the compensation protocol rules of the information enthalpy perturbation party.

[0070] Furthermore, the dual-end refund / replacement decision module 12 is also used to perform the following steps: The first and second refund / refund data are cross-checked. If they match, the power refund / refund restoration data is generated. If they do not match, the consistent refund / refund portion and the inconsistent refund / refund portion are divided. The inconsistent refund / refund portion is averaged and then integrated to generate the power refund / refund restoration data.

[0071] In Embodiment 3, based on the same inventive concept as the dynamic compensation and restoration method for power data in the foregoing embodiments, this application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method as described in Embodiment 1.

[0072] Through the foregoing detailed description of a dynamic power data compensation and restoration method, those skilled in the art can clearly understand the dynamic power data compensation and restoration method, system, and medium in this embodiment. Therefore, for the sake of brevity, further details are omitted here. As for the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be found in the method section.

[0073] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0074] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0075] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0076] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application intends to include such modifications and variations.

Claims

1. A method for dynamically compensating for and restoring electrical data, characterized in that, The method includes: Establish direct connections between the power data platform and the first and second data sources; A bidirectional asymmetric refund and compensation protocol is introduced, and a refund and compensation decision-maker is deployed in the power grid middle platform. Through data interaction with the first data source end and the second data source end, a refund and compensation decision under the bidirectional decision is executed to determine the power refund and compensation restoration data. The bidirectional decision includes a refund and compensation decision based on the quality level difference of the power quality spectrum and a refund and compensation decision based on the loss and distortion of metering information based on information enthalpy quantification. Based on the electricity refund / refund and restoration data, perform the refund / refund and restoration management of power grid metering.

2. The method for dynamic compensation and restoration of electricity data as described in claim 1, characterized in that, Before establishing direct connections between the power data platform and the first and second data sources, the following steps are included: The first data source is the intelligent terminal deployed at key nodes on the load side, and the second data source is the sensor array deployed in the power grid metering loop. The intelligent terminal is used to analyze the mass spectrum of the incoming electrical energy in real time and generate an additional mass fraction.

3. The method for dynamic compensation and restoration of electricity data as described in claim 2, characterized in that, Through data interaction with the first data source, including: Based on quality impairment events, a first quality loss class based on the load side and a second quality loss class based on the grid side are defined. Based on the intelligent terminal, real-time incoming power analysis is performed to determine the mass spectrum sequence, wherein the additional mass fraction of the first mass loss class is a positive value of the sequence, and the additional mass fraction of the second mass loss class is a negative value of the sequence. The mass spectrum sequence is transmitted to the power platform.

4. The method for dynamic compensation and restoration of electricity data as described in claim 3, characterized in that, Based on the power quality spectrum, the decision to cancel or supplement power based on quality level differentiation includes: The first branch of the withdrawal / replacement decision-maker receives the mass spectrum sequence and calls the first protocol part of the bidirectional asymmetric withdrawal / replacement protocol to perform protocol rule matching and decision-making on the sequence nodes of the mass spectrum sequence one by one to determine the first withdrawal / replacement data; The first agreement defines differentiated tariffs for different quality electricity, including load-side refund and subsidy directions and grid-side refund and subsidy directions.

5. The method for dynamic compensation and restoration of electricity data as described in claim 4, characterized in that, Before making decisions regarding the loss or distortion of measurement information based on information enthalpy quantification, the following should be included: Determine the first quantization relationship, wherein the first quantization relationship is the correlation between the waveform purity, signal-to-noise ratio, timing accuracy and information enthalpy of the electrical signal; For metering loops in the physical power grid, they are quantized into non-ideal information channels; Based on the first quantization relationship and the non-ideal information channel, a power grid information channel is constructed.

6. The method for dynamic compensation and restoration of electricity data as described in claim 5, characterized in that, Decision-making for loss and distortion of metrological information based on information enthalpy quantification includes: The full waveform of the electrical data of the metering circuit is sampled and transmitted back to the second branch of the compensation decision-maker; Spectral entropy and approximate entropy analysis are performed on electrical data to determine the information enthalpy value, wherein the spectral entropy is a waveform frequency domain feature quantization, and the approximate entropy is based on time domain complex quantization; Granger causality analysis is performed on the information enthalpy value to determine the information enthalpy disturbance, wherein the information enthalpy disturbance is either a load-side disturbance or a grid-side disturbance.

7. The method for dynamic compensation and restoration of electricity data as described in claim 6, characterized in that, After determining the perturbation source of the information enthalpy, the following is included: Based on the aforementioned information enthalpy value, counterfactual causal reasoning based on the electrical characteristics of the metering loop is performed to reconstruct the normal waveform pattern under undisturbed conditions. Using the normal waveform mode as a reference, the second protocol part of the bidirectional asymmetric compensation protocol is called to calculate the effective energy difference carried by the waveform distortion as the second compensation data. The second protocol part defines the conservation of information enthalpy. The calculation method based on the second protocol includes: Define a counter-counting quantum, wherein the counter-counting quantum is the smallest counter-counting unit; Using the normal waveform pattern as a reference, the information enthalpy difference based on the information enthalpy value is quantized, and the number of quantum units based on the counter-compensation quantum is calculated; The effective energy difference based on the quantum quantity is determined according to the return-and-replacement protocol rules of the information enthalpy perturbation party.

8. The method for dynamic compensation and restoration of electricity data as described in claim 7, characterized in that, After implementing the withdrawal / refund decision under the two-way decision-making system, the following are included: The first and second refund data are cross-verified. If they match, the power refund restoration data is generated. If the mutual verification is inconsistent, the consistent refund / refund portion and the inconsistent refund / refund portion are divided. The inconsistent refund / refund portion is averaged and then integrated to generate the power refund / refund restoration data.

9. A dynamic compensation and restoration system for electricity data, characterized in that, The system includes: The direct connection structure establishment module is used to establish direct connections between the power platform and the first data source end and the second data source end; The dual-end refund and compensation decision module is used to introduce a bidirectional asymmetric refund and compensation protocol, deploy a refund and compensation decision-maker in the power grid middle platform, and execute the refund and compensation decision under dual-end decision through data interaction with the first data source end and the second data source end to determine the power refund and compensation restoration data. The dual-end decision includes refund and compensation decision based on the quality level difference of power quality spectrum and refund and compensation decision based on the loss and distortion of metering information based on information enthalpy quantification. The refund / reduction / restoration management module is used to perform refund / reduction / restoration management of power grid metering based on the power refund / reduction / restoration data.

10. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the steps of the method according to any one of claims 1 to 8.

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