Electric energy metering error correction method and system based on vector analysis

Abstract

The invention provides an electric energy metering error correction method and system based on vector analysis. Belongs to the technical field of electric energy metering in an electric power system, and comprises the following steps: collecting current and voltage data in a power grid in real time by using a high-precision current and voltage sensor, and transmitting the data to an electric energy metering error correction system for processing; carrying out vector analysis on the collected current and voltage data based on a vector control technology to obtain vector information of the voltage and the current; by collecting current and voltage data in a power grid in real time and utilizing a vector analysis technology, an electric energy metering error can be calculated more accurately, so that the accuracy of electric energy metering is improved.

Description

Technical Field

[0001] The invention proposes an electric energy metering error correction method and system based on vector analysis, belonging to the technical field of electric energy metering in a power system. Background Art

[0002] Electric energy metering is an important part of the power system, and accurate electric energy metering is crucial for energy consumption and power grid management. Traditional electric energy metering methods have the problem of error accumulation, resulting in low metering accuracy. In recent years, vector control technology has become a research hotspot in the field of electric energy metering. By accurately measuring and controlling the voltage and current vectors in the power grid, accurate electric energy metering can be achieved. However, the existing vector control technology still has some technical difficulties, such as high cost and imperfect standard system. Therefore, the present invention proposes an electric energy metering error correction method and system based on vector analysis, aiming to solve the above problems. Summary of the invention

[0003] The present invention provides an electric energy metering error correction method and system based on vector analysis to solve the problems mentioned in the above background technology: The present invention proposes a method for correcting electric energy metering errors based on vector analysis, the method comprising: S1. Use high-precision current and voltage sensors to collect current and voltage data in the power grid in real time, and transmit the data to the electric energy metering error correction system for processing; S2. Perform vector analysis on the collected current and voltage data based on vector control technology to obtain vector information of voltage and current; S3. Calculate the electric energy metering error according to the vector information; and perform error correction on the electric energy meter according to the calculated error; S4. The corrected electric energy metering results are output in real time, and the deviations in the metering process are detected and corrected through feedback signals. At the same time, the electric energy metering results are transmitted to the data center of the electric energy metering system for real-time monitoring and management.

[0004] The present invention proposes an electric energy metering error correction system based on vector analysis, the system comprising: Data acquisition module: uses high-precision current and voltage sensors to collect current and voltage data in the power grid in real time, and transmits the data to the electric energy metering error correction system for processing; Vector analysis module: Based on vector control technology, the collected current and voltage data are vector analyzed to obtain the vector information of voltage and current; Error calculation module: calculates the electric energy metering error according to the vector information; performs error correction on the electric energy meter according to the calculated error; Monitoring and management module: The corrected electric energy metering results are output in real time, and the deviations in the metering process are detected and corrected through feedback signals. At the same time, the electric energy metering results are transmitted to the data center of the electric energy metering system for real-time monitoring and management.

[0005] The beneficial effects of the present invention are as follows: by collecting the current and voltage data in the power grid in real time and using the vector analysis technology, the electric energy metering error can be calculated more accurately, thereby improving the accuracy of electric energy metering; the corrected electric energy metering result is output in real time and transmitted to the data center for monitoring and management, which helps to timely discover and correct the deviation in the metering process and ensure the continuity and reliability of electric energy metering; through data compression, encryption processing and hash function verification and other measures, the security and integrity of the data in the transmission process are ensured, and the data is prevented from being tampered with or lost in the transmission process; the use of multi-channel transmission protocol and intelligent routing algorithm, combined with load balancing strategy, can improve the efficiency of data transmission, reduce data transmission delay, and improve the response speed of the system; based on the real-time status of the power grid and the data transmission requirements, the transmission channel and strategy are dynamically adjusted, and the use of genetic algorithm and machine learning The learning algorithm adaptively optimizes the transmission strategy, improving the flexibility and adaptability of the system; the fast Fourier transform algorithm is used for spectrum analysis, which can more accurately identify the harmonic components and their phase relationships in the power grid, and help to classify and manage the harmonic pollution of the power grid; according to the characteristics of different types of electricity meters, personalized error correction strategies are formulated, which can more effectively correct different types of electricity meters and improve the correction effect; the data center analyzes the performance change trend of the electricity meter based on historical data and correction records, which helps to more effectively maintain and optimize the electricity meter; through the user feedback mechanism, the system can continuously optimize the correction strategy according to user feedback, improve user satisfaction and system practicality; through the above technical solutions, the efficiency of energy management can be improved, energy waste can be reduced, and more accurate electricity metering services can be provided to power grid operators and users. BRIEF DESCRIPTION OF THE DRAWINGS

[0006] Figure 1 It is a step diagram of the method of the present invention; Figure 2 This is a system module diagram of the present invention. DETAILED DESCRIPTION

[0007] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0008] One embodiment of the present invention, as Figure 1 As shown, a method for correcting electric energy metering errors based on vector analysis comprises: S1. Use high-precision current and voltage sensors to collect current and voltage data in the power grid in real time, and transmit the data to the electric energy metering error correction system for processing; S2. Perform vector analysis on the collected current and voltage data based on vector control technology to obtain vector information of voltage and current; S3. Calculate the energy metering error based on the vector information. The error calculation can adopt the existing error calculation methods, such as the least square method, Kalman filter, etc., and perform error correction on the energy meter based on the calculated error. Different correction methods are used for different types of energy meters. For example, for mechanical energy meters, the error can be reduced by adjusting the capacitor and the eccentricity; for electronic energy meters, the error correction can be achieved by adjusting the sensor signal through the software program; for smart energy meters, the dynamic error compensation technology based on the adaptive algorithm can be used for error correction.

[0009] S4. The corrected electric energy metering results are output in real time, and the deviations in the metering process are detected and corrected through feedback signals. At the same time, the electric energy metering results are transmitted to the data center of the electric energy metering system for real-time monitoring and management.

[0010] The working principle of the above technical solution is: use high-precision current and voltage sensors to capture the current and voltage signals in the power grid in real time; convert these signals into digital data and transmit them to the electric energy metering error correction system for further processing; based on vector control technology, conduct in-depth analysis of the collected current and voltage data; through vector analysis, obtain vector information of voltage and current, including key parameters such as amplitude and phase; among them, vector information reveals the dynamic relationship between current and voltage, and provides necessary input for error calculation; according to the vector information, use existing error calculation methods (such as least squares method, Kalman filtering, etc.) to calculate the error of electric energy metering; according to the calculated error, use different correction methods for different types of electric energy meters (mechanical, electronic, smart electric energy meters); for mechanical For electric energy meters, errors are reduced by adjusting physical parameters such as capacitors and eccentricity. For electronic electric energy meters, errors are corrected by adjusting the signal output of the sensor by modifying the software program. For smart electric energy meters, dynamic error compensation technology based on adaptive algorithms is used to adjust the metering parameters in real time to compensate for errors. The correction process aims to improve the accuracy of electric energy metering and ensure the reliability of metering results. The corrected electric energy metering results are output in real time for users or systems to use. Deviations in the metering process are detected and corrected through feedback signals to ensure the continued accuracy of metering results. The electric energy metering results are transmitted to the data center of the electric energy metering system for real-time monitoring and management, so as to promptly discover and deal with potential problems. The real-time monitoring and management functions of the data center help maintain the stable operation of the power grid and improve energy utilization efficiency.

[0011] The effects of the above technical solutions are as follows: by using high-precision current and voltage sensors, the current and voltage data in the power grid can be collected in real time and accurately, providing a reliable basis for subsequent analysis and correction; by performing vector analysis on the collected data, the vector information of voltage and current, including amplitude, phase, etc., can be accurately obtained, thereby more accurately reflecting the actual state of the power grid; by calculating the error based on the vector information and adopting different correction methods for different types of electric energy meters, the error of electric energy metering can be significantly reduced and the accuracy of measurement can be improved; by outputting the corrected electric energy metering results in real time and detecting and correcting the deviations in the metering process, the stability of the metering system can be ensured and the system instability caused by error accumulation can be avoided; the electric energy metering results are transmitted to the data center of the electric energy metering system for real-time monitoring and management, which helps to timely discover and deal with potential The problem is solved, further enhancing the stability of the system; accurate electricity metering helps to accurately evaluate energy consumption and provide strong support for energy management and optimization; for smart electricity meters, the dynamic error compensation technology based on adaptive algorithms can achieve more intelligent and efficient error correction, further improving the energy utilization efficiency; through accurate error correction and real-time monitoring, the failure rate of electricity meters can be reduced, and the downtime and maintenance costs caused by failures can be reduced; accurate measurement and correction can also extend the service life of electricity meters and reduce replacement frequency and cost; this method combines advanced technologies such as high-precision sensors, vector analysis technology, and adaptive algorithms to promote the development of power grids towards intelligence and digitization; through real-time monitoring and management of electricity metering data, it can provide data support for the intelligent management of power grids and promote the intelligent upgrade and transformation of power grids.

[0012] In one embodiment of the present invention, the S1 includes: S11, real-time capture of current and voltage fluctuations in the power grid and current through high-precision current and voltage sensors deployed in the power grid; S12, the sensor transmits the captured current and voltage data to the data receiving module of the electric energy metering error correction system through a multi-channel transmission protocol; S13: The data receiving module pre-processes the received data and stores the pre-processed data in a database of the system.

[0013] The working principle of the above technical solution is: the small fluctuations of current and voltage in the power grid are captured in real time by high-precision current and voltage sensors deployed at key positions of the power grid; and the sensors use advanced measurement technology and signal processing algorithms to ensure that the instantaneous value, peak value, average value and other key parameters of current and voltage can be accurately captured in a complex and changeable power grid environment; after the sensor captures the data, it sends these data to the electric energy metering error correction system through a multi-channel transmission protocol. Among them, the multi-channel transmission protocol ensures the efficient and stable transmission of data, and can ensure the integrity and accuracy of the data even when the power grid load is large or the network condition is poor; during the data transmission process, a stable and reliable communication link is established between the sensor and the system to ensure the real-time and synchronization of the data; after the data receiving module of the electric energy metering error correction system receives the data transmitted by the sensor, it first performs preprocessing. The preprocessing process includes steps such as data format conversion, data verification, and data denoising; the raw data transmitted by the sensor is converted into a format that the system can recognize for subsequent processing and analysis; the received data is verified to ensure the integrity and accuracy of the data. The system will mark or remove abnormal or erroneous data; use advanced filtering algorithms to denoise the data, eliminate or reduce the interference of noise on the data, and improve the signal-to-noise ratio of the data; the pre-processed data is stored in the system's database for subsequent analysis and correction. The database uses efficient data storage and management technology to ensure fast access and efficient use of data.

[0014] The effects of the above technical solution are as follows: by deploying high-precision current and voltage sensors in the power grid, the current and voltage fluctuations in the power grid can be captured in real time and accurately, ensuring the authenticity and reliability of the data, and providing a solid foundation for subsequent analysis and correction; the sensors can respond to the current and voltage changes in the power grid in real time, ensuring the timeliness of the data, and can quickly identify and solve the problems in the power grid; the sensors use a multi-channel transmission protocol to send data to the electric energy metering error correction system, which improves the efficiency and stability of data transmission. Multi-channel transmission means that data can be transmitted simultaneously on multiple paths, reducing the risk of data loss or delay; a stable and reliable communication link is established between the sensor and the system, ensuring the real-time transmission and synchronization of the data, helping to maintain the consistency and integrity of the data, and providing a reliable data source for subsequent analysis and correction; the data receiving module pre-processes the received data, including data format conversion, data verification, data denoising and other steps, which helps to eliminate noise and outliers in the data and improve the signal-to-noise ratio and availability of the data; the pre-processed data is stored in the database of the system for subsequent analysis and correction. The efficient storage and management technology of the database ensures fast access and efficient use of data, providing strong support for the operation of the electric energy metering error correction system; this technical solution combines advanced technologies such as high-precision sensors, multi-channel transmission protocols, data preprocessing and database storage, and promotes the development of the power grid towards intelligence and digitalization; real-time and accurate data collection and transmission provide data support for the intelligent management of the power grid. By analyzing and utilizing these data, the operating status of the power grid can be more accurately grasped, the dispatching and management of the power grid can be optimized, and the reliability and efficiency of the power grid can be improved.

[0015] In one embodiment of the present invention, the S12 includes: S121, after capturing the current and voltage data, the sensor compresses the data using a lossless data compression algorithm, and encrypts the compressed data using an encryption algorithm; S122. Select a multi-channel transmission protocol according to the actual situation of the power grid and the demand for data transmission, and configure multiple transmission channels for each sensor; S123, based on intelligent routing algorithm, dynamically select the optimal transmission path according to network conditions and data transmission priority, and implement load balancing strategy among multiple transmission channels; S124. During data transmission, the data integrity is checked by a hash function. If data is lost or damaged due to network failure or other reasons, a retransmission mechanism is started, requiring the sensor to resend the data until the data is successfully received and verified to be correct.

[0016] The working principle of the above technical solution is as follows: after the sensor captures the current and voltage data, it first compresses the data through a lossless data compression algorithm (such as Huffman coding or arithmetic coding). The lossless compression algorithm can reduce the volume of data without losing data information, thereby reducing the occupancy of transmission bandwidth; the compressed data is encrypted by an encryption algorithm. The encryption algorithm uses symmetric or asymmetric encryption to ensure the security of data during transmission and prevent data from being illegally intercepted or tampered with; according to the actual situation of the power grid and the needs of data transmission, a suitable multi-channel transmission protocol is selected. Optional protocols include MQTT (message queue telemetry transmission), CoAP (restricted application protocol) or a custom TCP / IP protocol stack. These protocols have different characteristics and applicable scenarios, and can meet the data transmission requirements in different power grid environments; multiple transmission channels are configured for each sensor. The setting of multiple channels improves the flexibility and reliability of data transmission. Even if a channel fails, data can continue to be transmitted through other channels; based on the intelligent routing algorithm, the optimal transmission path is dynamically selected according to the network status and the priority of data transmission. The intelligent routing algorithm can monitor the network status in real time, including indicators such as bandwidth, delay, and packet loss rate, so as to select the optimal transmission path under the current network environment; implement load balancing strategies between multiple transmission channels. The load balancing strategy can reasonably allocate the data transmission volume of each channel to avoid the situation where one channel is overloaded while other channels are idle, thereby improving the overall data transmission efficiency; during the data transmission process, the data integrity is checked through the hash function. The hash function can generate a unique identifier (hash value) for the data. By comparing the hash value of the received data with the hash value when it was sent, it can be determined whether the data has been tampered with or damaged during the transmission process; for data loss or damage caused by network failure or other reasons, the retransmission mechanism is activated. The retransmission mechanism requires the sensor to resend the lost or damaged data until the data is successfully received and verified.

[0017] The effects of the above technical solutions are as follows: through lossless data compression algorithms such as Huffman coding or arithmetic coding, the sensor can effectively compress the current and voltage data after capturing it, significantly reducing the data volume, thereby reducing the transmission bandwidth occupancy and improving data transmission efficiency; the compressed data is encrypted through an encryption algorithm to ensure the security of the data during transmission, prevent the data from being illegally intercepted, tampered with or leaked, and protect the privacy and integrity of the power grid data; according to the actual situation of the power grid and the data transmission requirements, select multi-channel transmission protocols such as MQTT, CoAP or a customized TCP / IP protocol stack. These protocols have their own characteristics and can meet the data transmission requirements in different scenarios and enhance the flexibility of transmission; multiple transmission channels are configured for each sensor to improve the redundancy and reliability of data transmission. Even if a channel fails, data can continue to be transmitted through other channels to ensure the continuity of data transmission; based on the intelligent routing algorithm, the optimal transmission path is dynamically selected according to the network status and the priority of data transmission, which helps to reduce network congestion and improve the real-time and reliability of data transmission; a load balancing strategy is implemented between multiple transmission channels to reasonably allocate network resources to avoid the situation where a channel is overloaded while other channels are idle, which helps to optimize the utilization of network resources and improve the performance of the overall system; during the data transmission process, the data integrity is checked through the hash function, which can promptly detect and correct errors or damage that may occur in the data transmission process to ensure the accuracy of the data; for data loss or damage caused by network failure or other reasons, the retransmission mechanism is started to require the sensor to resend the data, which ensures the integrity and reliability of the data and provides an accurate data basis for the subsequent correction of electric energy metering errors.

[0018] In one embodiment of the present invention, the S122 includes: Conduct a comprehensive analysis of the grid's topology, data traffic patterns, transmission delay requirements, and security requirements to determine the type of transmission protocol that is appropriate for the grid's characteristics; Determine the type and number of transmission channels required based on the coverage of the power grid, the distribution of sensors, and the data transmission requirements; According to the real-time status of the power grid (such as network congestion, equipment failure, etc.), the transmission channel allocation of each sensor is dynamically adjusted through genetic algorithms; Based on the selected protocol and channel, perform interoperability tests between sensors and data receiving modules, and simulate abnormal situations to verify the fault recovery capabilities of protocols and channels; Monitor the performance indicators of the transmission channel (such as throughput, latency, packet loss rate, etc.) in real time, collect and analyze transmission data, identify potential performance bottlenecks, and adjust and optimize transmission strategies based on the results of performance monitoring and data analysis.

[0019] The working principle of the above technical solution is: conduct a comprehensive analysis of the topology of the power grid, data traffic pattern, transmission delay requirements and security requirements. For example, if the topology of the power grid is complex and the data traffic is large, it may be necessary to select a transmission protocol with high reliability and low latency, such as MQTT or a customized TCP / IP protocol stack; if the security requirements of the power grid are high, it may be necessary to select a transmission protocol with encryption function; determine the type and number of transmission channels: based on the coverage of the power grid, the distribution of sensors and the data transmission requirements, determine the required transmission channel type (such as wired, wireless) and number. For example, for a power grid with a wide coverage and sparse distribution of sensors, it may be necessary to select a wireless transmission channel; for a power grid with high data transmission requirements and delay sensitivity, it may be necessary to configure multiple wired transmission channels to ensure the stability and real-time performance of data transmission; according to the real-time status of the power grid (such as network congestion, equipment failure, etc.), the transmission channel allocation of each sensor is dynamically adjusted through a genetic algorithm. The genetic algorithm can simulate natural selection and genetic mechanisms, and find the optimal transmission channel allocation scheme through iterative optimization to adapt to the real-time changes of the power grid; based on the selected protocol and channel, the interoperability test between the sensor and the data receiving module is performed. This includes verifying whether the sensor can correctly send data to the data receiving module, and whether the data receiving module can accurately receive and parse the data sent by the sensor; simulating abnormal situations such as network failures and equipment failures to verify the fault recovery capabilities of protocols and channels. This includes verifying whether the system can automatically switch to a backup channel or protocol in the event of a failure to ensure the continuity of data transmission; real-time monitoring of the performance indicators of the transmission channel (such as throughput, latency, packet loss rate, etc.). These indicators can reflect the actual performance status of the transmission channel and provide a basis for subsequent optimization; collecting and analyzing transmission data to discover potential performance bottlenecks. For example, if the latency of a channel is too high or the packet loss rate is too large, the channel may need to be optimized or replaced; based on the results of performance monitoring and data analysis, the transmission strategy is adjusted and optimized. This includes reselecting the transmission protocol, adjusting the channel configuration, optimizing the load balancing strategy, etc., to further improve the efficiency, reliability and security of data transmission.

[0020] The effect of the above technical solution is: by comprehensively analyzing the topological structure, data traffic pattern, transmission delay requirements and security requirements of the power grid, the type of transmission protocol suitable for the characteristics of the power grid can be accurately determined. This precise matching helps to improve the overall efficiency and quality of power grid data transmission; based on the coverage of the power grid, the distribution of sensors and the data transmission requirements, the required transmission channel type (such as wired, wireless) and quantity are determined, which can achieve the reasonable allocation and utilization of transmission resources. Optimizing the configuration helps to reduce transmission costs and improve transmission efficiency. According to the real-time status of the power grid (such as network congestion, equipment failure, etc.), the transmission channel allocation of each sensor is dynamically adjusted through genetic algorithms, which can realize flexible scheduling and optimization of transmission resources. As an intelligent optimization algorithm, genetic algorithms can quickly find the optimal transmission channel allocation scheme to adapt to the real-time changes of the power grid. This dynamic adjustment helps to improve the flexibility and reliability of power grid data transmission. Based on the selected protocol and channel, the interoperability test between the sensor and the data receiving module is carried out, and abnormal conditions such as network failure and equipment failure are simulated to verify the fault recovery capability of the protocol and channel. This kind of testing and verification can ensure that when a power grid failure occurs, the transmission system can quickly switch to a backup channel or protocol to maintain the continuity of data transmission. This measure to enhance fault recovery capabilities helps to improve the robustness and stability of power grid data transmission. Real-time monitoring of the performance indicators of the transmission channel (such as throughput, latency, packet loss rate, etc.), collection and analysis of transmission data, can timely identify potential performance bottlenecks and optimize them. Based on the results of performance monitoring and data analysis, the transmission strategy can be adjusted and optimized, including protocol selection, channel configuration, load balancing strategy, etc. This real-time monitoring and optimization measure helps to improve the performance and efficiency of power grid data transmission and reduce transmission costs.

[0021] In one embodiment of the present invention, the S123 includes: Collect network parameters in real time through monitoring nodes deployed in the network or log information of existing network devices; The network parameters are analyzed based on a machine learning algorithm, and the overall status of the network and the performance of each transmission channel are evaluated based on the analysis results; the performance of each transmission channel is evaluated using the following formula:

[0022] in, represents the performance score of the i-th channel; represents the bandwidth of the i-th transmission channel; represents the delay of the i-th transmission channel; represents the packet loss rate of the i-th transmission channel; represents the queue length of the i-th transmission channel; represents the round trip time of the i-th transmission channel; , , , , They represent the weight coefficients of bandwidth, delay, packet loss rate, queue length, and round-trip time respectively; N represents the total number of transmission channels; Indicates the minimum delay among all channels; Indicates the maximum queue length among all channels; represents the round trip time of all channels; and They represent the penalty coefficients for queue length and round-trip time respectively; Indicates the exponential weight of the packet loss rate; Based on the network status assessment results, formulate intelligent routing strategies, and use intelligent routing algorithms to dynamically select the optimal transmission path according to the data transmission priority and changes in network status; By real-time monitoring of the load of each transmission channel, combined with historical data and machine learning algorithms, the load change trend of each channel in the future (for example, one day) is predicted; based on the load monitoring and prediction results, the load balancing strategy is dynamically adjusted; the load situation is obtained by the following formula:

[0023] in, represents the current load of the i-th transmission channel at time t; represents the maximum load capacity of the i-th book removal channel; represents the load change rate of the i-th transmission channel at time t; At time t, the load fluctuation of the i-th transmission channel; and Respectively represent the weight coefficients of load change rate and load fluctuation; Indicates the maximum load change rate among all channels; Indicates the maximum load fluctuation among all channels; and denotes the corresponding index weight; Collect performance indicators (such as throughput, delay, packet loss rate, etc.) during data transmission, combine user feedback, evaluate the effectiveness of the current transmission strategy, and use genetic algorithms to adaptively optimize the transmission strategy based on the performance evaluation results.

[0024] The working principle of the above technical solution is: using the log information of monitoring nodes deployed in the network or existing network equipment (such as routers and switches) to collect network parameters in real time, including bandwidth utilization, delay, jitter and packet loss rate. These parameters can reflect the real-time status of the network and provide a basis for subsequent analysis and strategy formulation; based on machine learning algorithms (such as random forests, support vector machines, etc.), in-depth analysis of network parameters is carried out. Through training and learning of historical data, the algorithm can accurately evaluate the overall status of the network and the performance of each transmission channel. The evaluation results will serve as the basis for the formulation of intelligent routing strategies and load balancing strategies; intelligent routing strategies are formulated based on the network status evaluation results. Intelligent routing strategies include key aspects such as path selection, traffic distribution and fault switching. Using intelligent routing algorithms (such as ant colony algorithms, genetic algorithms, reinforcement learning, etc.), the optimal transmission path is dynamically selected according to the priority of data transmission and changes in network conditions. At the same time, the intelligent routing strategy must also have the ability to respond quickly to network failures to ensure that it can quickly switch to an alternative path when a failure occurs to maintain the continuity of data transmission; by real-time monitoring of the load of each transmission channel, combined with historical data and machine learning algorithms, the load change trend of each channel in the future period is predicted. Based on the load monitoring and prediction results, the load balancing strategy is dynamically adjusted. For example, according to the prediction results, the data packet sending rate is adjusted, the transmission channel selection strategy is changed, etc., to balance the load of each channel and avoid overloading a single channel. This dynamic adjustment can ensure the reasonable allocation and utilization of network resources and improve the overall efficiency of data transmission; collect performance indicators (such as throughput, delay, packet loss rate, etc.) during data transmission, and combine user feedback to evaluate the effect of the current transmission strategy. Based on the performance evaluation results, the transmission strategy is adaptively optimized using optimization algorithms such as genetic algorithms. The optimization process will continue to iterate until the optimal combination of transmission strategies is found to meet the requirements of high efficiency, reliability and stability of data transmission.

[0025] The effect of the above technical solution is: by deploying monitoring nodes in the network or using the log information of existing network equipment, network parameters can be collected in real time, including key indicators such as bandwidth utilization, delay, jitter and packet loss rate. The real-time acquisition of these data provides a basis for the accurate evaluation of network conditions. Based on the analysis of machine learning algorithms, the overall condition of the network and the performance of each transmission channel can be further accurately evaluated, thereby ensuring a comprehensive understanding and grasp of the network status; based on the evaluation results of the network status, intelligent routing strategies can be formulated and implemented. These strategies cover multiple aspects such as path selection, traffic distribution and fault switching, aiming to dynamically select the optimal transmission path according to the priority of data transmission and changes in network conditions. By using intelligent routing algorithms such as ant colony algorithms, genetic algorithms or reinforcement learning, the efficiency and reliability of data transmission can be ensured, while reducing network congestion and transmission delays and improving user experience. By real-time monitoring of the load of each transmission channel, combined with historical data and machine learning algorithms, the load change trend of each channel in the future period is predicted. Based on these prediction results, the load balancing strategy can be adjusted dynamically, such as adjusting the data packet sending rate or changing the transmission channel selection strategy, so as to balance the load of each channel and avoid overloading a single channel. This dynamic load balancing mechanism can significantly improve the utilization of network resources, reduce transmission costs, and enhance the stability and scalability of the network; by collecting performance indicators (such as throughput, delay, packet loss rate, etc.) and user feedback during data transmission, the effect of the current transmission strategy can be evaluated. Based on these evaluation results, the transmission strategy can be adaptively optimized using optimization algorithms such as genetic algorithms. This optimization process can be continuously iterated until the optimal combination of transmission strategies is found to meet the data transmission requirements in different scenarios. By implementing the above technical solutions, the reliability and security of the network can be significantly improved. The intelligent routing strategy can ensure the continuity and stability of data transmission, and can quickly switch to the backup path even in the event of a network failure. At the same time, dynamic load balancing and adaptive optimization can reduce network congestion and transmission delay, and reduce the risk of data loss. These measures together provide a strong guarantee for the real-time, reliable and efficient transmission of network data. The above performance evaluation formula takes into account multiple factors such as bandwidth, latency, packet loss rate, queue length and round-trip time to comprehensively evaluate the performance of a transmission channel; the weight coefficient can be used to flexibly adjust the influence of each factor on the final score; by comparing the performance of the current channel with other channels, the quality of the channel can be relatively evaluated. The penalty coefficient allows for more severe deductions for certain unfavorable conditions (such as too long queues or high round-trip times) to ensure that network resources are used efficiently. The above load calculation formula not only takes into account the ratio of the current load to the maximum capacity, but also introduces load change rate and fluctuation as additional considerations. The load change rate reflects the growth rate of the load in the short term, while the load fluctuation measures the instability of the load.The weighting factor allows the importance of these dynamic characteristics to be adjusted according to the actual situation. By continuously monitoring the load conditions at different points in time and combining historical data for prediction, it helps to identify potential problems in advance and take preventive measures.

[0026] In one embodiment of the present invention, the S2 includes: S21. Based on the vector control technology, the pre-processed current and voltage data are vector-decomposed to obtain vector information of the voltage and current, where the vector information includes amplitude, phase, and frequency; S22, using the fast Fourier transform (FFT) algorithm to perform spectrum analysis on the vector information, and further extract each harmonic component and its phase relationship; S23. Based on the built-in vector space model, the vector information of voltage and current is mapped into the model, and the parameters of vector analysis are dynamically adjusted according to the actual operating status of the power grid.

[0027] The working principle of the above technical solution is: pre-process the collected current and voltage data, including filtering, denoising and other steps to ensure the accuracy and reliability of the data; the pre-processed data will be sent to the vector control module. This module uses vector control technology to vector decompose the current and voltage data; the purpose of vector decomposition is to convert the current and voltage signals from the time domain to the vector domain, so as to obtain their vector information, including amplitude, phase and frequency; these vector information can more intuitively reflect the waveform characteristics of the current and voltage, and provide a basis for subsequent analysis and processing; after obtaining the vector information of the current and voltage, the fast Fourier transform (FFT) algorithm is used to perform spectrum analysis on this information; the FFT algorithm can convert the time domain signal to the frequency domain, so as to extract the harmonic components and their phase relationship; through spectrum analysis, the fundamental wave and harmonic components in the current and voltage signals, as well as the phase relationship between them, can be clearly seen; on the basis of spectrum analysis, the harmonic components and their phase relationship are further extracted. This information is of great significance for assessing the harmonic pollution level of the power grid, diagnosing harmonic sources, and formulating harmonic suppression measures; the built-in vector space model is a mathematical model used to represent voltage and current vector information. The model can intuitively reflect the phase relationship and amplitude ratio between voltage and current; the vector information of voltage and current is mapped to the vector space model. Through the visual representation of the model, the phase difference and amplitude ratio between voltage and current can be intuitively seen; according to the actual operating status of the power grid (such as load changes, fault occurrence, etc.), the parameters of vector analysis are dynamically adjusted. These parameters include the accuracy of vector decomposition, the resolution of the FFT algorithm, and the parameters of the vector space model; the purpose of dynamic adjustment is to ensure that the vector analysis can adapt to the error correction requirements under different working conditions, thereby improving the accuracy and reliability of the analysis.

[0028] The effect of the above technical solution is: by vector control technology, the pre-processed current and voltage data are vector decomposed, and the amplitude, phase and frequency information of voltage and current can be accurately obtained. This information is the basis for evaluating the operation status of the power grid, fault diagnosis and optimization; vector decomposition technology can decompose complex current and voltage signals into simple vector components, thereby simplifying the data analysis process. At the same time, due to the high precision and stability of vector decomposition, the accuracy of data analysis can be significantly improved; the spectrum analysis of vector information using the fast Fourier transform (FFT) algorithm can be used to extract the harmonic components and their phase relationship in detail. This is of great significance for analyzing harmonic pollution in the power grid, identifying harmonic sources, and formulating effective harmonic suppression measures; through spectrum analysis, the fundamental wave and harmonic components in the current and voltage signals, as well as the phase relationship between them, can be clearly seen. This information helps to quickly locate the fault point in the power grid and improve the accuracy and efficiency of fault diagnosis; based on the built-in vector space model, the vector information of voltage and current is mapped into the model, and the parameters of vector analysis are dynamically adjusted according to the actual operation status of the power grid. This dynamic adaptability can ensure that accurate vector analysis results can be obtained under different working conditions, thus meeting the needs of error correction; by dynamically adjusting the parameters of vector analysis, real-time monitoring and feedback of the power grid operation status can be achieved. This helps to timely discover and solve potential problems in the power grid and improve the operational stability and reliability of the power grid; by accurately processing and analyzing current and voltage data, it provides strong support for the optimized operation of the power grid. By real-time monitoring of the power grid status, identifying harmonic sources and fault points, etc., effective optimization strategies can be formulated to reduce power grid losses and improve energy efficiency; through precise data analysis and processing, intelligent management and control of the power grid can be achieved, and the automation level and intelligence level of the power grid can be improved.

[0029] In one embodiment of the present invention, the S22 includes: According to the characteristics of vector information and analysis requirements, select data window function (such as rectangular window, Hanning window, Hamming window, etc.), sample the pre-processed vector information at equal intervals, and truncate the data according to the requirements of the FFT algorithm; The spectrum of the truncated data is calculated based on the fast Fourier transform algorithm to obtain the amplitude and phase information of each frequency component; According to the spectrum calculation results, the frequency, amplitude and phase information of each harmonic component are identified, and the phase relationship between each harmonic component is analyzed, including phase difference, phase angle, etc.; Compare and verify with the actual measurement data, and optimize and adjust the application parameters of the FFT algorithm (such as window function selection, sampling frequency, truncation length, etc.) according to the verification results; Key features that can reflect the harmonic characteristics of the power grid are extracted from the spectrum analysis results. The key features include harmonic amplitude ratio and phase difference distribution. Machine learning is used to perform pattern recognition on the extracted features to automatically classify the degree or type of harmonic pollution in the power grid.

[0030] The working principle of the above technical solution is: the pre-processed vector information is sampled at equal intervals to ensure the continuity and uniformity of the data; the sampled data will be truncated according to the requirements of the FFT algorithm to ensure that the data length meets the input requirements of the FFT algorithm; according to the characteristics of the vector information and analysis requirements, a suitable data window function (such as rectangular window, Hanning window, Hamming window, etc.) is selected. For example, when only the main lobe frequency is required to be accurately read without considering the amplitude accuracy, a rectangular window can be selected. If the test signal has multiple frequency components, the spectrum performance is very complex, and the purpose of the test is more focused on the frequency points, a Hanning window needs to be selected. In addition, if the measured signal is random or unknown, the Hanning window can also be selected; the selection of the window function is to reduce spectrum leakage and fence effect and improve the accuracy of spectrum analysis; the fast Fourier transform (FFT) algorithm is used to calculate the spectrum of the truncated data; the FFT algorithm converts the time domain signal into a frequency domain signal to obtain the amplitude and phase information of each frequency component; for the frequency range of interest or specific harmonic components, spectrum refinement technology (such as Zoom-FFT, Chirp-Z transform, etc.) is used; spectrum refinement technology can improve the spectrum resolution and more accurately extract the characteristics of the harmonic components, including frequency, amplitude and phase information; according to the spectrum calculation results, the frequency, amplitude and phase information of each harmonic component are identified; the identification results will be used for subsequent analysis and processing; the phase between each harmonic component The relationship between the two harmonic components is analyzed, including phase difference, phase angle, etc.; phase relationship analysis helps to understand the interaction between harmonic components and the harmonic characteristics of the power grid; compared with the actual measurement data for verification, the application parameters of the FFT algorithm (such as window function selection, sampling frequency, truncation length, etc.) are optimized and adjusted according to the verification results; the purpose of parameter optimization is to improve the accuracy and reliability of spectrum analysis; the verification results will be used to evaluate the performance and accuracy of the FFT algorithm, as well as subsequent feature extraction and pattern recognition; key features that can reflect the harmonic characteristics of the power grid are extracted from the spectrum analysis results; key features include harmonic amplitude ratio and phase difference distribution, etc.; machine learning algorithms are used to perform pattern recognition on the extracted features; the purpose of pattern recognition is to automatically classify the degree or type of harmonic pollution in the power grid, and provide decision support for subsequent harmonic governance and optimization.

[0031] The effects of the above technical solution are as follows: according to the characteristics of vector information and analysis requirements, selecting appropriate data window functions (such as rectangular windows, Hanning windows, Hamming windows, etc.) can significantly reduce spectrum leakage and fence effects, thereby improving the accuracy of spectrum analysis; using spectrum refinement technology (such as Zoom-FFT, Chirp-Z transform, etc.) to perform high-resolution analysis on the frequency range of interest or specific harmonic components, it is possible to more accurately extract the characteristics of harmonic components, including frequency, amplitude and phase information; the pre-processed vector information is sampled at equal intervals, and the data is truncated according to the requirements of the FFT algorithm to ensure that the data length meets the input requirements of the FFT algorithm. This flexibility enables the technical solution to adapt to data inputs of different lengths and types; the application parameters of the FFT algorithm (such as window function selection, sampling frequency, truncation length, etc.) can be optimized and adjusted according to the actual measurement data, which improves the adaptability and flexibility of the technical solution; by comparing and verifying with the actual measurement data, and optimizing and adjusting the application parameters of the FFT algorithm according to the verification results, the accuracy and reliability of the spectrum analysis can be further improved; by continuously optimizing the application parameters of the FFT algorithm and the spectrum refinement technology, the technical solution can continuously adapt to changes in the harmonic characteristics of the power grid and maintain the accuracy and effectiveness of the analysis; key features that can reflect the harmonic characteristics of the power grid, such as the harmonic amplitude ratio and phase difference distribution, are extracted from the spectrum analysis results, and pattern recognition is performed using machine learning algorithms to automatically classify the degree or type of harmonic pollution in the power grid. This intelligent analysis can significantly improve the efficiency and accuracy of harmonic analysis; through intelligent analysis, this technical solution can provide auxiliary decision support for power grid harmonic control and optimization, helping power grid managers to quickly identify harmonic problems and formulate effective control measures; accurate harmonic analysis helps to timely discover harmonic problems in the power grid and take effective control measures, thereby improving the operational stability and reliability of the power grid; through intelligent analysis and auxiliary decision support, this technical solution can reduce the degree of harmonic pollution in the power grid and protect the safety of power equipment and users. This technical solution provides strong support for harmonic monitoring and analysis of smart grids, and helps promote the development and popularization of smart grids.

[0032] In one embodiment of the present invention, the S23 includes: Initialize the vector space model, map the preprocessed voltage and current vector information into the initialized vector space model, and obtain the position and state of each vector in the model; Real-time monitoring of the dynamic changes of vectors in the model, as well as the relative positions and angle relationships between the vectors; and feedback of the monitoring results to the error correction system; According to the actual operation status of the power grid and the dynamic changes of vector information in the model, the principles and strategies of parameter adjustment are formulated; the principles include parameter adjustment based on load balancing and emergency parameter adjustment based on fault detection; By comparing and verifying with actual measurement data, the accuracy and reliability of the vector space model are evaluated. If the verification result does not meet the requirements, the model is revised or redesigned; According to the verification results and changes in the grid operation status, the vector space model is adaptively optimized, including adjusting the model parameters, improving the mathematical expression or physical meaning of the model, and introducing new monitoring and analysis technologies.

[0033] A comprehensive evaluation is conducted on the effect of error correction, including error comparison before and after correction, improvement in grid operating status, etc. Based on the evaluation results and continuous changes in grid operating status, the error correction strategy is continuously optimized and iterated.

[0034] The working principle of the above technical solution is as follows: first, the initial parameters of the vector space model are set, including the direction of the coordinate axis, the position of the origin, etc. These parameters will determine the basic framework and coordinate system of the model; at the same time, the initial state of each element in the model (such as voltage and current vectors) is defined, including their amplitude, phase and frequency, etc., to ensure that the model can accurately reflect the initial operation state of the power grid; the pre-processed voltage and current vector information is mapped to the initialized vector space model. This process includes converting the amplitude, phase and frequency information of each vector into the coordinate value in the model, so as to obtain the position and state of each vector in the model; real-time monitoring of the dynamic changes of the vector in the model, including the changes in amplitude, phase and frequency, and the relative position and angle relationship between the vectors. These changes will reflect the actual operation state of the power grid, such as load changes, fault occurrence, etc.; the monitoring results will be fed back to the error correction system. The error correction system will judge whether the operation state of the power grid deviates from the expectation based on these monitoring results, and formulate an error correction strategy accordingly; according to the actual operation state of the power grid (such as load changes, fault occurrence, etc.) and the dynamic changes of vector information in the model, formulate the principles and strategies for parameter adjustment. The principles include parameter adjustment based on load balancing, emergency parameter adjustment based on fault detection, etc.; according to the established principles, the parameters of the vector space model are adjusted to adapt to changes in the power grid operation status. These adjustments may involve the coordinate axis direction of the model, the origin position, the amplitude, phase and frequency of the vector elements, etc.; the accuracy and reliability of the vector space model are evaluated by comparison and verification with the actual measurement data. If the verification results do not meet the requirements, the model is revised or redesigned; according to the verification results and changes in the power grid operation status, the vector space model is adaptively optimized. Including adjusting the parameters of the model, improving the mathematical expression or physical meaning of the model, and introducing new monitoring and analysis technologies; comprehensively evaluating the effect of error correction, including error comparison before and after correction, and the improvement of the power grid operation status. These evaluation results will serve as the basis for subsequent optimization and iteration; based on the evaluation results and the continuous changes in the power grid operation status, the error correction strategy is continuously optimized and iterated. This includes adjusting the parameter adjustment strategy, improving the error correction algorithm, etc., to ensure that the vector space model can continuously and accurately reflect the operation status of the power grid.

[0035] The effects of the above technical solution are as follows: by initializing the vector space model and mapping the pre-processed voltage and current vector information, the state and position of each element in the power grid can be accurately reflected, providing an accurate data basis for power grid monitoring; real-time monitoring of the dynamic changes of the vector in the model, including amplitude, phase, frequency, and the relative position and angle relationship between each vector, improves the real-time and accuracy of power grid monitoring, and helps to timely discover abnormal conditions in the power grid; according to the actual operating status of the power grid and the dynamic changes of the vector information in the model, the principles and strategies of parameter adjustment are formulated, such as parameter adjustment based on load balancing and emergency parameter adjustment based on fault detection, which helps to achieve smooth operation of the power grid and rapid response to faults; by comparing and verifying with actual measured data, the accuracy and reliability of the vector space model are evaluated, and the model is corrected or redesigned according to the verification results, thereby improving the applicability and accuracy of the model; according to the verification results and changes in the operating status of the power grid, the model is adaptively optimized, including Adjusting parameters, improving mathematical expressions or physical meanings, and introducing new monitoring and analysis technologies can help continuously improve the analysis and optimization capabilities of the power grid; by real-time monitoring of the dynamic changes of vectors in the model, faults in the power grid, such as voltage fluctuations and current anomalies, can be discovered in a timely manner, which helps to quickly locate the fault point; a comprehensive evaluation of the effect of error correction, including error comparison before and after correction and the improvement of the power grid operation status, helps to verify the effectiveness of the error correction strategy; based on the evaluation results and the continuous changes in the power grid operation status, the error correction strategy is continuously optimized and iterated, which can continuously improve the efficiency and accuracy of fault handling; this technical solution integrates multiple links such as vector space model, real-time monitoring, parameter adjustment, model verification and optimization, realizes intelligent monitoring and analysis of the power grid, and helps to promote the development of the power grid towards intelligence and automation; through automated monitoring and analysis technology, it can reduce the workload of operation and maintenance personnel, improve operation and maintenance efficiency, and reduce operation and maintenance costs.

[0036] In one embodiment of the present invention, S3 includes: S31. Based on the Kalman filter algorithm, combined with the voltage and current vector information in the vector space model, the error between the theoretical value and the actual value of the electric energy metering is calculated; specifically:

[0037] in, represents the energy measurement error at the kth moment; and Respectively represent the actual measured values ​​of voltage and current at the kth moment; and They represent the theoretical predicted values ​​of voltage and current at the kth moment respectively; S32. Based on the error calculation results, the electric energy meters are classified to identify mechanical, electronic and smart electric energy meters; personalized error correction strategies are formulated for different types of electric energy meters; for mechanical electric energy meters, the errors are reduced by adjusting physical parameters such as capacitors and eccentricity; for electronic electric energy meters, the errors are corrected by adjusting the signal gain and phase compensation of the sensor through software programs; for smart electric energy meters, dynamic error compensation technology based on adaptive algorithms is used to adjust the metering parameters in real time to reduce errors.

[0038] S33. Based on the built-in error correction model, the correction strategy is converted into specific correction parameters and stored in the correction parameter library of the system.

[0039] The working principle of the above technical solution is: extracting voltage and current vector information from the vector space model, which is the basic data for electric energy metering; using the Kalman filter algorithm to filter the extracted voltage and current vector information to eliminate noise and interference and improve the accuracy and reliability of the data; the Kalman filter algorithm continuously iterates and updates the state estimate through two steps of prediction-update, thereby realizing the accurate calculation of the electric energy metering error; combining the filtered voltage and current vector information and the theoretical model of electric energy metering to calculate the error between the theoretical value and the actual value of electric energy metering; according to the error calculation results and the characteristics of the electric energy meter, the electric energy meter is divided into three categories: mechanical, electronic and intelligent electric energy meters.

[0040] Mechanical energy meter: By adjusting physical parameters such as capacitor and eccentricity, the mechanical characteristics of the energy meter are changed to reduce the error. This adjustment usually requires physical disassembly and recalibration of the energy meter.

[0041] Electronic energy meter: The sensor’s signal gain and phase compensation are adjusted by software program to correct the error. This adjustment is usually done remotely through a programming interface (such as a serial port, Ethernet port, etc.) without disassembling the energy meter.

[0042] Smart energy meter: Adopts dynamic error compensation technology based on adaptive algorithm to adjust metering parameters in real time to reduce errors. This technology uses the built-in processor and algorithm of the smart energy meter to dynamically adjust the metering parameters according to real-time data to achieve real-time compensation of errors.

[0043] According to the formulated personalized error correction strategy, the strategy is converted into specific correction parameters. The parameters include capacitor capacity, eccentricity, signal gain, phase compensation value, etc. The correction parameters are stored in the correction parameter library of the system. This library is a database or file storage system used to save and manage the correction parameters of all electric energy meters; the correction parameter library can be integrated with the electric energy meter management system to realize functions such as remote query, update and download of correction parameters.

[0044] The effect of the above technical solution is: by combining the voltage and current vector information in the vector space model, the Kalman filter algorithm is used to accurately calculate the error between the theoretical value and the actual value of the electric energy metering. This method can effectively filter out noise and interference, and improve the accuracy and reliability of error calculation; according to the error calculation results and the characteristics of the electric energy meter, the electric energy meter is divided into three categories: mechanical, electronic and intelligent electric energy meters. This classification helps to formulate personalized error correction strategies for the characteristics of different types of electric energy meters; for different types of electric energy meters, formulate and adjust physical parameters such as capacitors and eccentricity, adjust the signal gain and phase compensation of the sensor, and adopt personalized error correction strategies such as dynamic error compensation technology based on adaptive algorithms, which can more effectively reduce errors and improve the accuracy of electric energy metering; based on the built-in error correction model, the correction strategy is converted into specific correction parameters and stored in the correction parameter library of the system. This makes it easy to manage and query, and also provides data support for subsequent electric energy meter calibration; the calibration parameters stored in the calibration parameter library can be easily applied to the calibration process of the electric energy meter, without the need to recalculate or formulate the calibration strategy each time, which improves the calibration efficiency and accuracy; by continuously collecting and analyzing error data, the error correction strategy can be continuously optimized and improved, further improving the accuracy and reliability of the electric energy metering system; this technical solution can well adapt to the development needs of smart grids, provide strong support for the error correction of smart electric energy meters, and help promote the construction and development of smart grids. The above formula considers the dynamic model of the system, and the Kalman filter can effectively suppress the measurement noise, thereby providing an error estimate that is closer to the true value; the formula can quickly calculate the error in each data acquisition cycle, which is suitable for real-time monitoring systems; the algorithm can automatically adjust the error estimate according to the actual performance of the system, which means that it can adapt to different working conditions without manual intervention; even in the presence of model uncertainty and external interference, the Kalman filter can provide a relatively stable error estimate; if there are multiple sensors in the system, the Kalman filter can fuse the data of these sensors to provide a comprehensive error estimate; improving the measurement accuracy through software algorithms can reduce the dependence on high-precision hardware, thereby reducing the overall cost of the system.

[0045] In one embodiment of the present invention, the S4 includes: S41, outputting the corrected electric energy metering result to the display interface of the system in real time, and monitoring the deviation in the metering process in real time through the feedback signal detection module. If the deviation exceeds the preset threshold, the correction operation is triggered immediately; S42, transmitting the corrected electric energy metering results and correction operation records to the data center of the electric energy metering system for real-time monitoring and management; S43, the data center analyzes the performance change trend of the electric energy meter according to the historical data and calibration records, and performs subsequent maintenance and optimization based on the analysis results; S44. The user provides feedback on the correction results through the user feedback mechanism, and the system continuously optimizes the correction strategy based on the user feedback.

[0046] The working principle of the above technical solution is: the system outputs the partially corrected electric energy metering results to the display interface in real time for users to view and record. Through the feedback signal detection module, the system monitors the deviation in the metering process in real time. The module can capture the difference between the metering result and the expected value and evaluate it; if the deviation is detected to exceed the preset threshold, the system immediately triggers the correction operation. The system transmits the corrected electric energy metering results and correction operation records to the data center of the electric energy metering system. The data center is a place where electric energy metering data is centrally stored and managed; these data are stored in the database for subsequent real-time monitoring, query and analysis; the data center can monitor the changes in electric energy metering data in real time, including data comparison before and after correction, error changes, etc.; through the visual monitoring interface, managers can intuitively understand the operating status of the electric energy metering system, and promptly discover and solve potential problems; the data center analyzes the performance change trend of the electric energy meter based on the stored historical data and correction records. Including the trend of error changes, evaluation of correction effects, etc.; based on the analysis results, the system can perform subsequent maintenance and optimization of the electric energy meter. For example, for electric energy meters that frequently have errors, stricter correction strategies or more frequent maintenance can be formulated; this helps to extend the service life of the electric energy meter, improve measurement accuracy, and reduce operation and maintenance costs; users provide feedback on the correction results through the user feedback mechanism. This can be direct interface operations, such as filling out questionnaires, submitting opinions, etc.; it can also be indirect feedback, such as usage habits, etc.; the system continuously optimizes the correction strategy based on user feedback. This includes adjusting correction parameters, improving correction algorithms, adding new correction methods, etc.; through continuous iteration and optimization, the system can better meet user needs and improve the accuracy and reliability of electric energy metering.

[0047] The effect of the above technical solution is: by outputting the corrected electric energy metering results to the display interface in real time, users can view and record accurate data in real time. At the same time, the feedback signal detection module can monitor the deviation in the metering process in real time. Once the deviation exceeds the preset threshold, the correction operation is triggered immediately, thereby effectively avoiding error accumulation and improving the accuracy of electric energy metering; the real-time monitoring and immediate correction mechanism can timely discover and solve problems in the metering process, prevent the problem from expanding, and enhance the stability and reliability of the system; the corrected electric energy metering results and correction operation records are transmitted to the data center of the electric energy metering system to realize centralized storage and management of data. It is helpful for unified analysis and utilization of data and improves the efficiency of data management; the data center can monitor the changes in electric energy metering data in real time, discover abnormal data in time, and remind managers to deal with it through the early warning mechanism. It helps to prevent potential problems and ensure the normal operation of the metering system; the data center analyzes the performance change trend of the electric energy meter based on historical data and correction records. This provides managers with a scientific basis for decision-making and helps to formulate more reasonable maintenance and optimization strategies. By analyzing the performance of the energy meter, potential faults or wear can be discovered in a timely manner, so that preventive measures can be taken to extend the service life of the equipment. Based on the analysis results, the system can optimize the subsequent correction strategy to improve the accuracy and efficiency of the correction. Users provide feedback on the correction results through the user feedback mechanism, and the system continuously optimizes the correction strategy based on user feedback. This helps to improve user satisfaction and experience and enhance user trust in the system. User feedback is an important driving force for continuous improvement and innovation of the system. By collecting and analyzing user feedback, the system can continuously discover and improve its own shortcomings and promote technological progress and innovation.

[0048] In one embodiment of the present invention, the S43 includes: Clean the historical data and calibration records obtained from the data center, and integrate the cleaned data according to time sequence, meter model, calibration type and other dimensions to form a structured data set; Use time series analysis to identify the performance trends of electric energy meters over time, including amplitude changes, fluctuation range, and stability; use statistical methods and data mining techniques to detect whether there are outliers or abnormal patterns in the data set; Analyze the correlation between the performance change of the energy meter and external factors (such as ambient temperature, humidity, grid load, etc.), and formulate a preventive maintenance plan based on the performance change trend analysis and abnormal detection results; Based on the analysis results, adjust and optimize the correction strategy, and propose performance improvement plans for the energy meter performance degradation or existing design defects, including hardware upgrades, software optimization, algorithm improvements, etc.; Implement preventive maintenance, correction strategy adjustment, and performance improvement plans according to the formulated maintenance and optimization strategies, monitor the performance changes of electric energy meters after maintenance and optimization in real time, and conduct a comprehensive evaluation of the implementation effect of maintenance and optimization strategies based on the monitoring results; Through user feedback and on-site monitoring, we collect feedback and actual results after maintenance and optimization. Based on the feedback and actual results, we adjust and optimize the maintenance and optimization strategies to form a closed loop of continuous improvement.

[0049] The working principle of the above technical solution is as follows: the historical data and correction records obtained from the data center may contain noise, missing values ​​or inconsistent data. Therefore, it is necessary to clean these data first, remove invalid or erroneous data, and ensure data quality; the cleaned data is integrated according to the time sequence, meter model, correction type and other dimensions to form a structured data set. The time series analysis technology is used to identify the performance trend of the meter over time. This includes the analysis of amplitude change, fluctuation range, stability and other aspects. Time series analysis can reveal the law of data change over time, providing a basis for performance prediction and anomaly detection; based on statistical methods and data mining technology, detect whether there are outliers or abnormal patterns in the data set. Outliers may be caused by factors such as equipment failure, measurement error or external interference. Through anomaly detection, these problems can be discovered and handled in time to avoid affecting the normal operation of the energy metering system; analyze the correlation between the performance change of the energy meter and external factors (such as ambient temperature, humidity, grid load, etc.). This helps to understand the impact of external factors on the performance of the energy meter and provide a scientific basis for the formulation of preventive maintenance plans; formulate preventive maintenance plans based on performance change trend analysis and anomaly detection results. This includes planning for maintenance time, maintenance content, and required resources. Preventive maintenance aims to discover and solve potential problems in advance to avoid downtime or losses caused by equipment failure; based on the analysis results, adjust and optimize the correction strategy. This may include measures such as changing the correction cycle, adjusting correction parameters, and introducing new correction methods. By optimizing the correction strategy, the accuracy and efficiency of the correction can be improved, and the performance of the electric energy metering system can be further improved; performance improvement plans are proposed for the performance degradation or design defects of the electric energy meter. This includes suggestions on hardware upgrades, software optimization, and algorithm improvements. The performance improvement plan aims to improve the overall performance of the electric energy metering system by improving equipment or algorithms; implement preventive maintenance, correction strategy adjustments, performance improvement plans, etc. according to the formulated maintenance and optimization strategies. At the same time, real-time monitoring of the performance changes of the electric energy meter after maintenance and optimization, including the accuracy of the correction results, stability improvement, etc.; based on the monitoring results, a comprehensive evaluation of the implementation effect of the maintenance and optimization strategy is carried out. This includes considerations such as the degree of performance improvement and cost-benefit analysis. Through evaluation, we can understand the actual effect of maintenance and optimization strategies, and provide a basis for subsequent strategy adjustment and optimization; through user feedback and on-site monitoring, we can collect feedback and actual effects after maintenance and optimization. This helps to understand users' views on maintenance and optimization strategies and the performance of strategies in actual applications; according to feedback and actual effects, we can adjust and optimize maintenance and optimization strategies. This includes measures such as modifying maintenance plans, optimizing correction strategies, and improving performance improvement plans. Through continuous improvement, the performance and management level of the electric energy metering system can be continuously improved; the entire maintenance and optimization process forms a closed loop of continuous improvement.Starting from data cleaning and integration, to time series analysis and anomaly detection, performance change and external factor correlation analysis, formulation and implementation of maintenance and optimization strategies, implementation effect monitoring and evaluation, and then to continuous improvement and closed-loop management, each link is closely linked to form a complete workflow.

[0050] The effect of the above technical solution is: by cleaning and integrating historical data and correction records, a structured data set is formed to provide a solid foundation for subsequent analysis and decision-making. It helps to eliminate data redundancy and inconsistency, improve data quality and analysis efficiency; using time series analysis technology, it can accurately identify the performance trend of the electric energy meter over time, including key indicators such as amplitude change, fluctuation range and stability. At the same time, based on statistical methods and data mining technology, anomaly detection can timely discover abnormal values ​​or abnormal patterns in the data set, providing early warning for preventive maintenance; by analyzing the correlation between the performance change of the electric energy meter and external factors, such as ambient temperature, humidity, grid load, etc., it can more deeply understand the root cause of the performance change and provide a scientific basis for the formulation of an accurate preventive maintenance plan; according to the performance change trend analysis and anomaly detection results, a preventive maintenance plan including maintenance time, maintenance content, required resources, etc. is formulated. It helps to discover and solve potential problems in advance and avoid downtime or losses caused by equipment failure; based on the analysis results, adjust and optimize the correction strategy, such as changing the correction cycle, adjusting the correction parameters, introducing new correction methods, etc. At the same time, for the performance degradation or design defects of the electric energy meter, performance improvement solutions are proposed, including hardware upgrades, software optimization, algorithm improvements, etc. It helps to improve the accuracy and stability of the electric energy meter and extend the service life of the equipment; after the implementation of the formulated maintenance and optimization strategy, the performance changes of the electric energy meter are monitored in real time, including the accuracy and stability improvement of the correction results. Based on the monitoring results, a comprehensive evaluation of the implementation effect of the maintenance and optimization strategy is carried out, including the degree of performance improvement and cost-benefit analysis. It helps to understand the actual effect of the strategy and provide a basis for subsequent strategy adjustment and optimization; through user feedback and on-site monitoring, feedback and actual effects after maintenance and optimization are collected. It helps to understand the user's views on the strategy and the performance of the strategy in actual application, providing an important reference for continuous improvement; according to the feedback and actual effects, the maintenance and optimization strategy is adjusted and optimized. This includes measures such as modifying the maintenance plan, optimizing the correction strategy, and improving the performance improvement plan. Through continuous improvement, the overall performance and management level of the electric energy metering system can be continuously improved; the entire technical solution forms a closed-loop management system for continuous improvement. From data cleaning and integration to time series analysis and anomaly detection, correlation analysis, maintenance and optimization strategy formulation and implementation, implementation effect monitoring and evaluation, and user feedback and strategy adjustment and optimization, each link is closely linked to form a complete workflow. This helps to ensure the stability and reliability of the electric energy metering system and improve user satisfaction and trust.

[0051] One embodiment of the present invention, as Figure 2 As shown, an electric energy metering error correction system based on vector analysis, the system comprises: Data acquisition module: uses high-precision current and voltage sensors to collect current and voltage data in the power grid in real time, and transmits the data to the electric energy metering error correction system for processing; Vector analysis module: Based on vector control technology, the collected current and voltage data are vector analyzed to obtain the vector information of voltage and current; Error calculation module: calculates the electric energy metering error according to the vector information; performs error correction on the electric energy meter according to the calculated error; Monitoring and management module: The corrected electric energy metering results are output in real time, and the deviations in the metering process are detected and corrected through feedback signals. At the same time, the electric energy metering results are transmitted to the data center of the electric energy metering system for real-time monitoring and management.

[0052] The working principle of the above technical solution is: use high-precision current and voltage sensors to capture the current and voltage signals in the power grid in real time; convert these signals into digital data and transmit them to the electric energy metering error correction system for further processing; based on vector control technology, conduct in-depth analysis of the collected current and voltage data; through vector analysis, obtain vector information of voltage and current, including key parameters such as amplitude and phase; among them, vector information reveals the dynamic relationship between current and voltage, and provides necessary input for error calculation; according to the vector information, use existing error calculation methods (such as least squares method, Kalman filtering, etc.) to calculate the error of electric energy metering; according to the calculated error, use different correction methods for different types of electric energy meters (mechanical, electronic, smart electric energy meters); for mechanical For electric energy meters, errors are reduced by adjusting physical parameters such as capacitors and eccentricity. For electronic electric energy meters, errors are corrected by adjusting the signal output of the sensor by modifying the software program. For smart electric energy meters, dynamic error compensation technology based on adaptive algorithms is used to adjust the metering parameters in real time to compensate for errors. The correction process aims to improve the accuracy of electric energy metering and ensure the reliability of metering results. The corrected electric energy metering results are output in real time for users or systems to use. Deviations in the metering process are detected and corrected through feedback signals to ensure the continued accuracy of metering results. The electric energy metering results are transmitted to the data center of the electric energy metering system for real-time monitoring and management, so as to promptly discover and deal with potential problems. The real-time monitoring and management functions of the data center help maintain the stable operation of the power grid and improve energy utilization efficiency.

[0053] The effects of the above technical solutions are as follows: by using high-precision current and voltage sensors, the current and voltage data in the power grid can be collected in real time and accurately, providing a reliable basis for subsequent analysis and correction; by performing vector analysis on the collected data, the vector information of voltage and current, including amplitude, phase, etc., can be accurately obtained, thereby more accurately reflecting the actual state of the power grid; by calculating the error based on the vector information and adopting different correction methods for different types of electric energy meters, the error of electric energy metering can be significantly reduced and the accuracy of measurement can be improved; by outputting the corrected electric energy metering results in real time and detecting and correcting the deviations in the metering process, the stability of the metering system can be ensured and the system instability caused by error accumulation can be avoided; the electric energy metering results are transmitted to the data center of the electric energy metering system for real-time monitoring and management, which helps to timely discover and deal with potential The problem is solved, further enhancing the stability of the system; accurate electricity metering helps to accurately evaluate energy consumption and provide strong support for energy management and optimization; for smart electricity meters, the dynamic error compensation technology based on adaptive algorithms can achieve more intelligent and efficient error correction, further improving the energy utilization efficiency; through accurate error correction and real-time monitoring, the failure rate of electricity meters can be reduced, and the downtime and maintenance costs caused by failures can be reduced; accurate measurement and correction can also extend the service life of electricity meters and reduce replacement frequency and cost; this method combines advanced technologies such as high-precision sensors, vector analysis technology, and adaptive algorithms to promote the development of power grids towards intelligence and digitization; through real-time monitoring and management of electricity metering data, it can provide data support for the intelligent management of power grids and promote the intelligent upgrade and transformation of power grids.

[0054] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. The electric energy metering error correction method based on vector analysis is characterized in that: The method comprises: S1. Use high-precision current and voltage sensors to collect current and voltage data in the power grid in real time, and transmit the data to the electric energy metering error correction system for processing; S2. Perform vector analysis on the collected current and voltage data based on vector control technology to obtain vector information of voltage and current; S3. Calculate the electric energy metering error according to the vector information; and perform error correction on the electric energy meter according to the calculated error; S4. The corrected electric energy metering results are output in real time, and the deviations in the metering process are detected and corrected through feedback signals. At the same time, the electric energy metering results are transmitted to the data center of the electric energy metering system for real-time monitoring and management.

2. The method for correcting electric energy measurement errors based on vector analysis according to claim 1, characterized in that: Said S1 comprises: S11, real-time capture of current and voltage fluctuations in the power grid and current through high-precision current and voltage sensors deployed in the power grid; S12, the sensor transmits the captured current and voltage data to the data receiving module of the electric energy metering error correction system through a multi-channel transmission protocol; S13: The data receiving module pre-processes the received data and stores the pre-processed data in a database of the system.

3. The method for correcting electric energy measurement errors based on vector analysis according to claim 2, characterized in that: The S12 comprises: S121, after capturing the current and voltage data, the sensor compresses the data using a lossless data compression algorithm, and encrypts the compressed data using an encryption algorithm; S122. Select a multi-channel transmission protocol according to the actual situation of the power grid and the demand for data transmission, and configure multiple transmission channels for each sensor; S123, based on intelligent routing algorithm, dynamically select the optimal transmission path according to network conditions and data transmission priority, and implement load balancing strategy among multiple transmission channels; S124. During data transmission, the data integrity is checked by a hash function. If the data is lost or damaged, a retransmission mechanism is started, requiring the sensor to resend the data until the data is successfully received and verified to be correct.

4. The method for correcting electric energy measurement errors based on vector analysis according to claim 3 is characterized in that: The S122 includes: Conduct a comprehensive analysis of the power grid to determine the type of transmission protocol that matches the grid characteristics; determine the type and number of transmission channels required based on the grid coverage, sensor distribution, and data transmission requirements; According to the real-time status of the power grid, the transmission channel allocation of each sensor is dynamically adjusted through genetic algorithms; Based on the selected protocol and channel, perform interoperability tests between sensors and data receiving modules, and simulate abnormal situations to verify the fault recovery capabilities of protocols and channels; Monitor the performance indicators of the transmission channel in real time, collect and analyze transmission data, identify potential performance bottlenecks, and adjust and optimize the transmission strategy based on the results of performance monitoring and data analysis.

5. The method for correcting electric energy measurement errors based on vector analysis according to claim 3 is characterized in that: The S123 includes: Collect network parameters in real time through monitoring nodes deployed in the network or log information of existing network devices; Analyze network parameters based on machine learning algorithms, and evaluate the overall network status and the performance of each transmission channel based on the analysis results; Based on the network status assessment results, formulate intelligent routing strategies, and use intelligent routing algorithms to dynamically select the optimal transmission path according to the data transmission priority and changes in network status; By monitoring the load of each transmission channel in real time, combined with historical data and machine learning algorithms, the load change trend of each channel in the future is predicted; based on the load monitoring and prediction results, the load balancing strategy is dynamically adjusted; Collect performance indicators during data transmission, combine user feedback, evaluate the effectiveness of the current transmission strategy, and use genetic algorithms to adaptively optimize the transmission strategy based on the performance evaluation results.

6. The method for correcting electric energy measurement errors based on vector analysis according to claim 1, characterized in that: The S2 comprises: S21. Based on the vector control technology, the pre-processed current and voltage data are vector-decomposed to obtain the vector information of the voltage and current; S22, using a fast Fourier transform algorithm to perform spectrum analysis on the vector information, and further extract each harmonic component and its phase relationship; S23. Based on the built-in vector space model, the vector information of voltage and current is mapped into the model, and the parameters of vector analysis are dynamically adjusted according to the actual operating status of the power grid.

7. The method for correcting electric energy measurement errors based on vector analysis according to claim 6, characterized in that: The S22 comprises: According to the characteristics of vector information and analysis requirements, the data window function is selected, the pre-processed vector information is sampled at equal intervals, and the data is truncated; The spectrum of the truncated data is calculated based on the fast Fourier transform to obtain the amplitude and phase information of each frequency component; According to the spectrum calculation results, the frequency, amplitude and phase information of each harmonic component are identified, and the phase relationship between each harmonic component is analyzed; Compare and verify with the actual measurement data, and optimize and adjust the application parameters of the FFT algorithm based on the verification results; Extract key features from the spectrum analysis results, use machine learning to perform pattern recognition on the extracted features, and automatically classify the degree or type of harmonic pollution in the power grid.

8. The method for correcting electric energy measurement errors based on vector analysis according to claim 1, characterized in that: The S3 includes: S31, based on the Kalman filter algorithm, combined with the voltage and current vector information in the vector space model, the error between the theoretical value and the actual value of the electric energy metering is calculated; S32. Classify the electric energy meters according to the error calculation results, and identify mechanical, electronic and intelligent electric energy meters; formulate personalized error correction strategies for different types of electric energy meters; S33. Based on the built-in error correction model, the correction strategy is converted into specific correction parameters and stored in the correction parameter library of the system.

9. The method for correcting electric energy measurement errors based on vector analysis according to claim 1, characterized in that: The S4 comprises: S41, outputting the corrected electric energy metering result to the display interface of the system in real time, and monitoring the deviation in the metering process in real time through the feedback signal detection module. If the deviation exceeds the preset threshold, the correction operation is triggered immediately; S42, transmitting the corrected electric energy metering results and correction operation records to the data center of the electric energy metering system for real-time monitoring and management; S43, the data center analyzes the performance change trend of the electric energy meter according to the historical data and calibration records, and performs subsequent maintenance and optimization based on the analysis results; S44. The user provides feedback on the correction results through the user feedback mechanism, and the system continuously optimizes the correction strategy based on the user feedback.

10. The electric energy measurement error correction system based on vector analysis is characterized in that: The system comprises: Data acquisition module: uses high-precision current and voltage sensors to collect current and voltage data in the power grid in real time, and transmits the data to the electric energy metering error correction system for processing; Vector analysis module: Based on vector control technology, the collected current and voltage data are vector analyzed to obtain the vector information of voltage and current; Error calculation module: calculates the electric energy metering error according to the vector information; performs error correction on the electric energy meter according to the calculated error; Monitoring and management module: The corrected electric energy metering results are output in real time, and the deviations in the metering process are detected and corrected through feedback signals. At the same time, the electric energy metering results are transmitted to the data center of the electric energy metering system for real-time monitoring and management.

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