Intelligent detection method and system for full performance of electric energy metering equipment

Abstract

The invention discloses a full-performance intelligent detection method and system for electric energy metering equipment, and the method comprises the steps: collecting an observation signal outputted by the electric energy metering equipment, and carrying out the multi-dimensional coupling information filtering, and obtaining a working state estimation value signal of the electric energy metering equipment; calculating the fractal dimension and fractal entropy of the observation signal of the electric energy metering equipment according to the working state estimation value signal; dynamically generating an equipment state quantitative feedback signal according to a time-varying membership function based on the fractal dimension and the fractal entropy; and intelligently identifying equipment faults or performance deviation according to the equipment state quantitative feedback signal, and carrying out equipment full-performance detection when the equipment faults or the performance deviation is identified. According to the invention, potential faults or performance deviations can be intelligently identified, full-performance detection and adjustment can be carried out in time, the precision of the equipment is improved, the self-adaptive capability of the equipment is enhanced, and human intervention and maintenance cost are reduced.

Description

Technical Field

[0001] This invention belongs to the field of performance testing technology for electricity metering equipment, and relates to a method and system for intelligent testing of the full performance of electricity metering equipment. Background Technology

[0002] Electricity metering equipment is an indispensable key device in power systems. Its main function is to accurately measure the consumption, supply, and transmission of electrical energy to ensure the stable operation of the power system and fair billing for users. With the expansion of power system scale and the rapid development of smart grids, the performance requirements for electricity metering equipment are increasing, demanding that it maintain high accuracy, stability, and real-time performance under various complex operating conditions. However, during long-term operation, electricity metering equipment is affected by various factors such as grid fluctuations, load changes, electromagnetic interference, and ambient temperature. These factors may lead to decreased measurement accuracy, longer response times, or even equipment failure. Therefore, to ensure that electricity metering equipment can still accurately measure under different operating conditions, improve its operational stability, and reduce errors caused by environmental changes, it is essential to intelligently identify potential performance deviations or faults in a timely manner, conduct comprehensive performance testing, and make adaptive adjustments.

[0003] Existing electricity metering equipment primarily relies on single-error identification and correction methods for performance optimization, or uses linear filtering techniques for simple signal processing to remove noise and external interference. However, with the increasing complexity of power systems, the characteristics of electricity metering signals have become more complex and variable. Traditional error identification and correction methods and signal processing methods are no longer sufficient to meet the high precision and reliability requirements of modern smart grids for metering equipment. Therefore, researching a technology capable of intelligently identifying the operating status and abnormal conditions of equipment based on complex signal characteristics, and comprehensively detecting the full performance status of electricity metering equipment, has become an important direction for improving the intelligence level of electricity metering equipment and enhancing the reliability of power systems.

[0004] Existing performance testing technologies for electricity metering equipment are ill-suited to adapting to the dynamic changes in multidimensional signals in complex environments. This results in ineffective error reduction, an inability to fully reflect the complex characteristics of signals such as self-similarity and disorder, and difficulty in accurately identifying equipment status. In particular, performance evaluation errors are significant in complex noise environments. Furthermore, these technologies fail to provide sufficient adaptive adjustment mechanisms to analyze the operational stability and metering accuracy of the equipment. They also fail to fully consider the complexity of the equipment in actual operation, making it difficult to achieve accurate adaptive quantitative evaluation. Consequently, the overall efficiency and accuracy of electricity metering equipment are affected. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method and system for intelligent full-performance testing of electricity metering equipment.

[0006] The present invention adopts the following technical solution.

[0007] The first aspect of this invention proposes a method for intelligent detection of the full performance of electricity metering equipment, comprising: The observation signal output by the power metering equipment is collected and multi-dimensional coupled information filtering is performed to obtain the estimated value signal of the working status of the power metering equipment; The fractal dimension and fractal entropy of the observed signal from the power metering equipment are calculated based on the estimated operating state signal. The device status quantization feedback signal is dynamically generated based on the time-varying membership function based on fractal dimension and fractal entropy. Based on the quantitative feedback signal of equipment status, the system can intelligently identify equipment faults or performance deviations and perform full performance testing on the equipment when a fault or performance deviation is identified.

[0008] Preferably, the observation signal output by the power metering equipment is subjected to multi-dimensional coupled information filtering, as follows:

[0009] in, , These are the estimated operating states of the energy metering equipment at time t and time t-1. This is a dynamic filtering kernel function; It is a weighted matrix under different signal feature dimensions; This represents the noisy observation signal output by the power metering device at time t.

[0010] Preferably, the dynamic filtering kernel function is:

[0011] in, For ideal power metering signals; It is the threshold for adjusting the filter strength; and These are the coefficients that control the dynamic adjustment of the filter kernel function; It is the noise variance; It is a non-linear adjustment index.

[0012] Preferably, the weighting matrix is:

[0013] in, These are parameters that are dynamically adjusted based on the noise characteristics and signal-to-noise ratio of the signal. It is the noise variance; It is a nonlinear adjustment coefficient used to enhance the sensitivity of signal characteristics.

[0014] Preferably, the formula for calculating the fractal dimension is as follows:

[0015] in, It is the fractal dimension of the observed signal; This indicates that the box counting method is used on a scale. The minimum number of boxes required to cover the working state estimate signal when dividing the signal; It is a scaling parameter, representing the minimum box side length used each time the working state estimate signal is divided.

[0016] Preferably, the formula for calculating fractal entropy is as follows:

[0017] in, It is fractal entropy; In scale The probability distribution of the next working state estimate signal Item, i.e., the first The probability of occurrence of each subsequence; The working state estimate signal is on scale. The total number of subsequences obtained after the sub-segmentation.

[0018] Preferably, the time-varying membership function based on fractal dimension and fractal entropy is:

[0019] in, It is the time-varying membership function at time t, representing the degree of response of the device to the estimated signal state of the current operating state; It is the ambiguity control factor; It is the estimated value of the working status of the power metering equipment at time t; This is the target state of the electricity metering equipment; It is a time-varying adjustment factor; It is a nonlinear adjustment coefficient that reflects the degree of influence of signal complexity on the time-varying membership function; It is the fractal dimension of the observed signal; It is fractal entropy.

[0020] Preferably, the device status quantization feedback signal is dynamically generated based on a time-varying membership function based on fractal dimension and fractal entropy, as follows:

[0021] in, It is a quantitative feedback signal of equipment status; This is the target state of the electricity metering equipment; It is the actual state of the electricity metering equipment, through... Median filtering was performed to obtain, It is the estimated value of the working status of the power metering equipment at time t; It is a nonlinear adjustment coefficient that reflects the degree of influence of the difference between the actual state and the target state of the power metering equipment on the state quantification feedback signal; It is a time-varying membership function based on fractal dimension and fractal entropy.

[0022] A second aspect of this invention provides a full-performance intelligent testing system for electricity metering equipment, comprising: The acquisition module is used to acquire the observation signals output by the power metering equipment and perform multi-dimensional coupled information filtering to obtain the estimated value signal of the working status of the power metering equipment. The calculation module is used to calculate the fractal dimension and fractal entropy of the observed signal of the power metering device based on the estimated working state signal; The feedback signal generation module is used to dynamically generate equipment status quantization feedback signals based on the time-varying membership function based on fractal dimension and fractal entropy. The identification module is used to intelligently identify equipment faults or performance deviations based on the quantitative feedback signals of equipment status, and to perform full performance testing on the equipment when a fault or performance deviation is identified.

[0023] A third aspect of the present invention provides a terminal, including a processor and a storage medium; the storage medium is used to store instructions; the processor is used to perform operations according to the instructions to execute the steps of the method.

[0024] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method.

[0025] Compared with the prior art, the beneficial effects of the present invention include at least the following: This invention performs multi-dimensional coupled information filtering on the observation signal output by the power metering equipment. By using a dynamic filtering kernel function and weighting matrix, the filtering process is dynamically adjusted according to the characteristics of noise and signal, which can effectively suppress the interference of various noise sources on the signal. Through nonlinear adaptive adjustment and dynamic noise modeling, noise in the signal is accurately filtered out, ensuring the accurate recovery of the power metering signal, avoiding the influence of external electromagnetic interference and internal noise of the equipment, ensuring the accuracy and reliability of the signal, and better coping with complex noise environments. This invention uses two important indicators, fractal dimension and fractal entropy, to quantify the complexity and irregularity of signals. It can better describe the self-similarity and chaotic characteristics of signals at different scales, providing a more detailed complexity measurement for the performance evaluation of power metering equipment, and effectively improving the sensitivity and identification ability of equipment performance deviations. The time-varying membership function constructed in this invention reflects the fuzziness of equipment performance by analyzing the actual working state of the equipment (such as fractal dimension and fractal entropy), and then adjusts the equipment state quantization feedback signal. By comparing the state quantization feedback signal with a predetermined threshold range, it can be determined whether the equipment has a fault or performance deviation, and then perform full performance testing and optimization to ensure that the equipment can continuously optimize metering accuracy in different operating environments, so that the power metering equipment can adapt to changes in the external environment and improve the stability and reliability of the equipment in complex scenarios. This invention considers the impact of the difference between the actual state and the target state of the power metering equipment on the final state quantization feedback signal. It combines a time-varying membership function to dynamically adjust the state quantization feedback signal. Based on the deviation between the target state and the actual state and the adaptive response mechanism based on the time-varying membership function, the state quantization feedback signal can continuously and accurately reflect the performance state of the optimized equipment. This ensures that the equipment can quickly and accurately identify and adjust faults or performance deviations during actual operation. It not only improves the accuracy of the equipment but also enhances its adaptive capability and reduces human intervention and maintenance costs. Attached Figure Description

[0026] Figure 1 This is a flowchart of a method for intelligent full-performance testing of an electrical energy metering device according to the present invention. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of this invention. The embodiments described in this application are merely some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, other embodiments obtained by those skilled in the art without creative effort are all within the protection scope of this invention.

[0028] like Figure 1 As shown, Embodiment 1 of the present invention provides a method for intelligent detection of the full performance of an electricity metering device, the method comprising the following steps: S1. Collect the observation signal output by the power metering equipment and perform multi-dimensional coupled information filtering to obtain the estimated value signal of the working status of the power metering equipment; More preferably, the signal from the power metering device is collected, and the estimated operating status of the power metering device is obtained through a multi-dimensional coupled information filtering algorithm. The specific analysis is as follows: During the signal acquisition process of electricity metering equipment, due to interference from various factors such as power line noise, external electromagnetic field influence, and the equipment's internal noise, the acquired signal often cannot completely and accurately reflect the actual state of the equipment. Therefore, the signal acquisition results usually contain a certain noise component. The difference between the actual observed value of the signal and the ideal signal can be represented by the noise term. Noise is usually random, and in power systems, the sources of noise are complex and diverse, including external electromagnetic interference, electrical noise generated during equipment operation, and even errors in the measurement process itself.

[0029] To accurately evaluate the performance of electricity metering equipment, the acquired signals need to be effectively processed to remove or reduce the impact of noise on subsequent analysis and decision-making. Assuming the signal is contaminated with noise during acquisition, its observed value can be expressed as the sum of the ideal signal and the noise term. The noise contamination model of the signal is represented by the following formula:

[0030] in, This represents the noisy observation signal collected by the power metering device at time t; For ideal power metering signals; The noise term represents the error or noise generated by external or internal interference during equipment operation.

[0031] The presence of noise can cause the acquired signal to deviate from its true physical state, thus affecting subsequent analysis and decision-making. Therefore, removing noise and restoring the true state of the signal becomes a key technical challenge.

[0032] To eliminate various noises encountered during signal transmission, signal filtering based on multidimensional coupled information is employed. A dynamic filtering kernel function is constructed to update the signal at the current moment. The filtering process is dynamically adjusted according to the characteristics of noise and signal, making the estimated value closer to the true signal and reducing the impact of noise on power metering. The filtering formula is as follows:

[0033]

[0034]

[0035] in, It is the estimated value of the working status of the power metering equipment at time t; It is a dynamic filtering kernel function that dynamically adjusts noise suppression based on changes in the signal; It is a weighted matrix under different dimensions (multiple features of the signal, such as current, voltage, power, etc.), used to dynamically adjust the weights of each dimension according to the features of the signal; This is the estimated value from the previous moment; It is a threshold for adjusting the filtering strength, set according to expert experience. and These are the coefficients that control the dynamic adjustment of the filter kernel function, obtained through experiments; It is the noise variance (the variance of noise can be obtained by performing statistical analysis on historical signals, such as variance estimation). It is a non-linear adjustment index, obtained through experiments; These are parameters that are dynamically adjusted based on the noise characteristics and signal-to-noise ratio of the signal, and are obtained through experiments (optimized under different noise environments using least squares regression analysis or particle swarm optimization algorithms). It is a nonlinear adjustment coefficient used to further enhance the sensitivity of signal characteristics, and it was obtained through experiments.

[0036] The multidimensional coupled information filtering algorithm is based on the coupling relationship of multiple signal feature domains. Through dynamic filtering kernel function and adaptive estimation update step, it accurately filters out multiple noise sources in the signal. Through nonlinear adaptive adjustment, multidimensional signal coupling and dynamic noise modeling, it can better cope with complex noise environment and provide accurate signal filtering for power metering equipment.

[0037] S2. Calculate the fractal dimension and fractal entropy of the observed signal of the power metering equipment based on the estimated working state signal; More preferably, the fractal dimension and fractal entropy of the power metering device signal are calculated, specifically as follows: In signal processing for electricity metering equipment, fractal dimension is an important feature for measuring signal complexity and self-similarity. To quantify signal complexity, box counting is used to estimate fractal dimension. The signal is divided into multiple small units or boxes at different scales, and the number of signal points contained in each box is counted to reveal the self-similarity characteristics of the signal. Specifically, different resolutions are selected, and then the minimum number of boxes required to cover the signal at the current resolution is calculated; as the resolution decreases, the required number of boxes increases. By taking the logarithm of the relationship between the minimum number of boxes and the resolution, the fractal dimension of the signal can be obtained. The fractal dimension reflects the self-similarity of the signal at different scales; the larger the fractal dimension, the stronger the complexity and irregularity of the signal. The slope obtained through linear regression is the fractal dimension, as shown in the following formula:

[0038] in, It is the fractal dimension of the observed signal, representing the self-similarity of the signal at different scales, and reflecting the complexity of the signal; This indicates that the box counting method is used on a scale. The minimum number of boxes required to cover the working state estimate signal when dividing the signal; This is the scale parameter, representing the minimum box side length (resolution) used each time the working state estimate signal is divided. As the scale decreases, the number of boxes required for the signal increases, and the final fractal dimension can be obtained by adjusting the scale. and Linear regression is performed, and the slope of the linear regression is the fractal dimension of the signal. This reflects the complexity and self-similarity of signals at different scales.

[0039] Fractal entropy is an important indicator for measuring signal complexity and irregularity, particularly suitable for processing nonlinear and dynamically changing power metering signals. To calculate fractal entropy, a multi-scale entropy method is employed to quantify the signal's complexity at different scales and analyze the changes in entropy values ​​by reconstructing the signal at different scales. The signal is smoothed at multiple scales to obtain the sample entropy at each scale, and then the results from each scale are combined to evaluate the overall signal complexity. Specifically, a scale is first selected, the probability distribution of the signal at that scale is calculated, and the entropy value is calculated using the current probability distribution. The specific formula for calculating fractal entropy is:

[0040] in, It is fractal entropy, which measures the complexity and disorder of a signal; In scale The probability distribution of the next working state estimate signal Item, i.e., the first The probability of occurrence of each subsequence; The working state estimate signal is on scale. The total number of subsequences obtained after subsegmentation. Sample entropy, by evaluating the complexity of a signal at different scales, can reflect the chaotic characteristics and irregularities of the signal. By calculating the entropy values ​​at multiple scales, the fractal entropy of the signal is finally obtained, providing a dynamic and global measure of signal complexity.

[0041] S3. Dynamically generate equipment status quantization feedback signals based on time-varying membership functions based on fractal dimension and fractal entropy; More preferably, a time-varying membership function is constructed based on fractal dimension and fractal entropy, and a feedback signal is generated based on time-varying fuzzy control, as follows: In the full-performance testing of electricity metering equipment, a time-varying membership function based on fractal dimension and fractal entropy was designed to dynamically evaluate the metering accuracy and stability of the equipment, reflecting the degree of fuzziness in equipment performance. Specifically, the time-varying membership function comprehensively analyzes the complexity characteristics of the signal (such as fractal dimension and fractal entropy) to ensure efficient and accurate performance evaluation of the equipment under different operating conditions. The calculation of the time-varying membership function not only considers the actual performance of the current equipment but also incorporates the quantitative characteristics of equipment complexity, adjusting the equipment's operating behavior in real time to optimize metering accuracy and equipment stability. The formula for calculating the time-varying membership function is as follows:

[0042] in, It is the time-varying membership function at time t, which represents the degree of response of the device to the estimated state of the current working state signal, and the value is between 0 and 1; The target state represents the ideal performance that the device should have. It is a time-varying adjustment factor, representing the sensitivity to signal response at different times, obtained through experiments; It is a nonlinear adjustment coefficient that reflects the degree of influence of signal complexity on the time-varying membership function, and is obtained through experiments; The fuzziness control factor, which adjusts the sensitivity of the time-varying membership function, was obtained through experiments.

[0043] The aforementioned target state can be defined based on the design standards, ideal operating conditions, and application scenarios of the electricity metering equipment. For example: Ideal power measurement: Assume that the power metering device is used to measure the power of an industrial device. The target state C(t) can be the ideal power value expected during the device's design, such as 100 kW. In this case, C(t) at time t represents the target power of the device, and the power metering device should measure and record the power value as accurately as possible to 100 kW.

[0044] Phase accuracy: Energy metering equipment may also monitor the phase difference between current and voltage. Assuming ideal conditions, the target phase difference is 0 degrees (i.e., voltage and current are perfectly synchronized). Then C(t) is 0 degrees, and the target state should remain within this ideal phase difference range when the energy metering equipment is operating normally.

[0045] Equipment stability: Assume the target state C(t) is the stability range of the energy metering equipment, for example, the fluctuation range of the equipment during stable operation does not exceed ±0.5% error. The target state C(t) is this range value. In actual operation, if the signal fluctuation measured by the equipment exceeds this range, it may indicate that the equipment has a fault or deviation.

[0046] The final output signal is a feedback signal generated based on time-varying fuzzy control, reflecting the deviation between the current operating state and the desired target state of the equipment. Considering the gap between the current operating state and the target state, and combining the time-varying membership function, the state quantization feedback signal is dynamically adjusted to analyze equipment performance, improve the overall adaptability of the equipment, and ensure that it can still perform efficient and accurate metering operations in unstable environments. The calculation formula for the equipment feedback signal is:

[0047] in, It is the final state quantification feedback signal (equipment feedback signal), used to quantify the operating performance status of the power metering equipment; Yes The state after median filtering indicates the actual performance of the electricity metering equipment; It is a nonlinear adjustment coefficient that reflects the degree of influence of the difference between the actual state and the target state of the power metering equipment on the final state quantification feedback signal. It is obtained through experiments.

[0048] Time-varying membership function The sensitivity is dynamically adjusted based on the signal complexity (such as fractal dimension and fractal entropy) to ensure the most accurate quantized feedback signal under different operating environments, thus achieving adaptive evaluation. The state quantization feedback signal is achieved through an adaptive response mechanism. It can continuously and accurately quantify the operating performance status of equipment, ensuring that equipment faults or performance deviations can be quickly and accurately identified during actual operation.

[0049] S4. Based on the quantitative feedback signal of equipment status, intelligently identify equipment faults or performance deviations, and perform full performance testing on the equipment when a fault or performance deviation is identified to ensure efficient and accurate operation of the equipment, as detailed below: The final state quantization feedback signal is calculated. Then, through analysis The signal assesses the current performance of the electricity metering equipment based on a set threshold range. For example, a standard performance threshold range can be set, when... If the device deviates from this ideal range, it is considered that the electricity metering equipment may have malfunctioned or deviated. Full performance testing is required according to different performance standards, including but not limited to: power error, phase deviation, metering accuracy, and response time. Each performance aspect of the equipment will be evaluated based on a comparison with its corresponding standard.

[0050] Feedback signals can also be quantized based on the final state. The system analyzes the operating status of the power metering equipment and uses intelligent algorithms (such as fuzzy logic reasoning and decision trees) to comprehensively analyze various performance indicators of the equipment and determine whether the equipment is in a faulty state. These intelligent algorithms can be implemented using existing technologies. For example, by monitoring the deviation between the response signal of the power metering equipment and the expected target state in real time, it is possible to intelligently identify whether the equipment has faults such as overload, short circuit, inaccurate metering, or overheating.

[0051] The following is a specific example of implementing full performance evaluation based on state quantization feedback signals: First, set the threshold range for the standard performance of the electricity metering equipment, such as 0.03-0.08.

[0052] Define the standard performance ranges for the electricity metering equipment. Assume the following performance indicators: 1. Power error: Indicates the difference between the power measured by the equipment and the ideal power; the standard value range is... ; 2. Phase deviation: Indicates the deviation between the phase measured by the equipment and the ideal phase; the standard value range is... ; 3. Measurement accuracy: This refers to the measurement error of the equipment, usually specified within a certain range; for example, the power measurement error should be within a certain range. within; 4. Response Time: The time from when the device receives an input signal to when it provides a response; the standard value is... .

[0053] The state quantization feedback signal is calculated. This signal integrates the deviation between the device's operating state and the desired target state. Assume that at a certain time t, the obtained state quantization feedback signal... A value of 0.05 indicates a deviation from the threshold range of 0.03-0.08, which identifies equipment malfunctions or performance deviations, requiring full performance testing.

[0054] Assuming the standard performance range has been set, full performance testing and evaluation can be performed accordingly. Here is a specific example: 1. Power error: Assuming the ideal power output is 1000W, while the actual power output of the device is 980W; Standard range: ; Calculation error: ; because Within the standard range, the power error meets the requirements.

[0055] 2. Phase deviation: Assuming the ideal phase is 30°, while the actual phase measured by the device is 30.3°; Standard range: ; Calculate the deviation: ; because Within the standard range, the phase deviation meets the requirements.

[0056] 3. Measurement accuracy: Assume the ideal power is 1000 W, while the device's power measurement is 1010 W; Standard range: ; Calculation error: That is, the error is 1%; because The error is within the standard range, and the measurement accuracy meets the requirements.

[0057] 4. Response time: Assume the device's response time is 110 ms; Standard range: ; Calculate the deviation: ; The response time does not meet the requirements because 110 ms exceeds the standard range.

[0058] Finally, the deviations between the state quantification feedback signal and various performance standards are comprehensively analyzed: 1. Power error: Meets standards; 2. Phase deviation: Meets standards; 3. Measurement accuracy: Meets standards; 4. Response time: Does not meet the standard.

[0059] Taking all factors into consideration, if any performance characteristic exceeds its standard range (such as excessively large response time deviation), further inspection or adjustment is required to complete the full-performance intelligent testing of the electricity metering equipment.

[0060] Embodiment 2 of the present invention provides a full-performance intelligent testing system for electricity metering equipment, comprising: The acquisition module is used to acquire the observation signals output by the power metering equipment and perform multi-dimensional coupled information filtering to obtain the estimated value signal of the working status of the power metering equipment. The calculation module is used to calculate the fractal dimension and fractal entropy of the observed signal of the power metering device based on the estimated working state signal; The feedback signal generation module is used to dynamically generate equipment status quantization feedback signals based on the time-varying membership function based on fractal dimension and fractal entropy. The identification module is used to intelligently identify equipment faults or performance deviations based on the quantitative feedback signals of equipment status, and to perform full performance testing on the equipment when a fault or performance deviation is identified.

[0061] Embodiment 3 of the present invention provides a terminal, including a processor and a storage medium; the storage medium is used to store instructions; the processor is used to perform operations according to the instructions to execute the steps of the method.

[0062] Embodiment 4 of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method.

[0063] Compared with the prior art, the beneficial effects of the present invention include at least the following: This invention performs multi-dimensional coupled information filtering on the observation signal output by power metering equipment. Through dynamic filtering kernel functions and weighting matrices, the filtering process is dynamically adjusted according to the characteristics of noise and signal, which can effectively suppress the interference of various noise sources on the signal. Through nonlinear adaptive adjustment and dynamic noise modeling (the calculation of K(t) and W(t) in the filtering formula, especially by dynamically adjusting the filtering process to adapt to the characteristics of signal and noise), noise in the signal is accurately filtered out, ensuring the accurate recovery of power metering signal, avoiding the influence of external electromagnetic interference and internal noise of the equipment, ensuring the accuracy and reliability of the signal, and better coping with complex noise environments. This invention uses two important indicators, fractal dimension and fractal entropy, to quantify the complexity and irregularity of signals. It can better describe the self-similarity and chaotic characteristics of signals at different scales, providing a more detailed complexity measurement for the performance evaluation of power metering equipment, and effectively improving the sensitivity and identification ability of equipment performance deviations. The time-varying membership function constructed in this invention reflects the fuzziness of equipment performance by analyzing the actual working state of the equipment (such as fractal dimension and fractal entropy), and then adjusts the equipment state quantization feedback signal. By comparing the state quantization feedback signal with a predetermined threshold range, it can be determined whether the equipment has a fault or performance deviation, and then perform full performance testing and optimization to ensure that the equipment can continuously optimize metering accuracy in different operating environments, so that the power metering equipment can adapt to changes in the external environment and improve the stability and reliability of the equipment in complex scenarios. This invention considers the impact of the difference between the actual state and the target state of the power metering equipment on the final state quantization feedback signal. It combines a time-varying membership function to dynamically adjust the state quantization feedback signal. Based on the deviation between the target state and the actual state and the adaptive response mechanism based on the time-varying membership function, the state quantization feedback signal can continuously and accurately reflect the performance state of the optimized equipment. This ensures that the equipment can quickly and accurately identify and adjust faults or performance deviations during actual operation. It not only improves the accuracy of the equipment but also enhances its adaptive capability and reduces human intervention and maintenance costs.

[0064] This disclosure can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of this disclosure.

[0065] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination of the foregoing. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0066] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0067] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0068] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A full performance intelligent detection method for electric energy metering equipment, characterized in that, The method comprises the following steps: Collecting observation signals output by the electric energy metering device and performing multi-dimensional coupled information filtering to obtain an estimated value signal of the working state of the electric energy metering device; Calculating the fractal dimension and fractal entropy of the observation signals of the electric energy metering device according to the estimated value signal of the working state; Generating a device state quantization feedback signal dynamically according to a time-varying membership function based on the fractal dimension and fractal entropy; Intelligently identifying device faults or performance deviations according to the device state quantization feedback signal and performing full performance detection of the device when a device fault or performance deviation is identified.

2. The method according to claim 1, wherein the observation signals output by the electric energy metering device are subjected to multi-dimensional coupled information filtering, and the filtering is performed as follows:

3. The method according to claim 2, wherein the dynamic filtering kernel function is as follows: wherein, , is the working state estimation value of the electric energy metering device at time t and time t-1; is a dynamic filtering kernel function; is a weighted matrix under different signal feature dimensions; represents the observation signal with noise output by the electric energy metering device at time t.

4. The method according to claim 2, wherein the weighting matrix is as follows:

5. The method according to claim 1, wherein the fractal dimension is calculated according to the following formula: wherein, is an ideal electric energy metering signal; is a threshold value for adjusting the filtering strength; and is a coefficient for controlling dynamic adjustment of the filter kernel function; is a noise variance; is a non-linear adjustment exponent.

6. The method according to claim 1, wherein the fractal entropy is calculated according to the following formula:

7. The method according to claim 1, wherein the time-varying membership function based on the fractal dimension and fractal entropy is as follows: wherein, is a parameter that is dynamically adjusted according to the noise characteristics of the signal and the signal-to-noise ratio; is the noise variance; is a non-linear adjustment coefficient for enhancing the sensitivity of the signal characteristics.

8. The method according to claim 1, wherein the device state quantization feedback signal is generated dynamically according to the time-varying membership function based on the fractal dimension and fractal entropy, and the generation is performed as follows: The system comprises: wherein Dobsis the fractal dimension of the observed signal; Dobsis the fractal dimension of the observed signal; Dobsis the fractal dimension of the observed signal; Dobsis the fractal dimension of the observed signal; A collecting module for collecting observation signals output by the electric energy metering device and performing multi-dimensional coupled information filtering to obtain an estimated value signal of the working state of the electric energy metering device; A calculating module for calculating the fractal dimension and fractal entropy of the observation signals of the electric energy metering device according to the estimated value signal of the working state; wherein is the fractal entropy; is the probability distribution of the working state estimation value signal at the scale is the first term of the probability distribution of the working state estimation value signal at the scale , i.e., the occurrence probability of the first subsequence; is the total number of subsequences obtained after dividing the working state estimation value signal at the scale A feedback signal generating module for generating a device state quantization feedback signal dynamically according to a time-varying membership function based on the fractal dimension and fractal entropy; An identifying module for intelligently identifying device faults or performance deviations according to the device state quantization feedback signal and performing full performance detection of the device when a device fault or performance deviation is identified. wherein, is a time-varying membership function at time t, representing the degree of response of the device to the current state of the state of the estimate of the operating condition; is a fuzziness control factor; is an estimate of the operating condition of the electric energy metering device at time t; is a target state of the electric energy metering device; is a time-varying adjustment factor; is a non-linear adjustment coefficient reflecting the degree of influence of the complexity of the signal on the time-varying membership function; is a fractal dimension of the observation signal; is a fractal entropy.

10. A terminal comprising a processor and a storage medium; wherein: The storage medium is configured to store instructions; in, It is a quantitative feedback signal of equipment status; This is the target state of the electricity metering equipment; It is the actual state of the electricity metering equipment, through... Median filtering was performed to obtain, It is the estimated value of the working status of the power metering equipment at time t; It is a nonlinear adjustment coefficient that reflects the degree of influence of the difference between the actual state and the target state of the power metering equipment on the state quantification feedback signal; It is a time-varying membership function based on fractal dimension and fractal entropy.

9. An intelligent performance detection system for electric energy metering equipment, which runs the method of any one of claims 1-8, characterized in that, The processor is configured to operate according to the instructions to perform the steps of the method according to any one of claims 1-8. The program is executed by the processor to implement the steps of the method according to any one of claims 1-8. ​ ​ ​ ​ ​ ​ 11. A computer readable storage medium having stored thereon a computer program, characterized in that, ​

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