Intelligent management and health prediction method for electric energy meter operation data

Abstract

This invention relates to the field of data processing technology, and in particular to an intelligent management and health prediction method for electricity meter operating data. The method includes the following steps: preprocessing all collected electricity meter operating data to obtain an electricity meter operating dataset containing multi-dimensional raw operating data and basic time-series features; forming time-series feature clusters, adaptively identifying and optimizing these clusters, and based on the operating mode library of the time-series feature clusters, constructing a dedicated health status benchmark model corresponding to each typical operating mode of the electricity meter, where each typical operating mode corresponds to a specific electricity consumption behavior state of the electricity meter; calculating the real-time health of the electricity meter operating data based on the dedicated health status benchmark model corresponding to the typical operating mode matching the current operating data of the electricity meter, and providing dynamic early warnings for electricity meters with abnormal real-time health status. This invention improves the prediction accuracy of the real-time health of electricity meters.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to an intelligent management and health prediction method for electricity meter operation data. Background Technology

[0002] The stable and accurate operation of smart meters is a crucial prerequisite for ensuring fair electricity metering, grid security, and economical dispatch. With technological advancements, massive amounts of electricity meters continuously generate multi-dimensional, high-frequency time-series operational data, including voltage, current, power, and electricity consumption. This data not only records users' electricity consumption behavior but also contains rich information about the meters' own operating status, aging process, and potential faults. Therefore, intelligent management of electricity meter operating data, and the accurate prediction of their health status and fault early warning based on data-driven approaches, are of great value in reducing maintenance costs and improving power supply reliability.

[0003] Currently, in the field of electricity meter health prediction, existing technologies typically employ unified prediction models based on machine learning. The general processing paradigm of this type of method is as follows: collect historical operating data, extract global statistical features (such as mean, variance, skewness, kurtosis, etc.) or time-series features, and label them with corresponding health status tags (such as "normal," "requires maintenance," "faulty"). Then, a unified classification or regression model (such as random forest, support vector machine, LSTM, etc.) is trained. When new operating data is generated, it is input into this unified model to obtain a health score or fault probability. This method can, to a certain extent, achieve a mapping from data to status.

[0004] However, in-depth research into the actual operation of electricity meters reveals a fundamental limitation of the aforementioned unified model-based prediction method: it ignores the inherent "multi-mode" characteristics of electricity meter operating data. In practical applications, the same electricity meter serves different users (such as residents, shops, and factories), resulting in vastly different load characteristics. Even for the same user, electricity consumption patterns vary regularly and differently depending on whether it's day / night, weekday / weekend, or seasonal changes. Data under different operating modes exhibit drastically different statistical distributions, variation patterns, and normal fluctuation ranges. Existing technology mixes all these disparate data patterns together to train a unified prediction model, causing the model to learn multiple significantly different data patterns. Ultimately, this makes it difficult for the model to capture the unique health characteristics of each mode, blurring its decision boundaries. Consequently, the model becomes insensitive to slight anomalies on the edge of a certain mode, easily missing warnings; simultaneously, it may misjudge normal, drastic fluctuations in another mode as anomalies, leading to false alarms. Therefore, this decrease in prediction accuracy due to pattern confusion is one of the core reasons why current electricity meter health prediction technology is not performing well in real-world complex scenarios.

[0005] Therefore, improving the prediction accuracy of real-time health of electricity meters under multi-mode operation data has become an urgent problem to be solved. Summary of the Invention

[0006] In view of this, embodiments of the present invention provide an intelligent management and health prediction method for electricity meter operating data, in order to improve the problem of decreased prediction accuracy of real-time health of electricity meters under multi-mode operating data conditions.

[0007] In a first aspect, embodiments of the present invention provide an intelligent management and health prediction method for electricity meter operating data, which includes the following steps: Collect electricity meter operation data, preprocess all collected electricity meter operation data to obtain an electricity meter operation dataset containing multi-dimensional raw operation data and basic time series characteristics; Identify the basic time-series characteristics of all electricity meter operation data in the electricity meter operation dataset, form time-series feature clusters, adaptively identify and optimize the time-series feature clusters, and based on the operation mode library of the time-series feature clusters, construct an exclusive health status benchmark model corresponding to each typical operation mode of the electricity meter in the operation mode library, wherein each typical operation mode corresponds to a specific and recurring electricity consumption behavior state of the electricity meter. Based on a dedicated health status benchmark model corresponding to a typical operating mode that matches the current operating data of the electricity meter, the real-time health of the electricity meter's operating data is calculated, and dynamic warnings are issued for electricity meters with abnormal real-time health of their operating data.

[0008] As a preferred embodiment, the process of collecting electricity meter operating data and preprocessing all collected electricity meter operating data includes the following steps: Through the power information acquisition system, multi-dimensional operating data of the electricity meter that comprehensively reflects the operating status of the specified electricity meter is collected remotely at fixed intervals, and the timestamp of the electricity meter operating data collection is recorded. The collected multidimensional operating data of the electricity meter is cleaned and the collection time points of the cleaned multidimensional operating data of the electricity meter are aligned to form a unified multidimensional time series dataset. A moving average filter or a low-pass filter is used to smooth the high-frequency random disturbances caused by measurement noise or small power grid fluctuations in the unified multidimensional time series data to preserve the main load change trends of the electricity meter operation data. The numerical features of the smoothed multidimensional time series data are standardized, and the basic time series features of the multidimensional time series data are extracted within a specific moving time window.

[0009] As a preferred embodiment, the adaptive identification and optimization of the time-series feature clustering, and the construction of a dedicated health status benchmark model corresponding to each typical operating mode of the energy meter based on the operating mode library of the time-series feature clustering, includes the following steps: Preliminary adaptive identification of the operating mode based on the time-series feature clustering is performed to obtain a preliminary operating mode library for time-series feature clustering; The preliminary operation mode library of the time-series feature clustering is quantitatively evaluated to obtain the quantitative evaluation results. The key features defining each preliminary operation mode in the preliminary operation mode library are obtained, and the contribution of the key features to the definition of the preliminary operation mode is analyzed. Based on the quantitative evaluation results, the preliminary operation mode library is optimized. Based on the optimized preliminary operation mode library, a dedicated health status benchmark model corresponding to each typical operation mode of the electricity meter is constructed.

[0010] As a preferred embodiment, the preliminary adaptive identification of the operating mode based on the temporal feature clustering includes the following steps: The multidimensional raw operating data in the preprocessed electricity meter operating dataset is automatically and adaptively segmented and clustered from the historical normal data of the long time series to extract several operating mode fragments. For each obtained operation mode segment, a feature vector characterizing the operation mode of the operation mode segment is extracted. The operation mode based on the temporal feature clustering is initially adaptively identified by the feature vector characterizing the operation mode of the operation mode segment.

[0011] As a preferred embodiment, the feature vector characterizing the features of the operating mode segment specifically includes: Load profile feature vector, which is used to characterize the shape of the daily load curve; Three-phase unbalance, which is used to calculate the average and maximum values ​​of the three-phase current unbalance within a day; Load rate distribution, which is used to statistically analyze the percentage of time during which the daily power value is located in each rated power range; Power change activity, which is used to calculate the number of times the absolute value of the first-order difference of power exceeds a certain threshold.

[0012] As a preferred embodiment, the quantitative evaluation of the preliminary operating pattern library of the time-series feature clustering to obtain the quantitative evaluation results includes: The quantitative assessment results of the time-series feature clustering are affected by the parameter settings of the dedicated health status benchmark model or the noise in the electricity meter operating data.

[0013] As a preferred embodiment, the quantitative evaluation of the preliminary operating pattern library of the time-series feature clustering, obtaining the quantitative evaluation results, acquiring the key features defining each preliminary operating pattern in the preliminary operating pattern library, and analyzing the contribution of the key features to defining the preliminary operating pattern, includes the following steps: A quantitative evaluation method is used to construct a comprehensive quality score for each preliminary operating mode in the preliminary operating mode library of the time-series feature clustering. The preliminary operating mode library of the time-series feature clustering is quantitatively evaluated according to the quantitative evaluation method to obtain the quantitative evaluation result. Calculate the variance of the preliminary operating mode across all time-series feature dimensions, and analyze the contribution of the key features to defining the preliminary operating mode based on the variance of the key features of the preliminary operating mode across specific time-series feature dimensions.

[0014] As a preferred embodiment, the preliminary operation mode library is optimized based on the quantitative evaluation results. Based on the optimized preliminary operation mode library, a dedicated health status benchmark model corresponding to each typical operation mode of the electricity meter is constructed, including the following steps: Based on the quantitative evaluation results of each preliminary operation mode in the preliminary operation mode library and the key feature set of each preliminary operation mode, the preliminary operation mode library is optimized. For each of the optimized preliminary operating modes, a health status benchmark probability model describing the preliminary operating mode is constructed. Based on the health status benchmark probability model of each preliminary operating mode in the preliminary operating mode library, a dedicated health status benchmark model corresponding to each typical operating mode of the energy meter in the operating mode library is constructed.

[0015] As a preferred embodiment, the optimization of the preliminary operation mode library based on the quantitative evaluation results of each preliminary operation mode in the preliminary operation mode library and the key feature set of each preliminary operation mode includes the following steps: The initial operating mode to be quantified and evaluated is subjected to mode splitting to obtain at least two new operating modes split from the initial operating mode; The new operating mode with a data volume lower than the preset total number of segments and a quality score lower than the preset quality score is regarded as a noisy operating mode, and the noisy operating mode is removed from all new operating modes split from the initial operating mode in order to optimize the initial operating mode library.

[0016] As a preferred embodiment, the method of calculating the real-time health status of the electricity meter's operating data based on a dedicated health status benchmark model corresponding to a typical operating mode that matches the current operating data of the electricity meter, and providing dynamic early warning for electricity meters with abnormal real-time health status, includes the following steps: Determine which typical operating mode the electricity meter is currently operating in from the operating mode library, and match the corresponding typical operating mode from the operating mode library to the current operating data of the electricity meter, thereby obtaining the typical operating mode that matches the current operating data of the electricity meter. The system calls up the exclusive health status benchmark model corresponding to the typical operating mode that matches the current operating data of the electricity meter, extracts the current feature vector corresponding to the basic time series features of the current operating data of the electricity meter, calculates the Mahalanobis distance of the current feature vector relative to the exclusive health status benchmark model, and quantitatively calculates the real-time health of the electricity meter based on the Mahalanobis distance. A dynamic early warning threshold is set for each of the dedicated health status benchmark models of the electricity meter. When the Mahalanobis distance of the dedicated health status benchmark model of the electricity meter is greater than the dynamic early warning threshold, a dynamic early warning is issued indicating that the real-time health status of the electricity meter is abnormal.

[0017] Secondly, embodiments of the present invention provide an intelligent management and health prediction system for electricity meter operating data, the intelligent management and health prediction system for electricity meter operating data comprising: The system includes a data acquisition and preprocessing module, which is used to acquire electricity meter operation data and preprocess all the acquired electricity meter operation data to obtain an electricity meter operation dataset containing multi-dimensional raw operation data and basic time-series characteristics. A dedicated health status benchmark model construction module is used to identify the basic time-series characteristics of all electricity meter operation data in the electricity meter operation dataset, form time-series feature clusters, adaptively identify and optimize the time-series feature clusters, and construct a dedicated health status benchmark model corresponding to each typical operation mode of the electricity meter in the operation mode library based on the time-series feature clusters. Each typical operation mode corresponds to a specific and recurring electricity consumption behavior state of the electricity meter. The real-time health calculation and dynamic early warning module is used to calculate the real-time health of the electricity meter based on a dedicated health status benchmark model corresponding to a typical operating mode that matches the current operating data of the electricity meter, and to provide dynamic early warning for electricity meters with abnormal real-time health.

[0018] The beneficial effects of the embodiments of the present invention compared with the prior art are as follows: The intelligent management and health prediction method for electricity meter operating data of this invention adopts an adaptive mode adaptation approach. Its core is as follows: First, it automatically and accurately identifies and separates different typical operating modes of the electricity meter from historical data; then, it constructs a dedicated and refined health benchmark model for each identified operating mode. During real-time health prediction, it first determines the operating mode to which the current data belongs, and then calls the corresponding dedicated model for evaluation. Through this mode-adaptive analysis framework, the health status prediction model can closely match the actual operating modes of the electricity meter under various specific operating conditions, significantly improving the real-time perception sensitivity and judgment accuracy of subtle abnormal states within the mode. This enables earlier and more reliable intelligent real-time health status prediction and management, thereby effectively improving the prediction accuracy of real-time electricity meter health under multi-mode operating data scenarios. Attached Figure Description

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

[0020] Figure 1 This is a flowchart of an intelligent management and health prediction method for electricity meter operation data provided in Embodiment 1 of the present invention. Detailed Implementation

[0021] Embodiments of this disclosure are described in detail below, with examples of these embodiments illustrated in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this disclosure, and should not be construed as limiting it.

[0022] It should be noted that the terms "first," "second," etc., used in this disclosure and the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure.

[0023] To illustrate the technical solution of the present invention, specific embodiments are described below.

[0024] See Figure 1 This is a flowchart of a method for intelligent management and health prediction of electricity meter operating data provided in Embodiment 1 of the present invention. Figure 1 As shown, the intelligent management and health prediction method for the operating data of the electricity meter may specifically include the following steps: Step S1: Collect electricity meter operation data, preprocess all collected electricity meter operation data to obtain an electricity meter operation dataset containing multi-dimensional raw operation data and basic time series characteristics.

[0025] This step aims to provide a high-quality, standardized data foundation for subsequent pattern recognition and health modeling. Electricity meter operational data is characterized by its multi-dimensionality, time-series nature, and massive volume. This step may specifically include the following steps: 1. Data collection; The system uses a power information acquisition system (such as a power consumption information acquisition master station) to remotely collect operational data from designated electricity meters at fixed intervals (e.g., every 15 minutes). The collected data should comprehensively reflect the operating status of the meters, including but not limited to: Electrical parameter data: first phase voltage Ua, second phase voltage Ub, third phase voltage, each phase voltage Uc, current (Ia, Ib, Ic), active power (P), reactive power (Q), power factor (PF); Energy data: Total positive active energy, Total negative active energy; Event and status data: voltage sag / interruption records, overcurrent events, internal temperature of the energy meter, and clock battery voltage.

[0026] Ensure the continuity and integrity of data collection, and record accurate data collection timestamps.

[0027] 2. Data cleaning and reconstruction; Missing value handling: For a small amount of missing data due to communication interruptions or other reasons, linear interpolation of nearest neighbors or periodic sequence interpolation (based on historical data of the same day and time) is used to fill the gaps. For time periods with large areas of missing data, they are marked or removed.

[0028] Outlier handling: Based on physical constraints and statistical rules, preliminary screening is performed. Obvious erroneous data such as voltage values ​​exceeding the rated range (e.g., exceeding the rated range by ±20%), negative current values, and absolute power factor values ​​greater than 1 are removed or marked.

[0029] Data alignment: Ensure that the sampling time points of different data dimensions are strictly aligned to form a unified multidimensional time series dataset.

[0030] 3. Noise smoothing and initial feature extraction.

[0031] Smoothing: Use a moving average filter or a low-pass filter (such as a Butterworth filter) to smooth high-frequency random disturbances caused by measurement noise or small power grid fluctuations, while preserving the main load change trends.

[0032] Normalization: To eliminate the influence of different physical quantities in terms of dimensions, numerical characteristics such as voltage, current, and power are standardized by Z-score so that their mean is 0 and their standard deviation is 1.

[0033] Basic temporal feature extraction: To further characterize the operating state, basic features can be calculated within a sliding time window (e.g., a 1-hour window with a step size of 15 minutes), such as the mean, standard deviation, maximum, minimum power, and trend (slope of linear fitting) within the window; these extracted basic temporal features will serve as important inputs for pattern recognition in step S1.

[0034] Thus, after preprocessing, a well-organized electricity meter operation dataset containing multidimensional raw data and basic time-series characteristics can be obtained, laying a reliable data foundation for subsequent intelligent data analysis.

[0035] Step S2: Identify the basic time-series characteristics of all electricity meter operation data in the electricity meter operation dataset, form time-series feature clusters, adaptively identify and optimize the time-series feature clusters, and based on the operation mode library of the time-series feature clusters, construct an exclusive health status benchmark model corresponding to each typical operation mode of the electricity meter in the operation mode library. Each typical operation mode corresponds to a specific and recurring electricity consumption behavior state of the electricity meter.

[0036] The principle and core of this step is to achieve "mode adaptation." Existing traditional methods treat all data as a whole for modeling, which has drawbacks as mentioned earlier. The principle of this step is as follows: First, leveraging the multi-mode characteristics of electricity meter operation data, several representative "typical operation modes" are automatically extracted from historical normal operation data using unsupervised learning methods. Each typical operation mode corresponds to a specific, recurring electricity consumption behavior state (such as "nighttime sleep mode," "midday light load mode," "evening residential peak load mode," "weekend special mode," etc.). Then, all data segments belonging to the same mode are treated as a homogeneous set, and a model describing the "health status benchmark" under this mode is independently constructed for it. This model learns the "normal" performance pattern of various parameters (such as power curve shape, three-phase balance, voltage fluctuation range, etc.) under this specific electricity consumption mode.

[0037] Its beneficial effects are: a) Automation and adaptability: No manual pre-definition of patterns is required; the algorithm can discover patterns based on the inherent characteristics of the data. b) Refinement: Each health baseline model focuses on learning only one specific operating pattern, thus its ability to perceive subtle anomalies within it is far stronger than that of the global model. c) Providing a foundation for accurate prediction: During real-time prediction, by comparing the current data with each model baseline, it is possible not only to determine whether there is an anomaly, but also to indicate under what typical operating conditions the anomaly occurred, making the warning information more instructive. The overall operation, from initial pattern segmentation to pattern quality assessment, and then to pattern library optimization and model construction, gradually ensures that the final obtained pattern library and its health baseline are of high quality, stable, and highly discriminative.

[0038] The adaptive identification and optimization of the time-series feature clustering in step S2, and the construction of a dedicated health status benchmark model corresponding to each typical operating mode of the energy meter based on the operating mode library of the time-series feature clustering, are implemented in the following specific steps: Step A: Initially adaptively identify the operating mode based on the time-series feature clustering to obtain a preliminary operating mode library for time-series feature clustering; The goal of step A is to automatically segment and cluster different operational pattern segments from historical normal data in long-term series. First, the preprocessed continuous long-term series is divided using a simple and regular fixed-length method, which aligns with human understanding of the "daily cycle" of electricity consumption behavior and is a commonly used basic method. Therefore, this technical solution can use a sliding window with a fixed length of 24 hours for segmentation, with a sliding step size of 1 hour or several hours, to obtain sufficient and overlapping sample segments. Assume that N data segments are ultimately obtained: S 1, S 2, ..., S N .

[0039] For each obtained operating mode segment, a feature vector characterizing its operating mode is extracted, in addition to basic statistical features. F 1 (mean, variance, etc.); The focus should be on extracting features that reflect the characteristics of the pattern. This feature vector may include: 1. Load profile characteristics F 2. The 24-hour active power sequence is downsampled into 96 points (one point every 15 minutes). This vector directly depicts the shape of the daily load curve. (Significance: It directly reflects the electricity consumption of users during the day and is key to distinguishing between peak-valley-flat periods.) 2. Three-phase unbalance: Calculate the average value of the three-phase current unbalance over the day. F 3 and maximum value F 4. (Significance: The difference between residential single-phase electricity use and industrial / commercial three-phase electricity use is significant in this respect.) 3. Load rate distribution F 5. Statistical analysis of the percentage of time that the daily power consumption falls within different ranges (e.g., 0-10% of rated power, 10%-30%, ...). (Significance: This quantifies users' time habits under various power intensities; for example, the percentage of time spent in "nighttime sleep mode" during the idle period is extremely high.) 4. Power change activity F 6: Calculate the number of times the absolute value of the first-order difference of power exceeds a certain threshold. (Its significance: It reflects the frequency of load fluctuations. For example, a continuously running machine tool and frequently starting and stopping household appliances (such as air conditioners and water heaters) behave differently in this characteristic.)

[0040] Ultimately, each data fragment They are all mapped to one d 3D eigenvectors : ;

[0041] Then, the feature vectors of all data segments are combined to form a feature matrix. The set of feature vectors corresponding to each data segment This allows us to construct a feature space for clustering (a... A feature matrix of dimension 1 (where each point in the feature space represents a daily electricity consumption pattern); where 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 ... This indicates the total number of data segments. d The dimension of the feature matrix and the points in the feature space. Representing the Daily electricity consumption patterns F 1 represents the basic statistical characteristic. F 2 represents the load profile feature. F 3 represents the average value of the three-phase current imbalance during the day. F 4 represents the maximum value of the three-phase current imbalance during the day. F 5 represents the load factor distribution. F 6 represents the activity level of power changes.

[0042] Then, an unsupervised clustering algorithm (such as the density-based clustering method DBSCAN) is used to cluster the feature matrix. DBSCAN can automatically determine the number of categories and filter noise points, making it suitable for the scenario in this embodiment. Each cluster obtained after clustering is considered to initially correspond to a typical operating mode, and all data fragments within a cluster share similar electricity consumption behavior characteristics. Thus, the initial mode division results can be obtained. and the set of data fragments corresponding to each pattern, wherein, These represent the first, second, and third instances of the initial running pattern library for time-series feature clustering. k There are several operating modes.

[0043] Step B: Quantitatively evaluate the preliminary operation mode library of the time-series feature clustering, obtain the quantitative evaluation results, acquire the key features that define each preliminary operation mode in the preliminary operation mode library, and analyze the contribution of the key features to the definition of the preliminary operation mode. The preliminary clustering results obtained in Step B may be affected by parameter settings or data noise. Not all clusters are high-quality, stable, and physically meaningful "patterns." For example, there may be significant data differences within a cluster (the pattern is not pure), or the centers of two clusters may be too close (the pattern has low discriminative power). This step aims to quantitatively evaluate each preliminary pattern obtained in Step A and analyze which features dominate the formation of the pattern, providing a precise basis for the next optimization step.

[0044] Step B may specifically include the following steps: i. First, construct the initial operational model library for time-series feature clustering. i Operating modes Overall quality score :

[0045] in, This indicates the overall quality score. The first preliminary running pattern library representing temporal feature clustering i One operating mode, Representation pattern The number of data fragments contained Indicates belonging to the operating mode The feature vector of a certain data segment, The cluster centers (i.e., the mean of the feature vectors) represent the operating patterns. Representing the eigenvector To the center The Euclidean distance represents the degree of deviation of the data segment from the center of this operating mode; then the denominator... Representation pattern The average intra-class distance; its reciprocal reflects the operating mode. Pattern tightness: The smaller the intra-class distance, the more similar the data within the running patterns, and the higher the tightness; the second item... Indicates the operating mode The shortest Euclidean distance from the cluster center to the centers of all other preliminary operating patterns in the preliminary operating pattern library reflects the operating pattern. The degree of separation from other operating modes in the initial operating mode library; the higher the value of this separation, the better the operating mode. The stronger the uniqueness, the better.j This indicates that the initial runtime mode library contains runtime modes other than the runtime modes. Clustering centers with operating modes other than those in the past i and j All are positive integers; This indicates normalization processing; Let represent the first weighting coefficient and the second weighting coefficient, respectively, and satisfy the condition that the sum of the first weighting coefficient and the second weighting coefficient equals 1. The first weighting coefficient and the second weighting coefficient are used to balance the importance of intra-class tightness and inter-class separation in quality evaluation. Typically, they can be set as follows: , Then it means Both are equally important; The specific value can be set according to the needs of the scenario.

[0046] Based on the overall quality score Results classification: A comprehensive quality score was calculated for all preliminary operating modes. Then, set the evaluation threshold. ; Usually, all can be retrieved. The median or tertiary values ​​are used to classify the patterns into two categories: high-quality operating patterns and operating patterns to be evaluated. Among them, the high-quality operation mode must meet the comprehensive quality score. Greater than the evaluation threshold ,Right now: If a high-quality operating mode is compact and clearly distinct from others, it is a candidate stable operating mode; the operating mode to be evaluated must meet the comprehensive quality score. Less than the evaluation threshold If the condition is met, then the operating mode to be evaluated has internal looseness or similar problems to other operating modes, and needs to be optimized in subsequent steps.

[0047] ii. Analysis of the contribution of key features.

[0048] In this step, the operating mode is calculated. The variance across all time-series feature dimensions is calculated and compared with the variance of the global data on that feature dimension. For feature dimensions with significantly smaller variances than the global variance, it indicates that the operating mode exhibits high consistency across that feature dimension, and the features on that dimension are key features defining the operating mode. For example, for the "nighttime sleep mode," the intra-class variance of its feature "nighttime average power" will be much smaller than the global variance. The key feature set for each operating mode is recorded. This analysis helps to understand the physical meaning of the mode and allows for more refined adjustments to key features in subsequent optimizations.

[0049] Calculation operation mode In the Contribution of each feature dimension

[0050] , In the formula, Representing feature dimension Operating mode The contribution ratio, which has a range of [0, 1), indicates that the closer the contribution ratio is to 1, the more critical this feature dimension is to defining this operating mode. This indicates the operating mode. All data within the feature dimension variance on; This indicates the global dataset. (i.e., all historical data fragments) in the feature dimension The variance on.

[0051] The principle and significance of the above formula: If the variance of a feature dimension within a certain operating mode is much smaller than the overall variance of that feature dimension in all data, it means that the value of that feature dimension is very stable and concentrated in that operating mode. This stability and concentration is the mark that distinguishes this operating mode from other operating modes.

[0052] Construction of key feature set: for operating mode Calculate the contribution of all feature dimensions. And set a contribution threshold. (This solution can be set) 0.7; can also be set according to the scenario; can also retain the feature dimension corresponding to the maximum contribution value or the top two or three values ​​as the key feature dimension); will satisfy Feature dimensions are incorporated into the key feature dimension set of this operating mode. This set indicates the feature dimension subspaces that should be prioritized when optimizing or using this operating mode.

[0053] Step C: Based on the quantitative evaluation results, optimize the preliminary operation mode library, and based on the optimized preliminary operation mode library, construct a dedicated health status benchmark model corresponding to each typical operation mode of the electricity meter in the operation mode library.

[0054] The principle of step C is as follows: Based on the quantitative evaluation results obtained in step B, the preliminary operating mode library is optimized in a targeted manner, and a corresponding exclusive health status benchmark model is built for each finally determined operating mode; the specific operation steps are as follows: i. Optimize operation of the pattern library; based on the running mode Quality scores and key feature sets The following optimizations were performed: 1) Mode splitting: targeting the satisfaction to be quantified. Operating mode ; The main reason is poor density (large intra-class distance); therefore, its key feature set should be examined. This indicates that the operating mode may actually contain two sub-operating modes. Therefore, we can re-cluster all operating data points within this operating mode in the subspace of the key feature dimension (using K-means clustering, setting K=2). If the overall quality score of the two sub-clusters after splitting is higher in the original full feature space than before splitting, then we can use the clustering results to... Split into two new operating sub-modes .

[0055] 2) Noise pattern removal: Remove data with extremely small amounts of data (e.g., less than 1% of the total number of segments) and very low quality scores (e.g., ...). The mode that is considered a noise operation sub-mode is removed.

[0056] After the above optimizations, a stable and high-quality final typical operating mode library can be obtained. .

[0057] ii. Construction of a pattern-specific health status benchmark model; For the optimized final operating modes Build a description of the final operating mode The health status baseline probability model; firstly, assuming the final operating mode. Under healthy conditions, the feature vectors of data belonging to the same operating mode approximately follow a multivariate Gaussian distribution (a common, reasonable, and computationally efficient assumption that is the basis of existing logical theory). , in, Indicates the final operating mode The health status benchmark model Indicates a multivariate Gaussian distribution. Represents the mean vector. Represent the covariance matrix; for the final operating mode Its health status benchmark model The mean vector estimated from all historical healthy running data of this running mode (i.e., the running data segments used for training). Covariance Matrix Defined as follows: Mean vector Characterized by the normal central values ​​of each feature dimension under this operating mode; covariance matrix Defined as: a d*d symmetric matrix that represents the normal fluctuation range of each feature dimension (diagonal elements) and the normal linkage relationship between different features (off-diagonal elements) under the operating mode.

[0058] In conclusion, The normal center values ​​of each feature in this mode were characterized. It characterizes the range of normal fluctuations of each feature and their interrelationships. In real-time monitoring, the Mahalanobis distance of the current data feature relative to this distribution can be calculated to quantify its deviation from the healthy baseline.

[0059] Step S3: Based on the exclusive health status benchmark model corresponding to the typical operating mode that matches the current operating data of the electricity meter, calculate the real-time health of the electricity meter's operating data, and provide dynamic early warning for electricity meters with abnormal real-time health of operating data.

[0060] Step S3 applies the learned pattern library and health status benchmark model to the real-time data stream of the electricity meter to achieve online intelligent real-time health prediction and early warning management of the electricity meter, as detailed below: 1. After a new energy meter operating data segment (such as data from the past 24 hours) is preprocessed in step S1 and the same feature vector is extracted, the distance between it and the center of each final mode in the mode library is calculated (or the probability of belonging to the Gaussian distribution of each mode is calculated), and it is matched to the mode with the smallest distance (or the highest probability), thereby determining what typical operating mode the current energy meter is operating under.

[0061] 2. Quantitative Calculation of Real-Time Health: A dedicated health status benchmark model corresponding to the typical operating mode matching the current operating data of the electricity meter is invoked. The current feature vector corresponding to the basic time-series characteristics of the current operating data of the electricity meter is extracted. The Mahalanobis distance of the current feature vector relative to the dedicated health status benchmark model is calculated. Based on the Mahalanobis distance, the real-time health of the electricity meter is quantitatively calculated. The smaller the calculated Mahalanobis distance value, the higher the real-time health of the electricity meter; the larger the calculated Mahalanobis distance value, the lower the real-time health of the electricity meter, and the higher the probability of the electricity meter being abnormal.

[0062] 3. Dynamic Early Warning and Feedback: A dynamic early warning threshold is set for each mode; this threshold can be determined by the statistical quantile (e.g., 95th or 99th percentile) of the Mahalanobis distance of its historical health data samples; if the current Mahalanobis distance is greater than the dynamic early warning threshold, a dynamic early warning is triggered. The dynamic early warning information includes at least: the time of the electricity meter data anomaly, the matched typical operating mode (e.g., "evening peak load mode"), the degree of deviation of the electricity meter data anomaly, and the main characteristics that may affect its anomaly.

[0063] Therefore, by using automated mode discovery and a dedicated health status benchmark model, subtle anomalies in electricity meter data under specific electricity consumption patterns can be detected more accurately. This significantly reduces false alarms and missed alarms of electricity meter data anomalies, effectively improves the prediction accuracy of the real-time operating data health of electricity meters, and enables more intelligent and forward-looking management of the operating data health status of electricity meters, providing reliable decision support for the condition-based maintenance and management of electricity meters. The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for intelligent management and health prediction of electricity meter operating data, characterized in that, The intelligent management and health prediction method for electricity meter operating data includes: Collect electricity meter operation data, preprocess all the collected electricity meter operation data to obtain an electricity meter operation dataset containing multi-dimensional raw operation data and basic time series characteristics; Identify the basic time-series characteristics of all electricity meter operation data in the electricity meter operation dataset, form time-series feature clusters, adaptively identify and optimize the time-series feature clusters, and based on the operation mode library of the time-series feature clusters, construct an exclusive health status benchmark model corresponding to each typical operation mode of the electricity meter in the operation mode library, wherein each typical operation mode corresponds to a specific and recurring electricity consumption behavior state of the electricity meter. Based on a dedicated health status benchmark model corresponding to a typical operating mode that matches the current operating data of the electricity meter, the real-time health of the electricity meter's operating data is calculated, and dynamic warnings are issued for electricity meters with abnormal real-time health of their operating data.

2. The intelligent management and health prediction method for electricity meter operating data according to claim 1, characterized in that, The process of collecting electricity meter operating data includes preprocessing all collected electricity meter operating data, comprising the following steps: Through the power information acquisition system, multi-dimensional operating data of the electricity meter that comprehensively reflects the operating status of the specified electricity meter is collected remotely at fixed intervals, and the timestamp of the electricity meter operating data collection is recorded. The collected multidimensional operating data of the electricity meter is cleaned and the collection time points of the cleaned multidimensional operating data of the electricity meter are aligned to form a unified multidimensional time series dataset. A moving average filter or a low-pass filter is used to smooth the high-frequency random disturbances caused by measurement noise or small power grid fluctuations in the unified multidimensional time series data to preserve the main load change trends of the electricity meter operation data. The numerical features of the smoothed multidimensional time series data are standardized, and the basic time series features of the multidimensional time series data are extracted within a specific moving time window.

3. The intelligent management and health prediction method for electricity meter operation data according to claim 1, characterized in that, The adaptive identification and optimization of the time-series feature clustering, and the construction of a dedicated health status benchmark model corresponding to each typical operating mode of the energy meter based on the operating mode library of the time-series feature clustering, includes the following steps: Preliminary adaptive identification of the operating mode based on the time-series feature clustering is performed to obtain a preliminary operating mode library for time-series feature clustering; The preliminary operation mode library of the time-series feature clustering is quantitatively evaluated to obtain the quantitative evaluation results. The key features defining each preliminary operation mode in the preliminary operation mode library are obtained, and the contribution of the key features to the definition of the preliminary operation mode is analyzed. Based on the quantitative evaluation results, the preliminary operation mode library is optimized. Based on the optimized preliminary operation mode library, a dedicated health status benchmark model corresponding to each typical operation mode of the electricity meter is constructed.

4. The intelligent management and health prediction method for electricity meter operation data according to claim 3, characterized in that, The preliminary adaptive identification of the operating mode based on the temporal feature clustering includes the following steps: The multidimensional raw operating data in the preprocessed electricity meter operating dataset is automatically and adaptively segmented and clustered from the historical normal data of the long time series to extract several operating mode fragments. For each obtained operation mode segment, a feature vector characterizing the operation mode of the operation mode segment is extracted. The operation mode based on the temporal feature clustering is initially adaptively identified by the feature vector characterizing the operation mode of the operation mode segment.

5. The intelligent management and health prediction method for electricity meter operation data according to claim 4, characterized in that, The feature vector characterizing the operational mode of the operational mode segment specifically includes: Load profile feature vector, which is used to characterize the shape of the daily load curve; Three-phase unbalance, which is used to calculate the average and maximum values ​​of the three-phase current unbalance within a day; Load rate distribution, which is used to statistically analyze the percentage of time during which the daily power value is located in each rated power range; Power change activity, which is used to calculate the number of times the absolute value of the first-order difference of power exceeds a certain threshold.

6. The intelligent management and health prediction method for electricity meter operation data according to claim 3, characterized in that, The preliminary operational model library for the quantitative evaluation of the time-series feature clustering is used to obtain quantitative evaluation results, including: The quantitative assessment results of the time-series feature clustering are affected by the parameter settings of the dedicated health status benchmark model or the noise in the electricity meter operating data.

7. The intelligent management and health prediction method for electricity meter operation data according to claim 3, characterized in that, The quantitative evaluation of the preliminary operating pattern library of the time-series feature clustering, obtaining the quantitative evaluation results, acquiring the key features defining each preliminary operating pattern in the preliminary operating pattern library, and analyzing the contribution of the key features to the definition of the preliminary operating pattern, includes the following steps: A quantitative evaluation method is used to construct a comprehensive quality score for each preliminary operating mode in the preliminary operating mode library of the time-series feature clustering. The preliminary operating mode library of the time-series feature clustering is quantitatively evaluated according to the quantitative evaluation method to obtain the quantitative evaluation result. Calculate the variance of the preliminary operating mode across all time-series feature dimensions, and analyze the contribution of the key features to defining the preliminary operating mode based on the variance of the key features of the preliminary operating mode across specific time-series feature dimensions.

8. The intelligent management and health prediction method for electricity meter operation data according to claim 7, characterized in that, Based on the quantitative evaluation results, the preliminary operation mode library is optimized. Based on the optimized preliminary operation mode library, a dedicated health status benchmark model corresponding to each typical operation mode of the electricity meter is constructed, including the following steps: Based on the quantitative evaluation results of each preliminary operation mode in the preliminary operation mode library and the key feature set of each preliminary operation mode, the preliminary operation mode library is optimized. For each of the optimized preliminary operating modes, a health status benchmark probability model describing the preliminary operating mode is constructed. Based on the health status benchmark probability model of each preliminary operating mode in the preliminary operating mode library, a dedicated health status benchmark model corresponding to each typical operating mode of the energy meter in the operating mode library is constructed.

9. The intelligent management and health prediction method for electricity meter operation data according to claim 8, characterized in that, The optimization of the preliminary operation mode library based on the quantitative evaluation results of each preliminary operation mode in the library and the key feature set of each preliminary operation mode includes the following steps: The initial operating mode to be quantified and evaluated is subjected to mode splitting to obtain at least two new operating modes split from the initial operating mode; The new operating mode with a data volume lower than the preset total number of segments and a quality score lower than the preset quality score is regarded as a noisy operating mode, and the noisy operating mode is removed from all new operating modes split from the initial operating mode in order to optimize the initial operating mode library.

10. The intelligent management and health prediction method for electricity meter operation data according to claim 1, characterized in that, The method, based on a dedicated health status benchmark model corresponding to a typical operating mode that matches the current operating data of the electricity meter, calculates the real-time health of the electricity meter's operating data and provides dynamic early warnings for electricity meters with abnormal real-time health, including the following steps: Determine which typical operating mode the electricity meter is currently operating in from the operating mode library, and match the corresponding typical operating mode from the operating mode library to the current operating data of the electricity meter, thereby obtaining the typical operating mode that matches the current operating data of the electricity meter. The system calls up the exclusive health status benchmark model corresponding to the typical operating mode that matches the current operating data of the electricity meter, extracts the current feature vector corresponding to the basic time series features of the current operating data of the electricity meter, calculates the Mahalanobis distance of the current feature vector relative to the exclusive health status benchmark model, and quantitatively calculates the real-time health of the electricity meter based on the Mahalanobis distance. A dynamic early warning threshold is set for each of the dedicated health status benchmark models of the electricity meter. When the Mahalanobis distance of the dedicated health status benchmark model of the electricity meter is greater than the dynamic early warning threshold, a dynamic early warning is issued indicating that the real-time health status of the electricity meter is abnormal.

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