1574 results about "Smart meter" patented technology

The invention provides a multi-source ammeter data intelligent fusion and abnormity identification method. According to the method, original electric parameter data of a plurality of intelligent electric meters are collected under a unified time reference, a cross-equipment measurement data set indexed according to electric meter numbers is constructed, and electric parameter classification and associated information generation are completed in combination with electric wiring information and functional parameters; an electric parameter change curve is extracted through a continuous time window, a value change consistency index is calculated, a response offset feature vector is further introduced for joint judgment, and accurate recognition of an abnormal electric meter and an abnormal time period is achieved; for the intelligent electric meter which is identified as abnormal, constructing an estimated value through data of other electric meters, and calculating residual errors between the estimated value and an original value and between the estimated value and an adjacent estimated value according to a main conductive parameter so as to realize dual residual error verification; judging an anomaly type according to the consistency index and the residual result, and outputting an anomaly recognition result; according to the method, the anomaly detection precision in a multi-table collaboration scene is improved.

The invention discloses a power grid abnormal flow detection method based on multi-modal data fusion, and the method comprises the steps: collecting the multi-source heterogeneous data of a power grid through an edge calculation node, including the current waveform of an intelligent electric meter, the state variable of an SCADA system, network protocol metadata and an equipment log event; performing space-time alignment preprocessing on the original data, converting an unstructured log into a time sequence by adopting a sliding window mechanism, and eliminating sensor noise through an LSTM auto-encoder; constructing a multi-dimensional feature space which comprises a frequency domain feature, a spatial-temporal feature and a protocol feature, and obtaining a feature vector; the feature vectors are input into a hybrid detection model, the model is composed of an isolated forest algorithm, an improved CNN-LSTM classifier and an information entropy-based rule engine which are connected in parallel, and a dynamic weight fusion strategy is adopted to output an abnormal probability; and when the abnormal probability exceeds a dynamic threshold, triggering a multi-level response mechanism: sending a traffic shaping instruction to the edge device, generating a device fingerprint portrait on the cloud platform, and storing abnormal event features through a block chain.

The invention discloses a smart home energy management optimization method in an Internet of Things environment. According to the method, the Internet of Things technology is utilized, intelligent identification and classified management are carried out on equipment, multiple energy management strategies are preset, and a user can conveniently and autonomously adjust; energy data is monitored in real time through a sensor and an intelligent electric meter, an energy consumption rule is mined by using a big data analysis technology, and a demand trend is predicted; constructing an intelligent energy dispatching center, and formulating a dynamic energy distribution scheme by using an optimization algorithm; a mobile phone application and an intelligent control panel are developed, and a user is supported to check data, control equipment and carry out personalized customization; and meanwhile, equipment firmware upgrading and remote management are realized. According to the invention, energy consumption can be reduced, cost can be controlled, equipment collaboration and energy distribution efficiency can be improved, user experience can be enhanced, system maintenance and upgrading are facilitated, and intelligent household energy management is more efficient, intelligent and flexible so as to adapt to diversified household requirements.

The invention relates to the technical field of computers, and discloses a carbon emission calculation method based on deep learning, which comprises the following steps: S1, collecting multi-source data through a sensor, satellite remote sensing, an intelligent electric meter and a monitoring system, carrying out cleaning, denoising and missing value filling on the collected data, and in addition, carrying out data processing on the collected data; the structured data and the unstructured data are integrated through a multi-source data fusion technology; s2, performing feature extraction on the original data by adopting a deep feature learning model, and screening out key features contributing to carbon emission prediction; and S3, performing real-time decomposition and anomaly detection on the data by combining an autoregressive integral moving average model and an online learning algorithm. By combining an autoregressive integral moving average model and an online Bayesian learning method, carbon emission calculation can dynamically adjust parameters according to real-time data to adapt to environmental changes, seasonal fluctuations and emergencies, so that the flexibility and real-time performance of a prediction model are remarkably improved.

The invention discloses an intelligent electric meter abnormal electricity utilization detection method and system based on data analysis, and belongs to the technical field of electricity utilization abnormal detection, and the method specifically comprises the steps: obtaining the historical electricity utilization data of a user according to an intelligent electric meter, and carrying out the preprocessing of the obtained historical electricity utilization data of the user, extracting features of the preprocessed historical power consumption data of the user, carrying out three-layer clustering analysis on the extracted features, analyzing historical power consumption behaviors of the user, identifying abnormal behaviors obviously deviating from a normal power consumption mode according to the historical power consumption behaviors of the user, and adaptively adjusting model parameters through a feedback mechanism. The abnormal electricity utilization of the intelligent electric meter is monitored in real time, the method can adapt to the electricity utilization mode change of the user, the dynamic threshold value adjustment can be adaptively adjusted according to the electricity utilization behavior change of the user, and therefore more accurate anomaly detection is achieved.

The invention discloses a smart power grid fault early warning and diagnosis system, and relates to the technical field of smart power grids. Comprising a multi-modal data acquisition and fusion module, a deep reinforcement learning early warning module, a fault diagnosis module based on a graph neural network, an adaptive diagnosis module integrated with multi-task learning, an edge calculation and cloud collaboration module and a system test and verification module, and comprises the following steps: step 1, acquiring multi-source data through a power sensor, an intelligent electric meter and a meteorological station; dimension reduction and feature fusion are carried out on the data by using a depth auto-encoder model; 2, importing the collected data into a deep reinforcement learning early warning module, and dynamically adjusting an early warning strategy through interaction with a power grid operation environment; 3, a fault diagnosis module based on the graph neural network analyzes the topological structure of the power grid and identifies a fault position; 4, learning a model and processing a fault type; 5, edge calculation and cloud cooperative work are combined; and 6, testing, verifying and optimizing the model by the system testing and verifying module.

The invention relates to the field of electric meter box monitoring, and discloses an anti-electricity-stealing method and system for an intelligent electric meter box, which is used for solving the problem of false alarm caused by harmonic interference when anti-electricity-stealing early warning is carried out, and comprises the following steps: collecting time domain signals of current and voltage in the intelligent electric meter box in a historical detection time period; converting the time domain signal into a frequency domain signal, extracting harmonic information of each harmonic, and establishing a normal harmonic feature model to obtain a target harmonic range; the method comprises the following steps: acquiring time domain signals of current and voltage in an intelligent electric meter box in real time, extracting harmonic information, performing filtering processing according to a target harmonic range, performing first abnormality judgment by using an abnormality detection algorithm, performing multi-parameter joint evaluation if abnormality is judged for the first time, and performing second abnormality judgment according to a multi-parameter joint evaluation result. And electricity larceny early warning is carried out according to the second abnormal judgment result, so that false alarm and missing alarm are effectively reduced, and the adaptability of the system to a complex electricity larceny mode is remarkably enhanced.

The invention discloses a system for monitoring line loss rate abnormity of a power distribution network station area, and relates to the technical field of power distribution network monitoring. The line loss rate abnormity monitoring system for the power distribution network station area comprises a data acquisition module, a data processing module, a line loss rate calculation module, an abnormity identification and alarm module and an on-site troubleshooting and diagnosis module. Through integrating functions of intelligent electric meter data acquisition, power grid data collection and area division, data processing, line loss rate calculation, abnormity identification and alarm, on-site troubleshooting and diagnosis and the like, comprehensive and real-time monitoring of the line loss rate of the power distribution network station area is realized. And a regional line loss calculation model is established according to the characteristics of the power distribution network of each region, and the line loss rate can be monitored in real time, dynamically calculated and predicted, so that the efficiency and accuracy of line loss management are greatly improved. And the abnormity identification and alarm module can timely discover and give an alarm of line loss abnormity, provides probability analysis of abnormity reasons, and is helpful for operation and maintenance personnel to quickly respond and process.

The invention discloses an intelligent diagnosis system of a power distribution network fault self-judgment switch device, and relates to the technical field of power system automation and intelligent power grids, and the intelligent diagnosis system comprises an intelligent diagnosis system interconnection hub which is in communication connection with a power parameter monitoring module, an intelligent fault detection module and a visual decision support module; the electric power parameter monitoring module monitors the state of a power distribution network in real time through a mutual inductor, voltage, current, power factors and harmonic data are collected through an intelligent electric meter, the intelligent fault detection module achieves fault recognition, trend prediction and anomaly detection through an LSTM neural network, and the decision support module provides a graphical interface and automatically generates a fault response strategy. According to the method, autonomous judgment and quick response of the power distribution network fault are achieved, the accuracy and efficiency of fault detection are remarkably improved, the false alarm rate and the missing report rate are reduced, meanwhile, the decision support module of the graphical interface simplifies the fault processing flow, and the stability and safety of power grid operation are improved.

The invention relates to the technical field of power distribution network dispatching, in particular to an intelligent dispatching method and system for a power distribution network in an industrial park, and the method comprises the following steps: S1, data collection and preprocessing: collecting the real-time operation data of the power distribution network in the industrial park through an intelligent electric meter, a sensor and Internet of Things equipment; s2, performing real-time load prediction: performing load prediction on the industrial park power distribution network by using the load prediction model; s3, dynamic electric energy quality management: identifying electric energy quality abnormity and potential problems; s4, power supply scheme optimization and scheduling strategy generation: generating an optimal power supply scheme and scheduling strategy; s5, real-time monitoring and feedback: the running state of the power distribution network is monitored in real time; and S6, self-learning and optimization: analyzing historical scheduling data and an actual operation effect, and summarizing a scheduling rule and experience. According to the invention, the optimal power supply scheme is generated, and the economical efficiency and reliability of power supply are ensured.

The invention provides a distributed energy access-oriented intelligent electric meter multi-device collaborative optimization method and device, equipment and a medium, and the method comprises the steps: carrying out the interpolation of a unified electric meter, an inverter and energy storage data granularity, building a joint time error model, obtaining calibration reference data, and completing the multi-device time synchronization calibration; based on the reference data, acquiring operation data of three devices, and using a joint energy consumption model to distinguish electric meter hardware faults and multi-device collaborative anomalies to complete fault diagnosis; calculating local line loss by combining an inverter-to-ammeter line and electric quantity data, verifying residual electricity metering deviation, and tracing and correcting settlement data to complete line loss-bidirectional metering linkage verification; associating the fault result with the corresponding relation between the long-term statistical fault type of the metering data and the deviation rate and the line loss to establish a model to predict deviation trend early warning; and determining a multi-device cooperative abnormal source and transformer area load interference relation, calculating an interference coefficient, feeding back a scheduling system, and marking a line reconstruction priority. By adopting the method, the distributed energy consumption efficiency and the operation reliability of the power distribution network are improved.

The invention discloses an intelligent electric meter anomaly detection method and system based on a federal differential privacy and attention mechanism. The method comprises the following steps: firstly, collecting and regionally grouping intelligent electric meter data; then, a multi-level attention mechanism is adopted to extract regional specific initial features, and spatial correlation features are enhanced through fusion of a graph attention network and multi-head attention; carrying out distributed anomaly preliminary identification; for potential anomalies, user behavior log features are safely obtained and protected through a federated learning framework and an LDP mechanism; secondly, performing security aggregation and optimization on local model parameters which are trained by all parties and subjected to parameter-level differential privacy protection under a federated learning framework; local model fine tuning and secondary cross validation are carried out to confirm true abnormity; performing qualitative traceability on the abnormal event by utilizing multi-dimensional dynamic attention; and finally, dynamically adjusting a feature extraction strategy through federal feedback, and optimizing a data processing scheme in combination with resource awareness. According to the method, the problem of balance of precision, privacy protection and model adaptability in data anomaly detection of the intelligent electric meter is solved.

The invention discloses an intelligent power grid operation and maintenance system based on federated learning and edge calculation, and the system comprises an intelligent electric meter enhancement module which is disposed at a power grid monitoring node and is used for collecting electric energy quality parameters and environment data in real time; the edge calculation layer is used for carrying out data preprocessing, power quality parameter anomaly detection and building a federated learning model; the secure communication framework is used for establishing a hybrid communication network and multi-level security protection, carrying out routing decision and carrying out encrypted transmission on interactive data among the modules; and the end analysis platform is used for integrating the multi-source heterogeneous data of the power grid, predicting the state of the power grid and generating a maintenance plan. According to the invention, comprehensive monitoring and predictive maintenance of the operation state of the power grid are realized.

The invention discloses a power proxy service system and method based on artificial intelligence, and the method comprises the steps: collecting power data in real time through an intelligent electric meter and a sensor, and uploading the power data to a cloud platform through the Internet of Things for preprocessing and marking; predicting the power load by using time sequence analysis, regression, SVM and LSTM methods in combination with weather, season and holiday factors; power grid load dispatching is optimized through reinforcement learning or a genetic algorithm, and automatic allocation is achieved; the sensor monitors the state of the power equipment in real time, predicts the fault risk and gives an alarm in advance; the system provides energy-saving suggestions according to historical power consumption data of a user, and adjusts an equipment operation mode in real time; the intelligent customer service server pushes personalized electricity consumption suggestions based on natural language processing; an encryption technology and multiple identity authentication are adopted to guarantee data security, regular security audit and vulnerability repair; according to the electric power proxy service system and method based on artificial intelligence, integration with an AI system and construction and optimization of an intelligent power grid and renewable energy technology are promoted.

The invention discloses an intelligent electric meter fault early warning method and system based on multi-parameter synchronous measurement. The method comprises the step of synchronously collecting operation videos, reading data and environmental parameters of the electricity meter. Firstly, illumination compensation optimization is performed on a video, image features are extracted through a convolutional layer and a full connection layer, a comprehensive visual feature vector is constructed in combination with a cross attention mechanism and environmental parameters, and the overall shape of an electric meter is extracted to detail features in a layered manner; the change trend and fluctuation characteristics are extracted from the reading data by using time sequence analysis, and the influence of environmental factors is considered. Then, fusing multi-source features to generate a feature map reflecting the state of the electric meter; normal and fault state features are compared through similarity calculation, and a fault risk is identified and early warning is carried out; and evaluating a result by using ID matching and a machine learning algorithm, and optimizing fault early warning in combination with historical data. By implementing the method, multi-source information can be fused, the speed and accuracy of fault diagnosis can be remarkably improved, and continuous and stable operation of a power system is ensured.

The invention relates to an intelligent electric meter online health state adaptive evaluation method based on deep learning, and the method comprises the following steps: S1, obtaining the original time sequence data, spatial information, neighborhood electric meter interconnection information and electric meter operation environment information of an intelligent electric meter, and obtaining a structured original multi-modal data stream; s2, data preprocessing is carried out, multi-modal feature extraction and fusion are carried out, and a multi-modal feature matrix after alignment fusion is obtained; s3, constructing a health assessment model based on a space-time convolutional network and an adaptive graph neural network; s4, based on the trained multi-head deep health assessment model, feedforward is carried out on the model in batches according to real-time data, the state of each electric meter is assessed, and a health score and a risk level are fed back; and S5, based on the auxiliary information and backflow data of the actual area, adaptively adjusting the multi-head deep health assessment model, and carrying out local or global fine tuning to realize self-healing and self-evolution. According to the invention, the intelligence, accuracy and dynamic adaptive capacity of health assessment of the intelligent electric meter are effectively improved.

The invention discloses a meter reading system and a meter reading method based on dual-mode communication, and relates to the technical field of meter reading systems and methods, the meter reading system comprises a dual-mode communication module, intelligent data demand switching, strong NB-IoT penetration and far coverage; the BLE is short-distance, high-speed, low-power-consumption and fast in switching, the intelligent electric meter module and the high-precision metering chip measure electric energy accurately, historical data is stored in a large-capacity mode, the shell protection is high, the intelligent electric meter module adapts to severe environments, the data processing and storage module and the high-performance processor compress and process data, the SSD stores in a large-capacity mode and backups redundancy, and the user interaction module and the large-screen LCD clearly display power utilization information. And a button or a touch screen is convenient to operate, and a remote management and monitoring module is provided. Innovative technologies such as dual-mode communication are fused, high-precision metering, intelligent switching communication and convenient interaction are achieved, power line carrier standby and fault early warning diagnosis are achieved, meter reading efficiency, precision and reliability are improved, and development of an intelligent power grid is promoted.

The invention relates to the technical field of information, and provides an intelligent power consumption behavior inspection method and device based on multi-source data and a storage medium, and the method comprises the steps: processing a mass power consumption data flow collected by a sensor and an intelligent electric meter in real time through employing a distributed calculation framework, completing the integration and standardization of the multi-source data, and generating a dynamically updated power consumption monitoring original data set; extracting a multi-dimensional feature vector from time series data and historical electricity utilization records containing the basic identifier, the time information, the real-time change feature and the abnormal mark, and outputting a classification result of the abnormal behavior; finally confirmed abnormal behavior data are extracted, a real-time monitoring instrument panel is generated through the visualization technology, and the abnormal behaviors of power utilization inspection of the power supply enterprise are dynamically displayed. According to the invention, multi-source heterogeneous data are effectively integrated, intelligent analysis and real-time monitoring of power consumption behaviors are realized, the accuracy and efficiency of power consumption inspection of power supply enterprises are improved, and powerful support is provided for safe and stable operation of a power system.

The present invention relates to the technical field of load prediction of power systems, and discloses a user load prediction system based on adaptive learning of historical load data, comprising: a data acquisition and preprocessing module, a feature extraction module, an adaptive learning model module, a prediction module, an error evaluation and feedback module, and a historical database. The user load prediction system based on adaptive learning of historical load data takes data from smart meters and power monitoring equipment into a historical database through the data acquisition and preprocessing module, selects appropriate deep learning and mechanical learning in the adaptive learning model module, can better capture the law of load changes through adaptive learning, automatically adjusts the learning rate according to the changes in historical load data, uses training data to train the model, and continuously optimizes the parameters of the model, which can achieve accurate prediction of user loads, thereby improving the accuracy of system prediction.

The invention discloses an intelligent electric meter electricity utilization behavior interpretability analysis method based on a causal atlas. Through multi-source data fusion and a triple nested time window mechanism, a multi-level causal atlas is constructed; when abnormal power utilization is detected, identifying an affected area by adopting a local influence propagation algorithm, and correcting to realize dynamic map maintenance; a hierarchical lazy index updating mechanism is adopted, paths are divided into key, important and background levels according to importance, and only queried paths are verified; a causal path is quickly reconstructed through a multi-level degradation search strategy, and an interpretable analysis report is generated in combination with anti-factual reasoning. According to the method, the updating complexity and the index maintenance overhead are reduced, the query efficiency is improved, and an efficient interpretable technical scheme is provided for power consumption behavior analysis of the intelligent electric meter.

The invention discloses a high-precision electric power measurement method based on an intelligent electric meter, and relates to the technical field of electric power measurement. According to the invention, a plurality of high-precision voltage and current sensors with temperature compensation are uniformly arranged in a circuit, a low-noise front-end conditioning and multi-channel high-resolution ADC synchronous sampling technology is adopted, and a digital phase-locked loop is introduced to realize accurate correction of a sampling clock. And performing multi-stage weighted fusion, time domain instantaneous power calculation and FFT frequency domain decomposition compensation on the acquired data, and finally accurately calculating the real power in a power grid period. According to the method, measurement errors caused by temperature excursion, phase deviation and harmonic interference in the prior art are effectively solved, the measurement precision and stability of the system are remarkably improved, and the method is suitable for high-precision power monitoring in a complex power grid environment.

The invention relates to the technical field of intelligent power grid and household energy management, and provides a household ultra-short-term load prediction method, which comprises the following steps of: performing preprocessing and characteristic engineering processing on user power consumption data acquired by an intelligent electric meter; user type clusters are obtained through clustering analysis, and a standard user power consumption mode corresponding to each cluster is determined; constructing a deep learning prediction model based on the mode, and generating a model parameter matrix sequence and a timestamp; generating an ultra-short-term load prediction value by using the parameter matrix and historical data, and forming a prediction load curve according to a timestamp; and finally, the prediction curve and the management suggestion are sent to the household energy management terminal. According to the method, accurate prediction is realized through data cleaning, user grouping, pattern matching and deep learning modeling, and a visual result and an energy-saving suggestion are output. According to the invention, the household energy management efficiency and the power utilization economy can be improved.

The invention relates to the technical field of electric power intelligent judgment, in particular to an AI-based intelligent electric energy meter fault diagnosis system, which comprises an operation data acquisition module, a jump correlation judgment module, an operation deviation judgment module, a current waveform diagnosis module and an abnormity alarm module. According to the invention, through synchronous acquisition of the current data and the operation data of the refrigeration storage, whether the current jump is related to the gating action is judged, and the refrigeration performance abnormity of the refrigeration storage and the fault type of the intelligent electric meter are effectively identified by further combining multiple cross validation of baseline energy consumption and temperature change; an ideal current waveform is constructed through an AI algorithm and is compared with an actually measured waveform, so that intelligent judgment and classification of fault types are realized; meanwhile, the diagnosis result is linked with the operation and maintenance system, the response efficiency is improved, and the method is particularly suitable for scenes with high requirements on the stability of the electric meter, such as cold chain storage, and has good practical value and popularization prospect.

The invention relates to the technical field of power load decomposition, and discloses a non-intrusive power load decomposition method and system based on multi-modal feature learning. The method comprises the following steps: synchronously acquiring electric power parameter data of an intelligent electric meter, environmental parameter data of an environmental sensor and use behavior data of user equipment to obtain multi-modal load monitoring data; performing cross-modal feature extraction through a non-negative matrix factorization layer of the first equipment state recognition model to obtain a multi-modal fusion feature vector; carrying out load mode recognition through a first decomposition layer of the first equipment state recognition model to obtain a first decomposition load matrix; performing clustering optimization through a second decomposition layer of the first equipment state recognition model to obtain a second load decomposition matrix; and executing a dynamic fuzzy decision based on the second load decomposition matrix to obtain an equipment operation state identification result. According to the method, the limitation that a traditional method only depends on a single power signal is broken through, and high-precision and high-robustness non-intrusive power load decomposition is achieved.

The invention discloses an electric power measurement data management system based on cloud edge collaboration and an electric quantity distributed acquisition method, and relates to the technical field of electric power information acquisition and processing. The system comprises an intelligent electric meter terminal layer, an edge computing node layer, a regional collaborative gateway layer and a cloud data center. The intelligent terminal can dynamically adjust the sampling frequency according to the load change rate and the historical volatility, and identifies abnormity through an LSTM-ATT model; the edge nodes realize prediction caching based on Kalman filtering and RMSE judgment; the regional gateway executes dynamic time warping and consistency arbitration of topological weighting; and the cloud end completes digital twinborn modeling and exception evidence storage. According to the method, the real-time performance and accuracy of electric power data acquisition are improved, the communication bandwidth occupation is reduced, the system abnormity sensing capability and the operation stability are enhanced, and the method is suitable for an intelligent power distribution network and a distributed energy metering scene.

The invention relates to the technical field of intelligent power grids, and provides an intelligent terminal supporting multi-parameter fusion perception, which comprises a data acquisition module for acquiring parameters of a transformer body and parameters of a far-end intelligent electric meter through multiple types of sensors; the data processing module is used for executing a dynamic window cleaning algorithm on the collected data, and the data fusion module is used for outputting power grid state characteristics by adopting a fixed weight and dynamic weight dual-mode fusion mechanism; a multi-mode wireless communication unit is arranged in the communication module, and the communication module uploads the fused data and the early warning information to a cloud master station platform; according to the method, the defects in the prior art are overcome, a sliding window self-adaptive adjustment mechanism is established, and the window length is automatically adjusted and calculated by analyzing data distribution characteristics in real time; when abnormal fluctuation is detected, the window is contracted to improve the sensitivity, and the window is expanded to enhance the immunity under a steady-state working condition.

The invention relates to an intelligent electric meter reading method and system, and the method comprises the steps: enabling a high-definition camera to shoot an intelligent electric meter according to a preset meter reading time interval, obtaining an electric meter image, and uploading the electric meter image to an edge computing device through the Internet of Things; the high-definition camera is installed right in front of a dial plate of the intelligent electric meter. The edge computing device carries out image preprocessing on the electric meter image and transmits the electric meter image after image preprocessing to a server; the server recognizes the received electric meter images by adopting a plurality of image recognition models based on a deep learning algorithm, compares and verifies the electric meter readings obtained by recognition of the plurality of image recognition models, and if the comparison and verification are passed, sends the electric meter readings to the server; if yes, the current ammeter reading of the intelligent ammeter is obtained according to the ammeter reading obtained through identification processing of various image identification models; and the server performs associated storage according to the ammeter number of the intelligent ammeter and the current ammeter reading. According to the invention, the meter reading efficiency can be improved.

The invention relates to a user power consumption behavior portraying method and system based on non-intrusive load monitoring, and belongs to the technical field of residential user power consumption behavior analysis and energy management. According to the technical scheme, a data acquisition module collects user intelligent electric meter data; the user load clustering analysis module adopts a clustering algorithm to classify user load curves; the equipment-level behavior analysis module is used for analyzing the energy consumption ratio, the single-equipment use period preference and the multi-equipment cooperation mode of the equipment type for the user; the user portrait construction module constructs a multi-dimensional user power consumption behavior portrait; and the system application module formulates a personalized energy efficiency optimization scheme. According to the method, user load curve clustering analysis and device-level power consumption data are fused, the types and start-stop time of power consumption devices are identified through the NILM technology, and the total load clustering result is combined, so that fine-grained modeling of user power consumption behaviors is realized, and multi-dimensional mining of the device type energy consumption ratio, the single-device use period preference and the multi-device cooperation mode is carried out.

The invention provides a real-time network topology and parameter identification method and system for a power distribution system, and the method comprises the steps: collecting the data of a node for installing an intelligent electric meter in a historical power distribution network, and constructing a data set; calculating an admittance matrix, deducing a network topology structure, and preliminarily estimating line parameters; modeling the power grid topology based on a GCN (Graph Convolutional Network), calculating the importance of each node, and iteratively optimizing the placement strategy of the SMD through a loss function; on this basis, SMD data is adopted as the input of a graph neural network GNN, and real-time network topology and parameter identification are carried out on the system; and if the change of the network topology is identified, performing fine adjustment on the pre-trained GNN parameters through a transfer learning method to adapt to a new topological structure, and re-identifying the parameters. According to the method, under the condition of limited measurement equipment, the placement strategy of the measurement equipment can be optimized, and the identification real-time performance and precision of the topology and parameters of the power distribution network are improved.

The invention relates to the technical field of power load analysis, and discloses an intelligent ammeter power load analysis system and method, and the method comprises the steps: obtaining the current overall power consumption of a user, judging whether the power consumption of the user is abnormal or not, forming abnormal analysis data, and carrying out the corresponding abnormal prompt for the user according to the abnormal analysis data. According to the intelligent electric meter power load analysis system and method, whether the electric equipment is abnormal or not is judged according to the power utilization data collected by the electric meter, the abnormal reason of the electric equipment is judged according to the power utilization data, and a user is timely notified to carry out adjustment according to the abnormal reason of the electric equipment; therefore, the service life of the electric equipment is prevented from being shortened due to the fact that the use condition of the electric equipment cannot be adjusted in time, the equipment is prevented from being damaged due to the fact that equipment abnormity cannot be perceived in time, the service life of the equipment is prolonged, property loss of a user is reduced, fire catching of the electric equipment is reduced, and personal injury to personnel due to fire disasters is avoided.