1228 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 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 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 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 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 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 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 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 discloses an intelligent electric meter box operation and maintenance management method and system, and relates to the field of electric power operation and maintenance management. The intelligent electric meter box operation and maintenance management method comprises the steps of multi-source data acquisition and preprocessing, equipment health state dynamic evaluation, fault root cause positioning and prediction, maintenance task dynamic scheduling, adaptive maintenance execution and closed-loop optimization, feedback of a maintenance result to a health evaluation model, updating of a dynamic weight coefficient, and monthly generation of an equipment health white book. And continuously optimizing the operation and maintenance strategy. According to the intelligent electric meter box operation and maintenance management method, the fault prediction model is established based on the LSTM neural network, the typical fault mode is matched in combination with the knowledge graph, the fault prediction accuracy is improved, and non-planned power failure is reduced. The improved genetic algorithm improves the utilization rate of maintenance resources and shortens the emergency response time. Predictive maintenance replaces regular maintenance, invalid inspection is reduced, and the spare part inventory turnover rate is increased. Human errors are reduced through AR assistance; and the maintenance operation accuracy is improved.

The invention provides an intelligent electric meter calibration method and device, computer equipment and a storage medium, and the method comprises the steps: constructing a single meter-transformer area double-layer data system through a special transformer collection terminal, and collecting transformer area total intelligent electric meter data, user meter temperature and humidity, an electric energy observation value, transformer area line impedance and load unbalance degree data; constructing an energy conservation optimization function according to the user meter electric energy observation value and the total meter electric energy by combining the deviation with the observation value; determining line loss data according to total meter current and user temperature and humidity, determining power supply stability through total meter voltage, and determining optimization function constraint conditions according to corresponding data; and solving an optimal user table software compensation coefficient and a transformer area line loss correction coefficient by a generalized genetic algorithm, updating a humidity-error correlation coefficient and a line loss reference value by a feedback performance attenuation model to determine an adjusted single table calibration error range, and performing single table calibration by combining the user table temperature and humidity and an electric energy observation value. By adopting the scheme, the accuracy, the reliability and the environmental adaptability of the calibration of the intelligent electric meter are improved.

The invention belongs to the technical field of intelligent electric meters, and discloses an intelligent electric meter system with an abnormal power consumption behavior recognition function. The system is composed of a data acquisition module, an electric energy quality monitoring module, a data preprocessing module, a power utilization behavior feature extraction module, a short-time behavior monitoring module, an anomaly detection and diagnosis module, a behavior trend analysis module, an alarm and visualization module and a remote collaborative management module. Through deep mining and learning of historical power utilization data of a user, the system can accurately grasp power utilization habits, time period change rules and load characteristics of the user, along with dynamic evolution of power utilization conditions, the system can automatically optimize an anomaly detection threshold value, limitation brought by a traditional fixed threshold value is abandoned, and through the personalized and adaptive design, the power utilization efficiency of the user is improved. The system can exert the optimal efficiency in different regions and different types of users, and the application range and the practicability of the system are remarkably improved.

The invention relates to the field of data processing, in particular to an intelligent electric meter data optimization storage method, which comprises the following steps of: performing normalization, smoothing and abnormal value processing on multi-dimensional time series data such as electric energy, current, voltage and power; performing periodic analysis on time sequences of power, voltage, current and the like by using an autocorrelation function, and determining periodic intensity of the data; and finally, performing compression processing on the data through pulse code modulation (PCM), differential pulse code modulation (DPCM) and Huffman coding, thereby effectively improving the data compression rate, reducing redundancy, and ensuring optimization of data transmission efficiency and storage space. According to the invention, the ammeter data is compressed through periodic analysis and Huffman coding, so that the storage efficiency and the transmission efficiency are improved.

The invention provides an intelligent electric energy meter service life prediction method and device and a readable storage medium, and belongs to the technical field of device service life prediction.The method comprises the steps that multi-dimensional input is constructed by fusing key environment data and an operation time sequence, and the microcosmic, periodic and long-term characteristics of the input are extracted through multi-scale convolution support; the feature combination is dynamically optimized through an adaptive fusion module; constructing a time sequence prediction model, embedding a convolution attention mechanism in an encoder, and compressing redundant information of key features by combining sparse self-attention distillation through key features of feature combination of channel-space double attention dynamic enhancement optimization; the decoder adopts a mask probability sparse attention and iterative prediction strategy to generate an error prediction sequence based on the global features of the encoder to avoid future information interference; and calculating the residual life according to the intersection point of the error prediction sequence and the failure threshold. According to the method, the long sequence prediction precision, the key feature utilization rate and the calculation efficiency are improved, and reliable support is provided for preventive maintenance of the intelligent electric meter.

The invention provides a fault detection method and system based on an intelligent electric meter, and relates to the technical field of fault detection and diagnosis of the intelligent electric meter, and the key points of the technical scheme are that three-dimensional data are obtained, and the three-dimensional data comprise intelligent electric meter data, environment perception data, power grid topology data and line load data; predicting a normal data range of the next one minute in real time according to the three-dimensional data, and judging whether actual data exceed the normal data range or not; when the actual data exceed the normal data range, uploading all the data to a cloud end to analyze and detect faults; and collecting fault data in real time and generating a fault early warning list. The fault detection method and system based on the intelligent electric meter have the advantages that the fault false alarm rate is reduced, the fault detection response efficiency is enhanced, and fault risk early warning is achieved.

The invention provides a real-time three-dimensional city modeling and dynamic updating method and system based on deep learning. According to the method, building structure static load parameters of a target building are obtained, a micro-seismic sensor array is deployed at a node of a bearing wall and a frame beam column, and stress wave propagation path data are collected at a preset sampling frequency. And synchronously receiving electrical load data uploaded by the intelligent electric meter, and extracting a current abrupt change characteristic section in a time window corresponding to the abnormal vibration event. Building structure static load parameters, stress wave frequency domain energy distribution and the phase angle offset of the current abrupt change characteristic section are input into the deep learning model, the association rule of structural mechanical response and electrical load disturbance is coupled, and a three-dimensional model updating instruction is generated. And dynamically adjusting the grid vertex coordinates of the target building in the initial three-dimensional city model based on the instruction, and overlapping and displaying the space influence range boundary of the current abrupt change feature segment. The technical scheme provided by the invention is based on real-time three-dimensional city modeling and dynamic updating of deep learning.

The invention relates to the technical field of digital communication, in particular to an intelligent electric meter dual-mode communication self-adaptive switching method, and provides the following scheme that operation data are divided into a plurality of data units at a sending end, priority evaluation is carried out, and the time delay, the packet loss, the energy consumption and the bandwidth state of two links are combined, so that the intelligent electric meter dual-mode communication self-adaptive switching method is realized. Positioning clearing cut points in the priority sequence so as to realize differentiated distribution of data between a high-speed link and a low-speed link; when the data is distributed to a low-speed link, sparse coding is carried out by adopting a preset dictionary, and differential updating is triggered when an error exceeds a limit, so that decoding consistency is ensured; a receiving end realizes unified analysis of the original message and the sparse message through cross-link association, dictionary updating and residual synthesis; according to the invention, the timeliness of the key message can be guaranteed, the communication energy consumption and cost can be reduced, and the cooperation efficiency of the dual-mode link can be improved.

The invention discloses a non-intrusive household load identification method based on CUSUM event detection and a convolutional neural network, which is applied to total load data output by an intelligent electric meter installed at a user total incoming line, and comprises the following steps: data preprocessing; carrying out load event detection based on a sliding window bilateral CUSUM algorithm so as to determine an occurrence moment and a continuous interval of a load event; in a continuous interval corresponding to each load event, calculating an active power variation delta P, a reactive power variation delta Q, a current harmonic distortion variation delta Ihd and a current span Itc in steady-state data segments before and after the load event, and combining to form a multi-dimensional event feature vector; the method comprises the following steps: sequentially executing the steps on operation data of each electric device in a family in different operation modes to obtain a multi-dimensional event feature vector marked with a device type and an operation state label, and constructing a standardized device load feature database; and training a convolutional neural network classification model and realizing load identification.

The invention relates to an energy-saving method and an energy-saving system based on electric power big data, and belongs to the technical field of energy-saving optimization data processing. The method comprises the following steps: constructing a data model based on time-space fusion multi-source data through a cloud through an intelligent electric meter and external data access; establishing a dynamic reference of an operation scene based on a data model to perform group division on nodes, and then obtaining a multi-dimensional comprehensive sequence and generating an energy-saving reconstruction node list based on group evaluation index deviation; and completing energy-saving optimization by bidirectionally analyzing the node list, including upwards aggregating the nodes to generate a scheduling strategy and downwards decomposing the load identification equipment to optimize the energy consumption of the user. According to the method, a unified data model is constructed through an intelligent electric meter and multi-source data real-time acquisition and encrypted transmission, and node clustering and feature extraction are carried out by utilizing a machine learning clustering optimization operation reference; node deviation is calculated in real time, an energy-saving potential list is generated, and node resource scheduling and high-energy-consumption equipment optimization are achieved through bidirectional analysis.