969 results about "Smart meter" patented technology

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 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 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 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 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.

The invention relates to the technical field of power system automatic management, in particular to a virtual power plant optimal scheduling method and system.The virtual power plant optimal scheduling method comprises the steps that real-time multi-mode state data of all nodes are collected through an intelligent electric meter and a sensor, and a time sequence input tensor is constructed; a prediction tensor is generated based on the power system resource state and historical operation data, and abnormity is identified through difference operation with the real-time tensor; constructing an abnormal propagation map and identifying key linkage nodes; generating a candidate disposal path and an edge mapping strategy according to the key node data; testing a response index of each path in the virtual environment, collecting residual data, and selecting an optimal path; scheduling a processing sequence based on node priorities; and dynamically updating model parameters and a scoring function structure according to execution feedback. According to the method, the problem that tiny anomalies are difficult to perceive under the condition of complex power demands is solved, and model correction and strategy updating can be carried out according to disturbance response residual errors and simulation deviations after actual execution.

The invention relates to the technical field of ammeter fault detection, and provides an intelligent ammeter series arc fault detection method, system and device, a medium and equipment, and the method comprises the steps: firstly obtaining an ammeter data set of an intelligent ammeter, which comprises a pre-ammeter voltage, a post-ammeter voltage and a total loop current in a continuous power frequency period, and then carrying out the division through a sliding time window, obtaining a plurality of ammeter data windows; for each window, calculating a voltage difference sequence and a dynamic loss impedance sequence; when the absolute value of the total loop current is smaller than a preset threshold value, determining the current zero-break interval of each power frequency period and the current zero-break time corresponding to the window; determining an arc voltage estimation value according to a voltage difference between non-zero-break intervals in the zero-break interval; and finally, integrating the current zero break time, the arc voltage estimated value and the dynamic loss impedance of each window to judge whether a series arc fault exists, and if so, triggering a protection action. According to the embodiment of the invention, high-reliability identification of the series arc fault is realized.

The invention discloses a key sharing and security detection scheme based on an intelligent electric meter, and aims to solve the problems that an untrusted electric meter in an intelligent power grid is difficult to identify effectively, the detection scale is uncontrollable and the communication security is insufficient. According to the scheme, the elliptic curve cryptography, the threshold secret sharing mechanism and the physical unclonable function are combined, and hardware-level binding of the unique identity of the equipment and the secret key is achieved in the registration stage. Through a threshold password mechanism, a plurality of intelligent electric meters cooperatively participate in key reconstruction and encryption communication, and the intelligent electric meters can still work normally when part of the electric meters fail or are broken through. The center node can actively identify a fault or a malicious ammeter and dynamically adjust a communication strategy according to the integrity and correctness of the feedback information. Meanwhile, the scheme supports dynamic identity updating and integrity verification, and effectively resists modeling attacks, Sybil attacks, replay attacks and DoS / DDoS attacks. According to the invention, secure identity authentication, reliable key distribution and efficient anomaly detection are realized, and the security of smart grid terminal communication is improved.

The invention discloses a self-adaptive filtering method and system for an intelligent electric energy meter, and relates to the technical field of power grid signal processing, and the method comprises the steps: obtaining a power grid voltage and current signal set collected by a target intelligent electric energy meter historically, and obtaining a signal noise spectrum data set and a noise level evaluation result through spectrum analysis and noise level evaluation; if the result exceeds a preset noise threshold value, identifying an interference type and building a noise adaptive filtering multi-channel, integrating and deploying the noise adaptive filtering multi-channel into a target intelligent electric meter to complete signal filtering test and effect evaluation, and dynamically optimizing and generating an electric energy meter adaptive filtering multi-channel based on the obtained multi-channel filtering effect index parameter; and finally, signal filtering closed-loop control is realized. The technical problem that a traditional electric energy meter is weak in filtering pertinence and cannot adapt to dynamic noise of a power grid in electric power related scenes with complex dynamic interference is solved, and the technical effects that the electric variable measurement accuracy of the electric energy meter is improved, and the adaptive capacity of the electric energy meter to different interference scenes is enhanced are achieved.

The invention discloses a metering performance evaluation method and system for an intelligent electric energy meter, a medium and a product, and the method comprises the steps: employing a standard meter for synchronous operation for a to-be-evaluated intelligent electric energy meter, so as to obtain a field test value of a basic error of the intelligent electric energy meter, inputting the environment detection data and the operation time of the intelligent electric energy meter to be evaluated into the trained basic error prediction model corresponding to the model of the intelligent electric energy meter to be evaluated so as to obtain a model prediction value of a basic error; the basic error prediction model comprises an input layer, a one-dimensional CNN layer, an LSTM layer fused with a time attention mechanism, a full connection layer and a basic error output layer, and the metering performance level of the intelligent electric energy meter is determined according to a field test value and a model prediction value of a basic error. The invention aims to realize metering performance evaluation of the intelligent electric energy meter in a typical environment so as to realize higher operation efficiency and solve the bottleneck problem of long-term dependence on modeling and local feature extraction.

The invention relates to the technical field of power distribution network bidirectional power flow coordination control, and discloses a power distribution network bidirectional power flow coordination control method based on V2G aggregation, and the method comprises the following steps: S1, constructing a logic energy storage unit composed of a plurality of V2G charging piles and vehicles supporting reverse discharge; s2, aggregating the logic energy storage units into virtual power plant nodes which can be dispatched by a power grid through an information communication technology (ICT); and S3, bidirectional power flow sensing and real-time state estimation: utilizing an intelligent electric meter, a sensor and a topology identification algorithm. According to the method, a logic energy storage unit composed of a plurality of V2G charging piles and vehicles supporting reverse discharging is constructed, the logic energy storage unit is aggregated into a virtual power plant node capable of being dispatched by a power grid through an information communication technology (ICT), and specific charging and discharging actions are decomposed and executed, so that dispersed V2G resources can be effectively integrated, and the V2G resources can be effectively integrated. And collaborative optimization with operation states such as voltage and power flow of the power distribution network is carried out.

The invention discloses an intelligent power grid load prediction and dynamic response coordinated scheduling method, and relates to the technical field of power system automation, and the method comprises the steps: accessing intelligent ammeters, distributed power controllers and other devices of Modbus, IEC61850 and DL / T645 protocols through a multi-protocol adaptive gateway, and achieving data standardization; time stamps are calibrated by means of Beidou time service and an IEEE1588PTP protocol, and it is ensured that multi-source data synchronization errors are controllable; deploying an edge computing node cluster, distributing high-priority tasks to low-load nodes through an edge coordinator in combination with a load fluctuation level and a greedy algorithm, and ensuring real-time processing efficiency; the edge nodes generate short-term load prediction, and the cloud platform outputs medium and long-term prediction based on a historical data training model; and finally, the coordinated scheduling decision module fuses the two types of prediction results and the real-time parameters of the power grid, and generates a dynamic instruction to control the output of the adjustable load and the distributed power supply.

The invention provides an intelligent electric meter HPLC communication module dynamic calibration method suitable for environmental disturbance, and relates to the technical field of dynamic calibration, and the method comprises the steps: collecting multi-source data, collecting time sequence communication data of a communication module in real time, and carrying out the analysis to obtain a communication index. Initial calibration parameters are formulated, dynamic adjustment parameters are calculated, and the initial calibration parameters are adjusted according to the dynamic adjustment parameters to obtain real-time operation parameters. And judging whether the current communication index reaches a preset communication threshold, if so, maintaining the real-time operation parameter, and otherwise, optimizing the dynamic adjustment parameter. And analyzing the environmental disturbance data and the power line channel characteristic data to obtain associated influence data, carrying out feature extraction on the current communication index to obtain a communication abnormal feature, and adjusting the dynamic adjustment parameter in combination with the associated influence data to obtain a calibration correction parameter. According to the invention, intelligence and automation of communication module calibration are realized, manual intervention and experience dependence are reduced, and calibration efficiency and accuracy are improved.

The invention provides a power distribution network multi-source cooperative scheduling method and system based on big data, relates to the field of power distribution network scheduling, and solves the technical problem that an optimization strategy is not flexible enough in the prior art. The method comprises the following steps: acquiring multi-source real-time data of a power distribution network through an Internet of Things terminal and an intelligent electric meter; based on the multi-source real-time data, predicting a behavior mode of the electric vehicle and an output value of renewable energy by adopting a machine learning algorithm, and estimating a charging state of the electric vehicle by using a probability SOC prediction model; the behavior pattern is used for representing a travel rule of the user; generating a multi-time-scale scheduling strategy based on the behavior pattern, the output value of the renewable energy source and the charging state by adopting a collaborative optimization algorithm; and testing the scheduling strategy based on a simulation environment constructed by a digital twinning technology, and issuing a control signal based on a test result.

The invention discloses a data feature analysis method and system for an intelligent electric meter, and relates to the technical field of data processing analysis, and the method comprises the steps: collecting daily electricity consumption data of all electricity users in a target region in a preset time period, and carrying out the preprocessing, and obtaining a corresponding daily electricity consumption time sequence and a daily electricity consumption curve; calculating the similarity between the adjacent daily electricity consumption curves of each user, and obtaining the stability characteristic value of the electricity consumption change of the user; calculating an optimal Euclidean distance between the daily electricity consumption time sequences of different users in combination with the stability characteristic values of the electricity consumption changes of the users; clustering the users in the target area based on the optimal Euclidean distance; analyzing the power consumption behavior of the user based on the clustering result; by measuring the stability of the power consumption behavior of the user, the problem of clustering deviation caused by neglecting time sequence fluctuation in a traditional method is solved, and the clustering accuracy is improved, so that the precision of power consumption behavior analysis and the reliability of anomaly detection are effectively improved.

The invention relates to the technical field of intelligent power grids, and provides a remote software upgrading method for an intelligent electric energy meter, which comprises the following steps: S1, a server generates an upgrading package containing a version identifier, and transmits the upgrading package to the intelligent electric energy meter in a fragmented manner through a TLS encryption channel; s2, the intelligent electric energy meter doubly verifies the integrity of the upgrade package and the legality of the source through an SHA-256 check code and a digital signature, the upgrade package and the source legality are written into a backup storage area of the Flash memory after verification is passed, and a CRC write-in check value is recorded; s3, performing secondary verification on the upgrade package of the backup storage area, triggering restarting after verification is passed, and writing data of the backup area into the main partition in the restarting process; s4, if the main partition system is not started or the core function is abnormal within the set time, returning to an old version and generating a fault log containing a timestamp; through a dynamic subpackage strategy, differential upgrading, SHA256 verification, a watchdog mechanism and a fault log record, the upgrading efficiency and safety of the intelligent electric energy meter are remarkably improved, and the dynamic subpackage strategy dynamically adjusts the size of a data packet according to the network condition.