6736 results about "Power usage" patented technology

The invention relates to the technical field of power system dispatching, in particular to an emerging load power supply potential improvement method based on dynamic prediction, which comprises the following steps of: acquiring emerging load power consumption, meteorological environment and power grid schedulable resource data in a target area through an Internet of Things sensing terminal, and performing two-channel modeling to obtain a new load power supply potential improvement model; a deep space-time network is used to predict a load curve, a model is established to quantify resource regulation potential, a scheduling priority list and a capacity allocation strategy are generated by means of a matching rule base according to load fluctuation and resource evaluation results, an actual scheduling effect is fed back to the prediction model, parameters are corrected through error back propagation, a closed-loop optimization link is formed, and the scheduling efficiency is improved. The method improves load prediction accuracy and resource scheduling adaptability, is suitable for emerging load power supply optimization in a novel power system, and guarantees stable and efficient operation of a power grid.

The present disclosure relates to the technical field of intelligent monitoring and management of power systems, and specifically relates to an intelligent collaborative power consumption regulation and control method, apparatus and system for source-grid-load-storage, an electronic device and a storage medium. Said system comprises an energy regulation and control center and energy regulation and control units provided in microgrids; the energy regulation and control units use a temporal attention mechanism-based LRCN dual-layer network combined model to predict power consumption amounts, so as to generate power consumption surpluses and shortages within a future preset time; and on the basis of the power consumption surpluses and shortages and latest current electricity prices of the microgrids, the energy regulation and control center uses a fusion multi-objective algorithm based on a Pareto front curve and a fuzzy algorithm to generate a microgrid collaborative power consumption regulation and control solution, and sends the regulation and control solution to the energy regulation and control units for execution, so as to ensure the balance of energy supply and demand of the microgrids. Therefore, the present disclosure achieves efficient, intelligent and refined energy management for microgrid clusters, reducing energy consumption and costs, and providing solid support for sustainable development of microgrids.

The invention relates to an electrical load prediction and optimization regulation and control method and system for high-energy-consumption equipment, and solves the problems of inaccurate load prediction, single regulation and control means and difficulty in dynamic adaptation of the high-energy-consumption equipment, and the method comprises the steps: collecting multi-source data of the high-energy-consumption equipment in real time, constructing a dynamic equipment collaborative causal graph after preprocessing, and extracting key constraints; inputting the data and the constraints into the dynamic digital sample model to obtain a system state simulation result; based on the result, a multi-objective optimization regulation and control strategy is generated and executed by using a meta-learning + reinforcement learning decision framework; and collecting actual data comparison deviation, starting hierarchical federated learning when a threshold value is exceeded, grouping and aggregating similar experiences according to a causal graph topology, and dynamically calibrating model parameters and a decision framework. The method has the following effects that accurate load prediction and multi-target cooperative regulation and control of the high-energy-consumption equipment are achieved, working condition changes are dynamically adapted, the cost is reduced, and continuous production and the service life of the equipment are guaranteed.

The invention discloses a flexible load multi-target collaborative scheduling system and method, and relates to the technical field of collaborative optimization of power systems. The method is used for solving the problem of lack of accurate prediction and multi-target coordination of agricultural electricity and water utilization regulation and control. Firstly, based on meteorological data, soil moisture content and crop growth characteristics, an irrigation demand prediction model is constructed, irrigation water demand is predicted, and a water pump load power baseline is generated; then, a dynamic baseline constraint condition is generated in combination with historical behavior data and water pump start-stop logic; establishing a power grid side objective function, a user side objective function and a water affair side objective function, and introducing an underground water and carbon emission punishment mechanism; thirdly, dividing the power distribution network into sub-regions, adopting an alternating direction multiplier method to solve a region regulation and control strategy in parallel, coordinating water resource distribution conflicts through virtual interactive variables, and outputting a global scheduling instruction; and finally, collecting real-time response data and correcting the prediction model on line to realize closed-loop adaptive optimization.

The invention discloses a multi-domain collaborative flexible load schedulable potential evaluation and energy management method, and relates to the technical field of park energy management, and the method comprises the steps: obtaining space-time multi-source data of a smart park, constructing a graph neural network power consumer clustering model based on iterative self-organization analysis, and generating a power consumption behavior portrait of a power consumer; based on power utilization parameters of building air conditioners, electric vehicles, park ponds and energy storage batteries in the smart park, the schedulable potential of the multi-element flexible resources is evaluated; and based on the hybrid neural network and the Harris eagle optimization algorithm, constructing a load prediction model, and predicting various types of energy loads in a future time period. The invention aims to establish an accurate model and method, accurately evaluate the schedulable potential of different types of flexible loads in different scenes, realize efficient utilization, energy conservation and emission reduction and optimal configuration of park energy, and improve the overall energy management level and operation efficiency of the park.

The invention relates to the technical field of energy dispatching, in particular to a power dispatching optimization method based on optical energy storage, which comprises the following steps: extracting photovoltaic output and charge state fluctuation based on partitions, synchronously identifying abnormal intervals, analyzing direction matching and energy connectivity, screening abnormal units, and extracting linkage units in combination with load and power utilization characteristics. Evaluating power flow deviation, determining an unbalance level, matching a response list, and outputting an automatic linkage scheduling adjustment instruction set. According to the method, photovoltaic output and energy storage state fluctuation are identified in partitions, the accuracy of capturing an abnormal space-time coupling phenomenon is enhanced, the identification depth of scheduling imbalance hidden danger is improved, regional load distribution and power utilization behavior consistency judgment are combined, dynamic load feature evaluation of abnormal units is completed, the abnormal units are scheduled in a layered mode according to risk levels, and the scheduling efficiency is improved. The scheduling range is accurately adjusted, and the response precision and the handling capacity of the scheduling mechanism to the dynamic difference of the load side under new energy fluctuation are improved.

The invention relates to a source load storage control method for sustainable and stable output of new energy output power of an active power grid, and belongs to the technical field of new energy power systems. The method mainly comprises the following steps: based on ARIMA model prediction and load elastic response, guiding a user to optimize power consumption through real-time state perception, power prediction and time-of-use electricity price; a reference value is set and dynamically adjusted through multi-source data fusion, historical data and a scheduling instruction are integrated, and an output value of the source-storage combined system is corrected and output through an ARIMA model; a collaborative optimization mechanism is constructed, and load prediction, energy storage life and power grid interaction economy are optimized through multiple objective functions. Dynamic monitoring is realized through generalized power modeling and bidirectional flow discrimination, precision is improved by combining ARIMA prediction and rolling correction, and abandoned power is reduced. The load side actively participates in the electricity market, and the smooth curve relieves peak pressure; the intelligent charging and discharging strategy prolongs the energy storage life, optimizes economical efficiency and environmental protection, and provides a feasible technical path for a high-proportion new energy power grid.

The invention discloses a power distribution system flexible regulation and control system based on load side behavior recognition and a method thereof, and relates to the technical field of power distribution of power systems. The method comprises the following steps: acquiring power utilization power, equipment state, environment parameters and power utilization preference information of a user side in real time; dynamically generating and updating behavior inertia factor data, and reporting high-frequency change data when the behavior inertia factor data exceeds a preset threshold; receiving regional load feature abstract data, and uploading the abstract data when the change of the abstract data exceeds a threshold value; historical period abstract data are fused, and a prediction matrix containing the partition load trend and the peak probability in the T time window is generated through a behavior inertia dynamic evolution model; and when the load rate of the system exceeds a safety threshold value, calculating an individual adjustment amplitude, and generating a regulation and control instruction containing a time-phased target and a flexible adjustment amplitude. Accurate prediction and flexible regulation and control of the load of the power distribution system are realized, and the operation stability and the power supply quality of the system are remarkably improved.

The invention relates to an energy management method, device and equipment based on an air conditioning system and a medium. The method comprises the steps that in a preset period, a target cold storage capacity calculation model matched with the current working condition is selected from a plurality of candidate cold storage capacity calculation models according to the pre-obtained photovoltaic power generation power, the air conditioner system load and the change trend of the environment temperature; the environment temperature and the electric energy are input into the target cold storage capacity calculation model, and the cold storage capacity, output by the target cold storage capacity calculation model, of a phase change cold storage material in the air conditioner system is obtained; based on the cold storage capacity, the photovoltaic power generation power and pre-obtained planned electricity consumption probability distribution, analysis processing is conducted, and an energy management strategy of the air conditioning system is obtained; the energy management strategy is used for guiding the air conditioning system to adopt commercial power supply or photovoltaic power supply, whether to utilize the phase change cold storage material for cold storage or not and whether to start a compressor to assist in executing the cold storage process or not. By adopting the method, the power consumption cost can be reduced.

The invention relates to the technical field of electric energy metering and electric power system safety, and discloses an electricity larceny prevention method and system for multi-source data fusion of an electric energy meter, and the method comprises the steps: obtaining and preprocessing current and voltage waveform data and user electricity consumption behavior data; generating multi-dimensional power consumption characteristic parameters, performing screening to form a characteristic vector set, and constructing an abnormal power consumption detection reference set; dynamically verifying power utilization data based on the reference set, and analyzing a phase relation and a mismatch state of current and voltage signals; and generating an electricity consumption behavior deviation index to judge an electricity stealing behavior. The system comprises a multi-source data acquisition module, a feature modeling analysis module, an electrical relation verification module and an electricity stealing judgment module. According to the invention, through multi-source data fusion and multi-dimensional feature analysis, accurate detection of electricity larceny behaviors is realized, accuracy and reliability of electricity larceny prevention are improved, and the method is suitable for power utilization safety management of a smart power grid.

The invention relates to the technical field of power systems, in particular to a carbon neutralization-oriented peak-valley electricity price dynamic excitation mechanism design and user response prediction system, which comprises a data acquisition module, a load characteristic analysis module, a dynamic regulation and control module, a sample evaluation module and an execution module. The system obtains power grid operation, user power consumption behaviors and new energy power generation data through a multi-source data access interface, and realizes partition load optimization and new energy adaptation in combination with a load classification model, a dynamic excitation framework and an energy storage optimization scheduling scheme. The method can accurately predict the user response, optimizes the peak-valley electricity price strategy, improves the new energy consumption capability, and assists the realization of a carbon neutralization target.

The invention relates to the technical field of electric energy meter metering, and discloses an electric energy meter metering anomaly analysis method and system, and the method comprises the steps: collecting data, such as voltage waveforms, to generate a metering feature vector set, and constructing an anomaly detection rule base; setting a scene parameter type set, and establishing an abnormal association judgment model; dynamically correcting the threshold value by combining the model, and generating an optimized error threshold value set and an abnormal triggering condition set; and updating a metering analysis strategy to generate an abnormal judgment scheme, and correcting data verification sequential logic. The system comprises a data acquisition module, an abnormal rule base construction module, a scene parameter configuration module, a correlation model training module, a dynamic threshold optimization module, a strategy updating module and a time sequence correction module. Through multi-dimensional data modeling, scene-based threshold configuration and dynamic time sequence calibration, the accuracy and adaptability of electric energy meter measurement anomaly detection are improved, and the method is suitable for measurement anomaly analysis of diversified power consumption scenes in a smart power grid.

The invention provides an electricity stealing behavior detection method based on feature fusion and a CNN-LSTM hybrid model, and belongs to the technical field of electric power information technology and deep learning. According to the method, after power load data and user behavior characteristics are subjected to data processing and enhancement, a deep learning model is combined with a self-attention mechanism to process user electricity consumption data, user electricity stealing behavior anomaly detection is carried out, and the electricity stealing risk identification accuracy and calculation efficiency can be remarkably improved. According to the invention, power grid enterprises can be helped to efficiently deal with electricity stealing conditions, and comprehensive and accurate identification of electricity stealing behaviors is guaranteed. By learning user historical load data, integrating time sequence characteristics of user loads and optimizing a data abnormity diagnosis and judgment mechanism, the electricity stealing user detection accuracy is improved, and reliable support is provided for power grid enterprise decision making.

The invention relates to the field of intelligent early warning of photovoltaic electricity larceny prevention, and particularly discloses a photovoltaic electricity larceny prevention alarm device, which comprises the following steps of: firstly, acquiring an electricity utilization curve graph of a user to be analyzed in a plurality of preset time periods and transformer area line loss data of a plurality of preset time points in a power system as input data; and then performing deep convolutional coding and analysis on the input data by using a machine learning technology to obtain a classification result, wherein the classification result is used for representing whether the user to be analyzed has an electricity stealing behavior or not. Therefore, real-time early warning of photovoltaic electricity stealing behaviors can be realized, so that benefits of enterprises are effectively protected, the market order is maintained, and healthy development of the photovoltaic industry is promoted.

The invention relates to the technical field of power system optimization, and discloses a regional building group source network load storage demand response optimization method. Comprising the following steps of multi-source heterogeneous data fusion collection and intelligent preprocessing, power utilization behavior spatial-temporal characteristic deep mining, multi-dimensional response potential dynamic evaluation modeling, multi-target layered optimization decision generation, personalized excitation strategy self-adaptive generation and closed-loop cooperative regulation execution and feedback. According to the method, user strategy updating is simulated through a replication dynamic equation of an evolutionary game, efficient search of excitation parameters is realized by combining a Bayesian optimization Gaussian process and an expectation improvement function, a user group strategy evolution rule can be dynamically captured, parameters such as electricity price discount and subsidy gradient are accurately optimized in a limited sampling range, and the method is suitable for large-scale popularization and application. A'behavior modeling-data optimization 'closed loop is formed, users are stimulated to participate in demand response, optimal configuration of power resources is realized, and the flexibility and economy of the system are improved.

The invention relates to the technical field of intelligent power supplies, in particular to an intelligent power supply for an unattended station. Comprising a multi-source power supply scheduling unit; an intelligent energy storage management unit; a double-link communication unit; the double-link communication unit is used for constructing a multi-redundancy communication link of wired, wireless and power line carriers, and the double-link communication unit is based on link quality data, adopts an automatic switching strategy to guarantee data transmission continuity, preprocesses monitoring data by using an edge side data clustering algorithm, and realizes offline data caching and transmission flow optimization. Through a multi-source energy dynamic scheduling strategy, according to photovoltaic real-time output, commercial power time-of-use electricity price and load electricity demand, power supply paths of photovoltaic, commercial power and a storage battery can be dynamically optimized, the consumption proportion of clean energy is improved, the dependency degree of high-cost commercial power is reduced, a complex energy structure of photovoltaic, commercial power and storage battery multi-energy complementation is effectively adapted, and the energy utilization rate is improved. And power supply flexibility is enhanced.

The invention discloses an intelligent electric equipment monitoring and optimizing method, and relates to the technical field of electric power, and the method comprises the steps: collecting high-dimensional voltage-current time sequence data and transient event marks, calculating topological invariant features, inputting the topological invariant features to a lightweight neural network model, and recognizing the features of all electric equipment; performing abnormal attribution and anti-fact energy efficiency prediction by using causal reasoning and dynamic regularization regression based on the identified characteristics of each electric device, and constructing a multi-objective optimization function through the abnormal attribution and anti-fact energy efficiency prediction; and based on the multi-objective optimization function, generating an equipment operation scheduling strategy through a deep reinforcement learning agent, based on the operation scheduling strategy, sending a control instruction to the electric equipment, and collecting an operation result feedback in real time for optimization and updating. According to the method, through fusion of topological features, causal reasoning and safety reinforcement learning, the precision, robustness and safety of monitoring and optimization of the intelligent electric equipment are improved.

The invention relates to the technical field of user power utilization chain analysis, and discloses a special transformer user power utilization abnormal chain construction method fusing deep learning, which comprises the following steps: collecting time sequence power utilization data and power grid topological data of special transformer users, taking each special transformer user as a node, constructing a graph structure comprising a physical connection edge and a behavior association edge, and constructing a graph structure comprising a physical connection edge and a behavior association edge; and inputting the constructed graph structure into a space-time graph neural network model, introducing a power grid physical constraint condition into an optimization target, taking a power grid operation rule as a constraint embedding model, and outputting node-level, edge-level and sub-graph-level multi-level anomaly detection results. Constructing a heterogeneous graph containing user nodes, anomaly type nodes and time slice nodes through the multi-level anomaly detection result, performing path search in the heterogeneous graph through a predefined association mode template, generating a candidate anomaly chain, performing causal strength verification on the candidate anomaly chain by adopting a time sequence causal relationship verification model, and obtaining a multi-level anomaly detection result of the user nodes, the anomaly type nodes and the time slice nodes. And intelligent analysis of the user electricity consumption abnormity chain is realized.

The invention belongs to the technical field of electric power system information processing, and particularly relates to an electric power information analysis method based on big data, through semantic fusion of multi-source heterogeneous data and dynamic feature mining of a time sequence attention mechanism, in a load prediction scene, compared with a traditional single data source model, the electric power information analysis efficiency is improved. After the meteorological data, the user power consumption behavior data and the power grid operation data are fused, the prediction average error rate is reduced; in an equipment fault early warning scene, through multi-dimensional correlation analysis of vibration signals, oil temperature data and environmental factors, transformer latent faults can be early warned in advance, and the fault identification accuracy is improved; meanwhile, a self-adaptive modeling engine and a closed-loop feedback mechanism enable the system to have a self-evolution capability: when the power grid topology is adjusted or the new energy grid-connected proportion is changed, the model does not need to be manually retrained, and self-adaptive adaptation can be completed in two scheduling cycles through dynamic feature weight adjustment and meta-learner parameter optimization, so that the analysis performance is maintained to be stable.

The invention discloses an electricity utilization information acquisition intelligent configuration method based on a pattern recognition algorithm, and relates to the technical field of intelligent power grids, and the method comprises the steps: collecting a directional data stream, inputting a federal map neural network to construct a power distribution network physical connection relation, and generating a spatio-temporal topological feature vector; extracting a current effective value component of the directional data flow as an electrical load sequence, extracting an equipment state code to identify a voltage sag event, injecting voltage sag event associated disturbance into the electrical load sequence in combination with an event propagation path weight of a spatio-temporal topological feature vector, and generating an anti-fact sample set; and compressing the new configuration strategy through a knowledge distillation engine, and outputting an event response logic and a parameter adjustment instruction to form an executable configuration strategy. According to the method, through anti-fact sample generation and reinforcement learning optimization under spatial-temporal topological feature vector constraint, a physical rule deep embedding decision is realized.

The invention belongs to the technical field of electrical safety, and particularly relates to a human body electric shock protection method and system based on multi-parameter fusion, multi-dimensional characteristic parameters are formed by extracting a high-frequency harmonic component, a voltage abrupt change slope and a magnetic field intensity fluctuation value of a residual current waveform, and the weight is dynamically adjusted according to the instantaneous change rate of each characteristic, so that the human body electric shock protection is realized. Generating a weighted feature vector; and a comprehensive criterion value is calculated in combination with a preset rule base and is compared with a dynamic threshold value, so that whether the circuit breaker is triggered to act or not is determined. According to the method, through fusion analysis of a plurality of physical characteristics, the identification capability of the system in a complex electromagnetic environment is improved, the probability of misoperation caused by external interference is effectively reduced, the action accuracy and stability of the circuit breaker are improved, and the power utilization safety and the power supply continuity are guaranteed.

The invention relates to the field of energy storage power supply management, in particular to a power supply management method, system and equipment based on a household energy storage power supply system. The method comprises the following steps: collecting a household power utilization monitoring data stream, carrying out equipment use behavior preference mining and multi-scale state transition deep learning, and generating a user equipment behavior prediction model; mining power utilization behaviors of power utilization terminals one by one based on the household power utilization monitoring data flow, performing multi-time-point power utilization demand prediction based on a user equipment behavior prediction model, and constructing a multi-time-point power utilization demand prediction map of the user; and obtaining meteorological environment parameters of a current user family area, performing climate disturbance demand adjustment on the power demand prediction maps at the multiple time points based on the meteorological environment parameters, and constructing a climate disturbance power demand prediction map. According to the invention, through dynamic user demand changes, the energy efficiency management level and the energy allocation precision of long-term operation of the energy storage system are improved.

The invention relates to the technical field of data processing, in particular to an abnormal mode data processing system driven by power marketing big data, which comprises a distributed collaborative acquisition module for constructing a space-time alignment three-dimensional data stream, a multi-modal feature reconstruction module for separating periodic noise and quantizing environmental interference, and a data processing module for processing abnormal mode data. The resistance feature decoupling module generates a purification feature vector set and a noise confidence index through orthogonal projection, and the dynamic algorithm adaptation module dynamically schedules an isolated forest algorithm, a weighted distance measurement algorithm and a sparse self-encoding clustering algorithm according to the noise confidence index. The behavior chain verification module establishes a combined physical rule verification mechanism of an environment temperature threshold value, a load deviation degree and an equipment state, and the closed-loop strategy engine module adaptively adjusts a feature decoupling loss function weight according to a decision boundary offset, so that the accuracy and the environmental adaptability of real electricity consumption abnormity identification in a complex noise environment are effectively improved.

The invention relates to the field of load monitoring, in particular to an electric energy meter time-sharing load monitoring method and system based on multi-dimensional electricity utilization characteristics, and the method comprises the steps: collecting and preprocessing the electrical time sequence data of a user electric energy meter; extracting multi-dimensional statistical characteristics of current and voltage of the users under a plurality of analysis windows, and clustering to obtain a cluster and membership degree of each user; for the target user, respectively calculating a first anomaly degree of the target user and a historical sequence thereof and a second anomaly degree of the target user and a sequence of other users in the same period in the cluster to which the target user belongs; according to the membership degree of the user under each analysis window, carrying out weighted fusion on the second anomaly degree to obtain a third anomaly degree; and calculating a comprehensive abnormality based on the first abnormality and the third abnormality, and determining whether the load is abnormal. According to the invention, through dual reference of individual history and a dynamic group and multi-scale credible fusion, the accuracy, the adaptive ability and the reliability of load anomaly monitoring are significantly improved.

The invention discloses a regional orderly power utilization dynamic optimization monitoring method based on a self-adaptive threshold value, and particularly relates to the technical field of power utilization optimization. The method comprises the following steps: continuously monitoring real-time load and voltage data of a power utilization area, constructing an initial threshold by combining a historical behavior model and an extreme value theory, extracting key characteristic parameters after a continuous deviation event is triggered, quantifying a threshold adjustment trend and abnormal frequency change, and dividing a drift degree into a high level, a middle level and a low level; and under the medium level, further estimating the future identification performance of the system based on a dynamic identification accuracy prediction model, and dynamically limiting the threshold adjustment amplitude, thereby effectively preventing the system from mistakenly regarding the abnormal trend as a new normal state, avoiding the generation of a monitoring blind area, and improving the stability, intelligence and risk perception ability of the regional power utilization regulation and control system.

The invention discloses an electricity larceny identification method based on graph calculation, and particularly relates to the technical field of electricity utilization anomaly detection of an electric power system. Historical power consumption data and a power supply topological relation of power consumers are collected, and a multi-dimensional behavior graph model fusing behavior characteristics and structural information is constructed; performing structure disturbance analysis on each node in the graph, calculating information entropy change before and after node removal, performing attention fusion on a time sequence behavior feature of the node and a structure disturbance vector, constructing a joint feature vector, and enhancing feature expression through spectral clustering and linear reconstruction; a behavior propagation field and a disturbance adjustment mechanism are introduced into the graph to form a disturbance response graph, and an abnormal gathering area is identified through path energy analysis and focusing area fitting; calculating confidence scores of the nodes and outputting a suspicious user list; the method can realize efficient identification of electricity stealing behaviors with strong concealment and complex transmissibility, and has the advantages of high precision, strong interpretability and wide application scene adaptability.

The invention discloses a source-load interaction method based on an electricity utilization information acquisition system, and relates to the field of power distribution network operation and optimization. The method comprises the following four steps: S1, power consumption data acquisition: dynamically acquiring user power consumption, distributed energy output and power grid state data through intelligent equipment, and ensuring real-time accuracy; s2, load characteristic analysis: constructing a quantitative model to analyze characteristics such as a user load elastic coefficient and adjustable potential, establishing a supply and demand balance evaluation system in combination with distributed energy fluctuation, and determining peak and valley time periods and gaps; s3, generating an interactive strategy, and generating and issuing dynamic strategies such as electricity price excitation, load regulation and energy scheduling based on an analysis result; and S4, executing and feeding back the strategy, monitoring the execution effect in real time, and optimizing the strategy through a closed loop mechanism. According to the method, the problems of staticization, single mode and low efficiency of a traditional method are solved, the power grid supply and demand balance capability, the renewable energy consumption rate and the strategy accuracy are improved, and low-carbon and intelligent operation of the power distribution network is supported.

The invention discloses a high-frequency power data acquisition adaptive frequency and priority processing method and device based on resource load feedback, and a storage medium, and belongs to the technical field of high-frequency power data processing. The method comprises the following steps: acquiring resource load information of a distribution network side intelligent terminal node in real time; according to the real-time resource load information, judging whether a preset sampling period of the power monitoring data needs to be adjusted, if so, dynamically correcting through a double-layer fast and slow ring adjusting mechanism to obtain a final sampling period, and otherwise, maintaining an original period; acquiring power monitoring data acquired by the node in the corresponding sampling period; and on the basis of a preset priority queue grading rule, a tube queue algorithm is utilized to adopt a differential transmission strategy for the data, so that priority processing of different levels of data is realized. The method does not need to depend on a prediction model, and realizes acquisition link elastic control and key data delay guarantee through real-time quantification of node loads, dual-time-domain closed-loop adjustment of a sampling period and hierarchical queue management messages.

The invention relates to the technical field of load monitoring, in particular to a station electricity load feature recognition system based on operation situation awareness, which comprises a situation extraction module, a power focusing module, a sudden change judgment module, an anomaly recognition module and a state output module. According to the method, the behavior boundary definition between the loads is enhanced through a difference clustering mode based on the power linkage relation, the anomaly identification independence under the complex load coupling condition is improved, the high-consistency feature reference is constructed by using the operation stability index, the identification interference caused by short-time fluctuation is effectively eliminated, the misjudgment and missed judgment conditions are avoided, and the reliability of the system is improved. The reliability and adaptability of sudden load behavior recognition are guaranteed by combining tolerance period judgment of a sudden change sample interval and peeling off accidental disturbance factors, structured filing of an abnormal recognition result can be achieved by establishing a mapping list, a corresponding relation between a control path and an abnormal behavior is constructed in an auxiliary mode, and the reliability and adaptability of sudden load behavior recognition are improved. And the interpretability and traceability of station electricity load identification are enhanced.

The invention discloses an orderly power utilization system for a charging pile. The orderly power utilization system comprises a cloud platform, an orderly power utilization gateway and an orderly power utilization terminal, the cloud platform is used for judging the residual power of the power transformation box through the orderly power utilization gateway; the orderly power utilization gateway is used for collecting and controlling the charging power of the charging pile through the orderly power utilization terminal; the cloud platform is provided with a basic guarantee layer and a competition layer, and the basic guarantee layer is used for distributing the lowest power to each vehicle connected with the charging pile, so that the basic charging requirement is ensured; and the competition layer calculates priority scores according to the current electric quantity, the charging remaining time and the charging power of the charged vehicles, and distributes the remaining power of the power transformation box step by step according to the priority scores of the vehicles from large to small under the condition that the total power capacity of the charging pile is not exceeded. The competition layer calculates the priority score through the current electric quantity, the charging remaining time and the charging power of each vehicle, and then distributes the remaining power of the power transformation box according to the priority score, thereby adapting to the requirements of complex scenes, and improving the charging efficiency.