177054results about "Data processing applications" patented technology

The invention discloses an unmanned aerial vehicle-based riverway hydrological sampling inspection method and system. The method comprises the steps of collecting unmanned aerial vehicle multi-parameter sensor data, determining a dynamic sampling rate through an enhanced difference rate adaptive sampling algorithm, performing multi-parameter cross validation and environment compensation, and finally reconstructing a data stream. A hydrological feature extraction transformation algorithm and a hydrological mode recognition algorithm are adopted to recognize a hydrological mode, analyze a state evolution process and predict a development trend; a multi-scale anomaly detection algorithm is adopted to identify anomalies, an anomaly source is positioned through a causal chain tracing algorithm, risk assessment and grading are carried out, an emergency decision scheme is generated, and an inspection strategy is optimized; evaluating system performance, optimizing resource configuration, dynamically adjusting parameters, and updating a prediction model. The intelligent level of river hydrological monitoring is improved, the sampling efficiency and the anomaly detection accuracy are improved, and the decision response time is shortened.

The invention provides a building health monitoring and evaluation method and system based on a physical neural network, and the method comprises the steps: collecting the monitoring data of vibration, strain, displacement and the like of a building through a sensor network, and carrying out the data preprocessing based on wavelet transformation and a neural network; inputting the processed data into a physical constraint encoder, mapping the processed data to a physical space by adopting a Hamilton quantity encoding method, and constructing constraint conditions in combination with physical model parameters; performing feature matching by using a physical constraint neural network, and extracting key features; adopting a principal component analysis method to decompose early warning indexes, and constructing parameter mapping through a deep neural network; generating a hierarchical decision rule based on a decision tree algorithm; and carrying out constraint check and cost calculation on the candidate schemes, and finishing scheme sorting through an analytic hierarchy process. Physical knowledge is embedded into a deep learning framework, and the accuracy and reliability of monitoring evaluation are improved.

The present application relates to the technical field of load prediction, and provides an electrical load prediction method and device based on spatial-temporal correlation. The method comprises: performing spatial-temporal modeling on the basis of historical electrical load data and historical environment data to obtain a multi-layer spatial-temporal model, wherein each layer model is a directed graph and represents spatial causality in the fixed time, and a directed edge between the layer models represents temporal causality; on the basis of an interpretable spatial-temporal attention transformer, performing feature extraction and priori knowledge fusion on the multi-layer spatial-temporal model to obtain spatial-temporal characteristics of an electrical load, wherein the interpretable spatial-temporal attention transformer comprises a spatial causal attention network, a temporal attention network, and a spatial dependence comparison module; and on the basis of the spatial-temporal characteristics of the electrical load, and the real-time electrical load data and the real-time environment data of a target electrical node, predicting by using a trained STEF-DHNet model to obtain the predicted electrical load data of the target electrical node. The present application can improve the prediction accuracy and the model applicability.

The invention discloses a knowledge question-answering system based on a large language model. The method comprises the steps that a power operation and maintenance knowledge base module obtains data from a data source for preprocessing, constructs a power operation and maintenance field knowledge graph, converts text content into semantic vectors and integrates the semantic vectors to form a power operation and maintenance knowledge base; the problem understanding module is used for identifying and analyzing problems and converting the user problems into semantic vectors; the retrieval and reordering module is used for performing semantic retrieval in the electric power operation and maintenance knowledge base, recalling knowledge information related to question semantics, and ordering retrieval results based on semantic relevance, knowledge confidence and information importance; and the answer generation module is used for integrating the knowledge information and the user question into input of a large language model, generating prompt information, and generating an answer in a natural language form according to the prompt information by adopting the large language model based on data fine adjustment in the field of power operation and maintenance. According to the scheme, the deep learning technology is used for understanding semantics of user questions, and accurate retrieval is carried out in the knowledge base.

Disclosed in the present invention is a three-dimensional scene point cloud data extraction and recognition system based on a power distribution network. The system comprises: a data collection module used for collecting data for multiple times and fusing same to acquire a complete power distribution network scene; a data processing module used for using the least square method to calibrate closest point matching and perform point cloud registration; a data extraction and segmentation module used for using principal component analysis to calculate a normal vector of the points in a neighborhood of each point cloud data point, measuring the curvature change near each point of a curved surface of a three-dimensional scene of a power distribution network by means of Gaussian curvature, and segmenting and classifying point cloud data by means of a random sampling fitting method and a clustering method; a data recognition and monitoring module used for using PointNet to perform local and global feature learning on the features of each point cloud data point, and acquiring global features by means of a maximum pooling operation for coding and recognition; and a data visual analysis module used for connecting to an open-source point cloud data processing library PCL and a hardware device for visual interaction. The efficiency and sustainability of the power supply system are improved.

The invention discloses an ancient building risk prediction management and control method and system based on a large model, and relates to the technical field of ancient building protection. An ancient building risk prediction management and control system based on a large model comprises a multi-source data acquisition module, a multi-modal data fusion module, a large model risk prediction module, a risk assessment and early warning module, a digital twinborn dynamic management and control module and a repair decision module. According to the method, the structure state data, the visual image data and the environment data are fused, so that the state of the historic building is comprehensively perceived; the self-supervised learning pre-trained ViT-GPT model is combined with the LSTM time sequence model, so that the time sequence analysis capability and the multi-modal data processing capability of risk prediction are remarkably improved, the future damage probability and the risk trend of the historic building can be accurately predicted, and the defects of the prior art in long-time span risk prediction are overcome.

The invention discloses a transformer area intelligent fusion terminal data processing system based on edge calculation, relates to the field of edge intelligence, and solves the problems of poor real-time performance and poor data exception processing accuracy of an existing intelligent fusion terminal data processing system. According to the method, communication between micro-services is efficiently managed through the container arrangement engine and the service grid, the message transmission delay is reduced, and resource allocation is further optimized through a resource scheduling algorithm based on reinforcement learning, so that system resources are used most reasonably; according to the method, anomaly detection is rapidly and accurately carried out through a space-time heterogeneous fusion network model, hierarchical processing of data and on-cloud training of the model are realized through a cloud-edge collaborative architecture, edge nodes participate in model training through a federated learning mechanism, the model performance is optimized, the transmission time of the data in the network is shortened, and the problem positioning speed is increased; the real-time performance and the reliability of an existing system are greatly improved.

The invention discloses a power grid dispatching decision-making method based on a graph neural network and a large model, and the method comprises the steps: collecting an original power grid data set, and generating a fusion feature tensor through adaptive distribution mapping and recursive decomposition; importing a fusion feature tensor and a power grid topology matrix, and generating a dynamic graph embedding matrix through spectral decomposition and matrix manifold analysis; performing Markov jump process analysis and optimal transmission fusion on the dynamic graph embedded matrix to generate an integrated feature matrix; a multi-target Lyapunov function is constructed on the basis of the integrated feature matrix, and a robust decision strategy is generated by solving a Hamilton-Jacobi-Bellman equation; and performing multi-dimensional performance monitoring and statistical manifold learning on the robust decision strategy to realize strategy optimization. According to the method, the uncertainty in power grid operation can be effectively processed, and the accuracy and reliability of scheduling decisions are improved.

Disclosed is a microgrid optimal scheduling method taking into consideration a system operation risk and user satisfaction, comprising: generating a power prediction curve of wind power and photovoltaic new energy output in a microgrid, using a Monte Carlo simulation method to obtain a prediction error range to generate a typical scene set, and by means of a data fitting method, acquiring a wind-photovoltaic prediction error probability density distribution function; using value-at-risk and conditional value-at-risk theories to quantify a microgrid operation risk which is increased by slightly high or low output caused by wind power and photovoltaics; obtaining the scheduling cost of an adjustable load in a microgrid system; taking the minimum sum of conditional value-at-risk cost and microgrid system operation cost as an objective function and a user satisfaction level under a demand-side response as a constraint, establishing a microgrid optimal scheduling model taking into consideration the operation risk and the user satisfaction; and using a genetic algorithm to solve the model, and obtaining an optimal result of grid-connected microgrid system optimal scheduling. The safety, economic efficiency, stability and reliability of the microgrid are enhanced.

The present invention relates to a medium-voltage power distribution network source-grid-storage double-layer planning method considering flexibility. The method comprises the following steps: step 1, a power distribution network source-grid-storage double-layer planning framework based on flexibility assessment indicators; step 2, a power distribution network source-grid-storage double-layer planning model; step 3, solving of the power distribution network source-grid-storage double-layer planning model; and step 4, use analysis of demonstration cases. The present invention has the advantages of establishing assessment indicators for flexibility level from the aspects of flexibility supply-demand matching and network transmission capacity, and improving the economy and flexibility of expansion planning and system operation.

A system can process an indication of user data, wherein the user data comprises a food item including at least one recipe having one or more ingredients, and a query, wherein the query is associated with at least one ingredient, and generate, based at least in part on the query, nutrition data associated with the at least one ingredient, wherein the nutrition data comprises preparation data, sourcing data, delivery data, and a list of nutrients for the at least one ingredient. The system can further estimate, based at least in part on the nutrition data associated with the least one ingredient, a nutritional score, then compare the estimated nutritional score to a threshold, and based on the compared nutritional score, generate one or more instructions to cause an indication of the nutritional score on a user device.

The invention provides a building dynamic structure health monitoring method. The method comprises the steps that original data of a multi-mode sensor is acquired and preprocessed; constructing topology sensing network representation reflecting the spatial distribution and structure connection relation of the sensors; extracting multiple spatio-temporal scale features; local-to-global structure state mapping is realized by using a recurrence plot evolution predictor; integrating structural dynamics knowledge into the neural network model to perform physical constraint correction; and constructing a multi-dimensional health index set and outputting damage probability evaluation. According to the invention, accurate mapping from the local dense monitoring area to the global structure state under the non-uniformly distributed sensor network is realized, and the accuracy and reliability of structure health monitoring are improved.

The invention relates to the technical field of power plant data processing, in particular to a virtual power plant load prediction and dynamic adjustment optimization system and method, and the system comprises a data collection module, a preprocessing module, a prediction module, an adjustment module and a verification module. The data acquisition module acquires real-time operation data and power market signals of distributed energy nodes; the preprocessing module performs standardization processing on the data through a quantum space-time alignment and anomaly reconstruction technology, and extracts strong correlation vectors of meteorological features and loads; the prediction module adopts an adaptive noise complete set empirical mode decomposition algorithm to separate a trend term, a periodic term and a residual component of a load sequence, and the adjustment module constructs a multi-target optimization model. Efficient aggregation of distributed resources, high-precision load prediction in a meteorological sudden change scene and cooperation of multi-market dynamic scheduling strategies are realized; and the clean energy consumption capability and the virtual power plant market response efficiency are improved.

The present invention relates to a low-voltage power distribution network hybrid energy storage optimization configuration strategy considering an incentive-based demand response. The method comprises the following steps: step 1: an optical storage system considering an incentive-based demand side response; step 2: a hybrid energy storage system configuration strategy considering the incentive-based demand side response; step 3: a hybrid energy storage system optimization configuration model considering the incentive-based demand side response; and step 4: exemplary application analysis. The present invention has the advantages of fully using the complementation characteristic of resources, maximally exerting the adjustment capability of flexible resources, having a good effect of stabilizing the fluctuation of a net load curve, and being fast in solving speed and high in calculation precision.

The invention discloses a dam safety monitoring system and method based on digital twinning, and belongs to the technical field of data processing, and the method comprises the steps: collecting dam surface acoustic emission signals and internal infrared thermal image data in real time through a multi-source sensor, and generating a multi-dimensional damage feature matrix; constructing a disaster node topology network in combination with density peak clustering and dynamic weight; on the basis of gravitational field intensity calculation and improved Monte Carlo simulation, a damage chain reaction path is accurately predicted; generating a dynamic risk thermodynamic diagram through space-time folding mapping and a bidirectional diffraction equation; and extracting hidden danger indexes by adopting multi-axis domain characteristic distillation, dynamically comparing a safety envelope function, and generating a flood discharge scheduling and reinforcing scheme on line. According to the method, cross-modal fusion, a gravitational field dynamic topology network and a time-space folding mapping technology are adopted, deep fusion of accurate simulation of a damage energy conduction path, carbonization-deformation bidirectional coupling prediction and intelligent risk hidden danger disposal decision can be realized, and the accuracy and initiative of dam safety early warning are improved.

The invention provides a building safety alert level dynamic adjustment method and system based on artificial intelligence driving, and the method comprises the steps: receiving original monitoring data, spatial position data and data quality indexes of a sensor array, and sequentially carrying out the space-time alignment, feature extraction, quality evaluation and anomaly detection through a multi-source heterogeneous data preprocessing frame; performing multi-dimensional feature fusion and risk pattern recognition analysis on the preprocessed data, and sequentially realizing feature space construction, risk pattern extraction, correlation analysis and grade evaluation; feature fusion, threshold optimization, level adjustment and effect evaluation are realized through a warning level dynamic adjustment mechanism based on the risk level and the confidence coefficient; and finally, completing information fusion, content generation, grade classification and push optimization through early warning information intelligent generation and graded push processing. According to the invention, the accuracy and timeliness of building safety early warning are obviously improved.

The invention relates to a multi-modal dynamic optimization educational resource recommendation system and method, and the system comprises the following modules: a multi-modal data collection module integrates video behaviors, answer tracks, physiological signals and other data through edge calculation, and constructs a learning feature map; the student portrait module adopts an LSTM-Attention network in combination with a graph neural network to dynamically model knowledge mastery and learning styles; the resource matching engine realizes multi-objective optimization of knowledge gain, cognitive load and interest matching based on reinforcement learning and knowledge graph analysis; the tag adaptive module dynamically adjusts resource weights through causal inference and comparative learning, the personalized recommendation module generates a dynamic learning path and pushes adaptive resources based on student portraits and real-time behavior data, and the learning progress tracking module monitors a learning state in real time and feeds back the learning state to the resource matching engine to optimize a recommendation strategy in a closed loop mode. The technical defects that resource recommendation of a traditional education platform is rigid and personalized adaptation is lacked are overcome.

The invention discloses a tunnel multi-source fusion dynamic twin surrounding rock intelligent prediction and control method and system, and the method comprises the steps: collecting multi-source heterogeneous data in real time through intelligent drilling equipment, a distributed optical fiber sensing network and a mobile scanning device which are disposed on a tunnel construction surface; inputting the multi-source heterogeneous data into a space-time diagram convolutional network, constructing a space-time correlation model of geological features and construction disturbance parameters, and outputting a coupling feature matrix; based on the coupling characteristic matrix, simulating the dynamic response of the surrounding rock-support system through a streaming voxelization engine and a multi-physics coupling engine, and generating a risk evolution three-dimensional thermodynamic diagram; and inputting the risk evolution three-dimensional thermodynamic diagram into the deep reinforcement learning model, and dynamically optimizing to generate a construction parameter regulation and control instruction meeting a safety constraint condition. According to the technology, a full-link channel of'data perception-mechanical analysis-decision execution 'is opened in a closed-loop mode, and therefore the double risks of'data distortion misjudgment' and'response lag out-of-control 'of a traditional method are avoided.

Disclosed is a method for siting heat wave monitoring stations based on risk evaluation, including: acquiring historical meteorological data of a target region, and preprocessing the historical meteorological data to generate a gridded associated meteorological data set; identifying historical high-temperature heat wave events based on the associated meteorological data set, and calculating parameters and summary indexes of heat wave feature of grids; evaluating station building priority of the grids based on spatial distribution features of the summary indexes; acquiring multi-source data, and evaluating a heat wave risk to generate a heat wave risk map; performing iterative computation using an optimization algorithm based on current station building information, temporal-spatial distribution features of meteorological factors and the heat wave risk map to determine alternative station building positions; and acquiring on-site survey information of each alternative station building position, and determining a position where a station is to be built.

The invention discloses a dam safety monitoring system and method based on digital twinning, and particularly relates to the technical field of digital twinning. The method comprises the following steps: acquiring stress, deformation, displacement, inclination and environmental change data of a dam structure in real time, regularly carrying out high-precision calibration on a sensor, recording a time drift error, analyzing nonlinear sensitivity of a digital twin model under an extreme working condition, simulating the influence of the sensor error and the model sensitivity on dam evaluation, predicting a deviation degree, and calculating the evaluation accuracy of the dam. Based on a prediction result, a dynamic drift compensation algorithm is adopted to correct sensor data in real time, the sensitivity of the model is dynamically adjusted, the optimized data and model are integrated, the structural state and potential risk of the dam under normal and extreme working conditions are accurately evaluated, accurate early warning information and decision scheme are generated, and the accuracy of early warning is improved. The accuracy and reliability of dam safety monitoring and risk assessment are remarkably improved, decision delay or resource waste caused by misjudgment or missed judgment is reduced, and the dam safety guarantee capacity is improved.

The invention relates to the technical field of intelligent operation and maintenance of a power system, in particular to a 10KV substation equipment multi-source heterogeneous data collaborative fault prediction method and system, and the method comprises the steps: integrating equipment sensing data, environment data, operation and maintenance data and video image data, and forming a multi-mode heterogeneous data set; through depth feature extraction and semantic alignment, heterogeneous data is converted into unified feature representation, and dynamic calibration is carried out by using space-time consistency constraints; carrying out adaptive weighted fusion and contradiction resolution on the calibrated features to generate fused features; based on an equipment state knowledge graph, performing multi-dimensional evaluation on the fusion feature to obtain an equipment state vector; predicting a fault through a multi-level fault prediction model by using the vector and the fusion feature; and comparing a prediction result with actual fault data, feeding back and updating a knowledge graph and prediction model parameters, realizing continuous optimization, and realizing deep fusion of heterogeneous data through multi-modal heterogeneous data depth feature extraction and semantic alignment.

The invention relates to the technical field of communication command, in particular to an intelligent emergency command system and method based on scene twinning. Comprising a data acquisition module, a scene twinborn management module, an emergency situation analysis module, an emergency response decision module and a resource scheduling command module. Multi-source data of an emergency scene are collected through a sensor network, a scene twinning model is constructed by using augmented reality and digital twinning technologies, and real-time rendering of a virtual scene is realized. According to the method, emergency situation analysis, risk detection and trend prediction are performed in combination with a random matrix theory and a graph neural network algorithm, an emergency response strategy is generated by using a decision tree based on an analysis report, resources are scheduled and allocated through a multi-objective optimization algorithm, and an optimal emergency command scheme is ensured. According to the invention, the efficiency and scientificity of emergency command are improved, and the rapidity and accuracy of emergency response are enhanced.

The invention provides an unmanned intelligent inspection equipment cooperative scheduling method and system in a photovoltaic power generation scene, and relates to the technical field of photovoltaic power station intelligent inspection, and the method comprises the steps: carrying out the task priority classification and time sequence correlation analysis based on a dynamic adaptive neural network and a sequence predictor, constructing a task dependency graph, and carrying out the task priority classification and time sequence correlation analysis; a candidate task allocation scheme is generated based on a deep reinforcement learning framework, the candidate scheme is optimized in combination with a collaborative decision-making system constructed by a multi-target genetic algorithm and a graph attention mechanism by taking minimization of completion time, energy consumption and maximization of coverage rate as targets, and task allocation constraints are updated by using a confidence propagation network; and an abnormal condition is judged through a Bayesian reasoning algorithm, and an optimal scheduling scheme is finally output, so that the inspection efficiency of the photovoltaic power station can be effectively improved, the energy consumption is reduced, and the inspection coverage rate is improved.

The present invention belongs to the technical field of land change detection. Disclosed are a land change detection method combining matrix decomposition and adaptive propagation, and a system. A deep learning semantic segmentation model combining matrix decomposition and adaptive propagation is used for detecting a land change. The model is of a twin double-branch structure, which can receive both an input of a pre-temporal remote sensing image data set and an input of a post-temporal remote sensing image data set and can share network parameters, and which has the advantage of end-to-end feature learning. In the present invention, an attention method based on matrix decomposition is used to replace a conventional attention method based on manual design; moreover, in view of adaptive propagation technology for pixel offsets of neighboring points, a deep learning twin differential semantic segmentation network is established to realize automatic and precise land utilization / land coverage change detection on the basis of high-resolution optical data.

The invention relates to the technical field of data processing, and discloses an intelligent water conservancy inspection method, device and equipment and a storage medium. The method comprises the following steps: performing dynamic fusion processing on acquired multi-dimensional water conservancy environment data of a preset target area to obtain a target digital twinborn body; performing agent collaborative analysis on the digital twinborn body to obtain a self-adaptive inspection strategy; performing multi-modal sensing data acquisition on the target area according to the self-adaptive inspection strategy to obtain multi-modal data; performing federated learning processing on the multi-modal data to obtain holographic situation awareness data; performing causal reasoning analysis on the holographic situation awareness data to obtain a dynamic risk map; and inputting the dynamic risk map into a preset deep Q network for prevention and control strategy analysis to obtain an active prevention and control strategy and an inspection report. According to the invention, the efficiency and accuracy of intelligent water conservancy inspection are improved.

The invention provides a power distribution network optical fiber composite fault intelligent positioning and diagnosis method based on hybrid sensing, and relates to the technical field of power distribution networks, and the method comprises the steps: obtaining and preprocessing optical fiber and electrical sensing data, extracting time-frequency domain features through a multi-channel convolutional neural network, calculating a dynamic weight coefficient through an attention mechanism, and carrying out the feature fusion, a dynamic fault propagation graph is constructed, spatial-temporal feature extraction is performed by combining graph convolution and causal convolution, and fault positioning and type identification are realized. The method can make full use of multi-source heterogeneous data, improves the fault positioning precision and diagnosis reliability, and is suitable for fault diagnosis of a complex power distribution network.

The invention relates to power distribution network fault processing equipment applied to the technical field of power distribution, and aims to improve the fault prediction, diagnosis and processing capabilities of a power system. The equipment comprises a pre-training module, a neural network architecture module, a data acquisition layer, an edge calculation layer, a core processing layer, a context learning module, a reasoning processing module, an output and interaction layer and a continuous learning and improvement layer, and is characterized in that a large-scale pre-trained Transform model is utilized, and professional knowledge of a power system is combined; according to the method, deep understanding and analysis of a complex power grid state are realized, through multi-source data fusion, edge calculation and central cooperative processing, the equipment can monitor the power grid state in real time, accurately predict potential faults and provide intelligent diagnosis and repair suggestions, and model pre-training and optimization are performed by adopting self-supervised learning and federated learning technologies; using reinforcement learning to optimize a fault response strategy; the interpretability of the model is realized, and the decision transparency is improved.

The invention discloses an aerospace intelligent manufacturing large model construction method, which comprises the steps of collecting original data, performing preprocessing and data association, and constructing an aerospace intelligent manufacturing database; establishing a knowledge acquisition and structured conversion assembly line, a multi-dimensional associated domain knowledge graph, a knowledge quality control system and a dynamic updating mechanism, and constructing a professional knowledge base; aligning the cross-modal manufacturing data to generate a corpus; combining base general large model pre-training, injecting terminology semantics and multi-modal association capability, and completing knowledge migration; based on the pre-trained aerospace intelligent manufacturing large model, constructing an aerospace manufacturing cognitive agent, and forming a complex engineering problem solving framework; professional ability is optimized through a two-stage progressive multi-task training strategy, and dynamic adaptation of a production environment is realized in combination with an online learning and incremental updating mechanism. The intelligent level of aerospace intelligent manufacturing is remarkably improved.

The invention belongs to but not limited to the technical field of fault diagnosis and maintenance of power equipment, and discloses an intelligent diagnosis and maintenance system and method for power equipment based on a knowledge graph. A data acquisition and preprocessing module acquires real-time operation data and historical data of the power equipment; the knowledge graph construction module associates various kinds of information of the power equipment to construct a knowledge graph; the intelligent diagnosis module performs association analysis on power equipment faults based on a knowledge graph and a machine learning algorithm, identifies fault modes and potential faults, and predicts possible faults of the equipment; the maintenance suggestion module automatically generates a detailed maintenance scheme according to the diagnosis result; the operation and maintenance visualization module provides visual fault diagnosis results and maintenance suggestions, supports multi-level data display, and helps operation and maintenance personnel to clearly understand the equipment state. The operation safety of the power equipment is improved, the operation and maintenance cost is remarkably reduced, and the overall efficiency of equipment management in an intelligent power grid environment is improved.

The invention relates to the technical field of intelligent energy management, in particular to an energy consumption prediction and optimization system for energy-saving management and control, which comprises a data acquisition core module, a dynamic energy consumption prediction core module, an intelligent optimization control core module, a self-adaptive calibration core module, a user interaction core module and the like. The data acquisition module acquires energy consumption, equipment state and environment data from multiple sources; the dynamic energy consumption prediction module fuses improved time series decomposition and a multi-modal LSTM model to realize accurate prediction; the intelligent optimization control module is combined with strategies such as time-of-use electricity price and equipment linkage to generate an optimal instruction; the adaptive calibration module dynamically optimizes the model through Kalman filtering and incremental learning; the user interaction module supports visual display and strategy self-definition; in addition, the system is provided with an edge computing node to guarantee offline operation, an SM4 algorithm and a block chain technology are adopted to guarantee data security, and the system is compatible with various industrial protocols. The energy utilization efficiency is effectively improved, the operation cost is reduced, and the system safety and reliability are enhanced.