5586 results about "Feature matrix" patented technology

The invention provides an intelligent real-time interactive question-answering system based on a virtual digital human, and belongs to the technical field of voice signal processing and voice recognition, and the system comprises a data acquisition module which receives a voice or text interaction request input by a user, collects the expression dynamic parameter sequence and limb movement sequence data of the user in real time, and transmits the data to a user interaction module; obtaining a standardized voice feature vector and structured text data; the cross-modal fusion module is used for constructing an interactive feature matrix; the behavior decision module outputs a decision instruction set; the knowledge retrieval module is used for generating an answer text with emotional adaptability and voice features; and the voice generation module is used for generating a mouth shape animation key frame, a micro expression parameter sequence and a limb action track of the virtual digital human, generating a voice response in combination with the answer text and the voice characteristics, and pushing the voice response to the user terminal. According to the method, the interaction experience and adaptability of the virtual digital human are remarkably improved.

The invention discloses a smart park full life cycle management system and method based on digital twinning and Internet of Things, and relates to the technical field of smart park management, and the system comprises a sensing edge module, a data governance module, an intelligent analysis module, a life cycle module and a twinning modeling module. According to the invention, multi-protocol access and edge computing capability are supported, and the data transmission efficiency and stability are greatly improved; the intelligent analysis module outputs an accurate analysis result by constructing a multi-class feature matrix and deep multi-task joint modeling mechanism, and provides data support and model guidance for dynamic management and intelligent decision making of the park; the life cycle module integrates a Kepler optimization algorithm and a multi-agent reinforcement learning and simulated annealing algorithm, establishes a collaborative optimization mechanism, realizes combination of global search and local fine tuning of resource scheduling, and effectively optimizes energy consumption, response time, space utilization and safety risks; and the twin modeling module constructs a park three-dimensional model, so that the interactivity and operability of the system are improved.

The invention provides a knowledge graph generation method and system for science and technology project risk control, and the method comprises the steps: obtaining a multi-source heterogeneous data set of a target science and technology project, converting structured index data into a standard vector sequence through a heterogeneous data fusion mechanism, and converting unstructured text data into a semantic vector sequence; converting the time sequence behavior data into a behavior pattern vector sequence, inputting the three into a risk quantitative evaluation model, generating a risk entity feature matrix and a risk association strength matrix, and determining a node distribution topology of the knowledge graph according to entity feature vectors in the risk entity feature matrix; and according to association strength values in the risk association strength matrix, determining an entity relationship topology of the knowledge graph, generating a dynamic knowledge graph of the target science and technology project, and identifying a potential risk propagation path in the dynamic knowledge graph. According to the invention, the risk identification result has the dynamic characteristic of real-time updating, and the traceability of the multi-dimensional risk characteristic is maintained.

The invention relates to the technical field of network security, in particular to a network security big data state evaluation method based on pattern recognition, which comprises the following steps of: extracting multi-modal features from a network flow log, a system event log, a host behavior log and threat intelligence data, generating a feature matrix, performing feature dimensionality reduction by adopting an auto-encoding network, and obtaining a network security big data state evaluation result; carrying out attack behavior classification and abnormal mode identification in combination with unsupervised clustering and a graph neural network; constructing an attack transition probability matrix based on a Markov model; forming a time sequence attack chain; predicting an attack development trend; and a dynamic protection instruction is issued to the safety equipment. According to the method, the unknown attack detection capability can be improved, the time sequence attack traceability is enhanced, the security situation assessment is optimized, and the method is suitable for security situation awareness in cloud computing, industrial internet and large-scale network environments.

The invention provides a network traffic anomaly detection model training method and device and a readable storage medium, and the method comprises the steps: extracting a traffic statistical feature vector according to original network traffic data, and generating an initial mixed data set; generating a confrontation disturbance sample output enhanced feature matrix based on the initial mixed data set; constructing a self-adaptive feature fusion rule based on the enhanced feature matrix, embedding asset association degree parameters into an attention calculation layer of a feature encoder, and outputting encoding features fusing threat intelligence; inputting the coding features fused with the threat intelligence into a pre-constructed initial detection model, generating false report and missing report correction labels based on the suspicious traffic fragments, and outputting an adversarial sample correction data set; and performing adversarial training on the initial detection model through the adversarial sample correction data set to obtain an incremental detection model for network traffic anomaly detection. According to the invention, the detection precision, the anti-interference capability and the real-time defense response capability of the detection model to novel attacks can be improved.

The invention belongs to the technical field of equipment health monitoring and anomaly detection, and discloses an industrial time series data learning fusion and anomaly detection method, which comprises the steps of constructing an IP-PLC mapping relation table, collecting multi-modal data, constructing a physical constraint parameter list, and generating structured data and a storage index. Time domain features and frequency domain features are extracted, a spatial topological graph is constructed, node spatial feature vectors and edge association strength are extracted, spatial association feature vectors are generated, a constraint rule base is constructed, and an enhanced feature set is formed; aggregating the enhanced feature set and the constraint rule base, generating a multi-dimensional feature matrix and a global reference parameter table, further constructing a global reference system, obtaining an equipment-level anomaly probability matrix, and generating a working condition-level anomaly probability matrix; hierarchical optimization is carried out through hierarchical modeling, and an optimization parameter set is generated; constructing an alarm response mechanism, and performing reverse updating to form closed-loop iteration; and an interpretable and extensible solution is provided for equipment health management in a complex industrial scene.

The invention discloses an AI-based smart park multi-source data fusion method and system, and the method comprises the steps: generating a time-space aligned standardized data flow according to environment parameters, energy consumption waveforms, security signals and personnel trajectory data collected by a heterogeneous sensor network; generating a multi-modal fusion feature matrix based on the standardized data stream; according to the multi-modal fusion feature matrix, generating a three-dimensional twinborn body including the equipment state, the people flow density and the energy consumption hot spot in real time; inputting the three-dimensional twin into a multi-target constrained reinforcement learning algorithm, and fusing real-time data and prediction data to generate a Pareto optimal solution set; and based on the Pareto optimal solution set, generating a final instruction set for driving park equipment regulation and control, and triggering collaborative response of a security and protection system and an energy consumption system at the same time. According to the embodiment of the invention, intelligent upgrading of park management can be realized through cross-modal feature extraction, dynamic digital twin modeling and reinforcement learning optimization.

The invention discloses a perception intelligent driving complex traffic scene dynamic risk prediction method, and relates to the technical field of risk prediction, and the method comprises the steps: obtaining the multi-source traffic dynamic data of a target region in real time; performing space-time alignment processing on the multi-source traffic dynamic data; inputting the space-time coupling feature matrix into a pre-trained depth space-time prediction model to generate a risk thermodynamic map; calculating a dynamic risk index of each traffic sub-region, and generating a risk level distribution sequence; and triggering a self-adaptive early warning response mechanism according to the risk level distribution sequence, and dynamically adjusting operation parameters of the variable information sign and the traffic signal controller. The technical problems of frequent traffic congestion and high accident risk caused by inaccurate traffic risk prediction and difficulty in dynamic adjustment according to the real-time traffic condition in the prior art are solved, and the technical effects of accurately predicting the traffic risk and improving the safety and traffic efficiency in a complex traffic scene are achieved.

The invention relates to the technical field of data center intelligent management, in particular to a data center construction and intelligent operation and maintenance management system, which comprises a dynamic environment sensing module, a heterogeneous equipment protocol adaptation module, a multi-dimensional resource dynamic scheduling module, a hidden fault prediction module and an energy efficiency optimization execution module. Physical environment data such as temperature gradient, current harmonic component and optical fiber strain rate are acquired by deploying a multi-mode sensor, and an environment characteristic matrix is constructed; standard semantic mapping of the heterogeneous protocol is realized by using a semantic slot migration algorithm; establishing a resource topological graph based on the hypergraph neural network and dynamically updating the resource topological graph; a dual-channel space-time convolutional network is adopted to realize fault prediction; and combining the fault probability matrix to generate a dynamic tuning strategy of dimensions such as cooling, electric power, network and the like, and forming closed-loop optimization control. According to the invention, integrated collaboration of multi-source information fusion, equipment intelligent control and energy efficiency adaptive optimization is realized, and the intelligence, reliability and energy efficiency level of data center operation and maintenance are improved.

The invention discloses a bolt looseness online monitoring method and system, and the method comprises the steps: collecting an original data set containing a vibration signal of bolt connection, temperature gradient data and structural stress distribution through a multi-mode sensor array, and carrying out the time-space alignment and frequency domain decomposition processing of the original data set, obtaining a multi-dimensional feature matrix of the bolt nodes; based on the multi-dimensional feature matrix, a graph neural network model is adopted to carry out bolt looseness probability calculation, and a real-time looseness probability value of each bolt is output; generating a risk level map based on time evolution according to the real-time loosening probability value; and the risk level map is mapped in real time through a three-dimensional visual interface, and when it is detected that the bolt loosening risk level exceeds a preset threshold value, an early warning message is generated and uploaded to an operation and maintenance platform, and closed-loop monitoring response is completed. According to the embodiment of the invention, accurate assessment, dynamic prediction and visual early warning of the loosening risk can be realized, and the intelligent level of structure safety monitoring is improved.

The embodiment of the invention discloses an outer wall thermal insulation defect diagnosis method and system based on artificial intelligence, and the method comprises the steps: firstly obtaining an infrared thermal imaging and visible light image sequence of a target building outer wall, the former comprising continuous temperature distribution data, and the latter comprising textural feature data in time-space alignment with the latter; performing dynamic temperature gradient analysis on the infrared thermal imaging image sequence to generate a three-dimensional heat conduction abnormal map, extracting surface deformation characteristics from the visible light image sequence to generate a structure deformation distribution map, and performing multi-modal characteristic fusion on the two to obtain a joint defect characteristic matrix; performing defect type classification and region positioning on the matrix based on a pre-trained deep residual neural network model, outputting a defect type identifier and a corresponding region boundary coordinate, and finally generating a diagnosis report containing a repair priority score and a material matching suggestion according to the defect type identifier and the corresponding region boundary coordinate, and sending the diagnosis report to a user terminal for visual display. And efficient and accurate external wall thermal insulation defect diagnosis is realized.

The invention discloses a substation equipment state monitoring and intelligent fault early warning method based on deep learning. The method comprises the following steps: S1, obtaining a preprocessed multi-source state data set; s2, generating a high-dimensional equipment state feature matrix; s3, a fault sensitive deep belief network model is adopted to form a preliminary fault state recognition result; s4, obtaining an optimized sensitive depth belief network model; s5, performing online analysis on the multi-source state data acquired in real time by using the optimized sensitive deep belief network model, generating a real-time fault prediction result of the equipment state, and classifying and grading fault risks; and S6, according to a real-time fault prediction result, triggering a remote fault early warning mechanism, and sending fault early warning information including a fault risk level, an early warning signal and an emergency processing suggestion to a substation operation and maintenance center. According to the invention, intelligent alarm linkage and hierarchical control of the scheduling system are effectively supported.

The invention provides a photovoltaic active distribution network voltage out-of-limit problem tracing method comprising the following steps: receiving research data and carrying out multi-scale feature analysis to obtain a standardized multi-dimensional feature matrix and a time-space incidence matrix; constructing an initial causal graph based on basic research data and power grid physical constraints, and constructing a multi-layer causal network by calculating causal intensity; extracting a dynamic feature sequence, analyzing propagation features, and generating a propagation path set and a path weight matrix; performing tensor completion and state estimation on the research data to obtain a state estimation matrix and estimation uncertainty; and outputting an out-of-limit source position, an out-of-limit risk score and early warning information based on pattern matching analysis and risk assessment. According to the invention, through combination of multi-dimensional feature analysis and a multi-layer causal network, accurate positioning and risk early warning of a voltage out-of-limit source are realized, and traceability accuracy and analysis efficiency are improved.

The invention discloses a grassroots power supply enterprise compliance risk intelligent system and method based on big data analysis. The data acquisition unit is used for acquiring business operation data, historical violation records and policy and regulation update data of basic power supply enterprises to form a unified compliance data set. And the natural language processing unit performs text word segmentation and correlation analysis on the policy and regulation and violation record data, extracts key risk factors and labels compliance risk labels. And the risk feature construction unit performs multi-dimensional feature fusion on the business operation data and the compliance risk label data to generate a feature matrix for risk identification. And the intelligent risk assessment unit performs real-time analysis on the feature matrix by using a pre-trained machine learning model, identifies compliance risk categories and levels, and generates early warning information. According to the method, the accuracy of compliance risk identification is improved by using big data analysis and an intelligent algorithm, the compliance management cost of basic-level power supply enterprises is reduced, and the operation safety and compliance of the enterprises are improved.

The invention relates to the technical field of power system topology identification, in particular to a power distribution network frame topology identification method based on an improved graph neural network, and the method comprises the steps: collecting the real-time electric quantity data of nodes and edges of a power distribution network, and carrying out the modeling of a power distribution network graph structure; the method comprises the following steps: constructing a node, edge and hyperedge feature matrix by using real-time collected data, inputting an improved graph neural network topology identification model, dynamically weighting and adjusting an edge weight through a graph attention network, splicing and fusing local topological features and global topological features, and generating a prediction adjacency matrix; when the power distribution network is dynamically changed, a change area is positioned through adjacency matrix difference, and a sub-graph is extracted for incremental updating; and constructing a topological structure of the power distribution network based on the final adjacent matrix, and outputting a connection relationship between the physical positions of the nodes and the edges. Compared with the prior art, the real-time performance, generalization ability and applicability of the model are remarkably improved, and the problems of accuracy and efficiency of power distribution network topology identification in the dynamic environment are solved.

The invention relates to the technical field of signal devices, and discloses a traffic signal control method and system based on vehicle-road cloud multi-modal data fusion, and the method comprises the steps: collecting multi-modal traffic data synchronously in real time through a vehicle-end sensor, road-side sensing equipment and a cloud Internet platform; fusing the heterogeneous data by adopting a space-time alignment algorithm, and constructing a standardized space-time feature matrix; traffic flow prediction is carried out based on a multi-layer space-time diagram neural network trained by a federated learning mechanism, and a signal control instruction is generated through reinforcement learning and a multi-objective optimization model; and issuing the green wave parameter, the dynamic timing scheme and the cross-domain coordination strategy to a roadside signal machine through the cloud edge coordination architecture to execute control. The problems that in the prior art, low-delay private network communication cannot be achieved, the data fusion efficiency is low, unmanned driving is not supported, and the deployment cost is high are solved, and the purposes of low-delay communication, high reliability and low risk are achieved.

The invention relates to the technical field of fault positioning, and discloses a power distribution network grounding fault positioning method and system. The method comprises the following steps: acquiring a fault initial transient section signal, an arcing continuous section signal and an arc quenching recovery section signal of the power distribution network, and executing spectral analysis to obtain a fault feature matrix; performing zero-sequence transient frequency spectrum reconstruction on the fault feature matrix to obtain reconstructed frequency spectrum features and time-varying frequency spectrum features; calculating a transient characteristic index and a fault type based on the reconstructed spectrum characteristic and the time-varying spectrum characteristic; performing fault positioning calculation on the power distribution network according to the transient characteristic index and the fault type to obtain an initial fault point position; and performing transient fingerprint matching and probability density accumulation compensation on the initial fault point position to obtain a target fault point position. According to the method, high-precision characterization of the rapid change signal in the short time window is realized, interference of the compensation state on fault positioning is eliminated, and the grounding fault positioning accuracy of the power distribution network is improved.

The invention relates to the technical field of power system monitoring, and discloses a wire and cable fault early warning system based on intelligent monitoring, which comprises a data sensing module, a multi-mode fusion module, a characteristic evolution module, an abnormal early warning module and a dynamic optimization module. The data sensing module collects multi-source heterogeneous data, the multi-modal fusion module processes the data to generate a spatial-temporal feature matrix, the feature evolution module extracts cable degradation features, the abnormity early warning module performs fault early warning based on the cable degradation features, and the dynamic optimization module optimizes system parameters by using federal learning. In addition, the system also comprises a digital twin mapping and topology analysis module for assisting decision making and enhancing positioning. According to the invention, real-time monitoring, accurate fault early warning and system performance optimization of the operation state of the wire and cable are realized, the stability and reliability of power transmission are improved, and the system has the advantages of comprehensive multi-source data acquisition, efficient data processing, accurate early warning, data privacy protection and the like.

The invention relates to the technical field of data processing, and discloses a cross-border e-commerce commodity recommendation system and method based on multi-source data fusion. The system comprises an acquisition module for performing data acquisition to obtain a cross-border unified data warehouse and federal learning cooperation data; the classification module performs text classification processing to obtain a user preference analysis result and an interpretable attention mark; a quantization module carries out quantization processing to obtain a cross-border selection feature matrix; the fusion module carries out weighted fusion processing to obtain a basic comprehensive score and a weight convergence detection result; the evaluation module performs risk evaluation processing to obtain risk probability distribution and cross-border compliance evaluation results; and the sorting module carries out real-time processing through lightweight preprocessing and a flow-type calculation pipeline to obtain a comprehensive score sorting list of cross-border selected products. The problem that a traditional cross-border e-commerce product selection method cannot effectively integrate multi-source heterogeneous data and cannot analyze user feedback text semantic information is solved.

The invention belongs to the technical field of unmanned aerial vehicle intelligent navigation, and discloses a low-altitude economic unmanned aerial vehicle data processing method and system based on a large model, and the method comprises the steps: calculating the scene adaptation weight of multi-modal data through a dynamic attention mechanism based on a space-time alignment feature package, and carrying out the fusion to generate a multi-modal joint feature matrix; based on the multi-modal joint feature matrix, constructing a three-dimensional topological model of an urban airspace, predicting a dynamic obstacle trajectory in combination with a space-time diagram neural network, and generating a hierarchical navigation instruction set; the unmanned aerial vehicle executes a flight instruction according to the hierarchical navigation instruction set, and generates a flight state monitoring log by collecting data in flight of the unmanned aerial vehicle in real time; according to the method, the multi-modal joint feature matrix is generated through environment parameter driving weight distribution, and the complex scene sensing precision is remarkably improved.

The invention provides an image acquisition card multi-mode identification method and system based on intelligent security and protection. The method comprises the following steps: acquiring a multi-mode original data stream according to a global clock signal of an image acquisition card; performing space-time calibration on the multi-modal original data stream to generate a synchronous multi-modal data queue; extracting a multi-modal feature tensor of the synchronous multi-modal data queue through data preprocessing; performing hierarchical attention fusion on the multi-modal feature tensor to generate a fusion feature matrix; performing channel pruning on the fusion feature matrix through a lightweight convolutional neural network, and constructing a target detection model; and determining a detection result corresponding to the multi-modal original data stream according to the target detection model. Through the synergistic effect of a global clock signal and a dynamic space-time calibration algorithm, a time synchronization and space alignment compensation mechanism is constructed in a multi-modal data stream, and the problem of multi-modal information complementary advantage attenuation caused by space-time mismatch is effectively solved.

The invention relates to the technical field of data processing, and discloses a comprehensive evaluation method and system for the health degree of a distribution network cable. The method comprises the following steps: collecting cable joint multi-source monitoring signals, normalizing the monitoring signals to obtain a degradation degree feature vector, correcting multi-physics field coupling model parameters, obtaining a recessive degradation index through finite element calculation to obtain an enhanced feature vector, and performing time-frequency domain decomposition to extract multi-scale feature parameters to obtain a comprehensive feature matrix; a double attention mechanism calculates a feature weight and a time sequence correlation degree to obtain a deterioration trend prediction value, and fuzzy integral is fused with a multi-classifier output probability to obtain a health degree evaluation grade and an early warning result. According to the invention, the early defect identification accuracy and the degradation trend prediction precision are improved.

The invention relates to a railway traction substation state monitoring method and system, equipment and a medium. The monitoring method comprises the following steps: acquiring real-time monitoring data of the equipment in a railway traction substation; performing data preprocessing on the real-time monitoring data to obtain a preprocessed monitoring data set, performing protocol identification, and converting heterogeneous data in the monitoring data set into structured data according to a preset protocol template library; based on the structured data, time-frequency domain characteristic parameters of the equipment are extracted, and a multi-dimensional characteristic matrix is constructed; inputting the multi-dimensional feature matrix into a pre-trained hybrid diagnosis model, and generating an equipment health degree score and a fault probability value; according to the health degree score and the fault probability value, generating an early warning instruction in combination with a dynamic threshold algorithm; and generating a priority maintenance strategy through a maintenance strategy optimization model based on the early warning instruction and the equipment maintenance resource constraint condition. According to the invention, accurate perception and intelligent decision making of the equipment state are realized in a multi-source heterogeneous data environment.

The invention relates to an electromechanical fault prediction and diagnosis method and system based on big data, and the method comprises the steps: collecting the operation state data of electromechanical equipment in real time, and synchronously obtaining historical associated data; performing dynamic feature extraction on the operation state data and the historical associated data, constructing a sliding mean value feature matrix, and calculating dynamic weights of feature parameters; generating a fusion weight coefficient according to the dynamic weight and a preset fault threshold interval; extracting a distribution density curve of a historical fault occurrence probability, and calculating a dynamic threshold value; performing weighted reconstruction on the sliding mean feature matrix based on the fusion weight coefficient, and outputting a fault type and an occurrence probability through a pre-trained lightweight residual neural network model; and when the fault occurrence probability exceeds a dynamic threshold value adjusted based on a historical fault occurrence probability distribution density curve, generating an electromechanical fault diagnosis result so as to realize the purposes of real-time monitoring of the operation state of the electromechanical equipment and accurate fault prediction and diagnosis.

The invention provides a coal mine goaf coal spontaneous combustion risk intelligent monitoring and early warning method and system, and relates to the technical field of coal mine safety management. A dot-matrix wireless sensor network is deployed in a target area, temperature and gas concentration data are collected in real time, and data processing is carried out through edge calculation. A dynamic characteristic matrix is generated based on transfer learning and a space-time diagram convolutional network, a comprehensive risk index is calculated, four early warning levels are divided, efficient and accurate coal spontaneous combustion risk monitoring and early warning are achieved, and an innovative solution is provided for mine safety management.

The invention discloses a carbon emission prediction and optimization method, and the method comprises the steps: obtaining multi-source heterogeneous data including historical carbon emission data, meteorological data, economic indexes, energy consumption data, and Internet of Things sensor data, and constructing a three-dimensional feature matrix through employing an improved spatial-temporal feature extraction algorithm; based on a mixed architecture of a graph neural network GNN and a long and short term memory network LSTM, a prediction model is established in combination with an attention mechanism, and training is performed through an adaptive learning rate optimization algorithm; a prediction result is input into an improved NSGA-III algorithm, and three targets of total carbon emission, economic cost and social benefits are optimized at the same time; and establishing a feedback closed loop through reinforcement learning RL, and updating the model and the strategy on line according to real-time monitoring data. According to the method, multiple advanced technologies such as accurate data processing, dynamic prediction, multi-objective optimization and cross-domain collaboration are integrated, and a comprehensive and effective solution is provided for carbon emission management.

The invention discloses an access anomaly analysis method and system based on multi-dimensional features and user behaviors, and relates to the technical field of dynamic access anomaly detection, and the method comprises the steps: based on a dynamic hypergraph structure, extracting high-order correlation features of the user behaviors through a multilayer hypergraph convolutional network, and generating a high-order feature matrix; based on the high-order feature matrix, generating an authority approval threshold through a causal reinforcement learning framework, constructing a user behavior causal graph to generate strategy network parameters, and storing the strategy network parameters to distributed nodes of a regional data center; based on strategy network parameters stored by distributed nodes, security multi-party computing is adopted, cross-node collaborative optimization is carried out, and global defense strategy parameters are generated through a security aggregation algorithm. According to the method, security multi-party computing is adopted, cross-node collaborative optimization is performed, and the global defense strategy parameters are generated in combination with homomorphic encryption and a block chain fragmentation technology, so that the collaboration efficiency and strategy consistency among distributed nodes are improved on the premise of ensuring data privacy.

The invention discloses a power distribution network feeder terminal FTU intelligent monitoring method based on artificial intelligence. The method comprises the following steps that multi-dimensional operation parameter data of a power distribution network feeder terminal FTU are collected in real time through multiple sensors; carrying out dynamic preprocessing on the multi-dimensional operation parameter data to generate a standardized feature matrix, and matching the dimension of the standardized feature matrix with a preset multi-modal deep learning model input layer; inputting the standardized feature matrix into a pre-trained multi-modal deep learning model to generate a real-time monitoring result; wherein the real-time monitoring result comprises fault probability distribution and an equipment health degree score. Compared with the prior art, the method has the following advantages and effects that the data processing precision can be improved, and fault prediction and equipment health assessment can be optimized through the multi-modal deep learning model, so that more intelligent management of the power distribution network is realized.

The invention relates to the technical field of town sewage pipe network detection, and discloses a town sewage pipe network leakage detection system and method. The system comprises a data acquisition module which uses a multi-source sensor to acquire real-time operation data such as pipe network pressure, flow and the like; the feature extraction module extracts spatio-temporal features based on the multi-scale convolutional neural network, and generates a pipe network state feature matrix; the anomaly detection module inputs the feature matrix into a pre-training model and marks a potential leakage area; the optimization analysis module constructs a multi-constraint dynamic optimization model, and pipe network pressure parameters are optimized by using an adaptive particle swarm algorithm; the hierarchical execution module generates a global regulation and control sequence, dynamically matches local pressure parameters and adjusts the valve opening and the pump station power through a decision layer, a region coordination layer and an execution layer. The system and the method are accurate in detection and reasonable in regulation and control optimization, leakage risks can be effectively reduced, the operation management level of a pipe network is improved, and water resource waste and environmental pollution are reduced.

The invention discloses a self-adaptive nanosecond driving protection method for a high-voltage IGBT power module. The method comprises the steps that data such as high-voltage side voltage, collector current, grid voltage and junction temperature are collected and preprocessed, and standardized feature vectors are obtained; extracting statistical features, frequency domain features and trend features to obtain a comprehensive feature vector; density clustering is carried out based on historical fault data, and an extreme value model is established by using generalized Pareto distribution to generate an optimized threshold value; constructing a time sequence feature matrix, predicting a fault probability by adopting a time sequence convolutional network and a long-short term memory network, and generating a protection strategy parameter; and generating an optimal driving waveform and a control signal, and updating parameters according to an actual response effect. According to the invention, predictive protection and nanosecond-level precise control of faults are realized, and the reliability and performance of the high-voltage IGBT module are remarkably improved.