5111 results about "Prediction system" patented technology

The invention relates to the technical field of power equipment, and discloses a digital twin operation monitoring system for power equipment, which comprises a data sensing and acquisition system for acquiring key operation parameters of temperature, current, voltage, partial discharge, vibration and humidity of the power equipment in real time, and performing multi-dimensional data acquisition through a sensor and a data transmission module; the state evaluation and prediction system is used for performing equipment health evaluation and residual life prediction by using a prediction model LSTM based on the collected data, and updating a prediction result in real time; provided is a digital twin modeling system. Through the combination of edge calculation, an LSTM model and a digital twinning technology, the precision and real-time performance of health management of power equipment are improved, data quality is optimized through edge calculation, the LSTM model captures an equipment degradation trend, virtual-real fusion is realized through digital twinning, and accurate monitoring and early warning of the health state of the equipment are ensured, so that intelligent operation and maintenance decisions are optimized, the failure rate is reduced, and the safety of power equipment health management is improved. The equipment life is prolonged.

The invention discloses an intelligent analysis and remote control algorithm based on digital twinning. The intelligent analysis and remote control algorithm comprises a digital twinning model building and dynamic updating module, an intelligent analysis algorithm module based on digital twinning, a self-adaptive remote control algorithm module and an algorithm process and implementation module. Through multi-dimensional modeling and data fusion, the precision of the digital twin model is improved, and the system state can be predicted more accurately. In combination with deep learning and a multi-objective optimization algorithm, intelligence and optimization of a control strategy are realized, and the operation efficiency and safety of the system are improved. Based on model predictive control and a distributed architecture, real-time and accurate remote control of a physical system is realized, and the dynamic adaptability of the system is enhanced. The algorithm framework has good generalization ability, can be suitable for different types of complex systems, and supports to adapt to new application scenarios through modular extension.

The invention relates to a power transmission line forest fire risk assessment early warning system and method, and belongs to the technical field of power transmission line risk assessment. The system comprises an environment DNA power transmission line forest fire risk assessment and early warning system, a quantum sensing power transmission line fire remote monitoring system, a power transmission line forest fire early warning emergency training system, a digital twin power transmission line fire risk dynamic management system, and a multi-node power transmission line fire early warning system of an edge AI and a low orbit satellite. The system comprises a thermal imaging and deep learning integrated power transmission line fire risk distribution prediction system, an AI-driven power load dynamic adjustment and fire prevention system, an intelligent fire-fighting equipment linked power transmission line fire spreading path prediction system and a distributed quantum computing power transmission line fire emergency simulation and optimization response system. According to the method, the inflammable vegetation density and biocenosis change can be dynamically analyzed through a plant and microorganism gene sequencing technology.

The invention discloses a pulmonary infectious disease prediction system based on multi-modal data fusion, and aims to improve the accuracy and reliability of disease prediction through comprehensive analysis of multi-dimensional data. The system comprises a data acquisition module, a feature extraction module, a dynamic fusion module and a prediction modeling module. The data acquisition module is used for acquiring clinical and medical images and environmental factor data of a patient in real time; the feature extraction module extracts feature vectors of all data sources; the dynamic fusion module performs adaptive weight distribution on the multi-modal features and fuses the multi-modal features into multi-modal feature representation; the prediction modeling module is used for carrying out dynamic change trend modeling on the multi-modal feature representation through time sequence analysis so as to generate a probability prediction value of suffering from pulmonary infectious diseases and a disease trend prediction result of the patient; the invention provides a comprehensive and efficient disease prediction method by utilizing the collaborative advantage of multi-modal data and combining an attention mechanism and time sequence analysis, and can be widely applied to the fields of medical diagnosis and public health management.

The invention relates to the technical field of artificial intelligence and medical diagnosis, in particular to a disease diagnosis prediction method and system based on a graph neural network. The disease diagnosis prediction method based on the graph neural network comprises the five steps of heterogeneous medical knowledge graph construction, adaptive node embedding representation, hierarchical graph attention network modeling, incremental learning dynamic graph updating and multi-dimensional feature input and result output. The invention discloses a disease diagnosis and prediction system based on a graph neural network. The system comprises a multi-source data acquisition module, a heterogeneous graph construction module, a self-adaptive embedding module, a graph network calculation engine, a dynamic updating module, a disease prediction module and a feedback optimization module. According to the method, the multi-modal heterogeneous knowledge graph is constructed to integrate the multi-dimensional data of the patient, and the hierarchical graph attention network and the dynamic incremental learning are combined, so that the accurate prediction of the disease risk and the visual explanation of the pathological association path are realized.

The invention discloses an adapter life prediction system based on digital twinning and a dynamic early warning method. The system comprises a multi-source data acquisition module, a digital twinning model construction module, a data coordination module, a life prediction module and a calibration module. According to the method, the adapter full-life-cycle digital twins are constructed, the limitation of one-way static analysis of a traditional life prediction technology is broken through, and dynamic health assessment under multi-dimensional data driving is achieved; a cross-dimension feature fusion and closed-loop calibration mechanism is innovatively proposed, and the industrial problems that multi-source asynchronous data is weak in relevance and sudden abnormal response lags behind are effectively solved; through the synergistic effect of the generative adversarial network and the attention model, the stability and credibility of a prediction result are remarkably improved under a complex working condition; the technology can be adapted to a harsh use environment of an industrial adapter, and quantifiable and traceable decision support is provided for intelligent operation and maintenance of power electronic equipment.

The invention discloses an automatic instrument fault prediction system and method based on big data analysis, and belongs to the technical field of fault detection. The system comprises the following modules: an intelligent data processing and normalizing module which collects multi-source heterogeneous data of an instrument and an environment sensor in real time and performs data cleaning, standardization and quality evaluation; the working condition environment characteristic analysis module is used for identifying the current working condition state and quantitatively evaluating the influence degree of environmental factors on instrument operation; the multi-monitoring-parameter coupling analysis module is used for calculating and analyzing the mutual influence relationship among the monitoring parameters of the instrument and evaluating the coupling strength and influence links among the monitoring parameters in real time; the dynamic threshold calculation module is used for dynamically calculating and adjusting an early warning threshold system of each monitoring parameter; the fault prediction decision module is used for comprehensively evaluating various monitoring parameters and calculating a fault risk probability; and the early warning output and feedback module optimizes early warning output through an intelligent filtering mechanism, and collects early warning effect feedback for continuous optimization.

The invention discloses an intelligent monitoring and early warning system and method for agricultural non-point source pollution, and relates to the technical field of environmental monitoring, accurate prediction of water pollutant concentration is realized through multi-source heterogeneous data acquisition and fusion, a dynamic attention mechanism and a PINN-Transformer coupling model, the system extracts data spatial and temporal characteristics by using an optimized Transformer model, and the method is applied to the intelligent monitoring and early warning of agricultural non-point source pollution. A water pollution diffusion physical constraint is embedded, it is ensured that a prediction result conforms to an actual hydrodynamic law, and based on high-precision spatial-temporal distribution data, an intelligent algorithm is adopted to track a pollution diffusion path and rapidly lock a pollution source; meanwhile, the cellular automaton model simulates pollution risk dynamic diffusion and assists regional risk assessment, the system also combines a block chain technology to carry out credible evidence storage on key monitoring data, and real-time data processing and early warning pushing are realized through a cloud edge collaborative architecture. And an efficient and reliable technical solution is provided for agricultural water environment management and pollution prevention and control.

The invention discloses a coal mine hole fracture seepage analysis and prediction system based on a deep learning model, and relates to the technical field of coal mine safety. Comprising a geological data acquisition and three-dimensional model construction module, a data preprocessing and cleaning module, a deep learning feature extraction and training module, a graph neural network optimization and topology modeling module and a seepage prediction and risk assessment module, geological exploration data and hydrogeological information of a coal mine area are obtained, and a three-dimensional geological model containing pores and fractures is constructed. Through the deep learning method combining the convolutional neural network and the graph neural network, the accuracy of coal mine fracture seepage analysis is remarkably improved. The CNN extracts local features, the GNN optimizes a global topological structure, and the seepage prediction precision is improved. The model not only can accurately predict a seepage path, but also can evaluate safety risks, help mine managers to identify hidden dangers in advance, reduce water damage and gas accumulation accidents, and optimize mine safety management.

The invention relates to the technical field of hydraulic engineering safety monitoring, and particularly discloses a hydraulic engineering potential safety hazard assessment and prediction system and method based on image recognition. A multi-scale convolutional neural network is combined with a three-dimensional point cloud registration technology to extract surface visual feature parameters, and adaptive time-frequency analysis and a wavelet packet reconstruction algorithm are used to extract physical feature parameters of internal concealment defects; constructing a dual machine learning framework, eliminating environmental interference through a deep residual network, analyzing a causal relationship between features based on a gating cycle unit, and screening a key feature parameter set; a Gaussian process regression model of an adaptive kernel function is used for dynamic risk prediction, risk abrupt change points are identified in combination with multi-scale wavelet transform, and finally a safety state score and a grading early warning signal are generated through a fuzzy comprehensive evaluation algorithm.

The invention discloses a user behavior prediction system and method based on multi-modal data fusion, and particularly relates to the field of user behavior prediction, and the system comprises a multi-modal data collection module, a preprocessing and feature extraction module, a cross-modal fusion module, a user behavior prediction module, and a model optimization and feedback module. According to the system, multi-dimensional original data such as visual sense, auditory sense, text, physiological signals and environment context of a user are acquired in real time through a multi-modal data acquisition module; then, deep networks such as ResNet, VGGish and BERT are adopted to extract high-dimensional feature vectors of all modals, and contribution weights of features of different modals are dynamically learned through an attention mechanism; and finally, based on a time sequence model of Transform and LSTM, analyzing fusion features, and outputting probability distribution of future behavior intentions. And parameter joint optimization and continuous learning are realized through a multi-objective loss function and end-to-end back propagation.

The invention relates to the technical field of wind turbine generator data analysis, and discloses an offshore wind turbine generator operation data analysis and prediction system. The system comprises a marine environment data integration module for collecting data to generate a multi-source time-space synchronization data set; the multi-modal feature fusion module is used for extracting cross-modal correlation features to generate a high-dimensional fusion feature tensor; the dynamic fault prediction module is used for constructing a two-way gating circulation network model to predict the degradation probability and the residual life of key components of the equipment; and the self-adaptive optimization control module is used for constructing a multi-target dynamic programming model to optimize a fan operation strategy. In addition, the system is further provided with a feedback correction module for correcting prediction model parameters, and a virtual sensor module based on a physical information neural network is used for monitoring tower stress and diagnosing sensor faults. According to the system, comprehensive monitoring, accurate fault prediction and optimal control of the offshore wind turbine generator are realized, the operation efficiency, reliability and safety of the wind turbine generator are effectively improved, and the operation and maintenance cost is reduced.

The invention relates to the technical field of project management and artificial intelligence, in particular to a project progress tracking and risk prediction system and method based on an improved knowledge graph and a multi-view graph neural network, and the system comprises a knowledge graph construction and dynamic updating module, an improved graph neural network analysis module, and an anomaly detection and risk prediction module. An intelligent early warning and visualization module; the method has the beneficial effects that anomaly detection is carried out through the GAN, possible delay and risk of a project are predicted, a risk early warning report is automatically generated, and optimization suggestions are provided for project managers. The system has the advantages of dynamically updating the knowledge graph in real time, improving risk prediction accuracy and enhancing project management perspectiveness and intelligent decision support, and is suitable for progress tracking and risk control in complex project management.

The invention discloses a distributed optical storage micro-grid control system based on a large model and an energy management method, and the system collects various data through a data collection module, captures a time sequence long-term dependence relation based on a self-attention mechanism through a large model prediction system, and predicts the photovoltaic power generation amount, the load demand and the energy storage charging and discharging demand. The network-forming inverter integration module dynamically adjusts the output power, the energy storage strategy and the interaction power of the power generation system according to a prediction result, the distributed control strategy module adopts a distributed consensus algorithm to realize information sharing and collaborative decision making, and the energy management module makes a multi-time scale plan and introduces an economic optimization model. The energy management method comprises the steps of data collection, real-time monitoring, prediction modeling, plan making, distributed control, economic optimization, system monitoring, fault processing and the like. The method can improve the new energy utilization rate, the electric energy quality and the system stability, adapts to the change of environmental factors, and maximizes the economic and environmental benefits of the micro-grid.

The invention relates to the technical field of agricultural meteorological prediction, in particular to an agricultural meteorological disaster time sequence prediction system and method based on multi-modal data fusion, and the method comprises the steps: collecting and preprocessing agricultural meteorological disaster related data, constructing a dynamic semantic association graph, carrying out the multi-layer feature abstraction processing, and generating a semantic enhancement feature vector; the dual-branch prediction network processes time sequence dependence and local mode features, multi-granularity attention processing identifies key feature information, multi-scale feature fusion extracts different time scale feature information, and cascade fusion is carried out; the multi-target optimization module carries out model training based on the comprehensive feature representation and optimizes a plurality of targets; the multi-time scale prediction output module generates short-term accurate prediction, medium-term trend prediction and long-term risk assessment results, and provides prediction confidence, error range and risk level information; a dynamic semantic association graph and a multi-layer feature mapping mechanism are constructed, and deep fusion of multi-modal data on the semantic level is achieved.

The invention discloses an agricultural product quality abnormity propagation path prediction system and method based on a graph neural network. The method comprises the following steps: S1, collecting a quality data set in an agricultural product supply chain; s2, forming a feature vector used for image neural network input; s3, constructing an agricultural product supply chain quality anomaly propagation path prediction model based on the graph neural network; s4, inputting the preprocessed historical quality data set into the abnormal propagation path prediction model; s5, monitoring the quality state of each node in real time; s6, dynamically updating the weights of the edges in the graph based on an edge weight adaptive adjustment mechanism; s7, the abnormal propagation path prediction model recalculates the optimal propagation path with abnormal quality and generates a prediction path; s8, identifying potential risk nodes according to the re-calculated abnormal propagation path; and S9, the system updates the abnormal propagation path prediction model through real-time data. According to the invention, accurate prediction and real-time adjustment of the abnormal quality propagation path in the agricultural product supply chain network are realized.

The invention relates to the technical field of exception prediction, in particular to a storage cabinet exception trend prediction system based on time series data analysis, which comprises a state monitoring module, an interval sensing module, a path reconstruction module, a symptom activation module and an evolution prediction module. According to the method, the state vectors including the temperature, the voltage, the current and the door lock state are constructed and combined with the timestamp information to form the time sequence data sequence, and the dynamic expression mode of state change is established; a jump characteristic is analyzed by using a ratio of a time interval to a state change amplitude, a short-time disturbance path and a trend evolution path are distinguished by combining a jump rate statistical index, and an evolution activation signal is identified based on trend maintenance and non-fallback characteristics. On the basis, a neural network structure with long-time dependent learning ability is introduced to capture an aperiodic thermal anomaly trend in a state sequence, and the accuracy and timeliness of anomaly recognition are improved through multi-dimensional parameter cooperative processing and path construction logic.

The invention provides an intelligent operation and maintenance and fault prediction system and method for a data center, and the system comprises a self-healing decision module, a closed-loop verification module, an automatic execution module, a monitoring feedback module, and a strategy optimization module, and is characterized in that the self-healing decision module is used for determining the type and severity of a fault. The self-healing decision tree is generated according to the fault type and severity in combination with expert knowledge and historical data, the self-healing accuracy and safety are improved, the self-healing decision is executed through the automatic execution module, manual intervention is reduced, the operation and maintenance efficiency is improved, the self-healing process is monitored through the monitoring feedback module, feedback information is collected, and the operation and maintenance efficiency is improved. The self-healing effect is evaluated, the correctness and effectiveness of self-healing operation are ensured, a self-healing strategy is continuously optimized through a strategy optimization module according to feedback information and new data, the self-adaptive capacity and long-term performance of the system are improved, the system can continuously learn and adapt to a new fault mode, and therefore the self-healing capacity is continuously improved.

This application relates to the field of home kitchens, and discloses a social public opinion risk intelligent prediction system, including: a data collection module, which is used to collect text, image and video data from multi-source heterogeneous data sources in real time, including social media, news websites, forums, blogs and self-media; a data preprocessing module, which is used to clean, normalize, segment and tag the collected data to form a structured data set; an advanced natural language processing module, which is used to use the pre-trained Transformer model and graph neural network to perform deep semantic analysis on the pre-processed text data, and extract entity, relationship and context semantic features. By integrating advanced natural language processing, deep learning and multimodal data fusion technology, the system can monitor and accurately predict social public opinion in real time, quickly capture emergencies and public opinion changes, and deeply analyze complex semantics and emotional tendencies.

An intelligent monitoring, inversion and prediction system for a transient process of pumped storage units is provided, including: an equipment layer including sensors for obtaining hydraulic, mechanical and electrical operation data of the pumped storage units in real time, a data layer including a real-time database, a relational database, an expert knowledge base and a file system and used for storing hydraulic, mechanical and electrical operation parameters collected by the equipment layer in different levels, a twin layer including a modeling and an application of a digital twin model and used for a three-dimensional modeling and a key information visual display of a pumped storage system with BIM technology and a service layer displaying one-dimensional time domain parameters and designing and visualizing front-end interfaces for six functions: operation optimization, online monitoring, inversion, dynamic prediction, health assessment and fault warning.

The invention relates to the technical field of risk analysis, in particular to a dangerous chemical transportation risk prediction system and method based on big data analysis, and the system comprises a multi-dimensional data acquisition module which is used for collecting human-machine-ring-pipe four-dimensional data in the liquid ammonia water transportation process, and carrying out the standardization processing and time-space synchronization, and obtaining a multi-source heterogeneous data set; the risk level acquisition module is used for constructing a man-machine-ring-management collaborative risk assessment model and analyzing the multi-source heterogeneous data set to obtain a risk assessment result; the human-machine-environment-management collaborative risk assessment model comprises a human-machine interaction key node identification layer, a management behavior-equipment response association analysis layer, an environment-material interaction dynamic risk assessment layer and a multi-dimensional factor risk cascade assessment layer; and the emergency disposal scheme acquisition module constructs an emergency disposal scheme intelligent recommendation model to perform grading and classification analysis on the risk assessment result, generates a multi-level risk early warning and emergency disposal scheme, and optimizes the emergency disposal scheme.

The invention relates to the technical field of unmanned aerial vehicles, and discloses an unmanned aerial vehicle battery endurance flight capability prediction system, which comprises a multi-dimensional data acquisition module, a feature mapping module, a prediction module, an optimization module and a feedback optimization module, and can be additionally provided with an early warning module. The multi-dimensional data acquisition module acquires battery data and cleans the battery data to generate standardized data; the feature mapping module maps the data to a feature space, and generates a feature sequence cluster containing a multi-dimensional association relationship by using a time sequence segmentation algorithm; the prediction module divides prediction intervals based on a support vector machine algorithm and extracts prediction indexes; the optimization module generates an endurance prediction strategy by predicting and optimizing the network model; and the feedback optimization module performs multi-source data fusion optimization and outputs a prediction instruction. The early warning module can associate the prediction instruction with the battery health degree, output a grading early warning signal and trigger a response mechanism.

The invention provides a power equipment fault prediction method and system. The method comprises the steps of performing multi-modal fusion on acquired historical data of power equipment to obtain feature vectors of all modals; fusing the feature vectors by adopting principal component analysis to obtain fusion vectors; constructing the fusion vector into a graph to obtain a space-time diagram; learning spatial features of the spatial information extraction data by using a space-time diagram in a graph convolution mode to obtain a spatial feature representation vector; fusing the spatial feature representation vector in combination with the attention weight to generate an attention fusion vector; learning time sequence information by using a gating loop unit to obtain a time characteristic representation vector; performing weighted feature fusion on the spatial feature representation vector and the time feature representation vector to obtain a space-time fusion representation vector; and processing the space-time fusion representation vector by using a multi-layer perceptron, and outputting power equipment fault prediction. Based on the method, the invention further provides a power equipment fault prediction system. According to the invention, the accuracy and robustness of power equipment fault prediction are improved.

The invention provides a prospecting prediction system based on multi-scale big data fusion, and the system comprises a geological big data management module and a big data mining analysis module which are respectively used for carrying out the management and mining analysis of geological, geophysical, geochemical, remote sensing and mineral data; the multi-element extraction module is used for carrying out geological element extraction on the mining analysis result output by the big data mining analysis module; the multi-scale grid calculation module is used for carrying out subdivision and assignment on the geological elements through a multi-scale multi-element grid technology; the intelligent prospecting prediction module is used for compiling a result map; the intelligent prospecting evaluation module is used for compiling an optimal decision diagram; the auxiliary decision-making module generates a prospecting result report; and the prospecting dispatching command module is used for synchronously updating the data in the prospecting dispatching command module. By integrating the prospecting prediction basic data set, the knowledge base, the model base and the algorithm base, flexible scheduling and convenient use of various data, knowledge, models and algorithm tools are achieved.

The invention discloses an electrical equipment life prediction system and method based on deep learning, and belongs to the field of electrical equipment life prediction, and the method comprises the steps: constructing a data set, and carrying out the preprocessing; performing convolution operation on the time sequence data in the data set, extracting local features to obtain a convolutional neural network, capturing a long and short term dependency relationship of the time sequence data in the data set by using LSTM to obtain a recurrent neural network, and fusing output features of the convolutional neural network and the recurrent neural network to obtain comprehensive feature representation; constructing a deep learning model based on the fusion of a convolutional neural network and a recurrent neural network, adding an attention mechanism, adopting a mean square error as a loss function, optimizing model parameters, carrying out model training by utilizing a training data set, evaluating model performance by adopting a verification set, and adjusting hyper-parameters; and inputting real-time data into the trained model, predicting the residual life of the electrical equipment, and designing an online learning mechanism. According to the invention, the accuracy and real-time performance of prediction are improved.

The invention relates to the technical field of mining industry, and discloses an intelligent prediction method for gold ore beneficiation process parameters based on cloud and edge fusion, which realizes space-time correlation modeling of beneficiation process parameters and accurately depicts dynamic interaction influence among equipment. The cloud edge collaborative architecture considers global optimization and real-time response requirements, and the prediction stability under complex working conditions is effectively improved. The introduction of physical constraints enhances the applicability of the model in an actual production environment, a bidirectional feedback mechanism ensures the adaptive ability of the system in a dynamic change environment, and through the joint reasoning of a knowledge graph and a neural network, the consistency of a prediction result and a process principle is enhanced, and the risk of misjudgment under an abnormal working condition is reduced; the man-machine cooperation mechanism significantly improves the labeling efficiency of high-value samples, shortens the model iteration period, and ensures the continuous optimization capability of the prediction system in the actual production environment.

The invention provides a water pump residual life prediction method based on a large model, and the method comprises the following steps: S1, collecting the multi-source heterogeneous data of the operation of a water pump in real time through a vibration sensor, a temperature sensor, a pressure sensor and a monitoring unit, the temperature sensor monitors temperature gradient changes of the bearing and the sealing cavity, the pressure sensor records inlet and outlet pressure fluctuation characteristics, and the monitoring unit extracts three-phase current harmonic components of the motor; s2, carrying out lightweight preprocessing on the multi-source heterogeneous original sensing data at an edge computing node, wherein the lightweight preprocessing comprises vibration signal noise reduction processing based on wavelet transform, temperature and pressure data calibration normalization of load segments according to working conditions, and transient abnormal data flow filtering through a sliding time window; and S3, inputting the preprocessed data stream into a cloud large model platform, and analyzing the long-period dependency relationship of the vibration signals through a Transform encoder in a time sequence feature extraction module.

The invention relates to the technical field of equipment fault prediction, in particular to a coal mine equipment fault prediction system and method based on big data, and the method comprises the following steps: collecting the thickness of a biological membrane on the surface of equipment, synchronously collecting the vibration acceleration of the equipment and the environmental hydrogen sulfide concentration, and constructing a corrosion multi-mode data set; performing electrochemical noise separation on the corrosion multi-modal data set, and constructing a corrosion activity fingerprint spectrum; decoupling a coupling effect of mechanical vibration and microbial corrosion, and generating a corrosion-fatigue damage correlation map; and constructing a dual-channel life prediction model based on the corrosion-fatigue damage correlation map, and setting an early warning mechanism. Microbial corrosion characteristic current is accurately extracted through wavelet packet decomposition and wavelet entropy analysis, a corrosion-vibration heterogeneous graph is established by adopting a graph attention network, corrosion-induced structural vibration enhancement effect analysis is realized, and compared with a traditional prediction method based on a single signal, the method has the advantages that the method is simple and convenient to operate. And the problems of limited local corrosion influence range, unstable vibration response and the like are effectively solved.

The invention relates to the technical field of new energy automobile electric control, and discloses a new energy automobile electric control fault prediction system. The system comprises a real-time data acquisition module, a dynamic fault prediction model construction module, a fault difference calculation module, a multi-dimensional anomaly analysis module, a fault probability positioning module and a self-adaptive maintenance strategy module. The real-time data acquisition module acquires sensor data of the electric control system in real time; the dynamic fault prediction model construction module constructs a dynamic fault prediction model based on historical fault data; the fault difference calculation module inputs real-time data into the model and outputs theoretical fault indexes; the multi-dimensional anomaly analysis module compares the theory with the actually measured fault indexes to generate an anomaly difference matrix; the fault probability positioning module inputs the matrix into a space correlation network to generate a fault probability distribution diagram; and the adaptive maintenance strategy module configures maintenance parameters according to the distribution diagram. According to the system, the fault prediction accuracy and real-time performance can be improved, and stable operation of the new energy automobile electric control system is guaranteed.

The invention relates to the technical field of industrial automatic detection and intelligent operation and maintenance, in particular to a machine vision and big data fused linear slide rail defect identification and prediction system, which comprises a machine vision acquisition module, a time sequence feature extraction module, a big data analysis platform, a defect prediction and decision module and the like. The machine vision acquisition module acquires a slide rail image through an annular camera array and a multi-angle light source, and the acquisition quality is ensured through motion compensation; the time sequence feature extraction module utilizes a space-time attention model to mine defect features; the big data analysis platform is associated with the multi-source data to construct a defect evolution graph; the defect prediction and decision-making module outputs residual life prediction and maintenance instructions based on double-target reinforcement learning, and the dynamic model updating unit optimizes the model according to errors. According to the method, high-precision identification, prospective prediction and intelligent decision making of the defects of the linear sliding rail are realized, the equipment reliability can be effectively improved, the operation and maintenance cost is reduced, and the method is suitable for a complex industrial environment and has wide application value.