2089 results about "Multidimensional data" patented technology

The invention provides a supply chain multi-dimensional fluctuation prediction and inventory optimization method based on artificial intelligence, and relates to the technical field of supply chain management, and the method comprises the steps: obtaining multi-dimensional data, carrying out the preprocessing of the multi-dimensional data, generating a standardized time series data set and a knowledge graph, carrying out the multi-scale decomposition through wavelet transformation, and extracting fluctuation features; and constructing a state matrix by combining a graph attention network and a multi-head self-attention mechanism, predicting a risk conduction path based on historical fluctuation pattern matching and a graph convolutional network, and generating a risk prevention and control scheme. According to the invention, the fluctuation characteristics and the risk conduction path in the supply chain system can be accurately identified, and the early warning accuracy and the response timeliness are improved.

The invention discloses an electric bicycle lithium battery data real-time monitoring and early warning system and method, and relates to the technical field of battery management, and the system comprises a multi-dimensional data collection module which is used for obtaining a multi-source heterogeneous data set of a lithium battery system; the collaborative feature extraction module is used for generating a comprehensive evaluation parameter set; the dynamic threshold generation module is used for constructing a self-adaptive early warning boundary model according to the comprehensive evaluation parameter set; the intelligent decision module is used for generating a hierarchical control instruction set based on a multi-objective optimization algorithm; and the cloud collaboration module is used for synchronizing the hierarchical control instruction set to the edge computing node and the cloud management platform, and triggering a multi-level linkage protection mechanism based on the game theory when the thermal runaway risk index is detected to exceed a first dynamic threshold value. According to the electric bicycle lithium battery data real-time monitoring and early warning system and method provided by the invention, the safety and reliability of a battery system are improved.

The invention discloses an artificial intelligence-based advertisement effect evaluation method and system, and the method comprises the steps: synchronously obtaining multi-source data containing a user behavior data flow and an advertisement putting index flow through a distributed collection engine, and generating a time-space synchronous multi-dimensional data cube; performing feature decoupling on the multi-dimensional data cube, and outputting a dynamic feature topology network with a weight; inputting the dynamic feature topology network into an adversarial training framework, and finally outputting an advertisement conversion probability space-time distribution diagram; based on the advertisement conversion probability space-time distribution map, deploying an attribution calculation unit for real-time feedback, and generating an incremental attribution map with a confidence interval; and inputting the incremental attribution atlas into a strategy generation adversarial network, and outputting an adversarial optimization advertisement putting strategy set meeting Pareto optimum. According to the embodiment of the invention, the accuracy and real-time performance of advertisement effect evaluation can be improved.

The invention relates to the technical field of state monitoring of wind generating sets, and discloses a fault early warning and service life prediction method and system for a wind generating set, and the method comprises the steps: obtaining first state data, second state data and image data of a target wind generating set, and forming multi-dimensional data; fusing the multi-dimensional data by using a multi-modal fusion model to obtain multi-modal data fusion features of the target wind generating set; and performing fault early warning and / or life prediction on the target wind generating set based on the multi-modal data fusion features. By integrating the multi-modal data, the problem that fault features are difficult to comprehensively capture by a single data source is solved, fault early warning and service life prediction are performed by utilizing the multi-modal data fusion features, the false report and missing report rate of faults is reduced, accurate quantitative prediction of the remaining service life of the wind generating set is realized in combination with the data driving model, and the prediction efficiency is improved. By improving the accuracy of fault early warning and life prediction, the wind generating set is effectively operated and maintained in advance.

The invention provides a tunnel construction safety monitoring and early warning method and system based on multi-dimensional data fusion, and relates to the technical field of construction safety early warning, and the method comprises the steps: obtaining first information which comprises tunnel construction parameters and image information; performing space-time fusion processing and redundancy reduction processing on the first information to generate a four-dimensional hypergraph tensor space of tunnel construction; performing cross-scale analysis and topology extraction on the four-dimensional hypergraph tensor space, and constructing a dynamic heterogeneous graph network; performing space-time diagram convolution processing on the dynamic heterogeneous graph network, and generating a composite risk manifold based on a result obtained by processing and a multi-head attention mechanism; coupling processing is carried out according to the composite risk manifold, stability analysis and critical state judgment are carried out based on the risk phase change hypersurface obtained through processing, and real-time early warning is carried out based on a dynamic early warning boundary of tunnel construction obtained through judgment. According to the invention, the risk identification accuracy and the early warning response timeliness in the tunnel construction environment are improved.

The invention discloses an intelligent fault detection system and method for a wind driven generator, and relates to the technical field of deep learning, and the method comprises the steps: collecting multi-dimensional data, and carrying out the preprocessing; extracting multi-dimensional features from the preprocessed multi-dimensional data, fusing the multi-dimensional features through a graph neural network and short-time Fourier transform, and generating a time-frequency domain signal; key features are extracted from the time-frequency domain signals, and fault features of the operation state of the wind driven generator are detected through a support vector machine model; based on the fault features, a fusion neural network model is constructed, fault prediction is generated, a diagnosis report is output, and a maintenance suggestion is provided; according to the method, the neural network model is fused, the convolutional neural network and the long-short-term memory network are combined, the spatial features and the time sequence data are extracted, and multi-dimensional characterization of the fault features of the wind driven generator is achieved.

The invention discloses a deep well rock burst early warning system and method based on multi-dimensional monitoring, and belongs to the technical field of deep well rock burst early warning. According to the method, multi-dimensional data such as stress, strain and microseism are collected through a monitoring network, and an aligned multi-source data set is obtained through space-time registration; after dynamic noise suppression processing matched with physical characteristics is adopted, strong correlation characteristics are screened through mutual information entropy; frequency domain, time domain and time-frequency domain features are extracted through principal component extraction and phase-space reconstruction, and a multi-dimensional state space data set is formed; and inputting the prediction model to obtain a danger level and trigger a corresponding early warning signal, and finally dynamically adjusting the monitoring network layout and prevention and control measures based on the early warning signal. According to the method, the early warning accuracy and real-time performance are improved, and effective technical support is provided for deep well rock burst prevention and control.

The invention provides an HPLC (High Performance Liquid Chromatography) station area cooperative communication method based on clock synchronization, which comprises the following steps: step 1, multi-dimensional data acquisition and broadcasting: each concentrator periodically acquires clock precision, stability and reliability data, and sends a beacon frame containing a timestamp and a health degree score through a power line carrier broadcasting channel; step 2, dynamically calculating the index distinction degree by using an entropy weight method, adaptively allocating weights, and electing an optimal main CCO in real time; step 3, dual-mode PID cooperative control: the main CCO adopts a dual-mode switching strategy of a dynamic PID algorithm and a chaotic fractional order PID algorithm; 4, monitoring a synchronization state in real time by constructing a clock check network, and optimizing a fault isolation strategy in combination with weighted scoring to realize that the state switching time of the master node and the slave node is less than or equal to 50ms; cooperative communication is realized on the basis of completing synchronization of the concentrators CCO in the whole network. Redundant message transmission is reduced through a dynamic parameter resynchronization mechanism, and the utilization rate of network resources is optimized.

The invention relates to the field of wastewater treatment, and provides an industrial wastewater membrane process intelligent optimization method and system based on multi-dimensional data modeling. The method comprises the following steps: acquiring wastewater quality parameters of a target scene and a production line equipment state in real time through a multi-dimensional sensor network, and constructing a digital twinborn model of a wastewater treatment system in the target scene; the membrane surface pollution type of each node in the wastewater treatment system is identified through a digital twin model, and the inner membrane pollution evolution condition in the future preset duration is predicted; a multi-strategy evolutionary reinforcement learning algorithm is adopted, the wastewater quality parameters, the production line equipment state and the membrane pollution prediction result are combined, multi-target dynamic optimization is conducted on a wastewater treatment system through a multi-agent collaborative decision-making algorithm, an optimization strategy is generated based on the multi-target dynamic optimization result, and membrane process operation parameters and production control parameters are dynamically adjusted. According to the method, real-time intelligent adjustment of membrane process operation can be realized, the stability of membrane process operation is guaranteed, and the treatment efficiency of membrane process operation is improved.

The invention relates to the technical field of smart energy management, and discloses an energy consumption optimization method and system based on multi-dimensional data fusion, and the method comprises the steps: collecting energy consumption data, environment data and equipment operation state data of a target region in real time, carrying out the data preprocessing, and generating a sample data set; based on space-time correlation analysis, integrating the sample data set into an energy consumption feature matrix by adopting a multi-dimensional data fusion strategy combining feature-level fusion and decision-level fusion; inputting into an energy consumption prediction model constructed based on a deep learning algorithm, and outputting an energy consumption prediction result of the target area in a future time period; and according to an energy consumption prediction result, generating an optimal energy consumption control strategy through a dynamic programming algorithm. The dynamic optimal energy consumption control strategy is generated through linkage of the deep learning algorithm and the dynamic programming algorithm, and the problems that in the prior art, energy consumption optimization precision is insufficient, and adaptability is poor are solved.

The invention discloses a building engineering quality monitoring system, and relates to the technical field of building engineering monitoring, the building engineering quality monitoring system comprises a collection module, an analysis module, a monitoring module and an early warning module, the collection module collects first quality monitoring data of building engineering and obtains historical monitoring data, and transmits the data to the analysis module; performing feature extraction and data analysis on the first quality monitoring data to obtain second quality monitoring data, storing historical monitoring data and presetting quality standard data, transmitting the second quality monitoring data and the quality standard data to a monitoring module, performing dynamic comparison on the real-time second quality monitoring data and the preset quality standard data, and outputting the result. The method comprises the steps of generating quality anomaly feature parameters, transmitting the quality anomaly feature parameters to an early warning module, matching a preset early warning strategy according to the quality anomaly feature parameters, sending out graded early warning signals, and carrying out multi-dimensional early warning by integrating multi-dimensional data acquisition and analysis, dynamic feature index extraction and dynamic standard construction, so that the engineering quality monitoring accuracy and the management and control timeliness are improved.

A parallel processing unit comprises a plurality of processors each being coupled to a memory access hardware circuitry. Each memory access hardware circuitry is configured to receive, from the coupled processor, a memory access request specifying a coordinate of a multidimensional data structure, wherein the memory access hardware circuit is one of a plurality of memory access circuitry each coupled to a respective one of the processors; and, in response to the memory access request, translate the coordinate of the multidimensional data structure into plural memory addresses for the multidimensional data structure and using the plural memory addresses, asynchronously transfer at least a portion of the multidimensional data structure for processing by at least the coupled processor. The memory locations may be in the shared memory of the coupled processor and / or an external memory.

The invention discloses an offshore platform emergency control method based on a Beidou satellite system and related equipment, and relates to the technical field of Beidou satellites.The offshore platform emergency control method based on the Beidou satellite system comprises the steps that Beidou satellite time service signals and real-time meteorological data are obtained, the method comprises the following steps: synchronously acquiring structural deformation parameters, marine environment dynamic parameters and personnel positioning information of an offshore platform, and constructing multi-dimensional data of the offshore platform; fusing and analyzing the multidimensional data of the offshore platform, extracting space-time correlation characteristics through a pre-trained dynamic risk prediction model, constructing a multi-factor causal chain, and determining a platform risk level and accident type prediction result; and triggering graded emergency response according to the accident type prediction result, encrypting and transmitting a corresponding early warning instruction to a preset terminal through a Beidou short message, and executing a corresponding emergency control method. According to the invention, risk prediction and emergency response are carried out through the multi-dimensional data of time-space synchronization, so that the offshore platform can carry out timely emergency control.

The present invention discloses a health status assessment method and system for multi-source heterogeneous power data. The health status assessment method and system for multi-source heterogeneous power data include: S110, constructing a unified data model for multi-source heterogeneous power data; S120, extracting multidimensional data features based on the unified data model; S130, constructing a health status assessment model based on the extracted data features; S140, establishing a real-time health status monitoring and alarm mechanism based on the health status assessment model; the present application provides a real-time monitoring and alarm mechanism for the health status of multi-source heterogeneous power data, accurately detecting abnormal situations of power data through a multi-feature assessment model, and providing convenience for operation and maintenance personnel.

The invention discloses a construction site safety early warning method and system based on multi-dimensional data mining, and relates to the technical field of construction site early warning, and the method comprises the steps: collecting and preprocessing multi-dimensional data, and forming a multi-dimensional data set; performing feature extraction on the multi-dimensional data set by using a convolutional neural network, and fusing time sequence information through a space-time dynamic weighting mechanism to generate a multi-dimensional risk feature set; based on the multi-dimensional risk feature set, the risk features are converted into nodes, the incidence relation between the risk features is converted into edges, a causal correlation structure of the nodes and the edges is formed, and a dynamic risk map is constructed; analyzing the association strength among the risk factors by using the dynamic risk map, evaluating the overall risk level, and generating a risk event list; performing trend prediction on the risk events in the risk event list based on the time sequence to obtain a risk trend index; and performing dynamic early warning according to the risk trend index, and triggering a safety response. According to the invention, through multi-dimensional data mining and analysis, intelligentization of construction site safety early warning is realized.

The present invention discloses a coal mine disaster simulation prediction system based on multi-dimensional data collection, which relates to the field of coal mine safety technology. A variety of monitoring equipment such as geological radar and microseismic monitoring are integrated to build a real-time monitoring network covering key areas of coal mines, and comprehensively collect multi-dimensional data. The system includes modules such as data preprocessing, model construction and training, prediction and decision-making, early warning and emergency response, and user interaction interface. Data is processed through data cleaning, filtering, standardization and fusion technology, and disaster prediction is performed using a CNN and LSTM hybrid model to output disaster probability and risk areas, and automatically trigger early warning and emergency plans. The user interaction interface displays real-time monitoring, prediction results and early warning information, providing a comprehensive and intelligent solution for coal mine disaster prevention and control.

The invention discloses an electric power spot day-ahead market auxiliary quotation method, and relates to the technical field of electric power system automation, and the method comprises the steps: collecting power grid operation, weather, market and carbon market data through multi-source data fusion preprocessing, and carrying out the processing through a space-time generative adversarial network and a natural language processing model, so as to form a multi-dimensional data set; embedding a power grid power balance constraint by using a graph neural network to carry out topology modeling, and extracting spatio-temporal characteristics by combining a time sequence neural network and an attention mechanism; and a double-layer optimization model is constructed to realize collaborative decision-making of futures and spot markets, and carbon-electricity joint optimization and a dynamic shipping space adjustment mechanism are synchronously integrated. According to the method, the problems of data isolation, inaccurate prediction, insufficient risk control and the like of a traditional quotation method are solved, the quotation precision and the market competitiveness are improved, an auxiliary quotation scheme giving consideration to benefits and risks is provided for power generation enterprises, and the method is suitable for quotation strategy optimization of the current market of electric power spot goods.

The invention discloses an intelligent early warning method for pipeline blockage of a slurry circulation system of a slurry shield, which relates to the field of intelligent early warning, and comprises the following steps of: performing spatial-temporal feature analysis on a standardized multi-dimensional data stream, constructing a blockage feature knowledge graph based on pipeline position and time sequence correlation analysis, and generating a blockage feature vector through a graph neural network; based on the blockage feature vector, analyzing the dynamic change trend of particle distribution through a long-short-term memory network and predicting the particle blockage risk in combination with an acoustic signal, then performing adaptive judgment by fusing geological conditions and construction stage information to obtain a risk assessment result, and inputting the risk assessment result and the blockage feature vector into digital twinborn simulation to obtain the particle blockage risk. A blockage scene is predicted based on fluid dynamics and a particle sedimentation model, early warning parameters are adjusted through Bayesian optimization, and graded early warning signals are generated; according to the invention, by generating the blockage feature vector, the recognition capability of the early local abnormal propagation trend is enhanced, and a reliable basis is provided for accurately predicting the blockage risk.

The invention provides an intelligent retrieval matching method and system for multidimensional data of image-text features, and relates to the technical field of icon image retrieval. Comprising the following steps: extracting image features, text content and semantic features of an icon image by using a convolutional neural network, an image segmentation attention mechanism network, a converter optical character recognition model and a bidirectional semantic understanding model, constructing the extracted features into heterogeneous feature tensors, and performing singular value decomposition to obtain icon feature fingerprint vectors; and constructing a multi-level index based on locality sensitive hashing, realizing rapid retrieval, calculating visual, text and semantic similarities in combination with a deep metric learning model, weighting according to variances and discrimination coefficients of similarity features to obtain a comprehensive similarity score, and outputting a retrieval result with the highest similarity.

The invention discloses a ship control auxiliary method based on machine learning, and relates to the technical field of ship intelligent control, and the method comprises the steps of ship data collection, fuel oil energy consumption prediction, dynamic navigational speed planning and energy-saving feedback control. According to the method, the fuel energy consumption is predicted by adopting a prediction method combining multi-mode and disturbance self-adaption, modeling multi-dimensional data is unified, perception of environmental disturbance is enhanced, accurate prediction of the fuel energy consumption under complex navigation conditions is realized, and the reliability of subsequent dynamic navigational speed planning is effectively improved; dynamic speed planning is carried out by adopting a speed planning method combining multiple targets and segmented constraints, three core factors of fuel energy consumption, navigation time and safety risks are comprehensively considered, a multi-target cost function is constructed, segmented constraints are introduced, the physical feasibility and risk control of speed adjustment are guaranteed, the speed is optimized through a reinforcement learning mechanism, and the speed planning efficiency is improved. Intelligent self-adaptive navigational speed adjustment is achieved, and the energy-saving capacity, the navigation efficiency and the operation safety of the ship under the complex navigation condition are improved.

The invention discloses an underground mine operation state analysis system and method based on video monitoring data, and the system comprises a data collection module which is used for collecting mine video and environment parameter data through a distributed sensor network, and generating a multi-dimensional data fusion set based on a space-time label technology; the edge analysis module is used for extracting feature parameters through a convolutional neural network algorithm based on the multi-dimensional data fusion set and generating a mine operation state recognition result; the fence construction module is used for constructing a three-dimensional digital model and a dynamic safety boundary based on the mine operation state recognition result to form a real-time monitoring reference framework; and the decision execution module is used for performing hierarchical risk assessment on the monitoring data in the security boundary based on the real-time monitoring reference framework, and generating a security early warning and disposal scheme with a tracing identifier. Each piece of early warning and disposal information is attached with a unique tracing identification code, so that follow-up event backtracking analysis is facilitated, and the risk management and control capability is continuously improved.

The invention provides a BIM (Building Information Modeling)-based rail transit construction geological exploration profile generation method, which belongs to the technical field of exploration measurement and comprises the step of comprehensively improving the accuracy of rail transit construction geological exploration through an advanced data acquisition and intelligent analysis method. In a construction site, multi-dimensional data of geological drilling, geological radar, geophysical prospecting testing and the like are acquired through various precise instruments, and a three-dimensional coordinate system is firstly established; and deep feature extraction is performed on complex data by using a deep learning algorithm, and key information such as geological stratification, rock and soil parameters and underground water level is accurately obtained. Intelligent fusion of multi-source data is realized by constructing a geological stratification model, a rock and soil parameter model and an underground water level model, and a comprehensive geological information model is generated. And importing the extracted geological profile data into a BIM platform, and carrying out strict precision verification and optimization by adopting a professional geological information collaborative equation set to finally form a highly accurate and reliable rail transit construction geological exploration profile.

The invention relates to the technical field of multi-modal data processing and intelligent health management, in particular to a safety monitoring and evaluation method and system based on multi-dimensional data, and the method comprises the steps: mapping multi-modal feature data into health state vectors, and generating health state deviation and change trend by combining with time sequence analysis; identifying a health risk and generating a behavior intervention path; the system expands scarce scene data through a generative adversarial network, verifies the validity of a path in a virtual simulation environment, and optimizes parameters and association weights of key nodes in the path; dynamic collection of user feedback data further supports real-time adjustment of paths and models; finally, dynamic monitoring, accurate evaluation and personalized behavior intervention of the health state are achieved, and the method has high adaptability and perspectiveness.

The invention discloses a database management method and management system, and relates to the technical field of general database management, and the system comprises a user information management module, a work management module, a salary settlement module, an evaluation feedback module, an analysis prediction module, a multi-terminal integration module and a safety guarantee module. In the invention, a role-based access control permission model is constructed in a permission control layer and a module definition, and a field-level data control mechanism is implemented for three roles of a platform administrator, a customer enterprise and an employee, for example, the employee can only check a specific public field in a salary list, and sensitive fields such as a salary calculation formula are dynamically shielded; through a permission inheritance mechanism, the system automatically adds a filtering condition that employee ID = current user ID for a query request of an employee role, data isolation is ensured, a multi-dimensional data relation graph of employee-post-customer-contract is constructed, and a dynamic association path between entities is mapped in real time through a visual view.

The invention discloses a virtual reality scene three-dimensional reconstruction method and system based on multi-source data fusion, and relates to the technical field of underground commercial and traffic integrated space scene three-dimensional reconstruction. The system constructs a multi-dimensional data set through synchronous acquisition of point cloud, images, poses, positioning and semantic text information; a three-dimensional convolutional neural network is adopted to establish an AI reconstruction fusion model, and a three-dimensional model is generated; the system calculates a structural integrity evaluation coefficient JGPG, a fusion consistency evaluation coefficient RHPG and a VR interaction adaptability evaluation coefficient VRJH, compares the coefficients with corresponding thresholds, triggers an optimization strategy, and finally completes model adaptability optimization and packaging output, thereby improving the reduction degree and interaction performance of a virtual reality scene.

The invention provides a wetland vegetation intelligent monitoring method and system based on multi-dimensional data fusion, and the method comprises the following steps: collecting a water area unmanned plane image of a wetland water area through a monitoring unmanned plane, and obtaining a water area remote sensing image of the wetland water area through a remote sensing satellite; the water area unmanned aerial vehicle image and the water area remote sensing image are fused into a water area fusion image, the submerged vegetation distribution condition is recognized, and a submerged vegetation monitoring route is planned; the underwater monitoring robot is controlled to continuously detect a wetland water area, and a submerged vegetation standard image and a submerged vegetation multispectral image are collected at the same time; performing image enhancement on the submerged vegetation standard image through a submerged vegetation identification model, and identifying and marking target submerged vegetation from the submerged vegetation enhanced image; selecting a target multispectral region containing target submerged vegetation from the submerged vegetation multispectral image; and vegetation feature data of the target submerged vegetation are calculated based on the target multispectral region. According to the invention, related data of submerged vegetation can be continuously and accurately monitored.

The invention relates to an intelligent power security situation assessment method based on a multi-source sensor network, and the method comprises the steps: obtaining multi-dimensional data from a power system through the multi-source sensor network, and constructing an original data set containing time sequence features; preprocessing and denoising the original data set, and extracting dynamic change characteristics of key parameters to obtain a cleaned parameter change sequence; based on the cleaned parameter change sequence, analyzing an interaction relationship among the key parameters, and obtaining spectrum feature data; judging whether a resonance risk exists in the system or not according to the frequency spectrum characteristic data in combination with a resonance phenomenon theoretical model, and generating a risk assessment report; for the risk assessment report, identifying unstable factors and extracting a high-risk parameter combination, and generating unstable factor distribution feature data; and according to the system stability evaluation index, judging whether the power grid operation state is close to an unstable boundary, and generating a risk early warning signal and a parameter adjustment suggestion.

The invention discloses a complex industrial scene-oriented multi-dimensional data fusion monitoring method and system, and belongs to the technical field of industrial automation and data analysis, and the method comprises the following steps: carrying out credibility evaluation and self-repairing processing on multi-source heterogeneous data of industrial equipment; performing cross-modal time sequence alignment on the repaired multi-source heterogeneous data, and enhancing the time sequence alignment precision; dynamically adjusting the fusion weight of the multi-source heterogeneous data based on the real-time working condition of the equipment; taking the output value after the weight fusion as an edge weight to guide the fusion of the multi-source heterogeneous data; and performing abnormal propagation path simulation analysis in combination with a digital twinborn model. According to the method, through multi-dimensional data fusion, dynamic adjustment and application of the digital twinborn model, efficient industrial fault monitoring and prediction are realized, and high reliability and safety of equipment are ensured.

The invention discloses a road and bridge disease analysis and early warning method and system based on big data, and relates to the technical field of smart cities. Multiple groups of sensors are deployed, acquired related transverse wind load information data are associated with related vertical traffic load information data, and an external force load influence coefficient Xhz is constructed; after comparison, a preliminary early warning signal is sent out, related surface state data information and related vibration state data information of the current road and bridge are further collected and associated with an external force load influence coefficient Xhz, a comprehensive disease degree index Zbh is calculated through fitting in combination with a disease evaluation model, and the comprehensive disease degree index Zbh is compared with an evaluation threshold value K; according to the method and the system, the multi-dimensional data is acquired to judge whether the road bridge has a disease risk, a disease early warning instruction of a corresponding grade is generated and executed, and accurate identification and real-time early warning of the road bridge disease are realized through dynamic association and intelligent analysis of the multi-dimensional data.

The invention relates to the technical field of lithium battery safety management, and particularly discloses a lithium battery thermal runaway early warning and protection method based on an adaptive neural network, which comprises the following steps: acquiring temperature, internal resistance and voltage data of a lithium battery in real time, extracting multi-dimensional features based on discrete wavelet transform, singular value decomposition and principal component analysis algorithms, and performing early warning and protection on the lithium battery thermal runaway. The influence of internal resistance and voltage on abnormal temperature fluctuation is quantified; constructing a feature vector through the comprehensive influence coefficient, inputting the feature vector into an adaptive fuzzy neural network prediction model, outputting a temperature anomaly fluctuation coefficient, and accurately predicting the thermal runaway risk of the lithium battery; according to the lithium battery thermal runaway prediction method, multi-level protection measures such as early warning response triggering, system automatic charging and discharging strategy adjusting, power output reducing and heat dissipation system starting are taken based on the prediction result, development of the thermal runaway risk is restrained, multi-dimensional data fusion and an intelligent algorithm are combined, and the lithium battery thermal runaway prediction accuracy and the risk control real-time performance are improved.