890 results about "Optimal matching" patented technology

The invention provides an intelligent short message scheduling method and device based on multi-dimensional dynamic optimization, and the method comprises the steps: obtaining the performance data of a plurality of short message channels, and calculating a channel health score based on a weight dynamic adjustment model; determining a scheduling strategy according to the priority identifier of the to-be-sent message, and performing channel screening and optimal matching; executing message sending and monitoring a sending state; terminal state detection is carried out on the failure message through operator base station signaling, and a decision tree model is applied to determine a retry strategy; and performing Huffman coding compression processing on the P2-level marketing messages which fail in retry, and performing batch sending in an idle window. According to the method, a comprehensive performance evaluation index and reward function model is also constructed, and parameter optimization is performed by applying a reinforcement learning algorithm. According to the invention, multi-dimensional dynamic channel scoring, intelligent retry decision making based on terminal state perception, batch processing with balanced cost-time efficiency and a closed-loop self-optimization system are realized, the short message delivery rate is obviously improved, and the invalid retry rate and the sending cost are reduced.

The invention discloses an AI virtual coach training system based on standard action matching and deviation feedback, which relates to the technical field of AI virtual coach training systems and comprises a user modeling module, an action acquisition module, a template matching module, a deviation calculation module, a feedback generation module, an interactive presentation module and a learning optimization module. The user modeling module is used for modeling a registered user by adopting a body parameter acquisition and health data analysis method to obtain a user personalized feature vector; the action acquisition module is used for capturing actions executed by a user in real time by adopting a multi-source sensor fusion method to obtain a time sequence containing key point coordinates; and the template matching module is used for comparing the time sequence of the key point coordinates with corresponding actions in a preset standard action template library by adopting an improved dynamic time warping (DTW) algorithm to obtain an optimal matching path and a corresponding minimum matching cost.

The invention discloses a cost optimization method for resource scheduling management of a cloud data center, and relates to the technical field of cloud computing, and the method comprises the following steps: S1, collecting and modeling a multi-dimensional resource state of the cloud data center, and generating a resource change trend based on a sliding time window and a prediction model; and S2, constructing a multi-target game scheduling model taking calculation, storage, bandwidth and energy consumption as participants, outputting a scheduling game solution in combination with task modal adaptability parameters, and forming task-resource optimal matching. According to the method, through multi-dimensional resource state collection, a sliding time window and an advanced prediction model, resource dynamic changes and future trends can be captured more accurately, more reliable input is provided for scheduling decisions, resource waste or performance bottlenecks caused by information lag are avoided, calculation, storage, bandwidth and energy consumption are modeled as multi-party game participants, and the game efficiency is improved. Nash equilibrium is solved in combination with task modal adaptability parameters, and an optimal scheduling scheme giving consideration to resource utilization rate, performance and cost can be found.

The invention relates to the technical field of data processing, and discloses a cold and hot data exchange method and system based on optical storage and a storage medium. The method comprises the following steps: performing access feature acquisition and analysis on data in the optomagnetic hybrid storage system, calculating an access life cycle factor, generating a logic interval heat evaluation result, performing temperature grading, formulating a cold and hot data placement strategy, generating a data placement mapping table, executing batch migration according to priority, and updating a data position record. According to the method, the problem of how to realize logic interval hot degree accurate identification, temperature grade dynamic grading, storage mapping optimal matching and batch migration task efficient organization under the optomagnetic hybrid architecture is solved.

The invention relates to the technical field of safety supervision, in particular to a building engineering quality safety supervision system and method based on multi-source data. The method comprises the following steps: acquiring positioning data of engineers and instruments, identifying a movement track, generating a dynamic simulation building engineering model to analyze abnormal construction behaviors, performing incentive backtracking analysis to identify the root of the abnormal behaviors, predicting the development trend of the abnormal behaviors, delimiting a focusing supervision area according to prediction data, and performing focusing supervision on the abnormal behaviors. The method comprises the following steps: carrying out rejection path analysis, forming candidate paths, carrying out space mapping and speed optimal matching on the candidate paths, determining an optimal rejection path, and carrying out engineering repair management according to the optimal path when the safety quality is lower than a threshold value after the engineering safety quality is evaluated. According to the invention, a more efficient and safer construction engineering quality safety supervision method is realized.

The invention discloses a multi-modal entity connection method based on data enhancement, which relates to the technical field of data processing, and comprises the following steps: enhancing entities by using a large language model, carrying out abstract processing on entity description through a design instruction, and extracting and optimizing key information; semantic information is enhanced by using a multi-modal large language model, enhanced information description is generated by fusing texts and visual features, and the enhanced information description comprises names, types, function and behavior features and physical or structural attributes of entities as well as spatial structures, appearances, color textures and other visual features extracted from the visual information; a candidate entity set is expanded and optimized through a retrieval technology, and retrieval enhancement is achieved; and performing decision selection by using a large language model, and selecting an optimal matching item from a candidate entity set obtained by retrieval enhancement. According to the method, through multi-level data enhancement and an accurate entity selection process, the accuracy and efficiency of multi-modal entity linking are remarkably improved.

The invention relates to an AI online education intelligent question and answer information processing method, which comprises the following steps of S1, collecting teaching materials, and performing preprocessing and labeling to form a text data set with labels; s2, constructing a question and answer model based on XLNet; s3, introducing a specific context optimization question and answer model for a specific subject or topic to obtain an optimized question and answer model; s4, constructing an auxiliary understanding information knowledge graph by considering hierarchical design of age groups based on the labeled text data set; s5, inputting a query by the user, and obtaining an optimally matched answer based on the optimized question and answer model; according to the age group of the user and the best matching answer, auxiliary understanding information is obtained from the knowledge graph, and a complete answer is obtained; and S6, adjusting provided content and difficulty through user interaction data, and recommending related domain knowledge based on user behaviors and preferences. According to the method, the direct answers required by the user are provided, the supplementary background information is provided to enhance the understanding, and the requirements of the user are presented in a personalized manner according to the characteristics of the user.

The invention relates to the field of operation and maintenance management, in particular to an operation and maintenance management system and method based on artificial intelligence, and the system comprises an operation baseline modeling module, a resource consumption prediction module, an automatic resource allocation module, a data correlation analysis module, an abnormal data detection module and a fault path analysis module. According to the method, accurate data support is provided for resource management through real-time monitoring of operation data and baseline performance modeling, resource demands are analyzed and predicted in combination with a time sequence, shortage and excess in resource configuration are identified, automatic allocation of resources is achieved, optimal matching between resource supply and demands is ensured, and the optimal resource allocation efficiency is improved through construction of a dependency graph. The interaction between the data stream and the application is analyzed, abnormal data detection and fault path analysis are helped, fault problems are found in real time, reasons are identified, the downtime of the system is shortened, the continuity of services and the integrity of data are ensured, and the automation degree and the intelligent level of operation and maintenance management are improved.

The invention discloses an order-driven cross-factory collaborative production system, and relates to the technical field of intelligent manufacturing and supply chain collaboration, and the system obtains the productivity data, logistics cost and tax policies of a plurality of production bases such as Ningbo, Thailand and America in real time, and carries out the intelligent splitting and distribution of orders through a multi-base productivity game algorithm. And dynamic optimal matching of the order and the productivity is realized. Meanwhile, the system integrates WMS inventory data and third-party logistics real-time quotation, a transportation scheme with the lowest total cost is generated by adopting a genetic algorithm, and cross-border logistics and tax expenditure are remarkably reduced. The system overcomes the problems of information isolated island, response lag, extensive cost control and the like in traditional multi-factory production, realizes global productivity collaborative optimization and supply chain integrated intelligent decision, and improves the enterprise order performance efficiency and the overall resource utilization rate.

The invention relates to the technical field of smart community data management, in particular to a smart community metadata interaction method and system based on an edge computing framework, and the method comprises the steps: carrying out the dynamic storage of multi-dimensional data through a hierarchical rolling cache; generating a uniform resource vector through sequential progressive mapping; community semantic tags are added for the uniform resource vectors through extensible tag slots, connection is established according to association rules, and a resource semantic graph oriented to community services is constructed; receiving a resident task request, constructing a multi-factor weighted scoring engine based on the resource semantic graph, and generating a task data packet with a priority label; a two-channel hybrid scheduling network is constructed, a first channel outputs resource availability and node association degree, and a second channel outputs time sequence characteristics and load prediction parameters; and fusing dual-channel output to obtain task-node correlation and generate an optimal matching strategy. According to the method, task collaboration and resource adaptive management are realized through semantic resource modeling and hybrid scheduling.

The invention relates to a standardized project management and intelligent professional scheduling system, and belongs to the technical field of project management and human resource intelligent scheduling. The project standardization module is disassembled into standardization task steps through a business process association rule mining algorithm, and project complexity and resource tensity are adapted by using a task attribute dynamic weight algorithm; the archive matching module constructs professional archives, extracts feature vectors through a multi-criterion decision-weighted bipartite graph matching algorithm, and generates optimal matching pairs in combination with adaptive weight adjustment and an integer linear programming model; the dynamic scheduling module plans a task execution scheme according to a resource constraint scheduling mechanism, and realizes efficient scheduling in combination with a skill supply and demand prediction algorithm and calendar integration; and the quality optimization module adopts a deliverable anomaly detection algorithm to monitor compliance, feeds back an iterative matching and scheduling strategy through a time sequence prediction optimization algorithm, and perfects a skill map based on a map increment updating algorithm. The system realizes a project full-process closed loop.

The invention relates to the technical field of remote sensing data ocean application, and provides an ocean ship association identification method based on AIS and satellite-borne SAR data and electronic equipment, and the method constructs a full-process technical route from data acquisition, preprocessing, ship target detection to trace point association. The method runs through the whole process from SAR data and AIS trajectory preprocessing, intelligent identification of ship targets in SAR images and optimal matching of plots of the SAR data and the AIS trajectories, and a set of uniform, cooperative and efficient technical links is formed. According to the SAR-AIS fusion monitoring method, accurate alignment of AIS data and SAR imaging time is realized by introducing cubic spline interpolation, a ship target in an SAR image is automatically extracted in combination with a deep learning model, finally, global optimal association between trace points is completed by means of a Munkres algorithm, links from data preprocessing to result output are seamlessly connected, and the precision and efficiency of SAR-AIS fusion monitoring are remarkably improved.

The invention discloses a network construction type energy storage capacity optimal matching method based on a frequency response model, and belongs to the technical field of gas turbine power plant financial supervision and artificial intelligence, and the method comprises the steps: S1, quantifying the power vacancy of a typical station under an extreme short-circuit fault, determining a virtual inertia constant and a virtual droop coefficient of network construction type energy storage, and carrying out the calculation of the virtual inertia constant and the virtual droop coefficient; setting an initial output coefficient and adjusting a step length; s2, constructing and integrating a system frequency response model based on the inertia and frequency modulation characteristics of the conventional unit, the wind turbine generator and the network construction type energy storage; s3, applying power disturbance to the system, gradually increasing an initial output coefficient and performing simulation, and recording the maximum frequency deviation, the maximum frequency change rate and the quasi-steady-state frequency deviation; and S4, when the maximum frequency deviation, the maximum frequency change rate and the quasi-steady-state frequency deviation are respectively smaller than or equal to set threshold values, the energy storage capacity proportion corresponding to the current initial output coefficient is the optimal proportion. According to the method, the problem of accurately measuring the optimal configuration proportion of the network construction type energy storage in the new energy station is solved.

The invention discloses an AI marketing advertisement putting optimization system and method fusing multi-dimensional data, and relates to the technical field of data processing, and the system comprises a historical record obtaining module which extracts the historical putting record of a target optimization block; the terminal data interaction module interacts with the advertisement putting management platform based on the historical putting record to obtain multi-dimensional terminal data; a grid division module performs grid division on the target optimization block to generate a putting grid and time sequence label data; the mapping model construction module constructs a label mapping model based on the grid time sequence label data, the historical delivery record and the multi-dimensional terminal data; the advertisement task processing module generates an advertisement label cluster by using a label mapping model according to the advertisement task information; and the putting scheme optimization module realizes the optimal matching of the advertisement task and the label cluster through a multi-objective optimization algorithm, and outputs an advertisement putting scheme, thereby achieving the technical effects of improving the data utilization rate, providing predictive putting optimization and improving the putting income.

The invention relates to the technical field of information processing, and discloses a big data-based information matching management system, which comprises an acquisition module, an attribute analysis module, a multi-dimensional modeling module and a dynamic matching module. The acquisition module acquires a user behavior data set and extracts active information demand features; the attribute analysis module analyzes the real-time updating frequency, the semantic association degree, the historical matching success rate and the data topological structure characteristics of the target information resources; a multi-dimensional modeling module performs heterogeneous modeling on each feature to generate timeliness, semantic association, matching probability feature vectors and topological structure vectors; and the dynamic matching module generates an adaptation degree score and determines an optimal matching object through heterogeneous space projection and feature coupling operation. The system improves the accuracy, real-time performance and efficiency of information matching through multi-dimensional feature processing, topological structure analysis and a dynamic matching algorithm, and is suitable for an accurate information matching scene in a big data environment.

The invention discloses a task allocation method, device and equipment, a storage medium and a computer program product, and relates to the technical field of computers.The method comprises the steps that reasoning task information in a first preset time period and computing power information of all computing cards in a heterogeneous computing system are obtained; predicting a predicted number of the reasoning requests received in a second preset time period based on the reasoning task information; and according to the reasoning task information, the computing power information of each computing card and the predicted number, allocating a pre-filling task or a decoding task to each computing card, and determining the number of tasks allocated to each computing card, so that the time for the heterogeneous computing system to respond to the predicted number of reasoning requests is shortest. According to the method, the optimal matching of the heterogeneous resources and the reasoning tasks is realized, the utilization rate of the heterogeneous resources is improved, and the execution efficiency of the reasoning tasks is improved.

The invention discloses an ovarian cancer preoperative accurate prediction system and method based on multi-modal imageomics characteristics, and the method comprises the steps: taking a US image, a CT image and an MRI image as the basis, carrying out the radiomics characteristic extraction of the three types of modal image data, and carrying out the combined screening of the three types of modal image data, so as to determine a matched classifier, a prediction task and a corresponding optimal characteristic combination; in this way, the multi-modal radiomics features, the classifier and prediction tasks of different levels are optimally matched, then preoperative accurate diagnosis of ovarian cancer pathology, staging and accurate prediction of PDS effects are achieved through optimal matching, and the prediction process is close to the clinical practice diagnosis and treatment thought; and iconography detection suggestions with the highest diagnostic value are provided for different diagnosis and treatment layers.

The invention relates to the technical field of intelligent recommendation, and discloses an AI intelligent matching method based on a knowledge graph, and the method comprises the steps: constructing a quaternary knowledge graph containing a time dimension; identifying legal entities and semantic relationships by adopting an entity identification and relationship extraction technology; multi-level semantic features are extracted through a two-layer progressive semantic matching algorithm of a grammar layer, a semantic layer and a reasoning layer; constructing lawyer ability portraits based on a heterogeneous graph neural network and a time sequence perception graph convolution technology; progressive matching calculation is adopted, the optimal matching weight is learned through a multi-layer attention mechanism, and dynamically optimized intelligent matching is achieved. The technical problems of cold start, insufficient semantic understanding ability and poor timeliness processing ability in the existing legal consultation matching system can be solved, and the matching precision and the user satisfaction are improved.

The invention discloses a remote sensing image searching and matching method and device based on feature fusion, and relates to the technical field of remote sensing, a test image of an unmanned aerial vehicle or a heterogenous satellite is acquired, a feature library is established after preprocessing, and feature extraction and index establishment are performed on each base map in the base map library. And selecting a retrieval strategy according to the size of the test image, retrieving the name of the base image and cutting a related grid image. The method comprises the following steps: converting a test and grid image into gray scale and zooming, extracting feature points to form a set, estimating an affine transformation matrix by using a random sampling consensus algorithm, and preliminarily evaluating the number of matched inner points; if the first preset threshold value is exceeded, executing a fine matching step; and if the number of points in the fine matching step exceeds a second preset threshold value, returning to match the name of the base map and the longitude and latitude of the angular points. And if not, iterating precise matching until an optimal matching result and the longitude and latitude of the angular point are returned. The problem of how to efficiently and accurately find an image of a specific area for massive and multi-source remote sensing image data is solved.

The invention belongs to the technical field of target tracking, and discloses a multi-target tracking method and system and a computer storage medium. The method comprises the following steps: preprocessing target video data collected in extreme weather or a complex scene, and obtaining each target detection frame contained in each frame of image; predicting M target prediction frames contained in the current frame image; calculating the intersection-to-union ratio IoU between the N target detection frames and the M target prediction frames contained in the current frame image to construct an IoU cost matrix # imgabs0 # of M * N dimensions; calculating the ReID feature similarity between each target detection frame and the target prediction frame so as to construct an M * N-dimensional ReID feature similarity cost matrix # imgabs1 #; the # imgabs2 # and the # imgabs3 # are weighted to serve as a data association cost matrix # imgabs4 #; inputting # imgabs5 # into Hungary calculation to obtain an optimal matching scheme; and each target track is managed to realize multi-target tracking. According to the invention, the precision and tracking efficiency of multi-target tracking in extreme weather and complex scenes can be improved.

The invention provides a pile foundation buried pipe design optimization method, medium and system, and belongs to the technical field of ground source heat pump pile foundation buried pipe, and the method comprises the following steps: building a geologic model and a vibration parameter matrix by obtaining actually measured geology and vibration data of a to-be-placed pile foundation area; and a feature matching technology is utilized to find optimal matching in a preset typical soil database, clustering analysis is carried out, and the soil deformation amount of the region is determined. And in combination with load and vibration information of the pile foundation and the building, soil deformation in the whole using process is predicted. On the basis, an underground heat transfer model of the pile foundation buried pipe is established, and the heat exchange capacity of the ground source heat pump is calculated; meanwhile, the influence of the buried pipe on soil deformation is analyzed. And finally, the heat exchange capacity and soil deformation change serve as optimization targets, a game model is adopted for solving, and an optimal pile foundation buried pipe design scheme is obtained. In the whole process, the mode of combining measured data and modeling analysis is fully utilized, and intelligent optimization of pile foundation design is achieved.

The invention discloses an image registration method based on improved ORB feature extraction and grid feature matching. The method comprises the following steps: inputting an image to be extracted; using an improved FAST algorithm to extract feature points of the to-be-spliced image; establishing a BRIEF descriptor, and obtaining a BRIEF descriptor with a direction; according to the feature point set of the to-be-spliced image, establishing a Delaunay triangular grid taking the feature points as vertexes; judging the similarity of triangles, and obtaining similar triangles formed by the feature points; matching the similar triangle vertexes by using a Hamming distance to obtain an optimal matching feature point pair; obtaining affine matrix parameters by using an SVD method; according to the image registration method provided by the invention, the image quality is ensured, the feature extraction efficiency and the extraction precision can be improved, wrong matching is reduced, the stability is improved, the real-time performance of video stitching is improved, the waste of memory resources is reduced, the equipment cost is reduced, the working efficiency is greatly improved, and the method is suitable for popularization and application. And a better method is provided for image splicing.

The invention relates to the technical field of electric digital data processing, and discloses a multi-dimensional data logic processing method based on an artificial intelligence algorithm, which comprises the following steps: receiving a target discrete data packet, extracting metadata, and calculating to generate a dimension entropy feature vector representing data logic complexity; inputting the vector into a preset topological mapping model, and outputting an initial adjacent matrix; calling a feedback suppression mask matrix generated based on a historical operator utility state, and executing bitwise logic AND operation with the initial adjacent matrix to generate a corrected effective topological matrix; the matrix is analyzed, a logic operator function pointer is dynamically indexed in an instruction cache, and a directed acyclic execution linked list is constructed; according to the method, redundant logic nodes in AI prediction are definitely eliminated through a bit operation mask mechanism based on historical feedback, and deterministic convergence of processing delay and optimal matching of computing power resources are achieved.

The invention provides a multi-robot cooperative control method and system based on edge computing, and relates to the technical field of multi-robot control, and the method comprises the steps: dividing a search and rescue region into a plurality of sub-regions, generating a sub-region search and rescue task, and carrying out the search and rescue of the sub-regions according to the sub-region search and rescue task and the capability parameters and current states of ground robots. An optimal matching scheme of ground robots and sub-region search and rescue tasks is generated, different search and rescue strategies are adopted for different types of ground robots, and a heterogeneous robot cooperative search and rescue strategy is generated by defining formalized languages and lexical methods for cooperative control of the ground robots; each ground robot executes a search and rescue task in the corresponding sub-region according to the heterogeneous robot cooperative search and rescue strategy, when it is detected that the resource use condition or the network condition meets the preset condition, a calculation unloading decision is started, part of the search and rescue tasks are unloaded to other calculation nodes according to the decision result, and the search and rescue action continues to be executed. And the search and rescue task is completed.

The invention provides an elevator multi-mode crowd feature perception and intelligent advertisement putting method and system, and relates to the technical field of intelligent advertisement putting, and the method comprises the steps: collecting multi-mode perception data in an elevator through an edge computing terminal; performing feature decoupling on the data, performing cross-modal semantic alignment, establishing a directed association relationship between modals, and constructing a scene feature map; calculating the topology importance degree of map nodes, screening feature nodes, and extracting context information for semantic coding; mapping the scene semantic code and the advertisement audience semantic code to a two-dimensional coordinate system to construct a semantic matching graph, and extracting an optimal matching path to form a candidate set; predicting a scene evolution trend based on the scene characteristic spectrum evolution trajectory, and calculating an advertisement adaptive score to generate a playing sequence; putting and collecting user interaction data feedback according to the sequence to update the graph structure. According to the invention, accurate crowd feature recognition and advertisement dynamic matching are realized, and the advertisement putting efficiency and the user experience are improved.

The invention discloses a high-frequency vehicle track map matching method, which comprises the following steps of: (1) dividing a track T to be matched into a plurality of sub-tracks based on a reference line distance, and extracting key track points in the sub-tracks; (2) extracting a road where the key track point is located and a road type of the road, filtering the extracted road based on the road type, and storing filtered road data by adopting an R-Tree structure; and (3) matching an optimal matching path of the track T to be matched from the road data stored in the R-Tree structure through a hidden Markov probability matching model. From the perspective of reducing the scale of original data, trajectory and road network data are simplified. Then, space and direction analysis is carried out on the simplified trajectory, a micro-region splicing strategy is adopted to accelerate shortest path search, and a path deviation function is designed to improve the topology of a matching result; in addition, the extraction process of track motion key points is refined by fusing more road semantic information, so that a better matching result is obtained.

The invention provides an object grabbing method and system based on point cloud deep learning, and the method comprises the steps: obtaining a target object point cloud of a target object, carrying out the point cloud matching of the target object point cloud through a point cloud registration model based on a deep learning network, obtaining an initial matching position, optimizing the matching position through a nearest iteration algorithm, and obtaining an optimal matching position; obtaining an accurate transformation matrix of the target object relative to the template under a camera coordinate system; based on a pre-obtained hand-eye transformation matrix, calculating a transformation relation of the point cloud attitude of the target object relative to a tool coordinate system through teaching; the hand-eye transformation matrix is used for indicating a transformation relation between a sensor coordinate system and a mechanical arm tail end coordinate system; and according to the conversion relation and the accurate transformation matrix, the grabbing position coordinates of the target object under the mechanical arm base coordinate system are solved. According to the invention, point cloud registration is carried out through the deep learning network, and the position of the target object can be determined more accurately.

The invention provides a user credit scoring method and system based on multi-source behavior data, and the method comprises the steps: obtaining user behavior data, so as to construct a standardized multi-module behavior event record; taking a user as a unit, using the behavior event record as a node to construct a behavior graph, and constructing a directed edge according to a timestamp of event occurrence; based on the behavior map, constructing a user behavior path by using a constrained maximum edge weight path search algorithm; performing structure matching on the high-credit user behavior path template set and each user behavior path, calculating a path deviation degree of each user behavior path, and obtaining a minimum deviation score; and in combination with the minimum deviation score and the matched optimal matching path, predicting a risk tag of the user through a credit scoring model based on dichotomy learning, and outputting a customer credit score.

The invention provides a code reuse method and device based on AI drive, equipment and a medium. When a code submission event is detected, a code scanning process is triggered, a code analysis module driven by AI carries out multi-dimensional analysis on submitted codes, quantitative evaluation is carried out on the basis of a preset reusability evaluation model, and code snippets meeting a reuse standard are stored in a code knowledge base with a semantic index structure; analyzing annotation semantics based on a natural language processing technology through an AI retrieval engine, calculating semantic similarity between demand description and code snippets in a knowledge base in combination with a deep learning model, and positioning the code snippets which are optimally matched; after code multiplexing execution, performing incremental modification on the multiplexing code snippets and automatically generating change records; and carrying out value layering on the code snippets based on the Pareto analysis principle by using frequency trend prediction. Through combination of the AI technology and code reuse, the development efficiency can be remarkably improved.

The invention discloses a battery capacity prediction and state evaluation method and system based on multi-model collaborative learning, and belongs to the technical field of battery management. The method comprises the following steps: constructing a database containing multiple lithium ion battery long-term cycle data, and classifying according to a capacity attenuation trend; cleaning and preprocessing short-term cycle data of the to-be-tested battery; matching the to-be-tested data with the long-term attenuation trend in the database by using a clustering algorithm, and determining an optimal matching trend; distributing weights for the data in the matching trend by adopting a correlation algorithm, and generating initial capacity attenuation prediction; performing sequence correction on the preliminary prediction in combination with meta-learning and a related model, and generating a smooth future attenuation trend conforming to a physical law; and outputting a capacity prediction and health state evaluation result, and evaluating the prediction precision through a root-mean-square error and an average absolute percentage error. The method significantly improves the precision and generalization ability of long-term capacity prediction, and is suitable for various scenes such as electric vehicles, energy storage systems, consumer electronics and the like.