1913 results about "Data association" patented technology

Disclosed are a high-performance loosely-coupled multi-modal data fusion system for a smart driving environmental perception system and a vehicle-mounted device, comprising: a fusion detection model based on a modality-independent feature interaction strategy, which is configured for converting a LiDAR point cloud, a camera image, and a millimeter-wave radar point cloud into a unified bird's-eye view representation, and performing multi-modal fusion; and a fusion tracking model based on a motion-appearance feature cascaded coupling data association strategy, which is configured for performing subsequent trajectory tracking and matching according to multi-modal fusion feature information. A VoD data set and a K-Radar data set are selected for training, verifying, and testing the comprehensive performance of the models, and a TensorRT accelerated inference model is applied, then quantized, and deployed to a vehicle-mounted computational testing platform. The present invention is compatible with mainstream sensor deployment solutions, and achieves the efficient complementary fusion of multi-source heterogeneous sensor information, significantly improving the reliability, accuracy, and adaptability of vehicle-mounted perception systems, thereby effectively responding to extreme operating conditions such as complex traffic scenarios and inclement weather.

The invention relates to the technical field of tunnel power supply and distribution, in particular to a tunnel modular prefabricated cabin power supply and distribution intelligent substation self-adaptive regulation and control system based on edge calculation. Comprising an edge calculation and AI decision-making unit which is used for realizing rapid acquisition, processing and instant decision-making of tunnel power supply and distribution multi-dimensional data, generating a power supply and distribution adaptive regulation and control strategy by deploying calculation resources and a machine learning algorithm at edge nodes close to a data source, and converting the strategy into an executable regulation and control instruction; a cloud platform collaborative management unit; and an intelligent sensing and internet-of-things unit. According to the invention, hierarchical decision control of the tunnel power supply and distribution system is realized by constructing a hybrid architecture of edge computing and cloud platform collaboration and a priority judgment mechanism; according to the invention, multi-modal data are integrated through the multi-protocol communication link module and the full-scene data fusion analysis module, and data association analysis is realized through Kalman filtering, D-S evidence theory and other algorithms.

The invention provides a multi-source data real-time fusion processing method and system for a mobile intelligent device, and relates to the technical field of data processing.The method comprises the steps that 1, multi-dimensional original data streams are collected in real time through a heterogeneous sensor array integrated by the mobile intelligent device, data streams of different sensors are aligned by applying a space-time synchronization mechanism, and the data streams of different sensors are obtained; generating an original data set with consistent time and space; 2, dynamic interpolation compensation operation is executed on the original data set, and a dynamic calibration framework is constructed based on the internal topological relation of the data flow to form a dynamic sensing domain; and generating an evolution sequence according to the data unit evolution behavior of the domain boundary, generating a space correction value through the evolution sequence and the offset feature of the preset reference, and generating preprocessed data fused with the space correction value in combination with real-time data correlation analysis. According to the method, dynamic adjustment is triggered through anomaly detection, the fusion parameters are updated through the sliding window, and real-time efficient fusion processing of multi-source data of the mobile intelligent device is achieved.

The invention provides an electronic information data association method and system based on the Internet of Things. The method comprises the steps that a parallel processing architecture is adopted to execute rapid protocol conversion on a high-priority data packet, data recombination is directly carried out through a pre-compiled conversion rule template, a complex format verification link is skipped, and the integrity and format correctness of the converted data packet are determined; response time and error rate statistical information in the data transmission process are obtained, protocol conversion rules and scheduling strategies are optimized through a feedback mechanism, and if the success rate of certain protocol conversion is lower than a standard value, corresponding mapping rules and verification algorithms are updated; and carrying out standardized packaging on all converted data by adopting a unified data exchange standard, adding an equipment identifier, a timestamp and a data type label, establishing a cross-protocol general data format, and carrying out final distribution after judging whether a data packet meets a standard format requirement.

Systems and methods are disclosed for condition identification. One or more processors may receive a member data object with indicators and dimensions, access a member-specific graph network with nodes representing attributes and weighted edges indicating associations, modify the nodes and edges based on the member data object, generate a multi-modal graph database by combining the modified member-specific graph network and a disease graph network, apply the multi-modal graph database to an attention-based graph neural network (GNN) that identifies associations between nodes by dynamically allocating attention weights to edges, generate an embedding data object with node identifiers and vectors representing features and relationships, select a target node associated with condition data, apply the embedding data object to a classification layer that outputs predicted conditions for the target node, and generate the probability of predicted conditions appearing in the target node.

The invention relates to the technical field of business data insight, and discloses a method for disassembling insight business data through indexes. The method comprises the following steps: firstly, constructing a multi-dimensional index system, determining key business indexes and associated dimensions, and outputting an index disassembling framework; the method comprises the following steps: collecting multi-source data in a business process, performing cleaning and standardization processing, extracting index characteristics and dimension attributes, and constructing a business data association graph; introducing an adaptive weight distribution mechanism and a hierarchical iterative algorithm to optimize a preset attribution model, and generating a preliminary index contribution degree sequence based on an index disassembly framework; setting a monitoring threshold value of each dimension index, and tracking service data in real time; dynamically adjusting an index weight and a model parameter according to a tracking result, and recalculating and updating an index contribution degree sequence; and evaluating the service health degree, identifying abnormal index nodes, and starting a root cause analysis process for the abnormal index nodes. According to the method, multi-dimensional and dynamic insight of the business data can be realized.

The invention discloses a hyper-converged server resource pooling method and system, and belongs to the technical field of resource scheduling, and the method comprises the steps: collecting spatial information, generating a multi-dimensional resource topological graph, abstracting edge node and core node resources into virtual units with position labels, constructing a virtual resource pool, and generating a three-dimensional metadata table; constructing an independent double-queue architecture, configuring a token bucket, triggering a queue clearing mechanism based on a control period, and performing micro-batch merging on edge node short transactions; binding adjacent virtual units by taking the physical position of the equipment as a fragmentation primary key, monitoring cross-virtual unit access traffic, migrating a high-frequency data copy and redirecting a write request, and adjusting fragmentation granularity according to data relevance; the virtual unit closest to the equipment group is selected as a main node in the data fragments, write operations are combined, low-delay paths are selected for synchronization, an asynchronous confirmation mechanism is adopted to respond to edge nodes, the local resource hit rate and the task processing efficiency are improved, and the real-time performance and reliability of industrial control are guaranteed.

The invention relates to a data processing method for iterative sensor fusion. The method comprises receiving sensor data from a first data source type and a second data source type. The first data source type is adapted for providing sensor data for exclusively determining an entity and the second data source type is adapted for providing sensor data of a tracked entity. The method further comprises generating a data source identifier for each of the sensor data. The data source identifier comprises a data source type identifier corresponding to a data source type from which the sensor data is received. A first data source type identifier corresponds to the first data source type and a second data source type identifier corresponds to the second data source type. The method further comprises compiling a dataset that comprises newest sensor data received, and iteratively performing a data association step on the dataset.

The invention discloses a ship-shore cooperative tracking and positioning method based on multi-modal sensor fusion, and belongs to the technical field of target positioning. The method comprises the steps that multi-sensor layout and sensor fusion calibration are carried out on a target ship set and a shore end respectively, multi-view image sequence data and three-dimensional point cloud data are obtained, and the target ship set comprises a plurality of target ships; performing data fusion based on the multi-view image sequence data and the three-dimensional point cloud data to obtain fusion data of the target ship set, establishing an adaptive motion state model and an adaptive observation model based on the fusion data, and performing state prediction, state updating, data association and tracking management on the target ship set by adopting an unscented Kalman filtering algorithm; and establishing a space-time diagram model based on the Kalman filtering fusion observation factor and the Kalman filtering state prediction factor, and performing pose optimization on the target ship set in combination with the GPS factor. According to the method, the accuracy of ship and ship-shore cooperative positioning is improved.

The invention discloses a hospital project intelligent management and collaborative operation optimization system based on an AI algorithm technology, and the system comprises a multi-modal data collection module which is used for obtaining hospital operation data, medical equipment data, patient vital sign data and medical staff operation records in real time; the data fusion engine, the dynamic resource scheduling module, the risk prediction unit and the collaborative work platform provide intelligent task distribution, conflict detection and emergency plan generation functions; the self-adaptive optimization controller is used for continuously updating system parameters through an online learning mechanism; the method breaks through the limitation of traditional single-mode data processing, constructs a cross-department data association model through a graph neural network, integrates heterogeneous data sources such as radiation images, inspection reports and equipment logs, realizes dynamic mapping of diagnosis and treatment paths and resource states, introduces a space-time attention mechanism, and improves the diagnosis and treatment efficiency. Equipment operation cycle characteristics and patient movement tracks are subjected to combined modeling, so that the sensing precision of the system is improved to a minute level.

The invention discloses a low-carbon building monitoring system and method based on digital twinning, and relates to the technical field of digital twinning, the system comprises a data acquisition unit, a computing power cooperation unit, a regulation and control execution unit and a user interaction unit, the data acquisition unit carries out regional targeted timing monitoring, builds a unified semantic standard and a dynamic knowledge graph, and sends the unified semantic standard and dynamic knowledge graph to the user interaction unit; the comprehensiveness and efficiency of data association analysis are improved; a three-level computing power collaboration system is constructed through the computing power collaboration unit, building monitoring information is integrated, complex simulation of a digital twin module is carried out, an energy transmission path and equipment fault risks are accurately tracked, prediction optimization and long-term regulation and control management are carried out, and the construction efficiency is improved. Therefore, a full-life-cycle integrated management framework in a macroscopic geographical environment, a mesoscopic energy link and microscopic equipment of a building cluster is built, cross-domain collaborative analysis is realized, the real-time requirement is met, multi-target optimization design for balancing energy cost, energy efficiency and comfort is realized, and carbon neutralization targets in the building industry are assisted to fall into the ground.

The invention relates to the technical field of power distribution network dispatching. By providing a power grid load dynamic prediction and optimal scheduling method, device, equipment and medium, the method comprises the following steps: performing multi-source heterogeneous fusion processing on meteorological parameters, historical load curves and new energy output data to generate a dynamic load prediction map; constructing a dynamic network model, and performing power flow distribution simulation processing based on the dynamic network model to obtain a stability margin calculation result and a preset safety threshold boundary; performing stage decomposition processing on the global scheduling target to generate a progressive scheduling stage sequence; performing matching processing on the response characteristics of the power generation equipment to generate a self-adaptive progressive scheduling instruction sequence; and executing an adaptive progressive scheduling instruction sequence, performing feedback processing on the real-time state of the power grid, and generating a dynamic adjustment instruction so as to realize multi-dimensional data association modeling, stability margin quantitative analysis and dynamic instruction optimization, thereby improving the load prediction precision and reducing fault diffusion and transient oscillation.

The invention relates to the technical field of life cycle management, in particular to a catalyst industry chain-oriented full life cycle traceability method and system, and the method comprises the steps: obtaining the state attribute data of the same batch of recovered products, and carrying out the analysis to obtain an efficiency feature vector; inputting the efficiency feature vector into a preset quality evaluation model for matching, and identifying quality deviation data of the recycled product; the batch identity codes are analyzed, and resume data of the production full life cycle are obtained; a multi-dimensional data association model is constructed, quality deviation data is taken as a target variable, resume data is taken as an input feature, an influence weight coefficient and an attribution confidence index of each production control variable on the quality deviation are analyzed, and key control elements are screened out; and performing production regulation and control optimization operation based on the key control elements, calculating target parameter offset for correcting quality deviation, dynamically calculating a dynamic tolerance constraint range of a production control variable in combination with an attribution confidence index, and packaging into an execution management and control protocol.

The invention discloses an intelligent monitoring method and system for the construction progress of a building decoration curtain wall. The method comprises the steps that multi-source construction data of a construction site are collected through Internet of Things equipment and a high-definition camera; constructing a multi-dimensional tensor model and extracting data association features by applying a local reversible mapping algorithm to generate a standardized feature data set; performing parallel computing through shared memory hierarchical process mapping by utilizing the standardized feature data set, and outputting a project progress state evaluation result; constructing a decision diagram based on the project progress state evaluation result, and generating a construction progress deviation report; predicting a construction progress trend by adopting a sample optimal private regression algorithm and generating an optimization scheme; and visually displaying the construction progress prediction report and carrying out early warning. According to the invention, the problems of low data integration efficiency, delayed decision response and weak multi-dimensional data association analysis capability of traditional curtain wall construction monitoring are solved, and intelligent and efficient monitoring of the construction progress of the building decoration curtain wall is realized.

The invention relates to the technical field of community security, in particular to an intelligent community security management system and method, and the method comprises the steps: collecting equipment data, verifying the daily operation of equipment, collecting personnel and event data, and carrying out the data association and data integration; when an abnormal event is identified, determining a basic level according to the integrated data, determining an associated risk probability through a Bayesian network model, inputting the basic level and the associated risk probability into a dynamic weight adaptive grading model, and determining a final level of the event; inputting the obtained event final grade into a trend prediction model to obtain a risk prediction value, determining a comprehensive risk value of each region according to the risk prediction value, and generating a real-time risk thermodynamic diagram; and according to the final grade of the event and the associated data, screening the processing personnel meeting the conditions to perform task assignment, and performing event processing by the processing personnel. According to the scheme, by constructing the equipment, event and personnel association chain, dynamic verification and intelligent grading are realized, and the management efficiency is improved.

The invention belongs to the technical field of electric power environment monitoring, and particularly relates to an intelligent terminal data acquisition control system and method based on an electric power environment. The intelligent terminal data acquisition control method based on the electric power environment comprises the following steps that S10, an intelligent terminal acquires environment data and operation data in a target object, associates the environment data with the operation data according to acquisition time, generates an associated data set with a timestamp and stores the associated data set to the local; and S20, the intelligent terminal calls the local historical fault data and the associated data set, and an analysis model is constructed based on a multi-factor coupling analysis model. According to the scheme, the problem that the comprehensive assessment accuracy of the overall operation risk of the target object is low due to the fact that the intelligent terminal cannot capture the hidden risk formed by multi-factor coupling is solved through multi-factor coupling analysis, and meanwhile, the fault probability is cooperatively reduced, the environmental adaptability is enhanced, decision-making intelligence is achieved by combining hierarchical response and dynamic self-adaptive adjustment, and the risk assessment efficiency is improved. And full-life-cycle intelligent management of the power equipment is achieved.

The invention discloses a human resource data natural language query and SQL generation method and system, and relates to the technical field of SQL generation, and the method comprises the steps: preprocessing a natural language query text input by a user, and constructing a query semantic tree; traversing the query semantic tree to generate a candidate entity set, determining an unambiguous entity through a three-layer ambiguity resolution mechanism of semantic scoring, service verification and cross validation, identifying a composite intention, disassembling the composite intention into a sub-intention sequence, and generating an SQL operation instruction set in combination with a preset SQL operation mapping rule; based on the SQL operation instruction set and the human resource data association model, determining a data table set and an association path, generating a dynamic calculation logic and structured condition expression, and constructing a structured query skeleton; according to the method, the personalized SQL template is dynamically generated according to the query skeleton, after filling, grammar verification, strategy optimization and logic consistency verification are performed, and the optimized SQL statement is output, so that the query accuracy and performance are remarkably improved.

The invention relates to a fraud website identification early warning method, device and equipment and a storage medium thereof. The method comprises the following steps: firstly, acquiring network address information, then extracting feature data from dimensions such as structure, semantics, behavior and vision, accelerating similarity detection by applying a graphics processor parallel computing technology, and generating a composite feature vector containing a domain name similarity score and a registration feature; constructing fraud features in combination with the composite feature vector, mining a data association relationship through a graph neural network, and forming multi-dimensional risk feature data; and finally, processing the risk feature data by using a deep learning model, generating a risk probability, and when the risk probability exceeds a preset threshold, determining that the network address is a high-risk network address and generating an early warning report. By adopting the method, the comprehensiveness and accuracy of fraud website identification can be improved, complex fraud means such as domain name variation and semantic camouflage can be effectively dealt with, the real-time early warning capability for potential risks is enhanced, and efficient technical support is provided for network security protection.

The invention discloses a tunnel construction progress digital twinborn prediction and resource scheduling system and method, relates to the technical field of tunnel construction, and provides the following scheme that the method comprises the steps that real-time monitoring data of a tunnel construction area are collected, the real-time monitoring data comprise equipment position information and process progress quantitative data, and a BIM model of the tunnel construction area is constructed; dividing the equipment position information into a plurality of continuous time windows according to time nodes of the BIM model, and obtaining equipment operation parameters and corresponding energy consumption values in each time window; when the effective energy consumption is calculated, the geological condition grade weight and the construction process reference value are introduced to dynamically correct the initial efficiency coefficient, and meanwhile, the geological condition fluctuation, the equipment performance attenuation and the process connection change in the construction conditions are reflected through the time attenuation coefficient, so that the nonlinear influence of the geological difference and the process adjustment on the energy consumption efficiency is effectively eliminated; the strong relevance between the energy consumption and the progress is established, and a reliable data association basis is provided for progress prediction.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a personalized scheme generation method, device, equipment and medium, and the method comprises the steps: collecting multi-modal data, and carrying out the preprocessing of the multi-modal data to generate a preprocessed data set and a data gateway lead; the fusion processing module performs cross-modal interaction and attention interaction processing on the preprocessed data set based on the data gateway association to generate a comprehensive feature vector; inputting the comprehensive feature vector into a recognition model to generate an object dynamic portrait; generating a personalized optimization path set based on the dynamic image of the object; and extracting a demand intensity item, a preference weight item and an emotional state item, and generating a personalized scheme in combination with the personalized optimization path set. According to the method, the object dynamic portrait containing the demand, preference and emotional state is generated through multi-modal data fusion and feature extraction, and a personalized scheme is output in combination with an optimized path mechanism, so that demand description comprehensiveness and scheme generation precision are realized.

The invention provides a polyester fabric production whole process monitoring management method and system. The method belongs to the technical field of production process monitoring. The method comprises the following steps: deploying various types of sensors at each key node of a polyester fabric production line; various physical parameters and environment information related to polyester fabric production are collected in real time; meanwhile, an operation state monitoring system of the production equipment is integrated to obtain equipment data; carrying out deep fusion processing on multi-source heterogeneous data obtained from the polyester fabric production holographic sensing architecture; constructing a data association model; comprehensively evaluating the production situation based on the model in combination with the technological process and the quality standard of polyester fabric production; generating a production situation assessment report through production situation assessment; the whole-process data tracing system provides a traceable basis for quality disputes, raw material problems and the like, enhances the capability of controlling supply chain risks, meets the requirements of downstream customers on product traceability, and improves the brand credibility.

The invention provides a mass data association fusion method and device and a medium, and the method comprises the steps: firstly receiving mass data from a plurality of heterogeneous data sources, and carrying out the preprocessing of the data; then, the data stream is integrated into an annotated data stream; wherein the labeled data stream comprises a two-dimensional semantic vector and a dynamic quality label; and finally, inputting the data into an AI association fusion model to obtain a fusion result. Wherein the association fusion model is provided with an association fusion and semantic mapping layer, and is used for carrying out association fusion according to the layered dynamic knowledge graph and a preset fusion rule, and carrying out semantic mapping according to the twin-tower sub-model and a consistency judgment rule. According to the method, real-time updating of the incidence relation is achieved through the layered dynamic knowledge graph, and the real-time scene requirement is met; the association complexity of mass data is reduced by using a double-tower model, and the processing speed is increased; and in combination with the judgment rule, the conflict resolution precision is improved. Finally, efficient, accurate and real-time fusion of mass data is realized, and a reliable basis is provided for analysis and decision of various data.

Systems and methods for embedding-based LLM tuning include generating a first embedding of received user input data and utilizing a translation model trained to associate user input with device names to generate a second embedding that differs at least in part from the first embedding. Reference embeddings corresponding to devices associated with user account data may be generated and a subset of the reference embeddings that satisfy a threshold similarity to the second embedding may be determined. A large language model (LLM) configured to determine a response to the user input data may utilize data for a subset of devices that correspond to the subset of the reference embeddings for performing speech processing.

The invention relates to the technical field of data processing, and particularly provides a multi-source heterogeneous data integration method, system and device and a storage medium, and the method comprises the steps: obtaining multi-source heterogeneous data, and generating an identification attribute for each piece of heterogeneous data; dividing the heterogeneous data with the same identification attribute into the same data group; converting the heterogeneous data in the data group into standard data in a specified format; based on a predefined integration rule, integrating the plurality of pieces of standard data converted from the data set into one piece of complete data, the integration rule comprising a conflict resolution strategy and / or a data association rule; and storing the complete data to a corresponding position of a database according to a predefined storage strategy. According to the method, effective integration of multi-source heterogeneous data is realized, and the data analysis efficiency and quality are improved.

The invention relates to the field of semiconductors, in particular to an answer generation method and device based on a semiconductor knowledge base and a medium. The method comprises the steps of performing data association on structured knowledge according to a semiconductor domain knowledge structure to construct a class of knowledge bases; performing data recombination on the unstructured knowledge according to the layout so as to construct a second-class knowledge base; a query question is received, retrieval is executed in the first-class knowledge base and the second-class knowledge base based on a preset retrieval model, and a plurality of related knowledge fragments are obtained; determining a first fusion weight between the knowledge fragments of the same knowledge type based on the relevancy between each knowledge fragment and the query question and the update time of each knowledge fragment; determining an optimal knowledge type according to the group feature information, and determining a second fusion weight between different knowledge types according to the optimal knowledge type; and based on the first fusion weight and the second fusion weight, obtaining a reference answer according to the plurality of knowledge fragments. And the requirements of the semiconductor field on information comprehensiveness and answer accuracy are considered.

The invention relates to the technical field of form design, in particular to a business form design method based on a visual component. The method comprises the following steps: constructing a multi-type component resource pool in a business form design system, and presetting a standardized configuration parameter set for each component; realizing a component dragging interaction mechanism on the visual design interface, and generating initial layout data at the same time; constructing a component configuration context environment based on the initial layout data, and generating intermediate state form definition data; an index data association mechanism is achieved in the intermediate state form definition data, mapping binding is conducted on the intermediate state form definition data and form components, and a form data structure containing data flow direction definition is generated; and constructing a cross-platform rendering adaptation engine based on the form data structure to automatically generate a form rendering code so as to realize a form design real-time preview system, and providing an interactive debugging tool set for verifying the functional integrity of the form. According to the invention, efficient design and flexible deployment of the business form can be realized.

The invention relates to the technical field of point cloud data extraction, in particular to a river water surface extraction method based on SWOT point cloud and center line data. The method comprises the following steps: acquiring PIXC point cloud data; performing data preprocessing on the PIXC point cloud data, and performing spatial index construction to obtain point cloud KD tree index data and center line KD tree index data; based on the point cloud KD tree index data and the center line KD tree index data, carrying out topology shape-preserving cutting to obtain river cutting center line data; performing candidate water surface point set construction on the river cutting center line data; therefore, through multi-level spatial index construction, topological conformal cutting, combined space-elevation modeling and structural processing of geometric and hydrological feature fusion, the problems of difficulty in data association, much space isolated noise and insufficient water surface continuity recognition in traditional river water surface extraction are solved; and the extraction precision and the processing efficiency of the river water surface point cloud data are improved.

The invention relates to the technical field of pedestrian floor tile monitoring, and discloses a pedestrian floor tile road condition analysis method based on a knowledge graph. The method comprises the following steps: firstly, acquiring spatial topological data and historical maintenance records of road infrastructures, fusing real-time road condition monitoring data and environmental parameters acquired by a multi-source sensor, and constructing a pedestrian floor tile state knowledge graph structure; analyzing a data association relationship through a map entity alignment algorithm to generate road condition evaluation features, and updating map node weights by using a dynamic path optimization algorithm in combination with real-time data and environmental parameters; then, based on the updated node weight and evaluation features, an intelligent agent decision engine is operated to output a floor tile area risk evaluation result; and finally, collecting whole-process data, and optimizing a knowledge graph structure by using an incremental graph updating algorithm. According to the method, multi-source data fusion and dynamic analysis are realized, and delicacy management of pedestrian floor tile road conditions is assisted.

The invention provides a manufacturing abnormality diagnosis method and system based on a production data stream, and relates to the technical field of artificial intelligence, and the method comprises the steps: collecting an initial multi-source heterogeneous data stream of a production site, and implanting virtual data anchor points corresponding to key production equipment, process nodes and quality monitoring links in the initial multi-source heterogeneous data stream; performing spatial point set construction and tetrahedron inclusion detection based on the virtual data anchor points to perform data relevance quantitative evaluation and pruning on the initial multi-source heterogeneous data stream to generate an optimized multi-source heterogeneous data stream; and based on a pre-established unified semantic metadata dictionary, performing semantic analysis and normalization processing on the optimized multi-source heterogeneous data stream, and generating a standardized production event stream with unified semantics and time sequence and product identification. According to the invention, accurate diagnosis and root cause positioning of manufacturing abnormality can be realized.

The invention relates to the technical field of image recognition, in particular to an AI-based park intrusion detection and response system, which comprises a target trajectory acquisition module, an event density analysis module, a disturbance behavior recognition module and a response joint control execution module. According to the invention, through fusion of the spatial-temporal characteristics of the target trajectory and motion vector pattern recognition, intrusion behavior dynamic risk assessment and early warning collaborative optimization is realized, and through combination of multi-dimensional data association calculation and event density increment monitoring, abnormal situation sensing sensitivity and discrimination accuracy in a complex scene are improved. Motion vector time sequence modeling and disturbance feature extraction are utilized, the capacity of capturing camouflage behaviors and irregular motion is enhanced, an environment perception and response execution closed-loop control link is constructed by means of intelligent matching and parameter self-adaptive adjustment of multi-source sensing equipment, the dependency degree of manual review is remarkably reduced, the delay from recognition to treatment response is shortened, and the recognition efficiency is improved. And an early warning research and judgment and linkage disposal full-process automation system is formed.