72 results about "Spatial model" patented technology

The invention discloses a three-dimensional digital twin modeling method, system, equipment and medium taking a reservoir as an object, and relates to the technical field of reservoir monitoring, and the method comprises the steps: constructing a three-dimensional space model facing a reservoir entity, and forming a visual simulation environment; accessing multi-source data related to reservoir operation, and establishing a unified data aggregation and management mechanism; integrating a real-time operation state in the three-dimensional space model, and dynamically mapping perception information and business activities of the reservoir; and based on a set analysis model and a determination rule, carrying out intelligent processing on the reservoir operation data, identifying potential risks and generating an early warning result. According to the invention, by establishing a multi-type analysis model and a judgment rule system, real-time calculation and risk identification are carried out on key indexes, intelligent early warning of risks such as emergencies, trend anomalies and historical extreme value overrun is realized, and the risk prevention and control capability of the reservoir is effectively improved.

The invention discloses an AI-based building design scene automatic intelligent generation method and system, and relates to the technical field of building design, accurate basic data is provided for subsequent design by constructing a building related data set H, then a three-dimensional space model of an ancient building is generated by using 3D modeling and image recognition technologies, and the three-dimensional space model of the ancient building is obtained. According to the method, the to-be-repaired model area is marked, the recognition accuracy of the to-be-repaired model area is improved, in material suitability analysis, the building repair material is selected according to the material suitability score CL, the material compatibility and the repair effect are ensured, a first ancient building repair design scheme is obtained according to the AI model, meanwhile, finite element analysis is conducted, and the recognition accuracy of the to-be-repaired model area is improved. According to the method, it is ensured that the repaired historic building can bear loads and keep stability, finally, through target function optimization and simulation, an optimal historic building repair design scheme is output, a repair plan is executed, high efficiency and accuracy of the historic building repair process are ensured, and the repair quality of the historic building is improved.

The invention provides a traffic flow prediction method combining a dynamic graph neural network and a Mama mechanism. The future short-term traffic flow is predicted by using historical traffic data. According to the method, firstly, normalization preprocessing is carried out on traffic state data collected by multiple sensors, a training sample is generated by adopting a sliding window, and a traffic flow value in the next one hour is predicted according to data in the past 24 hours. On the basis of the model structure, a time modeling module composed of multiple layers of MambaBlocks is constructed and used for capturing historical time sequence dependence; constructing a spatial modeling module of dynamic graph convolution, and combining a static adjacency matrix of the road network with a learnable adaptive adjacency structure to extract spatial association; and finally, the outputs of the modules are fused, and a prediction result is obtained through a prediction output module. In the training process, a Huber loss function is used as an optimization target, and evaluation indexes such as a mean absolute error (MAE), a root mean square error (RMSE) and a mean absolute percentage error (MAPE) are used for evaluating the performance of the model. According to the method, the traffic space-time dynamic characteristics are effectively mined, and the long-range dependence modeling capability and the prediction precision are improved.

The invention discloses a foggy day real-time target detection method and system based on Fourier transform image defogging, and relates to the technical field of image target detection. The method comprises the following steps: preprocessing data, normalizing the size of an image, implementing a multi-dimensional image enhancement strategy, and designing a self-adaptive amplitude enhancement module based on fast Fourier transform to carry out defogging processing on the image; feature extraction: inputting the preprocessed image into a backbone network to generate feature maps with different scales, and enhancing the spatial modeling capability of the feature maps by a spatial modeling SimAM module by using a SimAM attention mechanism; feature fusion: adding a weight convolution pyramid module into the neck network to form a feature pyramid architecture, and performing multi-scale feature fusion operation on different-scale feature maps to obtain multi-scale semantic features; information post-processing decoding and prediction are carried out, after the multi-scale semantic features are decoupled through a detection head, two independent tensors of classification and positioning are generated, and tensor information is analyzed to obtain a detection result. The accuracy of the detection model is improved.

The invention relates to an urban green land high-resolution remote sensing monitoring method. The method comprises the following steps: data acquisition and preprocessing: synchronously acquiring point cloud data, hyperspectral data and multi-temporal remote sensing data; feature engineering: constructing a multi-dimensional feature space, and optimizing the multi-dimensional feature space through feature selection; model training: on the basis of the optimized multi-dimensional feature space, performing filling, differential normalization processing and regularization improvement on a U-net + model through pixel values of the edges of mirror images in sequence for urban green land features, and establishing the urban green land-oriented and multi-dimensional feature-based U-net + model, so that the urban green land-oriented and multi-dimensional feature-based U-net + model is obtained; adding the multi-dimensional feature data of the training sample into the U-net + model for training, predicting the spatial distribution of the urban green land after the training is completed, obtaining a prediction result of the U-net + model, and combining a mixed loss function with an adaptive training strategy; and post-processing and result generation: applying adaptive CRFs to optimize a U-net + model prediction result, generating a high-resolution urban green land distribution map, and outputting a precision evaluation report.

The invention relates to an inshore water quality day-by-day kilometer-level inversion method, system, device and medium, and belongs to the technical field of water quality monitoring, the method comprises the following steps: obtaining remote sensing image data, meteorological reanalysis data and ground water quality site measured data, preprocessing, carrying out space-time matching fusion, using the fusion data as a sample to carry out model training, and obtaining a model training model; obtaining a space model; a spatial model offshore water quality spatial distribution diagram; and constructing a CNN-BiLSTM mixed time sequence model, and training the CNN-BiLSTM mixed time sequence model by taking historical water quality data of a ground water quality station and the offshore water quality spatial distribution map as samples to obtain a prediction model. The method is suitable for red tide early warning, pollution tracing and marine ecological management, breaks through monthly monitoring limitation, and realizes day-by-day kilometer-level water quality dynamic tracking; remote sensing large-range coverage and site high-precision data are fused, and the problem of inshore environment heterogeneity is solved; the time-space double-model cascade architecture significantly improves the generalization ability under complex hydrological conditions.

The invention belongs to the technical field of storm surge disaster early warning, and relates to a coastal town storm surge disaster intelligent prevention and control system and method.The system comprises an internet-of-things sensing layer, a data resource platform and an application supporting platform, the application supporting platform comprises a forecasting module, and the forecasting module is based on a multi-layer nested storm surge model; through nesting calculation of an open sea grid and a near-shore grid, the dam overtopping water flow and the water level and range of a submerged area are predicted; the early warning module is used for dynamically analyzing hydrology and water quality data in combination with a risk threshold value, generating an early warning signal and visually issuing the early warning signal through a G IS engine; the rehearsal module is used for carrying out real-time spatial model rendering on a prediction result of the prediction module by adopting an OpenGLES underlying graphic API and a GPU rendering pipeline optimization technology so as to realize restoration of dynamic change of a physical world; according to the system, the precision and the real-time performance are considered, a forecasting, early warning, rehearsing and pre-planning full-linkage module is constructed, and the decision response speed is improved.

The invention provides a cultivated land non-cultivation data processing and analysis system and method. The system comprises a non-cultivation rate influence factor analysis module and a non-cultivation rate prediction module. The non-farming rate influence factor analysis module integrates a geographic detector model, a panel model, a space model, a space-time geographic weighted regression model and other modules to obtain a significant influence factor result of non-farming in a target area; and the non-farming rate prediction module comprises a panel model prediction module and a time t prediction module to predict the non-farming rate corresponding to each administrative region in the next year. Based on the technical scheme of the invention, multi-source heterogeneous data such as land utilization, climate environment and the like are integrated; index classification is more detailed and comprehensive, so that an analysis result is more effective; time and space factors are fused, and variables of different regions are analyzed more accurately; the factor identification accuracy is remarkably improved, the space prediction precision is remarkably improved, and the time sequence prediction error is remarkably reduced.

The present invention discloses a tag positioning method based on RFID technology, which mainly eliminates the positioning position interference problem caused by a single tag being read by multiple antennas. The method adopts the steps of designing a three-dimensional spatial model of the positioning area and combining it with a positioning algorithm for judgment. The interference values ​​are eliminated in sequence by using the advantages and disadvantages of the antenna spatial points, an RSSI ranking table combined with a tag data information table, and the weighted total score of the three major influencing factors of the tag data information. The method realizes the precise positioning of multiple tags in the application scenario, further reduces the misjudgment results, and improves the positioning accuracy of the tags.

The embodiment of the invention discloses a camera arrangement evaluation method and device based on operation data and a medium. The method comprises the steps that a three-dimensional space model of a to-be-evaluated area is acquired and visually displayed, and a virtual monitoring scene is generated; in response to an adding operation of camera point locations, displaying a visible range of each camera in the virtual monitoring scene in an overlapping manner; in response to an import operation of historical operation data, superposing and displaying a thermodynamic diagram of historical people flow density and / or work order density in the virtual monitoring scene; according to a superposition display result, determining a coverage condition of the camera on the people flow density and / or the work order density; and evaluating the point location layout of the camera according to the coverage condition. According to the embodiment of the invention, intelligent and precise camera arrangement evaluation is realized.

The invention discloses a real-time monitoring and early warning method and system for a port engineering construction ship machine, and relates to the technical field of intelligent early warning, and the method comprises the steps: obtaining the geographic space information of a target port, constructing a three-dimensional geographic space model, fusing construction ship machine equipment and a port facility model, and generating a comprehensive port space model; the method comprises the steps of receiving real-time task data and construction progress information, locking a key monitoring area, performing regional centralized monitoring, obtaining a multi-source monitoring index set, performing dual-channel real-time risk assessment in combination with a comprehensive port space model, generating a comprehensive risk assessment result, and performing risk early warning management. The technical problems that an existing construction ship machine monitoring means cannot be adaptively adjusted according to the construction task progress, so that monitoring is not targeted, and the accuracy of risk assessment is affected are solved, and the purposes of dynamically adjusting the monitoring strategy according to the construction progress, accurately locking the key area, extracting the monitoring index and improving the risk assessment accuracy are achieved. And the accuracy of risk assessment is improved.

The invention relates to the technical field of data acquisition, and discloses a three-dimensional data acquisition management system and method, and the method comprises the following steps: S1, obtaining a three-dimensional space model of a to-be-acquired region, and discretizing the space into a plurality of sampling units to form a voxel grid; s2, identifying a shielding area in the three-dimensional space model, and constructing an effective and reachable three-dimensional acquisition space; s3, generating an initial layout point location according to the equipment view field parameters; s4, acquiring parameters and sampling unit positions based on the equipment; s5, constructing a joint optimization objective function; and S6, under the constraint condition, solving an optimal equipment layout point location and path control function through an optimization method. According to the method, the optimal equipment layout scheme and path control strategy are obtained by constructing the three-dimensional space acquisition model and adopting a plurality of factors such as joint optimization of the objective function, dynamic optimization of equipment layout points and paths, energy consumption and shielding area punishment, and the acquisition efficiency and the reasonability of path planning are improved.

The present invention discloses a digital management system for drainage pipe network operation and maintenance data, which relates to the field of digital management and includes an operation and maintenance management center, in which a pipe network management module, a data acquisition module, a data analysis module, a data collaboration module and an operation and maintenance management module are provided; the pipe network management module is used to construct a three-dimensional spatial model of the pipe network and set an operation and maintenance management node; the data acquisition module is used to collect pipeline drainage data; the data analysis module is used to obtain pipeline load data at various positions and set drainage pipe links; the data collaboration module is used to set collaborative drainage information at the operation and maintenance management node according to the pipeline load data and the drainage pipe link; the operation and maintenance management module is used to optimize the collaborative drainage information, obtain operation and maintenance decision information, and adjust the pipeline flow according to the operation and maintenance decision information; the present invention improves the collaboration and rationality in the operation and maintenance process of the drainage pipe network.

The application belongs to the technical field of height data analysis, and provides a DEM space model height point automatic extraction method, the application carries out clustering on the height data of sampling points by setting different clustering radii, judges the terrain integrity of each clustering range, and divides subspaces; analyzes the reference value of different subspaces on the height interpolation of the projection position point, obtains the reference height value of the projection position point; then analyzes the distance interval condition of each height data in the same region in different subspaces, adjusts the coordinate position of the projection position point, and obtains the corrected DEM space model; finally, the difference condition between the water flow trend and the actual terrain in the corrected DEM space model is analyzed, the reference height value of part of the projection position points is adjusted, and the twice corrected DEM space model is obtained. The accuracy of the interpolation height points of different positions is improved by the method, and the accuracy of the DEM space model construction is improved.

The invention relates to the technical field of data security management, and discloses a security management system for data access control. The system comprises an access request receiving unit, a hierarchical analysis unit, a space mapping unit, a permission decision unit and a permission execution unit. The access request receiving unit obtains a data access request of a user, and analyzes a request main body identifier and a request resource identifier. The layering analysis unit carries out layering processing on the resource identification and extracts resource level feature information. And the space mapping unit retrieves the historical access log according to the main body identifier, and constructs an access behavior space distribution model. And the authority decision-making unit generates a dynamic access authority strategy in combination with the resource level characteristics and the access behavior space model. And the permission execution unit controls the access operation of the user to the resources according to the strategy. The system can adapt to a complex data resource structure and realize fine-grained dynamic access control.

This invention belongs to the field of campus management and discloses a method for monitoring campus personnel behavior trajectories based on multi-source data fusion, including the following steps: acquiring multi-source data within the campus; preprocessing the multi-source data using a data weighted fusion algorithm; performing differentiated scene optimization processing on the collected facial images to output clear facial image frames and effective feature data; constructing real-time behavior trajectories of campus personnel based on a temporal modeling model using clear facial image frames, effective feature data, and preprocessed multi-source data; analyzing the real-time behavior trajectories through an anomaly detection mechanism to determine the trajectory anomaly status and classify the anomaly level; executing multi-channel alarm push according to the anomaly level; visually restoring the real-time behavior trajectories and historical behavior trajectories based on a campus spatial model; and absorbing new data and anomaly judgment results through an incremental learning algorithm to update the parameters of the temporal modeling model, anomaly detection mechanism, and differentiated scene optimization processing related models.

A method for generating a spatial model tile data organization structure, relating to the field of geographic information technology, is characterized by the following steps: generating tile network leaf nodes; generating tile network intermediate nodes; selecting an initial aggregation point for the tile leaf nodes and performing multiple aggregation operations with each leaf-level tile until all leaf-level tiles are aggregated to obtain multiple tile aggregates; binding the tile network leaf nodes to their parent tile network intermediate nodes based on the correspondence between the tile network leaf nodes and the aggregated tile network intermediate nodes, ensuring that each tile network intermediate node has a logical relationship with its child nodes; and generating a tile network root node. Advantages: Effectively generates a multi-level detailed tile organization structure for model data in a given data block; the level of detail in each layer of tile data is reasonable, and the data organization is efficient; the data storage scale of a single tile is low and stable, saving network bandwidth and improving file transfer efficiency.

The invention provides a logging analysis model construction method and system based on multilevel regional feature association, and belongs to the technical field of oil-gas exploration artificial intelligence. In order to solve the problems of high cost, poor generalization and the like of a conventional method for performing data correction by using a standard well, the method comprises the following steps of: firstly, constructing multi-level geographic region embedding according to well location hierarchical codes, and capturing regional geologic features; then, constructing a regional relation graph based on well location space coordinates; and finally, aggregating features of adjacent regions through a graph neural network, and fusing multi-level region features and logging data through gating weight. According to the method, automatic learning of regional features is realized through a technology of performing gating weight fusion on the logging data by using the multistage regional nesting embedding module, the graph neural network spatial modeling module and the logging data, dependence and cost on standard well correction are removed, and cross-regional prediction precision is remarkably improved by establishing the regional features.

The invention relates to the field of intelligent video monitoring and early warning, and discloses a water area video intelligent analysis and early warning method based on an AI intelligent agent, which comprises the following steps: acquiring unmanned aerial vehicle aerial survey data of a target river channel to construct a river channel space model, identifying a monitoring area including a first type and / or a second type of risk areas, establishing a water area video monitoring system corresponding to the monitoring area, the edge computing device and the camera identifier; generating an unmanned aerial vehicle inspection strategy by combining the hydrological season state, and enabling the unmanned aerial vehicle to inspect according to the strategy; when abnormal event indication information is received, triggering an edge computing device to carry out AI analysis on camera videos and unmanned aerial vehicle real-time data to obtain target characteristic parameters, and dividing events into low, medium or high risks according to the target characteristic parameters; and performing selective storage and scheduling on the video data of the low-risk events and the medium-risk events, and generating early warning for the high-risk events and pushing the early warning to the management terminal. Therefore, graded monitoring and graded treatment of the key risky areas of the riverway are realized.

This invention relates to the field of machining and intelligent control technology, and discloses a multi-process collaborative control system for machining equipment based on multi-source data. The system includes a spatial modeling module, a multi-source sensing and feature mapping subsystem, a reference calibration module, and a collaborative execution module. The spatial modeling module constructs a hierarchical partitioned spatial mesh model enveloping the workpiece and drives dynamic node growth. The multi-source sensing and feature mapping subsystem synchronously acquires operating status data, converts it into spatial attribute values, and stores them in voxel units. After the workpiece is transferred to a new process, the reference calibration module constructs a cross-process coordinate transformation matrix and performs pose transformation on the spatial attribute values ​​stored in the model. The collaborative execution module generates machining compensation increments based on the transformed spatial attribute values ​​and injects them into the CNC system. This invention solves the problem of physical feature discontinuity in multi-process flow, realizing spatial mapping and adaptive compensation from previous physical states to the current process.

The application discloses a terrain self-adaptive rainstorm waterlogging spatio-temporal joint prediction method and system, and belongs to the field of alarm responding to disaster events. In order to overcome the defects of the existing method, such as insufficient terrain adaptability of fixed feature weight, and prediction fragmentation caused by independent operation of the spatio-temporal model, the technical scheme of the application is as follows: on the one hand, a terrain self-adaptive dynamic feature weight mechanism is constructed, an adjustment coefficient is generated by quantifying the terrain undulation, and the feature factor weight of the geographical spatio-temporal weighted regression model is dynamically adjusted to adapt to the complex underlying surface; on the other hand, a spatio-temporal joint weighted fusion framework is proposed, a random forest spatial model and a PredFormer time series model are combined, the spatial prediction result is taken as the initial condition of the time series, and the time series collaborative prediction of the flooded range and the water depth is realized. The application is suitable for rainstorm waterlogging prediction of different terrains in cities, and has the advantages of strong terrain adaptability, high prediction accuracy and stability, and unified spatio-temporal rapid response.

The invention provides a payment flow prediction method and device, electronic equipment and a storage medium, and is applied to the technical field of artificial intelligence. The method comprises the following steps: acquiring multi-dimensional time sequence data of life payment of a target area through a life payment system within a set historical duration; performing preprocessing of corresponding dimensions on each dimension of time series data in the multi-dimension time series data to obtain feature vectors of the corresponding dimension of time series data; according to a preset splicing sequence, sequentially splicing the feature vectors of the time sequence data of the corresponding dimensions to obtain multi-dimensional feature vectors; and the multi-dimensional feature vector is inputted to a time specialized model for fee-paying flow prediction, the predicted fee-paying flow of the target area in the future duration is obtained, and the time specialized model is obtained by removing a spatial modeling part irrelevant to fee-paying flow prediction in a space-time diagram convolutional network model based on geographical island characteristics. The method is used for achieving the effects of accurately predicting the payment flow and further accurately capturing the payment flow peak value.

An information processing system, method, and program are provided that evaluate the feasibility of a design system from the perspective of both spatial simulation and system specifications, and grasp the evaluation results in the context of their relevance to the specifications. [Solution] In an information processing system, information processing by an information processing device includes acquiring (S1) spatial model information and design information from a user terminal. The spatial model information describes a physical model in a virtual space of a design system to be designed and includes information on physical quantities that can be calculated in the virtual space, and the design information indicates specifications of the design system, the specifications being set by a user who designs the design system. The information processing also includes associating (S5) the spatial model information with the design information, calculating (S9) an evaluation index related to the feasibility of the specifications in the physical model based on the result of the association, and outputting (S10) the calculated evaluation index in association with the specifications included in the design information.

The present invention relates to the field of geographic information data technology, and specifically to a GIS-based CRM customer ecosystem dynamic visualization management system, comprising obtaining a customer's geographic location information, transaction data, customer relationship data, and time series data; constructing a customer ecosystem spatial model to analyze the geographic location information and customer relationship data, obtain spatial correlation strength and business correlation strength between customers, and generate a dynamic evolution matrix; performing real-time spatial clustering analysis on transaction data to identify customer distribution hotspots; constructing a customer spatiotemporal trajectory model based on the customer's time series data and geographic location information, analyzing the customer's spatiotemporal behavior pattern, and obtaining spatiotemporal trajectory prediction results including the customer's periodic behavior pattern and spatial activity trend; and performing three-dimensional rendering on the spatial correlation strength, business correlation strength, customer distribution hotspots, and spatiotemporal trajectory prediction results to obtain a three-dimensional visualization interface.

The present invention relates to the field of factory visualization technology, and in particular to a method and system for intelligent factory production visualization based on digital twins. By fusing laser point clouds and image data, a three-dimensional spatial model of the factory production line layout and equipment structure is automatically constructed, replacing manual annotation to achieve digital reconstruction of the physical workshop. A deep learning model is used to analyze product paths and state changes, identify exposed nodes and infer associated equipment, complete intelligent division of equipment, and reduce manual modeling work. By extracting the behavioral rhythm characteristics of operating parameters and comparing them with the historical patterns of equipment sub-models, a rhythm similarity matrix is ​​automatically constructed, and parameter binding relationships are determined to avoid tedious manual configuration. The solution has good flexibility and adaptability, and can quickly update the model after process changes or equipment replacements, ensuring that the digital twin system is synchronized with the actual factory state, and achieving efficient deployment and visualization updates.

The invention relates to the field of intelligent warehouse management, and discloses a safety operation management platform system for intelligent warehousing, which comprises a sensing and modeling module, a path scheduling module, a fault prediction module and a visual interaction module, and is characterized in that the sensing and modeling module constructs a real-time space model for the path scheduling and visual interaction module to use; the path scheduling module performs path planning based on a spatial model, executes event-driven path reconstruction when a sudden obstacle occurs, and optimizes a scheduling strategy in combination with equipment state evaluation of the fault prediction module and a user control instruction of the visual interaction module; the fault prediction module analyzes equipment operation data and feeds back risk information in advance; the visual interaction module integrates system state display and manual intervention instruction transmission to form a multi-module linkage intelligent safety management system.

The application discloses a method for improving transient stability of a network-forming converter in an islanded alternating-current microgrid, and the method comprises the following steps: building a spatial model of the islanded alternating-current microgrid based on a model predictive control strategy, and adding an additional adjustment term in a Q-V loop of a virtual synchronous generator control mode; when a grid-connected converter generates a frequency deviation due to a fault of the microgrid, an electromagnetic torque target function minimization method is used to rollingly optimize the additional adjustment term in the Q-V loop according to angular frequency data at each moment, so that an equivalent input torque and an output torque of the network-forming converter are balanced, and thus the stability of the network-forming converter is restored. The application can effectively reduce the frequency deviation and system oscillation after the fault, significantly shorten the system recovery time, and improve the operation reliability and stability of the microgrid.

This application provides a fire lane occupancy identification system and method using laser and radar collaboration, relating to the field of laser radar technology. The system includes: a point cloud generation module, used to emit laser beams to the target area using a solid-state laser radar, receive reflected signals, extract time difference and phase difference, convert them into polar coordinate data, and generate a point cloud dataset; a 3D spatial model construction module, used to perform point cloud filtering, clustering, and 3D reconstruction based on the point cloud dataset to construct a 3D spatial model of the target area; and an occupancy decision module, used to calculate the space occupancy rate based on the 3D spatial model, and when the detected space occupancy rate exceeds a preset dynamic threshold, to perform adaptive occupancy decision and push alarm information. This application can solve the technical problem of low occupancy detection accuracy in existing technologies, achieving the technical effects of improving detection stability, reducing false alarm rate, and enhancing system adaptability and linkage response capabilities.

The invention relates to a multi-view combined tracking method for a complex target in an industrial production scene, and the method comprises the following steps: S1, carrying out the calibration of internal parameters and external parameters of all cameras, constructing a physical space 3D model of a target production region, and forming a factory coordinate system; s2, collecting a multi-view image frame through the calibrated camera, and carrying out the preprocessing of the multi-view image frame; s3, detecting the position of each frame of target by using a deep learning detection model Detectron2, performing monocular inter-frame association on the detected target to obtain a local trajectory, and extracting a depth feature vector for each target; s4, when the same target appears at a plurality of visual angles, fusing the space coordinates to obtain accurate 3D positioning, and obtaining a physical 3D space coordinate candidate set of each actual target; and S5, according to the physical 3D space coordinate candidate set, constructing a matching candidate set, and adopting a joint matching algorithm to realize trajectory merging to obtain a complete 3D trajectory. According to the invention, multi-target efficient and multi-view collaborative accurate tracking in a complex industrial scene is realized.

The present application provides a method and device for generating a mobile path, which relates to the field of data processing technology and can be used to improve the accuracy of mobile path generation. The specific scheme includes: first obtaining video data captured by multiple shooting devices arranged in a target area; the target area is the area where the target to be identified is located; projecting the video data captured by the multiple shooting devices into a three-dimensional spatial model of the target area to obtain a fused video based on the three-dimensional spatial model; based on the layout position and maximum recognition distance of each shooting device in the multiple shooting devices, dividing the target area into multiple recognition areas; determining the target observation direction of the first recognition area based on the fused video; in the fused video, identifying the target to be identified with each target observation direction corresponding to each recognition area, so as to generate a movement path of the target to be identified in the target area.