119results about How to "Improve enforceability" patented technology

The invention provides a petrochemical pipeline full-life-cycle detection and evaluation method and equipment. The method comprises the following steps: constructing a pipeline detection digital twin model comprising a geometric model and material attributes; collecting detection data of the pipeline through the deployed multi-source sensor network; inputting the detection data into the pipeline detection digital twin model to perform feature extraction and analysis of the pipeline state; constructing a heterogeneous tree network and executing hierarchical evaluation; optimizing the maintenance plan based on the evaluation result; and predicting the residual life of the pipeline. Through the digital twinning technology and multi-source detection data fusion, full-life-cycle accurate evaluation of the petrochemical pipeline is realized.

The invention discloses a fermentation strain yield prediction method based on artificial intelligence, particularly relates to the field of biological fermentation production, and is used for solving the problems that in the prior art, due to the fact that the yield is difficult to directly map in concentration prediction, monitoring indexes are normal, the final yield deviates from expectation, and reliable strain comparison and production management decision cannot be supported. A historical batch process observation sequence and a material account sequence are aligned to generate a conversion efficiency characterization sequence to form a yield learning sample training yield prediction model, and the conversion efficiency characterization sequence is generated on batches and input into the model to obtain a yield prediction value. And calculating an accounting volume trajectory consistency parameter and a conversion contribution steady-state proportion parameter, inputting the parameters into a coefficient analysis model to obtain a decision credible coefficient, generating a yield risk judgment result for a yield prediction value based on the decision credible coefficient, and realizing a new-batch online prediction record.

The invention provides a floating structure anchoring system design analysis method suitable for a shallow water condition, and particularly belongs to the technical field of floating equipment mooring. The method comprises the steps that firstly, the platform deformation energy requirement is calculated according to a platform structure type, and the residual deformation energy of an anchoring line configuration scheme is calculated based on the deformation energy principle; preferably selecting an anchoring scheme with the maximum residual deformation energy; then, based on a numerical method of a viscous flow theory, an overlapping grid method is coupled with a mooring cable dynamic simulation method, the floating structure motion response and the anchoring safety coefficient of the optimal scheme are checked, the layout scheme of the mooring system is determined through the scheme design and safety checking process, the whole design process is scientific and meticulous in logic, and the design efficiency is high. The method is high in performability and has practical engineering application guiding significance.

The invention discloses a truck-unmanned aerial vehicle multi-target cooperative express delivery and pickup path planning method. The method comprises the following steps: acquiring related parameters and a constraint condition set of a vehicle-aircraft cooperative logistics service; constructing a target function set representing the path cost minimization of all client points of the vehicle-machine collaborative logistics service based on the related parameters; performing joint solution on the target function set and the constraint condition set in combination with a heuristic algorithm to obtain an optimal express delivery and pickup path scheme of the vehicle-machine collaborative logistics service; and based on the optimal express delivery and pickup path scheme, the unmanned aerial vehicle and the truck are controlled to realize delivery and pickup of the express delivery of the client. According to the method, the truck executes the express pickup task, and the unmanned aerial vehicle only executes the express delivery task on the premise of meeting the load and voyage constraints, so that the performability is increased; through subarea division and loading of the multiple unmanned aerial vehicles by the truck, each unmanned aerial vehicle only takes off once in each subarea, and delivery service can be provided for multiple client points, so that the service efficiency of the client points is greatly improved, and the total driving cost of the truck and the unmanned aerial vehicles is optimal through reasonable path planning.

The invention discloses an electromagnetic radiation anti-interference test method for an automobile steer-by-wire system. The method comprises the following steps: 1) placing a tested vehicle in an electromagnetic radiation anti-interference test environment; 2) installing a steering execution device to apply a steering torque to a steering wheel of the tested vehicle and collecting the torque; (3) installing a steering measuring device to collect a P-point motion trail of a steering wheel; (4) under the conditions that electromagnetic radiation interference is not applied and electromagnetic radiation interference is applied, steering wheel steering angle curves beta (t) and gamma (t) are obtained through P-point motion trail calculation; and 5) comparing the two curves, and judging whether the steering system meets the electromagnetic radiation anti-interference test requirement or not according to the two curves. According to the method, the defects that only the steering wheel is detected and the steering wheel is not detected in an existing method are overcome, in the method, when the steering angle of the steering wheel is measured, complex operation such as steering axis alignment is not needed, the coordinate track of the point P is collected, then the steering angle curve is obtained through fitting, operation is easy, the measuring error is small, compatibility is good, and the testing efficiency is high.

The invention discloses an automatic driving test and evaluation method and device based on a large language model, and solves the problems that the pertinence of a test scene is insufficient, the result understanding depends on manpower, the evaluation interpretation is weak and the like in a traditional method. By means of semantic analysis, logical reasoning and domain knowledge support of a large language model and in combination with an automatic driving test domain knowledge graph, generation of a structured test portrait of a tested system, construction of a test demand list, generation of a semantic test scene description file, interpretable test result attribution, multi-dimensional comprehensive evaluation and report output are completed in sequence. And a test portrait is updated through report feedback to form a closed loop. The method is suitable for function verification and performance evaluation of an automatic driving simulation test, can also be used for post-processing analysis of real vehicle test data, realizes automation of a whole test process and interpretability of an evaluation result, and improves test precision and efficiency.

The invention belongs to the crossing field of network space security, software engineering and artificial intelligence, and discloses a dynamic and static cooperative vulnerability full-link intelligent analysis and verification method based on a large language model and an MCP protocol, and the method comprises the steps: S1, task initialization and context construction; s2, target code deep analysis and static taint preanalysis are carried out; s3, carrying out Fuzzing Harness directional generation and iterative optimization on the basis of the stain paths; s4, performing dynamic and static collaborative sandbox execution and dynamic stain tracking; and S5, vulnerability accessibility judgment and report output of multi-source data fusion are carried out. According to the method, a dynamic and static combination mechanism is adopted, vulnerability analysis and verification can be automatically completed for software target codes, and accurate technical support is provided for vulnerability risk grading and repairing priority ranking.

The invention discloses a GIS-based territorial space planning optimization system and method, and relates to the technical field of Geographic Information System (GIS). Activity data of tourist users are collected to generate a thermodynamic diagram H of tourist activities, the thermodynamic diagram H is fused with GIS data to obtain a hot spot region Rhot, a development potential factor P (r) of each region is calculated in the hot spot region, and the development potential factor P (r) is calculated; therefore, more meticulous and personalized regional development evaluation is realized, the defect of insufficient consideration of tourist behaviors and regional characteristics in a traditional method is overcome, and in the optimization process, the total optimization cost Copt (r) of each region is analyzed and obtained by combining tourist flow and regional conveying capacity information, so that the optimal region development evaluation is realized. According to the method, the problem that the previous planning based on static data and experience cannot flexibly adapt to dynamically changing tourist flow and regional requirements is solved, and the matching degree between regional facility planning and actual tourist requirements is improved, so that more efficient and accurate regional development and resource allocation are realized.

The invention discloses a task allocation method for distributed cooperative formation reconstruction of underactuated unmanned surface vehicles. The method comprises the following steps: acquiring an initial cluster scale N of the underactuated unmanned surface vehicles and the position of a preset formation target point; according to the Durbin curve distance between the starting position and the target position of the underactuated unmanned ship, designing a cost function based on the Durbin curve, calculating the income value of the underactuated unmanned ship, enabling the distribution result to be closer to the path distance from the unmanned ship to the target point, and autonomously deciding the bid of the interested target point; and according to an auction algorithm principle, a distributed auction task allocation strategy is designed, and optimal matching of the unmanned ship formation and the target point position is realized.

The invention provides a Bayesian-based aviation equipment residual life uncertainty prediction method and system. The method comprises the following steps: S1, constructing an observation function equation and a state parameter description equation of residual life of key or important systems, modules and parts of aviation equipment; s2, constructing an agent model; s3, solving joint posterior probability distribution and performing residual life prediction; and S4, according to the prediction probability distribution of the remaining life of the aviation equipment, generating an optimization control instruction or a maintenance instruction for the aviation equipment, and acting the optimization control instruction or the maintenance instruction on the aviation equipment. The method provided by the invention not only provides a theoretical basis for solving the problem of prediction, analysis and prediction of the service life of the aviation equipment, but also clearly defines how information and uncertainty thereof flow and convert between a physical world and a digital world, and provides a brand new thought for prediction of the uncertainty of the residual service life of the aviation equipment.

The application discloses a kind of pumped storage power station multi-objective optimization scheduling method and system of water conservancy and hydropower engineering.The method comprises: obtaining the power grid regulation demand and power station operating state in scheduling window;Generation head interval under the unit operating condition reachable domain atlas, representing unit reachable operating condition and operating condition switching relationship;The regulation demand is split into regulation fragment, to find operating condition path in atlas with target response time as backtracking starting point and record path interruption event;Accordingly determine regulation gap and its physical constraint reason;Generation pre-compensation action access atlas, find compensation operating condition path again;Combined with two kinds of paths, generate multi-unit operating condition switching sequence and confirmable power scheduling trajectory.The present application can judge regulation demand bearing capacity and constraint reason in advance, correct scheduling trajectory in advance, and improve power station response certainty.

The present invention discloses a system and method for collecting and processing data for national land space planning, and specifically relates to the technical field of data processing. The system improves the detail accuracy of low-resolution satellite images in national land space planning through a data processing and analysis method for multi-resolution remote sensing images, obtains image data of different resolutions from different satellites according to needs, and evaluates the detail fidelity and captures the fluctuation state of low-resolution images through image segmentation and feature ratio analysis. After comprehensive analysis, the images are divided into high, medium and low accuracy levels, and abnormal prediction, data correction and optimization decision-making are performed for medium-accuracy images. This can not only effectively reduce the cumulative errors caused by low-resolution images, but also enhance the accuracy and long-term feasibility of planning, thereby ensuring the scientificity and practicality of national land space planning.

The invention discloses an under-driven dexterous hand grabbing pose generation method based on deep learning, and belongs to the technical field of robot operation and intelligent grabbing. According to the method, the prior knowledge of a large language model LLM to real capture is introduced into a data set, and a reasonable capture pose is realized by using object semantic-geometric joint features of a point cloud encoder Point Transform and the LLM. According to the method, three types of grabbing strategy classification models for the under-actuated five-finger dexterous hand are constructed, including two-finger kneading, three-finger kneading and five-finger grabbing, and are used for restraining and guiding generation of subsequent fine grabbing postures. According to the selected grabbing strategy, a network is generated to predict six-dimensional poses and angles of all joints of the dexterous hand, and a grabbing posture matched with the shape of the object is obtained. According to the method, two constraint loss items are additionally added to realize grabbing quality optimization. The method can be suitable for intelligent grabbing tasks of industrial robots, service robots and precision operation equipment under the condition that negative grabbing of samples and complex post-processing optimization are not needed.

The application discloses a layered continuous inverse kinematics solving method for fiber reciprocating winding of a cylindrical component and belongs to the technical field of robot end control. Based on a fiber winding task of a six-axis robot cylindrical component, trajectory modeling and pose generation are carried out; a kinematics model of the six-axis robot is established; a Jacobian matrix and singularity representation thereof are obtained; end pose error is defined; the fiber winding task of the six-axis robot cylindrical component is described as a continuous inverse kinematics problem; full-pose inverse kinematics iteration is carried out by using a damped least square method; initial value generation based on state prediction is carried out for the fiber winding task of the six-axis robot cylindrical component; a position rollback strategy is set; and the continuous inverse kinematics problem is solved. The application aims at solving the problems of poor continuity of the end trajectory of the six-axis robot, strong attitude constraint and unstable end reversing solving in the fiber winding process of the cylindrical component.

The present application relates to the technical field of unmanned aerial vehicle navigation, and relates to an unmanned aerial vehicle inspection path planning method, system, device and storage medium; wherein the unmanned aerial vehicle inspection path planning method comprises: obtaining a digital elevation model, a digital surface model of a plateau forest area and laser point cloud data of the plateau forest area; determining a three-dimensional voxel space of the plateau forest area based on the digital elevation model, the digital surface model and the laser point cloud data, and determining an unmanned aerial vehicle accessible region by performing accessibility determination on the three-dimensional voxel space based on a vertical safety margin; performing three-dimensional path search from a starting point to an ending point in the unmanned aerial vehicle accessible region by using an improved algorithm; performing smoothing processing on the searched three-dimensional path, and imposing a maximum turning radius constraint and a maximum climb rate constraint to generate a final three-dimensional track. The present application can improve the safety and executability of the planned route of the unmanned aerial vehicle.

This invention relates to the field of energy storage system control technology, and discloses an intelligent control and power regulation method for a string-type grid-connected energy storage system, comprising: S1, acquiring grid status and mode-defined demand; S2, acquiring cluster-level topology mapping; S3, collecting cluster state variables of each energy storage cluster; S4, determining capacity estimates and uncertainty metrics, and shrinking or correcting capacity boundary terms; S5, performing inter-cluster collaborative allocation based on support demand and available capacity to obtain support shares; S6, mapping control parameters and controlling converters; S7, event-triggered reconfiguration and reallocation. By introducing a safe availability capacity assessment mechanism based on cluster-level capacity estimation, uncertainty metrics, and boundary shrinkage, the method achieves precise on-demand supply of grid support while meeting current, voltage, and temperature constraints, significantly reducing the risks of overload, exceeding limits, and misjudgment, and improving system safety and support effectiveness.

The invention discloses a bearing capacity self-adaptive scheduling system and method for a high-proportion new energy power grid, and relates to the technical field of power system scheduling. The system adopts a three-layer structure: a multi-dimensional bearing capacity state generation layer constructs a power grid digital twinborn body which can be updated in a rolling manner, models bearing capacity into a multi-dimensional bearing capacity state variable and maps the multi-dimensional bearing capacity state variable into a bearing capacity constraint parameter, and constructs an operation feasible region; a rolling scheduling optimization decision-making layer establishes a rolling scheduling optimization model of cross-time scale coupling under the uncertainty of new energy output, and generates a scheduling scheme meeting the operation feasible region constraint and the consistency constraint; and the online safety check and feedback layer performs online safety check by adopting an agent model based on the digital twinborn body, outputs a corrected bearing capacity constraint parameter to update the operation feasible region and trigger re-solving, and issues an execution instruction and writes back operation feedback for continuous updating after the check is passed. According to the method, the real-time performance and the scheduling performability of the security boundary description can be improved, and the new energy consumption capability is improved.

The invention discloses a working condition self-adaption-based guide vane connecting rod stress trend diagnosis method, and relates to the technical field of intelligent diagnosis of hydroelectric generating sets, and the method comprises the steps: constructing an optical fiber stress monitoring system, setting an optical fiber grating sensor for a guide vane connecting rod, collecting a stress signal in real time, and collecting operation parameters; the method comprises the following steps: collecting multi-working-condition data within one month of normal operation after overhaul of a unit, calculating a correlation matrix between guide vane connecting rod stress and operation parameters, and establishing a stress working condition self-adaptive analysis model; and calculating a maximum value, a minimum value, an average value and a standard deviation of stress based on rolling statistics, determining a normal range of stress fluctuation, inputting real-time acquired data into a diagnosis algorithm, judging a stress deviation trend according to the reference model, outputting an alarm level, and updating the reference model. According to the method, the accuracy and timeliness of anomaly recognition are improved, the pertinence and the performability of maintenance decision making are enhanced, and the operation safety and the service life management effect of the guide vane connecting rod of the hydraulic unit are improved.

This application relates to a method, apparatus, storage medium, and electronic device for cross-platform migration of workflows. The method includes: parsing the native workflow file of the source platform to extract node information of each source node and the connection relationships between nodes; standardizing the node information and connection relationships of each source node according to the platform mapping table of the source platform, and constructing an intermediate representation based on the standardized node information and connection relationships; transforming each intermediate node in the intermediate representation into a target node corresponding to the target platform according to the capability characteristics and multi-level node transformation strategy of the target platform; adapting and reconstructing the connection relationships between the target nodes, and assembling them according to the workflow file structure of the target platform to generate a native workflow file that can be recognized and directly loaded and executed by the target platform. This application solves the technical problems of high cost and poor reliability in cross-platform workflow migration.

The invention relates to the technical field of urban intelligent traffic, in particular to an intelligent edge computing device with adaptive traffic signal control capability and a control method, and the method comprises the following steps: S1, collecting intersection traffic information detected by various traffic information detectors; s2, data preprocessing is carried out on the collected intersection traffic information, preprocessed data comprises a spatial dimension and a time dimension, and the spatial dimension is divided into section data and regional data; the time dimension is divided into real-time data, phase-level statistical data, period-level statistical data, time-period-level statistical data and day-level statistical data; s3, judging whether the data collected by the traffic information detector is seriously abnormal or not, if the data is seriously abnormal, giving an alarm, starting intersection reference signal timing, sending the intersection reference signal timing to a traffic signal controller, and executing by the traffic signal controller; the state of the traffic signal lamp can be detected in real time, and the real-time self-adaptive performability of the traffic signal is improved.

This invention relates to the field of trajectory planning technology, and discloses a robot trajectory optimization method, system, electronic device, and storage medium. The method includes: obtaining initial planned trajectories for different joints of the robot based on preset robot actions; mapping different joints of the robot and preset environmental obstacles into parameterized geometric primitives; constructing a cost functional for optimizing the initial planned trajectory; iteratively updating the trajectory using gradient descent on the cost functional until a preset termination condition is met, and controlling the robot's movement based on the updated trajectory. This invention maps robot joints and environmental obstacles into parameterized geometric primitives, transforming the calculation of the shortest distance and gradient from the reference trajectory of each point on the robot in the current frame to all obstacles in the environment into calculating the collision distance between parameterized geometric primitives using analytical distance formulas. This reduces the computational load and thus improves the efficiency of robot motion optimization.

The invention discloses a medicine supply chain intelligent management method based on big data processing, and the method comprises the steps: collecting multi-source data, and carrying out the preprocessing of the multi-source data to generate a standardized data set; constructing a unified entity set, and generating full-link tracing relation data; constructing a multivariable time sequence, and forming a variable Token; an improved iTransform model is constructed, and a structured result is obtained; outputting a scene set and a disposal record based on approximate nearest neighbor search; generating an executable instruction set according to the structured result, the scene set and the disposal record; and outputting to a service execution end, and generating an execution result and a tracing audit record. According to the invention, through combination of prediction and early warning of the improved iTransform model and similar scene retrieval of approximate nearest neighbor search, perceptible, traceable and executable closed-loop intelligent management of the medicine supply chain is realized.

The utility model discloses a hydrogen fuel cell carbon paper deformation detection device which comprises a universal testing machine, an experiment pressing block, an experiment cushion block, a polar plate carbon paper stacking structure, a video extensometer, a concentrated light source and a microscope, the polar plate carbon paper stacking structure is arranged between the experiment pressing block and the experiment cushion block, and the universal experiment machine is configured to provide continuously changing packaging force and uniformly transmit the packaging force to the polar plate carbon paper stacking structure through the experiment pressing block and the experiment cushion block, namely, the mechanical environment of carbon paper in a galvanic pile is simulated; the concentrated light source is configured to provide brightness, and the video extensometer and the microscope are configured to observe the form change of the carbon paper in the polar plate carbon paper stacking structure after continuous stress. The device simulates the in-pile mechanical environment through the universal testing machine, and directly observes the real form change of the carbon paper in the flow channel through the optical detection technology, thereby being beneficial to improving the cognition on the internal structure of the electric pile and guiding the design of the electric pile.

The invention provides a generation method of database query statements based on graphics. The generation method comprises the steps of obtaining a data query request for a target database; obtaining a database structure chart used for representing a target database structure, and performing graph extraction processing and character extraction processing on the database structure chart to obtain an extraction result, the extraction result at least comprises entity nodes in the database structure diagram, entity names and entity attribute names corresponding to the entity nodes, and an association relationship between the entity nodes; mapping the entity name into a data table name, mapping the entity attribute name into a field name, and mapping an association relationship between the entity nodes into an association relationship between data tables to obtain structure information of the target database; according to the data query request and the structure information, constructing a cue word at least comprising the data query request and the structure information; and inputting the cue word into an artificial intelligence model to obtain a database query statement.

This invention discloses a collaborative batch scheduling method and system for dual production lines in tire production, specifically for pre-fabricated rubber packs. Relating to the fields of intelligent manufacturing and production scheduling, the technical solution involves: acquiring basic tire production data; converting tire demand into rubber pack demand based on the mapping relationship between tire type and rubber pack type; generating predicted demand for each rubber pack type within a scheduling time window; establishing a collaborative batch scheduling model for both the rubber pack and tire production lines based on the predicted demand and the basic data; constructing a set of candidate scheduling schemes; and performing joint decoding, evaluation, and feedback correction on a unified time axis to output the optimal scheduling scheme. The beneficial effects of this invention are: achieving collaborative optimization between pre-fabricated rubber packs and tire production; reducing inventory and material shortages; improving equipment utilization and on-time order delivery capabilities; and enhancing the system's adaptability to demand fluctuations and resource changes.

The application discloses a kind of low-altitude logistics hub facing in and out field connection and supplement energy chain collaborative control method. Build task-unmanned aerial vehicle-take-off and landing position-supplement energy unit-route window heterogeneous association graph and resource linkage constraint state diagram;Using HRG-MambaNet to the heterogeneous association graph executes relationship coding and time sequence state joint modeling, and according to the relationship, the task connection direction, resource release order, supplement energy switching and route window redistribution range are implemented linkage constraint, form candidate control scheme, and delay, power deviation, window mismatch and supplement energy continuous connection deviation are written back to constraint state diagram structure as the input of next control time;Under the disturbance trigger, construct mismatch propagation inhibition boundary according to local subchain reconstruction threshold, only the local subchain of mismatch in boundary is reconstructed, and the in and out field connection, supplement energy chain switching and take-off and landing position release result are output. Compared with prior art, the application can improve the scheduling coordination, control stability and anti-disturbance ability in the operation process of low-altitude logistics hub.

The application belongs to the technical field of cloud computing software, and particularly relates to an agent closed-loop control method and system based on cloud computing power constraint and knowledge cooperation, which comprises the following steps: when an operation exception occurs, extracting feedback source node text data and converting the same into a reference attribute vector; obtaining a remaining search computing power quota based on a resource physical attenuation equation to control the graph traversal depth; when the search path is forced to terminate at a topological terminal leaf node, obtaining a recycled computing power quota and performing reverse backtracking; obtaining a feature similarity weight according to the reference attribute vector and a current attribute vector, and weighting and distributing the recycled computing power quota based on the weight to obtain an updated computing power quota; and finally extracting a local knowledge subgraph and inputting the same into a large language model to generate an abnormality processing action sequence instruction. The application solves the problems of memory deadlock and invalid dilution of the quota of an independent cloud container instance, prevents idle quotas from being invalidly distributed by low-relevance branches, guarantees the search depth of a core path, and improves the success rate of automatic recovery.

The present application relates to the technical field of software performance monitoring and artificial intelligence, in particular to a low-code platform performance diagnosis method and system based on multi-AI provider cooperation, the method of the present application receives batch page performance audit results, extracts page quantity, average performance score, common performance problems, performance budget and page distribution data; reads low-code platform component library description, visual building rules and configuration anti-pattern knowledge, constructs diagnostic prompt words containing low-code domain context and structured output constraints; in the automatic provider mode, cascade calling is performed in the order of Azure OpenAI, Anthropic and OpenAI, and different structured output constraint strategies are adopted for the interface capabilities of different AI providers; after field integrity verification and format standardization processing of the returned results, a performance diagnosis result file is generated persistently. The present application improves the availability, output stability and executability of low-code configuration level optimization suggestions of AI performance diagnosis service.

The application discloses an unmanned aerial vehicle (UAV) measurement system for highway subgrade bearing capacity, and particularly relates to the technical field of highway engineering detection, which generates an initial measurement path and a measurement point layout scheme through task planning, drives the UAV to complete measurement data and spatial position acquisition, aligns and analyzes the measurement data with a reference pavement model, divides sub-road sections along the direction of the subgrade, further generates structure deformation and measurement reliability evaluation factors for each sub-road section, then determines the distribution density of the measurement points, and adjusts the initial measurement path accordingly to form a measurement path planning that matches the state characteristics of different sub-road sections of the subgrade. The application improves the coverage integrity through path planning and measurement point layout, depicts the differences between the subgrades through segmented analysis, realizes targeted measurement by determining the density of the measurement points and adjusting the path, improves the accuracy of bearing capacity evaluation, reduces redundant operations, improves the efficiency and reliability, and supports the optimization of the level of engineering application and decision-making ability.

The invention discloses an intelligent vehicle test driving sales interaction method and device, and the method comprises the steps: obtaining the natural language input data of a user in response to the interaction triggering operation of the user for a target vehicle; analyzing a user demand tag set representing the car purchase intention and preference dimension of the user; screening a to-be-displayed target vehicle function set from a displayable function set of the target vehicle based on the user demand tag set, and determining a target experience scene set matched with the target vehicle function set according to a preset function scene mapping relationship; and acquiring the current position of the target vehicle as a route starting point, determining road network data based on the map data, and positioning a scene section corresponding to the target experience scene set in the road network data so as to plan and generate a personalized test driving route capable of covering the target experience scene set on the premise of meeting a preset route constraint condition. Therefore, the active test driving experience organization driven by the vehicle purchase intention and preference of the user is realized.