117 results about "Dynamic vehicle" patented technology

The invention discloses a logistics vehicle scheduling method and system, electronic equipment and a storage medium, and relates to the technical field of intelligent logistics, and the method comprises the steps: in a cloud center layer, an initial vehicle scheduling scheme is generated based on a dynamic vehicle scheduling model, and the dynamic vehicle scheduling model aims at minimizing the transportation cost; and the edge node layer receives the real-time traffic data and the order information, and dynamically adjusts the initial vehicle scheduling scheme to generate a dynamic scheduling scheme. And the vehicle-mounted terminal layer receives the dynamic scheduling scheme and performs path optimization based on the sensor data to obtain the execution state of the vehicle-mounted terminal. The execution state of the vehicle-mounted terminal is fed back to the edge node layer and the cloud center layer to update the dynamic vehicle scheduling model, a target vehicle scheduling scheme is determined based on the updated dynamic vehicle scheduling model, and the target vehicle scheduling scheme is used for scheduling multiple logistics vehicles. Through cloud side-end cooperative processing, the decision-making quality of logistics vehicle scheduling and the system response speed are improved.

The invention discloses an expressway dynamic vehicle behavior trajectory tracking induction monitoring system, which relates to the technical field of intelligent traffic monitoring, and comprises a multi-source data acquisition module used for responding to a trigger signal of a vehicle entering a monitoring area, acquiring multi-source data, and sending the multi-source data to the monitoring area; the multi-source data comprises three-dimensional point cloud data, a thermal infrared image, a multi-view visible light image, vehicle speed and lane data and a license plate image; and the preprocessing and calibration module is used for registering and fusing the thermal infrared image and the visible light image. According to the expressway dynamic vehicle behavior trajectory tracking induction monitoring system provided by the invention, through multi-source sensor fusion and intelligent algorithm innovation, high-precision tracking and real-time monitoring of vehicle behavior trajectories are realized, road safety and traffic efficiency are effectively improved, and the expressway dynamic vehicle behavior trajectory tracking induction monitoring system is suitable for popularization and application. Multi-dimensional sensors such as a laser radar, an infrared thermal inductance instrument and a visible light camera are adopted for cooperative work, and the capability of capturing vehicle information in a weak light environment is enhanced.

The invention provides an intelligent optimization method and system for a parking lot system. Wherein parking space pressure change data is collected through a parking space pressure sensor, vehicle size data of a vehicle identification system is synchronized, and a three-dimensional thermodynamic diagram containing parking demands is constructed based on spatial distribution; the stand column displacement sensing array monitors the height of a vehicle chassis, a dynamic vehicle size database is constructed by combining size data, and parking space physical constraints are matched; in cooperation with the parking demand and the constraint condition, screening and matching an idle parking space set according to the size of the entering vehicle and the thermodynamic diagram prediction trend; and a vehicle guiding path is generated by combining the incidence relation between the historical parking path and the thermodynamic diagram and integrating the parking space position and the path occupation state, so that intelligent optimization of the parking lot is realized. According to the technical scheme provided by the invention, precise parking space matching and dynamic path guiding of vehicle size self-adaption are realized, and the turnover efficiency and the space utilization rate of the parking lot are improved.

The invention relates to the technical field of intelligent mine and vehicle safety, in particular to a vehicle collision prediction method and prediction system for a mine laneway environment. The vehicle collision prediction method for the mine laneway environment comprises the following steps: constructing a static laneway environment map based on a mine digital map; at each time step t, constructing a dynamic vehicle interaction diagram according to the vehicle positioning information; extracting the spatial-temporal characteristics of the dynamic vehicle interaction diagram and the state characteristics of the driver, fusing the spatial-temporal characteristics and the state characteristics to form a comprehensive characteristic vector, and outputting a predicted value of the running speed of the current target vehicle at the next moment through a regression layer; and judging whether a collision risk exists or not according to the deviation between the predicted value of the vehicle speed and the real speed. According to the method, the space-time diagram neural network is constructed, vehicle dynamic interaction, static roadway environment topology and driver state multi-source information are fused, and high-precision and advanced prediction of the collision risk is achieved.

The invention relates to the technical field of state monitoring of rail transit equipment, and discloses a rack rail train gear online detection system and method, and the system comprises a laser sensor array module; a visual perception assembly module; a trigger module; and a data processing module. The method comprises the following steps: synchronously acquiring vertical distance data and an image sequence; applying an optical flow algorithm to track an image sequence to calculate a gear rotation angle; installing errors and vehicle vibration are separated through cross-correlation and low-pass filtering, and distance data are corrected; reconstructing the full-circle geometric contour of the gear according to the rotation angle; and finally, geometric deviation and surface damage are judged based on contour and image analysis. According to the invention, through multi-modal data fusion and error decoupling, high-precision automatic on-line detection of the gear of the rack rail railway vehicle under the dynamic passing working condition of the vehicle is realized, and the problems that the traditional off-line dismounting detection efficiency is low and early damage cannot be found in time are solved.

The invention discloses an Internet of Vehicles cooperative driving control method and system based on deep reinforcement learning, and belongs to the field of automatic drive.The method comprises the steps that state data of a main vehicle and state data of at least one adjacent vehicle received through Internet of Vehicles communication are received, generating smooth state estimation and uncertainty characteristics used for representing data credibility; constructing the main vehicle and the adjacent vehicles into a dynamic vehicle interaction diagram; inputting the dynamic vehicle interaction graph into a graph attention feature extraction model to extract space-time interaction features; inputting the space-time interaction characteristics into a deep reinforcement learning strategy network to generate a preliminary reference control instruction; a quadratic programming problem is constructed, and the quadratic programming problem takes minimization of deviation from a reference control instruction as an optimization target and takes security constraints derived from a control barrier function as constraint conditions; and solving the quadratic programming problem to obtain a final control instruction. According to the invention, efficient and intelligent cooperative driving can be realized on the premise of ensuring safety.

The utility model relates to a vehicle high-speed dynamic weighing device, and relates to the technical field of vehicle weighing, the vehicle high-speed dynamic weighing device comprises a weighing base and a weighing flat plate installed on the weighing base, a supporting module and a plurality of weighing modules are arranged in the weighing base, and the weighing flat plate is connected with the top of the supporting module so that the supporting module can be linked with the weighing flat plate; the weighing module comprises a weighing force sensor, a linkage block and a weighing column, the linkage block is installed on the supporting module, the weighing force sensor is arranged on the weighing base, a limiting assembly is installed at one end of the weighing column, the other end of the weighing column penetrates through and extends out of the linkage block to make contact with the weighing force sensor, and the weighing column is located over the weighing force sensor. The weighing column and the weighing weight sensor are installed in a split mode and are not in hard connection, and when the weighing flat plate is subjected to high-frequency impact, the posture of the weighing weight sensor is ensured to be in a natural state, so that the weighing precision of the device is ensured, the service life of the weighing weight sensor is prolonged, and the effect of maintenance work of the device is reduced.

The invention discloses a dynamic urban environment spectrum map construction method based on AOT-WTGAN, and the method comprises the steps: obtaining a mask matrix, carrying out the superposition of the mask matrix for a static spectrum map, constructing a dynamic spectrum map generation model, taking the static spectrum map and the mask matrix as the input, and taking the corresponding dynamic spectrum map as the output, and building a training data set. Training the generative adversarial network of the dynamic spectrum map generation model by using the training data to obtain a trained network model, and inputting the static building distribution map and the corresponding mask matrix into the dynamic spectrum map generation model to obtain a final dynamic spectrum map. According to the scheme, only the area affected by the dynamic vehicle is repaired, the radiation source and the vehicle position are used for obtaining the path shielding matrix to establish the mask matrix, the mask matrix and the static frequency spectrum map are combined for repairing the specific area, the complexity of a generator network is effectively reduced, and the reliability of the generator network is improved. The map construction efficiency is improved, and the precision of the spectrum map is improved.

The invention discloses a comprehensive guarantee order dispatching method and system for a large-scale business scene, and relates to the task distribution and data processing technology, and the method comprises the steps: obtaining a to-be-dispatched order, and enabling any order to be distributed to a corresponding vehicle; establishing a dynamic vehicle path planning problem DDVRPTW model with a time window according to the vehicle condition, the road network condition and the user demand when the vehicle executes the order task; establishing constraint conditions according to actual orders and vehicle conditions; for different order dispatching scenes, selecting a corresponding adaptation algorithm to solve the DDVRPTW model; and according to a solving result, the order to be dispatched is dispatched. According to the method, the dynamic vehicle path planning problem model with the time window is constructed, the spatial-temporal constraint and the dynamic disturbance of the guarantee task are accurately described and displayed, and efficient matching of resource demand and supply is realized.

This invention proposes a real-time road vehicle twinning method and system based on a monocular camera. Combining target detection, target tracking, and depth estimation techniques, it can perceive changes in multiple target vehicles in dynamic scenes in real time and accurately map their positions and features to a virtual twin scene, overcoming the shortcomings of existing road twinning methods in supporting dynamic vehicle twinning. A large visual question-answering model is used to extract vehicle features, reducing reliance on specific task data and scenes, and supporting generative reasoning and multi-turn interaction. This allows for dynamic question adjustment and rapid adaptation to new scenarios.

The utility model discloses an RVC camera module and a dynamic vehicle auxiliary line system. The RVC camera module comprises a lens, a sensor, a graphic signal processor ISP, a serializer Serdes, a POC circuit module, a connector and a power management chip PMIC (Power Management Integrated Circuit), the lens is in signal connection with the sensor; the sensor is in signal connection with the image signal processor ISP; the image signal processor ISP is in signal connection with the serializer Serdes; the serializer Serdes is in signal connection with the POC circuit module and the connector respectively; the POC circuit is in signal connection with the power management PMIC; the power management PMIC supplies power to the sensor, the image signal processor ISP and the serializer Serdes respectively. According to the utility model, the dynamic vehicle auxiliary line can be generated in the camera, so that the overall equipment cost of the reversing image system is reduced.

Trailer resetting system (100) in a vehicle (10) for automatically adjusting the vehicle dynamics while the vehicle (10) is coupled to a trailer (50, 450) to reduce the probability of trailer (50, 450) jackknifing, wherein the trailer resetting system (100) comprises a control (34) configured as follows: To obtain trailer profile information (201, 404); to obtain a dynamic vehicle steering angle, δ, (203, 412) and a dynamic trailer coupling articulation angle, θ, while the vehicle (10) is moving backwards; continuously maintains a maximum safe vehicle speed, v max, (207, 306, 414) to calculate with which reversing is possible without buckling, based on the trailer profile information (201, 404), of δ, of θ, a predetermined safe time until buckling, TTJ_ne, (410) and a maximum trailer coupling articulation angle, θ FK , (209, 406), where TTJ_ne is a calibratable time value that specifies a time safety margin before the occurrence of a buckling state is predicted; a current vehicle longitudinal speed, v, (211) with v max to compare; to automatically generate a speed correction signal (213) to cause the vehicle (10) to reduce speed below v max to slow down when v the v max exceeds; and to send the speed correction signal (213) to a vehicle motion control system to instruct the vehicle motion control system to adjust v below v maxto reduce, wherein the vehicle motion control system controls suitable vehicle actuators to reduce v, wherein the trailer profile information (201, 404) includes a vehicle wheelbase, L, a distance, d, between the rear axle of the vehicle (10) and the coupling point, and a trailer rear axle spacing, D, wherein: V max = LTTJ _ ne ⋅ E ln ( | [ ( C − B ) tan θ JK 2 + ( A − E ) ] [ ( C − B ) tan θ JK 2 + ( A + E ) ] [ ( C − B ) tan θ 2 + ( A + E ) ] [ ( C − B ) tan θ 2 + ( A − E ) ] | ) , A = LD , B = d D tan δ , C = tan δ , E = A 2 + B 2 − C 2 , d θ dt = VL [ tan δ ( 1 + d D cos θ ) + LD sin θ ] , if d θ dt If positive, then θ JK = θ JK , and if d θ dt If negative, then θ JK = -θ JK .

This invention provides a high-dynamic space-based cooperative guidance method and system for multi-level virtual target points, relating to the field of aircraft control technology. It involves acquiring initial flight state data of each aircraft and position data of the target point; generating at least one virtual target point based on the initial flight state data and position data, using a zero heading angle constraint; generating roll angle control command data for each aircraft based on the initial flight state data and the virtual target point, using a two-sided azimuth error corridor; and adjusting the attitude of each aircraft according to the roll angle control command data to ensure that each aircraft meets the terminal heading angle constraint and reaches the target point. This method enables precise control of the terminal heading angle of high-dynamic aircraft in complex dynamic environments, thereby enabling multiple aircraft to perform cooperative strike missions from different spatial orientations.

The invention discloses an underwater robot trajectory tracking method and a related device, and relates to the technical field of underwater robot autonomous navigation, and the method comprises the steps: synchronously collecting the original data of an underwater robot camera, an IMU and a DVL; performing time synchronization on IMU and DVL data by taking a camera timestamp as a reference, and converting DVL speed data into an IMU coordinate system; calculating a relative translation vector and a time interval based on the relative pose transformation matrix of the adjacent key frames, and determining an average speed estimation value of the underwater robot between the adjacent key frames; comparing the average speed estimation value with the actual measurement speed of the DVL, and determining a speed error function of the DVL; and constructing a weighted error cost function containing visual re-projection, IMU pre-integration and DVL speed error terms, solving optimal estimation of key frame poses and map point states through nonlinear least square optimization, and outputting an optimized trajectory of the underwater robot. According to the invention, the precision of trajectory tracking of the underwater robot is effectively improved.

Providing a vehicle antenna switching system and method. [Solution] An exemplary embodiment of a telematics system for a vehicle is disclosed. The exemplary telematics system comprises a plurality of antennas capable of transmitting and receiving radio signals, and a telematics unit configured to select one antenna from the plurality of antennas based on network information and dynamic vehicle information, and to connect the selected antenna to the telematics unit for transmitting and / or receiving radio signals via the selected antenna.

The present application belongs to the technical field of Internet of Vehicles and communication security, and discloses a federated learning training method based on vehicle state perception and applicable to dynamic Internet of Vehicles. The method collects vehicle running state information of vehicle clients, preliminarily screens candidate vehicle clients, calculates comprehensive scores of the screened vehicle clients, adopts a client selection strategy combining selection of the top-ranked clients with random supplementation to determine a set of vehicle clients participating in the current round of federated learning, configures local training parameters for the selected vehicle clients according to vehicle speed and computing capacity, performs local training on local data sets to obtain model updates, performs weighted aggregation on the model updates of the effective participating vehicle clients at the server end, introduces a momentum mechanism to smooth the model update process, and obtains new global model parameters. This method effectively improves the stability, robustness and training efficiency of federated learning in a high-dynamic Internet of Vehicles environment.

The invention discloses a tire pressure monitoring system for wireless information transmission. The tire pressure monitoring system comprises a programmable tire pressure sensor, a hardware-level security isolation module, a programming tool, a block chain hash traceability module and a multi-dimensional aging intelligent early warning module. And the hardware-level security isolation module realizes physical isolation of a DOT information area and other functional areas in the sensor through an independent storage controller and a hardware firewall. And the block chain Hash traceability module is composed of a Hash generation unit arranged in the programming tool, an alliance chain storage network and a verification unit arranged in the terminal equipment, and is used for performing Hash chaining and read verification on the DOT information and outputting a verification result. And the multi-dimensional aging intelligent early warning module obtains corresponding DOT original information according to the verification result, and performs fusion analysis in combination with dynamic vehicle condition data collected by a sensor to generate tire state early warning information. The safety, the credibility and the intelligent level of the tire pressure monitoring system are effectively improved.

The embodiment of the application belongs to the field of multi-camera systems, and relates to a multi-target region dynamic vehicle and pedestrian identification method, which comprises the following steps: acquiring image data of a target region collected by multiple cameras; pre-processing the image data to extract features of the target region; performing target detection on the image data to identify vehicle and pedestrian targets in the image data and determine positions and features of the targets; preliminarily positioning each target and predicting a motion trajectory of the target through a Kalman filtering algorithm or a particle filtering algorithm; and performing space-time correlation on the targets identified by the multiple cameras based on a joint data correlation method of the positions, features and motion trajectories of the targets. The application also provides a multi-target region dynamic vehicle and pedestrian identification system. The application improves the coverage and accuracy of target identification.

The invention is suitable for the technical field of vehicle intelligent control, and provides a terrain-task coupling constraint reconfigurable carrying platform dynamic recombination method, which comprises the following steps of: obtaining terrain information and task demand information of a vehicle driving environment, and respectively calculating a terrain cost factor and a task cost factor; taking minimization of a terrain cost factor, a task cost factor and energy consumption as optimization objectives, taking external cabin and vehicle configuration as decision variables, performing optimization solution through a multi-objective optimization algorithm, and generating an initial optimal configuration scheme of the reconfigurable carrying platform; constructing a dynamic vehicle model evaluation model based on a random forest algorithm; according to a prediction result output by the dynamic vehicle model evaluation model and a preset threshold feedback mechanism, whether vehicle configuration recombination is triggered or not is judged.

The present invention relates, in general, to systems and methods for generating hydrogen from ammonia on-board vehicles, where the produced hydrogen is used as fuel source for an internal combustion engine. The present invention comprises an exhaust heat cracking unit and an electric cracking unit fluidly connected by a controllable bypass path. When exhaust heat from the engine is unavailable, the bypass path redirects the gaseous ammonia flow away from the unheated exhaust heat cracking unit and directly into the electric cracking unit. This configuration prevents the introduction of cold or partially condensed ammonia into the exhaust heat cracking unit passages, thereby mitigating condensation, volume expansion, and thermal stress that could otherwise result in structural damage or thermal shock within the exhaust heat exchanger. The system enables seamless transition between heat sources, ensuring safe and efficient operation across dynamic vehicle or operating and power conditions.

The invention belongs to the field of vehicles, and discloses a vehicle window control method and system, and the method comprises the steps: obtaining the perception data of a vehicle; determining a demand score based on the sensing data, and determining a corresponding vehicle window control strategy based on the demand score; and controlling the vehicle window to move based on the vehicle window control strategy. According to the method, the vehicle sensing data are acquired, the demand score is determined, and the demand score can represent the emergency degree of vehicle window movement, so that the dynamic vehicle window control strategy adjustment based on the demand score is realized. By means of the process, the moving speed of the car window can better adapt to different driving environments, discomfort or potential safety hazards possibly caused by the fact that the car window is controlled to ascend and descend at the fixed speed in different environments are avoided, and user experience is improved.

The invention relates to the technical field of vehicle task scheduling, in particular to a dynamic vehicle task scheduling method and system based on two-stage differentiation and multiple targets. The method comprises the following steps: acquiring vehicle data and task data; performing time slice static modeling based on the task data; carrying out vehicle classification and attribute modeling based on the vehicle data; according to a modeling result, performing two-stage differentiation and multi-target dynamic vehicle task allocation, including a first stage of sparse bipartite graph matching allocation and a second stage of CMOEA-EG multi-target optimization; performing fairness evaluation and feedback based on a distribution result; and updating and optimizing based on a feedback result. The task execution result is fed back in real time through the dynamic updating module, and multi-round iterative optimization is supported.

The present application belongs to the field of unmanned driving, and proposes a point cloud dynamic vehicle detection method in an unmanned driving scene, which comprises: step 1. Dynamic vehicle labeling and construction of a training data set; step 2. Building a point cloud dynamic vehicle detection model and running a point cloud dynamic vehicle detection algorithm. The method of the present application provides a method that can directly perceive the motion of vehicles in an unmanned driving scene without the need for a large amount of prior knowledge, so that it can accurately determine whether the relative motion of the vehicle and the laser radar causes point cloud distortion, thereby improving the accuracy of intelligent perception of unmanned vehicles, and is expected to break through the obstacle of intelligent decision-making of unmanned motion behavior.

This invention discloses a method and system for vehicle access edge cloud scheduling based on multi-dimensional scenarios. The method includes: a scheduling scenario configuration step, which predefines multiple scheduling scenarios and configures corresponding scheduling strategies for each scenario; a vehicle request processing step, which receives and verifies the access request from the vehicle terminal and extracts the vehicle location and service requirements; a scheduling decision step, which determines the target scheduling scenario based on the service requirements and selects the optimal target edge cloud node based on the scenario's strategy, vehicle location, and the real-time resource status of each edge cloud node; and a service redirection step, which returns the target node information to the vehicle, enabling it to directly access and obtain services. This invention achieves intelligent and dynamic vehicle scheduling through scenario-based strategies and multi-factor decision-making, effectively improving the system's ability to cope with tidal loads, ensuring low-latency services, and optimizing resource utilization efficiency.

A method of detecting three dimensional (3D) changes in sites, comprising receiving a plurality of images of a site captured from a plurality of viewpoints by image sensor(s) mounted on dynamic vehicle(s) scanning the site, registering at least some of the images to each other, creating one or more merged representations of at least part of the site based on the at least some registered images, comparing between the merged representation(s) and one or more corresponding previous merged representations created based on a plurality of previous images captured during one or more previous scans of the site, detecting, based on the comparison, one or more 3D changes indicative of a depth change exceeding a certain threshold in one or more 3D features at the site, and generating a change indication indicative of the one or more detected 3D changes.

The invention relates to the technical field of Internet of Vehicles network security and intelligent intrusion detection, and provides a self-supervision vehicle-mounted intrusion detection method based on Transform. Preprocessing the vehicle-mounted network flow data and constructing a time sequence feature sequence by adopting a sliding window mode; carrying out self-supervision pre-training on normal traffic by using an auto-encoder so as to learn spatial-temporal feature representation of vehicle-mounted network communication; introducing a multi-task joint optimization mechanism of multi-classification detection, dichotomy detection and comparative learning, and performing fine tuning training on the model by using a small amount of labeled samples; and realizing anomaly detection and attack type identification of the vehicle-mounted network traffic through the trained model. According to the method, dependence on attack annotation data is remarkably reduced, the long-range time sequence dependency relationship in vehicle-mounted network communication is effectively modeled, the accuracy, robustness and generalization ability of intrusion detection are improved, and the method is suitable for complex and dynamic vehicle networking safety protection scenes.

This application discloses a vehicle reservation charging management method and system. The method includes: responding to a charging connection event of a target vehicle, obtaining target scenario information of the current location of the target vehicle, wherein the charging connection event includes: the charging gun being inserted into the target vehicle and being identified as a valid charging connection; the target scenario information includes at least one of the following: date information and location information; determining a target charging strategy matching the target scenario information from a preset scenario strategy configuration library, wherein the scenario strategy configuration library stores multiple pre-configured charging strategies and the effective scenario information corresponding to each charging strategy; and performing a charging operation on the target vehicle according to the target charging strategy. This application solves the technical problem that a single timed charging mode in the reservation charging scenario for new energy vehicles lacks flexibility and is difficult to meet the dynamic vehicle usage needs of the target users.

The invention discloses a traffic emergency disposal system and method based on big data, and the system comprises a collection terminal which mainly comprises a remote sensing satellite, a traffic probe and a traffic unmanned plane, and carries out the multi-point video collection of the information of an accident situation on a road, an accident range area, queuing vehicles, slowly driving vehicles, and a plurality of lanes; the vehicle-mounted terminal is used for guiding a driver to keep driving according to a specified speed range per hour, a specified lane and lane change information; the terminal command system is used for summarizing the lane emergency area model and the dynamic vehicle data acquired by the acquisition terminal and building a three-dimensional traffic system; the scheduling terminal is used for scheduling and guiding non-accident lanes for scheduling; according to the system and the method, a perfect divided emergency area can be quickly established according to the situation of a site at the first time after an alarm is received, and a congested vehicle is helped to select a priority passing route, so that the scheduling efficiency is greatly improved, early warning is performed in time, and the probability of occurrence of a secondary accident is reduced.

The invention discloses an automatic parking simulation system and method based on road surface obstacles and a related device, and the system comprises an obstacle model selection and parameter configuration device which is used for obtaining a target obstacle model selected by a user and input configuration parameter information of the target obstacle model, and according to the target obstacle model and the configuration parameter information, obtaining a target obstacle model according to the target obstacle model and the configuration parameter information; determining a trajectory function on a target plane when the wheel crosses a target obstacle corresponding to the target obstacle model; a gradient calculator for calculating a gradient function according to the trajectory function; the obstacle editor is used for receiving the placement position and the geometrical shape of the target obstacle edited and set by the user; the obstacle description file generator is used for generating description file information of the target obstacle according to the gradient function, the placement position and the geometrical shape; and the simulator is used for calculating the total resistance borne by the vehicle according to the description file information of the target obstacle and the attribute information of the wheels, and inputting the total resistance into the dynamic vehicle model for simulation.

The invention discloses an emergency rescue vehicle intelligent scheduling method based on Beidou positioning and related equipment, and relates to the technical field of emergency management, the emergency rescue vehicle intelligent scheduling method based on Beidou positioning comprises the following steps: obtaining multi-mode state sensing data returned by a plurality of emergency rescue nodes in real time, the multi-modal state sensing data is composed of Beidou high-precision positioning information and a space-time label; the multi-modal state sensing data is input into a preset intelligent scheduling model, a dynamic vehicle scheduling strategy and collaborative path planning are generated, and the intelligent scheduling model is obtained through pre-training based on historical disaster situation data, a rescue resource game relationship and a group survival entropy optimization target; and sending a scheduling instruction to a corresponding target emergency rescue node according to the dynamic vehicle scheduling strategy and the cooperative path planning. According to the invention, the technical problem of low scheduling efficiency in a multi-concurrence scene is solved.