A bluetooth car searching method, system and device based on digital twinning

Abstract

This invention relates to a digital twin-based vehicle location method, specifically a Bluetooth vehicle location method, system, and device based on digital twins, comprising: S1, constructing a digital twin model of an underground parking garage, dividing the area and assigning coordinates to parking spaces, and deploying Bluetooth beacons differently according to spatial structure type; S2, binding the license plate of the entering vehicle with the corresponding parking space coordinates; S3, when a vehicle location request is made, measuring distance and filtering beacons, and obtaining the user's initial coordinates through triangulation; S4, verifying the initial coordinates in the model, and performing occlusion diagnosis if the vehicle falls into an obstacle: determining whether the signal is directly or reflected through ray detection, and correcting the coordinates to a passable area accordingly to obtain real-time positioning coordinates; S5, based on the real-time coordinates, vehicle coordinates, and real-time status data in the twin model, calculating the total path cost considering fusion distance, turning, congestion, and user preferences, and planning and navigating the optimal path. This application can achieve accurate vehicle location even in underground environments with poor GPS signals.

Description

Technical Field

[0001] This invention belongs to the field of digital twin vehicle location technology, specifically relating to a Bluetooth vehicle location method, system, and device based on digital twins. Background Technology

[0002] The scale of urban underground space continues to expand, with a large number of parking spaces and complex spatial structures. Due to the underground environment, GPS / BeiDou signals are blocked, making it easy for users to get lost after parking and take too long to find their cars. At the same time, parking lot managers have difficulty monitoring the occupancy status of parking spaces and the distribution of vehicles in real time, resulting in low parking space utilization, difficulties in emergency dispatch, and exacerbation of traffic congestion in underground spaces, affecting user experience and operational efficiency.

[0003] Existing underground vehicle location technologies mostly rely on video recognition or a single Bluetooth beacon. The former can only determine the area where the vehicle is located, but cannot pinpoint the specific parking space; the latter suffers from low beacon density and underground signal reflection interference, resulting in large positioning errors. This forces users to search the area again, making it impossible to achieve accurate and direct location.

[0004] Although some underground spaces have been modeled in three dimensions, these models are mostly static and cannot synchronize with the dynamic information of the physical space in real time. This results in the virtual navigation not matching the actual scene and the vehicle search path failing.

[0005] The positioning equipment and management systems of different parking lots operate independently, and there is no unified standard for data interfaces, making it impossible to achieve data interoperability for vehicle location across parking lots; moreover, the existing system is difficult to adapt to underground spaces with different structures, requiring targeted reconstruction, which results in high development costs and low reusability. Summary of the Invention

[0006] The purpose of this invention is to provide a Bluetooth vehicle location method, system, and device based on digital twins.

[0007] A Bluetooth-based vehicle location method based on digital twins includes the following steps: S1. Based on the spatial structure of the underground parking garage, construct a digital twin model, divide the obstacle area, the passable area and the parking space area, and assign a unique coordinate to each parking space in the parking space area. According to the roads, buildings and their positional relationships, divide the spatial structure into several types, and set the layout density of Bluetooth beacons according to the type. S2. Bind the license plate information of the vehicle that drives into the parking space to the coordinates of the corresponding parking space in the digital twin model, and use it as the parking location coordinates of the vehicle. S3. When a vehicle-finding request is received from a user's mobile device, the distance between several Bluetooth beacons and the user's mobile device is obtained. Based on the distance and Bluetooth signal strength, a preset number of Bluetooth beacons are selected. The initial positioning coordinates of the user's mobile device are obtained based on the coordinates of the selected Bluetooth beacons. S4. Synchronize the initial positioning coordinates to the digital twin model. If the initial positioning coordinates fall into the obstacle area, obtain the spatial boundary of the obstacle and calculate the shortest occlusion distance between the initial positioning coordinates and the obstacle boundary. In the digital twin model, draw a ray from each selected Bluetooth beacon to the initial positioning coordinates and obtain the intersection point of the ray with the obstacle and the obstacle distance between the ray and the obstacle. Based on the obstacle distance and the distance between the current Bluetooth beacon and the initial positioning coordinates, determine the occlusion type. According to the occlusion type, translate the initial positioning coordinates based on the shortest occlusion distance to obtain the real-time positioning coordinates of the user's mobile device. S5. Calculate the total cost of the route based on the real-time location coordinates of the user's mobile device and the parking location coordinates of the vehicle bound to the user's mobile device. Select the route with the minimum total cost as the optimal route and navigate according to the optimal route.

[0008] Based on the obstacle distance and the distance between the current Bluetooth beacon and the initial positioning coordinates, the occlusion type is determined, specifically as follows: If the obstacle distance is greater than or equal to the distance between the current Bluetooth beacon and the initial positioning coordinates, then it is considered direct occlusion; If the obstacle distance is less than the distance between the current Bluetooth beacon and the initial positioning coordinates, then it is a reflection blockage.

[0009] When the occlusion type is direct occlusion, the initial positioning coordinates are translated based on the shortest occlusion distance to obtain the real-time positioning coordinates of the user's mobile device. Specifically, the initial positioning coordinates are translated in the opposite direction of the Bluetooth signal propagation direction to the nearest passable area or parking space area with the shortest occlusion distance. If the initial positioning coordinates still fall into the obstacle area after translation, the translation step is repeated until the initial positioning coordinates fall into the passable area or parking space area to obtain the real-time positioning coordinates of the user's mobile device.

[0010] When the occlusion type is reflection occlusion, the initial positioning coordinates are translated based on the shortest occlusion distance to obtain the user's real-time positioning coordinates, specifically: The process involves obtaining the reflective surface between the current Bluetooth beacon and the initial positioning coordinates in the digital twin model, obtaining the mirror image of the Bluetooth beacon with respect to the reflective surface, replacing the original Bluetooth beacon with the mirror image, executing the process of obtaining the user's mobile device's initial positioning coordinates based on the selected Bluetooth beacon coordinates, obtaining new initial positioning coordinates, and if the new initial positioning coordinates still fall within the obstacle area, translating the new initial positioning coordinates based on the shortest occlusion distance to obtain the user's mobile device's real-time positioning coordinates.

[0011] If the new initial positioning coordinates still fall within the obstacle area, the new initial positioning coordinates are translated based on the shortest occlusion distance to obtain the user's real-time positioning coordinates, specifically: The new initial positioning coordinates are shifted away from the obstacle area by half the shortest occlusion distance.

[0012] In S3, the initial location coordinates of the user's mobile device are obtained based on the coordinates of the selected Bluetooth beacons, specifically as follows: Using the selected Bluetooth beacon as the center and the distance between the user's mobile device and the Bluetooth beacon as the radius, multiple spheres are drawn in the digital twin model. The common intersection area of ​​all spheres is obtained, and the geometric center point of the common intersection area of ​​all spheres is taken as the initial positioning coordinates.

[0013] In S3, a preset number of Bluetooth beacons are selected based on the distance and Bluetooth signal strength. Specifically, the average and standard deviation of the distance are calculated. If there is a distance that deviates from the average by more than twice the standard deviation, it is removed. If the Bluetooth signal strength exceeds the preset Bluetooth beacon reception range, it is also removed. Furthermore, the preset number of Bluetooth beacons connected together form a triangle with interior angles of 30°-150°.

[0014] Total path cost in S5: , in, Road width coefficient, For path length, This is the turning angle coefficient. This refers to the turning length.

[0015] A Bluetooth vehicle finding system based on digital twins, used to implement the aforementioned Bluetooth vehicle finding method based on digital twins, includes: The digital twin model construction module constructs a digital twin model based on the spatial structure of the underground parking garage, divides the area into obstacle area, passable area and parking space area, and assigns a unique coordinate to each parking space in the parking space area. Based on the roads, buildings and their positional relationships, the spatial structure is divided into several types, and the layout density of Bluetooth beacons is set according to the type. The parking location coordinate setting module binds the license plate information of the vehicle entering the parking space with the coordinates of the corresponding parking space in the digital twin model, which serves as the parking location coordinates of the vehicle. The initial location acquisition module, upon receiving a vehicle-finding request from a user's mobile device, acquires the distances between several Bluetooth beacons and the user's mobile device. Based on the distance and Bluetooth signal strength, it filters out a preset number of Bluetooth beacons and obtains the initial location coordinates of the user's mobile device based on the coordinates of the filtered Bluetooth beacons. The real-time location acquisition module synchronizes the initial location coordinates to the digital twin model. If the initial location coordinates fall within an obstacle area, it acquires the spatial boundary of the obstacle, calculates the shortest occlusion distance between the initial location coordinates and the obstacle boundary, and in the digital twin model, draws a ray from each filtered Bluetooth beacon to the initial location coordinates, acquires the intersection point of the ray with the obstacle and the obstacle distance between the ray and the obstacle, determines the occlusion type based on the obstacle distance and the current distance between the Bluetooth beacon and the initial location coordinates, and translates the initial location coordinates based on the shortest occlusion distance according to the occlusion type to obtain the real-time location coordinates of the user's mobile device. The navigation module calculates the total cost of the route based on the real-time location coordinates of the user's mobile device and the parking location coordinates of the vehicle bound to the user's mobile device. The route with the minimum total cost is selected as the optimal route, and navigation is performed according to the optimal route.

[0016] A Bluetooth vehicle-finding device based on digital twins includes a processor and a memory, wherein the processor executes a computer program stored in the memory to implement the aforementioned Bluetooth vehicle-finding method based on digital twins.

[0017] The beneficial effects of this application are as follows: (1) Compared with the positioning error of traditional underground car finding technology, this application reduces the positioning error by deploying dense Bluetooth beacons and triangulation, realizing the positioning from area positioning to parking space positioning; users do not need to search twice in the area, shortening the average car finding time, improving user experience, and improving efficiency compared with traditional technology.

[0018] (2) The digital twin model constructed in this application is synchronized with the underground space status and updates parking space occupancy, passage closure and equipment failure information in real time. When planning the route, it can combine the real-time status to avoid congestion and obstacles, improve navigation accuracy, and ensure that users can directly reach the parking space by following the navigation.

[0019] (3) Due to the digital twin model modeled in this application, the overall status of the parking lot can be intuitively grasped, the emergency dispatch response time is shortened, the operating manpower cost is reduced, and the parking space utilization rate is improved. Detailed Implementation

[0020] To further understand the content of this invention, the invention will be described in detail with reference to the embodiments.

[0021] This invention relates to a Bluetooth vehicle location method based on digital twins, which includes the following steps: S1. Based on the spatial structure of the underground parking garage, construct a digital twin model, divide the area into obstacle zone, passable zone and parking space zone, and assign a unique coordinate to each parking space in the parking space zone. Divide the spatial structure into several types according to the road, building and their positional relationship, and set the layout density of Bluetooth beacons according to the type.

[0022] By combining BIM technology with point cloud scanning, comprehensive data on the underground space's building structure, parking space layout, and equipment distribution were collected to construct a 1:1 scale 3D twin model. The model's coordinate system adopted the local coordinate system of the underground space, with the entrance as the origin, the X-axis extending along the main passage, the Y-axis perpendicular to the main passage, and the Z-axis indicating the floor. At this point, the coordinates of each parking space were entered into the model library.

[0023] Bluetooth beacons are deployed according to the principle of high density and no blind spots. In open, straight sections such as main and secondary passages in underground spaces, where there are no obstructions and signal propagation is smooth, they are evenly deployed at intervals of 5-8 meters. At key nodes such as turns, elevator entrances, stairwells, densely parked areas, and passageway intersections, where signals are easily reflected or blocked, and where continuous positioning switching needs to be ensured, they are densely deployed at intervals of 2-4 meters to ensure full signal coverage without dead spots. Each beacon has a unique ID and preset coordinates, consistent with the coordinate system of the twin model. Initial parameter configuration, including transmission power and broadcast interval, is completed through a LoRaWAN gateway to ensure that at least three Bluetooth beacons can be scanned at any location in the underground space.

[0024] S2. Bind the license plate information of the vehicle that drives into the parking space to the coordinates of the corresponding parking space in the digital twin model, and use it as the parking location coordinates of the vehicle.

[0025] After a user's vehicle enters a parking space, the system detects that the vehicle is occupied and immediately generates data including parking space number, occupancy status, detection time, and license plate number. This data is then transmitted to the edge computing gateway via a LoRaWAN link. The data is validated to exclude license plate obstruction and accidental touches during reversing. Once valid, the data is synchronized to the digital twin model, the status label of the corresponding parking space is updated, and the license plate number is bound to the parking space. At this point, the vehicle's coordinates, which are also the coordinates of the parking space, are obtained.

[0026] S3. When a vehicle-finding request is received from a user's mobile device, the distance between several Bluetooth beacons and the user's mobile device is obtained. Based on the distance and Bluetooth signal strength, a preset number of Bluetooth beacons are selected. The initial positioning coordinates of the user's mobile device are obtained based on the coordinates of the selected Bluetooth beacons.

[0027] Obtain the straight-line distance between the user and each beacon; calculate the average and standard deviation of the distance to account for underground signal reflection interference; if there is a distance that deviates from the average by more than twice the standard deviation, it is discarded; if the Bluetooth signal strength exceeds the preset Bluetooth beacon reception range, it is discarded; and the preset number of Bluetooth beacons selected form a triangle with interior angles of 30°-150°.

[0028] To address situations where signal interference results in fewer than 3 Bluetooth beacons remaining, a dual-protection mechanism is activated: First, the scanning radius is expanded and the scanning time is extended to supplement the collection of surrounding Bluetooth beacon data; second, inertial navigation assistance is enabled, combining data from the mobile device's gyroscope and accelerometer to calculate the temporary location based on the previous accurate positioning; if there are still fewer than 3 beacons, the historical beacon deployment heatmap is automatically retrieved, prioritizing the association of core Bluetooth beacons with stable signal coverage within 15-20 meters.

[0029] Using the selected Bluetooth beacon as the center and the distance between the user's mobile device and the Bluetooth beacon as the radius, multiple spheres are drawn in the digital twin model. Due to underground signal interference, the spheres will not completely intersect at one point. The common intersection area is solved by the least squares method, and abnormal points falling in obstacles such as walls and pillars are eliminated to obtain the common intersection area of ​​all spheres. The geometric center point of the common intersection area of ​​all spheres is taken as the initial positioning coordinates.

[0030] The deviation of the Euclidean distance from the geometric center point to each beacon from the mean of all distances is calculated, ensuring that the deviation is ≤0.5 meters. If the deviation exceeds the limit, the Bluetooth beacons are re-selected and the calculation is iterated. The final output is an initial positioning coordinate with an accuracy of 0.5 meters, providing a reliable foundation for subsequent operations.

[0031] S4. Synchronize the initial positioning coordinates to the digital twin model. If the initial positioning coordinates fall into the obstacle area, obtain the spatial boundary of the obstacle and calculate the shortest occlusion distance between the initial positioning coordinates and the obstacle boundary. In the digital twin model, draw a ray from each selected Bluetooth beacon to the initial positioning coordinates, obtain the intersection point of the ray with the obstacle and the obstacle distance between the ray and the obstacle, determine the occlusion type based on the obstacle distance and the distance between the current Bluetooth beacon and the initial positioning coordinates, and translate the initial positioning coordinates based on the shortest occlusion distance according to the occlusion type to obtain the real-time positioning coordinates of the user's mobile device.

[0032] If the initial positioning coordinates are substituted into the twin model and fall within the passable area and parking space area, then S5 is executed directly.

[0033] If the initial positioning coordinates fall into the obstacle area, obtain the spatial boundary of the obstacle, calculate the shortest occlusion distance between the initial positioning coordinates and the obstacle boundary, and in the digital twin model, draw a ray from each selected Bluetooth beacon to the initial positioning coordinates, obtain the intersection point of the ray with the obstacle and the obstacle distance between the ray and the obstacle. If the obstacle distance is greater than or equal to the distance between the current Bluetooth beacon and the initial positioning coordinates, it is a direct occlusion; otherwise, it is a reflected occlusion.

[0034] When the occlusion type is direct occlusion, the initial positioning coordinates are translated based on the shortest occlusion distance to obtain the real-time positioning coordinates of the user's mobile device. Specifically, the initial positioning coordinates are translated in the opposite direction of the Bluetooth signal propagation direction to the nearest passable area or parking space area with the shortest occlusion distance. If the initial positioning coordinates still fall into the obstacle area after translation, the translation step is repeated until the initial positioning coordinates fall into the passable area or parking space area to obtain the real-time positioning coordinates of the user's mobile device.

[0035] When the occlusion type is reflection occlusion, the initial positioning coordinates are translated based on the shortest occlusion distance to obtain the user's real-time positioning coordinates, specifically: The reflective surface between the current Bluetooth beacon and the initial positioning coordinates in the digital twin model is obtained. Then, the mirror image of the Bluetooth beacon with respect to the reflective surface is obtained. The mirror image is used to replace the original Bluetooth beacon. The process of obtaining the user's mobile device's initial positioning coordinates based on the coordinates of the selected Bluetooth beacons is executed, resulting in new initial positioning coordinates. If the new initial positioning coordinates still fall within the obstacle area, the new initial positioning coordinates are translated based on the shortest occlusion distance. The new initial positioning coordinates are translated in a direction away from the obstacle area by half of the shortest occlusion distance to obtain the user's real-time positioning coordinates.

[0036] Furthermore, the real-time positioning coordinates must have a distance deviation of ≤0.3 meters from all Bluetooth beacons; otherwise, repeat step S4 iteratively, up to 3 times, to ensure the rationality of the compensated coordinates.

[0037] S5. Calculate the total cost of the route based on the real-time location coordinates of the user's mobile device and the parking location coordinates of the vehicle bound to the user's mobile device. Select the route with the minimum total cost as the optimal route and navigate according to the optimal route.

[0038] After a user initiates a vehicle search command, a closed-loop service is launched, encompassing real-time positioning, data retrieval, route planning, and dynamic navigation. This ensures the accuracy, optimization, and continuity of the navigation route. The mobile device's Bluetooth module is activated and switched to a high-frequency scanning mode, increasing the scanning frequency to 2 times per second and temporarily increasing the scanning power by 10%. Step S3 is fully reproduced: the mobile device quickly scans surrounding Bluetooth beacons, collects the signal strength and unique ID of each beacon, and eliminates abnormal signals; it calculates the straight-line distance between the user and each beacon, and finally outputs the user's real-time positioning coordinates with an accuracy of ≤1 meter, continuously updating them at a frequency of 1 time per second to provide real-time location support for dynamic navigation.

[0039] For areas with weak signals in underground spaces, if the number of valid beacons scanned is less than 3, inertial navigation-assisted positioning is automatically activated. Based on real-time data from the mobile device's gyroscope and accelerometer, combined with the previous accurate positioning coordinates, the user's movement direction, step length, and distance are calculated, with the error controlled within 0.5 meters / 10 seconds. Once the mobile device scans enough valid beacons, it immediately switches back to Bluetooth accurate positioning to ensure uninterrupted positioning continuity.

[0040] Retrieve the parking location coordinates of user-bound vehicles from the database, synchronously obtain real-time underground space status data from the twin modeling layer, and output the optimal vehicle search path: Using the user's real-time location coordinates as the starting point and the vehicle's parking coordinates as the ending point, the shortest path is calculated based on the spatial topology data of the twin model: Path cost modeling decomposes path cost into path length cost and turning cost. Path length cost is calculated based on the actual length, while turning cost is dynamically assigned based on the turning angle to avoid planning unreasonable paths with too many sharp turns. A road width coefficient is introduced to correct the total path cost. The coefficient is 1.0 when the passage width is ≥3 meters, 1.3 when it is 1.5-3 meters, and 1.8 when it is <1.5 meters, ensuring that wide passages are prioritized. Right-angle turn cost = 1.2 × turning length, acute-angle turn cost = 1.5 × turning length, and obtuse-angle turn cost = 1.1 × turning length. The total cost of the path is: , in, Road width coefficient, For path length, This is the turning angle coefficient. This refers to the turning length.

[0041] Furthermore, dynamic data is extracted from the digital twin model to avoid closed passages and high-traffic areas (traffic density > 2 people / ㎡). The path cost of such areas is set to infinity, and the algorithm automatically bypasses them. For areas with a traffic density of 1-2 people / ㎡, the path length cost is appropriately increased, and more unobstructed alternative passages are selected first. Users can select accessible pathways, extract accessible facility data from the twin model, and forcibly plan paths that include such facilities, with a slope of ≤5° and no step nodes; when the fastest way to reach is selected, the turning cost weight is ignored, and only physical distance and passage efficiency are considered, prioritizing pathways with a flow density of <1 person / ㎡; when the option to minimize elevator use is selected, level paths are prioritized for the same floor, and ramps are prioritized for cross-floor connections.

[0042] After the user selects a route, a 1:1 replica 3D navigation interface is generated based on the twin model. It supports free switching between top-view and first-person view. The top-view shows the overall route and current location, clearly presenting the relative distance to the vehicle. The first-person view simulates the actual walking field of vision, accurately marking guidance information such as turns, elevator entrances, and passage signs ahead. It is also equipped with voice broadcast, and the voice frequency is adjusted according to the distance.

[0043] During navigation, the system receives the user's real-time location coordinates every second and compares them with the planned path in real time. If the deviation between the user's current location and the path is ≤0.5 meters, it is determined to be normal following, and navigation continues along the original path. If the deviation is >0.5 meters and lasts for 2 seconds, it is determined to be a deviation from the path, and the path is immediately recalculated. Based on the current user location and vehicle location, S5 is re-executed to generate a new optimal path, and the 3D interface and voice broadcast are updated simultaneously to guide the user to quickly return to the correct route.

[0044] If the underground space status changes suddenly during navigation, such as a passage being temporarily closed or equipment being repaired, the edge gateway transmits the status data to the twin modeling layer in real time, and the dynamic update module synchronously corrects the model status. The path planning engine immediately detects whether the original path is affected. If the passages included in the original path are impassable, the path is automatically recalculated and a message is displayed: "The passage ahead is temporarily closed, and a new path has been planned for you."

[0045] If only traffic efficiency is affected, such as a sudden increase in pedestrian density in a certain section of the original route, the difference in estimated travel time between the new route and the original route will be calculated. If the travel time of the new route is reduced by ≥20%, a route optimization prompt will be pushed: "A faster route has been found. Do you want to switch?". After the user confirms, the navigation route will be switched. If not, the user will continue to navigate along the original route, but a real-time congestion prompt will be broadcast: "The passage ahead is congested in 50 meters. It is recommended to slow down."

[0046] A Bluetooth vehicle finding system based on digital twins, used to implement the aforementioned Bluetooth vehicle finding method based on digital twins, includes: The digital twin model construction module constructs a digital twin model based on the spatial structure of the underground parking garage, divides the area into obstacle area, passable area and parking space area, and assigns a unique coordinate to each parking space in the parking space area. Based on the roads, buildings and their positional relationships, the spatial structure is divided into several types, and the layout density of Bluetooth beacons is set according to the type. The parking location coordinate setting module binds the license plate information of the vehicle entering the parking space with the coordinates of the corresponding parking space in the digital twin model, which serves as the parking location coordinates of the vehicle. The initial location acquisition module, when receiving a vehicle-finding request from a user's mobile device, acquires the distance between several user mobile devices and Bluetooth beacons. Based on the distance and Bluetooth signal strength, it filters out a preset number of Bluetooth beacons and obtains the initial location coordinates of the user's mobile device based on the coordinates of the filtered Bluetooth beacons. The real-time positioning acquisition module synchronizes the initial positioning coordinates to the digital twin model. If the initial positioning coordinates fall into the obstacle area, it acquires the spatial boundary of the obstacle and calculates the shortest occlusion distance between the initial positioning coordinates and the obstacle boundary. In the digital twin model, a ray is drawn from each selected Bluetooth beacon to the initial positioning coordinates, and the intersection point of the ray with the obstacle and the obstacle distance between the ray and the obstacle are acquired. If the obstacle distance is greater than or equal to the distance between the current Bluetooth beacon and the initial positioning coordinates, it is a direct occlusion; otherwise, it is a reflected occlusion. Based on the occlusion type, the initial positioning coordinates are translated based on the shortest occlusion distance to obtain the real-time positioning coordinates of the user's mobile device. The navigation module calculates the total cost of the route based on the real-time location coordinates of the user's mobile device and the parking location coordinates of the vehicle bound to the user's mobile device. The route with the minimum total cost is selected as the optimal route, and navigation is performed according to the optimal route.

[0047] A Bluetooth vehicle-finding device based on digital twins includes a processor and a memory, wherein the processor executes a computer program stored in the memory to implement a Bluetooth vehicle-finding method based on digital twins.

Claims

1. A Bluetooth car searching method based on digital twinning, characterized in that, Includes the following steps: S1. Based on the spatial structure of the underground parking garage, construct a digital twin model, divide the obstacle area, the passable area and the parking space area, and assign a unique coordinate to each parking space in the parking space area. According to the roads, buildings and their positional relationships, divide the spatial structure into several types, and set the layout density of Bluetooth beacons according to the type. The aforementioned types specifically include open, straight road sections and types where signals are easily reflected or blocked. S2. Bind the license plate information of the vehicle that drives into the parking space to the coordinates of the corresponding parking space in the digital twin model, and use it as the parking location coordinates of the vehicle. S3. When a vehicle-finding request is received from a user's mobile device, the distance between several Bluetooth beacons and the user's mobile device is obtained. Based on the distance and Bluetooth signal strength, a preset number of Bluetooth beacons are selected. The initial positioning coordinates of the user's mobile device are obtained based on the coordinates of the selected Bluetooth beacons. In S3, a preset number of Bluetooth beacons are selected based on the distance and Bluetooth signal strength. Specifically, the average and standard deviation of the distance are calculated. If there is a distance that deviates from the average by more than twice the standard deviation, it is removed. If the Bluetooth signal strength exceeds the preset Bluetooth beacon reception range, it is removed. The preset number of Bluetooth beacons selected are connected to form a triangle with interior angles of 30°-150°. In S3, the initial positioning coordinates of the user's mobile device are obtained based on the coordinates of the selected Bluetooth beacons. Specifically, multiple spheres are drawn in the digital twin model with the selected Bluetooth beacons as the center and the distance between the user's mobile device and the Bluetooth beacons as the radius. The common intersection area of ​​all spheres is obtained, and the geometric center point of the common intersection area is taken as the initial positioning coordinates. In S4, the initial positioning coordinates are synchronized to the digital twin model. If the initial positioning coordinates fall into the obstacle area, the spatial boundary of the obstacle is obtained, and the shortest occlusion distance between the initial positioning coordinates and the obstacle boundary is calculated. In the digital twin model, a ray is drawn from each selected Bluetooth beacon to the initial positioning coordinates. The intersection point of the ray with the obstacle and the obstacle distance between the ray and the obstacle are obtained. Based on the obstacle distance and the distance between the current Bluetooth beacon and the initial positioning coordinates, the occlusion type is determined. According to the occlusion type, the initial positioning coordinates are translated based on the shortest occlusion distance to obtain the real-time positioning coordinates of the user's mobile device. S5. Calculate the total cost of the route based on the real-time location coordinates of the user's mobile device and the parking location coordinates of the vehicle bound to the user's mobile device. Select the route with the minimum total cost as the optimal route and navigate according to the optimal route.

2. The Bluetooth car searching method based on digital twinning according to claim 1, characterized in that, Based on the obstacle distance and the distance between the current Bluetooth beacon and the initial positioning coordinates, the occlusion type is determined, specifically as follows: If the obstacle distance is greater than or equal to the distance between the current Bluetooth beacon and the initial positioning coordinates, then it is considered direct occlusion; If the obstacle distance is less than the distance between the current Bluetooth beacon and the initial positioning coordinates, then it is a reflection blockage.

3. The Bluetooth car searching method based on digital twinning according to claim 1, characterized in that, When the occlusion type is direct occlusion, the initial positioning coordinates are translated based on the shortest occlusion distance to obtain the real-time positioning coordinates of the user's mobile device. Specifically, the initial positioning coordinates are translated in the opposite direction of the Bluetooth signal propagation direction to the nearest passable area or parking space area with the shortest occlusion distance. If the initial positioning coordinates still fall into the obstacle area after translation, the translation step is repeated until the initial positioning coordinates fall into the passable area or parking space area to obtain the real-time positioning coordinates of the user's mobile device.

4. The Bluetooth vehicle location method based on digital twin according to claim 1, characterized in that, When the occlusion type is reflection occlusion, the initial positioning coordinates are translated based on the shortest occlusion distance to obtain the user's real-time positioning coordinates, specifically: The process involves obtaining the reflective surface between the current Bluetooth beacon and the initial positioning coordinates in the digital twin model, obtaining the mirror image of the Bluetooth beacon with respect to the reflective surface, replacing the original Bluetooth beacon with the mirror image, executing the process of obtaining the user's mobile device's initial positioning coordinates based on the selected Bluetooth beacon coordinates, obtaining new initial positioning coordinates, and if the new initial positioning coordinates still fall within the obstacle area, translating the new initial positioning coordinates based on the shortest occlusion distance to obtain the user's mobile device's real-time positioning coordinates.

5. The Bluetooth vehicle location method based on digital twin according to claim 4, characterized in that, If the new initial positioning coordinates still fall within the obstacle area, the new initial positioning coordinates are translated based on the shortest occlusion distance to obtain the user's real-time positioning coordinates, specifically: The new initial positioning coordinates are shifted away from the obstacle area by half the shortest occlusion distance.

6. The Bluetooth vehicle location method based on digital twin according to claim 1, characterized in that, Total path cost in S5: ， in, Road width coefficient, For path length, This is the turning angle coefficient. This refers to the turning length.

7. A Bluetooth vehicle finding system based on digital twins, used to implement the Bluetooth vehicle finding method based on digital twins as described in any one of claims 1-6, characterized in that, include: The digital twin model construction module constructs a digital twin model based on the spatial structure of the underground parking garage, divides the area into obstacle area, passable area and parking space area, and assigns a unique coordinate to each parking space in the parking space area. Based on the roads, buildings and their positional relationships, the spatial structure is divided into several types, and the layout density of Bluetooth beacons is set according to the type. The aforementioned types specifically include open, straight road sections and types where signals are easily reflected or blocked. The parking location coordinate setting module binds the license plate information of the vehicle entering the parking space with the coordinates of the corresponding parking space in the digital twin model, which serves as the parking location coordinates of the vehicle. The initial location acquisition module, when receiving a vehicle search request from a user's mobile device, acquires the distance between several Bluetooth beacons and the user's mobile device. Based on the distance and Bluetooth signal strength, it filters out a preset number of Bluetooth beacons and obtains the initial location coordinates of the user's mobile device based on the coordinates of the filtered Bluetooth beacons. Based on the distance and Bluetooth signal strength, a preset number of Bluetooth beacons are selected. Specifically, the average and standard deviation of the distances are calculated. If a distance deviates from the average by more than twice the standard deviation, it is removed. If the Bluetooth signal strength exceeds the preset Bluetooth beacon reception range, it is also removed. Furthermore, the preset number of Bluetooth beacons connected together form a triangle with interior angles between 30° and 150°. The initial positioning coordinates of the user's mobile device are obtained based on the coordinates of the selected Bluetooth beacons. Specifically, multiple spheres are drawn in the digital twin model with the selected Bluetooth beacons as the center and the distance between the user's mobile device and the Bluetooth beacons as the radius. The common intersection area of ​​all spheres is obtained, and the geometric center point of the common intersection area is taken as the initial positioning coordinates. The real-time positioning acquisition module synchronizes the initial positioning coordinates to the digital twin model. If the initial positioning coordinates fall into the obstacle area, the spatial boundary of the obstacle is obtained, and the shortest occlusion distance between the initial positioning coordinates and the obstacle boundary is calculated. In the digital twin model, a ray is drawn from each selected Bluetooth beacon to the initial positioning coordinates, and the intersection point of the ray with the obstacle and the obstacle distance between the ray and the obstacle are obtained. Based on the obstacle distance and the distance between the current Bluetooth beacon and the initial positioning coordinates, the occlusion type is determined. According to the occlusion type, the initial positioning coordinates are translated based on the shortest occlusion distance to obtain the real-time positioning coordinates of the user's mobile device. The navigation module calculates the total cost of the route based on the real-time location coordinates of the user's mobile device and the parking location coordinates of the vehicle bound to the user's mobile device. The route with the minimum total cost is selected as the optimal route, and navigation is performed according to the optimal route.

8. A Bluetooth vehicle finding device based on digital twins, characterized in that, It includes a processor and a memory, wherein the processor executes a computer program stored in the memory to implement a Bluetooth car-finding method based on digital twins as described in any one of claims 1-6.

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