Underground space personalized intelligent guidance system based on spatial layout

Abstract

The invention discloses an underground space personalized intelligent guidance system based on spatial layout, which comprises a positioning hardware network, a navigation calculation server, user terminal equipment and a data transmission network, and is characterized in that the navigation calculation server is internally provided with a complex spatial database and stores a three-dimensional map of an underground space and function distribution data of each region; the system further comprises a special positioning algorithm processing unit and a path planning processing unit, the positioning algorithm processing unit is responsible for fusing multi-source data from Wi-Fi, Bluetooth and a high-precision inertial measurement unit IMU to achieve accurate positioning, and the path planning processing unit is based on a NavMesh navigation grid and an improved A * algorithm. And a multi-target path is generated in combination with real-time personnel density distribution data, an optimal path and a plurality of recommended paths are provided, and personalized route suggestions are generated according to specific requirements of users. According to the method, the limitation of a traditional navigation mode in dealing with the complexity of the underground space is overcome, and accurate navigation and personalized guidance of the user in the underground space are realized.

Description

Technical Field

[0001] The present invention relates to the field of underground space indoor navigation systems, and in particular to an underground space personalized intelligent guidance system based on space layout. Background Art

[0002] At present, the navigation system of underground space mainly relies on static sign systems and flat maps. Such methods usually help users understand their current location and find their destination by setting fixed signs or providing static maps in the space. However, this navigation method has limitations, especially in underground spaces where information changes frequently, such as shopping malls, subway stations, or underground parking lots. Static signs and flat maps cannot be updated dynamically, and cannot provide personalized navigation services according to user needs. In addition, users are prone to getting lost in complex underground spaces, especially when they are unfamiliar with the environment, and the experience of using traditional navigation tools is poor. Especially in crowded or emergency situations, existing navigation methods cannot provide real-time optimal path planning, which affects the utilization efficiency of underground space and user experience.

[0003] The defect of the existing technology is that it cannot effectively solve the problem of high-precision positioning in underground space environments. Due to the serious signal shielding in underground space, traditional GPS positioning methods are not feasible, and the accuracy of single Wi-Fi or Bluetooth positioning is also difficult to meet the needs. In addition, the existing technology lacks an updateable three-dimensional space map and cannot adapt to changes in the underground space layout. For the complex and changeable underground space environment, how to integrate multi-source data to improve the accuracy of positioning and navigation is still the bottleneck of the existing technology. At the same time, it is difficult for existing navigation systems to realize personalized real-time path planning under limited hardware resources, especially in emergency evacuation situations, and it is impossible to provide fast and safe guidance paths for different users. Solving these key technical problems is crucial to improving the performance of underground space navigation systems and user experience. Summary of the invention

[0004] In view of the shortcomings of the existing technology, the present invention proposes a personalized intelligent guidance system for underground space based on spatial layout, which aims to overcome the limitations of traditional navigation methods in dealing with the complexity of underground space. By combining multi-source data fusion technology, high-precision positioning algorithm and real-time path planning, accurate navigation and personalized guidance of users in underground space can be achieved.

[0005] To achieve the above object, the technical solution of the present invention is as follows:

[0006] A personalized intelligent guidance system for underground space based on spatial layout, the personalized intelligent guidance system for underground space comprising:

[0007] The positioning hardware network consists of multiple Wi-Fi access points and Bluetooth beacons, which are evenly distributed at key locations in the underground space to form a high-density positioning grid. Each Wi-Fi access point and Bluetooth beacon has a unique identifier and precise coordinates for high-precision positioning.

[0008] A navigation computing server is arranged in the control room of the underground space. The navigation computing server has a built-in complex spatial database, which stores a three-dimensional map of the underground space and functional distribution data of each area. The navigation computing server also includes a dedicated positioning algorithm processing unit and a path planning processing unit. The positioning algorithm processing unit is responsible for integrating multi-source data from Wi-Fi, Bluetooth and a high-precision inertial measurement unit IMU to achieve precise positioning. The path planning processing unit generates a multi-target path based on the NavMesh navigation grid and the improved A* algorithm, combined with real-time personnel density distribution data, provides the best path and multiple recommended paths, and generates personalized route suggestions according to the specific needs of users;

[0009] A user terminal device, equipped with a Wi-Fi module, a Bluetooth module and a high-precision inertial measurement unit IMU, wherein the user terminal device collects and sends multi-source positioning data through a dedicated navigation application;

[0010] The data transmission network consists of a high-speed wired network and a wireless mesh network. The high-speed wired network connects all Wi-Fi access points and navigation computing servers, and the wireless mesh network connects all Bluetooth beacons to improve the redundancy and reliability of the system.

[0011] Preferably, the navigation calculation server also has a plurality of network interfaces for connecting the positioning hardware network and user terminal devices.

[0012] Preferably, the navigation calculation server generates NavMesh through three-dimensional map data to provide basic spatial data for path planning, and initializes path planning through an improved A* algorithm based on the user's current location, target location, real-time crowd density data and user functional requirements to generate a multi-target path. The path planning processing unit evaluates and sorts each path according to the multi-target path generation results, and outputs the best path and multiple recommended paths.

[0013] Preferably, the navigation computing server has an emergency mode switching function. When an emergency occurs in the underground space, the navigation computing server re-plans the user's evacuation route according to the event location and personnel flow.

[0014] Preferably, the positioning algorithm processing unit achieves sub-meter positioning accuracy in the complex underground space by combining Wi-Fi fingerprint matching, Bluetooth triangulation positioning and high-precision inertial measurement unit IMU data through a data fusion algorithm.

[0015] Preferably, the path planning processing unit generates personalized route suggestions based on the user's specific needs, wherein the specific needs include shopping, dining and transportation. The path planning processing unit also considers user preferences when generating personalized route suggestions.

[0016] Preferably, the dedicated navigation application also integrates augmented reality (AR) technology to display the navigation path and surrounding environment information in an augmented reality manner. The navigation information is superimposed on the real-time camera image to provide users with intuitive visual guidance.

[0017] Preferably, the dedicated navigation application is capable of updating the navigation path in real time and automatically adjusting the path suggestion when the layout or traffic status of the underground space changes.

[0018] Preferably, the underground space personalized intelligent guidance system adopts a microservice architecture, each functional module is deployed independently, and communicates through a RESTful API.

[0019] Preferably, the underground space personalized intelligent guidance system adopts end-to-end encryption technology to protect the security of data transmission, the user location data is anonymized on the server side, and differential privacy technology is used to add noise to the data before storage to prevent user data from being tracked by a third party.

[0020] The beneficial effects of the present invention are as follows: the personalized intelligent guidance system for underground space of the present invention significantly improves the positioning accuracy of underground space. By combining Wi-Fi, Bluetooth and IMU multi-source data, high-precision complementary positioning is achieved, which effectively overcomes the limitations of a single positioning method in a complex underground environment, so that the system can achieve sub-meter positioning accuracy, surpassing the existing positioning technology. The strategy based on multi-source data fusion ensures the stability and accuracy of positioning, and can provide reliable positioning services even in an environment with severe signal shielding. The present invention also greatly enhances the user experience. By providing personalized real-time navigation services based on the user's current location, users can obtain optimal path suggestions according to their own needs (such as shopping, dining, transportation, etc.). At the same time, the integrated augmented reality (AR) technology enables navigation information to be presented on the user's terminal device in an intuitive way. The superposition display of the navigation path and the actual environment greatly enhances the user's interactive experience and navigation immersion. In addition, the present invention improves the utilization efficiency of underground space. Through precise path planning and intelligent diversion mechanism, the system can significantly reduce the time and congestion of users getting lost, thereby improving the traffic efficiency of people. At the same time, the real-time acquired population density information provides support for the dynamic management and optimization of underground space, improving the management efficiency of the entire space. The real-time dynamic update capability and multi-functional navigation characteristics of the underground space personalized intelligent guidance system of the present invention enable it to effectively respond to changes in the layout and functions of underground space, ensuring the accuracy and timeliness of navigation information. This feature enables the system to maintain high efficiency in long-term use and adapt to the complex changes in underground space and the diverse needs of different users. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is the overall architecture diagram of the personalized intelligent guidance system for underground space;

[0022] Figure 2 It is the workflow diagram of the personalized intelligent guidance system for underground space;

[0023] Figure 3 It is a flow chart of multi-source data fusion positioning calculation method;

[0024] Figure 4 It is a schematic diagram of the hardware composition of the user terminal equipment;

[0025] Figure 5 The navigation calculation server calculates the navigation path algorithm;

[0026] Figure 6 It is a block diagram of the internal structure of the navigation computer server;

[0027] Figure 7 It is a schematic diagram of the positioning hardware network structure;

[0028] Figure 8It is a schematic diagram of the user terminal navigation interface. DETAILED DESCRIPTION

[0029] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention belong to the present invention.

[0030] In addition, described feature, structure or characteristic can be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present application. However, those skilled in the art will appreciate that the technical scheme of the present application can be put into practice without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, known methods, devices, realizations or operations are not shown or described in detail to avoid blurring the various aspects of the application.

[0031] See also Figure 1-8 This embodiment provides a personalized intelligent guidance system for underground space based on spatial layout. The personalized intelligent guidance system for underground space includes a positioning hardware network, a navigation computing server, a user terminal device and a data transmission network.

[0032] The positioning hardware network consists of multiple Wi-Fi access points and Bluetooth beacons, which are evenly distributed in key locations in the underground space to form a high-density positioning grid. The selection and layout of Wi-Fi access points and Bluetooth beacons are very important. These devices must have high signal strength and stability to ensure the positioning accuracy of the system. The layout of these devices has been optimized and distributed in locations that can cover all important areas in the underground space, such as passages, entrances and exits, and shopping areas. This distribution method ensures that users can be accurately located in the system no matter where they are. Each Wi-Fi access point and Bluetooth beacon has a unique identifier and precise coordinates. This information has been entered when the system is initialized, in order to facilitate the system to quickly and accurately achieve high-precision positioning through signal strength and device coordinates.

[0033] The navigation computing server is installed in the control room of the underground space. The server has a built-in complex spatial database, which stores the three-dimensional map information of the underground space and the functional distribution data of each area. The three-dimensional map data is generated through precise measurement and modeling of the underground space, and can accurately reflect the layout structure and functional area division of the underground space, such as the location and attribute information of stores, restaurants, rest areas and transportation hubs. These data are the basis of navigation calculation. The navigation computing server also includes a dedicated positioning algorithm processing unit and a path planning processing unit. The positioning algorithm processing unit is responsible for fusing multi-source data from Wi-Fi, Bluetooth and high-precision inertial measurement unit IMU. Through multi-source data fusion, the system can achieve accurate positioning, especially in complex underground environments, where the positioning accuracy can reach the sub-meter level; the path planning processing unit works based on the NavMesh navigation grid and the improved A* algorithm. The NavMesh navigation grid is used to represent the navigable area in the three-dimensional map. The improved A* algorithm combines real-time personnel density distribution data to generate multi-target paths, provide the best path and multiple recommended paths, and the navigation computing server will also generate personalized route suggestions based on user needs. The user's personalized needs may include finding a specific store, restaurant location or traffic exit, and the system will generate recommended paths based on these needs.

[0034] The user terminal device is equipped with a Wi-Fi module, a Bluetooth module and a high-precision inertial measurement unit IMU. The user terminal device is usually a smartphone or other portable device with these functions. The user terminal device collects and sends multi-source positioning data through a dedicated navigation application. After the user enters the underground space, the dedicated navigation application will automatically start or be started by the APP, and begin to collect signal strength data of surrounding Wi-Fi access points and Bluetooth beacons. At the same time, combined with the IMU data, this information is sent to the navigation calculation server in real time. After processing this data, the navigation calculation server returns the user's accurate location and recommended path. The user can view the navigation path in real time through the terminal device. Augmented reality (AR) technology may be realized in this application. The virtual navigation path is combined with the real environment through the camera, allowing users to understand the navigation information more intuitively.

[0035] In order to further improve the positioning accuracy, the positioning algorithm processing unit uses the extended Kalman filter (EKF) for multi-source data fusion. The algorithm combines Wi-Fi, Bluetooth signal strength data and IMU data to achieve high-precision positioning in complex underground spaces. Specifically, Wi-Fi and Bluetooth signal strength are used for initial position estimation, and IMU data is used to predict the user's motion state. The extended Kalman filter calculates the user's current precise location by combining the data of these three. The specific implementation process is as follows:

[0036] State vector definition: , where (x, y) represents the user's 2D position and (vx, vy) represents the user's velocity.

[0037] Prediction step: state prediction based on IMU data:

[0038] ; Where F is the state transfer matrix:

[0039] ;

[0040] dt is the time interval and w(k) is the process noise.

[0041] Update step: Use Wi-Fi and Bluetooth signal strength data for location correction:

[0042] , where Z(k) is the observation value, h(.) is the nonlinear observation function, and K(k) is the Kalman gain.

[0043] Specific algorithm flow: Initialization: Set the initial state estimate X(0|0) and covariance matrix P(0|0).

[0044] For each time step k:

[0045] a) Prediction:

[0046] ;

[0047] ;

[0048] b) Calculate the Kalman gain:

[0049] ;

[0051] c) Update:

[0052] ;

[0053] ;

[0054] Where Q is the process noise covariance matrix, R is the observation noise covariance matrix, and H is the observation matrix (for nonlinear observations, the Jacobian matrix of h(.) is used).

[0055] The following is a calculation example: Assume that at a certain moment, there are the following data:

[0056] State estimation at the previous moment: ;

[0057] Time interval: dt = 0.1s;

[0058] Acceleration measured by IMU: ax = 0.2 m / s 2 , ay = 0.1 m / s 2 ;

[0059] Wi-Fi positioning results: (x wifi , y wifi ) = (10.8, 15.5);

[0060] Bluetooth positioning results: (x ble , y ble ) = (10.6, 15.3);

[0061] Step 1: Prediction

[0062] ;

[0063] X(k|k-1) = [10.05; 15.03; 0.52; 0.31];

[0064] Step 2: Update

[0065] ;

[0066] Assume that the calculated Kalman gain is ;

[0067] ;

[0068] ;

[0069] This result shows that the fused position estimate is (10.31, 15.18) and the velocity estimate is (0.585, 0.355).

[0070] In this way, the system can effectively combine data from different sensors to achieve more accurate and stable positioning. The algorithm is particularly suitable for dealing with complex environments in underground spaces, and can use IMU data to maintain the continuity and accuracy of positioning when Wi-Fi or Bluetooth signals are unstable.

[0071] The data transmission network consists of a high-speed wired network and a wireless mesh network. The high-speed wired network is mainly responsible for connecting all Wi-Fi access points and navigation computing servers to ensure fast data transmission and processing. Due to the complex layout of underground space and severe signal shielding, wired connections between Wi-Fi access points are essential, which can effectively reduce signal interference and increase data transmission speed. The wireless mesh network connects all Bluetooth beacons to improve the redundancy and reliability of the system. The wireless mesh network forms a self-healing network structure. Even if some Bluetooth beacons fail, other beacons can automatically re-establish connections to ensure continuous and stable operation of the system.

[0072] Furthermore, the navigation calculation server also has multiple network interfaces for connecting the positioning hardware network and the user terminal device. The setting of the network interface enables the system to exchange data efficiently, ensuring that the signal data obtained from the positioning hardware network can be transmitted to the server in a timely manner, and at the same time, the processed path planning results can be quickly sent back to the user terminal device to ensure that the user can obtain navigation information in real time. The existence of multiple network interfaces also enhances the system's fault tolerance and data transmission redundancy. Even if some interfaces fail, the system can still operate normally without affecting the overall navigation and positioning functions.

[0073] The navigation computing server generates NavMesh through three-dimensional map data, providing basic spatial data for path planning. The three-dimensional map data is generated through detailed measurement and modeling of the underground space, which contains accurate information about all physical structures such as buildings, passages, obstacles, etc. NavMesh is generated based on these three-dimensional data, which can define the walkable areas in the underground space and mark different spatial features, such as passable areas, stairs, obstacles, etc. As the basic data for path planning, NavMesh ensures that the system can generate feasible navigation routes in complex underground spaces. Based on the user's current location, target location, real-time crowd density data and user functional requirements, the improved A* algorithm is used to initialize path planning, which increases the consideration of real-time personnel density distribution and can dynamically adjust the weight of the path to prevent users from entering congested areas.

[0074] Based on the user's current location, target location, real-time crowd density data and user functional requirements, the system uses the improved A algorithm to initialize path planning. In the traditional A algorithm, the heuristic function is:

[0075] ;

[0076] Among them, f(n) is the total cost from the starting point to the end point, g(n) is the actual cost from the starting point to node n, and h(n) is the heuristic estimate from node n to the end point (such as Euclidean distance or Manhattan distance). To improve the traditional A* algorithm, the system introduces a dynamic weight factor w(n) in path planning, so that the heuristic function becomes:

[0077] ;

[0078] Among them, w(n) is the real-time density of people flow at the node location, indicating the current congestion level of the node. The greater the density of people flow, the greater the value of w(n), thereby increasing the cost of the node and enabling the system to avoid congested areas. This mechanism of dynamically adjusting path weights allows the system to provide users with more reasonable and faster route options based on changes in the current environment.

[0079] The advantage of this algorithm is that it can not only find the shortest path, but also provide users with more reasonable and faster route options based on current environmental changes. After generating multi-target paths, the path planning processing unit further optimizes and screens these paths. The system not only considers the shortest distance from the user's starting point to the target, but also performs multi-target path planning based on the user's needs and real-time environment. For example, in a shopping mall, users may need to pass through specific stores or dining areas, and the system will generate multiple recommended paths that meet these needs. The path planning processing unit evaluates and sorts each path based on the generated multi-target path results, and outputs the best path and multiple recommended paths. The evaluation and sorting criteria are not limited to the path length, but also include factors such as the path's speed, the density of people, the feasibility of emergency evacuation, and ultimately provide users with path recommendations that best suit their needs.

[0080] In addition, the navigation computing server has an emergency mode switching function. When an emergency occurs in the underground space, the server can respond quickly and switch the system to emergency evacuation mode. In emergency situations, such as fires, earthquakes or other emergencies, ordinary path planning can no longer meet the safety needs of users. Therefore, the system needs to adjust the navigation strategy in real time according to the current event location and the flow of people in the underground space. The event location is detected and sent to the navigation server by the fire or security system, and the flow of people is provided by the real-time crowd density monitoring module in the system. After integrating this information, the navigation computing server can re-plan the user's evacuation route and recommend the safest and fastest path to the user. In emergency mode, the system will give priority to the exit farthest from the accident area and without congestion, and try to avoid users passing through high-density crowd areas, so as to maximize the safety of users.

[0081] Furthermore, the positioning algorithm processing unit uses a data fusion algorithm to combine the data of Wi-Fi fingerprint matching, Bluetooth triangulation and high-precision inertial measurement unit IMU to achieve sub-meter positioning accuracy in complex underground spaces. Wi-Fi fingerprint matching technology uses the signal strength feature library of Wi-Fi access points in underground spaces to compare with the real-time received signals to determine the approximate location of the user. Bluetooth triangulation further accurately locates the user by analyzing the signal strength differences of different Bluetooth beacons. The high-precision inertial measurement unit IMU provides the user's motion status data, especially when the Wi-Fi and Bluetooth signals are unstable, the IMU data can maintain continuous tracking of the user's position. Through the fusion of these technologies, the system can achieve sub-meter high-precision positioning in the underground space environment with complex and changeable signals, greatly improving the accuracy and stability of positioning.

[0082] Further, the path planning processing unit generates personalized route suggestions according to the specific needs of the user, which usually include shopping, dining and transportation, for example, the user enters the destination or function requirement "find the nearest restaurant". After receiving the user's needs, the system will generate a path that best meets the user's needs based on the three-dimensional map and spatial function distribution data. For example, if the user chooses shopping as a demand, the system will prioritize planning a path that passes through multiple stores; if the user chooses dining, the system will recommend the path closest to the dining area. Similarly, if the user needs to go to a transportation hub, the system will provide the best path to the transportation area. When generating personalized route suggestions, the path planning processing unit will also consider the user's preferences. For example, some users may prefer a quieter or less crowded path, while some users may prefer to choose a path that passes through a shopping area or entertainment venue. The user's preferences can be considered and recorded in two ways: the first way is for the user to actively set the preference. The system provides a settings interface that allows users to select their path preferences in the navigation application, such as selecting a quiet area, a less congested route, or a path that wants to pass through a specific store or entertainment area. These user preference information will be recorded by the system and given priority when generating future routes. The second way is to allow users to choose from multiple candidate routes through multi-route recommendations. When the system generates multiple feasible routes, it will give multiple options based on different criteria (such as the fastest route, the quietest route, shopping or entertainment routes) and recommend these routes to users. After a user selects a certain type of route multiple times, the system will automatically record the user's selection tendency through an algorithm, gradually learn and optimize the route planning, so that future route recommendations are more in line with the user's preferences. The algorithm for recording preferences can use a user preference model based on machine learning, or by weighting and scoring the path selected by the user each time, such as using a collaborative filtering algorithm or a recommendation algorithm based on selection history.

[0083] Through these two methods, the system can gradually learn the user's path selection habits and continuously optimize personalized route suggestions, ensuring that it can not only provide routes that meet the user's real-time needs, but also gradually adapt to and match the user's personal preferences.

[0084] The high-precision inertial measurement unit (IMU) contains an accelerometer, a gyroscope, and a magnetometer. The accelerometer is used to detect the user's movement speed and direction changes, and can record the user's walking pace or acceleration information. The gyroscope is used to sense the rotation angle and inclination of the user's device, helping the system to determine the user's turns and body inclinations when walking. The magnetometer is used to detect the direction of the geomagnetic field, which is equivalent to an electronic compass and can provide an absolute reference value for the user's direction of travel. Through the data fusion of these sensors, the system can accurately track the user's movement trajectory in the underground space, especially in areas where Wi-Fi and Bluetooth signals are weak or completely missing. The IMU data can effectively compensate for the lack of signals and maintain accurate tracking of the user's position.

[0085] This system adopts a microservice architecture, with each functional module deployed independently and communicating through RESTful API. This architectural design allows each functional module of the system to be updated and expanded independently, making it easier to insert new algorithm modules or expand other functions in the future. For example, in the path planning process, the path planning algorithm can be optimized by introducing a machine learning model in the future, making it more intelligent to adapt to complex environments or dynamically changing conditions. Since each module is independent of each other, the system can dynamically upgrade or adjust one of the modules according to demand without affecting the normal operation of other functional modules.

[0086] In addition, the system also uses end-to-end encryption technology to ensure the security of data transmission. The transmission process of user location data from the terminal device to the server is encrypted throughout to prevent the data from being intercepted or tampered with by a third party during the transmission process. In order to further protect user privacy, the user's location information is anonymized on the server side. The processed data is stored using differential privacy technology, which hides the individual's precise location by adding noise, thereby effectively preventing third parties from tracking the user's whereabouts by analyzing the data. This data processing method protects the user's privacy security to the greatest extent while ensuring the accuracy and practicality of the navigation function.

[0087] The dedicated navigation application also integrates augmented reality (AR) technology. The system uses the existing high-precision positioning results and pre-built 3D maps to achieve a simple and efficient AR navigation effect. First, the system accurately aligns the user's real-time position with the corresponding position in the 3D map to ensure that the user's position in the virtual environment is completely matched with the actual environment. Next, the system uses the built-in sensors of the smart device (such as magnetometers and gyroscopes) to determine the orientation of the device to ensure that the navigation information can be accurately presented according to the user's current perspective. Based on the user's position information and device orientation, the system calculates the current perspective parameters and uses a graphics rendering library (such as OpenGL ES) to present the corresponding 3D scene in real time on the screen of the user's device. Through this rendering process, users can see a 3D virtual image of their environment, and the system overlays virtual navigation elements such as navigation arrows, path lines, and target locations on this scene. The specific position and direction of these virtual elements will be dynamically adjusted according to the user's current position and target path, allowing users to intuitively see the direction and path of travel, enhancing the immersive experience of navigation. In addition, the system will update the direction of the navigation arrows and paths in real time according to the dynamic changes when the user moves, ensuring that the virtual navigation information is always consistent with the user's actual position. This AR navigation method, which combines high-precision positioning and sensor data, can not only provide users with accurate navigation guidance, but also significantly improve the user experience in complex environments. Specifically, when users use navigation applications, the system will obtain real-time environmental images through the camera and overlay virtual navigation information on these images. For example, the navigation path can be displayed in the form of virtual arrows on the channel or route captured by the camera in real time, providing users with intuitive visual guidance. This AR method greatly improves the user experience of navigation. Users no longer need to rely on traditional flat maps or text guidance, but can directly see navigation information that matches the environment in the real scene, making it easier to find the direction to go. At the same time, dedicated navigation applications also provide traditional 2D / 3D map views and voice commands. In addition, dedicated navigation applications can update the navigation path in real time, and the system will automatically adjust the path suggestions when the layout or traffic status of the underground space changes. For example, when some areas in the underground space are temporarily closed due to construction or maintenance, the system will automatically identify these changes through the update of the background server and immediately re-plan the optimal path for the user. At the same time, when the system detects areas with dense traffic, it will also adjust the route according to real-time personnel distribution data to prevent users from entering congested areas. The entire process is completed automatically in the background. Users can obtain the latest route suggestions without manual intervention, thus ensuring the timeliness and accuracy of navigation.

[0088] The following is an example of a large transfer subway station to illustrate the specific implementation of this system: In this subway station, 200 Bluetooth beacons are arranged in key areas such as platforms, passages and transfer areas, and the spacing of Bluetooth beacons is set to 5-10 meters to ensure sufficient signal coverage in the underground space. These beacons work together to form a positioning network, and improve the accuracy of passenger positioning through triangulation positioning technology. 50 Wi-Fi access points are installed in the station hall and platform level to provide wider signal coverage, ensuring that the system's multi-source data fusion positioning algorithm can run smoothly, especially when switching between different areas, Wi-Fi and Bluetooth signals can be received seamlessly.

[0089] A high-performance central processing unit server is installed in the control room of the subway station. The server is configured with a 32-core CPU and 256GB memory, and has powerful computing and data processing capabilities. The server has a built-in accurate three-dimensional map of the underground space, and the location information of all channels, exits and facilities is marked in detail in the map. The system provides basic data for path planning through this three-dimensional map, and calculates the path in real time according to user needs. In order to ensure efficient data transmission, Gigabit Ethernet is laid in the subway station, connecting all Wi-Fi access points to the central processing unit. At the same time, Bluetooth beacons are also interconnected through a wireless mesh network to form a redundant structure. Even if individual beacons fail, the system can still maintain signal coverage and positioning accuracy through other beacons.

[0090] When passengers enter the subway station, the dedicated navigation app on their smartphones is automatically activated. The app transmits all information to the central processor in real time through signal strength data received by Bluetooth beacons and Wi-Fi access points, as well as data from the phone's built-in high-precision inertial measurement unit (IMU). The central processor uses the system's proprietary multi-source data fusion algorithm, combined with data from Wi-Fi, Bluetooth and IMU, to quickly calculate the passenger's precise location. Passengers can enter their destination in the app, such as an exit or a platform on a certain line, and the central processor will plan the optimal route for the passenger based on the current three-dimensional map and real-time crowd density. When planning the route, factors such as the passenger's walking speed, crowd density and travel time are taken into account to ensure that the recommended route is both the fastest and most in line with user needs.

[0091] Navigation information is sent to passengers' smartphones in real time through a data transmission network, and passengers can view navigation information through the APP. The navigation information is presented in the form of 3D maps, augmented reality (AR) virtual arrows and voice commands, providing users with intuitive navigation guidance. For example, passengers can view the actual environment of the subway station through the mobile phone camera, and the system will superimpose virtual navigation arrows on the screen to guide passengers in the direction of travel, greatly improving the convenience and experience of navigation.

[0092] In an emergency, such as a fire, the system can quickly switch to emergency evacuation mode. The central processor will plan the safest and fastest evacuation route for each user based on the specific location of the fire and the spread of the fire. The system will prioritize guiding passengers to avoid high-density crowds and dangerous areas, and push these evacuation routes to the user's terminal device in real time. At the same time, the evacuation route will also be displayed on the large screen in the subway station to ensure that all passengers can get safe evacuation guidance. During the entire evacuation process, the system will adjust the evacuation route in real time according to changes on the scene to ensure that each passenger can evacuate to a safe area safely and quickly.

[0093] In summary, the present invention discloses a personalized intelligent guidance system for underground space based on spatial layout, which integrates a high-precision indoor positioning hardware network, a pathfinding computing and processing device, and a user terminal system to achieve precise navigation based on the actual spatial structure. Through the multi-source data fusion of Wi-Fi, Bluetooth beacons, and high-precision inertial measurement units (IMUs), the system can achieve sub-meter positioning accuracy in complex underground spaces. The novelty of the system is reflected in the combination of underground space layout information and intelligent guidance, the comprehensive use of the improved A* algorithm and NavMesh navigation grid for multi-target pathfinding planning, and the introduction of augmented reality (AR) technology to provide users with intuitive visual guidance, which greatly enhances the user experience. In addition, the practicality of the system is reflected in many aspects. It can adapt to various complex underground environments, such as subway stations, underground shopping malls, and parking lots, provide personalized services, and generate optimal path suggestions according to the specific needs of users. The system supports real-time updates and can dynamically adjust the path planning according to the layout changes and traffic status of the underground space, especially in emergency situations, to provide the safest evacuation route. With the advancement of smart city construction and the sustainable development of underground space, this invention is expected to become the standard configuration for underground space management and services, providing strong technical support for the efficient and safe use of urban underground space. This system can not only greatly improve the efficiency of underground space use, but also provide users with a more convenient and safe navigation experience.

[0094] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them. Under the concept of the present invention, the technical features in the above embodiments or different embodiments may also be combined, the steps may be implemented in any order, and there are many other changes in different aspects of the present invention as described above, which are not provided in detail for the sake of simplicity. Although the present invention has been described in detail with reference to the above embodiments, a person of ordinary skill in the art should understand that the technical solutions described in the above embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A personalized intelligent guidance system for underground space based on spatial layout, characterized in that: The underground space personalized intelligent guidance system includes: The positioning hardware network consists of multiple Wi-Fi access points and Bluetooth beacons, which are evenly distributed at key locations in the underground space to form a high-density positioning grid. Each Wi-Fi access point and Bluetooth beacon has a unique identifier and precise coordinates for high-precision positioning. A navigation computing server is arranged in the control room of the underground space. The navigation computing server has a built-in complex spatial database, which stores a three-dimensional map of the underground space and functional distribution data of each area. The navigation computing server also includes a dedicated positioning algorithm processing unit and a path planning processing unit. The positioning algorithm processing unit is responsible for integrating multi-source data from Wi-Fi, Bluetooth and a high-precision inertial measurement unit IMU to achieve precise positioning. The path planning processing unit generates a multi-target path based on the NavMesh navigation grid and the improved A* algorithm, combined with real-time personnel density distribution data, provides the best path and multiple recommended paths, and generates personalized route suggestions according to the specific needs of users; A user terminal device, equipped with a Wi-Fi module, a Bluetooth module and a high-precision inertial measurement unit IMU, wherein the user terminal device collects and sends multi-source positioning data through a dedicated navigation application; The data transmission network consists of a high-speed wired network and a wireless mesh network. The high-speed wired network connects all Wi-Fi access points and navigation computing servers, and the wireless mesh network connects all Bluetooth beacons to improve the redundancy and reliability of the system.

2. The underground space personalized intelligent guidance system based on spatial layout according to claim 1 is characterized in that: The navigation calculation server also has multiple network interfaces for connecting the positioning hardware network and user terminal devices.

3. The underground space personalized intelligent guidance system based on spatial layout according to claim 2 is characterized in that: The navigation computing server generates NavMesh through three-dimensional map data to provide basic spatial data for path planning, and initializes path planning through an improved A* algorithm based on the user's current location, target location, real-time crowd density data and user functional requirements to generate a multi-target path. The path planning processing unit evaluates and sorts each path based on the multi-target path generation result, and outputs the best path and multiple recommended paths.

4. The underground space personalized intelligent guidance system based on spatial layout according to claim 3 is characterized in that: The navigation computing server has an emergency mode switching function. When an emergency occurs in the underground space, the navigation computing server replans the user's evacuation route according to the event location and personnel flow.

5. The underground space personalized intelligent guidance system based on spatial layout according to claim 4 is characterized in that: The positioning algorithm processing unit achieves sub-meter positioning accuracy in the complex underground space by combining Wi-Fi fingerprint matching, Bluetooth triangulation positioning and high-precision inertial measurement unit IMU data through a data fusion algorithm.

6. The underground space personalized intelligent guidance system based on spatial layout according to claim 4 is characterized in that: The path planning processing unit generates personalized route suggestions based on the user's specific needs, wherein the specific needs include shopping, dining and transportation. The path planning processing unit also considers the user's preferences when generating the personalized route suggestions.

7. The underground space personalized intelligent guidance system based on spatial layout according to claim 6 is characterized in that: The dedicated navigation application also integrates augmented reality (AR) technology to display the navigation path and surrounding environment information in an augmented reality manner. The navigation information is superimposed on the real-time camera image to provide users with intuitive visual guidance.

8. The underground space personalized intelligent guidance system based on spatial layout according to claim 7 is characterized in that: The dedicated navigation application is capable of updating the navigation path in real time and automatically adjusting the path suggestions when the layout or traffic conditions of the underground space change.

9. The underground space personalized intelligent guidance system based on spatial layout according to any one of claims 1 to 8, characterized in that: The underground space personalized intelligent guidance system adopts a microservice architecture, and each functional module is deployed independently and communicates through a RESTful API.

10. The underground space personalized intelligent guidance system based on spatial layout according to claim 9, characterized in that: The underground space personalized intelligent guidance system adopts end-to-end encryption technology to protect the security of data transmission. The user location data is anonymized on the server side, and differential privacy technology is used to add noise to the data before storage to prevent user data from being tracked by a third party.

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