AI leader navigation method, system and device

Abstract

The invention relates to the technical field of artificial intelligence navigation, in particular to an AI leader navigation method, system and equipment, and the method comprises the following steps: based on a quadtree compression algorithm, extracting key path data from a map and storing the key path data; generating an outdoor hiking path through a heuristic function; acquiring real-time position data of a user; based on the real-time position data of the user, matching a target path for the user from the outdoor hiking paths through a machine learning algorithm, or adjusting the outdoor hiking paths to generate the target path; teaching prompts are generated based on the outdoor hiking teaching points and the safety prompting points, navigation prompts are generated based on the real-time position data of the user and the target path, and navigation is carried out; and obtaining the health data and the environment data of the user for safety analysis, and performing safety early warning based on an analysis result. Therefore, for outdoor hiking, the path generation and optimization efficiency can be greatly improved, the data storage amount is reduced, and meanwhile, various navigation requirements of outdoor hiking are met.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence navigation technology, and in particular to an AI leader navigation method, system and device. Background Art

[0002] Traditional navigation systems have many shortcomings in path planning, data storage, trajectory recording, and real-time monitoring. For example, inefficient path planning algorithms, large data storage volumes, redundant trajectory recording, and insufficient real-time monitoring capabilities limit the user experience and reliability of the navigation system. For outdoor hiking instruction, due to the large fluctuations in user hiking trajectories, complex hiking instruction information, and high real-time monitoring requirements, traditional navigation systems clearly cannot meet the relevant navigation needs of outdoor hiking. Therefore, how to solve the problem of outdoor hiking navigation has become a technical problem that needs to be solved urgently. Summary of the Invention

[0003] In view of this, the purpose of the present invention is to provide an AI leader navigation method, system and device to overcome the problem that the current traditional navigation system cannot be applied to outdoor hiking navigation.

[0004] To achieve the above objectives, the present invention adopts the following technical solutions:

[0005] In a first aspect, the present application provides an AI leader navigation method, comprising:

[0006] Based on the quadtree compression algorithm, key path data is extracted from the map and stored;

[0007] Generate outdoor hiking paths through heuristic functions based on stored data;

[0008] Obtain user real-time location data;

[0009] Based on the real-time location data of the user, matching a target path for the user from the outdoor hiking paths by using a machine learning algorithm, or adjusting the outdoor hiking paths by using a machine learning algorithm to generate a target path;

[0010] Generate teaching tips based on outdoor hiking teaching points and safety reminder points, and generate navigation tips and provide navigation based on the user's real-time location data and target path;

[0011] Obtain user health data and environmental data for security analysis, and issue security warnings based on the analysis results.

[0012] Furthermore, in some embodiments of the present application, generating an outdoor hiking route by a heuristic function based on stored data includes:

[0013] Determine the starting node and target node, and generate multiple initial paths;

[0014] A heuristic value of each initial path is calculated based on the geographic distance and the path weight, and an outdoor hiking path is determined from the plurality of initial paths based on the heuristic value.

[0015] Furthermore, in some embodiments of the present application, the following is further included:

[0016] Obtaining user real-time trajectory data, including timestamp and latitude and longitude information;

[0017] Simplify user real-time trajectory data based on the Douglas-Peucker algorithm;

[0018] Trajectory features are extracted based on the simplified result, and the trajectory features are used to determine the path weight.

[0019] Furthermore, in some embodiments of the present application, the outdoor hiking teaching points and safety reminder points are generated based on the outdoor hiking route, and / or the user's real-time location data, and / or the user's real-time trajectory data.

[0020] Furthermore, in some embodiments of the present application, obtaining user health data and environmental data for security analysis, and issuing a security warning based on the analysis results, includes:

[0021] Acquire user health data and environmental data through preset sensor devices;

[0022] Extract key indicators from the acquired data based on data fusion algorithms;

[0023] Analyzing the key indicators and / or the user health data and environmental data through a support vector machine to determine whether there are any safety hazards;

[0024] If there are any safety hazards, a reminder will be issued.

[0025] Furthermore, in some embodiments of the present application, the user health data includes the user's heart rate and blood pressure, and the environmental data includes temperature.

[0026] In a second aspect, the present application provides an AI leader navigation system, comprising:

[0027] an extraction module, configured to extract key path data from a map and store the key path data based on a quadtree compression algorithm;

[0028] A first generation module is used to generate an outdoor hiking path through a heuristic function based on the stored data;

[0029] Acquisition module, used to obtain user real-time location data;

[0030] a second generating module, configured to match a target path for the user from the outdoor hiking paths using a machine learning algorithm based on the user's real-time location data, or to adjust the outdoor hiking paths using a machine learning algorithm to generate a target path;

[0031] The navigation module is used to generate teaching tips based on outdoor hiking teaching points and safety reminder points, and to generate navigation tips and perform navigation based on the user's real-time location data and target path;

[0032] The security warning module is used to obtain user health data and environmental data for security analysis and issue security warnings based on the analysis results.

[0033] In a third aspect, the present application provides an AI leader navigation device, including a processor and a memory, wherein the processor is connected to the memory:

[0034] The processor is configured to call and execute the program stored in the memory;

[0035] The memory is used to store the program, and the program is at least used to execute the above-mentioned AI leader navigation method.

[0036] The present invention relates to the field of artificial intelligence navigation technology, and more specifically to an AI leader navigation method, system, and device. The method comprises: extracting and storing key path data from a map based on a quadtree compression algorithm; generating an outdoor hiking route using a heuristic function based on the stored data; obtaining real-time user location data; matching a target path for the user from the outdoor hiking route using a machine learning algorithm based on the user's real-time location data, or adjusting the outdoor hiking route using a machine learning algorithm to generate a target path; generating teaching prompts based on outdoor hiking teaching points and safety prompt points, and generating navigation prompts based on the user's real-time location data and the target path, and performing navigation; obtaining user health data and environmental data for safety analysis, and issuing safety warnings based on the analysis results. In this way, for outdoor hiking, the efficiency of route generation and optimization can be greatly improved, the amount of system data stored can be reduced, and teaching information can be included at the same time. The real-time monitoring effect is better, thereby meeting the navigation needs of outdoor hiking. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0038] Figure 11 is a flow chart of an AI team leader navigation method provided by an embodiment of the present invention;

[0039] Figure 2 1 is a schematic diagram of the process of extracting features in the AI ​​leader navigation method provided by an embodiment of the present invention;

[0040] Figure 3 Schematic diagram of the structure of the AI ​​leader navigation device provided by an embodiment of the present invention;

[0041] Figure 4 It is a structural diagram of the AI ​​leader navigation device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0042] To make the objectives, technical solutions, and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail below. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other implementations obtained by those of ordinary skill in the art without inventive effort are within the scope of protection of the present invention.

[0043] Figure 1 This is a flowchart of the AI ​​leader navigation method provided by an embodiment of the present invention. Figure 1 , this embodiment may include the following steps:

[0044] S101. Extract and store key path data from the map based on a quadtree compression algorithm.

[0045] S102: Generate an outdoor hiking path through a heuristic function based on the stored data.

[0046] Specifically, in this application, key path data, such as road networks and POIs, can be extracted from high-definition maps and stored based on the quadtree compression algorithm, and the data can be optimized through the quadtree compression algorithm to reduce storage space.

[0047] In practical applications, the A-star algorithm can be combined with a heuristic function to optimize path generation and search efficiency, generating multiple outdoor hiking paths and achieving optimized path planning. It should be noted that in this process, outdoor hiking paths can be generated based solely on actual scenarios or based on the user's initial needs. For example, the starting and destination nodes can be determined based on the user's needs, and multiple initial paths can be generated. Then, heuristic values ​​for each initial path can be calculated based on geographic distance and path weights, and an outdoor hiking path can be determined from the multiple initial paths based on the heuristic values.

[0048] It should be noted that the above steps of this application can be implemented based on offline maps, and the generated outdoor hiking paths can also be directly used by users. Therefore, it is possible to provide users with efficient and accurate navigation services in an offline environment.

[0049] S103: Obtain user real-time location data.

[0050] S104. Based on the user's real-time location data, a target path is matched for the user from outdoor hiking paths through a machine learning algorithm, or the outdoor hiking paths are adjusted through a machine learning algorithm to generate a target path.

[0051] S105: Generate teaching prompts based on outdoor hiking teaching points and safety prompt points, and generate navigation prompts based on the user's real-time location data and target path and perform navigation.

[0052] Specifically, the user's real-time location data can be obtained through the GPS module, and then the user's location is matched with the outdoor hiking path generated in the above steps based on a machine learning algorithm such as the KNN algorithm to determine the target path.

[0053] In addition, in other embodiments of the present application, when determining a target path based on an outdoor hiking path, the existing outdoor hiking path may be adjusted and improved according to the user's location and the existing outdoor hiking path, thereby generating a more suitable target path.

[0054] On this basis, corresponding navigation prompts such as road prompts and turn prompts are generated according to the user's location and target path, as well as teaching prompts based on outdoor hiking teaching points and safety prompts. During the navigation process, intelligent navigation and teaching prompts are provided to enhance the user experience and realize AI leader intelligent prompts based on machine learning.

[0055] S106. Obtain user health data and environmental data for security analysis, and issue a security warning based on the analysis results.

[0056] Specifically, real-time user health and environmental data can be collected through sensor devices such as heart rate monitors, blood pressure monitors, and environmental sensors. Data fusion algorithms, such as the Kalman filter, are then used to process these sensor data and extract key indicators. Machine learning algorithms, such as support vector machines (SVMs), are then used to analyze the data and determine whether there are safety hazards. This allows real-time monitoring of user health and environmental data during navigation, providing safety warnings and enabling real-time monitoring and safety detection based on data fusion.

[0057] The AI ​​leader navigation method, system and device provided in this application include: extracting and storing key path data from a map based on a quadtree compression algorithm; generating an outdoor hiking path through a heuristic function based on the stored data; obtaining the user's real-time location data; matching the target path for the user from the outdoor hiking path through a machine learning algorithm based on the user's real-time location data, or adjusting the outdoor hiking path through a machine learning algorithm to generate a target path; generating teaching prompts based on outdoor hiking teaching points and safety prompt points, and generating navigation prompts based on the user's real-time location data and the target path and navigating; obtaining the user's health data and environmental data for safety analysis, and issuing a safety warning based on the analysis results. In this way, for outdoor hiking, the efficiency of path generation and optimization can be greatly improved, the amount of system data storage can be reduced, and teaching information can be included at the same time, and the real-time monitoring effect is better, thereby meeting the navigation needs of outdoor hiking.

[0058] Furthermore, in some embodiments of the present application, the AI ​​leader navigation method also includes: obtaining real-time user trajectory data; simplifying the real-time user trajectory data based on the Douglas-Peucker algorithm; and extracting trajectory features based on the simplified results.

[0059] Specifically, a GPS device is first used to record the user's trajectory data in real time, including information such as timestamps, longitude and latitude. The Douglas-Peucker algorithm is then used to simplify the trajectory data and remove redundant points. Trajectory features such as length and complexity are then extracted. These trajectory features can be used to determine the path weights used in the aforementioned steps, providing efficient and accurate data support for route design.

[0060] In addition, it should be noted that in this application, the above-mentioned outdoor hiking teaching points and safety reminder points can be generated based on any one or more of the outdoor hiking path, the user's real-time location data, and the user's real-time trajectory data. For example, they can be generated directly based on the outdoor hiking path, and the corresponding outdoor hiking teaching points and safety reminder point information can be automatically obtained after the target path is determined; or the outdoor hiking teaching points and safety reminder points corresponding to the outdoor hiking path can be generated first, and after the target path is adjusted based on the user's real-time location data, new outdoor hiking teaching points and safety reminder points can be determined based on the user's real-time location data and the outdoor hiking teaching points and safety reminder points corresponding to the outdoor hiking path before the modification, as well as the modification information. This allows for an inspection record based on trajectory optimization.

[0061] In addition, in other embodiments of the present application, outdoor hiking teaching points and safety reminder points can be directly identified based on the information initially extracted from the high-definition map through a machine learning algorithm, and then the corresponding outdoor hiking teaching points and safety reminder points can be matched when generating an outdoor hiking path, or the corresponding outdoor hiking teaching points and safety reminder points can be matched for the target path based on the user's real-time location data.

[0062] The AI ​​leader navigation method provided by the present invention optimizes path planning in an offline environment to provide users with efficient and accurate navigation services, while reducing the amount of data storage, thereby realizing offline map navigation based on optimized path planning; at the same time, by optimizing trajectory records and extracting trajectory features, efficient and accurate data support is provided for route design, thereby realizing field investigation records based on trajectory optimization; and through AI algorithms, intelligent navigation and teaching prompts are provided according to the user's real-time location and path, thereby improving user experience and realizing an AI leader intelligent prompt algorithm based on machine learning; and through algorithms, real-time monitoring data is analyzed to realize real-time detection and early warning of safety hazards, thereby improving user safety and realizing a real-time monitoring and safety detection algorithm based on data fusion.

[0063] It should be noted that the quadtree compression algorithm used in this application specifically includes: first, the map is divided into four quadrants (as the root nodes of the quadtree), and then each quadrant is further divided into four sub-quadrants to form a multi-level node. Then, for each node, key path information is retained and redundant data is removed, and the data is saved. In practical applications, the quadtree compression algorithm can achieve a 50% reduction in data storage.

[0064] Figure 2 This is a flow chart of feature extraction in the AI ​​leader navigation method provided by an embodiment of the present invention. Figure 2 As shown, the process may include:

[0065] First, the trajectory data is cleaned and formatted. Based on the processed results, trajectory features such as trajectory length and complexity are calculated. Key trajectory points are identified based on the trajectory feature values, and finally, the trajectory features and key points are output. This allows the user's trajectory data to be used to determine path weights when generating outdoor hiking routes. For example, higher path weights can be assigned to key trajectory points frequently visited by existing users, allowing initial paths with high path weights to be used as outdoor hiking routes. This feature extraction based on trajectory data can significantly improve the efficiency and effectiveness of path generation.

[0066] In addition, it should be noted that in the present application, after determining the target path based on the user's real-time location data, and generating navigation prompts such as road prompts and turn prompts, and generating teaching prompts in combination with teaching points, safety warnings such as dangerous area reminders can be generated in advance or generated in real time based on the user's real-time location data, and the prompt information can be fed back to the user in real time, thereby improving the user experience and ensuring user safety.

[0067] Based on the same inventive concept, this application also provides an AI leader navigation system. Figure 3 : is a structural diagram of the AI ​​leader navigation device provided by an embodiment of the present invention, such as Figure 3 As shown, the system includes:

[0068] The extraction module 11 is used to extract and store key path data from the map based on a quadtree compression algorithm.

[0069] The first generating module 12 is configured to generate an outdoor hiking route by using a heuristic function based on the stored data.

[0070] The acquisition module 13 is used to acquire the user's real-time location data.

[0071] The second generating module 14 is used to match a target path for the user from outdoor hiking paths based on the user's real-time location data through a machine learning algorithm, or to adjust the outdoor hiking paths through a machine learning algorithm to generate a target path.

[0072] The navigation module 15 is used to generate teaching tips based on outdoor hiking teaching points and safety tips, and to generate navigation tips and perform navigation based on the user's real-time location data and target path.

[0073] The security warning module 16 is used to obtain user health data and environmental data for security analysis and issue security warnings based on the analysis results.

[0074] Regarding the system in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0075] Based on the same inventive concept, the present application also provides an AI leader navigation device for implementing the above method embodiment. Figure 4 This is a schematic diagram of the structure of the AI ​​leader navigation device provided by an embodiment of the present invention. Figure 4 As shown, the AI ​​leader navigation device of this embodiment includes a processor 21 and a memory 22, and the processor 21 is connected to the memory 22. The processor 21 is used to call and execute the program stored in the memory 22; the memory 22 is used to store the program, which is at least used to execute the AI ​​leader navigation method in the above embodiment.

[0076] The specific implementation plan of the AI ​​leader navigation device provided in the embodiment of the present application can refer to the implementation plan of the AI ​​leader navigation method in any of the above embodiments, and will not be repeated here.

[0077] It can be understood that the same or similar parts of the above embodiments can be referenced to each other, and the contents not described in detail in some embodiments can refer to the same or similar contents in other embodiments.

[0078] It should be noted that, in the description of the present invention, the terms "first", "second", etc. are used for descriptive purposes only and should not be understood as indicating or implying relative importance. In addition, in the description of the present invention, unless otherwise specified, the meaning of "plurality" is at least two.

[0079] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention pertain.

[0080] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0081] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0082] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing module, or each unit may exist physically separately, or two or more units may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or in the form of software functional modules. If the integrated modules are implemented in the form of software functional modules and sold or used as independent products, they may also be stored in a computer-readable storage medium.

[0083] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0084] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0085] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. An AI leader navigation method, characterized in that: include: Based on the quadtree compression algorithm, key path data is extracted from the map and stored; Generate outdoor hiking paths through heuristic functions based on stored data; Obtain user real-time location data; Based on the real-time location data of the user, matching a target path for the user from the outdoor hiking paths by using a machine learning algorithm, or adjusting the outdoor hiking paths by using a machine learning algorithm to generate a target path; Generate teaching tips based on outdoor hiking teaching points and safety reminder points, and generate navigation tips and provide navigation based on the user's real-time location data and target path; Obtain user health data and environmental data for security analysis, and issue security warnings based on the analysis results.

2. The AI ​​leader navigation method according to claim 1, characterized in that: The method of generating an outdoor hiking path based on the stored data by a heuristic function includes: Determine the starting node and target node, and generate multiple initial paths; A heuristic value of each initial path is calculated based on the geographic distance and the path weight, and an outdoor hiking path is determined from the plurality of initial paths based on the heuristic value.

3. The AI ​​leader navigation method according to claim 2, characterized in that: Also includes: Obtaining user real-time trajectory data, including timestamp and latitude and longitude information; Simplify user real-time trajectory data based on the Douglas-Peucker algorithm; Trajectory features are extracted based on the simplified result, and the trajectory features are used to determine the path weight.

4. The AI ​​leader navigation method according to claim 3, characterized in that: The outdoor hiking teaching points and safety reminder points are generated based on the outdoor hiking path, and / or the user's real-time location data, and / or the user's real-time trajectory data.

5. The AI ​​leader navigation method according to claim 1, characterized in that: The acquisition of user health data and environmental data for security analysis, and issuing security warnings based on the analysis results, includes: Acquire user health data and environmental data through preset sensor devices; Extract key indicators from the acquired data based on data fusion algorithms; Analyzing the key indicators and / or the user health data and environmental data through a support vector machine to determine whether there are any safety hazards; If there are any safety hazards, a reminder will be issued.

6. The AI ​​leader navigation method according to claim 5, characterized in that: The user health data includes the user's heart rate and blood pressure, and the environmental data includes temperature.

7. An AI leader navigation system, characterized in that: include: An extraction module, used to extract and store key path data from the map based on a quadtree compression algorithm; A first generation module is used to generate an outdoor hiking path through a heuristic function based on the stored data; Acquisition module, used to obtain user real-time location data; a second generating module, configured to match a target path for the user from the outdoor hiking paths using a machine learning algorithm based on the user's real-time location data, or to adjust the outdoor hiking paths using a machine learning algorithm to generate a target path; The navigation module is used to generate teaching tips based on outdoor hiking teaching points and safety reminder points, and to generate navigation tips and perform navigation based on the user's real-time location data and target path; The security warning module is used to obtain user health data and environmental data for security analysis and issue security warnings based on the analysis results.

8. An AI leader navigation device, characterized in that: The device comprises a processor and a memory, wherein the processor is connected to the memory: The processor is configured to call and execute the program stored in the memory; The memory is used to store the program, and the program is at least used to execute the AI ​​leader navigation method described in any one of claims 1-6.

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