Hybrid positioning method of moving object in massive ordered coordinate points

Abstract

The invention relates to the field of path planning, and provides a hybrid positioning method for a moving object in massive ordered coordinate points, which comprises the following steps: acquiring an electronic map, acquiring navigation point information from the electronic map, and grouping the navigation point information to obtain grouped navigation points; determining a navigation target, obtaining a first navigation point relative distance between the navigation target and the first navigation point of the grouped navigation points, and obtaining an optimal group; performing grouping optimal point search for multiple times in the optimal group to obtain an optimal point search result, and performing traversal search to obtain a nearest navigation point; the navigation target moves according to the nearest navigation point and searches in the mobile search window to obtain a future nearest point; the navigation target moves according to the future nearest point and obtains motion parameters of the navigation target, the future position is calculated through the motion parameters, the future position and the future nearest point are compared, the moving state of the navigation target is determined, the track of the navigation target is corrected, and positioning and control over the navigation target are completed.

Description

Technical Field

[0001] This invention relates to the field of path planning technology, and in particular to a hybrid positioning method for a moving object in a large number of ordered coordinate points. Background Technology

[0002] With the rapid development of autonomous driving technology, advanced driver assistance systems, and automated logistics, mobile devices are facing unprecedented demands on their precise positioning capabilities in complex environments. In many application scenarios, mobile devices need to travel along a pre-defined path composed of a large number of discrete, high-precision relative coordinate points.

[0003] When a mobile device first enters a path or loses its location for various reasons and cannot determine its exact position, the most basic and direct technical solution is brute-force search. While brute-force search algorithms are simple in logic, easy to implement, and effective when the number of coordinate points is small, they suffer from high computational latency, poor scalability, and high computational resource consumption when applied to scenarios with massive numbers of coordinate points. To overcome these shortcomings, the industry has explored some data structure optimization methods based on spatial indexes, such as kd-trees or quadtrees. These methods reduce the average computational complexity of nearest neighbor search by hierarchically partitioning spatial points. However, these methods also have some shortcomings in practical applications: First, building and maintaining these complex tree-like data structures requires additional memory overhead and preprocessing time; second, for the specific scenario of "ordered path points" addressed in this invention, the distribution of points is not uniform and random, but rather linear or curvilinear, which may lead to uneven tree partitioning and performance degradation in some cases; finally, these data structures are not flexible enough in supporting dynamically updated maps. Summary of the Invention

[0004] This invention aims to at least solve one of the technical problems existing in related technologies. To this end, this invention provides a hybrid positioning method for moving objects among a massive number of ordered coordinate points, enabling the positioning and control of navigation targets.

[0005] This invention provides a hybrid positioning method for moving objects in a massive number of ordered coordinate points, comprising: S1; Obtain an electronic map, retrieve navigation point information from the electronic map, group the navigation point information, and obtain grouped navigation points; S2: Determine the navigation target, obtain the relative distance between the first and first navigation points of the navigation target and the first and first navigation points of the grouped navigation points, and obtain the optimal grouping based on the relative distance between the first and first navigation points; S3: Perform multiple grouping optimal point searches within the optimal group to obtain the optimal point search results. Then, perform a traversal search based on the optimal point search results to obtain the nearest navigation point. S4: The navigation target moves based on the nearest navigation point, then the movement search window is determined, and a search is performed within the movement search window to obtain the nearest future point; S5: The navigation target moves according to the nearest future point and obtains its own motion parameters. The future position is calculated through the motion parameters. The future position is compared with the nearest future point to determine the movement state of the navigation target and correct the trajectory of the navigation target, thus completing the positioning and control of the navigation target.

[0006] According to the hybrid positioning method for a moving object in a massive number of ordered coordinate points provided by the present invention, step S1 further includes: S11: Obtain an electronic map, determine the target movement path, and obtain the navigation point information from the electronic map based on the target movement path; S12: Determine the number of navigation points in a group based on the navigation point information, and group the navigation point information according to the number of navigation points in the group to obtain multiple groups of grouped navigation points.

[0007] According to the present invention, a hybrid positioning method for a moving object in a large number of ordered coordinate points is provided. In step S2, the first navigation point in each group of navigation points is taken as the first and first navigation points, the relative distance between the navigation target and the first and first navigation points is obtained, and the group of navigation points with the closest relative distance to the first and first navigation points is taken as the optimal group.

[0008] According to the hybrid positioning method for a moving object in a massive number of ordered coordinate points provided by the present invention, step S3 further includes: S31: Group the optimal group and determine the first navigation point in each group. Perform the optimal group search based on the distance between the first navigation point in each group and the navigation target. Perform the optimal group search multiple times to obtain the optimal search result. S32: Determine the search range, and perform a traversal search within the search range centered on the optimal search result to obtain the nearest navigation point.

[0009] According to the hybrid positioning method for a moving object in a large number of ordered coordinate points provided by the present invention, in step S3, when the position of the navigation target is known, the nearest navigation point to the navigation target is determined based on the position of the navigation target, and the nearest navigation point is obtained.

[0010] According to the hybrid positioning method for a moving object in a massive number of ordered coordinate points provided by the present invention, step S5 further includes: S51: The navigation target moves toward the future nearest point, obtains the motion parameters through the inertial navigation device mounted on the navigation target, and calculates the future position through the motion parameters; S52: Determine the position deviation threshold, calculate the relative position deviation between the future position and the future nearest point, and when the relative position deviation is greater than the position deviation threshold, determine that the movement state is abnormal, correct the trajectory of the navigation target, and complete the positioning and control of the navigation target.

[0011] This invention also provides a hybrid positioning system for moving objects in a massive number of ordered coordinate points, comprising: Navigation point grouping module: This includes acquiring an electronic map, obtaining navigation point information from the electronic map, grouping the navigation point information, and obtaining grouped navigation points; The optimal grouping module includes determining the navigation target, obtaining the relative distance between the first and second navigation points of the navigation target and the first and second navigation points of the grouped navigation points, and obtaining the optimal grouping based on the relative distance between the first and second navigation points. The nearest navigation point module includes performing multiple grouping optimization searches within the optimal group to obtain the optimal search result, and then performing a traversal search based on the optimal search result to obtain the nearest navigation point. The Future Closest Point Module includes: moving the navigation target based on the nearest navigation point, determining the movement search window, searching within the movement search window, and obtaining the future closest point; The navigation target correction module includes the following components: the navigation target moves according to the nearest future point and obtains its own motion parameters; the future position is calculated using the motion parameters; the future position is compared with the nearest future point; the movement state of the navigation target is determined and the trajectory of the navigation target is corrected; and the positioning and control of the navigation target are completed.

[0012] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of a hybrid positioning method for a moving object in a large number of ordered coordinate points as described above.

[0013] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a hybrid positioning method for a moving object in a massive number of ordered coordinate points as described above.

[0014] The present invention also provides a computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, which, when executed by a computer, enable the computer to perform the steps of any of the above-described hybrid positioning methods for a moving object in a massive number of ordered coordinate points.

[0015] The above-described one or more technical solutions in the embodiments of the present invention have at least one of the following technical effects: This invention provides a hybrid positioning method for moving objects in a massive number of ordered coordinate points. By finding the optimal group and performing multiple optimal point searches, it can quickly find the nearest navigation point in a short time with less computing resources, effectively improving the speed at which the navigation target determines its own position. By comparing the future position with the future nearest point, abnormal movement of the navigation target can be quickly detected, thereby preventing the navigation target from deviating from the predetermined trajectory and guiding the navigation target to the target along the predetermined trajectory.

[0016] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating a hybrid positioning method for a moving object in a massive number of ordered coordinate points provided by the present invention.

[0019] Figure 2 This is a schematic diagram of the structure of a hybrid positioning system for a moving object in a massive number of ordered coordinate points provided by the present invention.

[0020] Figure 3 This is a schematic diagram of the structure of a hybrid positioning device for a moving object in a massive number of ordered coordinate points provided by the present invention.

[0021] Figure label: 100. Navigation point grouping module; 200. Optimal grouping module; 300. Nearest navigation point module; 400. Future nearest point module; 500. Navigation target correction module; 810. Processor; 820. Communication interface; 830. Memory; 840. Communication bus. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention. The following embodiments are used to illustrate this invention but cannot be used to limit the scope of this invention.

[0023] In the description of the embodiments of the present invention, it should be noted that the terms "first", "second" and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0024] In the description of the embodiments of the present invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "connected" and "linked" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms in the embodiments of the present invention based on the specific circumstances.

[0025] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions 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 one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0026] The following is combined Figures 1 to 3 Specific embodiments of the present invention are described below. Figure 1 This is a flowchart illustrating a hybrid positioning method for a moving object in a massive number of ordered coordinate points provided by the present invention, including: S1; Obtain an electronic map, retrieve navigation point information from the electronic map, group the navigation point information, and obtain grouped navigation points; Furthermore, the objective of this stage is to obtain waypoint information, thereby grouping the waypoints into groups. Specifically, step S1 further includes: S11: Obtain an electronic map, determine the target movement path, and obtain the navigation point information from the electronic map based on the target movement path; S12: Determine the number of navigation points in a group based on the navigation point information, and group the navigation point information according to the number of navigation points in the group to obtain multiple groups of grouped navigation points.

[0027] The specific implementation method for the above steps in this embodiment is as follows: First, an electronic map of the area needs to be obtained, which includes several navigation points. Next, the target movement path is determined, that is, a path is drawn based on the starting and ending points of the navigation target, and the target needs to follow this path to reach the destination. Then, navigation point information is retrieved from the electronic map based on the target movement path, which includes the locations of all navigation points along the target movement path.

[0028] Subsequently, the number of navigation points in a group is determined based on the navigation point information. The navigation point information is then grouped according to the number of navigation points in the group. That is, all navigation points are divided into several consecutive groups with an equal number of navigation points according to the number of navigation points on the target movement path included in the navigation point information. For example, in this embodiment, every 128 navigation points are consecutively grouped into one group, thereby obtaining multiple groups of grouped navigation points.

[0029] S2: Determine the navigation target, obtain the relative distance between the first and first navigation points of the navigation target and the first and first navigation points of the grouped navigation points, and obtain the optimal grouping based on the relative distance between the first and first navigation points; Furthermore, the objective of this stage is to obtain the relative distance between the first and second navigation points, thereby obtaining the optimal grouping. Specifically, in step S2, the first navigation point in each group of navigation points is taken as the first navigation point, the relative distance between the navigation target and the first navigation point is obtained, and the group of navigation points with the closest relative distance to the first navigation point is taken as the optimal grouping.

[0030] The specific implementation method for the above steps in this embodiment is as follows: First, the navigation target needs to be identified, which is the object that needs to move according to the target's movement path. Then, the first navigation point in each group of navigation points is designated as the primary navigation point. Each navigation point can emit a radio signal. After receiving the radio signal, the navigation target can determine the distance to itself from each navigation point on its movement path, thus determining which navigation point is closest. However, when there are too many navigation points, judging them one by one would waste too many computing resources and take too long, potentially affecting the normal operation of the navigation target. When the navigation target first enters an area of ​​the electronic map without its initial position or when a malfunction occurs during navigation, resulting in the loss of its initial position, the first navigation point in each group of navigation points needs to be designated as the primary navigation point. In this way, the navigation target only needs to determine its distance relative to each primary navigation point, that is, the relative distance between the primary and secondary navigation points, to know which group of navigation points contains the closest primary navigation point, thus obtaining the optimal group.

[0031] S3: Perform multiple grouping optimal point searches within the optimal group to obtain the optimal point search results. Then, perform a traversal search based on the optimal point search results to obtain the nearest navigation point. Furthermore, the objective of this stage is to perform multiple grouped optimal point searches to obtain the optimal search results and then further perform a traversal search to obtain the nearest navigation point. Specifically, step S3 further includes: S31: Group the optimal group and determine the first navigation point in each group. Perform the optimal group search based on the distance between the first navigation point in each group and the navigation target. Perform the optimal group search multiple times to obtain the optimal search result. S32: Determine the search range, and perform a traversal search within the search range centered on the optimal search result to obtain the nearest navigation point.

[0032] The specific implementation method for the above steps in this embodiment is as follows: Here, it is necessary to further perform a grouping optimal point search on the optimal group. That is, following the idea of ​​step S2, the optimal group is further subdivided. In this embodiment, the optimal group with 128 navigation points is further subdivided into groups of 32 navigation points each, and the first navigation point in each further subdivision is determined as the first navigation point of the group. The distance between the navigation target and the first navigation point in each group is obtained, and the nearest first navigation point in the group is found. This completes one grouping optimal point search. The group containing the nearest first navigation point in the group is used as the optimal group for the next grouping optimal point search, and the above grouping optimal point search process is repeated until a unique navigation point that is closest to the navigation target is found from two points, and the optimal point search result is obtained.

[0033] After obtaining the optimal search result, the search range is determined centered on the optimal search result. In this embodiment, the search range is the distance between the five surrounding navigation points. Within the search range, a traversal search is performed centered on the optimal search result, determining the distance between each navigation point and the navigation target. The navigation point closest to the navigation target is then identified and designated as the nearest navigation point. The user's own position can also be determined based on its distance from the nearest navigation point and the position of the nearest navigation point.

[0034] The repeated grouping described above applies to situations where the navigation target has just entered an area of ​​the electronic map, has no initial position, or has lost its initial position due to a malfunction during navigation. Once the navigation target knows its current position, it directly determines the nearest navigation point based on its current location.

[0035] S4: The navigation target moves based on the nearest navigation point, then the movement search window is determined, and a search is performed within the movement search window to obtain the nearest future point; Furthermore, the objective of this stage is to search within the moving search window to obtain the nearest future point. Specifically, after obtaining the nearest navigation point, the navigation target can move towards it. Simultaneously, the moving search window needs to be determined; in this embodiment, the moving search window is the distance between five surrounding navigation points. As the navigation target moves closer to other navigation points, it uses the closest navigation point after its movement as the center to search within the moving search window. Combining this with the navigation target's current movement speed and direction, the center of the next moving search window can be determined, thus obtaining the nearest future point. This nearest future point is continuously updated as the navigation target moves.

[0036] S5: The navigation target moves according to the nearest future point and obtains its own motion parameters. The future position is calculated through the motion parameters. The future position is compared with the nearest future point to determine the movement state of the navigation target and correct the trajectory of the navigation target, thus completing the positioning and control of the navigation target.

[0037] Furthermore, the objective of this stage is to move the navigation target, calculate its future position, compare the future position with the nearest future point, and complete the positioning and control of the navigation target. Specifically, step S5 further includes: S51: The navigation target moves toward the future nearest point, obtains the motion parameters through the inertial navigation device mounted on the navigation target, and calculates the future position through the motion parameters; S52: Determine the position deviation threshold, calculate the relative position deviation between the future position and the future nearest point, and when the relative position deviation is greater than the position deviation threshold, determine that the movement state is abnormal, correct the trajectory of the navigation target, and complete the positioning and control of the navigation target.

[0038] The specific implementation method for the above steps in this embodiment is as follows: The navigation target continuously moves towards its nearest future point. Using the inertial navigation system (INS) onboard the target, it obtains its current speed and direction, thus determining its position when it is closest to the nearest future point – its future position. A position deviation threshold is determined; in this embodiment, it is 1.5m. The relative position deviation between the future position and the nearest future point is calculated. If this relative deviation exceeds the threshold, the movement is considered abnormal. The target's trajectory is then corrected to prevent deviations until it reaches its destination, thus completing the positioning and control of the navigation target.

[0039] The following describes a hybrid positioning device for a moving object in a massive number of ordered coordinate points provided by the present invention. The hybrid positioning device for a moving object in a massive number of ordered coordinate points described below can be referred to in correspondence with the hybrid positioning method for a moving object in a massive number of ordered coordinate points described above.

[0040] Figure 2 This example illustrates the structure of a hybrid positioning system for moving objects within a massive number of ordered coordinate points, as shown in the diagram. Figure 2 As shown, a hybrid positioning method for a moving object in a massive number of ordered coordinate points, as described above, includes: Navigation point grouping module 100: includes acquiring an electronic map, acquiring navigation point information from the electronic map, grouping the navigation point information, and obtaining grouped navigation points; Optimal grouping module 200: includes determining the navigation target, obtaining the relative distance between the first and second navigation points of the navigation target and the first and second navigation points of the grouped navigation points, and obtaining the optimal grouping based on the relative distance between the first and second navigation points; Nearest navigation point module 300: includes performing multiple grouping optimal point searches within the optimal group to obtain the optimal point search results, and performing a traversal search based on the optimal point search results to obtain the nearest navigation point; The Future Closest Point Module 400 includes: the navigation target moves according to the nearest navigation point, then the movement search window is determined, and the search is performed within the movement search window to obtain the future closest point; The navigation target correction module 500 includes a navigation target moving according to the nearest future point and obtaining its own motion parameters. The future position is calculated through the motion parameters. The future position is compared with the nearest future point to determine the movement state of the navigation target and correct the trajectory of the navigation target, thus completing the positioning and control of the navigation target.

[0041] Figure 3 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 3 As shown, the electronic device may include: a processor 810, a communication interface 820, a memory 830, and a communication bus 840, wherein the processor 810, the communication interface 820, and the memory 830 communicate with each other via the communication bus 840. The processor 810 can call a computer program stored in the memory 830 to execute a hybrid positioning method for a moving object among a massive number of ordered coordinate points. This method includes: S1; Obtain an electronic map, retrieve navigation point information from the electronic map, group the navigation point information, and obtain grouped navigation points; S2: Determine the navigation target, obtain the relative distance between the first and first navigation points of the navigation target and the first and first navigation points of the grouped navigation points, and obtain the optimal grouping based on the relative distance between the first and first navigation points; S3: Perform multiple grouping optimal point searches within the optimal group to obtain the optimal point search results. Then, perform a traversal search based on the optimal point search results to obtain the nearest navigation point. S4: The navigation target moves based on the nearest navigation point, then the movement search window is determined, and a search is performed within the movement search window to obtain the nearest future point; S5: The navigation target moves according to the nearest future point and obtains its own motion parameters. The future position is calculated through the motion parameters. The future position is compared with the nearest future point to determine the movement state of the navigation target and correct the trajectory of the navigation target, thus completing the positioning and control of the navigation target.

[0042] Furthermore, when the computer program in the aforementioned memory 830 can be implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0043] On the other hand, the present invention also provides a computer program product, the computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, wherein when the program instructions are executed by a computer, the computer is able to execute a hybrid positioning method for a moving object in a massive number of ordered coordinate points provided by the above methods, the method comprising: S1; Obtain an electronic map, retrieve navigation point information from the electronic map, group the navigation point information, and obtain grouped navigation points; S2: Determine the navigation target, obtain the relative distance between the first and first navigation points of the navigation target and the first and first navigation points of the grouped navigation points, and obtain the optimal grouping based on the relative distance between the first and first navigation points; S3: Perform multiple grouping optimal point searches within the optimal group to obtain the optimal point search results. Then, perform a traversal search based on the optimal point search results to obtain the nearest navigation point. S4: The navigation target moves based on the nearest navigation point, then the movement search window is determined, and a search is performed within the movement search window to obtain the nearest future point; S5: The navigation target moves according to the nearest future point and obtains its own motion parameters. The future position is calculated through the motion parameters. The future position is compared with the nearest future point to determine the movement state of the navigation target and correct the trajectory of the navigation target, thus completing the positioning and control of the navigation target.

[0044] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a hybrid positioning method for a moving object in a massive number of ordered coordinate points as described above, the method comprising: S1; Obtain an electronic map, retrieve navigation point information from the electronic map, group the navigation point information, and obtain grouped navigation points; S2: Determine the navigation target, obtain the relative distance between the first and first navigation points of the navigation target and the first and first navigation points of the grouped navigation points, and obtain the optimal grouping based on the relative distance between the first and first navigation points; S3: Perform multiple grouping optimal point searches within the optimal group to obtain the optimal point search results. Then, perform a traversal search based on the optimal point search results to obtain the nearest navigation point. S4: The navigation target moves based on the nearest navigation point, then the movement search window is determined, and a search is performed within the movement search window to obtain the nearest future point; S5: The navigation target moves according to the nearest future point and obtains its own motion parameters. The future position is calculated through the motion parameters. The future position is compared with the nearest future point to determine the movement state of the navigation target and correct the trajectory of the navigation target, thus completing the positioning and control of the navigation target.

[0045] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0046] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0047] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A hybrid positioning method of a mobile object in a mass of ordered coordinate points, characterized by, Comprising: S1; obtain an electronic map, obtain navigation point information from the electronic map, group the navigation point information to obtain grouped navigation points; S2: determine a navigation target, obtain a first navigation point relative distance between the navigation target and the first navigation point between the grouped navigation points, and obtain an optimal group according to the first navigation point relative distance; S3: multiple optimal point searching in the optimal group, obtaining optimal point searching results, traversing searching according to the optimal point searching results, and obtaining the nearest navigation point; S4: the navigation target moves according to the nearest navigation point, then determines a moving search window, searches in the moving search window, and obtains a future nearest point; S5: the navigation target moves according to the future nearest point, obtains its motion parameters, calculates a future position through the motion parameters, compares the future position with the future nearest point, determines the moving state of the navigation target and corrects the trajectory of the navigation target, and completes the positioning and control of the navigation target.

2. The hybrid positioning method of a mobile object in a mass of ordered coordinate points according to claim 1, characterized in that, Step S1 further comprises: S11: obtaining an electronic map, determining a target motion path, and obtaining the navigation point information from the electronic map according to the target motion path; S12: determining the number of navigation points in a group according to the navigation point information, grouping the navigation point information according to the number of navigation points in the group, and obtaining multiple groups of grouped navigation points.

3. The hybrid positioning method of claim 1, wherein, In step S2, the first navigation point in each group of grouped navigation points is taken as the first navigation point, the first navigation point relative distance between the navigation target and the first navigation point is obtained, and the grouped navigation point with the closest first navigation point relative distance is taken as the optimal group.

4. The hybrid positioning method of a mobile object in a mass of ordered coordinate points according to claim 1, characterized in that, Step S3 further comprises: S31: grouping the optimal group and determining the first navigation point in the group, completing optimal point searching according to the distance between the first navigation point in the group and the navigation target, and performing multiple optimal point searching to obtain the optimal point searching results; S32: determining a search range, and performing traversal search in the search range with the optimal point searching results as the center to obtain the nearest navigation point.

5. The hybrid positioning method of a mobile object in a mass of ordered coordinate points according to claim 1, characterized in that, In step S3, when the position of the navigation target is known, the navigation point closest to the navigation target is determined according to the position of the navigation target, and the nearest navigation point is obtained.

6. The hybrid positioning method of a mobile object in a mass of ordered coordinate points according to claim 1, characterized in that, Step S5 further comprises: S51: the navigation target moves to the future nearest point, and the motion parameters are obtained through the inertial navigation equipment carried on the navigation target, and the future position is calculated through the motion parameters; S52: determining a position deviation threshold, calculating the relative position deviation of the future position and the future nearest point, when the relative position deviation is greater than the position deviation threshold, judging that the moving state is abnormal, correcting the trajectory of the navigation target, and completing the positioning and control of the navigation target.

7. A hybrid positioning system of a mobile object in a mass of ordered coordinate points for performing a hybrid positioning method of a mobile object in a mass of ordered coordinate points as claimed in any one of claims 1 to 6, characterized by Comprising: A navigation point grouping module comprising obtaining an electronic map, obtaining navigation point information from the electronic map, and grouping the navigation point information to obtain grouped navigation points; An optimal group module comprising determining a navigation target, obtaining a first navigation point relative distance between the navigation target and the first navigation point between the grouped navigation points, and obtaining an optimal group according to the first navigation point relative distance; The recent navigation point module includes multiple optimal point searching in the optimal group, obtains the optimal point searching result, performs traversal searching according to the optimal point searching result, and obtains the recent navigation point. The future nearest point module includes moving the navigation target according to the recent navigation point, then determining a moving search window, performing searching in the moving search window, and obtaining the future nearest point. The navigation target correction module includes moving the navigation target according to the future nearest point, obtaining the motion parameter of the navigation target, calculating the future position through the motion parameter, comparing the future position with the future nearest point, determining the moving state of the navigation target, correcting the trajectory of the navigation target, and completing the positioning and control of the navigation target.

8. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the computer program to realize the steps of the hybrid positioning method of a mobile object in a mass of ordered coordinate points according to any one of claims 1 to 6. 9.A non-transitory computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the steps of the hybrid positioning method of a mobile object in a mass of ordered coordinate points according to any one of claims 1 to 6.

10. A computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions, characterized in that, When the program instructions are executed by the computer, the computer can execute the steps of the hybrid positioning method of a mobile object in a mass of ordered coordinate points according to any one of claims 1 to 6.

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