A high-precision positioning and navigation system and method based on Beidou navigation

Abstract

The application relates to the technical field of navigation positioning, in particular to a high-precision positioning and navigation system and method based on Beidou navigation, which comprises the following steps: analyzing the driving difficulty degree of a motor vehicle on a pre-navigation route based on road state data and non-motor vehicle gathering data; analyzing the driving behavior risk of a motor vehicle driver on the pre-navigation route based on the driving habit data of the motor vehicle driver and the road state data; evaluating the driving risk of the driver on the pre-navigation route according to the driving difficulty degree analysis result of the motor vehicle on the pre-navigation route and the driving behavior risk analysis result of the motor vehicle driver on the pre-navigation route; and re-planning and warning the pre-navigation route according to the driving risk evaluation result of the driver on the pre-navigation route, so that the conflict between people and vehicles can be effectively relieved, and the accident rate of the motor vehicle can be reduced.

Description

Technical Field

[0001] This invention relates to the field of navigation and positioning technology, and in particular to a high-precision positioning and navigation system and method based on BeiDou navigation. Background Technology

[0002] With the acceleration of urbanization and the diversification of traffic participants, the modern urban road environment is becoming increasingly complex, especially in densely populated areas where mixed traffic of motor vehicles, non-motor vehicles, and pedestrians has become the norm. While this mixed traffic mode improves travel convenience, it also brings significant safety hazards. Traditional GPS navigation systems mainly rely on static map data and basic route planning algorithms, optimizing for "shortest distance" or "shortest time," lacking the ability to quantitatively assess the dynamic risks of the real-time traffic environment. In contrast, the BeiDou Navigation Satellite System, as China's independently developed global satellite positioning infrastructure, provides the technological foundation for the quantitative assessment of dynamic risks in the real-time traffic environment thanks to its high-precision positioning, short message communication, and wide-area augmentation service capabilities. By integrating BeiDou high-precision positioning, multi-source sensor data, and roadside intelligent devices, the BeiDou Navigation Satellite System can achieve precise perception of vehicle positions, non-motor vehicle distribution, and road topology.

[0003] However, existing technologies often overlook the safety hazards that the highly random movement trajectory of non-motorized vehicles and the ambiguous right-of-way division pose to motorized vehicles when performing high-precision positioning and navigation. At the same time, existing technologies do not take into account that speeding and frequent lane changes by motorized drivers in narrow sections will further amplify the safety hazards caused by the highly random movement trajectory of non-motorized vehicles and the ambiguous right-of-way division.

[0004] To address these issues, this application presents a high-precision positioning and navigation system and method based on the BeiDou Navigation Satellite System. Summary of the Invention

[0005] The purpose of this invention is to provide a high-precision positioning and navigation system and method based on BeiDou navigation. By analyzing the difficulty of driving a motor vehicle on a pre-navigation route and the driving behavior risk of the motor vehicle driver on the pre-navigation route, the invention achieves a quantitative assessment of the driving danger of the driver on the pre-navigation route, thereby effectively alleviating human-vehicle conflicts and reducing the accident rate of motor vehicles.

[0006] This invention is implemented as follows:

[0007] In a first aspect, the present invention provides a high-precision positioning and navigation method based on BeiDou navigation, comprising the following steps:

[0008] S1. Obtain road condition data and non-motorized vehicle aggregation data on the pre-navigation route of motor vehicles, and at the same time obtain driving habit data of motor vehicle drivers;

[0009] S2. Based on road condition data and non-motorized vehicle aggregation data, analyze the degree of difficulty for motor vehicles to drive on the pre-navigation route;

[0010] S3. Based on the driving habit data and road condition data of motor vehicle drivers, analyze the driving behavior risks of motor vehicle drivers on the pre-navigation route;

[0011] S4. Based on the analysis results of the difficulty of driving the motor vehicle on the pre-navigation route and the analysis results of the driving behavior risk of the motor vehicle driver on the pre-navigation route, assess the driving danger of the driver on the pre-navigation route;

[0012] S5. Based on the driver's driving hazard assessment results on the pre-navigation route, replan and issue a warning for the pre-navigation route.

[0013] Based on the above scheme, step S2, using road condition data and non-motorized vehicle aggregation data, analyzes the difficulty of motor vehicles driving on the pre-navigation route, specifically including:

[0014] S21. Extract road condition data and non-motor vehicle aggregation data from the pre-navigation route of motor vehicles;

[0015] S22. Based on road condition data and non-motorized vehicle aggregation data on the pre-navigation route of motor vehicles, analyze the difficulty of driving motor vehicles on the pre-navigation route and obtain the analysis results of the difficulty of driving motor vehicles on the pre-navigation route.

[0016] Based on the above scheme, the preferred method is to analyze the difficulty of driving the motor vehicle on the pre-navigation route in step S22, specifically including:

[0017] S221. Based on the non-motorized vehicle aggregation data on the pre-navigation route of motor vehicles, analyze the degree of dynamic interference of non-motorized vehicles by motor vehicle drivers on the pre-navigation route, and obtain the analysis results of the degree of dynamic interference of non-motorized vehicles by motor vehicle drivers on the pre-navigation route.

[0018] S222. Based on the road condition data on the pre-navigation route of the motor vehicle, analyze the road condition complexity of the motor vehicle driver on the pre-navigation route and obtain the analysis results of the road condition complexity of the motor vehicle driver on the pre-navigation route.

[0019] S223. Based on the analysis results of the degree of dynamic interference from non-motorized vehicles and the analysis results of the road condition complexity on the pre-navigation route, the driving difficulty of motor vehicles on the pre-navigation route is analyzed, and the analysis results of the driving difficulty of motor vehicles on the pre-navigation route are obtained.

[0020] Based on the above scheme, step S3, which optimizes the driving behavior risk of motor vehicle drivers on the pre-navigation route based on the driving habit data and road condition data of motor vehicle drivers, specifically includes the following steps:

[0021] S31. Extract driving habit data of motor vehicle drivers and road condition data on the motor vehicle pre-navigation route;

[0022] S32. Based on driving habit data and road condition data, analyze the driving behavior risks of motor vehicle drivers on the pre-navigation route and obtain the driving behavior risk analysis results of motor vehicle drivers on the pre-navigation route.

[0023] Based on the above scheme, step S32 analyzes the driving behavior risks of motor vehicle drivers on the pre-navigation route, including the following specific steps:

[0024] S321. Based on driving habit data and road condition data, analyze the road adaptability of motor vehicle drivers on the pre-navigation route and obtain the analysis results of the road adaptability of motor vehicle drivers on the pre-navigation route.

[0025] S322. Based on driving habit data, analyze the degree of aggressive driving of motor vehicle drivers on the pre-navigation route and obtain the analysis results of the degree of aggressive driving of motor vehicle drivers on the pre-navigation route.

[0026] S323. Based on the analysis results of the road adaptability and driving aggression of motor vehicle drivers on the pre-navigation route, the driving behavior risk of motor vehicle drivers on the pre-navigation route is analyzed, and the driving behavior risk analysis results of motor vehicle drivers on the pre-navigation route are obtained.

[0027] Based on the above scheme, in step S4, the driving hazard of the driver on the pre-navigation route is assessed according to the analysis results of the difficulty of driving the motor vehicle on the pre-navigation route and the analysis results of the driving behavior risk of the motor vehicle driver on the pre-navigation route. This includes the following specific steps:

[0028] S41. Obtain the analysis results of the difficulty of driving a motor vehicle on the pre-navigation route, and at the same time obtain the analysis results of the driving behavior risk of the motor vehicle driver on the pre-navigation route.

[0029] S42. Based on the analysis results of driving difficulty and driving behavior risk, assess the driving hazard of the driver on the pre-navigation route and obtain the driving hazard assessment results of the driver on the pre-navigation route.

[0030] Based on the above scheme, step S5, according to the driver's driving hazard assessment results on the pre-navigation route, re-plans and issues a warning for the pre-navigation route, specifically including:

[0031] S51. Obtain the assessment results of the driver's driving hazard assessment on the pre-navigation route;

[0032] S52. Preset driving hazard threshold: When the driver's driving hazard assessment result on the pre-navigation route is greater than the driving hazard threshold, the pre-navigation route is replanned and a warning is issued.

[0033] Secondly, the present invention provides a high-precision positioning and navigation system based on BeiDou navigation, comprising:

[0034] The data acquisition module is used to acquire road condition data and non-motorized vehicle aggregation data on the pre-navigation route of motor vehicles, as well as driving habit data of motor vehicle drivers.

[0035] The driving difficulty analysis module is used to analyze the driving difficulty of motor vehicles on the pre-navigation route based on road condition data and non-motorized vehicle aggregation data;

[0036] The driving behavior risk analysis module is used to analyze the driving behavior risks of motor vehicle drivers on the pre-navigation route based on the driving habit data and road condition data of motor vehicle drivers;

[0037] The driving hazard assessment module is used to assess the driving hazard of the driver on the pre-navigation route based on the analysis results of the difficulty of driving the motor vehicle on the pre-navigation route and the analysis results of the driving behavior risk of the motor vehicle driver on the pre-navigation route.

[0038] The replanning warning module is used to replan and warn drivers about the pre-navigation route based on the driver's assessment of driving hazards on the pre-navigation route.

[0039] The control module is used to control the operation of the data acquisition module, driving difficulty analysis module, driving behavior risk analysis module, driving hazard assessment module, and replanning warning module.

[0040] Thirdly, the present invention provides an electronic device, comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes a high-precision positioning and navigation method based on BeiDou navigation by calling the computer program stored in the memory.

[0041] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0042] This invention analyzes the difficulty of driving motor vehicles on pre-navigation routes based on road condition data and non-motorized vehicle aggregation data; it analyzes the driving behavior risks of motor vehicle drivers on pre-navigation routes based on driving habit data and road condition data; it assesses the driving hazards of drivers on pre-navigation routes based on the analysis results of the difficulty of driving motor vehicles on pre-navigation routes and the analysis results of the driving behavior risks of motor vehicle drivers on pre-navigation routes; and it re-plans and issues warnings for pre-navigation routes based on the assessment results of driving hazards of drivers on pre-navigation routes, thereby effectively alleviating conflicts between people and vehicles and reducing the accident rate of motor vehicles. Attached Figure Description

[0043] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0044] Figure 1 This is a schematic diagram of the overall process of a high-precision positioning and navigation method based on BeiDou navigation according to the present invention;

[0045] Figure 2 This is a schematic diagram of the structure of a high-precision positioning and navigation system based on BeiDou navigation according to the present invention;

[0046] Figure 3 This is a flowchart analyzing the steps of a high-precision positioning and navigation method based on BeiDou navigation according to the present invention. Detailed Implementation

[0047] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations thereof. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0048] Example 1

[0049] This embodiment provides a high-precision positioning and navigation method based on BeiDou navigation, applicable to various driving scenarios, such as: narrow roads and outdated infrastructure in old urban areas, dense street shops, high demand for non-motorized vehicles and pedestrians, and frequent human-vehicle interactions. In such areas, right-of-way demarcation is ambiguous, and existing technologies struggle to accurately identify dynamic obstacles or predict sudden risks. Therefore, this embodiment, through high-precision positioning and non-motorized vehicle aggregation analysis, can identify potential human-vehicle conflict points in advance and provide route replanning warnings, thereby mitigating accident risks caused by limited driver visibility or delayed reaction times. For example, in areas with concentrated non-motorized vehicle traffic, such as around schools, commercial areas, and transportation hubs, electric vehicles and shared bicycles often exhibit tidal aggregation characteristics. Especially during morning and evening rush hours, the mixing of motorized and non-motorized vehicles intensifies. At this time, existing technologies can only provide static road condition information and cannot quantify dynamic risks. Therefore, this embodiment can combine road condition data and non-motorized vehicle aggregation data to adaptively adjust route planning strategies, helping motor vehicle drivers prioritize main roads with well-maintained barriers or lower non-motorized vehicle interference, further helping drivers reduce high-risk maneuvers such as lane changes and sudden braking.

[0050] like Figure 1 , Figure 3 As shown, this embodiment provides a high-precision positioning and navigation method based on BeiDou navigation, which specifically includes the following steps:

[0051] S1. Obtain road condition data and non-motorized vehicle aggregation data on the pre-navigation route of motor vehicles, and at the same time obtain driving habit data of motor vehicle drivers;

[0052] S2. Based on road condition data and non-motorized vehicle aggregation data, analyze the degree of difficulty for motor vehicles to drive on the pre-navigation route;

[0053] S3. Based on the driving habit data and road condition data of motor vehicle drivers, analyze the driving behavior risks of motor vehicle drivers on the pre-navigation route;

[0054] S4. Based on the analysis results of the difficulty of driving the motor vehicle on the pre-navigation route and the analysis results of the driving behavior risk of the motor vehicle driver on the pre-navigation route, assess the driving danger of the driver on the pre-navigation route;

[0055] S5. Based on the driver's driving hazard assessment results on the pre-navigation route, replan and issue a warning for the pre-navigation route.

[0056] As a preferred technical solution of the present invention, step S2, based on road condition data and non-motorized vehicle aggregation data, analyzes the degree of difficulty for motor vehicles to travel on the pre-navigation route, specifically including:

[0057] S21. Extract road condition data and non-motor vehicle aggregation data from the pre-navigation route of motor vehicles;

[0058] S22. Based on road condition data and non-motorized vehicle aggregation data on the pre-navigation route of motor vehicles, analyze the degree of driving difficulty for motor vehicle drivers on the pre-navigation route and obtain the analysis results of the degree of driving difficulty for motor vehicle drivers on the pre-navigation route.

[0059] As a preferred technical solution of the present invention, step S22 involves analyzing the difficulty of driving the motor vehicle on the pre-navigation route, specifically including:

[0060] S221. Based on the non-motorized vehicle aggregation data on the pre-navigation route of motor vehicles, the dynamic interference degree of non-motorized vehicles on the pre-navigation route of motor vehicle drivers is analyzed to obtain the analysis results of the dynamic interference degree of non-motorized vehicles on the pre-navigation route of motor vehicle drivers. This step realizes the quantification of the dynamic interference of mixed traffic flow by analyzing the dynamic coupling effect of non-motorized vehicle flow intensity and spatial distribution pattern. The non-motorized vehicle flow intensity reflects the occupation of road resources by non-motorized vehicle density, and the randomness of non-motorized vehicle aggregation or dispersion is described by calculating the dispersion degree of non-motorized vehicle distribution. The formula for calculating the dynamic interference degree of non-motorized vehicles is: Wherein, FG represents the degree of dynamic interference from non-motorized vehicles by motor vehicle drivers on the pre-navigation route, n represents the mean number of non-motorized vehicles passing through each segment of the motor vehicle pre-navigation route per unit time in the non-motorized vehicle aggregation data, nf represents the mean number of non-motorized vehicles occupying the motor vehicle lanes each segment of the motor vehicle pre-navigation route per unit time in the non-motorized vehicle aggregation data, sf represents the standard deviation of the number of non-motorized vehicles occupying the motor vehicle lanes each segment of the motor vehicle pre-navigation route per unit time in the non-motorized vehicle aggregation data; L represents the average traffic area of ​​each segment of the motor vehicle pre-navigation route, and Lf represents the average traffic area of ​​the non-motorized vehicle lanes in each segment of the motor vehicle pre-navigation route;

[0061] S222. Based on road condition data along the pre-navigation route, the complexity of road conditions for drivers on the pre-navigation route is analyzed, yielding the analysis results. This step reflects the limitation of traffic capacity on curves along the navigation route by calculating the difference in road curvature radius, and quantifies the spatial compression effect of narrow roads along the navigation route by calculating the difference in road width. Simultaneously, based on exponential function analysis of building density along the street, this step also amplifies the risks in complex environments such as old urban areas. Based on the above factors, this step achieves a comprehensive risk assessment of areas with sharp curves, narrow roads, and high-density buildings by analyzing the synergistic effect of road geometry and environmental complexity. The formula for calculating road condition complexity is: Where Lk represents the road condition complexity for motor vehicle drivers on the pre-navigation route, Sq represents the standard deviation of the radius of curvature of all road segments on the pre-navigation route in the road condition data, Aq represents the mean of the radius of curvature of all road segments on the pre-navigation route in the road condition data, Wf represents the mean of the width of the non-motorized vehicle lane in all road segments on the pre-navigation route in the road condition data, W represents the mean of the lane width in all road segments on the pre-navigation route in the road condition data, and Zt represents the mean of the vertical projection area of ​​the street-facing buildings on all road segments on the pre-navigation route in the road condition data.

[0062] S223. Based on the analysis results of the degree of non-motorized vehicle dynamic interference and the analysis results of road condition complexity on the pre-navigation route, the driving difficulty of motor vehicles on the pre-navigation route is analyzed to obtain the analysis results of the driving difficulty of motor vehicles on the pre-navigation route. This step analyzes the different degrees of influence of the degree of non-motorized vehicle dynamic interference and the road condition complexity on the driving difficulty of motor vehicles on the pre-navigation route through weighted summation analysis, so as to more accurately quantify the driving difficulty of motor vehicles on the pre-navigation route. The calculation formula for the driving difficulty is: Xk=a1×FG+a2×Lk, where Xk is the driving difficulty of motor vehicles on the pre-navigation route, and a1 and a2 are the influence weights of non-motorized vehicle dynamic interference and road condition complexity, respectively.

[0063] As a preferred technical solution of the present invention, step S3 analyzes the driving behavior risk of motor vehicle drivers on the pre-navigation route based on the driving habit data and road condition data of motor vehicle drivers, specifically including the following steps:

[0064] S31. Extract driving habit data of motor vehicle drivers and road condition data on the motor vehicle pre-navigation route;

[0065] S32. Based on driving habit data and road condition data, analyze the driving behavior risks of motor vehicle drivers on the pre-navigation route and obtain the driving behavior risk analysis results of motor vehicle drivers on the pre-navigation route.

[0066] As a preferred technical solution of the present invention, step S32, which analyzes the driving behavior risk of motor vehicle drivers on the pre-navigation route, includes the following specific steps:

[0067] S321. Based on driving habit data and road condition data, the road adaptability of motor vehicle drivers on the pre-navigation route is analyzed to obtain the analysis results. This step quantifies the impact of lane width and vehicle width on the navigation route and the preset vehicle safety margin (i.e., the preset minimum lane width for safe vehicle driving in this embodiment) on the road adaptability. A logarithmic function is introduced to suppress the influence of extreme vehicle speed values. Furthermore, the historical average vehicle speed of similar road segments on all segments of the pre-navigation route is included in the road adaptability analysis process, thereby achieving an accurate assessment of the matching degree between road conditions and driving behavior. The formula for calculating the road adaptability is: Where Ra represents the degree of road adaptability for motor vehicle drivers on the pre-navigation route, w represents the average lane width of all road segments on the pre-navigation route in the road status data, ws represents the preset minimum lane width for safe vehicle driving, wc represents the width of motor vehicles, vh represents the average historical average speed of all similar road segments corresponding to all road segments on the pre-navigation route in the driving habit data, and vs represents the average speed limit of all road segments corresponding to all road segments on the pre-navigation route.

[0068] S322. Based on driving habit data, analyze the degree of aggressive driving by motor vehicle drivers on the pre-navigation route to obtain the analysis results of the degree of aggressive driving by motor vehicle drivers on the pre-navigation route. This step introduces a difference coefficient to analyze the differences in the driver's historical average speed on similar road sections and the differences in the number of aggressive operations (in this embodiment, the number of aggressive operations is the sum of the number of times the driver has historically accelerated / decelerated and changed lanes sharply on similar road sections), thereby comprehensively analyzing the degree of aggressive driving and achieving accurate quantification of the driver's speeding tendency and aggressive driving. The formula for calculating the degree of aggressive driving is: Wherein, Sj represents the degree of aggressive driving by the motor vehicle driver on the pre-navigation route, vh represents the mean of the historical average speed of all similar road segments corresponding to all road segments on the pre-navigation route in the driving habit data, Svh represents the standard deviation of the historical average speed of all similar road segments corresponding to all road segments on the pre-navigation route in the driving habit data, m represents the mean of the number of historical aggressive operations by the driver on all similar road segments corresponding to all road segments on the pre-navigation route in the driving habit data, and Sm represents the standard deviation of the number of historical aggressive operations by the driver on all similar road segments corresponding to all road segments on the pre-navigation route in the driving habit data.

[0069] S323. Based on the analysis results of the road adaptability and driving aggression of motor vehicle drivers on the pre-navigation route, the driving behavior risk of motor vehicle drivers on the pre-navigation route is analyzed to obtain the driving behavior risk analysis results of motor vehicle drivers on the pre-navigation route. This step is based on the differentiated impact of road adaptability and driving aggression on driving behavior risk. By introducing a weighted summation method, a comprehensive analysis of driving behavior risk is achieved. The calculation formula for driving behavior risk is: Jx=b1×Ra+b2×Sj, where Jx is the driving behavior risk of motor vehicle drivers on the pre-navigation route, and b1 and b2 are the influence weights of road adaptability and driving aggression, respectively.

[0070] As a preferred technical solution of the present invention, step S4 assesses the driving danger of the driver on the pre-navigation route based on the analysis results of the difficulty of driving the motor vehicle on the pre-navigation route and the analysis results of the driving behavior risk of the motor vehicle driver on the pre-navigation route, including the following specific steps:

[0071] S41. Obtain the analysis results of the difficulty of driving a motor vehicle on the pre-navigation route, and at the same time obtain the analysis results of the driving behavior risk of the motor vehicle driver on the pre-navigation route.

[0072] S42. Based on the analysis results of driving difficulty and driving behavior risk, the driving hazard of the driver on the pre-navigation route is assessed to obtain the driving hazard assessment result of the driver on the pre-navigation route. This step introduces a weighted summation method to dynamically balance the differentiated impact of driving difficulty and driving behavior risk on driving hazard, so as to achieve a more accurate driving hazard assessment. The limitation of single indicator assessment is solved by quantitative fusion, thereby improving driving safety on the navigation route. The driving hazard assessment formula is: Wx=c1×Xk+c2×Jx, where Wx is the driving hazard of the driver on the pre-navigation route, and c1 and c2 are the influence weights of driving difficulty and driving behavior risk, respectively.

[0073] As a preferred technical solution of the present invention, step S5 involves replanning and issuing a warning for the pre-navigation route based on the driver's driving hazard assessment results on the pre-navigation route, specifically including:

[0074] S51. Obtain the assessment results of the driver's driving hazard assessment on the pre-navigation route;

[0075] S52. A preset driving hazard threshold is established. When the driving hazard assessment result of the driver on the pre-navigation route exceeds the driving hazard threshold, the pre-navigation route is replanned and a warning is issued. The parameters (e.g., weights and thresholds) in this embodiment are obtained experimentally by those skilled in the art. Specifically, the experimental method involves: acquiring road state data and non-motorized vehicle aggregation data from multiple historical sets of pre-navigation routes, and simultaneously acquiring the corresponding driving habit data of motorized vehicle drivers; substituting the road state data, non-motorized vehicle aggregation data, and corresponding driving habit data of motorized vehicle drivers into each step of this embodiment to obtain the historical driving hazard assessment results of multiple sets of motorized vehicle drivers on the pre-navigation route; obtaining the judgment results of whether multiple sets of motorized vehicle drivers have engaged in dangerous driving on the pre-navigation route; importing the historical driving hazard assessment results of multiple sets of motorized vehicle drivers on the pre-navigation route and the corresponding judgment results of whether drivers have engaged in dangerous driving on the pre-navigation route obtained in each step of this embodiment into fitting software, and outputting the values ​​of the preset parameters (e.g., weights and thresholds) that meet the highest driving hazard judgment accuracy.

[0076] Example 2

[0077] like Figure 2 As shown, this embodiment provides a high-precision positioning and navigation system based on BeiDou navigation, including:

[0078] The data acquisition module is used to acquire road condition data and non-motorized vehicle aggregation data on the pre-navigation route of motor vehicles, as well as driving habit data of motor vehicle drivers.

[0079] The driving difficulty analysis module is used to analyze the driving difficulty of motor vehicles on the pre-navigation route based on road condition data and non-motorized vehicle aggregation data;

[0080] The driving behavior risk analysis module is used to analyze the driving behavior risks of motor vehicle drivers on the pre-navigation route based on the driving habit data and road condition data of motor vehicle drivers;

[0081] The driving hazard assessment module is used to assess the driving hazard of the driver on the pre-navigation route based on the analysis results of the difficulty of driving the motor vehicle on the pre-navigation route and the analysis results of the driving behavior risk of the motor vehicle driver on the pre-navigation route.

[0082] The replanning warning module is used to replan and warn drivers about the pre-navigation route based on the driver's assessment of driving hazards on the pre-navigation route.

[0083] The control module is used to control the operation of the data acquisition module, driving difficulty analysis module, driving behavior risk analysis module, driving hazard assessment module, and replanning warning module.

[0084] The parameters and steps for implementing the corresponding functions of each unit module in the high-precision positioning and navigation system based on BeiDou navigation described above can be referred to the parameters and steps in the embodiments of the high-precision positioning and navigation method based on BeiDou navigation described above, and will not be repeated here.

[0085] Example 3

[0086] An electronic device according to an embodiment of the present invention includes a processor and a memory. The memory stores a computer program that can be called by the processor. The processor executes a high-precision positioning and navigation method based on BeiDou navigation by calling the computer program stored in the memory. It should be noted that all computer programs for the high-precision positioning and navigation method based on BeiDou navigation are implemented using C language. The data acquisition module, driving difficulty analysis module, driving behavior risk analysis module, driving hazard assessment module, replanning warning module, and control module are all controlled by a remote server.

[0087] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, 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.

[0088] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A high-precision positioning and navigation method based on BeiDou navigation, characterized in that, Includes the following steps: S1. Obtain road condition data and non-motorized vehicle aggregation data on the pre-navigation route of motor vehicles, and at the same time obtain driving habit data of motor vehicle drivers; S2. Based on road condition data and non-motorized vehicle aggregation data, analyze the degree of difficulty for motor vehicles to drive on the pre-navigation route; Based on the analysis results of the degree of dynamic interference from non-motorized vehicles and the analysis results of road condition complexity on the pre-navigation route, the degree of driving difficulty of motor vehicles on the pre-navigation route is analyzed. The formula for calculating the degree of dynamic interference from non-motorized vehicles is: Where FG represents the degree of dynamic interference from non-motorized vehicles by motor vehicle drivers on the pre-navigation route, n represents the mean number of non-motorized vehicles passing through all road segments on the motor vehicle pre-navigation route per unit time in the non-motorized vehicle cluster data, nf represents the mean number of non-motorized vehicles occupying the motor vehicle lane per unit time on all road segments on the motor vehicle pre-navigation route in the non-motorized vehicle cluster data, sf represents the standard deviation of the number of non-motorized vehicles occupying the motor vehicle lane per unit time on all road segments on the motor vehicle pre-navigation route in the non-motorized vehicle cluster data; L represents the average traffic area of ​​all road segments on the motor vehicle pre-navigation route, and Lf represents the average traffic area of ​​the non-motorized vehicle lane in all road segments on the motor vehicle pre-navigation route; The formula for calculating road condition complexity is: Where Lk represents the road condition complexity for motor vehicle drivers on the pre-navigation route, Sq represents the standard deviation of the radius of curvature of all road segments on the pre-navigation route in the road condition data, Aq represents the mean of the radius of curvature of all road segments on the pre-navigation route in the road condition data, Wf represents the mean width of the non-motorized vehicle lane in all road segments on the pre-navigation route in the road condition data, W represents the mean lane width in all road segments on the pre-navigation route in the road condition data, and Zt represents the mean vertical projection area of ​​the street-facing buildings on all road segments on the pre-navigation route in the road condition data. S3. Based on the driving habit data and road condition data of motor vehicle drivers, analyze the driving behavior risks of motor vehicle drivers on the pre-navigation route; S4. Based on the analysis results of the difficulty of driving the motor vehicle on the pre-navigation route and the analysis results of the driving behavior risk of the motor vehicle driver on the pre-navigation route, assess the driving danger of the driver on the pre-navigation route; S5. Based on the driver's driving hazard assessment results on the pre-navigation route, replan and issue a warning for the pre-navigation route.

2. The high-precision positioning and navigation method based on BeiDou navigation according to claim 1, characterized in that, Step S2, based on road condition data and non-motorized vehicle aggregation data, analyzes the difficulty of motor vehicles driving on the pre-navigation route, specifically including: S21. Extract road condition data and non-motor vehicle aggregation data from the pre-navigation route of motor vehicles; S22. Based on road condition data and non-motorized vehicle aggregation data on the pre-navigation route of motor vehicles, analyze the difficulty of driving motor vehicles on the pre-navigation route and obtain the analysis results of the difficulty of driving motor vehicles on the pre-navigation route.

3. The high-precision positioning and navigation method based on BeiDou navigation according to claim 2, characterized in that, Step S22 involves analyzing the difficulty of driving the vehicle on the pre-navigation route, specifically including: S221. Based on the non-motorized vehicle aggregation data on the pre-navigation route of motor vehicles, analyze the degree of dynamic interference of non-motorized vehicles by motor vehicle drivers on the pre-navigation route, and obtain the analysis results of the degree of dynamic interference of non-motorized vehicles by motor vehicle drivers on the pre-navigation route. S222. Based on the road condition data on the pre-navigation route of the motor vehicle, analyze the road condition complexity of the motor vehicle driver on the pre-navigation route and obtain the analysis results of the road condition complexity of the motor vehicle driver on the pre-navigation route. S223. Based on the analysis results of the degree of dynamic interference from non-motorized vehicles and the analysis results of the road condition complexity on the pre-navigation route, the driving difficulty of motor vehicles on the pre-navigation route is analyzed, and the analysis results of the driving difficulty of motor vehicles on the pre-navigation route are obtained.

4. The high-precision positioning and navigation method based on BeiDou navigation according to claim 3, characterized in that, Step S3, based on the driver's driving habits and road condition data, analyzes the driving behavior risks of the driver on the pre-navigation route, specifically including the following steps: S31. Extract driving habit data of motor vehicle drivers and road condition data on the motor vehicle pre-navigation route; S32. Based on driving habit data and road condition data, analyze the driving behavior risks of motor vehicle drivers on the pre-navigation route and obtain the driving behavior risk analysis results of motor vehicle drivers on the pre-navigation route.

5. A high-precision positioning and navigation method based on BeiDou navigation according to claim 4, characterized in that, Step S32 involves analyzing the driving behavior risks of motor vehicle drivers on the pre-navigation route, including the following specific steps: S321. Based on driving habit data and road condition data, analyze the road adaptability of motor vehicle drivers on the pre-navigation route and obtain the analysis results of the road adaptability of motor vehicle drivers on the pre-navigation route. S322. Based on driving habit data, analyze the degree of aggressive driving of motor vehicle drivers on the pre-navigation route and obtain the analysis results of the degree of aggressive driving of motor vehicle drivers on the pre-navigation route. S323. Based on the analysis results of the road adaptability and driving aggression of motor vehicle drivers on the pre-navigation route, the driving behavior risk of motor vehicle drivers on the pre-navigation route is analyzed, and the driving behavior risk analysis results of motor vehicle drivers on the pre-navigation route are obtained.

6. A high-precision positioning and navigation method based on BeiDou navigation according to claim 5, characterized in that, In step S4, based on the analysis results of the difficulty of driving the motor vehicle on the pre-navigation route and the analysis results of the driving behavior risk of the motor vehicle driver on the pre-navigation route, the driving danger of the driver on the pre-navigation route is assessed, including the following specific steps: S41. Obtain the analysis results of the difficulty of driving a motor vehicle on the pre-navigation route, and at the same time obtain the analysis results of the driving behavior risk of the motor vehicle driver on the pre-navigation route. S42. Based on the analysis results of driving difficulty and driving behavior risk, assess the driving hazard of the driver on the pre-navigation route and obtain the driving hazard assessment results of the driver on the pre-navigation route.

7. A high-precision positioning and navigation method based on BeiDou navigation according to claim 6, characterized in that, In step S5, based on the driver's driving hazard assessment results on the pre-navigation route, the pre-navigation route is replanned and a warning is issued. This specifically includes: S51. Obtain the assessment results of the driver's driving hazard assessment on the pre-navigation route; S52. Preset driving hazard threshold: When the driver's driving hazard assessment result on the pre-navigation route is greater than the driving hazard threshold, the pre-navigation route is replanned and a warning is issued.

8. A high-precision positioning and navigation system based on BeiDou navigation, used to implement the high-precision positioning and navigation method based on BeiDou navigation as described in any one of claims 1-7, characterized in that, The system includes: The data acquisition module is used to acquire road condition data and non-motorized vehicle aggregation data on the pre-navigation route of motor vehicles, as well as driving habit data of motor vehicle drivers. The driving difficulty analysis module is used to analyze the driving difficulty of motor vehicles on the pre-navigation route based on road condition data and non-motorized vehicle aggregation data; The driving behavior risk analysis module is used to analyze the driving behavior risks of motor vehicle drivers on the pre-navigation route based on the driving habit data and road condition data of motor vehicle drivers; The driving hazard assessment module is used to assess the driving hazard of the driver on the pre-navigation route based on the analysis results of the difficulty of driving the motor vehicle on the pre-navigation route and the analysis results of the driving behavior risk of the motor vehicle driver on the pre-navigation route. The replanning warning module is used to replan and warn drivers about the pre-navigation route based on the driver's assessment of driving hazards on the pre-navigation route. The control module is used to control the operation of the data acquisition module, the driving difficulty analysis module, the driving behavior risk analysis module, the driving hazard assessment module, and the replanning early warning module.

9. An electronic device, comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; characterized in that the processor executes a high-precision positioning and navigation method based on BeiDou navigation as described in any one of claims 1-7 by calling the computer program stored in the memory.

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