A Beidou navigation and positioning optimization method and system

Abstract

The present invention relates to the technical field of path navigation and positioning, and particularly relates to a Beidou navigation and positioning optimization method and system. The method includes: collecting the positioning points of a vehicle on a plane map and the vehicle driving data at each moment; obtaining the surrounding paths of the vehicle according to the positioning points of the vehicle; calculating the positioning point error distance according to the vehicle driving data of the vehicle; obtaining the same-direction attribution discrimination index of the vehicle to the surrounding paths according to the distances between the surrounding paths of the vehicle and the distances between the positioning points of the vehicle and the surrounding paths; calculating the positioning data difference of the vehicle at each moment according to the vehicle driving data and the positioning points of the vehicle at adjacent moments; obtaining the navigation trajectory matching degree between the vehicle and the surrounding paths according to the same-direction attribution discrimination index and the positioning data difference; and obtaining the navigation and positioning paths of the vehicle at each moment according to the navigation trajectory matching degree, thereby completing the map-assisted positioning of the vehicle. Thus, the optimization of the Beidou navigation and positioning method is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of path navigation and positioning, and particularly relates to a Beidou navigation and positioning optimization method and system. Background Art

[0002] With the improvement of social living standards, urban transportation roads are gradually increasing, and the complexity of roads poses greater challenges to vehicle driving. Currently, most vehicles are equipped with satellite navigation and positioning systems, which can provide road selection for drivers and assist driving. The commonly used satellite navigation and positioning method is the BD / DR integrated positioning method based on satellite navigation and inertial navigation. This method uses the position information provided by the BD satellite navigation system and the motion information provided by inertial sensors, and uses the Kalman filter algorithm to achieve the fusion estimation of position and speed, combining the advantages of satellite navigation and inertial navigation, so as to achieve higher positioning accuracy and stability.

[0003] When using the BD / DR integrated positioning method for electronic map assisted positioning, the direct projection algorithm is often used as the map assisted positioning algorithm to locate the vehicle's position on the map. However, when the vehicle travels to a complex intersection, due to the intricate road conditions, the error existing in Beidou satellite positioning is likely to be greater than the distance between roads, and at this time, the positioning accuracy of the vehicle using the direct projection algorithm is poor. Summary of the Invention

[0004] In order to solve the above technical problems, the purpose of the present invention is to provide a Beidou navigation and positioning optimization method and system, and the specific technical solutions adopted are as follows:

[0005] In a first aspect, an embodiment of the present invention provides a Beidou navigation and positioning optimization method, and the method includes the following steps:

[0006] Collect the positioning points of the vehicle on the plane map and the vehicle driving data at each moment, and the vehicle driving data includes: vehicle speed, vehicle acceleration, and direction;

[0007] Obtain the surrounding paths of the vehicle according to the positioning points of the vehicle at each moment; calculate the positioning point error distance according to the vehicle driving data of the vehicle at each moment; obtain the center starting point of the surrounding path according to the endpoints of the surrounding path of the vehicle; obtain the direct projection accuracy of the vehicle at each moment according to the distance between the center starting points of each surrounding path and the distance between the positioning point of the vehicle and the center starting points of each surrounding path; obtain the same-direction attribution discrimination index of the vehicle to the surrounding path according to the direction of the vehicle, the surrounding path, the center starting point of the surrounding path, and the direct projection accuracy;

[0008] Calculate the positioning data difference of the vehicle at each moment based on the vehicle speed and positioning points of the vehicle at adjacent moments; obtain the matching degree of the vehicle's navigation trajectory with the surrounding paths at each moment according to the same-direction attribution discrimination index of the vehicle to the surrounding paths at each moment and the positioning data difference; obtain the navigation positioning path of the vehicle at each moment according to the navigation trajectory matching degree.

[0009] Further, obtaining the surrounding paths of the vehicle according to the positioning points of the vehicle at each moment includes:

[0010] Construct a surrounding area of the vehicle with the positioning point of the vehicle at each moment as the center and a preset length as the radius; regard the roads included in the surrounding area of the vehicle as the surrounding paths of the vehicle at each moment.

[0011] Further, calculating the positioning point error distance according to the vehicle driving data of the vehicle at each moment includes:

[0012] For the t-th moment, calculate the square of the preset time interval, and obtain the product of the vehicle acceleration at the (t - 1)-th moment of the vehicle and the square, denoted as the first product; calculate the product of the vehicle speed at the (t - 1)-th moment of the vehicle and the preset time interval, denoted as the second product;

[0013] Calculate the sum value of the vehicle speed at the (t - 1)-th moment of the vehicle and the vehicle speed at the t-th moment, denoted as the first sum value; obtain the ratio of the first sum value to 2, and denote the product of the ratio and the preset time interval as the third product;

[0014] Calculate the sum value of the first product and the second product, denoted as the second sum value, and take the absolute value of the difference between the second sum value and the third product as the positioning point error distance of the vehicle at the t-th moment.

[0015] Further, obtaining the central starting point of the surrounding path according to the endpoints of the surrounding paths of the vehicle includes:

[0016] Denote the connection line of the two endpoints of each surrounding path as the path starting line of each surrounding path, obtain the path median line of each surrounding path, and take the intersection point of the path starting line and the path median line as the central starting point of each surrounding path.

[0017] Further, obtaining the direct projection accuracy of the vehicle at each moment according to the distance between the central starting points of each surrounding path and the distance between the positioning point of the vehicle and the central starting points of each surrounding path includes:

[0018] For the t-th moment, take the preset number of moments before the t-th moment and including the t-th moment as the observation window, calculate the mean value of the positioning point error distances at all moments within the observation window, denoted as the first mean value;

[0019] Calculate the Euclidean distance between the central starting points of adjacent surrounding paths, and obtain the minimum value of the Euclidean distances of all adjacent surrounding paths;

[0020] Calculate the ratio of the minimum value to the first mean value; Denote the Euclidean distance between the positioning point of the vehicle and the central starting point of each surrounding path as the planar distance between the vehicle and each surrounding path; Obtain the mean value of the planar distances between the positioning point of the vehicle and all surrounding paths, denoted as the second mean value; Take the product of the ratio and the second mean value as the direct projection accuracy of the vehicle at the t-th moment.

[0021] Furthermore, the obtaining the same-direction attribution discrimination index of the vehicle to the surrounding path according to the direction of the vehicle, the surrounding path, the central starting point of the surrounding path, and the direct projection accuracy includes:

[0022] For the t-th moment, take the tangent line of the path midline of each surrounding path at the central starting point as the path projection line of the surrounding path; Obtain the perpendicular distance between the positioning point of the vehicle and the path projection line of the surrounding path, denoted as the direct projection distance;

[0023] Denote the acute angle between the direction of the vehicle and the path projection line of the surrounding path as the direction angle between the vehicle and the surrounding path; Calculate the product of the radian value of the direction angle and the positioning point error distance of the vehicle at the t-th moment, denoted as the first product;

[0024] Calculate the difference between 1 and the direct projection accuracy at the t-th moment, obtain the maximum value between the difference and 0, and take the product of the maximum value and the first product as the second product;

[0025] Calculate the product of the direct projection distance and the direct projection accuracy at the t-th moment, denoted as the third product; Obtain the sum value of the second product and the third product;

[0026] Calculate the product of the vehicle speed of the vehicle at the t-th moment and the preset time interval, denoted as the fourth product; Obtain the ratio of the sum value to the fourth product; Calculate the opposite number of the ratio, and take the calculation result of the exponential function with the natural constant as the base and the opposite number as the exponent as the same-direction attribution discrimination index of the vehicle to the surrounding path.

[0027] Furthermore, the calculating the positioning data difference of the vehicle at each moment according to the vehicle speed and positioning point of the vehicle at adjacent moments includes:

[0028] For the t-th moment, calculate the product of the vehicle speed of the vehicle at the (t - 1)-th moment and the preset time interval; Obtain the Euclidean distance between the positioning point of the vehicle at the (t - 1)-th moment and the positioning point of the vehicle at the t-th moment; Denote the absolute value of the difference between the product and the Euclidean distance as the positioning data difference of the vehicle at the t-th moment.

[0029] Further, obtaining the matching degree between the vehicle and the surrounding path at each moment according to the same-direction attribution discrimination index of the surrounding path at each moment of the vehicle and the positioning data difference includes:

[0030] For the t-th moment, a preset number of moments before and including the t-th moment are used as the projection adjustment window of the t-th moment; the maximum value of the positioning data differences of all moments in the projection adjustment window is obtained, and the ratio of the positioning data difference at the t-th moment to the maximum value is calculated;

[0031] Calculate the product of the same-direction attribution discrimination index of the vehicle to the surrounding path at the t-th moment and the ratio; the sum value of the products of all moments in the projection adjustment window is used as the matching degree between the vehicle and the surrounding path at the t-th moment.

[0032] Further, obtaining the navigation positioning path of the vehicle at each moment according to the matching degree of the navigation trajectory includes:

[0033] Obtain the maximum value of the matching degrees of the navigation trajectories between the vehicle and all surrounding paths at each moment, and use the surrounding path corresponding to the maximum value as the navigation positioning path of the vehicle at each moment.

[0034] In a second aspect, an embodiment of the present invention further provides a Beidou navigation positioning optimization system, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of the method described in any one of the above are implemented.

[0035] The present invention has at least the following beneficial effects:

[0036] The present invention provides a method and system for optimizing Beidou navigation positioning. By analyzing the traditional direct projection algorithm, the direct projection distance from the positioning point is used as an index for judging the vehicle's attributed path, that is, the road with the shortest direct projection distance is used as the vehicle's navigation positioning road, without fully considering the influence of the angle data of on-vehicle sensors and the error data of the positioning point on the calculation accuracy of the direct projection distance; since the vehicle positioning point in the traditional direct projection algorithm is calculated by combining the speed and acceleration of the vehicle at the previous moment, the error distance of the positioning point is calculated through the difference between the speed and acceleration of the vehicle at the previous moment and the vehicle speed at the current moment, realizing the quantification of the positioning error; by analyzing the influence of the path complexity in the surrounding area of the vehicle on the effectiveness of the direct projection algorithm, combining the position relationship between the vehicle positioning point and the surrounding paths and the positioning error to calculate the direct projection accuracy, reflecting the positioning effectiveness of using the direct projection algorithm at each moment; to improve the traditional direct projection algorithm and make the calculation of the direct projection distance more accurate, the projection error distance of the direct projection distance is calculated according to the error distance of the positioning point and the vehicle direction, and used as an index for judging the vehicle's attributed path in the improved algorithm, avoiding the influence of the positioning point error on the direct projection algorithm and improving the navigation positioning accuracy when the vehicle travels to complex intersections; weights are set for the traditional and improved vehicle attributed path judgment indexes respectively according to the direct projection accuracy, avoiding the problem that the positioning effect of the improved algorithm is poor due to the small angle data difference between the vehicle direction and the surrounding path directions at non-complex intersections; finally, to avoid the problem of positioning errors caused by large errors in individual data, the vehicle positioning data at multiple moments are jointly calculated to obtain the navigation trajectory matching degree, and the vehicle's navigation positioning path is obtained according to the navigation trajectory matching degree, improving the accuracy of vehicle navigation positioning during driving. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0038] Figure 1 It is a flowchart of the steps of a method for optimizing Beidou navigation positioning provided by an embodiment of the present invention;

[0039] Figure 2 It is a schematic diagram of the division of the surrounding paths of the vehicle;

[0040] Figure 3 It is a schematic diagram of the projection error distance of the vehicle during driving. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0041] To further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following specifically describes, in conjunction with the accompanying drawings and preferred embodiments, a Beidou navigation and positioning optimization method and system proposed according to the present invention, including its specific implementation manner, structure, features, and effects, as follows. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0042] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0043] The following specifically describes the specific solution of a Beidou navigation and positioning optimization method and system provided by the present invention with reference to the accompanying drawings.

[0044] Please refer to Figure 1 , which shows a flowchart of the steps of a Beidou navigation and positioning optimization method provided by an embodiment of the present invention. The method includes the following steps:

[0045] Step S001: Obtain the positioning points of the vehicle on the plane map, and use in-vehicle sensors to obtain the vehicle driving data.

[0046] Obtain map data, where the map data is a plane map marked with road areas. Every s, obtain the vehicle driving data through in-vehicle sensors. The vehicle driving data includes: vehicle speed , vehicle acceleration , direction . Obtain the positioning points of the vehicle on the plane map through BD / DR combined positioning technology.

[0047] At the th moment during the vehicle driving process, as shown in Figure 2 , in this embodiment, with the positioning point of the vehicle as the center, construct the surrounding area of the vehicle with the length as the radius; the roads included in the surrounding area of the vehicle are the surrounding paths of the vehicle, and a total of surrounding paths are obtained; connect the endpoints of the surrounding paths to obtain the path starting line; obtain the path center line of each surrounding path; and mark the intersection point of the path starting line and the path center line as the center starting point of the surrounding path.

[0048] So far, the positioning points of the vehicle and the vehicle driving data at each moment can be obtained according to the above method of this embodiment.

[0049] Step S002: Obtain the surrounding paths of the vehicle based on the positioning points of the vehicle; calculate the positioning point error distance according to the vehicle driving data of the vehicle; obtain the same-direction attribution discrimination index of the vehicle for the surrounding paths based on the distances between the surrounding paths of the vehicle and the distances between the positioning points of the vehicle and each surrounding path.

[0050] When using the direct projection algorithm for map-aided positioning, when the vehicle passes through an intersection, the road matching of the vehicle may be inaccurate because the distance between the positioning point coordinates of the vehicle and the actual vehicle coordinates is greater than the distance between different roads at the intersection. Therefore, the direct projection algorithm can be improved by combining the accuracy of satellite positioning and the direction data of the vehicle to obtain a better map-aided positioning effect. First, quantify the positioning error of the vehicle at each moment, and calculate the positioning point error distance according to the vehicle driving data. The calculation formula is:

[0051]

[0052] In the formula, is the positioning point error distance at time ; is the acceleration data at time ; and are the vehicle speed data at time and time respectively;

[0053] When using the BD / DR combined positioning method for positioning, the position where the vehicle will move to at the next moment will be predicted based on the motion state at the previous moment and combined with Newton's laws of motion, and then the positioning point will be corrected. However, in the actual vehicle operation process, the driver brakes or accelerates, resulting in a change in the vehicle's acceleration, which causes an error between the positioning point coordinates and the actual vehicle coordinates. In the formula, the motion of the vehicle within a short period of time is regarded as a uniformly variable rectilinear motion. The vehicle speed data at time calculated through the motion data at time is used to calculate the predicted displacement from time to time . According to the vehicle speed data at time and the actual vehicle speed data at time , calculate the actual displacement from time to time ; take the difference between the two as the error distance of the positioning point at time . The larger its value, the larger the error distance of the positioning point.

[0054] Further, according to the relationship between the vehicle positioning point and the surrounding paths, the complexity of the paths within the surrounding area of the vehicle is judged, and the effectiveness of the direct projection algorithm is judged in combination with the positioning error. First, the effectiveness of the direct projection algorithm is calculated according to the reasons for the inaccurate road matching of the vehicle analyzed above. For At a moment, the path where the vehicle is located is taken as the starting path, and the remaining surrounding paths are sorted in the clockwise direction to obtain the serial numbers of each surrounding path, and the direct projection accuracy is constructed:

[0055]

[0056] In the formula, is the direct projection accuracy at the moment of is the number of surrounding paths of the vehicle at the moment of is from the moment of to the moment of the mean value of the positioning point error distances at all moments within the time period, and the empirical value of is 10; is the th th Euclidean distance between the central starting points of the th surrounding path and the th surrounding path of the vehicle at the moment of is the minimum value function.

[0057] The above formula takes the time period from the moment of to the moment of as the observation window at the moment of and takes the mean value of the positioning point error distances

[0058] at each moment within the observation window as the predicted positioning point error distance at the next moment; further, the Euclidean distance between the central starting points of the surrounding paths is compared with the positioning point error distance at the next moment. When the ratio is less than or equal to 1, it means that the positioning point error distance at this time is relatively large and greater than or equal to the spacing of the surrounding paths. At this time, the effect of using the direct projection algorithm for map-aided positioning is poor, and the corresponding direct projection accuracy is low; the greater the Euclidean distance between the vehicle positioning point and the central starting points of each surrounding path at this time, the farther the vehicle is from the intersection, and the higher the effectiveness of the direct projection algorithm. Finally, the larger the value of the effectiveness of the direct projection algorithm, the better the effect of using the direct projection algorithm at this time, and when the value is greater than or equal to 1, the direct projection algorithm is fully capable of map-aided positioning. Take the The path center line of the surrounding path's tangent at the central starting point is used as the path projection line. Obtain the perpendicular distance from the vehicle's positioning point to the path projection line of the surrounding path, denoted as the direct projection distance ; Denote the acute angle between the vehicle's direction and the path projection line of the th surrounding path as the direction angle between the vehicle and the surrounding path at time .

[0059] Calculate the same-direction attribution discrimination index of the vehicle to the surrounding path:

[0060]

[0061] In the formula, is the same-direction attribution discrimination index of the vehicle to the th surrounding path at time; The direct projection distance between the vehicle and the th surrounding path at time; The positioning point error distance at time; is the direction angle between the vehicle and the th surrounding path at time; is the maximum value function; is the vehicle speed at time; is the exponential function with the natural constant as the base.

[0062] As Figure 3 shown, taking the vehicle's positioning point as the starting point, move a point with a length of the positioning point error distance along the vehicle's driving direction as the actual vehicle position; at this time, the vector from the vehicle's positioning point to the actual vehicle position forms a sector with the path direction, the radius is the positioning point error distance, the vehicle direction and the path direction are respectively used as the directions of the two sides of the sector, and the projection error distance of the direct projection distance is approximately the arc length of the sector; when the arc length of the sector is smaller and closer to 0, it means that the influence of the positioning point error on the direct projection distance is smaller and the vehicle driving direction is more consistent with the direction of the path, then the same-direction attribution discrimination index is larger and the vehicle is more likely to belong to this path. is the improved vehicle attribution path judgment index, is the vehicle attribution path judgment index of the traditional direct projection algorithm, and The weights for both are assigned according to the direct projection accuracy. When the direct projection algorithm is highly effective, that is, when the effectiveness of the direct projection algorithm is greater than 1, only the traditional direct projection algorithm is used to determine the vehicle's belonging path; when the direct projection algorithm is less effective, the improved algorithm will account for a larger weight in determining the vehicle's belonging path. The reason is that the improved algorithm combines the direction data of the road. When the vehicle is at a non-complex intersection, the direction difference between roads is small, and it is difficult for the improved algorithm to calculate the difference between paths, resulting in a poor positioning effect. Therefore, using the effectiveness of the direct projection algorithm as the weight to switch between the traditional direct projection algorithm and the improved direct projection algorithm avoids the problem that the improved algorithm has a poor positioning effect when the vehicle travels to a non-complex intersection. is the driving distance of the vehicle between two sampling moments. The greater the distance, the greater the tolerance of the calculation result of the same-direction belonging discrimination index to the error of the direct projection distance. At this time, the greater the same-direction belonging discrimination index, the more likely the vehicle belongs to this path.

[0063] Step S003: Calculate the positioning data difference of the vehicle at each moment; obtain the navigation trajectory matching degree between the vehicle and the surrounding paths according to the same-direction belonging discrimination index and the positioning data difference; obtain the navigation positioning path of the vehicle at each moment according to the navigation trajectory matching degree.

[0064] Although the error distance of the positioning point can be calculated, relying solely on the vehicle positioning data at a certain moment is likely to lead to vehicle positioning errors. Therefore, the vehicle positioning data at multiple moments should be calculated together to complete map-assisted positioning. Calculate the navigation trajectory matching degree:

[0065]

[0066]

[0067] In the formula, is the positioning data difference at the is the navigation trajectory matching degree between the vehicle at the moment and the is the vehicle speed of the vehicle at the moment; is the time interval; is the positioning point of the vehicle at the moment and the positioning point at the moment is the size of the projection adjustment window, with an empirical value ; is The same-direction attribution discrimination index of the vehicle for the th surrounding path at a moment; is the maximum value function.

[0068] Subtracting the Euclidean distance between the positioning points at moment from the driving distance length of the vehicle at moment represents the difference between the positioning point and the on-vehicle sensor data. The greater the positioning data difference, the less credible the same-direction attribution discrimination index calculated based on the positioning data at this moment; within the projection adjustment window of the data at this moment, taking the normalized value of the positioning data difference as the weight and adding the same-direction attribution discrimination indexes with weights to obtain the navigation trajectory matching degree. The larger the value, the more likely the vehicle belongs to the th path. th path.

[0069] At moment, calculate the navigation trajectory matching degree between the vehicle and all surrounding paths, and select the surrounding path corresponding to the maximum value of the navigation trajectory matching degree as the navigation positioning path of the vehicle at moment, that is, the driving road.

[0070] Thus, the map-assisted positioning of the vehicle is completed, and the optimization of the Beidou navigation positioning method is realized.

[0071] Based on the same inventive concept as the above method, the embodiment of the present invention also provides a Beidou navigation positioning optimization system, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of any one of the above Beidou navigation positioning optimization methods are implemented.

[0072] It should be noted that: the above sequence of the embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. And the above description of specific embodiments of this specification is made. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0073] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between each embodiment can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0074] The above are only the preferred embodiments of the present invention and are not used to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the principles of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for optimizing Beidou navigation and positioning, characterized in that, The method includes the following steps: Collect the positioning points of the vehicle on the plane map and the vehicle driving data at each moment. The vehicle driving data includes: vehicle speed, vehicle acceleration, and direction; Obtain the surrounding paths of the vehicle according to the positioning points of the vehicle at each moment; calculate the positioning point error distance according to the vehicle driving data of the vehicle at each moment; obtain the central starting point of the surrounding path according to the endpoints of the surrounding path of the vehicle; obtain the direct projection accuracy of the vehicle at each moment according to the distance between the central starting points of each surrounding path and the distance between the positioning point of the vehicle and the central starting points of each surrounding path; for the t-th moment, take the tangent line of the path center line of each surrounding path at the central starting point as the path projection line of the surrounding path; obtain the perpendicular distance between the positioning point of the vehicle and the path projection line of the surrounding path, denoted as the direct projection distance; denote the acute angle between the direction of the vehicle and the path projection line of the surrounding path as the direction angle between the vehicle and the surrounding path; calculate the product of the radian value of the direction angle and the positioning point error distance of the vehicle at the t-th moment, denoted as the first product; calculate the difference between 1 and the direct projection accuracy at the t-th moment, obtain the maximum value between the difference and 0, and denote the product of the maximum value and the first product as the second product; calculate the product of the direct projection distance and the direct projection accuracy at the t-th moment, denoted as the third product; obtain the sum value of the second product and the third product; calculate the product of the vehicle speed of the vehicle at the t-th moment and the preset time interval, denoted as the fourth product; obtain the ratio of the sum value to the fourth product; calculate the opposite number of the ratio, and take the calculation result of the exponential function with the natural constant as the base and the opposite number as the exponent as the same-direction attribution discrimination index of the vehicle to the surrounding path; Calculate the positioning data difference of the vehicle at each moment according to the vehicle speed and positioning points of the vehicle at adjacent moments; obtain the navigation trajectory matching degree of the vehicle at each moment to the surrounding path according to the same-direction attribution discrimination index of the vehicle to the surrounding path at each moment and the positioning data difference; obtain the navigation positioning path of the vehicle at each moment according to the navigation trajectory matching degree.

2. The Beidou navigation and positioning optimization method according to claim 1, wherein The obtaining of the surrounding paths of the vehicle according to the positioning points of the vehicle at each moment includes: Construct a surrounding area of the vehicle with the positioning points of the vehicle at each moment as the center and a preset length as the radius; take the roads included in the surrounding area of the vehicle as the surrounding paths of the vehicle at each moment.

3. The Beidou navigation and positioning optimization method according to claim 1, characterized in that The positioning point error distance is calculated according to the vehicle driving data at each moment, and the calculation formula is: In the formula, is the positioning point error distance at the moment of; is the acceleration data at the moment of; , are respectively the vehicle speed data at the moment of and the moment of; is the time interval.

4. The Beidou navigation and positioning optimization method according to claim 1, wherein The obtaining of the central starting point of the surrounding path according to the endpoints of the surrounding path of the vehicle includes: Denote the connection line of the two endpoints of each surrounding path as the path starting line of each surrounding path, obtain the path center line of each surrounding path, and take the intersection point of the path starting line and the path center line as the central starting point of each surrounding path.

5. A Beidou navigation and positioning optimization method according to claim 1, characterized in that, The obtaining of the direct projection accuracy of the vehicle at each moment according to the distance between the central starting points of each surrounding path and the distance between the positioning point of the vehicle and the central starting points of each surrounding path includes: For the t-th moment, take the preset number of moments before the t-th moment and including the t-th moment as the observation window, calculate the mean value of the positioning point error distances at all moments within the observation window, denoted as the first mean value; Calculate the Euclidean distance between the central starting points of adjacent surrounding paths, and obtain the minimum value of the Euclidean distances of all adjacent surrounding paths; Calculate the ratio of the minimum value to the first mean value; Denote the Euclidean distance between the vehicle's positioning point and the central starting point of each surrounding path as the planar distance between the vehicle and each surrounding path; Obtain the mean value of the planar distances between the vehicle's positioning point and all surrounding paths, denoted as the second mean value; Take the product of the ratio and the second mean value as the direct projection accuracy of the vehicle at the t-th moment.

6. The Beidou navigation and positioning optimization method according to claim 1, wherein The calculating the positioning data difference of the vehicle at each moment according to the vehicle speed and the positioning point of the vehicle at adjacent moments includes: For the t-th moment, calculate the product of the vehicle speed of the vehicle at the (t - 1)-th moment and the preset time interval; Obtain the Euclidean distance between the positioning point of the vehicle at the (t - 1)-th moment and the positioning point of the vehicle at the t-th moment; Denote the absolute value of the difference between the product and the Euclidean distance as the positioning data difference of the vehicle at the t-th moment.

7. A Beidou navigation and positioning optimization method according to claim 1, characterized in that, The obtaining the matching degree between the vehicle and the surrounding path at each moment according to the same-direction attribution discrimination index of the vehicle to the surrounding path at each moment and the positioning data difference includes: For the t-th moment, take the preset number of moments before and including the t-th moment as the projection adjustment window at the t-th moment; Obtain the maximum value of the positioning data differences of all moments in the projection adjustment window, and calculate the ratio of the positioning data difference at the t-th moment to the maximum value; Calculate the product of the same-direction attribution discrimination index of the vehicle to the surrounding path at the t-th moment and the ratio; Take the sum value of the products of all moments in the projection adjustment window as the matching degree between the vehicle and the surrounding path at the t-th moment.

8. A Beidou navigation and positioning optimization method according to claim 1, characterized in that, The obtaining the navigation positioning path of the vehicle at each moment according to the matching degree of the navigation trajectory includes: Obtain the maximum value of the matching degrees of the vehicle to all surrounding paths at each moment, and take the surrounding path corresponding to the maximum value as the navigation positioning path of the vehicle at each moment.

9. A Beidou navigation and positioning optimization system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1-8.

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