CN114819090A - A Beidou positioning data deviation correction method and deviation correction system - Google Patents

Abstract

The invention provides a Beidou positioning data deviation correction method and a deviation correction system, wherein the method comprises the following steps: constructing a neural tree structure based on high-precision road network lines, wherein the neural tree structure is a multi-level tree structure, and the high-precision road network lines are stored in the neural tree structure; searching the optimal matching road network line of the current Beidou positioning data in the neural tree structure; if the current Beidou positioning data needs to be corrected, calculating the corrected positioning data in a plane coordinate mode according to the optimal matching line, the current Beidou positioning data and the previous Beidou positioning data. The invention utilizes the neural network structure to store high-precision road network data, improves the data query efficiency, and adopts multi-thread parallel search and correction calculation to solve the problem of real-time positioning and correction.

Description

A Beidou positioning data deviation correction method and deviation correction system

technical field

The invention relates to the field of positioning data deviation correction, and more particularly, to a Beidou positioning data deviation correction method and a deviation correction system.

Background technique

During the driving of the vehicle, the driving route usually deviates. Therefore, correction is required, that is, the positioning data of the vehicle is corrected.

The current common deviation correction method is, for example, the application number is CN201810675702.8, the name is a grid-based Beidou GPS vehicle trajectory management system and its method patent, through the average quantization division and grid preprocessing of each Grid information, and use the grid center coordinates as an index to generate a hash table of the intermediate data of the road section, and finally realize the correction of the trajectory data through the relevant process processing.

The main disadvantages of using this method for deviation correction are:

1. The accuracy of the sample data is low. Directly using the road network data of a certain area on the AutoNavi map has the problem of low storage data accuracy. In order to achieve the matching speed, the grid hash is used for full matching.

2. The matching speed is slow. Although the grid hash is formed by dilution as a whole, the full matching mode is adopted as a whole, and each point needs to be compared in turn, and the query efficiency is O(n).

SUMMARY OF THE INVENTION

Aiming at the technical problems existing in the prior art, the present invention provides a Beidou positioning data deviation correction method and a deviation correction system.

According to the first aspect of the present invention, there is provided a Beidou positioning data deviation correction method, including:

Building a neural tree structure based on high-precision road network lines, the neural tree structure is a multi-level tree structure, and high-precision road network lines are stored in the neural tree structure;

Find the optimal matching road network line of the current Beidou positioning data in the neural tree structure;

If the current Beidou positioning data needs to be corrected, according to the optimal matching line, the current Beidou positioning data and the previous Beidou positioning data, the corrected positioning data is calculated by plane coordinates.

On the basis of the above technical solutions, the present invention can also make the following improvements.

Optionally, the construction of a neural tree structure based on high-precision road network line data includes:

According to the longitude and latitude of each data point in the high-precision road network, the neural tree structure is constructed in ascending or descending order.

Optionally, according to the longitude and latitude of each data point in the high-precision road network line, the neural tree structure is constructed in an ascending order mode or a descending order mode, including:

For any data point in the high-precision road network line, let the longitude of the any data point be x and the latitude to be y, where x, y include multiple bits, x=[x1x2x3...xn], y= [y1y2y3...ym];

Based on the longitude x of any one of the data points, construct a first neural tree structure, the root node of the first neural tree structure is x1, the first one is used as a parent node, and the last one is used as a child node;

as well as,

Based on the latitude y of any one of the data points, a second neural tree structure is constructed. The root node of the second neural tree structure is y1, the first one is a parent node, and the last one is a child node.

Optionally, the searching for the optimal matching road network line data of the current Beidou positioning data in the neural tree structure includes:

According to the breadth-first or depth-first search algorithm, based on the current Beidou positioning data, search for a matching road network line in the neural tree structure;

If a matching road network line is found, the route line is used as the optimal matching road network line of the current Beidou positioning data;

If multiple matching road network lines are found, the distance between the current Beidou positioning data and each matching road network line is calculated, and the road network line with the smallest distance is taken as the optimal road network line of the current Beidou positioning data.

Optionally, according to the breadth-first or depth-first search algorithm, based on the current Beidou positioning data, the neural tree structure is searched for matching road network lines, including:

Get the longitude and latitude of the current Beidou positioning data;

Search for matching longitude data in the first neural tree structure based on the longitude of the current Beidou positioning data;

Based on the latitude of the current Beidou positioning data, searching for matching latitude data in the second neural tree structure;

According to the precision data and latitude data of the search match, get the current Beidou positioning data to search and match the road network line.

Optionally, take the road network line with the smallest distance as the optimal road network line of the current Beidou positioning data, and also include:

If there are multiple road network lines with the smallest distance, the optimal matching road network line of the previous Beidou positioning data is used as the optimal road network line of the current Beidou positioning data.

Optionally, determine whether the current Beidou positioning data needs to be corrected in the following manner:

If the distance between the current Beidou positioning data and its optimal matching road network line is greater than the set distance threshold, the current Beidou positioning data needs to be corrected; otherwise, the current Beidou positioning data does not need to be corrected.

Optionally, according to the optimal matching line, the current Beidou positioning data and the previous Beidou positioning data, calculating the corrected positioning data by plane coordinates, including:

According to the average driving speed and travel time of the previous Beidou positioning data to the current Beidou positioning data, and the azimuth angle of the current Beidou positioning data, the positioning data after the deviation correction of the current Beidou positioning data is calculated by plane coordinates, and the corrected positioning data is located in The optimal matching road network line.

Optionally, according to the average driving speed and travel time of the previous Beidou positioning data to the current Beidou positioning data and the azimuth angle of the current Beidou positioning data, the positioning data after the deviation correction of the current Beidou positioning data is calculated by plane coordinates, including:

表示前一个北斗定位数据G_i-1′(lat，lon)到当前北斗定位数据G_i′(lat，lon)的平均行驶速度，Δt_i表示前一个北斗定位数据G_i-1′(lat，lon)到当前北斗定位数据G_i′(lat，lon)的行驶时间，A_i表示当前北斗定位数据G_i′(lat，lon)的方位角。Among them, G _i (lat, lon) represents the positioning data after the current Beidou positioning data G ₁ '(lat, lon) is corrected, and P _j (lat, lon) represents the current Beidou positioning data G _i '(lat, lon) belongs to road network,

Represents the average travel speed from the previous Beidou positioning data G _i-1 '(lat, lon) to the current Beidou positioning data G _i '(lat, lon), Δt _i represents the previous Beidou positioning data G _i-1 '(lat, lon) to the travel time of the current Beidou positioning data G _i '(lat, lon), A _i represents the azimuth angle of the current Beidou positioning data G _i '(lat, lon).

According to a second aspect of the present invention, a Beidou positioning data deviation correction system is provided, including:

a building module for constructing a neural tree structure based on high-precision road network lines, the neural tree structure is a multi-level tree structure, and high-precision road network lines are stored in the neural tree structure;

A search module, used to search the optimal matching road network line of the current Beidou positioning data in the neural tree structure;

The deviation correction module is used to calculate the corrected positioning data by plane coordinates according to the optimal matching line, the current Beidou positioning data and the previous Beidou positioning data if the current Beidou positioning data needs to be corrected.

According to a third aspect of the present invention, an electronic device is provided, including a memory and a processor, wherein the processor is used to implement the steps of the Beidou positioning data deviation correction method when executing a computer management program stored in the memory.

According to a fourth aspect of the present invention, a computer-readable storage medium is provided, on which a computer management program is stored, and when the computer management program is executed by a processor, the steps of the Beidou positioning data deviation correction method are implemented.

The invention provides a Beidou positioning data deviation correction method and a deviation correction system, which utilizes a neural network structure to store high-precision road network data, improves data query efficiency, and adopts multi-thread parallel search and deviation correction calculation, which can solve the problem of real-time positioning and deviation correction.

Description of drawings

Fig. 1 is a kind of Beidou positioning data deviation correction method flow chart provided by the present invention;

Figure 2 is a schematic diagram of the road network line data of the high-precision map;

3 is a schematic diagram of a neural tree structure;

Fig. 4 is a schematic diagram of neural tree search;

Fig. 5 is a schematic diagram of correction of current Beidou positioning data;

6 is a schematic structural diagram of a Beidou positioning data deviation correction system provided by the present invention;

7 is a schematic diagram of the hardware structure of a possible electronic device provided by the present invention;

FIG. 8 is a schematic diagram of the hardware structure of a possible computer-readable storage medium provided by the present invention.

Detailed ways

The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and embodiments. The following examples are intended to illustrate the present invention, but not to limit the scope of the present invention.

Example 1

A method for correction of Beidou positioning data is shown in Figure 1. The method for correction mainly includes the following steps:

S1, based on high-precision road network lines, construct a neural tree structure, the neural tree structure is a multi-level tree structure, and high-precision road network lines are stored in the neural tree structure.

It can be understood that the data based on which the Beidou positioning data is corrected in the embodiment of the present invention is high-precision road network data, wherein the data source is high-precision road network data collected by high-precision equipment. The high-precision road network data is cleaned and processed by tools to form professional and unified GIS data, and road network points are formed by data import to form road network lines L _j , j∈N, N is the number of road network lines, and the formed high-precision map road network The schematic diagram of the line data can be seen in Figure 2.

Based on the acquired high-precision road network data, it is stored in the neural tree structure model. As an embodiment, based on the high-precision road network line data, constructing a neural tree structure includes: according to the data of each data point in the high-precision road network line The latitude and longitude construct the neural tree structure in ascending or descending order.

Wherein, constructing a neural tree structure according to the longitude and latitude of each data point in the high-precision road network line in ascending or descending order mode includes: for any data point in the high-precision road network line, set the longitude of any data point is x, and the latitude is y, where x and y include multiple bits, x=[x1x2x3...xn], y=[y1y2y3...ym]; based on the longitude x of any data point, construct the first neural tree structure, the root node of the first neural tree structure is x1, the first one is used as a parent node, and the last one is used as a child node; and, based on the latitude y of any data point, a second neural tree structure is constructed, and the first The root node of the two-neural tree structure is y1, the first one is the parent node, and the last one is the child node.

It can be understood that for high-precision road network data points, its coordinates include longitude coordinates and latitude coordinates. For each data point, its longitude can be expressed as x, and its latitude can be expressed as y, where x, y include multiple digits, x =[x1x2x3...xn], y=[y1y2y3...ym], please refer to Table 1 for the data coordinate form.

Table 1 High-precision road network data

For all high-precision road network data points, a neural tree structure (called the first neural tree structure) is constructed according to the longitude, and a neural tree structure (called the second neural tree structure) is constructed according to the latitude. The specific construction method can refer to the neural tree structure in Figure 3. For example, the longitude form of a certain high-precision data point is 112.491322144001860, and its 112 is the root node of the neural tree. Bits are child nodes, and finally the first neural tree structure is constructed. For nodes at the same level, the longitudes of different data points are stored in the nodes of the neural tree in ascending order from left to right or descending from left to right. For high-precision road networks The latitude of the data, using the same method to build a second neural tree structure.

S2: Find the optimal matching road network line of the current Beidou positioning data in the neural tree structure.

It can be understood that, for the real-time Beidou positioning data of the vehicle, it is first necessary to find which road network line the Beidou positioning data belongs to in the neural tree structure, that is, the optimal matching road network line of the current Beidou positioning data.

When searching specifically, searching for the best matching road network line data of the current Beidou positioning data in the neural tree structure, including: searching in the neural tree structure based on the current Beidou positioning data according to the breadth-first or depth-first search algorithm The matching road network line; if a matching road network line is found, the route line is used as the optimal matching road network line of the current Beidou positioning data; if multiple matching road network lines are found, the The distance between the current Beidou positioning data and each matching road network line, the road network line with the smallest distance is taken as the optimal road network line of the current Beidou positioning data.

Understandably, when searching for matching road network data in the neural tree structure based on the current Beidou positioning data, the search can be performed according to a certain search algorithm, for example, the search can be performed according to a breadth-first search algorithm or a depth-first search algorithm.

Specifically, according to the breadth-first or depth-first search algorithm, based on the current Beidou positioning data, search for matching road network lines in the neural tree structure, including: obtaining the longitude and latitude of the current Beidou positioning data; based on the longitude of the current Beidou positioning data, Searching for matching longitude data in the first neural tree structure; searching for matching latitude data in the second neural tree structure based on the latitude of the current Beidou positioning data; obtaining the current matching accuracy data and latitude data according to the search Beidou positioning data searches for matching road network lines.

Understandably, obtain the current Beidou positioning data of the vehicle, including its longitude data and latitude data, search in the first neural tree structure based on the longitude data, and find the matching longitude data; and based on the latitude data in the second neural tree structure. Find matching latitude data in . According to the found matching longitude data and matching latitude data, obtain the road network line data matching the current Beidou positioning data. Referring to FIG. 4 , it is a data search process, in which, when the current Beidou positioning data of multiple vehicles needs to be searched at the same time, a multi-thread and multi-process parallel search method can be used to search to improve the efficiency of data search.

Among them, if the current Beidou positioning data can only find one matching road network line in the neural tree structure, the road network line is the optimal matching road network line of the current Beidou positioning data.

If the current Beidou positioning data finds multiple matching road network lines in the neural tree structure, then calculate the distance between the current Beidou positioning data and each matching road network line, which can be understood as calculating the current Beidou positioning data to each match. The vertical distance of the road network line is determined, and the road network line with the shortest distance is found, and the road network line with the shortest distance is the optimal matching road network line for the current Beidou positioning data.

It should be noted that if there are multiple road network lines with the shortest distance, that is, the distances from the current Beidou positioning data to multiple road network lines are equal and the shortest, then, at this time, you can refer to the previous Beidou positioning data, The optimal matching road network line of the previous Beidou positioning data is used as the optimal matching road network line of the current Beidou positioning data.

S3, if the current Beidou positioning data needs to be corrected, according to the optimal matching line, the current Beidou positioning data and the previous Beidou positioning data, calculate the corrected positioning data by means of plane coordinates.

It is understandable that when the optimal matching road network line of the current Beidou positioning data is found in the neural tree structure, it is judged whether the current Beidou positioning data needs to be corrected. Correction: If the distance between the current Beidou positioning data and its optimal matching road network line is greater than the set distance threshold, for example, the vertical distance between the current Beidou positioning data and its optimal matching road network line is greater than 20m, the current Beidou positioning data needs to be corrected; less than 20m , then within the error range, the current Beidou positioning data does not need to be corrected.

If the current Beidou positioning data needs to be corrected, according to the optimal matching line, the current Beidou positioning data and the previous Beidou positioning data, calculate the corrected positioning data by plane coordinates, including: according to the previous Beidou positioning data to the current Beidou The average travel speed, travel time of the positioning data, and the azimuth of the current Beidou positioning data, and the corrected positioning data of the current Beidou positioning data is calculated by plane coordinates, and the corrected positioning data is located on the optimal matching road network line .

It can be understood that, referring to Fig. 5, for the current Beidou positioning data G _i ', if the current Beidou positioning data needs to be corrected, then according to the previous Beidou positioning data G _i-1 ' and the current Beidou positioning data G _i ' Vehicles Average driving speed, vehicle travel time Δt _i of the previous Beidou positioning data G _i-1 ' and current Beidou positioning data G _i ', and the azimuth angle of the current Beidou positioning data G _i ', calculate the current Beidou positioning data G _i ' for deviation correction After the positioning data G _i , that is, correct the current Beidou positioning data G _i ' to G _i , and complete the deviation correction processing of the current Beidou positioning data.

Specifically, the corrected positioning data G _i can be obtained by calculating the average speed and azimuth angle of the last unit time by means of plane coordinates, which can be expressed by the following formula:

表示前一个北斗定位数据G_i-1′(lat，lon)到当前北斗定位数据G_i′(lat，lon)的平均行驶速度，Δt_i表示前一个北斗定位数据G_i-1′(lat，lon)到当前北斗定位数据G_i′(lat，lon)的行驶时间，A_i表示当前北斗定位数据G_i′(lat，lon)的方位角。Among them, G _i (lat, lon) represents the positioning data of the current Beidou positioning data G _i '(lat, lon) after the deviation correction, P _j (lat, lon) represents the current Beidou positioning data G _i '(lat, lon) belongs to road network,

Represents the average travel speed from the previous Beidou positioning data G _i-1 '(lat, lon) to the current Beidou positioning data G _i '(lat, lon), Δt _i represents the previous Beidou positioning data G _i-1 '(lat, lon) to the travel time of the current Beidou positioning data G _i '(lat, lon), A _i represents the azimuth angle of the current Beidou positioning data G _i '(lat, lon).

Embodiment 2

A Beidou positioning data deviation correction system, see FIG. 6, the deviation correction system includes a building module 61, a search module 62 and a deviation correction module 63, wherein:

The building module 61 is used to construct a neural tree structure based on high-precision road network lines, the neural tree structure is a multi-level tree structure, and high-precision road network lines are stored in the neural tree structure;

A search module 62 is used to search the optimal matching road network line of the current Beidou positioning data in the neural tree structure;

The deviation correction module 63 is configured to calculate the corrected positioning data by plane coordinates according to the optimal matching line, the current Beidou positioning data and the previous Beidou positioning data if the current Beidou positioning data needs to be corrected.

It can be understood that a Beidou positioning data deviation correction system provided by the present invention corresponds to the Beidou positioning data deviation correction method provided by the foregoing embodiments, and the relevant technical features of the Beidou positioning data deviation correction system can refer to the related technology of the Beidou positioning data deviation correction method. features, which are not repeated here.

Embodiment 3

Please refer to FIG. 7 , which is a schematic diagram of an embodiment of an electronic device provided by an embodiment of the present invention. As shown in FIG. 7 , an embodiment of the present invention provides an electronic device 700, including a memory 710, a processor 720, and a computer program 711 stored in the memory 710 and running on the processor 720, and the processor 720 executes the computer program Step 711 is to implement the Beidou positioning data deviation correction method of the first embodiment.

Embodiment 4

Please refer to FIG. 8 , which is a schematic diagram of an embodiment of a computer-readable storage medium provided by the present invention. As shown in FIG. 8 , this embodiment provides a computer-readable storage medium 800 on which a computer program 811 is stored, and when the computer program 811 is executed by a processor, implements the steps of the Beidou positioning data deviation correction method of the first embodiment.

The Beidou positioning data deviation correction method and deviation correction system provided by the embodiments of the present invention have the following advantages:

(1) The neural network structure is used to store high-precision road network data, which improves the data query efficiency, and the multi-thread parallel search and deviation correction calculation can solve the problem of real-time positioning and deviation correction;

(2) In the process of data search, when multiple matching road network lines are found, the optimal matching road network lines are obtained in a certain way, which improves the accuracy of the deviation correction data basis;

(3) The data deviation correction is based on the high-precision road network data collected by the high-precision equipment. Due to the high precision of the high-precision road network data, the data deviation correction accuracy of the present invention is significantly improved, and after the deviation correction, it is aimed at turning and jumping. Points are a good improvement.

It should be noted that, in the foregoing embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each process and/or block in the flowchart illustrations and/or block diagrams, and combinations of processes and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to the processor of a general purpose computer, special purpose computer, embedded computer or other programmable data processing device to produce a machine such that the instructions executed by the processor of the computer or other programmable data processing device produce Means for implementing the functions specified in one or more of the flowcharts and/or one or more blocks of the block diagrams.

These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory result in an article of manufacture comprising instruction means, the instructions The apparatus implements the functions specified in the flow or flow of the flowcharts and/or the block or blocks of the block diagrams.

These computer program instructions can also be loaded on a computer or other programmable data processing device to cause a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process such that The instructions provide steps for implementing the functions specified in the flow or blocks of the flowcharts and/or the block or blocks of the block diagrams.

Although preferred embodiments of the present invention have been described, additional changes and modifications to these embodiments may occur to those skilled in the art once the basic inventive concepts are known. Therefore, the appended claims are intended to be construed to include the preferred embodiment and all changes and modifications that fall within the scope of the present invention.

It will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the spirit and scope of the invention. Thus, provided that these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.

Claims (10)

1. a Beidou positioning data deviation correction method, is characterized in that, comprises: Building a neural tree structure based on high-precision road network lines, the neural tree structure is a multi-level tree structure, and high-precision road network lines are stored in the neural tree structure; Find the optimal matching road network line of the current Beidou positioning data in the neural tree structure; If the current Beidou positioning data needs to be corrected, according to the optimal matching line, the current Beidou positioning data and the previous Beidou positioning data, the corrected positioning data is calculated by plane coordinates. 2. The method for correcting deviation of Beidou positioning data according to claim 1, wherein the described construction of a neural tree structure based on high-precision road network line data comprises: According to the longitude and latitude of each data point in the high-precision road network, the neural tree structure is constructed in ascending or descending order. 3. Beidou positioning data deviation correction method according to claim 2, is characterized in that, described according to the longitude and latitude of each data point in the high-precision road network line according to ascending order pattern or descending order pattern builds neural tree structure, comprises: For any data point in the high-precision road network line, let the longitude of the any data point be x and the latitude to be y, where x, y include multiple bits, x=[x1x2x3...xn], y= [y1y2y3...ym]; Based on the longitude x of any one of the data points, construct a first neural tree structure, the root node of the first neural tree structure is x1, the first one is used as a parent node, and the last one is used as a child node; as well as, Based on the latitude y of any one of the data points, a second neural tree structure is constructed. The root node of the second neural tree structure is y1, the first one is a parent node, and the last one is a child node. 4. The method for correcting deviation of Beidou positioning data according to claim 3, characterized in that, searching for the optimal matching road network line data of current Beidou positioning data in the neural tree structure, comprising: According to the breadth-first or depth-first search algorithm, based on the current Beidou positioning data, search for a matching road network line in the neural tree structure; If a matching road network line is found, the route line is used as the optimal matching road network line of the current Beidou positioning data; If multiple matching road network lines are found, the distance between the current Beidou positioning data and each matching road network line is calculated, and the road network line with the smallest distance is taken as the optimal road network line of the current Beidou positioning data. 5. The Beidou positioning data deviation correction method according to claim 4, wherein, according to the breadth-first or depth-first search algorithm, based on the current Beidou positioning data, the neural tree structure is searched for matching road network lines, include: Get the longitude and latitude of the current Beidou positioning data; Search for matching longitude data in the first neural tree structure based on the longitude of the current Beidou positioning data; Based on the latitude of the current Beidou positioning data, searching for matching latitude data in the second neural tree structure; According to the precision data and latitude data of the search match, get the current Beidou positioning data to search and match the road network line. 6. The Beidou positioning data deviation correction method according to claim 4, wherein the road network line with the smallest distance is used as the optimal road network line of the current Beidou positioning data, further comprising: If there are multiple road network lines with the smallest distance, the optimal matching road network line of the previous Beidou positioning data is used as the optimal road network line of the current Beidou positioning data. 7. Big Dipper positioning data deviation correction method according to claim 1, is characterized in that, whether described current Big Dipper positioning data needs deviation correction is judged as follows: If the distance between the current Beidou positioning data and its optimal matching road network line is greater than the set distance threshold, the current Beidou positioning data needs to be corrected; otherwise, the current Beidou positioning data does not need to be corrected. 8. The method for correcting the deviation of Beidou positioning data according to claim 1 or 7, characterized in that, according to the optimal matching line, the current Beidou positioning data and the previous Beidou positioning data, calculate the corrected deviation by plane coordinates. Location data, including: According to the average driving speed and travel time of the previous Beidou positioning data to the current Beidou positioning data, and the azimuth angle of the current Beidou positioning data, the positioning data after the deviation correction of the current Beidou positioning data is calculated by plane coordinates, and the corrected positioning data is located in The optimal matching road network line. 9. Big Dipper positioning data deviation correction method according to claim 8, is characterized in that, described according to previous Big Dipper positioning data to the azimuth angle of average travel speed of current Big Dipper positioning data, travel time and current Big Dipper positioning data, by plane The coordinate method calculates the positioning data after the current Beidou positioning data has been corrected, including:

Among them, G _i (lat, lon) represents the positioning data of the current Beidou positioning data G _i '(lat, lon) after the deviation correction, P _j (lat, lon) represents the current Beidou positioning data G _i '(lat, lon) belongs to road network,

Represents the average travel speed from the previous Beidou positioning data G _i-1 '(lat, lon) to the current Beidou positioning data G _i '(lat, lon), Δt _i represents the previous Beidou positioning data G _i-1 '(lat, lon) to the travel time of the current Beidou positioning data G _i '(lat, lon), A _i represents the azimuth angle of the current Beidou positioning data G _i '(lat, lon). 10. A Beidou positioning data deviation correction system, characterized in that, comprising: a building module for constructing a neural tree structure based on high-precision road network lines, the neural tree structure is a multi-level tree structure, and high-precision road network lines are stored in the neural tree structure; A search module, used to search the optimal matching road network line of the current Beidou positioning data in the neural tree structure; The deviation correction module is used to calculate the corrected positioning data by plane coordinates according to the optimal matching line, the current Beidou positioning data and the previous Beidou positioning data if the current Beidou positioning data needs to be corrected.

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