Path planning method and system based on Beidou satellite positioning

Abstract

The invention discloses a path planning method and system based on Beidou satellite positioning, and relates to the technical field of Beidou satellite positioning, and the method comprises the steps: carrying out the high-precision positioning and error correction of Beidou satellite signals through employing a multi-source sensor fusion method, and obtaining the position information; matching the electronic map data with the position information by adopting an electronic map matching method to obtain electronic map data corresponding to the current position; constructing a traffic flow prediction model based on the position information and a machine learning algorithm, and inputting the position information into the traffic flow prediction model to obtain a traffic condition prediction result; based on the traffic condition prediction result, planning a path from the current position to the destination by adopting a path search algorithm to obtain a preliminary path; performing personalized adjustment on the initial path according to user preference setting to obtain a recommended path; and monitoring the final recommended path by adopting a real-time monitoring and feedback mechanism, evaluating the position change and traffic condition in the path, and obtaining a final path planning strategy.

Description

Technical Field

[0001] The present invention relates to the field of Beidou satellite positioning technology, and in particular to a path planning method and system based on Beidou satellite positioning. Background Art

[0002] BeiDou Navigation Satellite System (BDS) is a global satellite navigation system independently developed by China, providing high-precision position and time information. It can provide users with all-weather, all-day, high-precision positioning, navigation and timing services around the world. The BeiDou system consists of three parts: the space segment, the ground segment and the user segment. The space segment includes multiple geostationary orbit satellites, inclined geosynchronous orbit satellites and medium earth orbit satellites.

[0003] In the field of Beidou satellite positioning, existing electronic map matching algorithms are sometimes unable to accurately map the actual position of a vehicle onto a digital map, resulting in path planning errors. In complex urban environments, such as urban canyons or tunnels with tall buildings, traditional satellite positioning signals are easily interfered with, resulting in reduced positioning accuracy. At the same time, traditional traffic flow prediction models are difficult to accurately predict future traffic conditions, which seriously affects the safety of the method during use. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a path planning method based on Beidou satellite positioning to solve the problem that the existing electronic map matching algorithm sometimes cannot accurately map the actual position of the vehicle to the digital map, resulting in path planning errors, and in complex urban environments, such as urban canyons or tunnels with high-rise buildings, traditional satellite positioning signals are easily interfered, resulting in a decrease in positioning accuracy.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a path planning method based on Beidou satellite positioning, which comprises: The multi-source sensor fusion method is used to perform high-precision positioning and error correction on Beidou satellite signals to obtain position information; Using an electronic map matching method to match the electronic map data with the location information to obtain the electronic map data corresponding to the current location; A traffic flow prediction model is constructed based on location information and machine learning algorithms, and the location information is input into the traffic flow prediction model to obtain traffic condition prediction results; Based on the traffic condition prediction results, a path search algorithm is used to plan the path from the current location to the destination to obtain a preliminary path; The preliminary path is personalized according to the user's preference settings to obtain a recommended path; A real-time monitoring and feedback mechanism is used to monitor the final recommended path, evaluate the location changes and traffic conditions in the path, and obtain the final path planning strategy.

[0008] As a preferred solution of the path planning method based on Beidou satellite positioning described in the present invention, wherein: the multi-source sensor fusion method is used to perform high-precision positioning and error correction on Beidou satellite signals to obtain position information, and the specific steps are as follows: Receive current position information from Beidou satellites and simultaneously obtain acceleration and angular velocity data provided by the inertial navigation system IMU; Use the inertial navigation system IMU data to predict the state at the next moment; The state includes position, velocity and attitude; Use the position provided by BeiDou satellites to correct the predicted status; The position information is obtained by separating the position component from the updated state vector by extracting the matrix .

[0009] As a preferred solution of the path planning method based on Beidou satellite positioning of the present invention, wherein: the electronic map matching method is used to match the electronic map data with the position information to obtain the electronic map data corresponding to the current position, and the specific steps are: Load electronic map data covering the current location; The electronic map data includes geographical information of road networks, intersections and buildings; Define a point in the electronic map; Calculate the distance between the current position and each candidate point on the electronic map. The expression is: ; in, Indicates the current position coordinates, Indicates the coordinates of a point on an electronic map. , , The current location is , , The coordinates on the axis, , , They are the candidate points on the electronic map. , , Coordinates on the axis; Select the point with the smallest distance as the best matching point for the current position ; Use dynamic window technology DWA to predict the driving trajectory in the future and compare it with the road information on the electronic map; The predicted driving trajectory is defined as , and calculate the similarity between it and each road segment on the electronic map , the expression is: ; in, represents the predicted driving trajectory, represents the road segment on the electronic map, and the integral interval is the prediction time period. Indicates at time When The specific location or status of Indicates at time When the road section The specific location or status of Select the road segment with the highest similarity as the best matching road segment for the current location; The location information obtained by multi-source sensor fusion is matched with the electronic map data to determine the exact location and road information of the current location on the electronic map.

[0010] As a preferred solution of the path planning method based on Beidou satellite positioning described in the present invention, wherein: the traffic flow prediction model is constructed based on the location information and the machine learning algorithm, and the location information is input into the traffic flow prediction model to obtain the traffic condition prediction result, and the specific steps are as follows: Use long short-term memory network LSTM as the basis of machine learning algorithm; The traffic flow prediction model is constructed by combining the precise location information obtained from the multi-source sensor fusion method and the road sections determined by the electronic map matching method; Input the collected historical traffic data into the traffic flow prediction model The training is performed in , and the expression is: ; in, is the sample size, , , are the actual vehicle speed, vehicle density and congestion probability respectively, , , are the predicted vehicle speed, vehicle density and congestion probability respectively; Use the trained traffic flow prediction model to predict the current location and the road section where it is located Predict future traffic conditions and obtain specific traffic condition prediction results , the expression is: ; in, represents the future traffic conditions predicted by the traffic flow prediction model, is the current vehicle speed, is the vehicle density, is the current congestion probability, It is the current time information.

[0011] As a preferred solution of the path planning method based on Beidou satellite positioning of the present invention, wherein: based on the traffic condition prediction result, a path search algorithm is used to plan the path between the current position and the destination to obtain a preliminary path, and the specific steps are: Prediction results based on traffic conditions , calculate the dynamic weight of each road segment , reflects the current road traffic efficiency, the expression is: ; in, represents the predicted vehicle speed, represents the predicted congestion probability, is a tuning parameter, is the dynamic weight of each road segment; Loading electronic map data covering the range from the current location to the destination; The electronic map data within the range from the current location to the destination includes geographical information of road networks, intersections and obstacles, and defines the nodes in the electronic map as ; A-star algorithm is used as the path search algorithm, combined with dynamic weight Plan the path from the current location to the destination. The expression is: ; ; in, represents the actual cost from the starting point to the current node, represents the estimated cost from the current node to the target node after taking into account the dynamic weights, represents the original heuristic function, is the cost function; Initialize the data structure of the A-star algorithm; The data structure includes an open list and a closed list; Start the iterative search process by selecting the one with the lowest cost from the open list Node , and check whether the node is the target node; If not, move it from the open list to the closed list and expand all of the node's neighbor nodes; If the neighbor node is not in the open list, add it to the open list and set its parent node to the current node. ; If the neighbor node is already in the open list and the new cost is lower, update its cost and parent node; Repeat the above process until the target node is found or the open list is empty; By backtracking the parent node link of the target node, the optimal path from the current location to the destination is obtained. , the expression is: ; ; in, is the optimal path, is the target node, Is a node The parent node of is the starting point, is the operation of reversing the path sequence, It is the backtracking function.

[0012] As a preferred solution of the path planning method based on Beidou satellite positioning of the present invention, wherein: the preliminary path is personalized adjusted according to the user preference settings to obtain the recommended path, and the specific steps are: Define the user's preference vector and calculate the feature score vector for each path; The characteristic score vector includes highway usage, landscape score, and number of traffic lights; Define a personalized cost function to evaluate the cost value of each road segment after considering user preferences; Initialize the adjusted path set and initially set it as the preliminary path; Start the iterative adjustment process for the optimal path A section of , calculate its personalized adjusted cost, compare it with the adjacent optional sections, and update the path set , the expression is: ; Repeat the above process until all sections are traversed or no better alternative section can be found. Is the optimal path A section of the road, It is adjusted to replace new sections of roads; The set of paths adjusted by backtracking , and obtain the recommended path that meets the user's preferences.

[0013] The real-time monitoring and feedback mechanism is used to monitor the final recommended path, evaluate the location changes and traffic conditions in the path, and obtain the final path planning strategy. The specific steps are as follows: Receive current location information from the BeiDou satellite system , and compare it with the expected position on the recommended path to calculate the deviation, the expression is: ; in, Represents the expected position coordinates on the recommended path at the current time point, is the current location information, is the real-time position deviation; Use the short message function of the Beidou system to collect updated data on traffic conditions along the way; According to the real-time position deviation and traffic conditions to assess whether rerouting is necessary; When there is a significant deviation or new traffic problems are encountered, the route replanning process is triggered; Define the threshold judgment function , decide whether to re-plan the path, the expression is: ; in, is the maximum position deviation threshold allowed, It is the traffic status update data; The updated route information is fed back to the user, and real-time monitoring continues until the destination is reached.

[0014] In a second aspect, the present invention provides a path planning system based on Beidou satellite positioning, comprising: Positioning module, electronic map matching module, traffic flow prediction module, path search module, personalized adjustment module and monitoring module; The positioning module is used to perform high-precision positioning and error correction on Beidou satellite signals by adopting a multi-source sensor fusion method to obtain position information; The electronic map matching module is used to match the electronic map data with the location information using an electronic map matching method to obtain the electronic map data corresponding to the current location; The traffic flow prediction module is used to build a traffic flow prediction model based on location information and a machine learning algorithm, and input the location information into the traffic flow prediction model to obtain a traffic condition prediction result; The path search module is used to plan the path from the current location to the destination using a path search algorithm based on the traffic condition prediction result to obtain a preliminary path; The personalized adjustment module is used to perform personalized adjustment on the preliminary path according to the user's preference settings to obtain a recommended path; The monitoring module is used to monitor the final recommended path using a real-time monitoring and feedback mechanism, evaluate the position changes and traffic conditions in the path, and obtain the final path planning strategy.

[0015] In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the path planning method based on Beidou satellite positioning as described in the first aspect of the present invention is implemented.

[0016] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the path planning method based on Beidou satellite positioning as described in the first aspect of the present invention.

[0017] The beneficial effects of the present invention are as follows: by adopting a multi-source sensor fusion method to perform high-precision positioning and error correction on Beidou satellite signals, accurate location information acquisition is achieved, which not only improves the accuracy of location data, but also enhances the robustness and reliability of the system, making subsequent path planning more reliable and efficient; by adopting an electronic map matching method to match electronic map data with location information, accurate positioning of the current location on the electronic map is achieved; through accurate map matching, the accuracy of the starting point and end point of path planning is improved, and route errors caused by map errors are reduced; a traffic flow prediction model is constructed based on location information and a machine learning algorithm, and the location information is input into the traffic flow prediction model, so as to achieve prediction of future traffic conditions; the prediction capability enhances the adaptability and flexibility of the method, and can quickly respond to the impact of emergencies such as traffic accidents or road construction. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0019] Figure 1 This is a flow chart of the path planning method based on Beidou satellite positioning in Example 1.

[0020] Figure 2This is a schematic diagram of a path planning system based on Beidou satellite positioning in Example 1. DETAILED DESCRIPTION

[0021] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0022] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0023] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0024] Example 1, reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, provides a path planning method based on Beidou satellite positioning, comprising the following steps:

[0025] S1. Use multi-source sensor fusion method to perform high-precision positioning and error correction on Beidou satellite signals to obtain location information; Furthermore, the current position information is received from the BeiDou satellite, and the acceleration and angular velocity data provided by the inertial navigation system IMU are simultaneously obtained; Use the inertial navigation system IMU data to predict the state at the next moment; The state includes position, velocity and attitude; Use the position provided by BeiDou satellites to correct the predicted status; The position information is obtained by separating the position component from the updated state vector by extracting the matrix ; It should be noted that by combining Beidou satellite signals and IMU data, not only the positioning accuracy is improved, but also the robustness of the system is enhanced. In urban canyons, tunnels and other places where satellite signals are unstable or missing, IMU data can provide continuous position updates, ensuring that the navigation system can maintain high accuracy and reliability in various complex environments.

[0026] S2, using an electronic map matching method to match the electronic map data with the location information to obtain electronic map data corresponding to the current location; Furthermore, electronic map data covering the current location is loaded; Electronic map data contains geographic information of road networks, intersections, and buildings; Define a point in the electronic map; Calculate the distance between the current position and each candidate point on the electronic map. The expression is: ; in, Indicates the current position coordinates, Indicates the coordinates of a point on an electronic map. , , The current location is , , The coordinates on the axis, , , They are the candidate points on the electronic map. , , Coordinates on the axis; Select the point with the smallest distance as the best matching point for the current position ; Use dynamic window technology DWA to predict the driving trajectory in the future and compare it with the road information on the electronic map; The predicted driving trajectory is defined as , and calculate the similarity between it and each road segment on the electronic map , the expression is: ; in, represents the predicted driving trajectory, represents the road segment on the electronic map, and the integral interval is the prediction time period. Indicates at time When The specific location or status of Indicates at time When the road section The specific location or status of Select the road segment with the highest similarity as the best matching road segment for the current location; Match the location information obtained by multi-source sensor fusion with the electronic map data to determine the accurate location and road information of the current location on the electronic map; It should be noted that the electronic map matching step not only solves the mapping problem from physical location to digital map, but also predicts future driving trajectory through dynamic window technology DWA, thereby improving the foresight and accuracy of path planning. This method can effectively reduce route errors caused by map errors and provide users with more accurate navigation suggestions.

[0027] S3, building a traffic flow prediction model based on the location information and the machine learning algorithm, and inputting the location information into the traffic flow prediction model to obtain a traffic condition prediction result; Furthermore, Long Short-Term Memory Network (LSTM) is used as the basis of the machine learning algorithm; The traffic flow prediction model is constructed by combining the precise location information obtained from the multi-source sensor fusion method and the road sections determined by the electronic map matching method; Input the collected historical traffic data into the traffic flow prediction model The training is performed in , and the expression is: ; in, is the sample size, , , are the actual vehicle speed, vehicle density and congestion probability respectively, , , are the predicted vehicle speed, vehicle density and congestion probability respectively; Use the trained traffic flow prediction model to predict the current location and the road section where it is located Predict future traffic conditions and obtain specific traffic condition prediction results , the expression is: ; in, represents the future traffic conditions predicted by the traffic flow prediction model, is the current vehicle speed, is the vehicle density, is the current congestion probability, is the current time information; It should be noted that the use of long short-term memory networks (LSTMs) for traffic flow prediction can not only process long-term dependencies in historical data, but also adjust the prediction model according to real-time location information, so that the method can more accurately predict future traffic flow, speed, and congestion, helping users avoid peak hours or sections, thereby reducing travel time and improving travel efficiency.

[0028] S4, based on the traffic condition prediction result, a path search algorithm is used to plan the path from the current location to the destination to obtain a preliminary path; Furthermore, the traffic conditions are used to predict the results. , calculate the dynamic weight of each road segment , reflects the current road traffic efficiency, the expression is: ; in, represents the predicted vehicle speed, represents the predicted congestion probability, is a tuning parameter, is the dynamic weight of each road segment; Loading electronic map data covering the range from the current location to the destination; The electronic map data from the current location to the destination includes geographical information of road networks, intersections and obstacles, and defines the nodes in the electronic map as ; A-star algorithm is used as the path search algorithm, combined with dynamic weight Plan the path from the current location to the destination. The expression is: ; ; in, represents the actual cost from the starting point to the current node, represents the estimated cost from the current node to the target node after taking into account the dynamic weights, represents the original heuristic function, is the cost function; Initialize the data structure of the A-star algorithm; Data structures include open lists and closed lists; Start the iterative search process by selecting the one with the lowest cost from the open list Node , and check whether the node is the target node; If not, move it from the open list to the closed list and expand all of the node's neighbor nodes; If the neighbor node is not in the open list, add it to the open list and set its parent node to the current node. ; If the neighbor node is already in the open list and the new cost is lower, update its cost and parent node; Repeat the above process until the target node is found or the open list is empty; By backtracking the parent node link of the target node, the optimal path from the current location to the destination is obtained. , the expression is: ; ; in, is the optimal path, is the target node, Is a node The parent node of is the starting point, is the operation of reversing the path sequence, is the backtracking operation function; It should be noted that by calculating the dynamic weight of each road section and combining the A-star algorithm for path planning, the system can flexibly respond to real-time changing traffic conditions. The dynamic weight mechanism not only takes into account the current road traffic efficiency, but also continuously adjusts the optimal path based on the latest traffic information to ensure that users always follow the most appropriate route.

[0029] S5, personalizing the preliminary path according to the user's preference settings to obtain a recommended path; Furthermore, the user's preference vector is defined, and the feature score vector of each path is calculated; The feature score vector includes highway usage, landscape score, and number of traffic lights; Define a personalized cost function to evaluate the cost value of each road segment after considering user preferences; Initialize the adjusted path set and initially set it as the preliminary path; Start the iterative adjustment process for the optimal path A section of , calculate its personalized adjusted cost, compare it with the adjacent optional sections, and update the path set , the expression is: ; Repeat the above process until all sections are traversed or no better alternative section can be found. Is the optimal path A section of the road, It is adjusted to replace new sections of roads; The set of paths adjusted by backtracking , get the recommended path that meets the user's preferences; It should be noted that the personalized adjustment module achieves highly customized route recommendations by defining the user's preference setting vector and evaluating the cost value of each road section in combination with the characteristic scoring vector. This method can not only meet the special needs of different users, but also improve user experience and satisfaction, making each trip more in line with personal preferences.

[0030] S6. Use real-time monitoring and feedback mechanism to monitor the final recommended path, evaluate the location changes and traffic conditions in the path, and obtain the final path planning strategy; Furthermore, the current location information is received from the BeiDou satellite system , and compare it with the expected position on the recommended path to calculate the deviation, the expression is: ; in, Represents the expected position coordinates on the recommended path at the current time point, is the current location information, is the real-time position deviation; Use the short message function of the Beidou system to collect updated data on traffic conditions along the way; According to the real-time position deviation and traffic conditions to assess whether rerouting is necessary; When there is a significant deviation or new traffic problems are encountered, the route replanning process is triggered; Define the threshold judgment function , decide whether to re-plan the path, the expression is: ; in, is the maximum position deviation threshold allowed, It is the traffic status update data; Feedback the updated route information to the user and continue to perform real-time monitoring until the destination is reached; It should be noted that the real-time monitoring and feedback mechanism ensures the timeliness and accuracy of route planning by continuously comparing the current position with the expected position and dynamically adjusting it according to the latest traffic condition update data. The mechanism can respond quickly to emergencies and re-plan the route when necessary to ensure that the user is always on the optimal path, which greatly improves the reliability of the method and user experience.

[0031] This embodiment also provides a path planning system based on Beidou satellite positioning, including: Positioning module, electronic map matching module, traffic flow prediction module, path search module, personalized adjustment module and monitoring module; The positioning module is used to perform high-precision positioning and error correction on Beidou satellite signals using a multi-source sensor fusion method to obtain position information; An electronic map matching module is used to match the electronic map data with the location information using an electronic map matching method to obtain the electronic map data corresponding to the current location; Traffic flow prediction module, which is used to build a traffic flow prediction model based on location information and machine learning algorithms, and input location information into the traffic flow prediction model to obtain traffic condition prediction results; The path search module is used to plan the path from the current location to the destination using a path search algorithm based on the traffic condition prediction results to obtain a preliminary path; A personalized adjustment module is used to perform personalized adjustments on the preliminary path according to user preference settings to obtain a recommended path; The monitoring module is used to monitor the final recommended path using a real-time monitoring and feedback mechanism, evaluate the location changes and traffic conditions in the path, and obtain the final path planning strategy.

[0032] This embodiment also provides a computer device, which is applicable to the path planning method based on Beidou satellite positioning, including: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the path planning method based on Beidou satellite positioning as proposed in the above embodiment.

[0033] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through Wi-Fi, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0034] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by the processor, the path planning method based on Beidou satellite positioning as proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0035] In summary, the present invention achieves accurate location information acquisition by adopting a multi-source sensor fusion method to perform high-precision positioning and error correction on Beidou satellite signals, which not only improves the accuracy of location data, but also enhances the robustness and reliability of the system, making subsequent path planning more reliable and efficient. By adopting an electronic map matching method to match electronic map data with location information, the accurate positioning of the current position on the electronic map is achieved. Through accurate map matching, the accuracy of the starting point and end point of path planning is improved, and the route errors caused by map errors are reduced. A traffic flow prediction model is constructed based on location information and a machine learning algorithm, and the location information is input into the traffic flow prediction model, so that the prediction of future traffic conditions is achieved. The prediction capability enhances the adaptability and flexibility of the method, and can quickly respond to the impact of emergencies such as traffic accidents or road construction.

[0036] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A path planning method based on Beidou satellite positioning, characterized in that: include: The multi-source sensor fusion method is used to perform high-precision positioning and error correction on Beidou satellite signals to obtain position information; Using an electronic map matching method to match the electronic map data with the location information to obtain the electronic map data corresponding to the current location; A traffic flow prediction model is constructed based on location information and machine learning algorithms, and the location information is input into the traffic flow prediction model to obtain traffic condition prediction results; Based on the traffic condition prediction results, a path search algorithm is used to plan the path from the current location to the destination to obtain a preliminary path; The preliminary path is personalized according to the user's preference settings to obtain a recommended path; A real-time monitoring and feedback mechanism is used to monitor the final recommended path, evaluate the location changes and traffic conditions in the path, and obtain the final path planning strategy.

2. The path planning method based on Beidou satellite positioning as claimed in claim 1, characterized in that: The multi-source sensor fusion method is used to perform high-precision positioning and error correction on Beidou satellite signals to obtain position information. The specific steps are as follows: Receive current position information from Beidou satellites and simultaneously obtain acceleration and angular velocity data provided by the inertial navigation system IMU; Use the inertial navigation system IMU data to predict the state at the next moment; The state includes position, velocity and attitude; Use the position provided by BeiDou satellites to correct the predicted status; The position information is obtained by separating the position component from the updated state vector by extracting the matrix .

3. The path planning method based on Beidou satellite positioning as claimed in claim 2, characterized in that: The electronic map matching method is used to match the electronic map data with the location information to obtain the electronic map data corresponding to the current location. The specific steps are: Load electronic map data covering the current location; The electronic map data includes geographical information of road networks, intersections and buildings; Define a point in the electronic map; Calculate the distance between the current position and each candidate point on the electronic map. The expression is: ; in, Indicates the current position coordinates, Indicates the coordinates of a point on an electronic map. , , The current location is , , The coordinates on the axis, , , They are the candidate points on the electronic map. , , Coordinates on the axis; Select the point with the smallest distance as the best matching point for the current position ; Use dynamic window technology DWA to predict the driving trajectory in the future and compare it with the road information on the electronic map; The predicted driving trajectory is defined as , and calculate the similarity between it and each road segment on the electronic map , the expression is: ； in, represents the predicted driving trajectory, represents the road segment on the electronic map, and the integral interval is the prediction time period. Indicates at time Time prediction trajectory The specific location or status of Indicates at time Time road section The specific location or status of Select the road segment with the highest similarity as the best matching road segment for the current location; The location information obtained by multi-source sensor fusion is matched with the electronic map data to determine the exact location and road information of the current location on the electronic map.

4. The path planning method based on Beidou satellite positioning as claimed in claim 3, characterized in that: The traffic flow prediction model is constructed based on the location information and the machine learning algorithm, and the location information is input into the traffic flow prediction model to obtain the traffic condition prediction result. The specific steps are as follows: Use long short-term memory network LSTM as the basis of machine learning algorithm; The traffic flow prediction model is constructed by combining the precise location information obtained from the multi-source sensor fusion method and the road sections determined by the electronic map matching method; Input the collected historical traffic data into the traffic flow prediction model The training is performed in , and the expression is: ; in, is the sample size, , , are the actual vehicle speed, vehicle density and congestion probability respectively, , , are the predicted vehicle speed, vehicle density and congestion probability respectively; Use the trained traffic flow prediction model to predict the current location and the road section where it is located Predict future traffic conditions and obtain specific traffic condition prediction results , the expression is: ； in, represents the future traffic conditions predicted by the traffic flow prediction model, is the current vehicle speed, is the vehicle density, is the current congestion probability, It is the current time information.

5. The path planning method based on Beidou satellite positioning as claimed in claim 4, characterized in that: Based on the traffic condition prediction result, a path search algorithm is used to plan the path between the current location and the destination to obtain a preliminary path. The specific steps are as follows: Prediction results based on traffic conditions , calculate the dynamic weight of each road segment , reflects the current road traffic efficiency, the expression is: ; in, represents the predicted vehicle speed, represents the predicted congestion probability, is a tuning parameter, is the dynamic weight of each road segment; Loading electronic map data covering the range from the current location to the destination; The electronic map data within the range from the current location to the destination includes geographical information of road networks, intersections and obstacles, and defines the nodes in the electronic map as ; A-star algorithm is used as the path search algorithm, combined with dynamic weight Plan the path from the current location to the destination. The expression is: ； ； in, represents the actual cost from the starting point to the current node, represents the estimated cost from the current node to the target node after taking into account the dynamic weights, represents the original heuristic function, is the cost function; Initialize the data structure of the A-star algorithm; The data structure includes an open list and a closed list; Start the iterative search process by selecting the one with the lowest cost from the open list Node , and check whether the node is the target node; If not, move it from the open list to the closed list and expand all of the node's neighbor nodes; If the neighbor node is not in the open list, add it to the open list and set its parent node to the current node. ; If the neighbor node is already in the open list and the new cost is lower, update its cost and parent node; Repeat the above process until the target node is found or the open list is empty; By backtracking the parent node link of the target node, the optimal path from the current location to the destination is obtained. , the expression is: ； ； in, is the optimal path, is the target node, Is a node The parent node of is the starting point node, is the operation of reversing the path sequence, It is the backtracking function.

6. The path planning method based on Beidou satellite positioning as claimed in claim 5, characterized in that: The specific steps of performing personalized adjustment on the preliminary path according to the user preference setting to obtain the recommended path are as follows: Define the user's preference vector and calculate the feature score vector for each path; The characteristic score vector includes highway usage, landscape score, and number of traffic lights; Define a personalized cost function to evaluate the cost value of each road segment after considering user preferences; Initialize the adjusted path set and initially set it as the preliminary path; Start the iterative adjustment process for the optimal path A section of , calculate its personalized adjusted cost, compare it with the adjacent optional sections, and update the path set , the expression is: ； Repeat the above process until all sections are traversed or no better alternative section can be found. Is the optimal path A section of the road, It is adjusted to replace new sections of roads; The set of paths adjusted by backtracking , and obtain the recommended path that meets the user's preferences.

7. The path planning method based on Beidou satellite positioning as claimed in claim 6, characterized in that: The real-time monitoring and feedback mechanism is used to monitor the final recommended path, evaluate the location changes and traffic conditions in the path, and obtain the final path planning strategy. The specific steps are as follows: Receive current location information from the BeiDou satellite system , and compare it with the expected position on the recommended path to calculate the deviation, the expression is: ； in, Represents the expected position coordinates on the recommended path at the current time point, is the current location information, is the real-time position deviation; Use the short message function of the Beidou system to collect updated data on traffic conditions along the way; According to the real-time position deviation and traffic conditions to assess whether rerouting is necessary; When there is a significant deviation or new traffic problems are encountered, the route replanning process is triggered; Define the threshold judgment function , decide whether to re-plan the path, the expression is: ； in, is the maximum position deviation threshold allowed, It is the traffic status update data; The updated route information is fed back to the user, and real-time monitoring continues until the destination is reached.

8. A path planning system based on Beidou satellite positioning, based on the path planning method based on Beidou satellite positioning according to any one of claims 1 to 7, characterized in that: include: Positioning module, electronic map matching module, traffic flow prediction module, path search module, personalized adjustment module and monitoring module; The positioning module is used to perform high-precision positioning and error correction on Beidou satellite signals by adopting a multi-source sensor fusion method to obtain position information; The electronic map matching module is used to match the electronic map data with the location information using an electronic map matching method to obtain the electronic map data corresponding to the current location; The traffic flow prediction module is used to build a traffic flow prediction model based on location information and a machine learning algorithm, and input the location information into the traffic flow prediction model to obtain a traffic condition prediction result; The path search module is used to plan the path from the current location to the destination using a path search algorithm based on the traffic condition prediction result to obtain a preliminary path; The personalized adjustment module is used to perform personalized adjustment on the preliminary path according to the user's preference settings to obtain a recommended path; The monitoring module is used to monitor the final recommended path using a real-time monitoring and feedback mechanism, evaluate the position changes and traffic conditions in the path, and obtain the final path planning strategy.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the path planning method based on Beidou satellite positioning are implemented in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the path planning method based on Beidou satellite positioning described in any one of claims 1 to 7 are implemented.

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