A route planning method and system based on Beidou navigation

Abstract

The present invention relates to the field of route planning technology, and in particular, to a route planning method and system based on Beidou navigation. The method comprises the following steps: S1, obtaining the starting position of a car through Beidou satellites, and collecting the terminal position that the car needs to reach; S2, when the car is navigating, obtaining image data of the driving direction of the car, scanning the image data for license plates, and supplementing vehicles on the map according to the number of scanned license plates; S3, recording the vehicles as supplementary vehicles and navigation vehicles in the map, and then predicting and analyzing the supplementary vehicles for each section of each time period according to the supplementary vehicles in the historical records, and obtaining the predicted supplementary vehicles that will appear in each section of each time period; by comprehensively considering the predicted driving time and the section congestion score, the optimal navigation route can be selected, which will reduce the time the vehicle spends on the congested section and optimize the overall driving experience.

Description

Technical Field

[0001] The present invention relates to the technical field of route planning, and in particular to a route planning method and system based on Beidou navigation. Background Art

[0002] As people's living standards improve, cars are used more and more widely. People usually use navigation software to navigate when traveling to improve travel efficiency.

[0003] At present, when planning car driving, navigation route planning is generally carried out by combining real-time road conditions with user driving requirements. However, when the road is blocked, the navigation software generally recommends another road for driving, but does not control the recommended traffic flow, which easily leads to road congestion when many cars drive to the same section of road at the same time due to excessive traffic flow guided by navigation, thereby reducing the quality of navigation. Therefore, a route planning method and system based on Beidou navigation are proposed. Summary of the invention

[0004] The object of the present invention is to provide a route planning method and system based on Beidou navigation to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, one of the purposes of the present invention is to provide a route planning method based on Beidou navigation, comprising the following steps:

[0006] S1. Obtain the starting position of the car through Beidou satellite, and collect the destination position that the car needs to reach;

[0007] S2. When navigating the car, obtain image data of the direction of the car's travel, scan the license plate of the image data, and add vehicles on the map according to the number of scanned license plates;

[0008] S3, recording the vehicles as supplementary vehicles and navigation vehicles in the map, and then performing a forecast analysis on the supplementary vehicles for each road section in each time period based on the supplementary vehicles in the historical records, to obtain the forecasted supplementary vehicles that will appear in each road section in each time period;

[0009] S4, predicting the driving of the navigation vehicle in the map according to the navigation route combined with the road section and time period, and obtaining the predicted navigation vehicle that will appear on each road section in each time period;

[0010] S5. Obtain the traffic speed of each road section, and then perform congestion scoring by combining the predicted supplementary vehicles with the predicted navigation vehicles and the traffic speed, to obtain the congestion score of each road section corresponding to driving in each time period;

[0011] S6. The starting point location and the end point location are combined to plan the navigation route, and a driving prediction is performed based on the obtained navigation route. Then, the predicted road section corresponding to the predicted time period and the congestion score are combined to perform a comprehensive score on each navigation route, and finally the navigation route with the best comprehensive score is selected for car navigation.

[0012] As a further improvement of the technical solution, the step of S1 is as follows:

[0013] S1.1, by making navigation software, the car driver downloads it in the car control system, so that the car is equipped with navigation software;

[0014] S1.2. When the car is started, the navigation software automatically starts working in the background of the car control system, obtains the real-time position of the car through the Beidou satellite, and uses the real-time position of the car as the starting point of the car. Then, the driver of the car enters the destination that requires navigation assistance in the navigation software, and the input destination is used as the final destination that the car needs to reach.

[0015] As a further improvement of the technical solution, the steps of S2 are as follows:

[0016] S2.1, monitoring the working status of the navigation software. When the navigation software starts navigation, the image data of the driving direction of the vehicle is obtained by connecting the data with the driving recording device of the vehicle;

[0017] S2.2. The map is divided into multiple sections according to the road names, and a license plate number statistics library is created for each section. The license plates are then scanned in the image data, and the scanned license plates are uploaded to the license plate number statistics library of the corresponding section according to the real-time location of the car. The number of license plates in the license plate number statistics library is then used as the number of cars to complete vehicle addition on the map.

[0018] As a further improvement of the technical solution, when the same license plate appears in the license plate number statistics database during vehicle supplementation, the latest uploaded license plate is used as the update data, and the previously uploaded data of the license plate is deleted;

[0019] Set a license plate deletion time threshold. After a license plate is uploaded to the license plate number statistics database, if it is not updated after exceeding the license plate deletion time threshold, the license plate will be deleted from the license plate number statistics database.

[0020] As a further improvement of the technical solution, the steps of S3 are as follows:

[0021] S3.1. Record the vehicles as supplementary vehicles and navigation vehicles in the map, obtain the license plate number of the vehicle using the navigation software, extract the license plate number from the license plate number statistical database based on the obtained license plate number, and record the same license plate number as the navigation vehicle, and other different license plate numbers as supplementary vehicles;

[0022] S3.2. Extract the historical supplementary vehicle data recorded for each road section in each time period, and then perform prediction analysis on the supplementary vehicles for each time period of the road section based on the historical supplementary vehicle data, so as to obtain the predicted supplementary vehicles corresponding to each time period in each road section.

[0023] As a further improvement of the technical solution, the step of S5 is as follows:

[0024] S5.1. extracting road construction data for each road section in the map, and setting a traffic speed for the path according to the road construction data;

[0025] S5.2. Traffic simulation is performed by combining the predicted supplementary vehicles with the predicted navigation vehicles and the traffic speed. The time required for the predicted supplementary vehicles and the predicted navigation vehicles to pass through the road section is simulated, and a congestion score is performed based on the time, so as to obtain the congestion score of each road section corresponding to the driving in each time period.

[0026] As a further improvement of the present technical solution, when S5.2 scores the congestion of a road section, the shorter the time required to pass the road section, the higher the congestion score; conversely, the longer the time required to pass the road section, the lower the congestion score.

[0027] As a further improvement of the technical solution, the step of S6 is as follows:

[0028] S6.1. Plan a navigation route by combining the starting point with the end point, and obtain a navigation route that can be completed by driving from the starting point to the end point;

[0029] S6.2, predicting the navigation route obtained in S6.1, obtaining the corresponding road sections of the vehicle at different time periods, and then calculating it in combination with the congestion score obtained in S5.2, so as to obtain the comprehensive score corresponding to each navigation route, and then selecting the navigation route with the lowest score as the optimal navigation route for car navigation.

[0030] The second object of the present invention is to provide a route planning system based on Beidou navigation, including any one of the route planning methods based on Beidou navigation described above, including a position acquisition unit, a congestion scoring unit and a navigation determination unit;

[0031] The position acquisition unit is used to acquire the starting position and the ending position of the car;

[0032] The congestion scoring unit is used to record the supplementary vehicles and the navigation vehicles in the map, and then perform congestion scoring on each road section in the map in each time period;

[0033] The navigation determination unit is used to plan a navigation route based on the starting point and the end point, and to perform a comprehensive score on the navigation route obtained through planning in combination with the congestion scoring unit, and then use the navigation route with the best score to navigate the car.

[0034] Compared with the prior art, the present invention has the following beneficial effects: by comprehensively considering the predicted driving time and the road congestion score, the optimal navigation route can be selected, which will reduce the time the vehicle spends on congested roads and optimize the overall driving experience. At the same time, the route selection can be adjusted according to the predicted data of different time periods, which enables the navigation suggestions to adapt to changes in traffic conditions in real time, improves the flexibility of the system, and enables users to obtain the best route suggestions in different time periods, thereby avoiding congestion during peak hours. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is the overall flow chart of the present invention;

[0036] Figure 2 A flowchart of the present invention for equipping a car with navigation software;

[0037] Figure 3 A flowchart of completing vehicle supplement on a map according to the present invention;

[0038] Figure 4 A flowchart of the present invention for obtaining the congestion score of each road section corresponding to driving in each time period;

[0039] Figure 5 A flowchart of selecting the navigation route with the lowest score as the optimal navigation route for automobile navigation in the present invention;

[0040] Figure 6 It is a structural principle diagram of the position acquisition unit of the present invention.

[0041] The meaning of each number in the figure is:

[0042] 10. Position acquisition unit; 20. Congestion scoring unit; 30. Navigation determination unit. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] like Figure 1-6 As shown, one of the purposes of the present invention is to provide a route planning method based on Beidou navigation, comprising the following steps:

[0045] S1. Obtain the starting position of the car through the Beidou satellite and collect the destination position that the car needs to reach; the steps of S1 are as follows:

[0046] S1.1, by making navigation software, the car driver downloads it in the car control system, so that the car is equipped with navigation software;

[0047] S1.2. When the car is started, the navigation software starts automatically and works in the background of the vehicle control system. It obtains the real-time position of the car through the Beidou satellite and uses the real-time position of the car as the starting position of the car. Then the driver inputs the destination that needs navigation assistance in the navigation software, and then uses the input destination as the terminal position that the car needs to reach. The specific working steps are as follows:

[0048] System startup: When the car starts, the vehicle control system will perform self-checks and initialization. The vehicle control system usually includes a central processing unit and various sensors. The system will automatically start the navigation software to ensure that it runs normally in the background of the vehicle control system;

[0049] Get the real-time position of the car: The navigation software receives signals from satellites through the Beidou satellite positioning system BDS. The signals contain the real-time position of the car. The navigation software parses the data in the satellite signals and calculates the longitude and latitude coordinates of the car. Based on the real-time position received from the Beidou satellite, the navigation software sets the current car position as the starting position.

[0050] Enter the destination: The driver enters the destination to be navigated through the vehicle interface, which usually involves entering an address, place name or selecting a location through a map. The navigation software parses the information entered by the user, converts it into longitude and latitude coordinates, and then sets it as the destination location based on the longitude and latitude coordinates.

[0051] S2. When navigating the car, obtain image data of the direction of the car's travel, scan the license plate of the image data, and add vehicles on the map according to the number of scanned license plates;

[0052] The steps of S2 are as follows:

[0053] S2.1. Monitor the working status of the navigation software. When the navigation software starts navigation, the image data of the driving direction of the vehicle is obtained by connecting the data with the driving recording device of the vehicle:

[0054] By establishing data connections with the forward-facing camera, driving recorder, and panoramic camera system, these components can obtain real-time image data of the vehicle's driving direction while the vehicle is driving.

[0055] S2.2, divide the road into multiple sections according to the road name, and create a license plate number statistics library for each section. Then scan the license plate in the image data, upload the scanned license plate to the license plate number statistics library of the corresponding section according to the real-time position of the car, and then use the number of license plates in the license plate number statistics library as the number of cars to complete the vehicle addition on the map. The specific working steps are as follows:

[0056] Map segmentation: Use Google Maps to obtain map data, and then divide the map into multiple segments based on road names. Each segment should have a unique identifier ID and name;

[0057] Create a license plate number statistics database: Create a database table or file for each road segment to store the license plate numbers and their quantities detected on the road segment;

[0058] Image data acquisition and license plate scanning: Apply license plate recognition technology OCR to extract license plate numbers from images;

[0059] Real-time location acquisition: Based on the real-time location received from the Beidou satellite, the road section where the vehicle is currently located can be determined by comparing the geographical location with the road section boundary;

[0060] Upload license plate number: associate the scanned license plate number with the real-time location of the vehicle, upload the license plate number to the license plate number statistics database of the corresponding road section, and ensure the timestamp recorded in the database to track the movement of the vehicle;

[0061] Statistics and display: Count the number of license plates recorded in each road segment, update these numbers on the map, and display the number of vehicles in each road segment.

[0062] S2.2 When supplementing vehicles, if the same license plate appears in the license plate number statistics database, the latest uploaded license plate will be used as the update data, and the previously uploaded data of the license plate will be deleted;

[0063] Set a license plate deletion time threshold. After a license plate is uploaded to the license plate number statistics database, if it is not updated after exceeding the license plate deletion time threshold, the license plate will be deleted from the license plate number statistics database.

[0064] Set a time threshold, for example, 30 minutes. License plates that exceed this time threshold will be deleted. Perform cleanup tasks regularly to check and delete license plate records that exceed the time threshold and are not updated.

[0065] S3, recording the vehicles as supplementary vehicles and navigation vehicles in the map, and then performing a forecast analysis on the supplementary vehicles for each road section in each time period based on the supplementary vehicles in the historical records, to obtain the forecasted supplementary vehicles that will appear in each road section in each time period;

[0066] The steps for S3 are as follows:

[0067] S3.1. Record the vehicles as supplementary vehicles and navigation vehicles in the map, obtain the license plate number of the vehicle using the navigation software, extract the license plate number from the license plate number statistical database based on the obtained license plate number, and record the same license plate number as the navigation vehicle, and other different license plate numbers as supplementary vehicles;

[0068] S3.2, extract the historical supplementary vehicle data recorded in each time period of each road section, and then perform prediction analysis on the supplementary vehicles in each time period of the road section based on the historical supplementary vehicle data, so as to obtain the predicted supplementary vehicles corresponding to each time period in each road section. The specific working steps are as follows:

[0069] Data collection: Extract the historical supplementary vehicle data of each road section at each time period from the license plate number statistics database, and ensure that the data includes timestamp, road section information, number of supplementary vehicles, etc.;

[0070] Data cleaning: clean data, handle missing values ​​and outliers, standardize data formats, and ensure data consistency and integrity;

[0071] Data segmentation: Split the data into training sets and test sets according to time periods such as hours or days. Usually, the training set is used for model training and the test set is used for model validation;

[0072] Feature engineering: extract features that are helpful for prediction, such as time, date, holidays, weather, etc. You can consider using time series features, periodic features, etc.

[0073] Model formula:

[0074]

[0075] in, represents the number of supplementary vehicles in period t, φ i is the coefficient of the AR part, θ i is the coefficient of the MA part, ε t-iis the white noise residual, p is the order of the AR part, d is the number of differences to make the data stationary, and q is the order of the MA part. ACF (autocorrelation function) and PACF (partial autocorrelation function) plots can be used to help determine these parameters;

[0076] Model training: Use the training set data to train the selected model, adjust the model parameters to optimize the prediction performance, and then use the test set data to evaluate the performance of the model;

[0077] Predictive analysis: Use the trained model to make predictions for each road section and each time period to generate prediction results for supplementary vehicles.

[0078] S4. The navigation vehicle in the map is predicted to travel according to the navigation route, road section and time period, and the predicted navigation vehicle that will appear on each road section in each time period is obtained. The specific working steps are as follows:

[0079] First, define the variables:

[0080] L i is the length of road segment i, collected through the map;

[0081] V i (t) is the traffic flow of road section i during time period t, F i (t) is the traffic flow forecast value of road section i in time period t, and the traffic flow is the sum of supplementary vehicles and navigation vehicles;

[0082] T i is the travel time of road section i, which can be estimated from historical data;

[0083]

[0084] Among them, α, β and γ are model parameters, which need to be obtained through historical data training. i (t-1) is the traffic flow in the previous time period, F i (t-1) is the predicted flow in the previous time period, T i is the factor affecting the flow rate due to travel time;

[0085] After predicting each time period and each road section, the predicted number of navigation vehicles for each road section in each time period can be obtained by combining the navigation route. Assuming that the number of navigation vehicles N(t) on each route is evenly distributed, the following formula can be used:

[0086]

[0087] Where N(t) is the total number of all navigation vehicles in time period t, and R is the total number of sections of the route;

[0088] Applying the above formula to the actual data, we can get the predicted number of navigation vehicles for each road section in each time period. For time period t and road section i, use the following steps:

[0089] Calculate the traffic forecast F of road segment i at time t using historical data and model prediction formula i (t), and then calculate the number of navigation vehicles N in the time period t by combining the navigation route data i (t), and finally the predicted number of navigation vehicles on each road section in each time period is obtained.

[0090] S5. Obtain the traffic speed of each road section, and then perform congestion scoring by combining the predicted supplementary vehicles with the predicted navigation vehicles and the traffic speed, to obtain the congestion score of each road section corresponding to driving in each time period;

[0091] The steps of S5 are as follows:

[0092] S5.1. extracting road construction data for each road section in the map, and setting a traffic speed for the path according to the road construction data;

[0093] S5.2. Traffic simulation is performed by combining the predicted supplementary vehicles with the predicted navigation vehicles and the traffic speed. The time required for the predicted supplementary vehicles and the predicted navigation vehicles to pass through the road section is simulated, and a congestion score is performed based on the time, so as to obtain the congestion score of each road section corresponding to the driving in each time period.

[0094] S5.2 When scoring the congestion of a road section, the shorter the time required to pass the road section, the higher the congestion score. Conversely, the longer the time required to pass the road section, the lower the congestion score. The specific working steps are as follows:

[0095] Data extraction: Extract construction data of each road section from the map, such as length, number of lanes, road type, etc.;

[0096] Traffic flow rate setting: Set the traffic flow rate for each road section based on road construction data and historical traffic volume;

[0097] Path simulation: Combine the predicted supplementary vehicles and navigation vehicles with the traffic flow rate to perform traffic simulation and calculate the time required for each vehicle to pass through the road section;

[0098] Calculate the travel time of each road section. The time calculation formula is:

[0099]

[0100] Among them, T is time, D is road length, and S is traffic speed;

[0101] The congestion score is set according to the travel time. The congestion score formula is:

[0102]

[0103] Among them, C is the congestion score, T is the travel time, the shorter the time, the higher the congestion score; the longer the time, the lower the score;

[0104] Time period evaluation: Evaluate each road section at different time periods, and record and analyze the congestion scores at each time period.

[0105] S6. The starting point location and the end point location are combined to plan the navigation route, and a driving prediction is performed based on the obtained navigation route. Then, the predicted road section corresponding to the predicted time period and the congestion score are combined to perform a comprehensive score on each navigation route, and finally the navigation route with the best comprehensive score is selected for car navigation.

[0106] The steps of S6 are as follows:

[0107] S6.1. Plan a navigation route by combining the starting point with the end point, and obtain a navigation route that can be completed by driving from the starting point to the end point;

[0108] S6.2, predicting the navigation route obtained in S6.1, obtaining the corresponding road sections of the vehicle at different time periods, and then calculating the congestion score obtained in S5.2, so as to obtain the comprehensive score corresponding to each navigation route, and then selecting the navigation route with the lowest score as the optimal navigation route for car navigation. The specific working steps are as follows:

[0109] Navigation route planning: Use graph algorithms such as Dijkstra or A* to find all possible routes, and output a set of paths from the starting point to the end point. Each path consists of a series of road segments.

[0110] Calculate the comprehensive score: give a weighted score to each section of each navigation route and calculate the total score of the route;

[0111] Select the best route: Compare the comprehensive scores of all routes and select the one with the lowest score as the best route. The formula is as follows:

[0112]

[0113] Among them, n is the number of sections on the route, M is the comprehensive score, and C is the congestion score.

[0114] The second object of the present invention is to provide a route planning system based on Beidou navigation, including any one of the above-mentioned route planning methods based on Beidou navigation, including a position acquisition unit 10, a congestion scoring unit 20 and a navigation determination unit 30;

[0115] The position acquisition unit 10 is used to acquire the starting position and the ending position of the car;

[0116] The congestion scoring unit 20 is used to record the supplementary vehicles and the navigation vehicles in the map, and then perform a congestion score for each road section in the map in each time period;

[0117] The navigation determination unit 30 is used to plan a navigation route based on the starting point and the end point, and to perform a comprehensive score on the navigation route obtained through planning in combination with the congestion scoring unit 20, and then use the navigation route with the best score to navigate the car.

[0118] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and descriptions are only preferred examples of the present invention and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A route planning method based on Beidou navigation, characterized in that: The steps include: S1. Obtain the starting position of the car through Beidou satellite, and collect the destination position that the car needs to reach; S2. When navigating the car, obtain image data of the direction of the car's travel, scan the license plate of the image data, and add vehicles on the map according to the number of scanned license plates; S3, recording the vehicles as supplementary vehicles and navigation vehicles in the map, and then performing a forecast analysis on the supplementary vehicles for each road section in each time period based on the supplementary vehicles in the historical records, to obtain the forecasted supplementary vehicles that will appear in each road section in each time period; S4, predicting the driving of the navigation vehicle in the map according to the navigation route combined with the road section and time period, and obtaining the predicted navigation vehicle that will appear on each road section in each time period; S5. Obtain the traffic speed of each road section, and then perform congestion scoring by combining the predicted supplementary vehicles with the predicted navigation vehicles and the traffic speed, to obtain the congestion score of each road section corresponding to driving in each time period; S6. The starting point location and the end point location are combined to plan the navigation route, and a driving prediction is performed based on the obtained navigation route. Then, the predicted road section corresponding to the predicted time period and the congestion score are combined to perform a comprehensive score on each navigation route, and finally the navigation route with the best comprehensive score is selected for car navigation.

2. A route planning method based on Beidou navigation according to claim 1, characterized in that: The steps of S1 are as follows: S1.1, by making navigation software, the car driver downloads it in the car control system, so that the car is equipped with navigation software; S1.

2. When the car is started, the navigation software automatically starts working in the background of the car control system, obtains the real-time position of the car through the Beidou satellite, and uses the real-time position of the car as the starting point of the car. Then, the driver of the car enters the destination that requires navigation assistance in the navigation software, and the input destination is used as the final destination that the car needs to reach.

3. A route planning method based on Beidou navigation according to claim 1, characterized in that: The steps of S2 are as follows: S2.1, monitoring the working status of the navigation software. When the navigation software starts navigation, the image data of the driving direction of the vehicle is obtained by connecting the data with the driving recording device of the vehicle; S2.

2. The map is divided into multiple sections according to the road names, and a license plate number statistics library is created for each section. The license plates are then scanned in the image data, and the scanned license plates are uploaded to the license plate number statistics library of the corresponding section according to the real-time location of the car. The number of license plates in the license plate number statistics library is then used as the number of cars to complete vehicle addition on the map.

4. A route planning method based on Beidou navigation according to claim 3, characterized in that: When the vehicle is supplemented, if the same license plate appears in the license plate number statistics database, the latest uploaded license plate is used as the update data, and the previously uploaded data of the license plate is deleted; Set a license plate deletion time threshold. After a license plate is uploaded to the license plate number statistics database, if it is not updated after exceeding the license plate deletion time threshold, the license plate will be deleted from the license plate number statistics database.

5. The route planning method based on Beidou navigation according to claim 1, characterized in that: The steps of S3 are as follows: S3.

1. Record the vehicles as supplementary vehicles and navigation vehicles in the map, obtain the license plate number of the vehicle using the navigation software, extract the license plate number from the license plate number statistical database based on the obtained license plate number, and record the same license plate number as the navigation vehicle, and other different license plate numbers as supplementary vehicles; S3.

2. Extract the historical supplementary vehicle data recorded for each road section in each time period, and then perform prediction analysis on the supplementary vehicles for each time period of the road section based on the historical supplementary vehicle data, so as to obtain the predicted supplementary vehicles corresponding to each time period in each road section.

6. The route planning method based on Beidou navigation according to claim 1, characterized in that: The steps of S5 are as follows: S5.

1. extracting road construction data for each road section in the map, and setting a traffic speed for the path according to the road construction data; S5.

2. Traffic simulation is performed by combining the predicted supplementary vehicles with the predicted navigation vehicles and the traffic speed. The time required for the predicted supplementary vehicles and the predicted navigation vehicles to pass through the road section is simulated, and a congestion score is performed based on the time, so as to obtain the congestion score of each road section corresponding to the driving in each time period.

7. A route planning method based on Beidou navigation according to claim 6, characterized in that: When S5.2 scores the congestion of a road section, the shorter the time required to pass the road section, the higher the congestion score. Conversely, the longer the time required to pass the road section, the lower the congestion score.

8. The route planning method based on Beidou navigation according to claim 1, characterized in that: The steps of S6 are as follows: S6.

1. Planning a navigation route by combining the starting point location with the end point location to obtain a navigation route that can be completed by driving from the starting point location to the end point location; S6.2, predicting the navigation route obtained in S6.1, obtaining the corresponding road sections of the vehicle at different time periods, and then calculating it in combination with the congestion score obtained in S5.2, so as to obtain the comprehensive score corresponding to each navigation route, and then selecting the navigation route with the lowest score as the optimal navigation route for car navigation.

9. A method for implementing a route planning system based on Beidou navigation, comprising a route planning method based on Beidou navigation according to any one of claims 1 to 8, characterized in that: It includes a location acquisition unit (10), a congestion scoring unit (20) and a navigation determination unit (30); The position acquisition unit (10) is used to acquire the starting position and the ending position of the car; The congestion scoring unit (20) is used to record the supplementary vehicles and the navigation vehicles in the map, and then perform a congestion score on each road section in the map in each time period; The navigation determination unit (30) is used to plan a navigation route based on the starting point and the end point, and to perform a comprehensive score on the navigation route obtained through planning in combination with the congestion scoring unit (20), and then use the navigation route with the best score to navigate the car.

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