A big data based vehicle route planning system

Abstract

The application is a vehicle route planning system based on big data, comprising a monitoring terminal connected with a data collection module, a data processing module, a data analysis module and a route recommendation module; the data collection module is used for collecting driving route information data and vehicle information data; the data processing module is used for processing road information data and vehicle information data, obtaining driving route A and driving route B according to the processing result; the data analysis module is used for analyzing driving route A and driving route B, obtaining driving route obstacle rate according to the analysis result, and sorting the driving route obstacle rate to obtain the sorting of the driving route obstacle rate; the route recommendation module is used for recommending vehicle routes according to the sorting of the driving route obstacle rate; and the recommended driving route is safer under the condition of reaching the destination fastest.

Description

Technical Field

[0001] This invention relates to the field of route planning technology, specifically a vehicle route planning system based on big data. Background Technology

[0002] With the development of technology, many navigation software programs use roads as data and calculate the best route based on certain algorithms (shortest route, avoiding highways, etc.). These routes work well for routes with little variation and simple road conditions, such as highways. However, the recommended routes may have many problems for routes with more complex road conditions, such as urban roads.

[0003] When we arrive in an unfamiliar city, we rely solely on GPS navigation. However, GPS navigation often leads us to choose a poor route. For example, to alleviate traffic congestion, a city builds an outer ring road. Although it's a bit longer, many local drivers prefer this route. However, GPS navigation will usually select a congested section that leads through the city center, wasting driving time and compromising driving safety. Existing GPS positioning and navigation systems can only provide the destination route; they cannot assess the route conditions or choose a safe one. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a vehicle route planning system based on big data;

[0005] The objective of this invention can be achieved through the following technical solution: a vehicle route planning system based on big data, including a monitoring terminal, wherein the monitoring terminal is connected to a data acquisition module, a data processing module, a data analysis module and a route recommendation module;

[0006] The data acquisition module is used to collect driving route information data and vehicle information data;

[0007] The data processing module is used to process road information data and vehicle information data, and obtain driving route A and driving route B based on the processing results;

[0008] The data analysis module is used to analyze driving route A and driving route B, obtain the obstacle rate of the driving route based on the analysis results, and sort them to obtain the ranking of the obstacle rates of the driving routes.

[0009] The route recommendation module is used to recommend vehicle routes based on the ranking of obstacle rates along the driving routes.

[0010] Furthermore, the process by which the data acquisition module collects driving route information data and vehicle information data includes:

[0011] The driver inputs the destination and obtains several driving routes based on the GPS positioning and navigation system;

[0012] The driving route information data includes driving time, curves, weather conditions, road size, and obstacles.

[0013] The vehicle information data includes the vehicle size and the driver's driving experience;

[0014] The driving weather conditions include sunny, rainy, and snowy weather;

[0015] The driving curves include whether there are traffic lights on the curves and the number of curves;

[0016] The traffic congestion status of the driving route is obtained from the GPS positioning and navigation system;

[0017] Install environmental sensors to obtain weather information while driving.

[0018] Furthermore, the process by which the data processing module processes road information data and vehicle information data to obtain a driving safety index includes:

[0019] The route information data and vehicle information data of several driving routes are combined into several driving datasets and vehicle datasets;

[0020] The travel time, road size, and weather conditions are labeled as T, x, and C, respectively, where x represents the minimum width of the road in the travel route.

[0021] A curve with traffic lights is marked YES, a curve without traffic lights is marked NO, and the number of curves is marked N.

[0022] Vehicle size and driver experience are marked as X and E, respectively;

[0023] The driving dataset is represented as {T, N, x, C};

[0024] The vehicle dataset is represented as {X, E}, where X represents the width of the vehicle size;

[0025] Set a driving time threshold T' and a curve number threshold for the driving route, count the number of curves with traffic lights, and obtain the driving safety index of the driving route based on the number of curves with traffic lights.

[0026] If the driving time T in the driving dataset is greater than the driving time threshold T', then the driving route corresponding to that driving time will be removed.

[0027] If the travel time T in the driving dataset is less than the travel time threshold T', then the driving safety index of the route is obtained based on the number of curves and the number of traffic lights along the curves in the driving dataset.

[0028] If the number of curves with traffic lights exceeds 50% of the total number of curves, the driving safety index is 1; if the number of curves with traffic lights is less than 50%, the driving safety index is 0.

[0029] The driving safety index will be displayed on the driving route corresponding to the GPS positioning and navigation system.

[0030] Furthermore, based on the driving safety index, the process of obtaining driving route A and driving route B includes:

[0031] Routes with a driving safety index of 0 are eliminated. The remaining routes with a driving safety index of 1 are used to obtain routes A and B based on the minimum width of the roads in the routes and the vehicle dataset.

[0032] By eliminating several routes to the destination and retaining the optimal routes A and B, and further analyzing routes A and B, the optimal route can be selected. This not only helps drivers reach their destination faster but also more safely.

[0033] Set a minimum width threshold for the driving route. Match the vehicle size in the vehicle dataset. If the width of the vehicle size is greater than or equal to the minimum width threshold, the corresponding driving route is removed. If the width of the vehicle size is less than 80% of the minimum width threshold, the corresponding driving route is marked as driving route A. If the width of the vehicle size is greater than or equal to 80% of the minimum width threshold but less than 100%, the corresponding driving route is marked as driving route A.

[0034] Furthermore, the process by which the data analysis module analyzes driving route A and driving route B includes:

[0035] The traffic flow along the driving route is obtained from the GPS positioning and navigation system, and the congestion status of the driving route is determined based on the traffic flow.

[0036] The traffic congestion status of the driving route includes both congested road conditions and normal road conditions;

[0037] Set a traffic flow threshold for the driving route. If the traffic flow of the driving route is greater than or equal to the traffic flow threshold, the driving route is marked as congested. If the traffic flow of the driving route is less than the traffic flow threshold, the driving route is marked as normal.

[0038] The driving routes A and B correspond to two different traffic congestion states. The congested state in driving route A is marked as driving route A1, and the normal state is marked as driving route A2. Similarly, the congested state in driving route B is marked as driving route B1, and the normal state is marked as driving route B2.

[0039] Furthermore, based on the driving weather, driving routes A1 and A2, as well as driving routes B1 and B2, are analyzed to obtain the ranking of the obstacle rates of the corresponding driving routes.

[0040] If the weather is sunny, the order of obstacle rates for the corresponding driving route is A2 < B2 < A1 < B1;

[0041] If the driving weather is rain or snow, the order of obstacle rates for the corresponding driving route is A2 < A1 < B2 < B1.

[0042] Furthermore, the process by which the route recommendation module recommends vehicle routes based on the ranking of obstacle rates along the driving route includes:

[0043] Based on the ranking of obstacle rates on driving routes, different driving experience thresholds are set for different vehicle drivers, and driving routes corresponding to the ranking of obstacle rates on driving routes are recommended based on different driving experience thresholds.

[0044] The driver experience thresholds for vehicles are set in the order A2 < A1 < B2 < B1.

[0045] Furthermore, if the driver's driving experience is less than the threshold for the A2 driving route, the A2 route is recommended to ensure that the driver can reach the destination quickly and safely.

[0046] If the driver's driving experience is greater than the threshold for driving routes A2 and less than A1, then route A2 is recommended.

[0047] If the driver's driving experience is greater than the threshold for the B1 route, then the recommended routes are A2, A1, B2, and B1.

[0048] Compared with the prior art, the beneficial effects of the present invention are:

[0049] 1. The data acquisition module collects driving route information and vehicle information data; the collected driving route information and vehicle information data are sent to the data processing module for processing. Based on the processing results, a driving safety index is obtained, thus obtaining driving route A and driving route B. These are then sent to the data analysis module for analysis. Based on the analysis results, the obstacle rate of each driving route is obtained and sorted. The route recommendation module recommends routes based on the obstacle rate ranking, ensuring that the recommended driving route reaches the destination as quickly and safely as possible.

[0050] 2. Choosing a driving route requires not only reaching the destination as quickly as possible, but also ensuring driving safety. Whether there are traffic lights at curves, whether the road is wide, and whether the weather is good all affect the driver's safe driving. Therefore, existing GPS positioning and navigation systems can only select the destination route, but cannot judge the route conditions and choose a safe driving route. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0052] like Figure 1 This is a schematic diagram of the present invention. Detailed Implementation

[0053] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0054] like Figure 1 As shown, a vehicle route planning system based on big data includes a monitoring terminal, which is connected to a data acquisition module, a data processing module, a data analysis module, and a route recommendation module.

[0055] The data acquisition module is used to collect driving route information data and vehicle information data, and the specific process includes:

[0056] The driver inputs the destination, and the GPS positioning and navigation system obtains several driving routes for the vehicle. These routes are marked as number 1, 2, 3, ..., n, where n is a positive integer.

[0057] The driving route information data includes driving time, curves, weather conditions, road size, and obstacles.

[0058] The vehicle information data includes the vehicle size and the driver's driving experience;

[0059] The driving weather conditions include sunny, rainy, and snowy weather;

[0060] The driving curves include whether there are traffic lights on the curves and the number of curves;

[0061] The driving obstacles include traffic congestion on the driving route, the width of the driving route, and traffic accident conditions;

[0062] The traffic congestion status of the driving route is obtained from the GPS positioning and navigation system;

[0063] Install environmental sensors to obtain weather information while driving;

[0064] Wireless communication is used to connect with traffic management agencies to obtain information on traffic accidents along the travel route;

[0065] The obtained route information and vehicle information data are sent to the data processing module for processing.

[0066] The data processing module is used to process road information data and vehicle information data, and obtain driving route A and driving route B based on the processing results. The specific process includes:

[0067] The route information data and vehicle information data of several driving routes are combined into several driving datasets and vehicle datasets;

[0068] The travel time, road size, and weather conditions are labeled as T, x, and C, respectively, where x represents the minimum width of the road in the travel route.

[0069] A curve with traffic lights is marked YES, a curve without traffic lights is marked NO, and the number of curves is marked N.

[0070] Vehicle size and driver experience are marked as X and E, respectively;

[0071] The driving dataset is represented as {T, N, x, C};

[0072] The vehicle dataset is represented as {X, E}, where X represents the width of the vehicle size;

[0073] It should be further explained that, in the specific implementation process, the dataset contains, but is not limited to, the data collected mentioned above. The collected data can be added, deleted, or modified according to the specific implementation to ensure that the data is more representative and complete. The dataset is used to integrate the data and process and analyze the data more clearly.

[0074] Set a driving time threshold T' and a curve number threshold for the driving route, count the number of curves with traffic lights, and obtain the driving safety index of the driving route based on the number of curves with traffic lights.

[0075] If the driving time T in the driving dataset is greater than the driving time threshold T', then the driving route corresponding to that driving time will be removed.

[0076] If the travel time T in the driving dataset is less than the travel time threshold T', then the driving safety index of the route is obtained based on the number of curves and the number of traffic lights along the curves in the driving dataset.

[0077] Right now:

[0078] If the number of curves with traffic lights exceeds 50% of the total number of curves, the driving safety index is 1; if the number of curves with traffic lights is less than 50%, the driving safety index is 0.

[0079] The driving safety index will be displayed on the driving route corresponding to the GPS positioning and navigation system;

[0080] Routes with a driving safety index of 0 are eliminated. The remaining routes with a driving safety index of 1 are used to obtain routes A and B based on the minimum width of the roads in the routes and the vehicle dataset.

[0081] Set a minimum width threshold for the driving route. Match the vehicle size in the vehicle dataset. If the width of the vehicle size is greater than or equal to the minimum width threshold, the corresponding driving route is removed. If the width of the vehicle size is less than 80% of the minimum width threshold, the corresponding driving route is marked as driving route A. If the width of the vehicle size is greater than or equal to 80% of the minimum width threshold but less than 100%, the corresponding driving route is marked as driving route B.

[0082] It should be further explained that in the specific implementation process, several driving routes to the destination are eliminated, and the optimal driving routes A and B are retained. Further analysis of driving routes A and B will select the optimal driving route, which can not only help drivers reach the destination faster, but also more safely.

[0083] The obtained driving routes A and B will be sent to the data analysis module.

[0084] The data analysis module is used to analyze driving routes A and B, and based on the analysis results, obtains a ranking of the obstacle rates of the driving routes. The specific process includes:

[0085] The system receives driving routes A and B, and analyzes them based on the traffic congestion status and weather conditions in the driving data set.

[0086] The traffic congestion status of the driving route includes both congested road conditions and normal road conditions;

[0087] The traffic flow along the driving route is obtained from the GPS positioning and navigation system, and the congestion status of the driving route is determined based on the traffic flow.

[0088] Set a traffic flow threshold for the driving route. If the traffic flow of the driving route is greater than or equal to the traffic flow threshold, the driving route is marked as congested. If the traffic flow of the driving route is less than the traffic flow threshold, the driving route is marked as normal.

[0089] The driving routes A and B correspond to two different traffic congestion states. The traffic congestion state in driving route A is marked as driving route A1, and the normal road state is marked as driving route A2. The traffic congestion state in driving route B is marked as driving route B1, and the normal road state is marked as driving route B2.

[0090] Based on the weather conditions, the obstacle rates of driving routes A1 and A2, as well as driving routes B1 and B2, are analyzed to obtain the corresponding driving route obstacle rates.

[0091] If the weather is sunny, the order of obstacle rates for the corresponding driving route is A2 < B2 < A1 < B1;

[0092] If the driving weather is rain or snow, the order of obstacle rates for the corresponding driving route is A2 < A1 < B2 < B1;

[0093] It should be further explained that in the specific implementation process, the selection of driving route not only requires the fastest speed, but also ensures driving safety. Whether there are traffic lights on the curves, whether the road is wide, and whether the weather is good will all affect the driver's safe driving. Therefore, the existing GPS positioning and navigation system can only select the destination route when it obtains the driving route, and cannot judge the route conditions and select a safe driving route.

[0094] The obtained obstacle rates of the driving routes are sorted and sent to the route recommendation module.

[0095] The route recommendation module is used to recommend vehicle routes based on the ranking of obstacle rates along the driving routes. The specific process includes:

[0096] Based on the ranking of obstacle rates on driving routes, different driving experience thresholds are set for different vehicle drivers, and driving routes corresponding to the ranking of obstacle rates on driving routes are recommended based on different driving experience thresholds.

[0097] The driver experience thresholds for vehicles are set in the order A2 < A1 < B2 < B1;

[0098] Recommended routes based on the driver's driving experience and driving experience threshold;

[0099] If the driver's driving experience is less than the threshold for the A2 driving route, then the A2 route is recommended to ensure that the driver can reach the destination quickly and safely.

[0100] If the driver's driving experience is greater than the threshold for driving routes A2 and less than A1, then route A2 is recommended.

[0101] If the driver's driving experience is greater than the threshold for the driver's driving experience for route B1, then the recommended routes are A2, A1, B2, and B1.

[0102] It should be further explained that, in the specific implementation process, if the driver has a long driving experience and has more driving routes to choose from, the driving route can be recommended based on the driving time and the driver's driving habits.

[0103] Working principle: The system uses a data acquisition module to collect driving route information and vehicle information data. This data is then sent to a data processing module for processing. Based on the processing results, a driving safety index is obtained, leading to driving routes A and B. These routes are then sent to a data analysis module for analysis. The analysis results determine the obstacle rate of each route, and these obstacles are ranked. The route recommendation module then recommends routes based on this ranking, ensuring the recommended routes reach the destination as quickly and safely as possible.

[0104] The embodiments described above are merely some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without creative effort are within the scope of protection of the present invention.

[0105] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A vehicle route planning system based on big data, comprising a monitoring terminal, characterized in that, The monitoring terminal is connected to a data acquisition module, a data processing module, a data analysis module, and a route recommendation module; The data acquisition module is used to collect driving route information data and vehicle information data; The data processing module is used to process road information data and vehicle information data, and obtain driving route A and driving route B based on the processing results; The data analysis module is used to analyze driving route A and driving route B, obtain the obstacle rate of the driving route based on the analysis results, and sort them. The route recommendation module is used to recommend vehicle routes based on the ranking of obstacle rates along the driving routes; The process by which the data acquisition module collects driving route information data and vehicle information data includes: The driver inputs the destination and obtains several driving routes based on the GPS positioning and navigation system; The driving route information data includes driving time, curves, weather conditions, road size, and obstacles. The vehicle information data includes the vehicle size and the driver's driving experience; The driving weather conditions include sunny, rainy, and snowy weather; The driving curves include whether there are traffic lights on the curves and the number of curves; The traffic congestion status of the driving route is obtained from the GPS positioning and navigation system; Install environmental sensors to obtain weather information while driving; The process by which the data processing module processes road information data and vehicle information data to obtain a driving safety index includes: Set a driving time threshold T' and a curve number threshold for the driving route, count the number of curves with traffic lights, and obtain the driving safety index of the driving route based on the number of curves with traffic lights. If the travel time T is greater than the travel time threshold T', then the travel route corresponding to that travel time will be removed. If the travel time T is less than the travel time threshold T', then the driving safety index of the route is obtained based on the number of curves and the number of traffic lights along the curves. If the number of curves with traffic lights exceeds 50% of the total number of curves, the driving safety index is 1; if the number of curves with traffic lights is less than 50%, the driving safety index is 0. The driving safety index will be displayed on the driving route corresponding to the GPS positioning and navigation system; The process of obtaining driving route A and driving route B based on the driving safety index includes: Routes with a driving safety index of 0 are eliminated. The remaining routes with a driving safety index of 1 are then used to determine routes A and B based on the minimum width of the road and the size of the vehicle. The minimum width threshold for the driving route is set and matched with the vehicle size. If the width of the vehicle is greater than or equal to the minimum width threshold, the corresponding driving route is removed. If the width of the vehicle is less than 80% of the minimum width threshold, the corresponding driving route is marked as driving route A. If the width of the vehicle is greater than or equal to 80% of the minimum width threshold but less than 100%, the corresponding driving route is marked as a driving route.

2. The vehicle route planning system based on big data according to claim 1, characterized in that, The data analysis module analyzes driving route A and driving route B in the following ways: The traffic flow along the driving route is obtained from the GPS positioning and navigation system, and the congestion status of the driving route is determined based on the traffic flow. The traffic congestion status of the driving route includes both congested road conditions and normal road conditions; Set a traffic flow threshold for the driving route. If the traffic flow of the driving route is greater than or equal to the traffic flow threshold, the driving route is marked as congested. If the traffic flow of the driving route is less than the traffic flow threshold, the driving route is marked as normal. The driving routes A and B correspond to two different traffic congestion states. The congested state in driving route A is marked as driving route A1, and the normal state is marked as driving route A2. Similarly, the congested state in driving route B is marked as driving route B1, and the normal state is marked as driving route B2.

3. The vehicle route planning system based on big data according to claim 2, characterized in that, Based on the driving weather, the obstacle rates of driving routes A1 and A2, as well as driving routes B1 and B2, are analyzed to obtain a ranking of the obstacle rates of the corresponding driving routes. If the weather is sunny, the order of obstacle rates for the corresponding driving route is A2 < B2 < A1 < B1; If the driving weather is rain or snow, the order of obstacle rates for the corresponding driving route is A2 < A1 < B2 < B1.

4. The vehicle route planning system based on big data according to claim 3, characterized in that, The process by which the route recommendation module recommends vehicle routes based on the ranking of obstacle rates includes: Based on the ranking of obstacle rates on driving routes, different driving experience thresholds are set for different vehicle drivers, and driving routes corresponding to the ranking of obstacle rates on driving routes are recommended based on different driving experience thresholds. The driver experience thresholds for vehicles are set in the order A2 < A1 < B2 < B1.

5. A vehicle route planning system based on big data according to claim 4, characterized in that, If the driver's driving experience is less than the threshold for the A2 driving route, then the recommended driving route is A2. If the driver's driving experience is greater than the threshold for driving routes A2 and less than A1, then route A2 is recommended. If the driver's driving experience is greater than the threshold for the B1 route, then the recommended routes are A2, A1, B2, and B1.

Images