Trip planning method based on vehicle historical navigation data

Abstract

The present invention discloses a method for itinerary traffic planning based on vehicle historical navigation data, which belongs to the field of vehicle navigation technology and includes the following steps: S1: acquiring and confirming the vehicle's historical navigation data, training a itinerary traffic planning model based on the vehicle's historical navigation data, and determining the optimal itinerary traffic planning model; S2: acquiring the vehicle's real-time traffic data, performing traffic analysis and prediction on the vehicle's real-time traffic data based on the optimal itinerary traffic planning model, and generating one or more itinerary traffic planning solutions. The present invention solves the existing problem that the vehicle's itinerary traffic cannot be optimally planned based on the vehicle's historical navigation data, and that an efficient and comfortable itinerary traffic planning route cannot be provided to users, thereby reducing the vehicle's driving safety. The present invention can optimally plan the vehicle's itinerary traffic based on the vehicle's historical navigation data, can provide users with an efficient and comfortable itinerary traffic planning route, and can improve the vehicle's driving safety.

Description

Technical Field

[0001] The invention relates to the technical field of vehicle navigation, and in particular to a method for planning a trip road condition based on historical vehicle navigation data. Background Art

[0002] With the construction and popularization of intelligent transportation systems, driving safety has become an issue that people are increasingly concerned about. During travel, vehicles and drivers will be affected by various factors, such as traffic congestion, road disasters, etc. The above factors will cause the vehicle's driving speed to decrease, the driving time to increase, and may even cause traffic accidents. Therefore, in order to improve driving safety, it is necessary to optimize the vehicle's driving route to avoid encountering bad road conditions.

[0003] The Chinese patent with publication number CN115638801A discloses a system and method for suggesting a charging station for an electric vehicle, the method comprising determining a destination of the electric vehicle, accessing a schedule of a user of the electric vehicle, identifying in the user's schedule an event scheduled during a driving period to the destination, and selecting a suggested charging station based at least on the destination and the identified event scheduled, and generating a suggested charging station at a display for presentation; however, the patent has the following defects:

[0004] The existing system cannot optimally plan the vehicle's travel conditions based on the vehicle's historical navigation data, cannot provide users with efficient and comfortable travel condition planning routes, and reduces vehicle driving safety. Summary of the invention

[0005] The purpose of the present invention is to provide a trip traffic planning method based on vehicle historical navigation data, which can optimally plan the vehicle trip traffic based on the vehicle historical navigation data, provide users with efficient and comfortable trip traffic planning routes, improve vehicle driving safety, and solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The method for planning a trip based on historical vehicle navigation data includes the following steps:

[0008] S1: Acquire historical navigation data of the vehicle, confirm the historical navigation data of the vehicle, train a trip traffic planning model based on the historical navigation data of the vehicle according to the trip traffic planning requirements based on the historical navigation data of the vehicle, test and optimize the trip traffic planning model, and determine the optimal trip traffic planning model;

[0009] S2: Analyze the driving conditions of the vehicle at different times and on different routes, obtain the real-time traffic data of the vehicle, perform traffic analysis and prediction on the real-time traffic data of the vehicle based on the optimal trip traffic planning model, and generate one or more trip traffic planning solutions;

[0010] S3: Select the optimal itinerary traffic planning scheme, guide the vehicle to travel based on the optimal itinerary traffic planning scheme, and monitor the driving vehicle in real time to ensure that the vehicle is always on the optimal itinerary traffic planning route.

[0011] Preferably, in S1, historical navigation data of the vehicle is obtained by performing the following operations:

[0012] Acquiring the driving route of the vehicle based on the on-board equipment to determine the driving route data of the vehicle;

[0013] Acquiring the driving time of the vehicle based on the on-board equipment to determine the driving time data of the vehicle;

[0014] Acquiring the driving speed of the vehicle based on the on-board equipment to determine the driving speed data of the vehicle;

[0015] Acquiring the parking point of the vehicle based on the on-board equipment to determine the parking point data of the vehicle;

[0016] Acquiring the vehicle's passing locations based on the vehicle-mounted equipment to determine the vehicle's passing location data;

[0017] The historical navigation data of the vehicle is determined based on the vehicle's driving route data, driving time data, driving speed data, parking point data and passing location data.

[0018] Preferably, in S1, the historical navigation data of the vehicle is confirmed, and the following operations are performed:

[0019] Get the historical navigation data of the vehicle;

[0020] Clean the historical navigation data of the vehicle, including:

[0021] Perform consistency check on the vehicle's historical navigation data;

[0022] Check whether the historical navigation data of the vehicle meets the requirements according to the reasonable value range and mutual relationship of each parameter in the historical navigation data of the vehicle;

[0023] Remove inconsistent data that is beyond the normal range, logically unreasonable or contradictory in the historical navigation data of the vehicle;

[0024] Process invalid and missing values ​​of the vehicle's historical navigation data;

[0025] Remove invalid and missing data in historical navigation data that is of no value to vehicle travel and road condition planning;

[0026] Identify historical navigation data that is valuable for vehicle trip planning.

[0027] Preferably, in S1, a trip traffic planning model based on historical vehicle navigation data is trained by performing the following operations:

[0028] Get the historical navigation data of the vehicle;

[0029] Dividing the historical navigation data of the vehicle;

[0030] Determine a trip traffic planning training set and a trip traffic planning test set;

[0031] According to the needs of itinerary planning based on the historical navigation data of the vehicle, a neural network model framework suitable for itinerary planning is selected;

[0032] Based on the trip traffic planning training set, training the selected neural network model framework suitable for vehicle trip traffic planning;

[0033] Train a trip planning model based on the vehicle's historical navigation data.

[0034] Preferably, in S1, the trip traffic planning model is tested and optimized by performing the following operations:

[0035] Obtain a trip traffic planning model based on the vehicle's historical navigation data;

[0036] Based on the trip planning test set, the performance of the trip planning model based on the vehicle's historical navigation data is tested and evaluated;

[0037] Determine the performance test evaluation report based on the trip traffic planning model;

[0038] Mining and analyzing the performance test evaluation report based on the trip traffic planning model;

[0039] Determine the parameter adjustment optimization scheme based on the trip traffic planning model;

[0040] Optimizing the parameters of the trip traffic planning model according to a parameter adjustment optimization scheme based on the trip traffic planning model;

[0041] Determine the optimal travel traffic planning model.

[0042] Preferably, in S2, real-time traffic data of the vehicle is obtained, and the following operations are performed:

[0043] Based on the on-board equipment, the road congestion situation of vehicles at different times and on different routes is obtained in real time to determine the vehicle road congestion data;

[0044] Based on the on-board equipment, the road accident situation of the vehicle at different times and on different routes is acquired in real time to determine the vehicle road accident data;

[0045] Based on the on-board equipment, the road construction conditions of the vehicle at different times and on different routes are acquired in real time to determine the vehicle road construction data;

[0046] Among them, the real-time road condition data of the vehicle is determined based on the vehicle's road congestion data, road accident data and road construction data.

[0047] Preferably, in S2, the real-time traffic data of the vehicle is analyzed and predicted, and the following operations are performed:

[0048] Obtain real-time traffic data of the vehicle;

[0049] Input the vehicle's real-time traffic data into the optimal trip traffic planning model;

[0050] Analyze and predict the traffic conditions of the vehicle's real-time traffic data based on the optimal trip traffic planning model;

[0051] One or more travel traffic planning schemes are generated based on user preferences, and the one or more travel traffic planning schemes are displayed to the navigation terminal for display.

[0052] Preferably, in S3, the optimal travel traffic planning scheme is selected, and the following operations are performed:

[0053] Obtain one or more travel traffic planning plans;

[0054] The user selects the best itinerary traffic planning scheme from one or more itinerary traffic planning schemes according to their own needs;

[0055] The navigation terminal performs itinerary traffic planning based on the optimal itinerary traffic planning scheme, and guides the vehicle to travel based on the optimal itinerary traffic planning scheme.

[0056] Preferably, in S3, the moving vehicle is monitored in real time, and the following operations are performed:

[0057] When the vehicle is traveling based on the optimal travel and road condition planning scheme, the vehicle is monitored in real time to determine whether the vehicle is always on the optimal travel and road condition planning route;

[0058] When the vehicle deviates from the optimal planned route based on road conditions, the vehicle will be given a real-time warning and road condition control measures will be taken in time to guide the vehicle.

[0059] Preferably, when the vehicle deviates from the optimal travel and road condition planning route, a real-time warning is given to the vehicle, and travel and road condition control measures are taken in time to guide the vehicle to travel, including:

[0060] When there are multiple travel traffic planning schemes obtained, after selecting the optimal travel traffic planning scheme, performing correlation determination on multiple travel traffic planning schemes other than the optimal travel traffic planning scheme, and obtaining a first correlation value between each of the travel traffic planning schemes and the optimal travel traffic planning scheme;

[0061] When the vehicle deviates from the optimal travel and road condition planning route, the deviation parameters between the vehicle and the optimal travel and road condition planning route are monitored in real time; wherein the deviation parameters include the direction deviation angle between the vehicle and the optimal travel and road condition planning route, the straight-line distance between the vehicle and the optimal travel and road condition planning route, and the deviation driving speed;

[0062] The vehicle deviation evaluation parameter is obtained according to the deviation parameter between the vehicle and the optimal travel road condition planning route; wherein the vehicle deviation evaluation parameter is obtained by the following formula:

[0063] ;

[0064] Where P represents the vehicle deviation assessment parameter; w 01 、w 02 and w 03 Respectively represent the preset first weight value, the second weight value and the third weight value; θ represents the direction deviation angle between the current vehicle and the optimal travel road condition planning route; d represents the straight-line distance between the vehicle and the optimal travel road condition planning route; α represents the preset basic proportional constant, and the value range is 0.02-0.08; β represents the preset speed index, which is used to represent the influence of speed, and the value range of the speed index is (-1,0); γ ​​represents the preset road condition complexity parameter, and the value range of the road condition complexity parameter is 0.27-2.38; L represents the rate of change of the straight-line distance deviation; v represents the deviation speed of the current vehicle;

[0065] comparing the vehicle deviation assessment parameter with a preset vehicle deviation assessment parameter threshold;

[0066] When the vehicle deviation assessment parameter exceeds a preset vehicle deviation assessment parameter threshold, it is determined that travel road condition adjustment is required, and an alternative route is obtained for recommendation to the user.

[0067] Preferably, performing correlation determination on a plurality of the travel traffic planning schemes other than the optimal travel traffic planning scheme, and obtaining a first correlation value between each of the travel traffic planning schemes and the optimal travel traffic planning scheme, comprises:

[0068] When there are multiple travel traffic planning schemes obtained, after selecting the optimal travel traffic planning scheme, multiple travel traffic planning schemes other than the optimal travel traffic planning scheme are retrieved as candidate travel traffic planning schemes;

[0069] Extracting path parameters of the path corresponding to the candidate travel traffic planning scheme and the path corresponding to the optimal travel traffic planning scheme; wherein the path parameters include path travel distance, overlapping path distance and number of traffic lights;

[0070] Obtaining a first correlation value between each of the candidate travel traffic planning solutions and the optimal travel traffic planning solution by using the path parameters of the path corresponding to the candidate travel traffic planning solution and the path corresponding to the optimal travel traffic planning solution;

[0071] The first correlation value between each of the travel traffic planning schemes and the optimal travel traffic planning scheme is obtained by the following formula:

[0072] ;

[0073] Among them, G 01 Indicates the first correlation value; x 01 represents the preset first weight parameter value; x 02 represents the preset second weight parameter value; D c represents the overlapped path distance between the path corresponding to the candidate trip traffic planning solution and the path corresponding to the optimal trip traffic planning solution; D z represents the path travel distance of the path corresponding to the candidate travel traffic planning scheme; D yz N represents the path travel distance of the path corresponding to the optimal travel traffic planning solution; z Indicates the number of traffic lights on the path corresponding to the candidate trip traffic planning solution; N yz Indicates the number of traffic lights on the path corresponding to the optimal travel traffic planning solution.

[0074] Preferably, when the vehicle deviation evaluation parameter exceeds a preset vehicle deviation evaluation parameter threshold, it is determined that travel road condition regulation is required, and alternative routes are obtained for recommendation to the user, including:

[0075] When the vehicle deviation assessment parameter exceeds a preset vehicle deviation assessment parameter threshold, a path association parameter between the current vehicle driving path and the candidate trip road condition planning scheme is retrieved;

[0076] The path association parameters include the overlapped path distance and straight-line distance between the current vehicle driving path and the path corresponding to the candidate trip road condition planning scheme, and the path travel distance between the current vehicle driving path and the path corresponding to the candidate trip road condition planning scheme;

[0077] Obtaining a second correlation value between the current vehicle driving path and the candidate travel road condition planning scheme using a path correlation parameter between the current vehicle driving path and the candidate travel road condition planning scheme;

[0078] The second correlation value is obtained by the following formula:

[0079] ;

[0080] Among them, G 02 Indicates the second correlation value; x 01 represents the preset first weight parameter value; x 02 represents the preset second weight parameter value; D cs represents the overlapped path distance between the current vehicle driving path and the path corresponding to the candidate trip road condition planning scheme; D z represents the path travel distance of the path corresponding to the candidate travel traffic planning scheme; D yz represents the path travel distance of the path corresponding to the optimal travel traffic planning solution; D f Indicates the straight-line distance between the current vehicle driving path and the path corresponding to the candidate trip road condition planning scheme;

[0081] The first correlation value and the second correlation value are used to obtain a comprehensive correlation value corresponding to each candidate trip road condition planning scheme; wherein the comprehensive correlation value is obtained by the following formula:

[0082] ;

[0083] Among them, G represents the comprehensive correlation value, G 01 Represents the first correlation value; G 02 represents the second correlation value;

[0084] The candidate trip road condition planning scheme corresponding to the maximum comprehensive correlation value is used as an alternative route;

[0085] The alternative path is recommended to the user.

[0086] Compared with the prior art, the present invention has the following beneficial effects:

[0087] The present invention obtains historical navigation data of the vehicle, confirms the historical navigation data of the vehicle, trains a trip traffic planning model based on the historical navigation data of the vehicle according to the trip traffic planning requirements based on the historical navigation data of the vehicle, tests and optimizes the trip traffic planning model, determines the optimal trip traffic planning model, analyzes the driving conditions of the vehicle at different times and on different routes, obtains the real-time traffic data of the vehicle, performs traffic analysis and prediction on the real-time traffic data of the vehicle based on the optimal trip traffic planning model, generates one or more trip traffic planning schemes, and displays them on the navigation terminal for display, the user selects the optimal trip traffic planning scheme, guides the vehicle to travel based on the optimal trip traffic planning scheme, and monitors the driving vehicle in real time, so that the vehicle is always on the optimal trip traffic planning route, can optimally plan the vehicle trip traffic based on the historical navigation data of the vehicle, can provide the user with an efficient and comfortable trip traffic planning route, and can improve the driving safety of the vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0088] Figure 1 The present invention is a flow chart of a method for planning a trip based on historical vehicle navigation data. DETAILED DESCRIPTION

[0089] 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.

[0090] In order to solve the existing problem that the vehicle's travel conditions cannot be optimally planned based on the vehicle's historical navigation data, and that the user cannot be provided with an efficient and comfortable travel route, which reduces the vehicle's driving safety, please refer to Figure 1 , this embodiment provides the following technical solutions:

[0091] The method for planning a trip based on historical vehicle navigation data includes the following steps:

[0092] S1: Acquire historical navigation data of the vehicle, confirm the historical navigation data of the vehicle, train a trip traffic planning model based on the historical navigation data of the vehicle according to the trip traffic planning requirements based on the historical navigation data of the vehicle, test and optimize the trip traffic planning model, and determine the optimal trip traffic planning model;

[0093] In this embodiment, the historical navigation data of the vehicle is obtained by performing the following operations:

[0094] Acquiring the driving route of the vehicle based on the on-board equipment to determine the driving route data of the vehicle;

[0095] Acquiring the driving time of the vehicle based on the on-board equipment to determine the driving time data of the vehicle;

[0096] Acquiring the driving speed of the vehicle based on the on-board equipment to determine the driving speed data of the vehicle;

[0097] Acquiring the parking point of the vehicle based on the on-board equipment to determine the parking point data of the vehicle;

[0098] Acquiring the vehicle's passing locations based on the vehicle-mounted equipment to determine the vehicle's passing location data;

[0099] The historical navigation data of the vehicle is determined based on the vehicle's driving route data, driving time data, driving speed data, parking point data and passing location data.

[0100] It should be noted that by collecting the vehicle's driving route data, driving time data, driving speed data, parking point data and passing location data, the vehicle's historical navigation data can be determined, which facilitates the construction of a travel traffic planning model based on the vehicle's historical navigation data.

[0101] In this embodiment, the historical navigation data of the vehicle is confirmed by performing the following operations:

[0102] Get the historical navigation data of the vehicle;

[0103] Clean the historical navigation data of the vehicle, including:

[0104] Perform consistency check on the vehicle's historical navigation data;

[0105] Check whether the historical navigation data of the vehicle meets the requirements according to the reasonable value range and mutual relationship of each parameter in the historical navigation data of the vehicle;

[0106] Remove inconsistent data that is beyond the normal range, logically unreasonable or contradictory in the historical navigation data of the vehicle;

[0107] Process invalid and missing values ​​of the vehicle's historical navigation data;

[0108] Remove invalid and missing data in historical navigation data that is of no value to vehicle travel and road condition planning;

[0109] Identify historical navigation data that is valuable for vehicle trip planning.

[0110] It should be noted that by confirming the vehicle's historical navigation data, inconsistent data, invalid data and missing data in the vehicle's historical navigation data can be removed, and then historical navigation data that is valuable for the vehicle's itinerary planning can be determined, which facilitates the construction of a itinerary planning model based on the vehicle's historical navigation data.

[0111] In this embodiment, the trip traffic planning model based on the historical navigation data of the vehicle is trained by performing the following operations:

[0112] Get the historical navigation data of the vehicle;

[0113] Dividing the historical navigation data of the vehicle;

[0114] Determine a trip traffic planning training set and a trip traffic planning test set;

[0115] According to the needs of itinerary planning based on the historical navigation data of the vehicle, a neural network model framework suitable for itinerary planning is selected;

[0116] Based on the trip traffic planning training set, training the selected neural network model framework suitable for vehicle trip traffic planning;

[0117] Train a trip planning model based on the vehicle's historical navigation data.

[0118] It should be noted that, by dividing the historical navigation data of the vehicle, a trip traffic planning model based on the historical navigation data of the vehicle is trained based on the trip traffic planning training set.

[0119] In this embodiment, the trip traffic planning model is tested and optimized by performing the following operations:

[0120] Obtain a trip traffic planning model based on the vehicle's historical navigation data;

[0121] Based on the trip planning test set, the performance of the trip planning model based on the vehicle's historical navigation data is tested and evaluated;

[0122] Determine the performance test evaluation report based on the trip traffic planning model;

[0123] Mining and analyzing the performance test evaluation report based on the trip traffic planning model;

[0124] Determine the parameter adjustment optimization scheme based on the trip traffic planning model;

[0125] Optimizing the parameters of the trip traffic planning model according to a parameter adjustment optimization scheme based on the trip traffic planning model;

[0126] Determine the optimal travel traffic planning model.

[0127] It should be noted that the optimal trip traffic planning model can be determined by performing performance testing and evaluation and adjusting parameters of the trip traffic planning model.

[0128] S2: Analyze the driving conditions of the vehicle at different times and on different routes, obtain the real-time traffic data of the vehicle, perform traffic analysis and prediction on the real-time traffic data of the vehicle based on the optimal trip traffic planning model, and generate one or more trip traffic planning solutions;

[0129] In this embodiment, to obtain the real-time traffic data of the vehicle, the following operations are performed:

[0130] Based on the on-board equipment, the road congestion situation of vehicles at different times and on different routes is obtained in real time to determine the vehicle road congestion data;

[0131] Based on the on-board equipment, the road accident situation of the vehicle at different times and on different routes is acquired in real time to determine the vehicle road accident data;

[0132] Based on the on-board equipment, the road construction conditions of the vehicle at different times and on different routes are acquired in real time to determine the vehicle road construction data;

[0133] Among them, the real-time road condition data of the vehicle is determined based on the vehicle's road congestion data, road accident data and road construction data.

[0134] In this embodiment, the real-time traffic data of the vehicle is analyzed and predicted, and the following operations are performed:

[0135] Obtain real-time traffic data of the vehicle;

[0136] Input the vehicle's real-time traffic data into the optimal trip traffic planning model;

[0137] Analyze and predict the traffic conditions of the vehicle's real-time traffic data based on the optimal trip traffic planning model;

[0138] One or more travel traffic planning schemes are generated based on user preferences, and the one or more travel traffic planning schemes are displayed to the navigation terminal for display.

[0139] It should be noted that the real-time traffic data of the vehicle is obtained, and traffic analysis and prediction are performed on the real-time traffic data of the vehicle based on the optimal trip traffic planning model, and one or more trip traffic planning schemes are generated for users to choose. The user selects the optimal trip traffic planning scheme from one or more trip traffic planning schemes according to his or her own needs, and guides the vehicle to travel based on the optimal trip traffic planning scheme.

[0140] S3: Select the optimal itinerary traffic planning scheme, guide the vehicle to travel based on the optimal itinerary traffic planning scheme, and monitor the driving vehicle in real time to ensure that the vehicle is always on the optimal itinerary traffic planning route.

[0141] In this embodiment, the optimal travel traffic planning scheme is selected, and the following operations are performed:

[0142] Obtain one or more travel traffic planning plans;

[0143] The user selects the best itinerary traffic planning scheme from one or more itinerary traffic planning schemes according to their own needs;

[0144] The navigation terminal performs itinerary traffic planning based on the optimal itinerary traffic planning scheme, and guides the vehicle to travel based on the optimal itinerary traffic planning scheme.

[0145] In this embodiment, the moving vehicle is monitored in real time, and the following operations are performed:

[0146] When the vehicle is traveling based on the optimal travel and road condition planning scheme, the vehicle is monitored in real time to determine whether the vehicle is always on the optimal travel and road condition planning route;

[0147] When the vehicle deviates from the optimal planned route based on road conditions, the vehicle will be given a real-time warning and road condition control measures will be taken in time to guide the vehicle.

[0148] Therefore, by acquiring the historical navigation data of the vehicle, confirming the historical navigation data of the vehicle, according to the itinerary traffic planning requirements based on the historical navigation data of the vehicle, training the itinerary traffic planning model based on the historical navigation data of the vehicle, testing and optimizing the itinerary traffic planning model, determining the optimal itinerary traffic planning model, analyzing the driving conditions of the vehicle at different times and on different routes, acquiring the real-time traffic data of the vehicle, performing traffic analysis and prediction on the real-time traffic data of the vehicle based on the optimal itinerary traffic planning model, generating one or more itinerary traffic planning schemes, and displaying them on the navigation terminal for display, the user selects the optimal itinerary traffic planning scheme, guiding the vehicle to travel based on the optimal itinerary traffic planning scheme, and monitoring the driving vehicle in real time, so that the vehicle is always on the optimal itinerary traffic planning route, and can optimally plan the vehicle's itinerary traffic based on the historical navigation data of the vehicle, can provide users with an efficient and comfortable itinerary traffic planning route, and can improve the driving safety of the vehicle.

[0149] Specifically, when a vehicle deviates from the optimal travel and road condition planning route, a real-time warning is issued to the vehicle, and travel and road condition control measures are taken in time to guide the vehicle's driving, including:

[0150] When there are multiple travel traffic planning schemes obtained, after selecting the optimal travel traffic planning scheme, performing correlation determination on multiple travel traffic planning schemes other than the optimal travel traffic planning scheme, and obtaining a first correlation value between each of the travel traffic planning schemes and the optimal travel traffic planning scheme;

[0151] When the vehicle deviates from the optimal travel and road condition planning route, the deviation parameters between the vehicle and the optimal travel and road condition planning route are monitored in real time; wherein the deviation parameters include the direction deviation angle between the vehicle and the optimal travel and road condition planning route, the straight-line distance between the vehicle and the optimal travel and road condition planning route, and the deviation driving speed;

[0152] The vehicle deviation evaluation parameter is obtained according to the deviation parameter between the vehicle and the optimal travel road condition planning route; wherein the vehicle deviation evaluation parameter is obtained by the following formula:

[0153] ;

[0154] Where P represents the vehicle deviation assessment parameter; w 01 、w 02 and w 03 Respectively represent the preset first weight value, the second weight value and the third weight value; θ represents the direction deviation angle between the current vehicle and the optimal travel road condition planning route; d represents the straight-line distance between the vehicle and the optimal travel road condition planning route; α represents the preset basic proportional constant, and the value range is 0.02-0.08; β represents the preset speed index, which is used to represent the influence of speed, and the value range of the speed index is (-1,0); γ ​​represents the preset road condition complexity parameter, and the value range of the road condition complexity parameter is 0.27-2.38; L represents the rate of change of the straight-line distance deviation; v represents the deviation speed of the current vehicle;

[0155] comparing the vehicle deviation assessment parameter with a preset vehicle deviation assessment parameter threshold;

[0156] When the vehicle deviation assessment parameter exceeds a preset vehicle deviation assessment parameter threshold, it is determined that travel road condition adjustment is required, and an alternative route is obtained for recommendation to the user.

[0157] The technical effect of the above technical solution is: by real-time monitoring of the deviation of the vehicle from the optimal travel road condition planning route, the system can quickly detect the deviation behavior of the vehicle and immediately trigger the early warning mechanism. This real-time performance ensures that the driver or the autonomous driving system can quickly obtain feedback, so that there is an opportunity to correct the driving route in time to avoid further deviation or entering an unfavorable road condition area. By comprehensively considering multiple parameters such as the direction deviation angle, straight-line distance, deviation driving speed between the vehicle and the optimal route, and combining the preset weight value, basic proportional constant, speed index and road condition complexity parameter, a comprehensive vehicle deviation evaluation parameter is calculated. This multi-dimensional evaluation method improves the accuracy and scientificity of deviation judgment, allowing the system to more accurately judge the severity and potential risks of vehicle deviation.

[0158] When the system determines that it is necessary to adjust the travel conditions, it will automatically obtain and recommend alternative routes to the user. This intelligent route recommendation function not only helps users quickly find alternative routes, but also alleviates the inconvenience caused by road congestion, construction or other emergencies to a certain extent, and improves user experience and travel efficiency. Through real-time warning and timely regulation, this technical solution can largely avoid safety accidents caused by vehicles entering dangerous sections or encountering adverse traffic conditions. At the same time, the intelligent route recommendation function also enhances the flexibility of driving and the ability to respond to emergencies, further improving the safety and stability of driving. Selecting the best option from multiple travel road condition planning options and determining the relevance of other options will help the system to have a more comprehensive understanding of the current traffic conditions, so as to make more scientific and reasonable scheduling decisions. Optimize the use of traffic resources, reduce congestion and waste, and improve the operating efficiency of the overall transportation system.

[0159] In summary, this technical solution effectively improves the safety, stability and efficiency of vehicle driving through real-time warning, multi-dimensional deviation assessment, intelligent route recommendation and other means, while also optimizing the utilization of traffic resources.

[0160] Specifically, performing correlation determination on a plurality of the travel traffic planning schemes other than the optimal travel traffic planning scheme, and obtaining a first correlation value between each of the travel traffic planning schemes and the optimal travel traffic planning scheme, includes:

[0161] When there are multiple travel traffic planning schemes obtained, after selecting the optimal travel traffic planning scheme, multiple travel traffic planning schemes other than the optimal travel traffic planning scheme are retrieved as candidate travel traffic planning schemes;

[0162] Extracting path parameters of the path corresponding to the candidate travel traffic planning scheme and the path corresponding to the optimal travel traffic planning scheme; wherein the path parameters include path travel distance, overlapping path distance and number of traffic lights;

[0163] Obtaining a first correlation value between each of the candidate travel traffic planning solutions and the optimal travel traffic planning solution by using the path parameters of the path corresponding to the candidate travel traffic planning solution and the path corresponding to the optimal travel traffic planning solution;

[0164] The first correlation value between each of the travel traffic planning schemes and the optimal travel traffic planning scheme is obtained by the following formula:

[0165] ;

[0166] Among them, G 01 Indicates the first correlation value; x 01 represents the preset first weight parameter value; x 02 represents the preset second weight parameter value; D c represents the overlapped path distance between the path corresponding to the candidate trip traffic planning solution and the path corresponding to the optimal trip traffic planning solution; D z represents the path travel distance of the path corresponding to the candidate travel traffic planning scheme; D yz N represents the path travel distance of the path corresponding to the optimal travel traffic planning solution; z Indicates the number of traffic lights on the path corresponding to the candidate trip traffic planning solution; N yz Indicates the number of traffic lights on the path corresponding to the optimal travel traffic planning solution.

[0167] The technical effect of the above technical solution is: by determining the correlation of multiple other trip traffic planning schemes except the optimal trip traffic planning scheme, the system can more comprehensively understand the characteristics of the above alternative schemes and their similarities or differences with the optimal scheme. When it is necessary to adjust the driving route, quickly select a relatively better alternative scheme with a higher correlation with the optimal scheme, so as to maintain the continuity and efficiency of the driving path. By extracting the path parameters (such as path travel distance, overlapping path distance and number of traffic lights) between the candidate trip traffic planning scheme and the optimal scheme, and using the above parameters to calculate the first correlation value, the system can evaluate the pros and cons of the alternative schemes based on quantitative indicators. This data-based decision-making method improves the scientificity and accuracy of decision-making and reduces the uncertainty caused by subjective judgment.

[0168] In a real-time traffic environment, changes in road conditions are the norm. By pre-determining the relevance of multiple travel and road condition planning schemes and storing the corresponding first correlation values, the system can quickly call the above information when needed to provide flexible path adjustment schemes for vehicle driving. This enhances the system's adaptability and response capabilities to complex traffic environments. When a vehicle needs to deviate from the optimal driving route for some reason, the system can quickly recommend an alternative path with a high degree of correlation with the optimal solution. This intelligent path recommendation function not only reduces the user's decision-making burden, but also improves the stability and continuity of driving, thereby enhancing the user's overall travel experience. By comprehensively considering factors such as path travel distance, overlapping path distance, and the number of traffic lights, the system can more accurately evaluate the pros and cons of different travel and road condition planning schemes. Guide vehicles to choose more efficient and smooth driving routes, reduce congestion and waiting time, and promote the rational use of traffic resources and improve overall traffic efficiency.

[0169] In summary, this technical solution improves the scientificity and accuracy of decision-making, enhances the flexibility and adaptability of the system, improves user experience, and promotes the rational use of transportation resources by determining the correlation between multiple travel planning schemes and evaluating the pros and cons of alternative schemes based on quantitative indicators.

[0170] Specifically, when the vehicle deviation evaluation parameter exceeds a preset vehicle deviation evaluation parameter threshold, it is determined that travel road condition adjustment is required, and an alternative route is obtained for recommendation to the user, including:

[0171] When the vehicle deviation assessment parameter exceeds a preset vehicle deviation assessment parameter threshold, a path association parameter between the current vehicle driving path and the candidate trip road condition planning scheme is retrieved;

[0172] The path association parameters include the overlapped path distance and straight-line distance between the current vehicle driving path and the path corresponding to the candidate trip road condition planning scheme, and the path travel distance between the current vehicle driving path and the path corresponding to the candidate trip road condition planning scheme;

[0173] Obtaining a second correlation value between the current vehicle driving path and the candidate travel road condition planning scheme using a path correlation parameter between the current vehicle driving path and the candidate travel road condition planning scheme;

[0174] The second correlation value is obtained by the following formula:

[0175] ;

[0176] Among them, G 02 Indicates the second correlation value; x 01 represents the preset first weight parameter value; x02 represents the preset second weight parameter value; D cs represents the overlapped path distance between the current vehicle driving path and the path corresponding to the candidate trip road condition planning scheme; D z represents the path travel distance of the path corresponding to the candidate travel traffic planning scheme; D yz represents the path travel distance of the path corresponding to the optimal travel traffic planning solution; D f Indicates the straight-line distance between the current vehicle driving path and the path corresponding to the candidate trip road condition planning scheme;

[0177] The first correlation value and the second correlation value are used to obtain a comprehensive correlation value corresponding to each candidate trip road condition planning scheme; wherein the comprehensive correlation value is obtained by the following formula:

[0178] ;

[0179] Among them, G represents the comprehensive correlation value, G 01 Represents the first correlation value; G 02 represents the second correlation value;

[0180] The candidate trip road condition planning scheme corresponding to the maximum comprehensive correlation value is used as an alternative route;

[0181] The alternative path is recommended to the user.

[0182] The technical effect of the above technical solution is: by real-time monitoring whether the vehicle deviates from the preset evaluation parameter threshold, once the deviation is detected, the travel road condition control mechanism is immediately triggered. Timely discover and correct potential driving risks, such as straying into dangerous sections of road, traffic congestion areas, etc., thereby improving the safety of drivers and passengers. After detecting vehicle deviation, the system not only considers the overlapping path distance and straight-line distance between the current vehicle driving path and the candidate travel road condition planning scheme, but also introduces the path travel distance as an evaluation factor. By comprehensively calculating the second correlation value, the pros and cons of the candidate path can be more comprehensively evaluated. Provide drivers with more reasonable and efficient alternative paths and optimize driving routes.

[0183] The system not only recommends alternative routes based on the current location and driving status, but also combines the first correlation value (although not described in detail in the original text, it usually involves multi-dimensional evaluations such as road conditions, time, fuel consumption, etc.) and the second correlation value for comprehensive evaluation to ensure that the recommended route is both in line with user preferences and practical. This personalized recommendation method can significantly improve the user's driving experience and satisfaction. The above overall solution relies on advanced navigation, positioning and data processing technologies, which can analyze the vehicle's driving status and road conditions in real time and make intelligent decisions based on this. This reflects the improvement of the vehicle's intelligence level, making the driving process more convenient and efficient. At the same time, by optimizing the driving route and reducing unnecessary detours and waiting time, vehicle fuel consumption and emissions can be reduced to a certain extent, which has positive significance for environmental protection and energy conservation and emission reduction.

[0184] At the same time, by comprehensively considering the multiple path association parameters (such as overlapping path distance, straight-line distance, and path travel distance) between the current vehicle driving path and the candidate travel planning scheme, and calculating the second correlation value based on this, the system can more accurately evaluate the matching degree between the candidate path and the current vehicle driving state. Subsequently, by comprehensively processing the first correlation value (based on the correlation between the candidate path and the optimal path) and the second correlation value, the candidate path closest to the current vehicle driving state is selected as the alternative path for recommendation. This precise recommendation mechanism can significantly improve user acceptance and satisfaction.

[0185] When the vehicle deviation evaluation parameter exceeds the preset threshold, the system can respond quickly and immediately start the screening and recommendation process of alternative routes. This timely travel control can effectively prevent the vehicle from continuing to deviate from the optimal route, reduce unnecessary driving time and distance, and improve the overall travel efficiency. By intelligently recommending alternative routes that are highly matched with the user's current driving status, the system can provide users with more personalized and convenient navigation services. Users can get the best driving suggestions without having to find alternative routes on their own, which greatly improves the user's travel experience and satisfaction. By guiding vehicles to choose more reasonable and efficient driving routes, the system can alleviate traffic congestion and optimize traffic flow distribution to a certain extent. Improve the operating efficiency of the overall transportation system and reduce energy consumption and environmental pollution. This technical solution realizes real-time monitoring and precise control of vehicle driving status by introducing intelligent means such as correlation calculation and path correlation parameter extraction. This not only improves the response speed and accuracy of the system, but also enhances the system's intelligence level and autonomous learning ability.

[0186] In summary, this technical solution effectively improves driving safety and user experience through real-time monitoring, intelligent evaluation and personalized recommendations, while promoting the realization of vehicle intelligence and energy conservation and emission reduction goals. This technical solution realizes intelligent management and control of vehicle driving status by accurately recommending alternative routes, improving the timeliness of itinerary control, enhancing user experience, optimizing traffic flow and improving the intelligence level of the system.

[0187] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between the above entities or operations. Moreover, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0188] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments described above without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for planning a trip based on historical vehicle navigation data, characterized in that: The steps include: S1: Acquire historical navigation data of the vehicle, confirm the historical navigation data of the vehicle, train a trip traffic planning model based on the historical navigation data of the vehicle according to the trip traffic planning requirements based on the historical navigation data of the vehicle, test and optimize the trip traffic planning model, and determine the optimal trip traffic planning model; S2: Analyze the driving conditions of the vehicle at different times and on different routes, obtain the real-time traffic data of the vehicle, perform traffic analysis and prediction on the real-time traffic data of the vehicle based on the optimal trip traffic planning model, and generate one or more trip traffic planning solutions; S3: Selecting the optimal itinerary road condition planning scheme, guiding the vehicle to travel based on the optimal itinerary road condition planning scheme, and monitoring the traveling vehicle in real time to ensure that the vehicle is always on the optimal itinerary road condition planning route; In S3, the moving vehicle is monitored in real time, and the following operations are performed: When the vehicle is traveling based on the optimal travel and road condition planning scheme, the vehicle is monitored in real time to determine whether the vehicle is always on the optimal travel and road condition planning route; When a vehicle deviates from the optimal route planned for travel and road conditions, it will issue a real-time warning to the vehicle and take timely travel and road condition control measures to guide the vehicle. When the vehicle deviates from the optimal route planned for travel conditions, the vehicle will be given a real-time warning and timely travel condition control measures will be taken to guide the vehicle, including: When there are multiple travel traffic planning schemes obtained, after selecting the optimal travel traffic planning scheme, performing correlation determination on multiple travel traffic planning schemes other than the optimal travel traffic planning scheme, and obtaining a first correlation value between each of the travel traffic planning schemes and the optimal travel traffic planning scheme; When the vehicle deviates from the optimal travel and road condition planning route, the deviation parameters between the vehicle and the optimal travel and road condition planning route are monitored in real time; wherein the deviation parameters include the direction deviation angle between the vehicle and the optimal travel and road condition planning route, the straight-line distance between the vehicle and the optimal travel and road condition planning route, and the deviation driving speed; The vehicle deviation evaluation parameter is obtained according to the deviation parameter between the vehicle and the optimal travel road condition planning route; wherein the vehicle deviation evaluation parameter is obtained by the following formula: ； Where P represents the vehicle deviation assessment parameter; w 01 、w 02 and w 03 Respectively represent the preset first weight value, the second weight value and the third weight value; θ represents the direction deviation angle between the current vehicle and the optimal travel road condition planning route; d represents the straight-line distance between the vehicle and the optimal travel road condition planning route; α represents the preset basic proportional constant, and the value range is 0.02-0.08; β represents the preset speed index, which is used to represent the influence of speed, and the value range of the speed index is (-1,0); γ ​​represents the preset road condition complexity parameter, and the value range of the road condition complexity parameter is 0.27-2.38; L represents the rate of change of the straight-line distance deviation; v represents the deviation speed of the current vehicle; comparing the vehicle deviation assessment parameter with a preset vehicle deviation assessment parameter threshold; When the vehicle deviation assessment parameter exceeds a preset vehicle deviation assessment parameter threshold, it is determined that a travel road condition adjustment is required, and an alternative route is obtained for recommendation to the user; When the vehicle deviation evaluation parameter exceeds a preset vehicle deviation evaluation parameter threshold, it is determined that a travel road condition adjustment is required, and an alternative route is obtained for recommendation to the user, including: When the vehicle deviation assessment parameter exceeds a preset vehicle deviation assessment parameter threshold, a path association parameter between the current vehicle driving path and the candidate trip road condition planning scheme is retrieved; The path association parameters include the overlapped path distance and straight-line distance between the current vehicle driving path and the path corresponding to the candidate trip road condition planning scheme, and the path travel distance between the current vehicle driving path and the path corresponding to the candidate trip road condition planning scheme; Obtaining a second correlation value between the current vehicle driving path and the candidate travel road condition planning scheme using a path correlation parameter between the current vehicle driving path and the candidate travel road condition planning scheme; The second correlation value is obtained by the following formula: ； Among them, G 02 Indicates the second correlation value; x 01 represents the preset first weight parameter value; x 02 represents the preset second weight parameter value; D cs represents the overlapped path distance between the current vehicle driving path and the path corresponding to the candidate trip road condition planning scheme; D z represents the path travel distance of the path corresponding to the candidate travel traffic planning scheme; D yz represents the path travel distance of the path corresponding to the optimal travel traffic planning solution; D f Indicates the straight-line distance between the current vehicle driving path and the path corresponding to the candidate trip road condition planning scheme; The first correlation value and the second correlation value are used to obtain a comprehensive correlation value corresponding to each candidate trip road condition planning scheme; wherein the comprehensive correlation value is obtained by the following formula: ； Among them, G represents the comprehensive correlation value, G 01 Represents the first correlation value; G 02 represents the second correlation value; The candidate trip road condition planning scheme corresponding to the maximum comprehensive correlation value is used as an alternative route; The alternative path is recommended to the user.

2. The method for planning a trip based on historical vehicle navigation data according to claim 1, characterized in that: In S1, historical navigation data of the vehicle is obtained, and the following operations are performed: Acquiring the driving route of the vehicle based on the on-board equipment to determine the driving route data of the vehicle; Acquiring the driving time of the vehicle based on the on-board equipment to determine the driving time data of the vehicle; Acquiring the driving speed of the vehicle based on the on-board equipment to determine the driving speed data of the vehicle; Acquiring the parking point of the vehicle based on the on-board equipment to determine the parking point data of the vehicle; Acquiring the vehicle's passing locations based on the vehicle-mounted equipment to determine the vehicle's passing location data; The historical navigation data of the vehicle is determined based on the vehicle's driving route data, driving time data, driving speed data, parking point data and passing location data.

3. The method for planning a trip based on historical vehicle navigation data according to claim 2, characterized in that: In S1, the historical navigation data of the vehicle is confirmed, and the following operations are performed: Get the historical navigation data of the vehicle; Clean the historical navigation data of the vehicle, including: Perform consistency check on the vehicle's historical navigation data; Check whether the historical navigation data of the vehicle meets the requirements according to the reasonable value range and mutual relationship of each parameter in the historical navigation data of the vehicle; Remove inconsistent data that is beyond the normal range, logically unreasonable or contradictory in the historical navigation data of the vehicle; Process invalid and missing values ​​of the vehicle's historical navigation data; Remove invalid and missing data in historical navigation data that is of no value to vehicle travel and road condition planning; Identify historical navigation data that is valuable for vehicle trip planning.

4. The method for planning a trip based on historical vehicle navigation data according to claim 3, characterized in that: In S1, a trip traffic planning model based on historical vehicle navigation data is trained, and the trip traffic planning model is tested and optimized, and the following operations are performed: Get the historical navigation data of the vehicle; Dividing the historical navigation data of the vehicle; Determine a trip traffic planning training set and a trip traffic planning test set; According to the needs of itinerary planning based on the historical navigation data of the vehicle, a neural network model framework suitable for itinerary planning is selected; Based on the trip traffic planning training set, training the selected neural network model framework suitable for vehicle trip traffic planning; Train a trip traffic planning model based on the vehicle's historical navigation data; Based on the trip planning test set, the performance of the trip planning model based on the vehicle's historical navigation data is tested and evaluated; Determine the performance test evaluation report based on the trip traffic planning model; Mining and analyzing the performance test evaluation report based on the trip traffic planning model; Determine the parameter adjustment optimization scheme based on the trip traffic planning model; Optimizing the parameters of the trip traffic planning model according to a parameter adjustment optimization scheme based on the trip traffic planning model; Determine the optimal travel traffic planning model.

5. The method for planning a trip based on historical vehicle navigation data according to claim 4, characterized in that: In S2, the real-time traffic data of the vehicle is obtained, and the traffic analysis and prediction are performed on the real-time traffic data of the vehicle, and the following operations are performed: Based on the on-board equipment, the road congestion situation of vehicles at different times and on different routes is obtained in real time to determine the vehicle road congestion data; Based on the on-board equipment, the road accident situation of the vehicle at different times and on different routes is acquired in real time to determine the vehicle road accident data; Based on the on-board equipment, the road construction conditions of the vehicle at different times and on different routes are acquired in real time to determine the vehicle road construction data; Among them, based on the vehicle's road congestion data, road accident data and road construction data, the vehicle's real-time road condition data is determined; Input the vehicle's real-time traffic data into the optimal trip traffic planning model; Analyze and predict the traffic conditions of the vehicle's real-time traffic data based on the optimal trip traffic planning model; One or more travel traffic planning schemes are generated based on user preferences, and the one or more travel traffic planning schemes are displayed to the navigation terminal for display.

6. The method for planning a trip based on historical vehicle navigation data according to claim 5, characterized in that: In S3, the optimal travel traffic planning scheme is selected, and the following operations are performed: Obtain one or more travel traffic planning plans; The user selects the best itinerary traffic planning scheme from one or more itinerary traffic planning schemes according to their own needs; The navigation terminal performs itinerary traffic planning based on the optimal itinerary traffic planning scheme, and guides the vehicle to travel based on the optimal itinerary traffic planning scheme.

7. The method for planning a trip based on historical vehicle navigation data according to claim 6, characterized in that: Determining the relevance of the other multiple travel traffic planning schemes except the optimal travel traffic planning scheme, and obtaining a first relevance value between each of the travel traffic planning schemes and the optimal travel traffic planning scheme, including: When there are multiple travel traffic planning schemes obtained, after selecting the optimal travel traffic planning scheme, multiple travel traffic planning schemes other than the optimal travel traffic planning scheme are retrieved as candidate travel traffic planning schemes; Extracting path parameters of the path corresponding to the candidate travel traffic planning scheme and the path corresponding to the optimal travel traffic planning scheme; wherein the path parameters include path travel distance, overlapping path distance and number of traffic lights; Obtaining a first correlation value between each of the candidate travel traffic planning solutions and the optimal travel traffic planning solution by using the path parameters of the path corresponding to the candidate travel traffic planning solution and the path corresponding to the optimal travel traffic planning solution; The first correlation value between each of the travel traffic planning schemes and the optimal travel traffic planning scheme is obtained by the following formula: ； Among them, G 01 Indicates the first correlation value; x 01 represents the preset first weight parameter value; x 02 represents the preset second weight parameter value; D c represents the overlapped path distance between the path corresponding to the candidate trip traffic planning solution and the path corresponding to the optimal trip traffic planning solution; D z represents the path travel distance of the path corresponding to the candidate travel traffic planning scheme; D yz N represents the path travel distance of the path corresponding to the optimal travel traffic planning solution; z Indicates the number of traffic lights on the path corresponding to the candidate trip traffic planning solution; N yz Indicates the number of traffic lights on the path corresponding to the optimal travel traffic planning solution.

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