Electric vehicle navigation route planning method and related equipment

Abstract

The invention discloses an electric vehicle navigation route planning method and related equipment, and relates to the technical field of intelligent driving, the method comprises the following steps: obtaining preference information of a target user and route information of an alternative route, the route information comprising traffic information, weather information, route length, route landscape quantity and predicted travel time; according to the traffic information, the weather information, the route length, the number of route landscapes, the predicted travel time and the preference information, the comprehensive integrating degree of the alternative routes is determined; and when the comprehensive integrating degree is greater than a preset integrating degree threshold value, taking the alternative route as a target route for navigation. According to the method, the optimal travel route which is more personalized and efficient and meets the requirements of the electric vehicle is provided by intelligently integrating user preferences and real-time information.

Description

Technical Field

[0001] The present application relates to the field of intelligent driving technology, and more specifically, to an electric vehicle navigation route planning method and related equipment. Background Art

[0002] With the rapid development of electric vehicle technology and the increasing demand for intelligence, navigation route planning for electric vehicles during travel has become an important part of improving user experience. In the context of increasingly complex urban road environments and increasingly diverse user travel needs, how to provide personalized route planning services has gradually become a focus of attention. However, existing electric vehicle navigation route planning methods mostly focus on the selection of the shortest path or shortest time, and have certain limitations in meeting personalized travel needs.

[0003] In current technology, electric vehicle navigation route planning methods usually lack detailed consideration of user preference factors, such as comprehensive analysis of the number of route landscapes, road complexity, traffic information and weather information, which makes the navigation results fail to fully match the personalized needs of users; this simple path planning method ignores the diversity of travel experience and is difficult to achieve deep fit with different user preferences in practical applications, which may lead to poor user experience and reduce the market competitiveness of electric vehicles; especially in terms of personalized and multi-dimensional considerations of route planning, existing technical solutions often find it difficult to balance the relationship between navigation accuracy and user preferences, resulting in a large deviation between meeting personalized needs and maintaining route rationality. Therefore, the existing technology has the technical problem of insufficient personalization of electric vehicle navigation route planning solutions. Summary of the invention

[0004] A series of simplified concepts are introduced in the summary of the invention, which will be further described in detail in the detailed description. The summary of the invention of this application does not mean to attempt to define the key features and essential technical features of the technical solution claimed for protection, nor does it mean to attempt to determine the scope of protection of the technical solution claimed for protection.

[0005] The electric vehicle navigation route planning method and related devices provided in this application can provide a more personalized, efficient and optimal travel route that meets the needs of electric vehicles by intelligently integrating user preferences and real-time information.

[0006] In a first aspect, the present application provides an electric vehicle navigation route planning method, comprising: obtaining preference information of a target user and route information of an alternative route, wherein the route information includes traffic information, weather information, route length, number of route landscapes, and estimated travel time; determining a comprehensive fit of the alternative route based on the traffic information, the weather information, the route length, the number of route landscapes, the estimated travel time, and the preference information; when the comprehensive fit is greater than a preset fit threshold, navigating the alternative route as a target route.

[0007] In a feasible implementation manner, the comprehensive compatibility of the alternative route is determined based on the traffic information, the weather information, the route length, the number of route landscapes, the expected travel time and the preference information, including: determining the vehicle energy consumption of the alternative route based on the expected travel time, the traffic information, the weather information and the route length; determining the user preference compatibility of the alternative route based on the expected travel time, the number of route landscapes, the expected travel time and the preference information; determining the comprehensive compatibility of the alternative route based on the expected travel time, the vehicle energy consumption and the user preference compatibility.

[0008] In a feasible implementation manner, the preference information includes a preferred route and an expected travel time; determining the user preference fit of the alternative route based on the expected travel time, the number of route landscapes, the expected travel time and the preference information includes: calculating the ratio of the expected travel time of the preferred route to the expected travel time of all the alternative routes, whichever is smaller, to obtain a first ratio, and adjusting the first ratio according to the travel time sensitivity weight to obtain a first weight result; calculating the ratio of the route length of the preferred route to the route length of the alternative route to obtain a second ratio, and adjusting the second ratio according to the route type preference weight to obtain a second weight result; calculating the ratio of the number of route landscapes of the alternative route to the larger value of the number of route landscapes of all the alternative routes to obtain a third ratio, which reflects the scenic advantage of the route; adjusting the third ratio according to the scenic preference weight to obtain a third weight result; accumulating the first weight result, the second weight result and the third weight result to obtain the user preference fit.

[0009] In a feasible implementation manner, determining the vehicle energy consumption of the alternative route based on the estimated travel time, the traffic information, the weather information and the route length includes: determining the vehicle acceleration duration and the vehicle deceleration duration of the alternative route based on the traffic information and the route length; determining the vehicle speed adjustment loss of the alternative route based on the weather information and the route length; determining the vehicle energy consumption of the alternative route based on the vehicle acceleration duration, the vehicle deceleration duration and the vehicle speed adjustment loss.

[0010] In a feasible implementation manner, the route information also includes charging station information, wherein the charging station information includes the number of charging vehicles, charging efficiency, and charging station location; the electric vehicle navigation route planning method also includes: determining the charging station compatibility of the alternative route based on the current position of the vehicle and the number of charging vehicles, charging efficiency, and charging station location of each charging station in the alternative route; determining the comprehensive compatibility of the alternative route based on the estimated travel time, the vehicle energy consumption, and the user preference compatibility includes: determining the comprehensive compatibility of the alternative route based on the estimated travel time, the vehicle energy consumption, the user preference compatibility, and the charging station compatibility.

[0011] In a feasible implementation manner, the determination of the charging station compatibility of the alternative route based on the current position of the vehicle and the number of charging vehicles, the charging efficiency, and the location of the charging station at each charging station in the alternative route includes: calculating the ratio of the number of charging vehicles to the charging efficiency to obtain a fourth ratio, and adjusting the fourth ratio according to the queuing time sensitivity weight to obtain a fourth weighted result; determining the charging station distance according to the charging station location and the current position of the vehicle, and adjusting the charging station distance according to the charging distance sensitivity weight to obtain a fifth weighted result; and determining the smaller value of the sum of the fourth weighted result and the fifth weighted result of each charging station in the alternative route as the charging station compatibility of the alternative route.

[0012] In a feasible implementation, the electric vehicle navigation route planning method further includes: determining the group type of the target user based on the portrait information of the target user; and determining the travel time sensitivity weight, the route type preference weight, the queuing time sensitivity weight and the charging distance sensitivity weight based on the group type.

[0013] In a second aspect, the present application also provides an electric vehicle navigation route planning device, comprising: a data acquisition unit, used to acquire preference information of a target user and route information of an alternative route, wherein the route information includes traffic information, weather information, route length, number of route landscapes and estimated travel time; a fit determination unit, used to determine the comprehensive fit of the alternative route based on the traffic information, the weather information, the route length, the number of route landscapes, the estimated travel time and the preference information; a route navigation unit, used to navigate the alternative route as a target route when the comprehensive fit is greater than a preset fit threshold.

[0014] In a third aspect, the present application further provides an electronic device comprising: a memory and a processor, wherein the processor is used to implement the steps of the electric vehicle navigation route planning method described in the first aspect when executing a computer program stored in the memory.

[0015] In a fourth aspect, the present application further provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the electric vehicle navigation route planning method described in the first aspect.

[0016] In a fifth aspect, the present application further provides a computer program product, including a computer program or a computer executable instruction, which, when executed by a processor, implements the electric vehicle navigation route planning method provided in the embodiment of the present application.

[0017] In summary, the present application combines user preference information (such as speed priority, scenery priority, etc.) with real-time route information for comprehensive evaluation, generates a fit score, and customizes routes according to user needs, so that users can obtain more personalized navigation services; through comprehensive consideration of traffic and weather information, the recommended routes can dynamically adapt to real-time traffic conditions and weather changes, avoid congestion and adverse weather effects, thereby reducing the user's driving time and the difficulty of responding to emergencies, and improving overall travel efficiency; by considering route length and traffic conditions, it can help electric vehicle users to reasonably plan their driving routes, avoid problems such as stops and charging due to insufficient battery life or excessive distances; especially in long-distance travel, route fit screening helps to avoid high-energy-consuming routes and further optimize the battery life of electric vehicles; based on the comprehensive scoring of factors such as the number of landscapes, users can choose more beautiful routes. For leisure travel or scenery enthusiasts, it can greatly enhance the viewing experience of travel and make the navigation solution more in line with the personalized needs of different users. In summary, the electric vehicle navigation route planning method provided in this application provides a more personalized, efficient and optimal travel route that meets the needs of electric vehicles by intelligently integrating user preferences and real-time information. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present specification. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0019] Figure 1 A flowchart of a method for planning a navigation route for an electric vehicle provided in an embodiment of the present application;

[0020] Figure 2 A schematic diagram of the structure of a navigation route planning device for an electric vehicle provided in an embodiment of the present application;

[0021] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0022] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, rather than to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "is" and "has" and any variations thereof involved in the present application are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0023] The term "module" or "unit" in this application refers to a computer program or a part of a computer program that has a predetermined function and works together with other related parts to achieve a predetermined goal, and can be implemented in whole or in part by using software, hardware (such as processing circuits or memories) or a combination thereof. Similarly, a processor (or multiple processors or memories) can be used to implement one or more modules or units. In addition, each module or unit can be part of an overall module or unit that includes the function of the module or unit.

[0024] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. In the following description, it is related to "some embodiments", which describes a subset of all possible embodiments, but it can be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict.

[0025] See also Figure 1 , Figure 1 1 is a flow chart of a method for planning a navigation route for an electric vehicle provided in an embodiment of the present application. The method may specifically include the following steps 101 to 103:

[0026] Step 101, obtaining preference information of a target user and route information of an alternative route, wherein the route information may include traffic information, weather information, route length, number of route landscapes, and estimated travel time;

[0027] Specifically, the target user is a specific user who uses the electric vehicle navigation route planning method, that is, an individual for whom the navigation service is to provide route planning services. The alternative route is any different route that may be selected from the current location of the target user to the destination. The preference information refers to a series of personal preferences and needs of the target user. The route information refers to various data and information related to the alternative routes, which is used to evaluate and compare the advantages and disadvantages of different routes.

[0028] For example, first, the user's personalized preferences during travel can be obtained through a user interface (such as an in-vehicle system or a mobile application), such as the preferred route type, expected travel time, preference for scenery along the way, etc. These preferences reflect the user's special needs in selecting navigation routes, such as the desire to choose a power-saving route, a route that arrives quickly, or a route with beautiful scenery, thereby ensuring that the navigation service is more personalized; based on the acquisition of the user's preference information, relevant information for each alternative route can be collected in real time.

[0029] Through the implementation of step 101, detailed information on user preferences and alternative routes (including traffic, weather, route length, number of landscapes, etc.) is collected, providing basic data for subsequent fit calculations, enabling personalized understanding of user needs and real-time traffic conditions, and tailoring navigation solutions for users, thereby improving users' travel experience and the accuracy of route selection.

[0030] Step 102, determining the comprehensive suitability of the alternative routes based on traffic information, weather information, route length, number of route landscapes, estimated travel time, and preference information;

[0031] Specifically, comprehensive fit is a quantitative indicator used to measure the degree of match between alternative routes and the needs and preferences of target users. It is a score that takes into account multiple different factors and reflects the overall suitability and attractiveness of the route. The fit of each alternative route can be calculated by taking into account factors such as traffic, weather, route length, landscape, and estimated time, as well as the user's personal preferences; for example, if the user likes scenery and has plenty of time, a slightly longer but scenic route may receive a higher comprehensive fit and be recommended to the user.

[0032] Through the implementation of step 102, traffic information, weather impact, route length, number of landscapes, estimated time and user preferences are comprehensively analyzed, and alternative routes are scored from multiple dimensions, ensuring that the recommended route not only meets the user's preferences but also adapts to the current environment and road conditions, ultimately providing the user with a preferred route that meets both efficiency and comfort.

[0033] Step 103, when the comprehensive degree of fit is greater than a preset degree of fit threshold, the alternative route is used as the target route for navigation;

[0034] Specifically, the preset compatibility threshold is a pre-set standard or limit value used to determine whether the alternative route is sufficiently consistent with the user's travel needs and preferences, thereby deciding whether to use it as a target route for navigation.

[0035] For example, assuming that the preset compatibility threshold is 80 points, the compatibility of two alternative routes is calculated for user A: Route 1 is 85 points and Route 2 is 75 points; since the comprehensive compatibility of Route 1 of 85 points is greater than the threshold of 80 points, Route 1 will be determined as the navigation target route, and the compatibility of Route 2 does not reach the threshold, so Route 2 will not be recommended as a navigation route.

[0036] By implementing step 103, the compatibility threshold is screened, and only high-compatibility routes that meet the preset standards are used as navigation targets, thereby automatically excluding routes that do not meet user needs. This can greatly improve the accuracy of navigation recommendations, reduce the risk of users choosing inappropriate routes, and ensure the high quality and satisfaction of navigation results.

[0037] In summary, the embodiment of the present application combines user preference information (such as speed priority, scenery priority, etc.) with real-time route information for comprehensive evaluation, generates a fit score, and customizes routes according to user needs, so that users can obtain more personalized navigation services; through comprehensive consideration of traffic and weather information, the recommended route can dynamically adapt to real-time traffic conditions and weather changes, avoid congestion and adverse weather effects, thereby reducing the user's driving time and the difficulty of dealing with emergencies, and improving overall travel efficiency; by considering the route length and traffic conditions, it can help electric vehicle users to reasonably plan their driving routes and avoid problems such as midway stops and charging due to insufficient battery life or too long distance. Especially in long-distance travel, route fit screening helps to avoid high-energy-consuming routes and further optimize the battery life of electric vehicles; under the comprehensive score of factors such as the number of landscapes, users can choose a more beautiful route, which can greatly improve the viewing experience of travel for leisure travel or scenery enthusiasts, making the navigation solution more in line with the personalized needs of different users. In summary, the electric vehicle navigation route planning method provided in the embodiment of the present application provides a more personalized, efficient and optimal travel route that meets the needs of electric vehicles by intelligently integrating user preferences and real-time information.

[0038] In some embodiments, the aforementioned step 102 may include: determining the vehicle energy consumption of the alternative route based on the expected travel time, traffic information, weather information and route length; determining the user preference compatibility of the alternative route based on the expected travel time, the number of route landscapes, the expected travel time and preference information; determining the comprehensive compatibility of the alternative route based on the expected travel time, the vehicle energy consumption and the user preference compatibility.

[0039] Specifically, first, the vehicle energy consumption of the alternative route can be calculated based on the expected travel time, traffic information, weather conditions and route length to ensure that the route selection is not only in line with the itinerary plan, but also optimal in terms of electric vehicle energy consumption. The impact of road congestion, road section length and weather on vehicle battery life can be evaluated to estimate the energy consumption of the entire route; for example, if an alternative route is short and has smooth traffic and clear weather, it can be predicted that the vehicle energy consumption is low, thereby increasing the energy compatibility score for the route. Secondly, the user preference compatibility of the alternative route can be calculated; the user preference compatibility is based on the expected travel time, the number of route landscapes, the expected travel time and user-specific preference information (such as "scenery priority" or "power saving priority"). The number of landscapes and travel time in the route can be analyzed to evaluate whether the route meets the user's visual and itinerary expectations; for example, assuming that the user prefers "scenery priority" and has more travel time, the preference compatibility score can be increased for routes with rich landscapes and more scenic spots along the way to consider the vehicle energy consumption and the user's personalized needs.

[0040] For example, suppose the user chooses to go from city A to city B, and there are two alternative routes, and the user prefers "scenery first" and hopes to "save power"; the energy consumption can be calculated first, assuming that the estimated energy consumption of route 1 is 18 kWh, while the energy consumption of route 2 is 20 kWh, so route 1 has a higher score; next, the user's preference fit can be evaluated: Route 1 has many natural scenic spots along the way, with rich landscapes; Route 2 is mainly high-speed sections, with fewer scenery; therefore, in terms of preference fit, route 1 also gets a higher score. After combining these two scores, route 1 has a higher overall fit and may eventually be recommended as the best navigation route.

[0041] For example, the comprehensive compatibility of the alternative routes can be calculated by the following formula:

[0042]

[0043] In the formula, S(P) represents the comprehensive fit of the alternative route P, which is used to comprehensively measure the applicability of the route. The higher the value, the more the route meets the user's needs and preferences; ω 1 The weight coefficient of the estimated travel time indicates the user's sensitivity to travel time. If the user is more concerned about travel time, this weight will be higher, increasing the impact of time on the overall fit; T max It represents the longest estimated travel time among all the alternative routes, which is used as a benchmark to calculate the relative efficiency of the alternative route P in terms of time; T(P) represents the estimated travel time of the alternative route P. The shorter the travel time, the higher the score will be in the calculation of comprehensive fit; α represents the adjustment index of travel time, which controls the nonlinear effect of time on comprehensive fit; ω 2 represents the weight coefficient of vehicle energy consumption, indicating the user's emphasis on vehicle energy consumption. For users who prefer energy saving, this value is higher so that energy consumption has a greater impact on the overall fit; λ represents the energy consumption adjustment factor, which controls the influence curve of energy consumption on the overall fit. The larger the value, the more significant the impact of energy consumption; C(P) represents the vehicle energy consumption of the alternative route P, that is, the total energy consumption from the starting point to the destination. The route with lower energy consumption scores higher in this evaluation; ω 3 The weight coefficient of the user preference fit is used to control the importance of user preferences in the comprehensive fit. The higher the weight of the user preference fit, the greater the impact of the user's specific preference on the route selection. U(P) represents the user preference fit of the alternative route P, which measures the degree of conformity of the route to the user's preferences (such as scenery priority, energy saving priority, etc.). The higher the score, the more the route meets the user's personalized needs.

[0044] Through the implementation of the above embodiments, real-time data is used to calculate the vehicle energy consumption of alternative routes, so that route planning is more in line with the endurance characteristics of electric vehicles, helping users reduce energy consumption and achieve energy-saving travel, and being able to provide users with travel plans that are both energy-saving and meet personalized needs.

[0045] In some embodiments, the aforementioned preference information may include a preferred route and an expected travel time; the aforementioned determination of the user preference fit of the alternative routes based on the expected travel time, the number of route landscapes, the expected travel time and the preference information may include: calculating the ratio of the expected travel time of the preferred route to the smaller value of the expected travel time of all the alternative routes to obtain a first ratio, and adjusting the first ratio according to the travel time sensitivity weight to obtain a first weight result; calculating the ratio of the route length of the preferred route to the route length of the alternative route to obtain a second ratio, and adjusting the second ratio according to the route type preference weight to obtain a second weight result; calculating the ratio of the number of route landscapes of the alternative route to the larger value of the number of route landscapes of all the alternative routes to obtain a third ratio, which ratio reflects the scenic advantage of the route; adjusting the third ratio according to the scenic preference weight to obtain a third weight result; accumulating the first weight result, the second weight result and the third weight result to obtain the user preference fit.

[0046] Specifically, the ratio of the alternative routes can be calculated according to the user's preferred travel time, and the estimated travel time of the user's preferred route can be compared with the shortest estimated travel time of all alternative routes to obtain a first ratio; this ratio indicates the relative efficiency of the preferred route in terms of time; if the user prefers the shortest time, the first ratio can be adjusted according to the user's travel time sensitivity weight to generate a first weight result, which can more accurately reflect the user's demand for travel time. Next, the matching degree of the alternative routes in terms of length and number of landscapes can be calculated; first, the length of the preferred route is calculated by ratio with the length of the alternative route to obtain a second ratio, and this ratio is adjusted according to the user's route type preference weight to generate a second weight result, ensuring that the route length can reflect the user's preference; if the user pays attention to the scenery along the way, the number of landscapes of the alternative route will be calculated by ratio with the highest number of landscapes among all alternative routes to generate a third ratio, and the user's scenery preference weight will be applied to generate a third weight result; finally, the three weight results are accumulated to obtain the user preference fit of the alternative route for recommending the best route.

[0047] Exemplarily, the user preference fit can be calculated according to the following formula:

[0048]

[0049] In the formula, U(P) represents the user preference fit of the alternative route P, which is used to measure the personalized recommendation effect of the route. The higher the value, the more the route meets the user's needs; ω t The weight coefficient of travel time represents the user's sensitivity to travel time. The higher the user's sensitivity to time, the greater the weight, which is used to amplify or weaken the impact of time on the final preference fit; t(P) represents the estimated travel time of alternative route P, that is, the total time from the starting point to the destination of route P; t min represents the shortest travel time among all alternative routes, which is used for normalization so that the time ratio of the preferred route can measure its relative difference with the shortest time route; ω r The weight coefficient of route length reflects the importance users place on route length. If users care more about route length (e.g., prefer shorter or longer routes), this value is higher to adjust the impact of route length on preference fit. r represents the length of the preferred route, which is used to compare the adaptability of the route in user preferences; L represents the total length of the alternative routes; ω q The weight coefficient of the scenery preference represents the importance the user places on the number of landscapes. Users who prefer scenery should set a higher weight value so that the number of landscapes has a greater impact on the matching degree calculation; N s The number of landscapes in the alternative route reflects the advantage of the route in terms of landscapes along the way; N represents the maximum number of landscapes among all alternative routes, which is used for normalized comparison to reflect the relative advantage of route P in terms of landscapes. The three weighted items in the formula respectively reflect the contribution of time, route length, and number of landscapes to the fit of user preferences. By calculating the comprehensive weighted results of these factors, the fit of alternative routes to user preferences can be quantified, helping to more accurately recommend the best route that meets user needs.

[0050] Through the implementation of the above embodiments, a multi-dimensional analysis of user preferences (such as travel time, route scenery) is performed to provide route planning that meets the user's personalized needs, thereby increasing the pleasure and satisfaction of travel.

[0051] In some embodiments, the aforementioned determination of the vehicle energy consumption of the alternative route based on the estimated travel time, traffic information, weather information and route length may include: determining the vehicle acceleration duration and the vehicle deceleration duration of the alternative route based on the traffic information and route length; determining the vehicle speed adjustment loss of the alternative route based on the weather information and route length; determining the vehicle energy consumption of the alternative route based on the vehicle acceleration duration, the vehicle deceleration duration and the vehicle speed adjustment loss.

[0052] Specifically, first, the acceleration and deceleration time of the vehicle in the alternative route can be calculated based on traffic information and route length to more accurately estimate the vehicle energy consumption, because acceleration and deceleration operations usually consume more power; for example, in sections with traffic congestion or more traffic lights, vehicles will frequently accelerate and decelerate, thereby increasing energy consumption; on the contrary, in smoother highway sections, the acceleration and deceleration time of the vehicle is shorter, and the energy consumption is relatively lower. Next, the speed adjustment loss in the route can be further estimated by combining weather information and route length; weather factors (such as rainfall, low temperature and strong wind) will affect the endurance of electric vehicles, so corresponding adjustments can be made based on these factors; for example, in cold weather, the battery of an electric vehicle may consume more power to maintain performance; and in windy weather, wind resistance will cause the vehicle to require additional power to maintain a stable speed; the acceleration time, deceleration time and speed adjustment loss can finally be combined to calculate the overall energy consumption of the route, forming a more accurate estimate of vehicle energy consumption.

[0053] For example, assume that a user has two alternative routes from city A to city B; Route 1 is a city road, 50 kilometers long, with multiple congestion points, and the estimated acceleration and deceleration times are 15 minutes and 10 minutes respectively, and the weather forecast shows a drop in temperature, which will increase battery energy consumption; Route 2 is a highway section, 60 kilometers long, with smooth road conditions, acceleration and deceleration time of only 5 minutes, and clear weather; it can be calculated that Route 1 consumes more energy due to frequent acceleration and deceleration and low temperatures, while although Route 2 is longer, the overall energy consumption is lower due to the smooth vehicle speed and clear weather.

[0054] Through the implementation of the above embodiments, the acceleration and deceleration time of the vehicle and the loss of speed adjustment are dynamically calculated based on traffic information, weather information, etc., and the actual energy consumption of the vehicle is accurately estimated, so that the planned route is more in line with the real-time road conditions, energy management is optimized, and users are provided with more energy-saving options in route selection.

[0055] In some embodiments, the aforementioned route information may also include charging station information, wherein the aforementioned charging station information may include the number of charging vehicles, charging efficiency, and charging station location; the aforementioned electric vehicle navigation route planning method may also include: determining the charging station compatibility of the alternative route based on the current position of the vehicle and the number of charging vehicles, charging efficiency, and charging station location of each charging station in the aforementioned alternative route; the aforementioned determination of the comprehensive compatibility of the alternative route based on the estimated travel time, vehicle energy consumption, and user preference compatibility may include: determining the comprehensive compatibility of the alternative route based on the estimated travel time, vehicle energy consumption, user preference compatibility, and charging station compatibility.

[0056] Specifically, when obtaining route information, relevant data of charging stations can also be included to better meet the endurance requirements of electric vehicles; these charging station information include the vehicle queue situation, charging efficiency (charging speed) and location of the charging station, which can be combined with these data to calculate the charging station fit according to the current vehicle position and the status of each charging station in the alternative route; the calculation of charging station fit takes into account factors such as the distance to the charging station, the waiting time in the queue and the charging speed. For example, charging stations with a large number of vehicles in the queue may lead to a long wait; charging stations with low charging efficiency will increase the charging time. Therefore, charging stations with fewer queues, high charging efficiency and closer to the driving route can be given priority, thereby improving the convenience and efficiency of users when stopping at charging stations; when calculating the comprehensive fit of the alternative route, the four aspects of expected travel time, vehicle energy consumption, user preference fit and charging station fit can be integrated to provide a more comprehensive score.

[0057] For example, assume that the user has two alternative routes from city A to city B, and can find several charging stations along the route. Route 1 passes through charging stations A and B, where charging station A has a short queue time, high charging efficiency, and is located close to the main road; although charging station B is also on the road, there are many vehicles queuing and the charging speed is slow. Route 2 passes through charging stations C and D, both of which have medium charging efficiency, but no queues. Based on the user's current location and route requirements, it can be calculated that the charging station fit of route 1 is high. Combined with the charging station fit, user preferences, and energy consumption evaluation, route 1 can be recommended as the best choice to ensure that the user's charging needs during the trip are efficiently met.

[0058] For example, the comprehensive compatibility of the alternative routes can be calculated by the following formula:

[0059]

[0060] In the formula, S(P) represents the comprehensive fit of the alternative route P, which is used to comprehensively measure the applicability of the route. The higher the value, the more the route meets the user's needs and preferences; ω 1 The weight coefficient of the estimated travel time indicates the user's sensitivity to travel time. If the user is more concerned about travel time, this weight will be higher, increasing the impact of time on the overall fit; T max It represents the longest estimated travel time among all the alternative routes, which is used as a benchmark to calculate the relative efficiency of the alternative route P in terms of time; T(P) represents the estimated travel time of the alternative route P. The shorter the travel time, the higher the score will be in the calculation of comprehensive fit; α represents the adjustment index of travel time, which controls the nonlinear effect of time on comprehensive fit; ω 2represents the weight coefficient of vehicle energy consumption, indicating the user's emphasis on vehicle energy consumption. For users who prefer energy saving, this value is higher so that energy consumption has a greater impact on the overall fit; λ represents the energy consumption adjustment factor, which controls the influence curve of energy consumption on the overall fit. The larger the value, the more significant the impact of energy consumption; C(P) represents the vehicle energy consumption of the alternative route P, that is, the total energy consumption from the starting point to the destination. The route with lower energy consumption scores higher in this evaluation; ω 3 The weight coefficient of the user preference fit is used to control the importance of user preferences in the comprehensive fit. The higher the weight of the user preference fit, the greater the impact of the user's specific preference on the route selection. U(P) represents the user preference fit of the alternative route P, which measures the degree of conformity of the route to the user's preferences (such as scenery priority, energy saving priority, etc.). The higher the score, the more the route meets the user's personalized needs. opt Indicates the charging station compatibility of the alternative route. This value reflects the adaptability of the charging station on the alternative route. The higher the value, the more the alternative route meets the user's charging needs; ω 4 The weight coefficient representing the charging station's suitability.

[0061] Through the implementation of the above embodiments, the real-time queuing situation, charging efficiency, location and other information of the charging stations are integrated, and the optimal charging station is intelligently selected, which can reduce the waiting time and inconvenience of users during the charging process and optimize the charging arrangement. It not only meets the travel needs and preferences of users, but also reasonably plans the charging station stops to avoid interruptions to the journey due to insufficient battery life.

[0062] In some embodiments, the aforementioned determination of the charging station compatibility of the alternative route based on the current position of the vehicle and the number of charging vehicles, the charging efficiency, and the location of the charging station at each charging station in the alternative route may include: calculating the ratio of the number of charging vehicles to the charging efficiency to obtain a fourth ratio, and adjusting the fourth ratio according to the queuing time sensitivity weight to obtain a fourth weighted result; determining the charging station distance according to the charging station location and the current position of the vehicle, and adjusting the charging station distance according to the charging distance sensitivity weight to obtain a fifth weighted result; and determining the smaller value of the sum of the fourth weighted result and the fifth weighted result of each charging station in the alternative route as the charging station compatibility of the alternative route.

[0063] Specifically, first, the ratio of the number of charging vehicles to the charging efficiency of each charging station can be calculated to obtain the fourth ratio, which reflects the combination of the queuing situation and the charging speed of the charging station. Generally, the charging station with fewer queuing vehicles and higher charging efficiency will get a lower ratio, indicating that the user has a shorter waiting time at the station; the queuing time sensitivity weight can also be applied to the ratio according to the user's queuing tolerance to generate a fourth weighted result, so that the user's preference for queuing time is also taken into consideration. Next, the distance between the location of each charging station and the current vehicle location can be calculated to obtain the charging station distance, and the charging distance sensitivity weight can be applied to the distance according to the user's charging distance preference to generate a fifth weighted result. This weight ensures that those charging stations that are close to the current driving route and meet the user's preference will receive a higher score. Finally, the fourth weighted result and the fifth weighted result of each charging station can be added, and the smaller value of the summed result can be selected as the charging station fit for the alternative route.

[0064] Exemplarily, the suitability of the charging stations of the alternative routes can be calculated by the following formula:

[0065]

[0066] In the formula, C opt represents the charging station compatibility of the alternative route. This value reflects the adaptability of the charging station on the alternative route. The higher the value, the more the alternative route meets the user's charging needs. min represents the operator with a smaller value. η represents the queue time sensitivity weight coefficient, which is used to adjust the user's tolerance to the queue time. Q(C i ) indicates charging station C i The more vehicles in the queue, the longer the waiting time is, which will increase the user's stay time at the charging station; R(C i ) indicates charging station C i The charging efficiency is the charging speed per unit time. The higher the charging efficiency, the shorter the time required for vehicle charging, thus improving the user's charging experience. θ represents the charging distance sensitivity weight coefficient, which represents the user's attention to the distance of the charging station. The higher this value is, the more users prefer to choose a charging station that is closer. i ,P) represents the current vehicle position P and the charging station C i The distance between the charging stations is smaller, and the charging station with smaller distance can meet the charging demand without significantly deviating from the driving route; C i It represents the i-th charging station on the alternative route, i ranges from 1 to n, and n represents the number of all charging stations on the alternative route.

[0067] Through the implementation of the above embodiments, the queuing time and distance sensitivity weights of the charging stations are calculated, the suitability of the charging stations is intelligently evaluated, and the intelligence of navigation planning is further improved to ensure that users can obtain the best charging service within a reasonable distance. It is ensured that the selected charging station not only has less queues and high charging efficiency, but is also as close to the driving route as possible, reducing the extra time cost of users during the journey.

[0068] In some embodiments, the aforementioned electric vehicle navigation route planning method may further include: determining the group type of the target user based on the portrait information of the target user; and determining the travel time sensitivity weight, route type preference weight, queue time sensitivity weight and charging distance sensitivity weight based on the group type.

[0069] Specifically, user portrait information can include characteristics such as the user's daily travel habits, age, driving style, etc., to help identify the user's specific preferences while driving; for example, by analyzing the user's past travel data, it can be identified whether the user is a commuter user who focuses on time efficiency, or a leisure user who pays more attention to the experience along the way; according to the type of different groups, different sensitivity weights can be set for factors such as travel time, route type, waiting in line, and distance to charging stations, so as to provide users with navigation solutions that better meet their needs. Specifically, the weight settings for different types of user groups will be different; for commuter users, the travel time sensitivity weight can be set to a higher value to give priority to routes with the shortest time and smooth traffic; and for leisure users, the route type preference weight and charging station distance sensitivity weight can be increased to choose routes with more beautiful scenery and convenient charging stations.

[0070] For example, assume that user A is an office worker who prioritizes time efficiency in his commuting route, while user B is a weekend self-driving enthusiast who focuses on the travel experience. The travel time sensitivity weight can be increased based on user A's commuting needs, and routes that arrive faster can be recommended. For user B, the route type preference weight can be increased to choose routes that pass through scenic areas. At the same time, the sensitivity weight of the charging station location can be set to ensure a reasonable layout of charging stations along the way. Through such differentiated weight adjustments, the personalization and accuracy of navigation recommendations can be effectively improved, making navigation more in line with the needs of different user groups.

[0071] Through the implementation of the above embodiments, different weight settings are optimized according to the portrait information of the target user, so that the planning is more in line with the travel needs of different user groups, further improving the flexibility and personalization level of the electric vehicle navigation route planning method, and enhancing the user experience.

[0072] Furthermore, as an implementation of the aforementioned method embodiment, the present application also provides an electric vehicle navigation route planning device, which is used to implement the aforementioned method embodiment. The device embodiment corresponds to the aforementioned method embodiment. For ease of reading, the present electric vehicle navigation route planning device embodiment will no longer repeat the details of the aforementioned method embodiment one by one, but it should be clear that the device in the embodiment of the present application can correspond to and implement all the contents of the aforementioned method embodiment. Figure 2 As shown, the electric vehicle navigation route planning device 20 includes: a data acquisition unit 201, a compatibility determination unit 202 and a line navigation unit 203, wherein the data acquisition unit 201 is used to obtain the preference information of the target user and the route information of the alternative route, wherein the aforementioned route information may include traffic information, weather information, route length, number of route landscapes and estimated travel time; the compatibility determination unit 202 is used to determine the comprehensive compatibility of the alternative route according to the traffic information, weather information, route length, number of route landscapes, estimated travel time and preference information; the line navigation unit 203 is used to navigate the alternative route as the target route when the comprehensive compatibility is greater than a preset compatibility threshold.

[0073] In some embodiments, the compatibility determination unit 202 is also used to determine the vehicle energy consumption of the alternative route based on the expected travel time, traffic information, weather information and route length; determine the user preference compatibility of the alternative route based on the expected travel time, the number of route landscapes, the expected travel time and preference information; and determine the comprehensive compatibility of the alternative route based on the expected travel time, the vehicle energy consumption and the user preference compatibility.

[0074] In some embodiments, the preference information includes a preferred route and an expected travel time; the fit determination unit 202 is further used to calculate the ratio of the expected travel time of the preferred route to the smaller value of the expected travel time of all alternative routes to obtain a first ratio, and adjust the first ratio according to the travel time sensitivity weight to obtain a first weight result; calculate the ratio of the route length of the preferred route to the route length of the alternative route to obtain a second ratio, and adjust the second ratio according to the route type preference weight to obtain a second weight result; calculate the ratio of the number of route landscapes of the alternative route to the larger value of the number of route landscapes of all alternative routes to obtain a third ratio, which reflects the scenic advantage of the route; adjust the third ratio according to the scenery preference weight to obtain a third weight result; accumulate the first weight result, the second weight result and the third weight result to obtain the user preference fit.

[0075] In some embodiments, the compatibility determination unit 202 is also used to determine the vehicle acceleration time and vehicle deceleration time of the alternative route based on traffic information and route length; determine the vehicle speed adjustment loss of the alternative route based on weather information and route length; determine the vehicle energy consumption of the alternative route based on the vehicle acceleration time, vehicle deceleration time and vehicle speed adjustment loss.

[0076] In some embodiments, the route information also includes charging station information, wherein the charging station information includes the number of charging vehicles, charging efficiency, and charging station location; the compatibility determination unit 202 is also used to determine the charging station compatibility of the alternative route based on the current position of the vehicle and the number of charging vehicles, charging efficiency, and charging station location of each charging station in the alternative route; and determine the comprehensive compatibility of the alternative route based on the estimated travel time, vehicle energy consumption, user preference compatibility, and charging station compatibility.

[0077] In some embodiments, the compatibility determination unit 202 is also used to calculate the ratio of the number of charging vehicles to the charging efficiency to obtain a fourth ratio, and adjust the fourth ratio according to the queuing time sensitivity weight to obtain a fourth weight result; determine the distance of the charging station according to the location of the charging station and the current location of the vehicle, and adjust the distance of the charging station according to the charging distance sensitivity weight to obtain a fifth weight result; the smaller value of the sum of the fourth weight result and the fifth weight result of each charging station in the alternative route is determined as the charging station compatibility of the alternative route.

[0078] In some embodiments, the route navigation unit 203 is also used to determine the group type of the target user based on the portrait information of the target user; and determine the travel time sensitivity weight, route type preference weight, queue time sensitivity weight and charging distance sensitivity weight based on the group type.

[0079] The present application also provides a computer-readable storage medium, which stores computer-executable instructions or computer programs. When the computer-executable instructions or computer programs are executed by a processor, the processor will be caused to execute any step of the electric vehicle navigation route planning method provided in the present application.

[0080] In some embodiments, the computer-readable storage medium may be a memory such as RAM, read-only memory (ROM), flash memory, magnetic surface memory, optical disk, or compact disc read-only memory (CD-ROM); or it may be various devices including one or any combination of the above memories.

[0081] In some embodiments, computer executable instructions may be in the form of a program, software, software module, script or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including as a stand-alone program or as a module, component, subroutine or other unit suitable for use in a computing environment.

[0082] In some embodiments, computer-executable instructions may, but need not, correspond to a file in a file system, may be stored as part of a file storing other programs or data, for example, in one or more scripts in a HyperText Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files storing one or more modules, subroutines, or code portions).

[0083] In some embodiments, computer executable instructions may be deployed to be executed on one electronic device, or on multiple electronic devices located at one site, or on multiple electronic devices distributed at multiple sites and interconnected by a communication network.

[0084] like Figure 3 As shown, the present application also provides an electronic device 30, including a memory 310, a processor 320, and a computer program 311 stored in the memory 310 and executable on the processor, and when the processor 320 executes the computer program 311, any step of the above-mentioned electric vehicle navigation route planning method is implemented.

[0085] The present application also provides a computer program product, which includes a computer program or a computer executable instruction, and the computer program or the computer executable instruction is stored in a computer-readable storage medium. The processor of the electronic device reads the computer program or the computer executable instruction from the computer-readable storage medium, and the processor executes the computer program or the computer executable instruction, so that the electronic device performs any step of the electric vehicle navigation route planning method described above in the present application.

[0086] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for planning a navigation route for an electric vehicle, characterized in that: include: Obtaining preference information of a target user and route information of an alternative route, wherein the route information includes traffic information, weather information, route length, number of route landscapes, and estimated travel time; Determining the comprehensive suitability of the alternative routes according to the traffic information, the weather information, the route length, the number of route landscapes, the estimated travel time, and the preference information; When the comprehensive degree of fit is greater than a preset degree of fit threshold, the alternative route is used as a target route for navigation.

2. The electric vehicle navigation route planning method according to claim 1, characterized in that: Determining the comprehensive suitability of the alternative routes according to the traffic information, the weather information, the route length, the number of route landscapes, the estimated travel time and the preference information includes: determining the vehicle energy consumption of the alternative route based on the estimated travel time, the traffic information, the weather information and the route length; Determining the user preference compatibility of the alternative route according to the estimated travel time, the number of route landscapes, the estimated travel time, and the preference information; The comprehensive compatibility of the alternative routes is determined according to the estimated travel time, the vehicle energy consumption and the user preference compatibility.

3. The electric vehicle navigation route planning method according to claim 2, characterized in that: The preference information includes a preferred route and a desired travel time; Determining the user preference compatibility of the alternative route according to the estimated travel time, the number of route landscapes, the estimated travel time and the preference information includes: Calculating the ratio of the estimated travel time of the preferred route to the smaller value of the estimated travel time of all the alternative routes to obtain a first ratio, and adjusting the first ratio according to the travel time sensitivity weight to obtain a first weight result; Calculating the ratio of the route length of the preferred route to the route length of the alternative route to obtain a second ratio, and adjusting the second ratio according to the route type preference weight to obtain a second weight result; Calculating the ratio of the number of route landscapes of the alternative route to the larger value of the number of route landscapes of all alternative routes to obtain a third ratio, which reflects the advantage of the route in terms of scenery; adjusting the third ratio according to the scenery preference weight to obtain a third weighted result; The first weight result, the second weight result and the third weight result are accumulated to obtain the user preference fit.

4. The electric vehicle navigation route planning method according to claim 2, characterized in that: The determining the vehicle energy consumption of the alternative route according to the estimated travel time, the traffic information, the weather information and the route length comprises: Determining a vehicle acceleration time and a vehicle deceleration time of the alternative route according to the traffic information and the route length; Determining a vehicle speed adjustment loss of the alternative route according to the weather information and the route length; The vehicle energy consumption of the alternative route is determined according to the vehicle acceleration time, the vehicle deceleration time and the vehicle speed adjustment loss.

5. The electric vehicle navigation route planning method according to any one of claims 1 to 4, characterized in that: The route information also includes charging station information, wherein the charging station information includes the number of charging vehicles, charging efficiency, and charging station location; The electric vehicle navigation route planning method further includes: Determine the suitability of the charging station for the alternative route according to the current position of the vehicle and the number of charging vehicles, charging efficiency, and location of the charging station at each charging station in the alternative route; Determining the comprehensive compatibility of the alternative routes according to the estimated travel time, the vehicle energy consumption and the user preference compatibility includes: The comprehensive compatibility of the alternative routes is determined according to the estimated travel time, the vehicle energy consumption, the user preference compatibility and the charging station compatibility.

6. The electric vehicle navigation route planning method according to claim 5, characterized in that: The determining the suitability of the charging station for the alternative route according to the current position of the vehicle and the number of charging vehicles, the charging efficiency, and the location of the charging station at each charging station in the alternative route includes: Calculating a ratio of the number of charging vehicles to the charging efficiency to obtain a fourth ratio, and adjusting the fourth ratio according to a queue time sensitivity weight to obtain a fourth weight result; Determining a distance to the charging station according to the location of the charging station and the current location of the vehicle, and adjusting the distance to the charging station according to a charging distance sensitivity weight to obtain a fifth weighted result; The smaller value of the sum of the fourth weight result and the fifth weight result of each charging station in the alternative route is determined as the charging station compatibility of the alternative route.

7. The electric vehicle navigation route planning method according to claim 6, characterized in that: The electric vehicle navigation route planning method further includes: Determining the group type of the target user according to the portrait information of the target user; The travel time sensitivity weight, the route type preference weight, the queue time sensitivity weight, and the charging distance sensitivity weight are determined according to the group type.

8. An electric vehicle navigation route planning device, characterized in that: include: A data acquisition unit, used to acquire preference information of a target user and route information of an alternative route, wherein the route information includes traffic information, weather information, route length, number of route landscapes, and estimated travel time; a compatibility determination unit, configured to determine a comprehensive compatibility of the alternative route according to the traffic information, the weather information, the route length, the number of route landscapes, the estimated travel time and the preference information; The route navigation unit is used to navigate the alternative route as the target route when the comprehensive compatibility is greater than a preset compatibility threshold.

9. An electronic device, comprising: A memory and a processor, wherein the processor is used to implement the steps of the electric vehicle navigation route planning method as described in any one of claims 1-7 when executing the computer program stored in the memory.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the electric vehicle navigation route planning method according to any one of claims 1 to 7 are implemented.

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