Navigation route recommendation method and device, electronic equipment and medium

Abstract

The invention relates to the technical field of navigation planning, and provides a navigation route recommendation method and device, electronic equipment and a medium. The method comprises the steps of determining a route starting point, a route ending point and target time information based on a user instruction; determining a plurality of initial routes from the route starting point to the route end point and route distances thereof based on map information; determining all travel schemes based on the historical driving data set; based on the historical driving data set, determining passing efficiency corresponding to each travel scheme; calculating an expected function value of each travel scheme according to a driving expected function scheme based on the corresponding traffic efficiency and the route distance; and taking a preset number of travel schemes with minimum expected function values as recommendation results. According to the method, calculation is more accurate and thorough, the user can directly obtain an effective recommendation result meeting user requirements, and the user experience is improved.

Description

Technical Field

[0001] The present application relates to the field of navigation planning technology, and in particular to a method, device, electronic device and medium for recommending a navigation route. Background Art

[0002] In recent years, navigation software has been widely used in various mobile terminals and car systems, providing users with map-based route planning and real-time navigation functions. With the refinement of user needs and the development of big data analysis, navigation software has added route congestion factors to the time-consuming calculation of route planning, so that users can understand the route conditions more accurately and plan their own travel plans.

[0003] At present, the time calculation of route planning is relatively simple, usually using the historical congestion situation in big data to directly estimate the time consumption of a certain planned route. The current estimation method does not describe the congestion situation in detail enough, the calculation is not thorough enough, and the estimation result is not accurate enough. Moreover, the application software cannot provide users with information on all routes every time they plan. Users must issue multiple instructions to the application software to obtain specific information on different planned routes, and compare all planned routes by themselves to finally determine the target route that meets their own arrangements, resulting in a poor user experience.

[0004] Therefore, how to provide a solution to the above technical problems is a problem that those skilled in the art need to solve. Summary of the invention

[0005] In view of this, the embodiments of the present application provide a method, device, electronic device and medium for recommending a navigation route to solve the problem that path planning in the prior art does not meet user needs.

[0006] According to a first aspect of an embodiment of the present application, a method for recommending a navigation route is provided, comprising:

[0007] Obtaining a user instruction, and determining a route starting point, a route end point, and target time information based on the user instruction, wherein the target time information includes at least one of a target departure time interval and a target arrival time interval;

[0008] Based on the map information, determining a plurality of initial routes from a route starting point to a route ending point and a route distance of each initial route;

[0009] Based on the historical driving data set corresponding to the map information, all travel plans are determined, each travel plan includes an initial route and a set of driving action moments corresponding to the initial route that completely meet the target time information, and each set of driving action moments includes at least one of a departure time corresponding to the target departure time interval and an arrival time corresponding to the target arrival time interval;

[0010] Based on the historical driving data set, determine the traffic efficiency corresponding to each travel plan; traffic efficiency is used to indicate the degree of road smoothness;

[0011] For each travel plan, based on the corresponding traffic efficiency and route distance, the expected function value at each departure time is calculated according to the driving expectation function scheme; in the driving expectation function scheme, the expected function value is negatively correlated with the traffic efficiency, and the expected function value is positively correlated with the route distance;

[0012] The travel plans with the smallest preset expected function values ​​are sent to the display terminal as the recommended results of the user's instructions.

[0013] According to a second aspect of an embodiment of the present application, a navigation route recommendation device is provided, comprising:

[0014] A preprocessing module, used to obtain a user instruction, and determine a route starting point, a route end point, and target time information based on the user instruction, wherein the target time information includes at least one of a target departure time interval and a target arrival time interval;

[0015] An initial route module, used for determining a plurality of initial routes from a route start point to a route end point and a route distance of each initial route based on map information;

[0016] A travel plan module, used to determine all travel plans based on the historical travel data set corresponding to the map information, each travel plan includes an initial route and a set of travel action moments corresponding to the initial route that completely meet the target time information, each set of travel action moments includes at least one of a departure time corresponding to the target departure time interval and an arrival time corresponding to the target arrival time interval;

[0017] The traffic efficiency module is used to determine the traffic efficiency corresponding to each travel plan based on the historical driving data set; the traffic efficiency is used to indicate the degree of road smoothness;

[0018] The expected calculation module is used to calculate the expected function value of each departure time for each travel plan based on the corresponding traffic efficiency and route distance according to the driving expected function scheme; in the driving expected function scheme, the expected function value is negatively correlated with the traffic efficiency, and the expected function value is positively correlated with the route distance;

[0019] The recommendation module is used to send a preset number of travel plans with the smallest expected function values ​​as the recommendation results of the user's instructions to the display terminal.

[0020] According to a third aspect of an embodiment of the present application, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0021] According to a fourth aspect of an embodiment of the present application, a computer-readable storage medium is provided, which stores a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0022] Compared with the prior art, the beneficial effects of the embodiments of the present application include at least the following: the embodiments of the present application determine a travel plan that meets the user's instructions, calculate the traffic efficiency based on the historical driving data set for each travel plan, and comprehensively calculate the expected function value based on the traffic efficiency and route distance as the basis for determining the final recommendation result. The calculation of the traffic efficiency and driving expectation function plan based on the historical driving data set characterized by non-linear road delays is more accurate and thorough. The user does not need to perform multiple operations and manual analysis to directly obtain effective recommendation results that meet the user's needs, thereby improving the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0024] Figure 1 It is a schematic diagram of an application scenario of an embodiment of the present application;

[0025] Figure 2 It is a flowchart of a method for recommending a navigation route provided in an embodiment of the present application;

[0026] Figure 3 is a structural schematic diagram of a navigation route recommendation device provided in an embodiment of the present application;

[0027] Figure 4 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0028] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0029] A method, device, electronic device and medium for recommending a navigation route according to an embodiment of the present application will be described in detail below with reference to the accompanying drawings.

[0030] Figure 1 1 is a schematic diagram of an application scenario of an embodiment of the present application. The application scenario may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a server 104 and a network 105.

[0031] The first terminal device 101, the second terminal device 102, and the third terminal device 103 can be hardware or software. When the first terminal device 101, the second terminal device 102, and the third terminal device 103 are hardware, they can be various electronic devices with display screens and support communication with the server 104, including but not limited to vehicle systems, smart phones, tablet computers, laptop portable computers, and desktop computers; when the first terminal device 101, the second terminal device 102, and the third terminal device 103 are software, they can be installed in the above electronic devices. The first terminal device 101, the second terminal device 102, and the third terminal device 103 can be implemented as multiple software or software modules, or as a single software or software module, which is not limited in the embodiment of the present application. Further, various applications can be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103, such as data processing applications, instant messaging tools, social platform software, search applications, shopping applications, etc.

[0032] The server 104 may be a server that provides various services, for example, a background server that receives a request sent by a terminal device that establishes a communication connection with the server, and the background server may receive and analyze the request sent by the terminal device, and generate a processing result. The server 104 may be a single server, or a server cluster composed of several servers, or a cloud computing service center, which is not limited in the present embodiment of the application.

[0033] It should be noted that the server 104 can be hardware or software. When the server 104 is hardware, it can be various electronic devices that provide various services for the first terminal device 101, the second terminal device 102, and the third terminal device 103. When the server 104 is software, it can be multiple software or software modules that provide various services for the first terminal device 101, the second terminal device 102, and the third terminal device 103, or it can be a single software or software module that provides various services for the first terminal device 101, the second terminal device 102, and the third terminal device 103, and the embodiment of the present application does not limit this.

[0034] The network 105 can be a wired network connected by coaxial cable, twisted pair and optical fiber, or it can be a wireless network that can interconnect various communication devices without wiring, such as Bluetooth, Near Field Communication (NFC), infrared, etc., which is not limited in the embodiments of the present application.

[0035] It should be noted that the specific types, quantities and combinations of the first terminal device 101, the second terminal device 102, the third terminal device 103, the server 104 and the network 105 can be adjusted according to the actual needs of the application scenario, and the embodiments of the present application do not limit this.

[0036] Figure 2 It is a flowchart of a method for recommending a navigation route provided in an embodiment of the present application. Figure 2 The recommended method for navigation routes is performed by the controller, which can be Figure 1 The first terminal device, the second terminal device, the third terminal device or the server is implemented. Figure 2 As shown, the recommended method includes:

[0037] S201: Obtaining a user instruction, and determining a route starting point, a route end point, and target time information based on the user instruction, wherein the target time information includes at least one of a target departure time interval and a target arrival time interval;

[0038] S202: Determine, based on the map information, a plurality of initial routes from a route start point to a route end point and a route distance of each initial route;

[0039] S203: determining all travel plans based on the historical travel data set corresponding to the map information, each travel plan including an initial route and a set of travel action moments corresponding to the initial route that completely meet the target time information, each set of travel action moments including at least one of a departure time corresponding to the target departure time interval and an arrival time corresponding to the target arrival time interval;

[0040] S204: Determine the traffic efficiency corresponding to each travel plan based on the historical driving data set; the traffic efficiency is used to indicate the degree of road smoothness;

[0041] S205: For each travel plan, based on the corresponding traffic efficiency and route distance, the expected function value of each departure time is calculated according to the driving expected function plan; in the driving expected function plan, the expected function value is negatively correlated with the traffic efficiency, and the expected function value is positively correlated with the route distance;

[0042] S206: Send a preset number of travel plans with the smallest expected function values ​​as the recommendation results of the user instruction to the display terminal.

[0043] It can be understood that after obtaining the user instruction, the method of this embodiment determines the route starting point and route end point according to the user instruction for planning multiple initial routes, determines the target time information according to the user instruction, and traverses and determines all travel plans in combination with the historical driving data set. Each travel plan includes an initial route and a set of driving action moments corresponding to the initial route that completely meet the target time information. Then, for each travel plan, the traffic efficiency is determined based on the historical driving data set, and the driving expectation function plan is comprehensively calculated based on the traffic efficiency and route distance. The expected function value obtained is used as a measure of the driving effect of the travel plan. The smaller the preset function value, the better the driving experience. Multiple travel plans with the smallest preset function values ​​are selected as recommended plans and sent to the display terminal. The user obtains the recommendation results through the display terminal without multiple operations and manual analysis. The recommendation results can directly show the most accurate and comprehensive driving plan with the best driving experience to the user, so the user experience of path planning is improved.

[0044] It can be understood that the user sends user instructions to the execution subject controller of this embodiment through the human-computer interaction interface. The form of user instructions specifically includes but is not limited to text, voice, etc., and the user instructions are parsed to obtain the route starting point, route end point and target time information.

[0045] Among them, the route starting point and the route end point are the most basic basis for route planning. The route finally planned must be a route starting from the route starting point and reaching the route end point. When executing step S202 according to the route starting point and the route end point, the map information is used as the premise for determining the route distance. The map information includes the geographic information of all roads in the current city or current area. Based on the map information, conventional path planning strategies such as Dijkstra, A*, RRT (Rapidly-Exploring Random Trees), RRT*, etc. are used to determine multiple initial routes. The number of initial routes is determined by the pre-set number of initial routes. The number of initial routes can be set to 3 or 4, which can be set according to the actual situation of hardware and computing resources and user needs.

[0046] It can be understood that the starting point of each initial route determined in step S202 is the route starting point mentioned above, and the end point is the route end point mentioned above. For each initial route, the route distance will not change with the change of departure time or arrival time, and will no longer change after being determined in step S202. However, the smoothness and driving time of each initial route are not fixed quantities. At different departure times or arrival times, the smoothness is likely to be different, which will lead to different driving times.

[0047] It is understandable that the specific information on the degree of smoothness and driving time is implicit in the specific numerical changes in the speed and acceleration of the vehicle when it is driving. Therefore, the historical data of the speed and acceleration of vehicles on all roads are collected, and the data sets obtained after the merger are sorted out as the historical driving data set. Based on the nonlinear characteristics of the historical driving data set, the nonlinear road section delay model is applied for calculation, so that the smoothness of each road at a specific time can be known, and the driving time of each road can be further calculated. Therefore, based on the historical driving data set, steps S203 and S204 are executed to determine the travel plan corresponding to each initial route that meets the target time information and the corresponding traffic efficiency.

[0048] The target time information is also determined based on the user's instructions. Compared with the fixed and unique route start point and route end point, the target time information is more flexible, and the target time information can be at least one of the target departure time interval and the target arrival time interval.

[0049] Specifically, if the user instruction includes a determined departure time interval with two left and right endpoints, the departure time interval is directly used as the target departure time interval. Similarly, if the user instruction includes a determined arrival time interval with two left and right endpoints, the arrival time interval is directly used as the target arrival time interval.

[0050] If a time point and a time relationship are set in the user instruction regarding the departure time, and the time relationship is, for example, no earlier than the time point, later than the time point, no later than the time point, or earlier than the time point, the corresponding target departure time interval can also be determined according to the time point and the time relationship. At this time, a time length suggestion option can be returned based on the user instruction, so that the user can add a user instruction about the interval length based on the time point, and the time length can be selected and set by the user according to the actual situation. Finally, the time point in the user instruction is determined as an interval endpoint of the target departure time interval, and the interval endpoint is determined to be the left endpoint or the right endpoint according to the time relationship in the user instruction. The time length in the user instruction is used as the target departure time interval length, and finally an accurate target departure time interval is determined. For example, the time point is 7 o'clock in the morning, the time relationship is no earlier than the time point, and the time length is 30 minutes, then the target departure time interval length is from 7 o'clock to 7:30. The setting of the target arrival time interval is similar, and is also determined according to the time point, time relationship and time length.

[0051] If the user instruction only sets one time point for the departure time without setting a time relationship, the target departure time interval only includes this time point, and the time point can be determined as the only departure time without executing step S203. Correspondingly, if the user instruction only sets one time point for the arrival time without setting a time relationship, the target arrival time interval only includes this time point.

[0052] Specifically, when executing step S203, the target time information is used as the calculation basis of the route time consumption, and the calculation is performed based on the historical driving data set. At the same time, the target time information is used as a constraint condition. For example, if the target time information is the target departure time interval, the departure time is initially determined from the target departure time interval according to the preset unit time length. For example, the target departure time interval is from 7:00 to 7:30 in the morning, and the preset unit time length can be set to 5 minutes or 10 minutes. The initially determined departure times include 7:00, 7:05, 7:10, 7:15, 7:20, 7:25, and 7:30. Then, for each departure time, the total time consumption of traveling each initial route is calculated based on the historical driving data set, and then the arrival time corresponding to each departure time is obtained. It is currently known that the traffic efficiency of each road will change with time. For example, the traffic efficiency of the road will be lower during the morning and evening peak hours, and the time consumption of traveling through the road will increase. Therefore, for different departure times, it is necessary to calculate the total time consumption from the departure time. The target arrival time interval is used as a constraint condition for the arrival time. After all arrival times are obtained, the arrival time is analyzed to see whether it satisfies the target arrival time interval in the target time information. Of course, if there is no target arrival time interval in the target time information, that is, there is no constraint condition for the arrival time, there is no need to analyze whether the arrival time satisfies the target time information.

[0053] Correspondingly, if there is no target departure time interval in the target time information, but only a target arrival time interval, one or more arrival times are preliminarily determined from the target arrival time interval according to the preset unit duration, and then based on the historical driving data set, the total time and departure time of each initial route are reversed for each arrival time.

[0054] It can be understood that when the travel plan is determined based on the historical driving data set in step S203, the travel action moment of each initial route that meets the target time information is calculated. If the target time information only contains the target departure time interval, one or more departure moments can be directly determined from the target departure time interval according to the preset unit time length; if the target time information only contains the target arrival time interval, one or more arrival moments can be directly determined from the target arrival time interval according to the preset unit time length; if the target time information includes both the target departure time interval and the target arrival time interval, then the interval with a smaller interval length can be selected from the target departure time interval and the target arrival time interval, and the moment within the interval can be preliminarily determined according to the preset unit time length, and another moment can be calculated based on the moment, and then the moment that meets the corresponding interval can be screened out from the calculated moments, for example, preliminarily. Once the departure time is determined, the corresponding arrival time is calculated, and the arrival time that meets the target arrival time interval is screened out. For another example, once the arrival time is preliminarily determined, the corresponding departure time is calculated, and the departure time that meets the target departure time interval is screened out. In addition, when the target time information includes both the target departure time interval and the target arrival time interval, it is also possible not to compare the interval lengths. According to a preset calculation order, the moment of an interval is first determined from the interval, and another corresponding moment is calculated, and then the calculated moment is filtered based on the other interval. The calculation order here can be first determining the departure time from the target departure time interval, calculating the corresponding arrival time, and filtering the arrival time that meets the target arrival time interval, or first determining the arrival time from the target arrival time interval, calculating the corresponding departure time, and filtering the departure time that meets the target departure time interval.

[0055] It is understandable that, when the target time information includes both the target departure time interval and the target arrival time interval, there may be a situation where there is no departure time that meets the target time information: one situation is that the journey time is short and the arrival time will be early, for example, the target departure time interval is from 7:00 to 7:30, the target arrival time interval is from 11:30 to 12:30, and the longest time of each initial route is 3 hours. Under the condition of ensuring that the target departure time interval is met, even if the departure time is the latest 7:30, according to the longest time of 3 hours, the calculated arrival time is earlier than the earliest time of 11:30 in the target arrival time interval; another situation is that the journey time is long and it is impossible to arrive in time, for example, the target departure time interval is from 7:00 to 7:30, the target arrival time interval is from 11:30 to 12:30, and the shortest time of each initial route is 6 hours. Even if the departure time is the earliest 7:00, according to the shortest time of 6 hours, the arrival time is later than the latest time of 12:30 in the target arrival time interval.

[0056] For the situation where there is no travel plan that meets the target time information, the information that the planning result is empty can be directly returned, or the constraints can be relaxed to determine an alternative travel plan, and the subsequent steps can be carried out with the alternative travel plan, and a prompt about the constraints satisfied by the current route can be added when outputting the recommendation results, that is, the most stringent constraint (the departure time meets both the target departure time interval and the target arrival time interval) is temporarily canceled, and only the alternative travel plan with the departure time meeting the target departure time interval is determined based on the historical driving data set, without considering whether the arrival time meets the target arrival time interval, and, only the alternative travel plan with the arrival time meeting the target arrival time interval is determined based on the historical driving data set, without considering whether the derived departure time meets the target departure time interval, thereby providing users with more possible alternative options.

[0057] It can be understood that after canceling the constraint condition that requires both the departure time in the same set of driving action times to meet the target departure time interval and the arrival time to meet the target arrival time interval, there are two situations in the alternative travel plan: the departure time meets the target departure time interval, but the arrival time corresponding to the departure time does not meet the target arrival time interval; the arrival time meets the target arrival time interval, but the departure time corresponding to the arrival time does not meet the target departure time interval.

[0058] At this time, multiple alternative options for departure time are obtained, and these alternative travel plans can be used directly to execute steps S204-S205, or one or more alternative travel plans can be first selected from the alternative travel plans according to the screening conditions determined by the user's needs, and then the subsequent steps S204-S205 are executed. For example, if the user's demand is to be as close to the target time information as possible, that is, late departure or late arrival is allowed but the timeout is short, and early departure or early arrival is allowed but the early time is short, then the screening condition is that the departure time or arrival time is the shortest difference from the target time information; for another example, if the user's demand is not to be late, that is, early arrival or early departure is allowed but not to be late, then the screening condition is that the arrival time is not later than the right end point of the target arrival time interval; for another example, if the user's demand is not to depart early, that is, late arrival or late departure is allowed but not to depart early, then the screening condition is that the departure time is not later than the left end point of the target departure time interval.

[0059] Further, after the departure time is determined in step S203, step S204 is executed to calculate the traffic efficiency based on the historical driving data set corresponding to the travel plan. It should be noted that each travel plan of steps S204-S206 includes a determined and unique initial route and a determined and unique set of driving action moments. For example, the initial routes are r1 and r2, and the departure times in the driving action moments of the initial route r1 are t11, t12, and t13, and the departure times in the driving action moments of the initial route r2 are t21, t22, and t23. Even if there are the same time values ​​in t11-t13 and t21-t23, for example, t11 and t23 are both 7:15, but since the initial routes corresponding to the two departure times are not the same, the two departure times with the same time values ​​are not considered to be the same departure time, and there is no correlation between two departure times belonging to different initial routes, and each is used as an independent parameter for subsequent steps.

[0060] Among them, the traffic efficiency of a certain point on the initial route is used to characterize whether the vehicle has smooth traffic when passing through the point. If the traffic efficiency is high, there is no congestion at the point, and the vehicle can travel at a high and uniform speed when passing through the point. If the traffic efficiency is low, there is a certain degree of congestion at the point, and the vehicle's speed is usually low when passing through the point, or braking and acceleration are often required.

[0061] Therefore, the calculation of the traffic efficiency of a certain location point is usually determined by the driving performance of the vehicle, that is, the speed and acceleration. Furthermore, the maximum traffic efficiency of the location point corresponds to the expected driving performance of the vehicle. The expected driving performance refers to the driving performance of the vehicle on a smooth road. The smooth road here can be other roads on the initial route, or it can be the current location point to be calculated in a smooth state. Under normal conditions, the vehicle can maintain the maximum speed limit and the expected acceleration, that is, at a higher and uniform speed. It can be understood that based on the comparison of the actual driving performance and the expected driving performance of the location point in the historical driving data set, it can be analyzed whether the traffic efficiency of the location point has reached the maximum efficiency.

[0062] It can be understood that, according to steps S201-S204, all feasible travel plans have been determined, each of which includes a specific driving action time and an initial route. When the vehicle executes a certain travel plan, it will travel according to the driving action time and the initial route. For all the travel plans currently obtained, how to determine the travel plan that best meets the user's needs and return it as a recommendation result is the problem to be solved in step S205. To this end, step S205 uses the driving expectation function scheme to calculate the traffic efficiency and route distance of each travel plan. The independent variables of the driving expectation function scheme include the traffic efficiency and route distance of the travel plan, and the function result is the expected function value. User needs usually tend to have a shorter route distance and a higher traffic efficiency. In this method, the relationship between the function result and each independent variable in the driving expectation function scheme specifically includes: the expected function value is negatively correlated with the traffic efficiency, and the expected function value is positively correlated with the route distance. Therefore, when the route distance is larger, the expected function value is larger, and when the traffic efficiency is higher, the expected function value is smaller. In other words, a smaller expected function value indicates that the route distance is smaller or the traffic efficiency is higher, so the user demand tends to have a smaller expected function value.

[0063] It is understandable that after the expected function value of each travel plan is obtained in step S205, all expected function values ​​are compared, and a preset number of travel plans with the smallest expected function values ​​are used as recommended plans. For example, if the preset number is 3, the travel plans corresponding to the smallest 3 expected function values ​​are output as recommended results. The recommended results take into account both route distance and traffic efficiency, and can more comprehensively meet the needs of users in the driving navigation scenario.

[0064] The method of the embodiment of the present application determines a travel plan that meets the user's instructions, calculates the traffic efficiency based on the historical driving data set for each travel plan, and comprehensively calculates the expected function value based on the traffic efficiency and route distance as the basis for determining the final recommendation result. The calculation of the traffic efficiency and the driving expectation function plan based on the historical driving data set characterized by non-linear road section delays is more accurate and thorough. The user does not need to perform multiple operations and manual analysis to directly obtain effective recommendation results that meet the user's needs, thereby improving the user experience. In some embodiments, since the traffic efficiency of consecutive location points is usually similar, the initial route can be segmented according to the historical driving data set to obtain multiple traffic sections, and the traffic efficiency within each traffic section should have a uniform performance. Furthermore, the traffic efficiency is used to represent the congestion level of the traffic section, which can be measured by a non-linear road section delay model. At this time, step S204 determines the traffic efficiency corresponding to each travel plan based on the historical driving data set, which may include:

[0065] For each travel plan, the corresponding initial route is divided into multiple passable sections based on the historical driving data set;

[0066] Based on the historical driving data set, the traffic rate ratio within the time period of driving through each passable road section is integrated to obtain the traffic efficiency of the corresponding passable road section;

[0067] The traffic rate ratio is the ratio of the real-time traffic rate corresponding to the travel plan at the same location point to the preset traffic rate. The real-time traffic rate is the ratio of the speed and acceleration of the corresponding travel plan in the historical driving data set.

[0068] Accordingly, the calculation of the traffic efficiency of each traffic section applies the nonlinear section delay model, and the calculation process is as follows:

[0069]

[0070] Among them, L ij is the traffic efficiency of the i-th road section corresponding to the j-th travel plan, c ij is the real-time traffic rate of a certain point in the ith road section corresponding to the jth travel plan, c ref-i is the preset traffic rate of the corresponding position point in the ith traffic section, is the traffic rate ratio. All traffic rate ratios on the traffic section are integrated at the location point x or at time t to obtain the traffic efficiency L. ij The reason why the integration can be performed according to time t is that there is an implicit correspondence between the location points and time that the vehicle passes through. Therefore, the integral of the traffic rate of all location points x on the pass section can also be integrated according to time t;

[0071] The real-time traffic rate is determined based on the real-time driving performance. The specific value of the real-time driving performance here is selected from the historical driving data set corresponding to the historical data. The real-time traffic rate is specifically:

[0072]

[0073] where v ij and a ij are the speed and acceleration of a certain point in the historical driving data set corresponding to the j-th travel plan and the i-th travel section respectively;

[0074] The preset traffic rate may be a predetermined value or a value calculated based on a predetermined expected driving performance, the calculation process including:

[0075]

[0076] where v ref-i and a ref-i are the speed and acceleration of the expected driving performance corresponding to the i-th road section.

[0077] It can be understood that, whether it is the expected driving performance or the real-time driving performance, there is a corresponding formula between speed and acceleration v(x) = u×e a(x)×Δt , where v(x) and a(x) are the velocity and acceleration of position point x, respectively, u is the historical velocity at the previous moment, and △t is the time difference between u and the current v(x).

[0078] Furthermore, after the initial route is divided into multiple passable sections, the calculation of the initial route based on the driving expectation function scheme is equivalent to the sum of the calculations of multiple passable sections. Specifically, according to the specific calculation of the pass efficiency mentioned above, an example of a driving expectation function scheme can be the ratio of distance to speed, where the speed is the product of the expected pass speed and the pass efficiency. In this case, the driving expectation function scheme corresponding to any travel scheme is specifically:

[0079] The sum of the expected travel time of all travel sections corresponding to the travel plan;

[0080] The expected travel time of each passable road section is specifically the ratio of the passable road section distance to the expected travel speed of the passable road section;

[0081] The expected speed is the product of the preset speed and the corresponding speed efficiency.

[0082] Specifically, the driving expectation function scheme can be expressed as:

[0083] Among them, T j is the expected function value corresponding to the j-th travel plan, d i and V i is the traffic efficiency and preset speed of the i-th traffic section corresponding to the j-th travel plan, V i ×L ij is the expected traffic speed, is the expected travel time of the ith road segment. The preset speed is the speed in the preset driving performance mentioned above.

[0084] Furthermore, if there is no congestion in a certain traffic section corresponding to the travel plan, it is called a non-congested section, and the traffic efficiency is very high, which can be directly recorded as 100% to save computing resources on traffic efficiency. Based on this, the complete initial route corresponding to the travel plan can be analyzed in advance to see if there are congested sections. The traffic efficiency of the non-congested section is directly recorded as 100%, and the calculation of the traffic efficiency is skipped. The traffic efficiency of the congested section is low and there are different degrees of congestion. The specific traffic rate ratio and traffic efficiency are calculated for the congested section. Therefore, for each travel plan, based on the historical driving data set, the process of dividing the corresponding initial route into multiple traffic sections can include:

[0085] For each departure time, determine whether there is a congested section based on the historical driving data set;

[0086] If yes, for each travel plan, the corresponding initial route is divided into a plurality of passable sections based on the historical driving data set; each passable section is a congested section or a non-congested section;

[0087] If not, it is determined that the initial route only includes non-congested sections;

[0088] Among them, the traffic efficiency of non-congested sections is preset to 1.

[0089] It can be understood that the driving expectation function scheme is essentially a functional relationship between traffic efficiency and route distance corresponding to the expected function value, which can be described as: expected function value = f (traffic efficiency, route distance). Both traffic efficiency and route distance will affect the expected function value. Considering the different inclinations of traffic efficiency and route distance in user needs, the weights of traffic efficiency and route distance can be flexibly adjusted to meet user needs. At this time, the weight of traffic efficiency is recorded as the first weight, and the weight of route distance is recorded as the second weight. The first weight and the second weight are also included in the driving expectation function scheme as influencing variables. At this time, we have:

[0090] Expected function value = f (traffic efficiency, first weight, route distance, second weight).

[0091] Therefore, in some embodiments, the degree of negative correlation between the expected function value and the traffic efficiency is determined by the first weight.

[0092] The degree of positive correlation between the expected function value and the route distance is determined by the second weight.

[0093] It can be understood that the correlation between the independent variable and the expected function value is positively correlated with the numerical value of the weight: when the first weight increases, the correlation between the expected function value and the traffic efficiency increases; when the first weight decreases, the correlation between the expected function value and the traffic efficiency decreases; the same applies to the second weight.

[0094] When user needs are more concerned with distance rather than travel efficiency, the second weight can be set to be larger and the first weight can be set to be smaller. When user needs are more concerned with travel efficiency rather than distance, the first weight can be set to be larger and the second weight can be set to be smaller.

[0095] Furthermore, the above travel expectation function scheme does not consider the influence of travel time. In fact, travel time is also important for users to choose travel plans. For example, users expect the total journey time to be earlier, or expect the departure time to be later, or expect the arrival time to be earlier, etc. Therefore, the travel time information can be added as an independent variable in the travel expectation function scheme. In this case, the travel expectation function scheme is:

[0096] Expected function value = f (traffic efficiency, route distance, travel time information).

[0097] Accordingly, for each travel plan, based on the corresponding traffic efficiency and route distance, the expected function value at each departure time is calculated according to the travel expectation function plan, including:

[0098] For each travel plan, based on the corresponding travel time information, corresponding travel efficiency and route distance, the expected function value at each departure time is calculated according to the travel expectation function plan;

[0099] The travel time information is at least one of the departure time, the arrival time, and the journey time corresponding to the travel plan.

[0100] It is understandable that when the specific expected content of user needs is different, the correlation between the travel time information and the expected function value will also change, including whether the correlation is negative or positive, and the degree of correlation. Usually, the travel time information can be set by default to include the journey time, and the expected function value is set to be positively correlated with the journey time, and the degree of positive correlation is determined by a weight. The user can issue the setting information corresponding to the user's needs through user instructions: travel time information, correlation, and degree of correlation. Therefore, for each travel plan, based on the corresponding travel time information, the corresponding travel efficiency and route distance, according to the travel expectation function plan, before obtaining the expected function value of each travel plan, it also includes:

[0101] Based on the user instruction, determining one or more travel time information, and determining the correlation between the expected function value and each travel time information and the correlation degree of the correlation;

[0102] The correlation is either negative or positive.

[0103] It can be understood that if the travel time information is determined as the arrival time based on user instructions and the correlation between the expected function value and the arrival time is positively correlated, the travel plan with an earlier arrival time has a smaller expected function value and is more likely to be included in the recommendation results, which means that the user needs to expect the vehicle to arrive earlier; similarly, if the travel time information is determined as the departure time based on user instructions and the correlation between the expected function value and the departure time is negatively correlated, the travel plan with a later departure time has a smaller expected function value and is more likely to be included in the recommendation results, which means that the user needs to expect the vehicle to depart later.

[0104] It can be understood that the weights of the above-mentioned variables are quantitative expressions of the importance of different independent variables in the driving expectation function scheme of the present method. The larger the weight value, the higher the importance of the corresponding independent variable in determining the value of the entire expectation function. Assuming that the independent variables of the driving expectation function scheme in one embodiment include departure time, distance and traffic efficiency, if the user instruction does not make any decision and does not set any priority, it will run according to the default priority. The default priority may include distance is better than traffic efficiency and better than departure time, and the weights of the respective variables are determined according to the priority. If the user instruction adjusts the priority or weight, the driving expectation function scheme is calculated according to the adjusted weight. In addition, the subsequent execution of the recommended results can further reflect the user's demand for each independent variable. Therefore, the weight of each independent variable can be adjusted according to the travel plan selected for execution in the recommended results.

[0105] In addition, the priority setting can also be used to specify the step-by-step screening of each travel plan by the driving expectation function scheme, that is, the execution process of the function scheme includes: performing the screening operation step by step in order of priority from high to low, and finally obtaining the remaining recommended results. The screening operation includes: for each priority, taking the sorting number as the expected function value, sorting the travel plans according to the correlation between the expected function value and the independent variable corresponding to each priority, and selecting the corresponding number of travel plans with the smallest expected function value.

[0106] For example, the order of priority from high to low is that the distance is better than the travel efficiency and better than the travel time. First, the screening operation is performed based on the distance. Since the expected function value is positively correlated with the distance, the final goal is to have a smaller expected function value. Therefore, the five travel plans with the shortest distance are selected from all the travel plans; then the screening operation is performed based on the travel efficiency. Since the expected function value is negatively correlated with the travel efficiency, the final goal is to have a smaller expected function value. Therefore, the three travel plans with the highest travel efficiency are selected from the five travel plans just now; then the screening operation is performed based on the travel time. Assuming that the expected function value is positively correlated with the travel time, the final goal is to have a smaller expected function value. Therefore, the two travel plans with the shortest travel time are selected from the remaining three travel plans, and two departure times and corresponding initial routes are obtained as the recommended results of the user's instructions and sent to the display terminal. The specific numbers of the travel plans above are all examples. When applied, they can be adjusted and set according to the actual situation and user needs, and there is no restriction here.

[0107] It can be understood that the specific implementation method of the expected driving function scheme, in addition to the functional relationship expression and step-by-step screening operation mentioned above, can also be set to other forms according to actual conditions and user needs, and is not limited here.

[0108] All the above optional technical solutions can be combined in any way to form optional embodiments of the present application, which will not be described one by one here. It should be understood that the size of the sequence number of each step in the above embodiment does not mean the order of execution, and the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present application. The description of "when..." in the above embodiment is only used to describe the corresponding information, environment, and state, and does not constitute a limitation on the timing.

[0109] The following is an embodiment of the device of the present application, which can be used to execute the embodiment of the method of the present application. For details not disclosed in the embodiment of the device of the present application, please refer to the embodiment of the method of the present application.

[0110] Figure 3 is a schematic diagram of a navigation route recommendation device provided in an embodiment of the present application. Figure 3 As shown, the recommended device includes:

[0111] A preprocessing module 301 is used to obtain a user instruction, and determine a route starting point, a route end point, and target time information based on the user instruction, where the target time information includes at least one of a target departure time interval and a target arrival time interval;

[0112] An initial route module 302, for determining a plurality of initial routes from a route start point to a route end point and a route distance of each initial route based on map information;

[0113] The departure time module 303 is used to determine all departure times that meet the target time information when traveling along each initial route based on the historical travel data set;

[0114] The traffic efficiency module 304 is used to determine the traffic efficiency corresponding to the initial route at each departure time based on the historical driving data set; the traffic efficiency is used to indicate the degree of road smoothness;

[0115] The expected calculation module 305 is used to calculate the expected function value of each departure time according to the driving expected function scheme based on the corresponding traffic efficiency and route distance; in the driving expected function scheme, the expected function value is negatively correlated with the traffic efficiency, and the expected function value is positively correlated with the route distance;

[0116] The recommendation module 306 is used to send a preset number of departure times and corresponding initial routes with the smallest expected function values ​​as the recommendation results of the user instruction to the display terminal.

[0117] The device of the embodiment of the present application determines a travel plan that meets the user's instructions, calculates the traffic efficiency based on the historical driving data set for each travel plan, and comprehensively calculates the expected function value based on the traffic efficiency and route distance as the basis for determining the final recommendation result. The calculation of the traffic efficiency and the driving expectation function plan based on the historical driving data set characterized by non-linear road delays is more accurate and thorough. The user does not need to perform multiple operations and manual analysis to directly obtain effective recommendation results that meet the user's needs, thereby improving the user experience.

[0118] In an exemplary embodiment, the traffic efficiency module is specifically used to:

[0119] For each travel plan, the corresponding initial route is divided into multiple passable sections based on the historical driving data set;

[0120] Based on the historical driving data set, the traffic rate ratio within the time period of driving through each passable road section is integrated to obtain the traffic efficiency of the corresponding passable road section;

[0121] The traffic rate ratio is the ratio of the real-time traffic rate corresponding to the travel plan at the same location point to the preset traffic rate. The real-time traffic rate is the ratio of the speed and acceleration corresponding to the travel plan and location point in the historical driving data set.

[0122] In an exemplary embodiment, the driving expectation function scheme corresponding to any travel scheme is specifically:

[0123] The sum of the expected travel time of all travel sections corresponding to the travel plan;

[0124] The expected travel time of each passable road section is specifically the ratio of the passable road section distance to the expected travel speed of the passable road section;

[0125] The expected speed is the product of the preset speed and the traffic efficiency.

[0126] In an exemplary embodiment, the traffic efficiency module is specifically used to:

[0127] For each travel plan, determine whether there is a congested section based on the historical driving data set;

[0128] If yes, for each travel plan, the corresponding initial route is divided into a plurality of passable sections based on the historical driving data set; each passable section is a congested section or a non-congested section;

[0129] If not, it is determined that the initial route only includes non-congested sections;

[0130] The traffic efficiency of non-congested sections is preset to 1.

[0131] In an exemplary embodiment, the negative correlation degree between the expected function value and the traffic efficiency is determined by a first weight, and the positive correlation degree between the expected function value and the route distance is determined by a second weight.

[0132] In an exemplary embodiment, the expected calculation module is specifically used for:

[0133] For each travel plan, based on the corresponding travel time information, corresponding travel efficiency and route distance, the expected function value of each travel plan is calculated according to the driving expectation function plan;

[0134] The travel time information is at least one of the departure time, the arrival time, and the journey time corresponding to the travel plan.

[0135] In an exemplary embodiment, the preprocessing module is further configured to:

[0136] Based on the user instruction, determining one or more travel time information, and determining the correlation between the expected function value and each travel time information and the correlation degree of the correlation;

[0137] The correlation is either negative or positive.

[0138] Figure 4 Schematic diagram of an electronic device 4 provided in an embodiment of the present application. Figure 4 As shown, the electronic device 4 of this embodiment includes: a processor 401, a memory 402, and a computer program 403 stored in the memory 402 and executable on the processor 401. When the processor 401 executes the computer program 403, the steps in the above-mentioned method embodiments are implemented. Alternatively, when the processor 401 executes the computer program 403, the functions of the modules / units in the above-mentioned device embodiments are implemented.

[0139] The electronic device 4 may be a desktop computer, a notebook, a PDA, a cloud server, or other electronic device. The electronic device 4 may include, but is not limited to, a processor 401 and a memory 402. Those skilled in the art will appreciate that Figure 4 The electronic device 4 is merely an example and does not limit the electronic device 4 , and may include more or less components than those shown in the figure, or different components.

[0140] The processor 401 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0141] The memory 402 may be an internal storage unit of the electronic device 4, for example, a hard disk or memory of the electronic device 4. The memory 402 may also be an external storage device of the electronic device 4, for example, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 4. The memory 402 may also include both an internal storage unit and an external storage device of the electronic device 4. The memory 402 is used to store computer programs and other programs and data required by the electronic device.

[0142] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units.

[0143] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. The computer program may include computer program code, which may be in source code form, object code form, executable file or some intermediate form. The computer-readable storage medium may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0144] 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. 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, and should all be included in the protection scope of the present application.

Claims

1. A method for recommending a navigation route, characterized in that: include: Obtaining a user instruction, and determining a route starting point, a route end point, and target time information based on the user instruction, wherein the target time information includes at least one of a target departure time interval and a target arrival time interval; Based on the map information, determining a plurality of initial routes from the route starting point to the route ending point and a route distance of each of the initial routes; Determine all travel plans based on the historical travel data set corresponding to the map information, each of the travel plans includes an initial route and a set of travel action moments corresponding to the initial route that completely meet the target time information, each set of travel action moments includes at least one of a departure time corresponding to the target departure time interval and an arrival time corresponding to the target arrival time interval; Determine the traffic efficiency corresponding to each of the travel plans based on the historical travel data set; the traffic efficiency is used to indicate the degree of road smoothness; For each of the travel plans, based on the corresponding traffic efficiency and the route distance, the expected function value at each of the departure times is calculated according to the travel expected function plan; in the travel expected function plan, the expected function value is negatively correlated with the traffic efficiency, and the expected function value is positively correlated with the route distance; A preset number of travel plans with the smallest expected function values ​​are sent to a display terminal as recommendation results of the user instruction.

2. The method according to claim 1, characterized in that The process of determining the traffic efficiency corresponding to each of the travel plans based on the historical travel data set includes: For each of the travel plans, based on the historical travel data set, the corresponding initial route is divided into a plurality of passable road sections; Based on the historical driving data set, integrating the traffic rate ratio within the time period of driving through each of the passable road sections to obtain the traffic efficiency corresponding to the passable road section; The traffic rate ratio is the ratio of the real-time traffic rate corresponding to the travel plan at the same location point to the preset traffic rate, and the real-time traffic rate is the ratio of the speed and acceleration corresponding to the travel plan and the location point in the historical driving data set.

3. The method according to claim 2, characterized in that The driving expectation function scheme corresponding to any of the travel schemes is specifically: the sum of the expected travel time of all the travel sections corresponding to the travel scheme; The expected travel time of each of the passable sections is specifically a ratio of the passable section distance of the passable section to the expected travel speed; The expected passing speed is the product of a preset speed and the corresponding passing efficiency.

4. The method according to claim 2, characterized in that: For each of the travel plans, based on the historical travel data set, the process of dividing the corresponding initial route into a plurality of passable road segments includes: For each of the travel plans, judging whether there is a congested road section based on the historical travel data set; If yes, executing the action of dividing the corresponding initial route into a plurality of passable road sections for each of the travel plans based on the historical travel data set; each of the passable road sections is the congested road section or the non-congested road section; If not, determining that the initial route only includes the non-congested road section; The traffic efficiency of the non-congested road section is preset to 1.

5. The method according to claim 1, characterized in that The degree of negative correlation between the expected function value and the traffic efficiency is determined by a first weight; The degree of positive correlation between the expected function value and the route distance is determined by a second weight.

6. The method according to any one of claims 1 to 5, characterized in that: For each of the travel plans, based on the corresponding traffic efficiency and the route distance, the process of calculating according to the travel expectation function plan to obtain the expected function value of each of the departure times includes: For each of the travel plans, based on the corresponding travel time information, the corresponding travel efficiency and the route distance, the expected function value of each of the travel plans is calculated according to the travel expected function plan; The travel time information is at least one of the departure time, the arrival time, and the travel time corresponding to the travel plan.

7. The method according to claim 6, characterized in that For each of the travel plans, based on the corresponding travel time information, the corresponding travel efficiency and the route distance, the travel expectation function plan is calculated to obtain the expected function value of each of the travel plans, and the method further includes: Based on the user instruction, determining one or more of the travel time information, and determining a correlation between the expected function value and each of the travel time information and a correlation degree of the correlation; The correlation is a negative correlation or a positive correlation.

8. A navigation route recommendation device, characterized in that: include: A preprocessing module, configured to obtain a user instruction, and determine a route starting point, a route end point, and target time information based on the user instruction, wherein the target time information includes at least one of a target departure time interval and a target arrival time interval; An initial route module, used for determining a plurality of initial routes from the route starting point to the route end point and a route distance of each of the initial routes based on map information; A travel plan module, for determining all travel plans based on a historical travel data set corresponding to the map information, each of the travel plans comprising an initial route and a set of travel action moments corresponding to the initial route that completely conform to the target time information, each set of the travel action moments comprising at least one of a departure time corresponding to the target departure time interval and an arrival time corresponding to the target arrival time interval; A traffic efficiency module, used to determine the traffic efficiency corresponding to each of the travel plans based on the historical travel data set; the traffic efficiency is used to indicate the degree of road smoothness; an expectation calculation module, for calculating, for each of the travel plans, based on the corresponding traffic efficiency and the route distance, according to a travel expectation function scheme, to obtain an expectation function value for each of the departure times; in the travel expectation function scheme, the expectation function value is negatively correlated with the traffic efficiency, and the expectation function value is positively correlated with the route distance; The recommendation module is used to send a preset number of the travel plans with the smallest expected function values ​​as the recommendation results of the user instruction to the display terminal.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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