CN112818243A - Navigation route recommendation method, device, equipment and storage medium - Google Patents
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Abstract
The application discloses a recommendation method, a recommendation device, equipment and a storage medium of a navigation route, which belong to the technical field of internet, and the method comprises the following steps: acquiring at least one navigation route according to a route query request of a target user; acquiring a potential violation prediction value of any navigation route in at least one violation scene based on the historical violation occurrence probability of the target user in at least one violation scene; acquiring a recommended weight of any one navigation route based on a potential violation prediction value in at least one violation scene; and recommending the target navigation route to the target user based on the recommendation weight. According to the method, a quantifiable recommendation calculation method of the navigation route is provided according to the potential violation prediction value, and in addition, the probability of violation occurrence can be reduced by the user according to the recommended target navigation route, so that the navigation is more intelligent, the recommended navigation route is more accurate and optimized, the navigation experience of the user is improved, and the utilization rate of the recommendation result is improved.
Description
Technical Field
The embodiment of the application relates to the technical field of internet, in particular to a navigation route recommendation method, a navigation route recommendation device, navigation route recommendation equipment and a storage medium.
Background
With the continuous development of social economy and the continuous improvement of living standard, the demand of people for traffic information is increasing day by day. And a proper travel route is selected through navigation, so that great convenience is brought to the life of people. How to select a better navigation route is particularly important, so that the travel requirement of the user is better met.
In the related art, the server may obtain the driving feature tag of the user according to the driving age data and the historical driving behavior data of the user, and recommend the navigation route to the user according to the driving feature tag and the route query request. Therefore, the method can be used for carrying out proper route inquiry on different users, and the applicability of the route inquiry method is improved.
However, the method adopted in the related art has a large dimension, and can only recommend the navigation route directly according to the algorithm, and cannot perform quantitative sequencing on the navigation route.
Disclosure of Invention
The embodiment of the application provides a recommendation method, a recommendation device and a recommendation storage medium for a navigation route, which can solve the problems in the related art. The technical scheme is as follows:
in one aspect, an embodiment of the present application provides a method for recommending a navigation route, where the method includes:
acquiring a route query request of a target user;
acquiring at least one navigation route according to the route query request;
for any one navigation route in the at least one navigation route, acquiring a potential violation prediction value of the any one navigation route in at least one violation scene based on the historical violation occurrence probability of the target user in the at least one violation scene, wherein the potential violation prediction value is used for predicting violation conditions;
acquiring a recommended weight of any navigation route based on a potential violation prediction value of the navigation route in the at least one violation scene;
recommending a target navigation route to the target user based on the recommended weight of the at least one navigation route.
In a possible embodiment, before obtaining a potential violation prediction value of any one navigation route in at least one violation scenario based on the historical violation occurrence probability of the target user in at least one violation scenario, the method further includes:
acquiring historical violation record data of the target user in the at least one violation scene and historical driving record data of the target user;
and for any violation scene in the at least one violation scene, acquiring the historical violation occurrence probability of the target user in any violation scene according to the historical violation record data of the target user in any violation scene and the historical driving record data.
In a possible embodiment, for any violation scenario in the at least one violation scenario, the obtaining, according to the historical violation record data and the historical driving record data of the target user in any violation scenario, a historical violation occurrence probability of the target user in any violation scenario includes:
acquiring the historical violation times of the target user in any violation scene based on the historical violation record data of the target user in any violation scene;
based on the historical driving record data, acquiring a historical driving route corresponding to the historical violation record data of the target user in any violation scene, and acquiring the number of first violation detection points corresponding to any violation scene included in the historical driving route;
and acquiring the historical violation occurrence probability of the target user in any violation scene based on the historical violation times and the number of the first violation detection points.
In a possible implementation manner, the obtaining of the historical violation occurrence probability of the target user in any violation scenario based on the historical violation times and the number of first violation detection points includes:
and taking the ratio of the historical violation times to the number of the first violation detection points as the historical violation occurrence probability of the target user in any violation scene.
In a possible embodiment, the obtaining of the potential violation prediction value of any one navigation route in at least one violation scenario based on the historical violation occurrence probability of the target user in at least one violation scenario includes:
for any violation scene in the at least one violation scene, acquiring the number of second violation detection points corresponding to any violation scene included on any navigation route;
and acquiring a potential violation prediction value of any navigation route in any violation scene based on the historical violation occurrence probability of the target user in any violation scene and the number of the second violation detection points.
In a possible implementation manner, the obtaining a potential violation prediction value of any navigation route in any violation scenario based on the historical violation occurrence probability of the target user in any violation scenario and the number of second violation detection points includes:
acquiring the product of the historical violation occurrence probability of the target user in any violation scene and the number of second violation detection points; and taking the result of the multiplication as a potential violation prediction value of any navigation route in any violation scene.
In a possible embodiment, the obtaining of the recommended weight of any one navigation route based on the potential violation prediction value of any one navigation route in the at least one violation scenario includes:
and performing summation operation on the potential violation prediction value of any navigation route in the at least one violation scene, and taking the result of the summation operation as the recommended weight of any navigation route.
In one possible embodiment, the recommending a target navigation route to the target user based on the recommendation weight of the at least one navigation route includes:
and taking the navigation route with the minimum recommended weight in the at least one navigation route as a target navigation route, and recommending the target navigation route to the target user.
In another aspect, there is provided a recommendation apparatus for a navigation route, the apparatus including:
the first acquisition module is used for acquiring a route query request of a target user;
the second acquisition module is used for acquiring at least one navigation route according to the route inquiry request;
the third obtaining module is used for obtaining a potential violation predicted value of any navigation route in at least one violation scene based on the historical violation occurrence probability of the target user in at least one violation scene, and the potential violation predicted value is used for predicting violation conditions;
the fourth obtaining module is used for obtaining the recommended weight of any one navigation route based on the potential violation prediction value of any one navigation route in the at least one violation scene;
and the recommending module is used for recommending a target navigation route to the target user based on the recommending weight of the at least one navigation route.
In one possible implementation, the apparatus further includes:
the fifth acquisition module is used for acquiring historical violation record data of the target user in the at least one violation scene and historical driving record data of the target user;
and the sixth acquisition module is used for acquiring the historical violation occurrence probability of the target user in any violation scene according to the historical violation record data of the target user in any violation scene and the historical driving record data.
In a possible implementation manner, the sixth obtaining module is further configured to obtain the historical violation times of the target user in any violation scenario based on historical violation record data of the target user in any violation scenario; based on the historical driving record data, acquiring a historical driving route corresponding to the historical violation record data of the target user in any violation scene, and acquiring the number of first violation detection points corresponding to any violation scene included in the historical driving route; and acquiring the historical violation occurrence probability of the target user in any violation scene based on the historical violation times and the number of the first violation detection points.
In a possible implementation manner, the sixth obtaining module is further configured to use a ratio of the number of historical violations to the number of first violation detection points as a historical violation occurrence probability of the target user in any violation scenario.
In a possible implementation manner, the third obtaining module is configured to obtain, for any violation scene in the at least one violation scene, the number of second violation detection points corresponding to the violation scene included on the any navigation route; and acquiring a potential violation prediction value of any navigation route in any violation scene based on the historical violation occurrence probability of the target user in any violation scene and the number of the second violation detection points.
In a possible implementation manner, the third obtaining module is configured to obtain a product of a historical violation occurrence probability of the target user in any violation scenario and the number of second violation detection points; and taking the result of the multiplication as a potential violation prediction value of any navigation route in any violation scene.
In a possible implementation manner, the fourth obtaining module is configured to perform summation operation on the potential violation prediction values of any one navigation route in the at least one violation scenario, and use a result of the summation operation as a recommended weight of any one navigation route.
In a possible implementation manner, the recommending module is configured to recommend the navigation route with the smallest recommended weight in the at least one navigation route as a target navigation route, and recommend the target navigation route to the target user.
In another aspect, a computer device is further provided, and the computer device includes a processor and a memory, where at least one program code is stored in the memory, and the at least one program code is loaded and executed by the processor to implement any one of the above methods for recommending a navigation route.
In another aspect, a computer-readable storage medium is provided, in which at least one program code is stored, and the at least one program code is loaded and executed by a processor to implement any one of the above methods for recommending a navigation route.
In another aspect, a computer program product or a computer program is also provided, comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer readable storage medium, and executes the computer instructions, so that the computer device executes any one of the above-mentioned navigation route recommendation methods.
The technical scheme provided by the embodiment of the application can bring the following beneficial effects:
according to the method provided by the embodiment of the application, the potential violation prediction value of the user is obtained according to the historical violation occurrence probability of the user, the recommendation weight of each navigation route is obtained according to the potential violation prediction value, and the target navigation route is recommended to the user according to the recommendation weight. According to the method, the navigation route is recommended according to the recommended weight, and the recommended weight is expressed as a specific numerical value, so that a measurable calculation method based on the navigation route is provided, the target navigation route recommended to the user can avoid the occurrence of violation events as far as possible, the violation occurrence rate of roads is reduced, the navigation is more intelligent, the recommended navigation route is more accurate and optimized, the navigation experience of the user is improved, and the utilization rate of the recommended result is improved.
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In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic diagram illustrating an implementation environment of a method for recommending a navigation route according to an embodiment of the present application;
FIG. 2 is a flowchart of a method for recommending a navigation route according to an embodiment of the present application;
FIG. 3 is a schematic diagram of a device for recommending a navigation route according to an embodiment of the present application;
FIG. 4 is a schematic diagram of a device for recommending a navigation route according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a device for recommending a navigation route according to an embodiment of the present application.
Detailed Description
To make the objects, technical solutions and advantages of the present application more clear, embodiments of the present application will be described in further detail below with reference to the accompanying drawings.
Referring to fig. 1, a schematic diagram of an implementation environment of a method for recommending a navigation route provided in an embodiment of the present application is shown. The implementation environment includes: a terminal 101 and a server 102.
The terminal 101 may be an electronic device such as a mobile phone, a tablet Computer, an e-book reader, a multimedia playing device, a wearable device, a Personal Computer (PC), and the like. Alternatively, the terminal 101 is a portable terminal, and the terminal 101 is used by a certain user, such as a first user, a second user, a third user, and the like. The terminal 101 may have a client (or called application program) installed therein, where the client is capable of sending a route query request to the server 102, receiving a recommendation result of a navigation route sent by the server 102, and displaying the recommendation result on a navigation user interface, for example, the client is a map-type application program. In the embodiment of the present application, the type of the client is not limited, and the client may be a map navigation application, a social application, an instant messaging application, a web shopping application, a payment application, a news application, a video application, or the like.
The server 102 is configured to provide a background service for a client in the terminal 101, where the background service includes, but is not limited to: at least one of navigation service, information service, and instant messaging service. For example, the server 102 may be a backend server of the clients described above. The server 102 may be a server, a server cluster composed of a plurality of servers, or a cloud computing service center.
The terminal 101 and the server 102 can communicate with each other via a network. The network may be a wired network or a wireless network.
Those skilled in the art will appreciate that the terminal 101 and the server 102 are only examples, and other existing or future terminals or servers may be suitable for the application, and are included within the scope of the present application and are incorporated by reference herein.
Fig. 2 shows a flowchart of a recommendation method for a navigation route according to an exemplary embodiment of the present application, which may be applied to the server 102 shown in fig. 1, and includes the following steps 201 to 205.
Wherein the route query request is a request for obtaining a navigation route planned by the server by a target user. The route inquiry request sent by the target user at least comprises destination information, and optionally, the route inquiry request also comprises starting place information. The start location information corresponds to start location information of a navigation route generated by the route inquiry request, the destination information corresponds to end location information of the navigation route generated by the route inquiry request, and in the case that the start location information is not included in the route inquiry request, the terminal can be located, and the located position information is used as the start location information. In the case where the route query request includes the origin information, the origin information may be input by the target user or selected based on the history input information. I.e., initially may be the current geographic location of the target user or may be another geographic location selected by the target user.
Illustratively, the route query request acquired by the server includes starting location information of a cell as a starting location, and destination information of b square as a destination. Illustratively, the starting place information included in the route query request acquired by the server is the current geographic position of the user at the starting place; the destination information is the destination C supermarket.
In the embodiment of the present application, the manner of obtaining the current geographic location of the target user may be obtained based on a Global Positioning System (GPS), which is a System for performing Positioning and navigation in real time in a Global scope, and may provide functions of vehicle Positioning, theft prevention, anti-hijacking, driving route monitoring, call command, and the like, which are not described in detail herein.
In one possible implementation, the server may perform a search in the map database based on a route query request sent by the target user to obtain at least one navigation route. The at least one navigation route may be one or multiple, and the embodiment of the present application is not limited thereto.
Illustratively, a target user Liu somebody sends a route query request comprising an initial place A and a destination B, and a server searches in a map database to obtain 3 different navigation routes L1、L2、L3All 3 navigation routes can finish reaching the B place from the A place.
Wherein, the at least one violation scene comprises but is not limited to at least one of running a red light, speeding, not wearing a safety belt and occupying an emergency lane; the potential violation predictor is used to predict a violation condition.
In a possible implementation manner, for any one of the at least one navigation route, before the potential violation prediction value of any one navigation route in the at least one violation scene is obtained based on the historical violation occurrence probability of the target user in the at least one violation scene, the method further includes step 2031-step 2032.
Step 2031, obtaining historical violation record data of the target user in at least one violation scene and historical driving record data of the target user.
In a possible implementation manner, a target user needs to register in advance and acquire account information, and the target user needs to provide information such as an identity card, a license plate number, a driving license and the like of the target user in the registration process. And when the target user logs in the account information, the server acquires corresponding data according to the associated information of the account information.
Optionally, the obtaining of the historical violation record data of the target user in at least one violation scenario and the historical driving record data of the target user may be: inquiring the violation record of the user from a third-party platform according to the driver license information provided by the target user in advance to obtain historical violation record data of the target user, wherein the historical violation record data of the target user comprises the historical violation record data in at least one violation scene; and acquiring historical driving record data of the target user according to license plate number information provided by the target user in advance and navigation historical records or automobile data, and acquiring a historical driving route of the target user through the historical driving record data.
In a possible implementation mode, historical violation record data and historical driving record data of a target user in at least one violation scene are obtained, an effective time value can be preset, and historical violation records and historical driving records in a preset effective time range are obtained to be effective historical violation record data and effective historical driving record data.
Illustratively, the valid time value is set in advance to 100 hours. If the total time of all the historical driving records of the target user is more than or equal to 100 hours, starting to obtain the latest historical driving record, sequentially obtaining other historical driving records from the beginning according to the time sequence of the historical driving records until the total time of the historical driving records reaches 100 hours, and taking the obtained historical driving record data as effective historical driving record data; and acquiring a history violation record generated on the effective historical driving record data according to the acquired effective historical driving record data, and taking the acquired history violation record as the effective historical violation record data. If the total time of all the historical driving records of the target user is less than 100 hours, directly acquiring all the historical driving records of the target user as effective historical driving record data; and taking all historical violation records of the target user as effective historical violation record data.
In one possible implementation mode, when the vehicle runs, the running data information is collected in real time, so that the server updates the data information, and deletes the data information beyond the preset effective time.
Step 2032, for any violation scene in at least one violation scene, obtaining the historical violation occurrence probability of the target user in any violation scene according to the historical violation record data and the historical driving record data of the target user in any violation scene.
In the embodiment of the application, the historical violation occurrence probability refers to the probability of violation events occurring in the historical driving process of the target user. For any two violation scenes, such as overtaking violation and red light running violation, if 8 overspeed detection points and 8 red light running detection points are arranged on a certain historical driving route, the target user has 2 overspeed violations and 1 red light running violation on the historical driving route, and the probability representing that the target user has overspeed violations is higher than that of red light running.
In a possible implementation manner, for any violation scene in at least one violation scene, the historical violation occurrence probability of the target user in any violation scene is obtained according to the historical violation record data and the historical driving record data of the target user in any violation scene, and the method comprises the following 3 steps.
1. And acquiring the historical violation times of the target user in any violation scene based on the historical violation record data of the target user in any violation scene.
Optionally, the historical violation record data includes, but is not limited to, target user identification, violation scenario information, violation occurrence time information, and violation occurrence location information. Therefore, the historical violation record data of the target user in any violation scene represents all historical record data of any violation occurring by the target user, and the number of the historical violation record data is counted to be the historical violation frequency of the target user in any violation scene.
2. Based on the historical driving record data, acquiring a historical driving route corresponding to the historical violation record data of the target user in any violation scene, and acquiring the number of first violation detection points corresponding to any violation scene included in the historical driving route.
In the embodiment of the present application, the historical driving record data includes, but is not limited to, a historical driving route of the target user, and time information corresponding to the historical driving route. Because the historical violation record data comprises violation occurrence time information and violation occurrence place information, the historical driving route corresponding to the time and place information can be obtained from the historical driving record data according to the time and place information, and the historical driving route is a driving route from a starting place to a destination including violation occurrence time. After the historical driving route corresponding to the historical violation record data of the target user in any violation scene is obtained based on the historical driving record data, the number of violation detection points corresponding to any violation scene included in the historical driving route can be obtained, and the number is called as the number of first violation detection points.
Exemplarily, if any violation scene is red light running, the place provided with the traffic light is the place where the violation of red light running is possible to happen, all the places provided with the traffic light on the historical driving route are searched, and the number of the places is counted to be the number of the first violation detection points; if any violation scene is overspeed, the place provided with the overspeed violation detection points is the place where the overspeed violation detection points are possibly caused, all the places provided with the overspeed violation detection points included in the historical driving route are searched, and the number of the places is counted to be the number of the first violation detection points; if any violation scene is that the safety belt is not fastened, the place provided with the violation detection points without the safety belt is the place where the violation detection points without the safety belt are possibly fastened, all the violation detection points with the safety belt are searched for on the historical driving route, and the number of the places is counted to be the number of the first violation detection points; and if any violation scene is that the emergency lane is occupied, the place where the violation detection points of the emergency lane are occupied is the place where the violation detection points of the emergency lane are possibly occupied, all the violation detection points of the emergency lane are found in the historical driving route, and the number of the places is counted to be the number of the first violation detection points.
In a possible implementation mode, the map database comprises the condition information of the violation detection points arranged on each route, for any violation scene, the server can obtain the violation detection point condition of any violation scene included on the target route through the query of the map database, and the number of the violation detection points corresponding to any violation scene can be obtained through data statistics.
In a possible implementation manner, the obtaining of the number of first violation detection points corresponding to any violation scenario included in the historical driving route may further be: and acquiring all effective historical driving routes of the target user based on the historical driving record data, and acquiring the number of violation detection points included on all the effective historical driving routes as the number of the first violation detection points. The valid historical driving routes are all historical driving routes included in the historical driving records in the preset valid time range. That is, if there is no history violation occurrence record on a history running route, the number of violation detection points included in the history running route is counted to the number of first violation detection points. Therefore, the number of first violation detection points includes the number of violation detection points included in the historical travel route corresponding to the historical violation record data, and also includes the number of violation detection points included in other historical travel routes than the historical travel route corresponding to the historical violation record data.
3. And acquiring the historical violation occurrence probability of the target user in any violation scene based on the historical violation times and the number of the first violation detection points.
In the embodiment of the application, the violation detection points are places where the violation is likely to occur, and the occurrence probability corresponding to the violation of the target user can be obtained by counting the number of the places where the violation is likely to occur and according to the number of the historical violations.
In one possible implementation mode, the method for acquiring the historical violation occurrence probability of the target user in any violation scene based on the historical violation times and the number of first violation detection points comprises the following steps: and taking the ratio of the historical violation times to the number of the first violation detection points as the historical violation occurrence probability of the target user in any violation scene.
Illustratively, the violation scenes included in the historical violation record data of the target user liu include red light running, overspeed, no safety belt fastening and emergency lane occupation, and the historical violation occurrence probability of the liu under any violation scene is acquired as follows: probability P of red light running1The number of traffic light places contained in the historical red light running violation times/historical driving route; probability of overspeed occurrence P2The number of overspeed violation detection points included in the historical speed violation times/historical driving route; probability of unbelted belt P3The number of the illegal safety belt fastening detection points included in the historical safety belt fastening violation times/historical driving route; probability of occuring emergency lane P4The number of occupied emergency lane detection points included in the historical occupied emergency lane violation times/historical driving route.
It should be noted that what is introduced in the above steps 1 to 3 is an implementation process for acquiring the historical violation occurrence probability of the target user in any violation scene based on the historical violation record data and the historical driving record data of the target user under the condition that at least one violation scene includes red light running, overspeed, no safety belt fastening and emergency lane occupation. The condition of the at least one violation scene is not limited to this, and the at least one violation scene may include any one or any two of red light running, overspeed, no safety belt fastening and emergency lane occupation, in addition to the red light running, overspeed, no safety belt fastening and emergency lane occupation. For the condition that at least one violation scene comprises any one or any two of red light running, overspeed, no safety belt fastening and emergency lane occupation, the process of determining the historical violation occurrence probability respectively corresponding to the target user in at least one violation scene can be realized by referring to the steps 1 to 3 based on the historical violation record data and the historical driving record data, and the details are not repeated here.
In a possible implementation manner, the process of obtaining the potential violation prediction value of any navigation route in at least one violation scene based on the historical violation occurrence probability of the target user in at least one violation scene is as follows: for any violation scene in at least one violation scene, acquiring the number of second violation detection points corresponding to any violation scene included on any navigation route; and acquiring a potential violation prediction value of any navigation route in any violation scene based on the historical violation occurrence probability of the target user in any violation scene and the number of second violation detection points.
In a possible implementation manner, for any one of the at least one navigation route, the same manner as the manner of obtaining the number of the first violation detection points is adopted, the server can obtain the conditions of the violation detection points included in the any one navigation route through the query of the map database, and the number of the violation detection points corresponding to any violation scene included in the any one navigation route can be obtained through data statistics, which is called the number of the second violation detection points.
Illustratively, based on a route query request sent by a target user Liu, including a starting place A and a destination B, 3 different navigation routes L are obtained1、L2、L3. The violation scenes included in historical violation record data of the target user Liu include red light running, overspeed, no safety belt fastening and emergency lane occupation. Then, for the navigation route L1Obtained byGet the navigation route L1The number of scenes in which red light violation is likely to occur, namely the navigation route L1The number of the violation monitoring points which are provided with traffic lights is X11(ii) a Obtaining a navigation route L1The number of scenes in which overspeed violation is likely to occur, i.e. the navigation route L1The number of the violation monitoring points which are provided with overspeed detection points is X12(ii) a Obtaining a navigation route L1The number of scenes in which the violation of belt fastening is not possible, i.e. the navigation route L1The number of the violation monitoring points which are provided with the detection points without the safety belt is X13(ii) a Obtaining a navigation route L1The number of scenes in which emergency lane violation may occur, i.e., the navigation route L1The number of the violation monitoring points occupying the emergency lane detection points is X14。
In a possible implementation mode, based on the historical violation occurrence probability and the number of second violation detection points of the target user in any violation scene, the process of obtaining the potential violation prediction value of any navigation route in any violation scene is as follows: acquiring the product of the historical violation occurrence probability of the target user in any violation scene and the number of second violation detection points; and taking the result of the multiplication as a potential violation prediction value of any navigation route in any violation scene.
Illustratively, based on the above example, the violation scenes included in the historical violation record data of the target user liu include red light running, overspeed, no safety belt fastening and emergency lane occupation, and for the navigation route L1In particular, the potential red light violation prediction value E11＝X11*P1(ii) a Potential overspeed violation prediction value E12＝X12*P2(ii) a Potential non-belted violation prediction value E13＝X13*P3(ii) a Potential emergency lane occupation violation prediction value E14＝X14*P4。
And 204, acquiring a recommended weight of any navigation route based on the potential violation prediction value of any navigation route in at least one violation scene.
In a possible implementation manner, based on the potential violation prediction value of any navigation route in at least one violation scene, the process of obtaining the recommended weight of any navigation route is as follows: and carrying out summation operation on the potential violation prediction values of any navigation route in at least one violation scene, and taking the result of the summation operation as the recommended weight of any navigation route.
Illustratively, based on the above example, for the navigation route L1In other words, the navigation route L1Is SUM (L)1)＝E11+E12+E13+E14. Similarly, for the navigation route L2、L3In other words, the navigation route L can be acquired based on the same method2Is SUM (L)2) And a navigation route L3Is SUM (L)3)。
And step 205, recommending a target navigation route to the target user based on the recommendation weight of the at least one navigation route.
In one possible embodiment, the process of recommending the target navigation route to the target user based on the recommendation weight of the at least one navigation route is as follows: and taking the navigation route with the minimum recommended weight in the at least one navigation route as a target navigation route, and recommending the target navigation route to the target user. Because the recommendation weight is obtained according to the incidence probability of the historical violation, the recommendation weight is increased according to the increase of the incidence probability of the historical violation, and the user wants to reduce the probability of the possible violation as much as possible. Therefore, in the embodiment of the application, the smaller the recommended weight is, the less the comprehensive probability of representing all possible violations of the navigation route is minimized.
In a possible implementation manner, while a target navigation route is recommended to a target user based on the recommendation weight of at least one navigation route, the recommendation weight corresponding to each navigation route and each navigation route is sent to the target user, the target user can see the recommendation weight of each navigation route, quantitative ranking results of all navigation routes are obtained, and the target navigation route is selected according to requirements.
According to the method provided by the embodiment of the application, the potential violation prediction value of the user is obtained according to the historical violation occurrence probability of the user, the recommendation weight of each navigation route is obtained according to the potential violation prediction value, and the target navigation route is recommended to the user according to the recommendation weight. According to the method, the navigation route is recommended according to the recommended weight, and the recommended weight is expressed as a specific numerical value, so that a measurable calculation method based on the navigation route is provided, the target navigation route recommended to the user can avoid the occurrence of violation events as far as possible, the violation occurrence rate of roads is reduced, the navigation is more intelligent, the recommended navigation route is more accurate and optimized, the navigation experience of the user is improved, and the utilization rate of the recommended result is improved.
Referring to fig. 3, an embodiment of the present application provides a device for recommending a navigation route, including:
a first obtaining module 301, configured to obtain a route query request of a target user;
a second obtaining module 302, configured to obtain at least one navigation route according to the route query request;
the third obtaining module 303 is configured to obtain, for any one of the at least one navigation route, a potential violation prediction value of the any one navigation route in the at least one violation scene based on the historical violation occurrence probability of the target user in the at least one violation scene, where the potential violation prediction value is used to predict a violation condition;
the fourth obtaining module 304 is configured to obtain a recommended weight of any one navigation route based on a potential violation prediction value of any one navigation route in at least one violation scene;
a recommending module 305 for recommending a target navigation route to the target user based on the recommendation weight of the at least one navigation route.
In one possible implementation, referring to fig. 4, the apparatus further includes:
the fifth obtaining module 306 is configured to obtain historical violation record data of the target user in at least one violation scenario and historical driving record data of the target user;
the sixth obtaining module 307 is configured to, for any violation scene in the at least one violation scene, obtain a historical violation occurrence probability of the target user in any violation scene according to the historical violation record data and the historical driving record data of the target user in any violation scene.
In a possible implementation manner, the sixth obtaining module 307 is further configured to obtain the historical violation times of the target user in any violation scenario based on historical violation record data of the target user in any violation scenario; based on the historical driving record data, acquiring a historical driving route corresponding to the historical violation record data of the target user in any violation scene, and acquiring the number of first violation detection points corresponding to any violation scene included in the historical driving route; and acquiring the historical violation occurrence probability of the target user in any violation scene based on the historical violation times and the number of the first violation detection points.
In a possible implementation manner, the sixth obtaining module 307 is further configured to use a ratio of the number of historical violations to the number of first violation detection points as a historical violation occurrence probability of the target user in any violation scenario.
In a possible implementation manner, the third obtaining module 303 is configured to obtain, for any violation scene in the at least one violation scene, the number of second violation detection points corresponding to any violation scene included on any navigation route; and acquiring a potential violation prediction value of any navigation route in any violation scene based on the historical violation occurrence probability of the target user in any violation scene and the number of second violation detection points.
In a possible implementation manner, the third obtaining module 303 is configured to obtain a product of the historical violation occurrence probability of the target user in any violation scenario and the number of second violation detection points; and taking the result of the multiplication as a potential violation prediction value of any navigation route in any violation scene.
In a possible implementation manner, the fourth obtaining module 304 is configured to perform a summation operation on potential violation prediction values of any one navigation route in at least one violation scenario, and use a result of the summation operation as a recommended weight of any one navigation route.
In a possible implementation manner, the recommending module 305 is configured to recommend the target navigation route to the target user by taking the navigation route with the minimum recommended weight in the at least one navigation route as the target navigation route.
According to the device provided by the embodiment of the application, the potential violation prediction value of the user is obtained according to the historical violation occurrence probability of the user, the recommendation weight of each navigation route is obtained according to the potential violation prediction value, and the target navigation route is recommended to the user according to the recommendation weight. According to the method, the navigation route is recommended according to the recommended weight, and the recommended weight is expressed as a specific numerical value, so that a measurable calculation method based on the navigation route is provided, the target navigation route recommended to the user can avoid the occurrence of violation events as far as possible, the violation occurrence rate of roads is reduced, the navigation is more intelligent, the recommended navigation route is more accurate and optimized, the navigation experience of the user is improved, and the utilization rate of the recommended result is improved.
It should be understood that, when the apparatus provided in the foregoing embodiment implements the functions thereof, the foregoing division of the functional modules is merely illustrated, and in practical applications, the above functions may be distributed by different functional modules according to needs, that is, the internal structure of the apparatus is divided into different functional modules to implement all or part of the functions described above. In addition, the apparatus and method embodiments provided by the above embodiments belong to the same concept, and specific implementation processes thereof are described in the method embodiments for details, which are not described herein again.
Referring to fig. 5, a schematic structural diagram of a recommendation device for a navigation route according to an embodiment of the present application is shown. The device may be a terminal, and may be, for example: smart phones, tablet computers, vehicle-mounted terminals, notebook computers or desktop computers. A terminal may also be referred to by other names such as user equipment, portable terminal, laptop terminal, desktop terminal, etc.
Generally, a terminal includes: a processor 501 and a memory 502.
The processor 501 may include one or more processing cores, such as a 4-core processor, an 8-core processor, and so on. The processor 501 may be implemented in at least one hardware form of a DSP (Digital Signal Processing), an FPGA (Field-Programmable Gate Array), and a PLA (Programmable Logic Array). The processor 501 may also include a main processor and a coprocessor, where the main processor is a processor for Processing data in an awake state, and is also called a Central Processing Unit (CPU); a coprocessor is a low power processor for processing data in a standby state. In some embodiments, the processor 501 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content required to be displayed by the display screen. In some embodiments, processor 501 may also include an AI (Artificial Intelligence) processor for processing computational operations related to machine learning.
In some embodiments, the terminal may further include: a peripheral interface 503 and at least one peripheral. The processor 501, memory 502 and peripheral interface 503 may be connected by a bus or signal lines. Each peripheral may be connected to the peripheral interface 503 by a bus, signal line, or circuit board. Specifically, the peripheral device includes: at least one of radio frequency circuitry 504, display screen 505, camera assembly 506, audio circuitry 507, positioning assembly 508, and power supply 509.
The peripheral interface 503 may be used to connect at least one peripheral related to I/O (Input/Output) to the processor 501 and the memory 502. In some embodiments, the processor 501, memory 502, and peripheral interface 503 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 501, the memory 502, and the peripheral interface 503 may be implemented on a separate chip or circuit board, which is not limited in this embodiment.
The Radio Frequency circuit 504 is used for receiving and transmitting RF (Radio Frequency) signals, also called electromagnetic signals. The radio frequency circuitry 504 communicates with communication networks and other communication devices via electromagnetic signals. The rf circuit 504 converts an electrical signal into an electromagnetic signal to transmit, or converts a received electromagnetic signal into an electrical signal. Optionally, the radio frequency circuit 504 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a subscriber identity module card, and so forth. The radio frequency circuitry 504 may communicate with other terminals via at least one wireless communication protocol. The wireless communication protocols include, but are not limited to: metropolitan area networks, various generation mobile communication networks (2G, 3G, 4G, and 5G), Wireless local area networks, and/or WiFi (Wireless Fidelity) networks. In some embodiments, the rf circuit 504 may further include NFC (Near Field Communication) related circuits, which are not limited in this application.
The display screen 505 is used to display a UI (User Interface). The UI may include graphics, text, icons, video, and any combination thereof. When the display screen 505 is a touch display screen, the display screen 505 also has the ability to capture touch signals on or over the surface of the display screen 505. The touch signal may be input to the processor 501 as a control signal for processing. At this point, the display screen 505 may also be used to provide virtual buttons and/or a virtual keyboard, also referred to as soft buttons and/or a soft keyboard. In some embodiments, the display screen 505 may be one, disposed on the front panel of the terminal; in other embodiments, the display 505 may be at least two, respectively disposed on different surfaces of the terminal or in a folded design; in still other embodiments, the display 505 may be a flexible display disposed on a curved surface or on a folded surface of the terminal. Even more, the display screen 505 can be arranged in a non-rectangular irregular figure, i.e. a shaped screen. The Display screen 505 may be made of LCD (Liquid Crystal Display), OLED (Organic Light-Emitting Diode), and other materials.
The camera assembly 506 is used to capture images or video. Optionally, camera assembly 506 includes a front camera and a rear camera. Generally, a front camera is disposed at a front panel of the terminal, and a rear camera is disposed at a rear surface of the terminal. In some embodiments, the number of the rear cameras is at least two, and each rear camera is any one of a main camera, a depth-of-field camera, a wide-angle camera and a telephoto camera, so that the main camera and the depth-of-field camera are fused to realize a background blurring function, and the main camera and the wide-angle camera are fused to realize panoramic shooting and VR (Virtual Reality) shooting functions or other fusion shooting functions. In some embodiments, camera assembly 506 may also include a flash. The flash lamp can be a monochrome temperature flash lamp or a bicolor temperature flash lamp. The double-color-temperature flash lamp is a combination of a warm-light flash lamp and a cold-light flash lamp, and can be used for light compensation at different color temperatures.
The positioning component 508 is used for locating the current geographic Location of the terminal to implement navigation or LBS (Location Based Service). The Positioning component 508 may be a Positioning component based on the united states GPS (Global Positioning System), the chinese beidou System, the russian graves System, or the european union's galileo System.
A power supply 509 is used to power the various components in the terminal. The power source 509 may be alternating current, direct current, disposable or rechargeable. When power supply 509 includes a rechargeable battery, the rechargeable battery may support wired or wireless charging. The rechargeable battery may also be used to support fast charge technology.
In some embodiments, the terminal also includes one or more sensors 510. The one or more sensors 510 include, but are not limited to: acceleration sensor 511, gyro sensor 512, pressure sensor 513, fingerprint sensor 514, optical sensor 515, and proximity sensor 516.
The acceleration sensor 511 may detect the magnitude of acceleration on three coordinate axes of a coordinate system established with the terminal. For example, the acceleration sensor 511 may be used to detect components of the gravitational acceleration in three coordinate axes. The processor 501 may control the display screen 505 to display the user interface in a landscape view or a portrait view according to the gravitational acceleration signal collected by the acceleration sensor 511. The acceleration sensor 511 may also be used for acquisition of motion data of a game or a user.
The gyroscope sensor 512 can detect the body direction and the rotation angle of the terminal, and the gyroscope sensor 512 and the acceleration sensor 511 can cooperate to acquire the 3D action of the user on the terminal. The processor 501 may implement the following functions according to the data collected by the gyro sensor 512: motion sensing (such as changing the UI according to a user's tilting operation), image stabilization at the time of photographing, game control, and inertial navigation.
The pressure sensor 513 may be disposed on a side frame of the terminal and/or underneath the display screen 505. When the pressure sensor 513 is disposed on the side frame of the terminal, the holding signal of the user to the terminal can be detected, and the processor 501 performs left-right hand recognition or shortcut operation according to the holding signal collected by the pressure sensor 513. When the pressure sensor 513 is disposed at the lower layer of the display screen 505, the processor 501 controls the operability control on the UI interface according to the pressure operation of the user on the display screen 505. The operability control comprises at least one of a button control, a scroll bar control, an icon control and a menu control.
The fingerprint sensor 514 is used for collecting a fingerprint of the user, and the processor 501 identifies the identity of the user according to the fingerprint collected by the fingerprint sensor 514, or the fingerprint sensor 514 identifies the identity of the user according to the collected fingerprint. Upon recognizing that the user's identity is a trusted identity, the processor 501 authorizes the user to perform relevant sensitive operations including unlocking the screen, viewing encrypted information, downloading software, paying, and changing settings, etc. The fingerprint sensor 514 may be disposed on the front, back, or side of the terminal. When a physical button or vendor Logo is provided on the terminal, the fingerprint sensor 514 may be integrated with the physical button or vendor Logo.
The optical sensor 515 is used to collect the ambient light intensity. In one embodiment, the processor 501 may control the display brightness of the display screen 505 based on the ambient light intensity collected by the optical sensor 515. Specifically, when the ambient light intensity is high, the display brightness of the display screen 505 is increased; when the ambient light intensity is low, the display brightness of the display screen 505 is reduced. In another embodiment, processor 501 may also dynamically adjust the shooting parameters of camera head assembly 506 based on the ambient light intensity collected by optical sensor 515.
A proximity sensor 516, also known as a distance sensor, is typically provided on the front panel of the terminal. The proximity sensor 516 is used to capture the distance between the user and the front face of the terminal. In one embodiment, when the proximity sensor 516 detects that the distance between the user and the front surface of the terminal gradually decreases, the processor 501 controls the display screen 505 to switch from the bright screen state to the dark screen state; when the proximity sensor 516 detects that the distance between the user and the front surface of the terminal is gradually increased, the display screen 505 is controlled by the processor 501 to switch from the breath screen state to the bright screen state.
Those skilled in the art will appreciate that the configuration shown in fig. 5 is not intended to be limiting and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components may be used.
In an exemplary embodiment, a computer device is also provided that includes a processor and a memory having at least one program code stored therein. The at least one program code is loaded and executed by one or more processors to implement any of the above-described methods of recommending navigation routes.
In an exemplary embodiment, there is also provided a computer-readable storage medium having at least one program code stored therein, the at least one program code being loaded and executed by a processor of a computer device to implement any of the above-mentioned methods of recommending a navigation route.
Alternatively, the computer-readable storage medium may be a Read-Only Memory (ROM), a Random Access Memory (RAM), a Compact Disc Read-Only Memory (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, and the like.
In an exemplary embodiment, a computer program product or computer program is also provided, the computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer readable storage medium, and the processor executes the computer instructions to cause the computer device to execute any one of the above-mentioned methods for recommending a navigation route.
It is to be understood that reference to "at least one" herein means one or more. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship.
The above description is only exemplary of the present application and should not be taken as limiting, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (11)
1. A method for recommending a navigation route, the method comprising:
acquiring a route query request of a target user;
acquiring at least one navigation route according to the route query request;
for any one navigation route in the at least one navigation route, acquiring a potential violation prediction value of the any one navigation route in at least one violation scene based on the historical violation occurrence probability of the target user in the at least one violation scene, wherein the potential violation prediction value is used for predicting violation conditions;
acquiring a recommended weight of any navigation route based on a potential violation prediction value of the navigation route in the at least one violation scene;
recommending a target navigation route to the target user based on the recommended weight of the at least one navigation route.
2. The method of claim 1, wherein the method further comprises, prior to obtaining a potential violation predictor for the any one of the navigation routes in the at least one violation scenario based on the historical violation occurrence probability of the target user in the at least one violation scenario:
acquiring historical violation record data of the target user in the at least one violation scene and historical driving record data of the target user;
and for any violation scene in the at least one violation scene, acquiring the historical violation occurrence probability of the target user in any violation scene according to the historical violation record data of the target user in any violation scene and the historical driving record data.
3. The method of claim 2 wherein the step of obtaining, for any one of the at least one violation scenario, a historical violation occurrence probability of the target user in the any violation scenario based on historical violation record data of the target user in the any violation scenario and the historical travel record data comprises:
acquiring the historical violation times of the target user in any violation scene based on the historical violation record data of the target user in any violation scene;
based on the historical driving record data, acquiring a historical driving route corresponding to the historical violation record data of the target user in any violation scene, and acquiring the number of first violation detection points corresponding to any violation scene included in the historical driving route;
and acquiring the historical violation occurrence probability of the target user in any violation scene based on the historical violation times and the number of the first violation detection points.
4. The method of claim 3 wherein said obtaining a historical violation probability of said target user for said any violation scenario based on said historical number of violations and said number of first violation detection points comprises:
and taking the ratio of the historical violation times to the number of the first violation detection points as the historical violation occurrence probability of the target user in any violation scene.
5. The method of claim 1, wherein the obtaining a potential violation predictor of the any one navigation route in at least one violation scenario based on the historical violation occurrence probability of the target user in the at least one violation scenario comprises:
for any violation scene in the at least one violation scene, acquiring the number of second violation detection points corresponding to any violation scene included on any navigation route;
and acquiring a potential violation prediction value of any navigation route in any violation scene based on the historical violation occurrence probability of the target user in any violation scene and the number of the second violation detection points.
6. The method of claim 5, wherein the obtaining of the potential violation prediction value of the any navigation route in any violation scenario based on the historical violation occurrence probability of the target user in any violation scenario and the number of second violation detection points comprises:
acquiring the product of the historical violation occurrence probability of the target user in any violation scene and the number of second violation detection points; and taking the result of the multiplication as a potential violation prediction value of any navigation route in any violation scene.
7. The method of claim 1, wherein obtaining the recommended weight of the any one navigation route based on the potential violation prediction value of the any one navigation route under the at least one violation scenario comprises:
and performing summation operation on the potential violation prediction value of any navigation route in the at least one violation scene, and taking the result of the summation operation as the recommended weight of any navigation route.
8. The method according to any one of claims 1-7, wherein the recommending a target navigation route to the target user based on the recommendation weight of the at least one navigation route comprises:
and taking the navigation route with the minimum recommended weight in the at least one navigation route as a target navigation route, and recommending the target navigation route to the target user.
9. An apparatus for recommending a navigation route, said apparatus comprising:
the first acquisition module is used for acquiring a route query request of a target user;
the second acquisition module is used for acquiring at least one navigation route according to the route inquiry request;
the third obtaining module is used for obtaining a potential violation predicted value of any navigation route in at least one violation scene based on the historical violation occurrence probability of the target user in at least one violation scene, and the potential violation predicted value is used for predicting violation conditions;
the fourth obtaining module is used for obtaining the recommended weight of any one navigation route based on the potential violation prediction value of any one navigation route in the at least one violation scene;
and the recommending module is used for recommending a target navigation route to the target user based on the recommending weight of the at least one navigation route.
10. A computer device comprising a processor and a memory, the memory having stored therein at least one program code, the at least one program code being loaded and executed by the processor to implement the method of recommending a navigation route according to any of claims 1 to 8.
11. A computer-readable storage medium having at least one program code stored therein, the at least one program code being loaded and executed by a processor to implement the method of recommending a navigation route according to any of claims 1 to 8.
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