CN113834489A - Navigation path planning method and device - Google Patents
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- G—PHYSICS
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- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/28—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network with correlation of data from several navigational instruments
- G01C21/30—Map- or contour-matching
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/28—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network with correlation of data from several navigational instruments
- G01C21/30—Map- or contour-matching
- G01C21/32—Structuring or formatting of map data
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
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- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/3407—Route searching; Route guidance specially adapted for specific applications
- G01C21/3438—Rendez-vous, i.e. searching a destination where several users can meet, and the routes to this destination for these users; Ride sharing, i.e. searching a route such that at least two users can share a vehicle for at least part of the route
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- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
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- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/3453—Special cost functions, i.e. other than distance or default speed limit of road segments
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Abstract
The invention discloses a method and a device for planning a navigation path, wherein the method comprises the following steps: acquiring initial point position information contained in a navigation request triggered by a navigation user; acquiring first navigation data matched with the position information of the starting point from a preset navigation database, and planning a path according to the first navigation data; if the planning fails, acquiring second navigation data matched with the initial point position information from a preset auxiliary database; planning a path according to the combination of the first navigation data and the second navigation data; the preset auxiliary database is generated in the following way: acquiring position track information of each navigation user, and judging whether the acquired position track information is matched with the existing data content in a navigation database; if not, the position track information is stored in a preset auxiliary database. In this way, the position track information of the navigation user is not used for updating the navigation database, thereby avoiding the phenomenon of updating errors.
Description
Technical Field
The invention relates to the technical field of internet, in particular to a navigation path planning method and a navigation path planning device.
Background
Along with the development of internet technology, wisdom trip also arises, map navigation class smart mobile phone APP has become the application that the frequency of use is very high in daily life, current intelligent APP navigation is that road network data through the collection of map businessman calculate the way navigation, the main life regional scope of people has basically been covered, but because map data volume is too big, the data update degree of difficulty is great, the frequency is lower, and along with the improvement of standard of living, more and more people select outdoor trip, explore some new areas, but these areas are usually more remote, do not have newest road network data, want direct navigation to go to this type of destination, APP software is difficult to plan out effectual route very likely.
In some regions, due to geography or some special reasons, the map operator cannot measure the real data, but people can enter the regions to search, such as some special mountain roads or some scenic spots, so that the user trajectory data can be considered to assist in supplementing the navigation route. For example, chinese patent application No. 201110442518.7 discloses a method for collecting user's trajectory and updating navigation route. The method comprises the following steps: dividing the collected user track into track sections according to the navigation map; determining road sections related to the track road sections in the navigation map sheet to which each track road section belongs; and updating the navigation data of the navigation map sheet to which each track road section belongs according to the topological relation between each track road section and the related road section.
However, the inventor finds that the above mode in the prior art has at least the following defects in the process of implementing the invention: the collected user track data can be divided into two types, one type is user track data suitable for navigation, and most of the data is collected by a graph businessman, and road related information is collected and confirmed as navigable road network data; the other type is user track data which is not suitable for navigation and cannot be used for updating navigation data, and if the navigation data is directly updated, invalid data is likely to exist, particularly track data of outdoor travel or unknown regional exploration.
Disclosure of Invention
In view of the above, the present invention is proposed to provide a navigation path planning method and apparatus that overcomes or at least partially solves the above problems.
According to an aspect of the present invention, there is provided a method for planning a navigation path, including:
acquiring initial point position information contained in a navigation request triggered by a navigation user;
acquiring first navigation data matched with the initial point position information from a preset navigation database, and planning a path according to the first navigation data;
if the planning fails, acquiring second navigation data matched with the initial point position information from a preset auxiliary database;
planning a path according to the combination of the first navigation data and the second navigation data;
wherein the preset auxiliary database is generated by the following method: acquiring position track information of each navigation user, and judging whether the acquired position track information is matched with the existing data content in the navigation database; and if not, storing the position track information to the preset auxiliary database.
According to another aspect of the present invention, there is provided a navigation path planning apparatus, including:
the acquisition module is suitable for acquiring the position information of the starting point contained in the navigation request triggered by the navigation user;
the route planning module is suitable for acquiring first navigation data matched with the initial point position information from a preset navigation database and planning a route according to the first navigation data;
the auxiliary navigation module is suitable for acquiring second navigation data matched with the initial point position information from a preset auxiliary database if the planning fails; planning a path according to the combination of the first navigation data and the second navigation data;
the auxiliary database generation module is suitable for acquiring the position track information of each navigation user and judging whether the acquired position track information is matched with the existing data content in the navigation database; and if not, storing the position track information to the preset auxiliary database.
According to still another aspect of the present invention, there is provided an electronic apparatus including: the system comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete mutual communication through the communication bus;
the memory is used for storing at least one executable instruction, and the executable instruction enables the processor to execute the operation corresponding to the navigation path planning method.
According to still another aspect of the present invention, there is provided a computer storage medium, in which at least one executable instruction is stored, and the executable instruction causes a processor to perform an operation corresponding to the navigation path planning method.
In the navigation path planning method and the navigation path planning device, the position track information of each navigation user can be obtained in advance, and whether the obtained position track information is matched with the existing data content in the navigation database or not is judged; if not, the position track information is stored in a preset auxiliary database. Correspondingly, according to the starting point position information contained in the navigation request triggered by the navigation user, first navigation data matched with the starting point position information is obtained from a preset navigation database, and when planning fails according to the first navigation data, second navigation data matched with the starting point position information is obtained from the auxiliary database, so that path planning is performed according to the combination of the first navigation data and the second navigation data. Therefore, in the mode, on one hand, navigation can be performed by combining the position track information of the navigation user so as to improve the accuracy and accessibility of the navigation result; on the other hand, the position track information of the navigation user is not used for updating the navigation database, so that the phenomenon of updating errors is avoided.
The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
FIG. 1 shows a flow diagram of a method of planning a navigation path according to one embodiment of the invention;
FIG. 2 shows a flow diagram of a method of planning a navigation path according to another embodiment of the invention;
FIG. 3 is a schematic structural diagram of a navigation path planning apparatus according to yet another embodiment of the present invention;
FIG. 4 shows a schematic structural diagram of an electronic device according to the present invention;
fig. 5 shows a topological structure diagram of a bayesian network.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
Fig. 1 shows a flow chart of a method for planning a navigation path according to an embodiment of the present invention, as shown in fig. 1, the method includes:
step S110: and acquiring the position information of the starting point contained in the navigation request triggered by the navigation user.
The navigation user can actively trigger a navigation request, and the navigation request at least comprises starting point position information and ending point position information of navigation. The start point position information and the end point position information are collectively referred to as start point position information.
Step S120: and acquiring first navigation data matched with the position information of the starting point from a preset navigation database, and planning a path according to the first navigation data.
Specifically, the navigation database is generated from historical navigation information. Correspondingly, first navigation data respectively matched with the starting point position information and the end point position information are obtained from a navigation database, and the first navigation data comprise path information from the starting point position to the end point position. Correspondingly, path planning can be carried out according to the first navigation data. If only one path can be determined according to the first navigation data, directly determining the path as a navigation path; if a plurality of routes can be determined based on the first navigation data, at least one route is preferentially selected from the plurality of routes to be determined as the navigation route.
Step S130: and if the planning fails, acquiring second navigation data matched with the position information of the starting point from a preset auxiliary database.
Specifically, when the path planning fails, it is indicated that the navigation database does not have content precisely matched with the start point position information and the end point position information, and at this time, the second navigation data matched with the start point position information is acquired from the preset auxiliary database.
The preset auxiliary database is generated by the following method: acquiring position track information of each navigation user, and judging whether the acquired position track information is matched with the existing data content in a navigation database; if not, the position track information is stored in a preset auxiliary database. It follows that the assistance database is used to store position trajectory information for the navigation user to supplement existing navigation data.
Step S140: and planning a path according to the combination of the first navigation data and the second navigation data.
Specifically, on the basis of the first navigation data, path planning is performed by combining with the second navigation data, and since the second navigation data is supplementary data realized according to the position track of the navigation user, path planning can be completed more accurately by means of the second navigation data, so that the path which is not recorded in the existing navigation database can be determined conveniently.
Therefore, in the mode, on one hand, navigation can be performed by combining the position track information of the navigation user so as to improve the accuracy and accessibility of the navigation result; on the other hand, the position track information of the navigation user is not used for updating the navigation database, so that the phenomenon of updating errors is avoided.
Fig. 2 shows a flow diagram of a method for planning a navigation path according to another embodiment of the present invention. In this embodiment, the navigation method is applied to a network car booking scene, and correspondingly, the navigation user is a car owner user.
As shown in fig. 2, the method includes:
step S210: and when a car booking order triggered by a car booking user is received, matching the first historical trip data of the car booking user with the second historical trip data of each candidate car owner user.
Specifically, when matching the first historical travel data of the car booking user with the second historical travel data of each candidate car owner user, the following method is specifically adopted:
the method comprises the steps of firstly, obtaining first base station class data contained in first terminal signaling data of a car booking user, generating a first base station position sequence corresponding to the car booking user according to the first base station class data, predicting a first actual position sequence corresponding to the first base station position sequence through a hidden Markov model, and obtaining first historical trip data according to the first actual position sequence.
Then, second base station class data contained in second terminal signaling data of the vehicle owner user are obtained, a second base station position sequence corresponding to the vehicle owner user is generated according to the second base station class data, a second actual position sequence corresponding to the second base station position sequence is predicted through a hidden Markov model, and second historical trip data are obtained according to the second actual position sequence.
Step S220: and screening target vehicle owner users matched with the car booking order from the candidate vehicle owner users according to the matching result, and pushing the car booking order to the target vehicle owner users.
Step S230: and receiving the starting point position information contained in the navigation request triggered when the target owner user is used as the navigation user.
The navigation user can actively trigger a navigation request, and the navigation request at least comprises starting point position information and ending point position information of navigation. The start point position information and the end point position information are collectively referred to as start point position information.
Step S240: and acquiring first navigation data matched with the position information of the starting point from a preset navigation database, and planning a path according to the first navigation data.
Specifically, the navigation database is generated from historical navigation information. Correspondingly, first navigation data respectively matched with the starting point position information and the end point position information are obtained from a navigation database, and the first navigation data comprise path information from the starting point position to the end point position. Correspondingly, path planning can be carried out according to the first navigation data. If only one path can be determined according to the first navigation data, directly determining the path as a navigation path; if a plurality of routes can be determined based on the first navigation data, at least one route is preferentially selected from the plurality of routes to be determined as the navigation route.
Step S250: and if the planning fails, acquiring second navigation data matched with the position information of the starting point from a preset auxiliary database.
Specifically, when the path planning fails, it is indicated that the navigation database does not have content precisely matched with the start point position information and the end point position information, and at this time, the second navigation data matched with the start point position information is acquired from the preset auxiliary database.
The preset auxiliary database is generated by the following method: acquiring position track information of each navigation user, and judging whether the acquired position track information is matched with the existing data content in a navigation database; if not, the position track information is stored in a preset auxiliary database. It follows that the assistance database is used to store position trajectory information for the navigation user to supplement existing navigation data.
Specifically, when acquiring the position track information of each navigation user, the following method is implemented: when navigation failure or navigation completion of a navigation user is detected, a prompt message for prompting the user whether to start a route exploring mode is generated; and if a confirmation response message triggered by the navigation user aiming at the prompt message is received, starting a route exploring mode, and acquiring the position track information of each navigation user in the route exploring mode.
In addition, in order to enrich the connotation of the position track information, when the position track information of each navigation user is acquired, the method is realized by the following steps:
firstly, according to a characteristic point marking request triggered by a navigation user, identifying characteristic points contained in position track information. Then, feature attribute information corresponding to the feature points is acquired. And finally, determining road section attribute information of the track road section where the feature point is located according to the feature attribute information corresponding to the feature point, and storing the feature point, the feature attribute information corresponding to the feature point and the road section attribute information of the track road section where the feature point is located into an auxiliary database.
Wherein the feature point marking request is used for inputting image information, text information and/or audio information corresponding to the feature point; correspondingly, when the characteristic attribute information corresponding to the characteristic point is obtained, the characteristic attribute information corresponding to the characteristic point is determined through an image recognition mode, a text recognition mode and/or an audio semantic recognition mode. Correspondingly, the section attribute information of the track section where the feature point is located includes: position attribute information, driving speed attribute information, driving mode attribute information, altitude information and road type information; wherein the road type information includes: mountain types, and/or waterway types.
Step S260: and planning a path according to the combination of the first navigation data and the second navigation data.
Specifically, on the basis of the first navigation data, path planning is performed by combining with the second navigation data, and since the second navigation data is supplementary data realized according to the position track of the navigation user, path planning can be completed more accurately by means of the second navigation data, so that the path which is not recorded in the existing navigation database can be determined conveniently.
In specific implementation, when path planning is performed according to the combination of the first navigation data and the second navigation data, the method is implemented in the following manner: judging whether to switch the driving mode or not according to the road section attribute information of the track road section where the feature point is contained in the second navigation data; if so, generating prompt information for prompting the user to switch the driving mode; wherein the driving mode includes: a driving mode, a walking mode, a riding mode, and/or a boat-riding mode.
For the convenience of understanding, the following describes the details of the implementation of the present embodiment in detail by taking two specific examples as examples:
examples one,
This example focuses on the link of path planning according to the auxiliary database, i.e., the above-mentioned steps S240 to S260. Specifically, in this example, the user actively reports the track route through the APP, but does not update the navigation data, but stores the track route in one database (i.e., an auxiliary database), and when the user performs the navigation route calculation, the track data reported by the user and the existing navigable road data are matched with a route that can reach or is closer to the destination through a route planning algorithm, and the navigation mode is switched according to different attribute information of the route.
The following is a detailed explanation of the present example, and the specific process steps are as follows:
step one, a user tracking route-finding function is developed in the map navigation APP application, namely, under the condition that the user finishes navigation or fails in navigation, the user is prompted to select to enter a tracking route-finding mode, namely, the user is guided to start the route-finding mode through the prompt message mentioned above.
And step two, after entering a tracking route detection mode, the APP records the position track and other related information of the user.
And step three, the user can report and retain the recorded track data or share the recorded track data to more people through the APP, and the data are independently stored in the auxiliary database and are not used for updating the navigation road network data in the existing navigation database.
And step four, the user can mark some special points as mileage points while recording the track data by the APP, and the user can add characters to the mileage points or share photos. The mileage points are the above-mentioned feature points.
And step five, in order to enrich route data, the system is provided with an open module to provide a special route data recording function for units or organizations such as scenic spots or outdoor clubs.
Step six, the background system obtains attributes (namely characteristic attribute information corresponding to the characteristic points) such as position, speed, altitude, starting point, ending point, direction, track shape and the like in the user track data after statistical analysis by a big data technology, records, combines and deduplicates the same track, meanwhile, a weight w is set for the track route, the more the same track merging times n are, the greater the weight w is, the more w is n +1, the linear relationship is formed, then the technologies such as artificial intelligence semantic recognition and image recognition are utilized to recognize the information of character marking of mileage points or shared photos to assist in analyzing the information such as road types, road conditions and pedestrian flow distribution (namely the road section attribute information of the track road section where the feature points are located), the existing mountain road picture data set is used for recognizing the location where the user is located as a mountain road through an artificial intelligence model, and the relevant image data possibly lacking in a navigation database are made up.
Step seven, the processed data is assisted to carry out navigation and route calculation, if the user inputs the end position coordinate in the navigation route planning, the route planning can be carried out through the road network data in the navigation database, the navigation and route calculation is normally carried out, otherwise, the route is matched according to the method in the step eight.
And step eight, if the target position cannot be finally reached, the navigation route calculation may fail, and in such a case, the track route reported by the user or the specific route recorded by some unit organizations is searched in a certain circular radius area range near the terminal point.
Step nine, at the same time, the situation that the navigation route is not failed but can not reach the destination finally can also exist, at the moment, the navigation route is usually bound to a navigable position point which is closer to the destination, in this situation, the reported track route is searched in a certain circular radius area range near the destination, the shortest distance from the route to the navigation destination is required to be less than the distance from the bound navigable position point to the navigation destination, and a binding point e of the destination position coordinate to the nearest distance from the user to the reported route is calculated;
step ten, sorting the first 3 routes according to the shortest path sequence generated based on the two conditions from the step eight to the step nine, if the length difference between the first three routes is within a certain range (such as within 50 m), re-sorting the routes from high to low according to the weight w, re-sorting the routes randomly according to the same weight, if the user performs navigation calculation in a mode of least time, sorting the first 3 routes according to the shortest time sequence, if the predicted time difference between the first three routes is within a certain range (such as within 2 min), re-sorting the routes from high to low according to the weight w, re-sorting the routes according to the same weight, selecting the routes with more times of passage and higher weight as much as possible, and taking all routes when less than three routes are available.
Step eleven, respectively planning the navigation routes by using the starting points, the end points, the binding points e of the routes and the intersection points of the routes and the circular radius areas, and possibly forming a plurality of navigation routes.
Step twelve, if the navigation route is not matched according to the mode, the circle radius in a certain range near the terminal point is enlarged by one time, and the steps eight to step eleven are executed again until the route is matched, and the navigation route can be abandoned after two rounds of unsuccessful circulation according to the actual calculation speed in the actual application.
And step thirteen, after a plurality of navigation routes are formed, the destination route can be reached or more close to the destination route based on the navigation routes in the navigation database and the track route generated by the user report through the Dijkstra route planning algorithm matching.
And step fourteen, after the navigation of the user based on the normal road network data is finished, entering a route based on the data reported by the user, and prompting the user to enter a navigation and route-exploring mode.
Step fifteen, prompting the user of steering information and the condition that the user deviates from the route according to the shape of the route and the current position of the user in the navigation and route finding mode, prompting the user whether to continue driving or to turn into riding or walking according to information such as road speed, type and the like, realizing smooth switching from the driving navigation to the walking navigation and route finding mode according to the related data of the route attribute, and guiding the user to a final destination; the whole process is ended.
In summary, in this example, the trajectory reported by the user through the APP and the existing navigation route can be generated into a route reaching or closer to the end point through Dijkstra route planning algorithm in combination with the specific matching method of the present invention. And the user track can be collected after the user fails to navigate or the navigation is finished and the user enters the exploring tracking mode. In addition, the artificial intelligence semantic recognition and image recognition technology is used for assisting in judging the attribute, type, people flow distribution and other information of the route for the special mileage points of the user track, and the smooth switching from driving navigation to walking navigation is realized by judging the attribute, type and other information of the route.
It can be seen that the approach in this example has at least the following advantages:
1. the track route of the user is combined with the navigation route of the navigation database to provide a more comprehensive and complete navigation route. Meanwhile, errors caused by directly updating navigation data of track data (such as mountain roads, water roads, street roads and the like) which are not suitable for navigation users are avoided, and the navigation path calculation function of the APP software is further optimized.
2. And collecting track data of the user by adopting a clear method, and judging information such as the attribute, the type and the like of the route in an auxiliary manner by using an analysis method of artificial intelligence semantic recognition and image recognition on the mileage points in the track of the user.
3. And the smooth switching from driving navigation to walking navigation is realized by judging the attribute, the type and other information of the route.
Examples two,
The example focuses on the network car booking process, i.e., steps S210 to S230.
Along with the development of internet technology, intelligent travel also comes to the future, smart phone APPs of internet car booking class have become applications with very high use frequency in daily life, users can go out in time to book cars through such APPs, including taxis, windmills, express trains and the like, also can use APPs to designate specific time and specific places to book cars, the current car booking software sets reservation time at the users, a car booking route travel order can be generated by a system platform after the places, then the system can push reservation orders of passengers to car owners in a certain time range, the car owners select whether to take orders or not according to actual conditions, the existing technical scheme does not fully consider data combining historical route data and travel time of the users, the success rate of network car booking reservation is to be further improved, and the accuracy of order message pushing also needs to be further improved. The existing network car booking appointment recommendation method at least has the following defects: 1. and the related data of the historical travel time and the historical travel track of the user are lacked. 2. Reservation recommendation is not fully performed based on the historical travel time and the historical travel track of the user. 3. The success rate and accuracy of network appointment booking are further improved.
To address the above issues, in this example, matching recommendations for route planning are facilitated based on the user's historical location trajectory data. Accordingly, the present example provides a network appointment recommendation method based on historical navigation data and mobile phone signaling data to implement the above steps S210 to S230. Firstly, analyzing an approximate travel route of a user through a historical navigation route, a historical positioning track, historical mobile phone signaling data and real map road network data of the user so as to enrich data of the historical travel route of the user; secondly, if a user inputs a starting point, a terminal point and a time reservation network car reservation through car reservation software, the system analyzes and predicts a route and performs route matching through big data analysis and by using a dynamic Bayesian network model in combination with simulated route data based on historical navigation data and mobile phone signaling of all users, and statistical analysis is performed fully based on historical travel time and historical travel tracks of the users; and finally, a more effective car booking route with specific travel time is provided for car booking users, and the vehicle booking reservation recommendation is carried out on the vehicle master users with higher route matching degree, so that the success rate and the accuracy of the vehicle booking reservation of the network are further improved. The following is a detailed explanation of the present example, which mainly includes the following steps:
step one, obtaining user trip historical data.
The example mainly obtains historical travel data of two types of users:
the historical travel data of the first type of users are as follows: the method comprises the steps that a navigation route and a track route generated by a user through a map navigation APP and a trip APP are reported and stored, trip data of passengers and a vehicle owner are contained, and information such as a trip track route, trip time and user attributes of the user is extracted from the data;
the historical travel data of the second type of users are as follows: mobile phone signaling data of a user in a recent period of time is obtained, base station positions are obtained according to a common base station positioning algorithm or directly obtained according to the signaling data through base station related space-time data (namely base station class data) in mobile phone signaling, base station position sequences are formed according to a time sequence, and a user trip approximate road section track sequence (namely an actual position sequence) is predicted through a hidden Markov chain model:
there are two sequences in the model, one is the clear sequence (A1, A2, A3 … …) and one is the cryptic sequence (B1, B2, B3 … …). The clear sequence is the base station position sequence, and the hidden sequence is the actual position sequence. In the model, the clear sequence (A1, A2, A3 … …) represents the base station sequence passed by the user, and the hidden sequence (B1, B2, B3 … …) is the actual position sequence of the user. The treatment was carried out as follows:
suppose that the user is traveling on a road and the user is traveling at a constant speed. The existing real road data in the coverage area of the base station is decomposed into one road section, and the road section sequence is used for replacing the position sequence of the user. And obtaining the matching probability between the base station roads by adopting the existing road and base station matching probability calculation method. And predicting the maximum possible track route of the user by combining the data through a hidden Markov chain model according to the coverage condition of the circular range of the base station passed by the user.
And step two, route matching and prediction based on historical data.
Firstly, a user inputs a starting point, an end point and a reservation time through car reservation software to perform car reservation, a system plans a route from the starting point to the end point, and a passenger route and a user (a passenger user and a car owner user) historical route acquired by the process can be subjected to route matching by referring to the following rules:
(1) calculating the starting point distance of the vehicle owner and the passenger;
(2) calculating the distance between the vehicle owner and the passenger terminal;
(3) calculating the route information of the vehicle owner;
(4) calculating the binding distance of the passenger starting point corresponding to the route of the vehicle owner, namely the point of the passenger starting point corresponding to the main route of the vehicle, which is the closest to the main route;
(5) calculating the binding distance of the passenger terminal corresponding to the route of the vehicle owner, namely the point with the shortest distance from the passenger terminal corresponding to the main route of the vehicle;
(6) calculating the route proportion from the starting point of the passenger to the route of the vehicle owner;
(7) calculating whether the basic binding information is met according to the starting point distance, the end point distance and the starting point and end point distance of the vehicle owner corresponding to the passenger: the distance from the starting point is less than 5km, the distance from the ending point is less than 1km, the distance from the starting point to the route is less than 5km, and the distance from the ending point to the route is less than 5 km;
(8) and calculating a basic matching degree rule when the starting point is less than 5km and the end point is less than 1 km:
100- ((starting point distance + end point distance)/6000X 10)
And calculating a basic matching degree rule when the distance from the starting point to the route is less than 5km and the distance from the end point to the route is less than 5 km:
100- (start point distance/5000 x 10) - (end point to route distance/5000) × 5- (route ratio/100 x 10);
and calculating a basic matching degree rule according to the following conditions that the distance from the starting point to the route is less than 5km and the distance from the end point is less than 1 km:
100- (start to route distance/5000 x 5) -end distance/1000 x 10- (route ratio/100 x 10);
and calculating a basic matching rule when the route of the starting point road is less than 5km and the distance from the end point to the route is less than 5 km:
100- (start point to route distance/5000 x 5) - (end point to route distance/5000) × 5- (route ratio/100 x 10);
if the rule is not satisfied, the matching degree is directly set to 10, and the matching is considered to be failed;
(9) calculating the route angles of a passenger route and a vehicle owner route, calculating whether a connecting line of a starting point and a destination of the passenger intersects with the connecting line of the starting point and the destination of the vehicle owner, and calculating a proportional value of an angle according to the angle, whether the angle intersects with the connecting line of the starting point and the destination of the passenger, and the distance between the starting points and the destination, so as to improve the proportional value and the weight of a route close to the direction of the starting point and the destination of the passenger;
(10) calculating the linear distance between the starting point and the ending point of the passenger and the linear distance between the starting point and the ending point of the owner, and calculating the proportional value of the linear distance according to the proportion of the distance between the passenger and the owner and the distance interval by using the smaller distance/the larger distance so as to improve the weight of the proportional value of the distance close route;
(11) setting a floating coefficient according to the fact that the current matched vehicle owner is the number of routes, for example, the matched first route is 1, the second route is 0.95, and the like;
(12) and then obtaining a final matching result according to the basic matching degree, the main route floating coefficient, the distance proportion value and the angle proportion value, and marking as p.
Then, all routes of the matching result p >50 are taken, the route matching is considered to be successful, the historical time corresponding to each route is recorded, the reservation time of the passenger user is recorded as T, the time is in days, a route data set in a recent certain time range (such as one month) is taken, the matching in time series is carried out according to a dynamic Bayesian model, and the network topology of the model is shown in FIG. 5. C is a set of a first route of the same owner user every day, M is a set of all routes in one day, O is a matching route set, T is a time sequence according to the day, a matching result (T > T) of reservation time T of a passenger is predicted through matching of the T time sequence on the basis of a dynamic Bayesian model for all user data, and a route with high matching probability of the owner user and a route with high matching probability of other passenger users are found out.
And if the routes are matched at the time t, the final matching degree P is (1- (n-t) × 0.01) P, n is historical data of the latest time, according to the matching degree formula, the closer the corresponding time of the matching routes is, the higher the matching degree is, and the final matching routes are sorted according to the final matching degree.
Step three, entering car appointment recommendation according to the route matching degree
Firstly, taking the first 100 routes sequenced according to the final matching degree P, obtaining users corresponding to the routes, and recommending car appointment routes to car owner users;
then, if no owner user confirms the order within a certain time range (such as within one hour), recommending an appointment route to the owner users with matching degrees sorted between 100 th and 200 th after the next time period, and so on;
and finally, when recommending a car appointment route to the car owner user, if other passenger users have routes with higher matching degree, initiating car pooling recommendation information of the online car appointment to the user.
In summary, in this example, the real navigation route calculation data and the user positioning track data reported by the APP are combined with the mobile phone signaling data and the route data predicted by the real road data through the hidden markov chain model to be used as a basis for analyzing the historical data of the user trip, so that the data accuracy is improved; based on the historical data of the user trip, matching the route in space and time through a special route matching algorithm and a dynamic Bayesian network model; and carrying out car booking recommendation to the car owner user based on the matching degree, and carrying out car sharing recommendation to the passenger user.
It can be seen that the approach in this example has at least the following advantages: the historical travel data of the user can be enriched by the travel track of the historical navigation route data and the historical mobile phone signaling data during traveling, and particularly, the user can not report the data by using software on a familiar route, so that the defects can be avoided; through big data analysis and statistical analysis based on the historical travel data of the user by utilizing a specific matching rule and a matching model, the route matching effect is better; the invention can further improve the success rate and the accuracy of the network car booking reservation.
The two examples can be combined with each other to implement the steps in the second embodiment.
Fig. 3 is a schematic structural diagram of a navigation path planning apparatus according to another embodiment of the present invention, and as shown in fig. 3, the system includes:
an obtaining module 31, adapted to obtain start point location information included in a navigation request triggered by a navigation user;
the path planning module 32 is adapted to acquire first navigation data matched with the starting point position information from a preset navigation database, and perform path planning according to the first navigation data;
the auxiliary navigation module 33 is adapted to obtain second navigation data matched with the starting point position information from a preset auxiliary database if the planning fails; planning a path according to the combination of the first navigation data and the second navigation data;
an auxiliary database generation module 34 adapted to obtain position track information of each navigation user, and determine whether the obtained position track information matches with existing data content in the navigation database; and if not, storing the position track information to the preset auxiliary database.
Optionally, the auxiliary database generation module is specifically adapted to:
when navigation failure or navigation completion of a navigation user is detected, a prompt message for prompting the user whether to start a route exploring mode is generated;
and if a confirmation response message triggered by the navigation user aiming at the prompt message is received, starting a route exploring mode, and acquiring the position track information of each navigation user in the route exploring mode.
Optionally, the auxiliary database generation module is specifically adapted to:
identifying the characteristic points contained in the position track information according to the characteristic point marking request triggered by the navigation user;
acquiring feature attribute information corresponding to the feature points;
determining road section attribute information of a track road section where the feature points are located according to the feature attribute information corresponding to the feature points;
and storing the characteristic points and the corresponding characteristic attribute information thereof and the road section attribute information of the track road section where the characteristic points are located in the auxiliary database.
Optionally, the feature point marking request is used for inputting image information, text information and/or audio information corresponding to the feature point;
the secondary database generation module is specifically adapted to: determining feature attribute information corresponding to the feature points through an image recognition mode, a text recognition mode and/or an audio semantic recognition mode;
the link attribute information of the track link where the feature point is located includes: position attribute information, driving speed attribute information, driving mode attribute information, altitude information and road type information; wherein the road type information includes: mountain types, and/or waterway types.
Optionally, the auxiliary navigation module is specifically adapted to:
judging whether to switch the driving mode according to the road section attribute information of the track road section where the feature point is contained in the second navigation data;
if so, generating prompt information for prompting the user to switch the driving mode; wherein the driving mode includes: a driving mode, a walking mode, a riding mode, and/or a boat-riding mode.
Optionally, the obtaining module is further adapted to:
when a car booking order triggered by a car booking user is received, matching first historical trip data of the car booking user with second historical trip data of each candidate car owner user;
screening target vehicle owner users matched with the car booking order from all candidate vehicle owner users according to the matching result, and pushing the car booking order to the target vehicle owner users;
and receiving the starting point position information contained in the navigation request triggered when the target vehicle owner user is used as the navigation user.
Optionally, the obtaining module is specifically adapted to:
acquiring first base station class data contained in first terminal signaling data of the car booking user, generating a first base station position sequence corresponding to the car booking user according to the first base station class data, predicting a first actual position sequence corresponding to the first base station position sequence through a hidden Markov model, and obtaining first historical trip data according to the first actual position sequence;
acquiring second base station class data contained in second terminal signaling data of the vehicle owner user, generating a second base station position sequence corresponding to the vehicle owner user according to the second base station class data, predicting a second actual position sequence corresponding to the second base station position sequence through a hidden Markov model, and obtaining second historical travel data according to the second actual position sequence.
The specific structure and the working principle of each module may refer to the description of the corresponding step in the method embodiment, and are not described herein again.
The embodiment of the application provides a nonvolatile computer storage medium, wherein the computer storage medium stores at least one executable instruction, and the computer executable instruction can execute the navigation path planning method in any method embodiment.
Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, and the specific embodiment of the present invention does not limit the specific implementation of the electronic device.
As shown in fig. 4, the electronic device may include: a processor (processor)402, a Communications Interface 404, a memory 406, and a Communications bus 408.
Wherein:
the processor 402, communication interface 404, and memory 406 communicate with each other via a communication bus 408.
A communication interface 404 for communicating with network elements of other devices, such as clients or other servers.
The processor 402 is configured to execute the program 410, and may specifically perform relevant steps in the above embodiments of the domain name resolution method.
In particular, program 410 may include program code comprising computer operating instructions.
The processor 402 may be a central processing unit CPU or an application Specific Integrated circuit asic or one or more Integrated circuits configured to implement embodiments of the present invention. The electronic device comprises one or more processors, which can be the same type of processor, such as one or more CPUs; or may be different types of processors such as one or more CPUs and one or more ASICs.
And a memory 406 for storing a program 410. Memory 406 may comprise high-speed RAM memory, and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
The program 410 may be specifically configured to cause the processor 402 to perform the operations in the above-described method embodiments.
The algorithms and displays presented herein are not inherently related to any particular computer, virtual machine, or other apparatus. Various general purpose systems may also be used with the teachings herein. The required structure for constructing such a system will be apparent from the description above. Moreover, the present invention is not directed to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of the present invention as described herein, and any descriptions of specific languages are provided above to disclose the best mode of the invention.
In the description provided herein, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.
The various component embodiments of the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functionality of some or all of the components in an electronic device according to embodiments of the present invention. The present invention may also be embodied as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present invention may be stored on computer-readable media or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
Claims (10)
1. A method for planning a navigation path includes:
acquiring initial point position information contained in a navigation request triggered by a navigation user;
acquiring first navigation data matched with the initial point position information from a preset navigation database, and planning a path according to the first navigation data;
if the planning fails, acquiring second navigation data matched with the initial point position information from a preset auxiliary database;
planning a path according to the combination of the first navigation data and the second navigation data;
wherein the preset auxiliary database is generated by the following method: acquiring position track information of each navigation user, and judging whether the acquired position track information is matched with the existing data content in the navigation database; and if not, storing the position track information to the preset auxiliary database.
2. The method of claim 1, wherein the obtaining of the position trajectory information of each navigation user comprises:
when navigation failure or navigation completion of a navigation user is detected, a prompt message for prompting the user whether to start a route exploring mode is generated;
and if a confirmation response message triggered by the navigation user aiming at the prompt message is received, starting a route exploring mode, and acquiring the position track information of each navigation user in the route exploring mode.
3. The method of claim 1, wherein the obtaining of the position trajectory information of each navigation user comprises:
identifying the characteristic points contained in the position track information according to the characteristic point marking request triggered by the navigation user;
acquiring feature attribute information corresponding to the feature points;
determining road section attribute information of a track road section where the feature points are located according to the feature attribute information corresponding to the feature points;
and storing the characteristic points and the corresponding characteristic attribute information thereof and the road section attribute information of the track road section where the characteristic points are located in the auxiliary database.
4. The method according to claim 3, wherein the feature point marking request is for inputting image information, text information, and/or audio information corresponding to the feature point;
the obtaining of the feature attribute information corresponding to the feature point includes: determining feature attribute information corresponding to the feature points through an image recognition mode, a text recognition mode and/or an audio semantic recognition mode;
the link attribute information of the track link where the feature point is located includes: position attribute information, driving speed attribute information, driving mode attribute information, altitude information and road type information; wherein the road type information includes: mountain types, and/or waterway types.
5. The method of claim 4, wherein the path planning according to the combination of the first navigation data and the second navigation data comprises:
judging whether to switch the driving mode according to the road section attribute information of the track road section where the feature point is contained in the second navigation data;
if so, generating prompt information for prompting the user to switch the driving mode; wherein the driving mode includes: a driving mode, a walking mode, a riding mode, and/or a boat-riding mode.
6. The method of claim 1, wherein before the obtaining of the starting point location information included in the navigation request triggered by the navigation user, further comprising:
when a car booking order triggered by a car booking user is received, matching first historical trip data of the car booking user with second historical trip data of each candidate car owner user;
screening target vehicle owner users matched with the car booking order from all candidate vehicle owner users according to the matching result, and pushing the car booking order to the target vehicle owner users;
and receiving the starting point position information contained in the navigation request triggered when the target vehicle owner user is used as the navigation user.
7. The method of claim 6, wherein the matching the first historical travel data of the car appointment user with the second historical travel data of each candidate owner user comprises:
acquiring first base station class data contained in first terminal signaling data of the car booking user, generating a first base station position sequence corresponding to the car booking user according to the first base station class data, predicting a first actual position sequence corresponding to the first base station position sequence through a hidden Markov model, and obtaining first historical trip data according to the first actual position sequence;
acquiring second base station class data contained in second terminal signaling data of the vehicle owner user, generating a second base station position sequence corresponding to the vehicle owner user according to the second base station class data, predicting a second actual position sequence corresponding to the second base station position sequence through a hidden Markov model, and obtaining second historical travel data according to the second actual position sequence.
8. An apparatus for planning a navigation path, comprising:
the acquisition module is suitable for acquiring the position information of the starting point contained in the navigation request triggered by the navigation user;
the route planning module is suitable for acquiring first navigation data matched with the initial point position information from a preset navigation database and planning a route according to the first navigation data;
the auxiliary navigation module is suitable for acquiring second navigation data matched with the initial point position information from a preset auxiliary database if the planning fails; planning a path according to the combination of the first navigation data and the second navigation data;
the auxiliary database generation module is suitable for acquiring the position track information of each navigation user and judging whether the acquired position track information is matched with the existing data content in the navigation database; and if not, storing the position track information to the preset auxiliary database.
9. An electronic device, comprising: the system comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete mutual communication through the communication bus;
the memory is used for storing at least one executable instruction, and the executable instruction causes the processor to execute the operation corresponding to the navigation path planning method according to any one of claims 1-7.
10. A computer storage medium having stored therein at least one executable instruction for causing a processor to perform operations corresponding to the method of planning a navigation path according to any one of claims 1-7.
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Application publication date: 20211224 |