CN110530381B

CN110530381B - Map presence detection method and device based on navigation data and storage medium

Info

Publication number: CN110530381B
Application number: CN201810501066.7A
Authority: CN
Inventors: 杜逸康; 杨璧嘉; 陈永全; 龚剑
Original assignee: Tencent Technology Shenzhen Co Ltd; Tencent Dadi Tongtu Beijing Technology Co Ltd
Current assignee: Tencent Technology Shenzhen Co Ltd; Tencent Dadi Tongtu Beijing Technology Co Ltd
Priority date: 2018-05-23
Filing date: 2018-05-23
Publication date: 2022-01-25
Anticipated expiration: 2038-05-23
Also published as: CN110530381A

Abstract

The application provides a map presence detection method, a map presence detection device and a storage medium based on navigation data, wherein the method comprises the following steps: acquiring a navigation data set associated with a target interest point; performing statistical analysis on each navigation data in the navigation data set based on a mapping relation between a preset matrix element and an area position, and determining a value corresponding to each element in a preset matrix at present; and determining the current map data of the target interest point according to the current corresponding numerical value of each element. By the method, the accuracy and timeliness of the target interest point situation detection can be improved, and the technical problem that the situation updating time and judgment precision are difficult to guarantee in the prior art is solved.

Description

Map presence detection method and device based on navigation data and storage medium

Technical Field

The present application relates to the field of navigation technologies, and in particular, to a map presence detection method and apparatus based on navigation data, and a storage medium.

Background

The electronic map is used as a mainstream navigation tool, and the use experience of a user is seriously influenced by the accuracy of the map. The presence refers to the current situation of the geospatial information provided by the map. In order to ensure the accuracy of the electronic map route recommendation, the situational nature of the geospatial information provided by the map (such as sights, shopping malls, gas stations, etc. displayed on the map) needs to be updated.

Currently, the methods for the determination of the presence are: collecting information on the spot, and judging the situation according to the industry operation standard; or, the manual confirmation is carried out through telephone communication; or, determining the situation according to the information fed back by the user; and acquiring the situation according to data provided by a third party calling the map.

However, the conventional determination method has large uncontrollable property and uncertainty, and cannot ensure the update time and determination accuracy of the situation, which affects the accuracy of the electronic map.

Disclosure of Invention

The present application is directed to solving, at least to some extent, one of the technical problems in the related art.

Therefore, in the first aspect of the present application, a map presence detection method based on navigation data is provided, which detects the presence of current map data of a target interest point according to a navigation data set associated with the target interest point, thereby improving the accuracy and timeliness of the target interest point presence detection, and solving the technical problem in the prior art that the update time and the judgment precision of the presence are difficult to be ensured.

The second aspect of the present application provides a map presence detection apparatus based on navigation data.

A third aspect of the present application provides a computer device.

A fourth aspect of the present application is directed to a computer-readable storage medium.

An embodiment of a first aspect of the present application provides a map presence detection method based on navigation data, including:

acquiring a navigation data set associated with a target interest point;

performing statistical analysis on each navigation data in the navigation data set based on a mapping relation between a preset matrix element and an area position, and determining a value corresponding to each element in a preset matrix at present;

and determining the current map data of the target interest point according to the current corresponding numerical value of each element.

According to the map situation detection method based on the navigation data, the navigation data set associated with the target interest point is obtained, statistical analysis is conducted on the navigation data in the navigation data set based on the mapping relation between the preset matrix elements and the area positions, the value corresponding to each element in the preset matrix at present is determined, and then the situation of the current map data of the target interest point is determined according to the value corresponding to each element at present. Therefore, the purpose of judging the situation by using navigation data with wide coverage and high real-time performance is achieved, the accuracy and timeliness of the situation detection of the target interest point are improved, the accuracy of the recommended route of the map is improved, and the user experience is improved.

An embodiment of a second aspect of the present application provides a map presence detection apparatus based on navigation data, including:

an acquisition module for acquiring a navigation data set associated with a target point of interest;

the analysis module is used for carrying out statistical analysis on each navigation data in the navigation data set based on the mapping relation between the preset matrix elements and the area positions, and determining the current corresponding numerical value of each element in the preset matrix;

and the determining module is used for determining the current map data of the target interest point according to the current corresponding numerical value of each element.

The map situation detection device based on the navigation data obtains the navigation data set associated with the target interest point, performs statistical analysis on the navigation data in the navigation data set based on the mapping relation between the preset matrix elements and the area positions, determines the current corresponding numerical value of each element in the preset matrix, and further determines the situation of the current map data of the target interest point according to the current corresponding numerical value of each element. Therefore, the purpose of judging the situation by using navigation data with wide coverage and high real-time performance is achieved, the accuracy and timeliness of the situation detection of the target interest point are improved, the accuracy of the recommended route of the map is improved, and the user experience is improved.

An embodiment of a third aspect of the present application provides a computer device, including: a processor and a memory; wherein the processor executes a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to implement the map situation detection method based on navigation data according to the embodiment of the first aspect.

An embodiment of a fourth aspect of the present application provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements the map presence detection method based on navigation data according to the embodiment of the first aspect.

Additional aspects and advantages of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application.

Drawings

The foregoing and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:

fig. 1 is a schematic flowchart illustrating a map presence detection method based on navigation data according to an embodiment of the present disclosure;

FIG. 2(a) is an exemplary diagram of a navigation track when a point of interest is normal;

FIG. 2(b) is an exemplary diagram of a navigation trajectory when a point of interest is closed;

FIG. 3 is an exemplary diagram of a piece of navigation data;

FIG. 4 is an exemplary diagram of the correspondence between the tracks of the numbers and the matrix elements;

FIG. 5(a) is a diagram illustrating a matrix transformation example of a navigation trajectory;

FIG. 5(b) is a second exemplary matrix transformation diagram of a navigation trajectory;

fig. 6 is a schematic flowchart of another map presence detection method based on navigation data according to an embodiment of the present application;

fig. 7 is a schematic flowchart of another map presence detection method based on navigation data according to an embodiment of the present application;

fig. 8 is a schematic flowchart of another map presence detection method based on navigation data according to an embodiment of the present application;

fig. 9 is an exemplary diagram of a correspondence relationship of the region division granularity and the number of associated link pieces;

fig. 10 is a schematic structural diagram of a map presence detection apparatus based on navigation data according to an embodiment of the present application;

fig. 11 is a schematic structural diagram of another map presence detection apparatus based on navigation data according to an embodiment of the present application;

fig. 12 is a schematic structural diagram of another map presence detection apparatus based on navigation data according to an embodiment of the present application;

fig. 13 is a schematic structural diagram of another map presence detection apparatus based on navigation data according to an embodiment of the present application; and

fig. 14 is a schematic structural diagram of a computer device according to an embodiment of the present application.

Detailed Description

Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary and intended to be used for explaining the present application and should not be construed as limiting the present application.

A map presence detection method, apparatus, and storage medium based on navigation data according to an embodiment of the present application are described below with reference to the accompanying drawings.

A Point of Interest (POI) is the most core data of a location-based service, and has a wide range of operating scenes on an electronic map, such as a destination selected during navigation, a nearby hotel, a gas station on the electronic map, a restaurant, and the like.

The timely updating of the POI data is a very important link in the electronic map, and whether the present situation of the POI is accurate or not has a great influence on the accuracy of the navigation of the electronic map, so that the present situation of the POI in the electronic map needs to be accurately judged and timely updated in order to ensure that the electronic map provides an effective navigation service for users.

The traditional methods for judging the POI presence mainly include the following:

(1) and (5) field collection. In this way, professional surveying and mapping collection personnel need to shoot and collect POIs on the roadside on the spot, and the situation of the POIs is obtained through the confirmation of the operation standards in the industry. By adopting the on-site collection mode, one-time on-site collection is often carried out at a long time interval for one place, the situation change of the POI cannot be known in time, the timeliness is difficult to guarantee, and the on-site collection needs higher labor and material cost.

(2) And (6) manual auditing. This is a way for the homeworker to determine the presence of the POI by communicating with the existing POI or its neighboring POIs by telephone. This method requires a large labor cost.

(3) And (5) user feedback. The method provides an entrance for reporting information for a user through different channels such as a map application end, a forum, a public number and the like, and the user feeds back the situation state of the POI through the entrance. However, there is a large uncertainty in this way because it is uncertain whether the user will provide feedback, and when the user will provide feedback, so that the accuracy of the judgment and the update time are not guaranteed.

(4) Obtained from a third party application. In the public information of the third-party application calling the electronic map, the situation state marking may be carried out on the POI, so that the situation state of the POI can be obtained from the public information of the third-party application. The information disclosed by the third-party application is also in an uncontrollable state, so that the judgment accuracy and the updating time of the POI situation cannot be guaranteed.

In summary, the conventional method for determining the POI presence has a large uncontrollable property and uncertainty, and cannot ensure the update time and the determination accuracy of the POI presence, thereby affecting the accuracy of the electronic map.

In view of the above problems, the present application provides a map presence detection method based on navigation data, so as to improve the update speed and accuracy of POI presence update and improve the timeliness of the presence update.

Fig. 1 is a schematic flowchart of a map presence detection method based on navigation data according to an embodiment of the present disclosure.

As shown in fig. 1, the map presence detecting method based on navigation data includes the following steps:

step 101, a navigation data set associated with a target point of interest is obtained.

The target interest point is an interest point to be analyzed for a current situation on the electronic map, and may be, for example, a certain gas station, a certain mall, and the like on the electronic map.

When a user uses an electronic map for route guidance, two situations may be encountered: firstly, the user smoothly arrives at the destination and stops around the destination (interest point) or drives into the destination, as shown in fig. 2 (a); secondly, after the navigation reaches the destination, the navigation finds that the destination does not exist, and continues to drive forwards without driving into the destination, as shown in fig. 2 (b). Therefore, the navigation track data around the interest point in the electronic map can reflect the current state of the interest point.

The electronic map has massive user resources, the times of using the electronic map by a user are increased along with the time, and more navigation track data are generated when the user uses the electronic map. For each point of interest on the map, especially for points of interest frequently visited by the user, a large amount of data may correspond to the navigation track data of the electronic map. Therefore, in the embodiment, for the target interest point to be analyzed, the navigation data set associated with the target interest point may be acquired from the navigation track data of the electronic map. The navigation data in the navigation data set may include a plurality of information, such as an address of a destination, each positioning point in the way, and a time when navigation occurs.

As an example, fig. 3 is an exemplary diagram of a piece of navigation data. As shown in fig. 3, the navigation data includes the POI corresponding to the destination (as shown in the sentence marked as a in fig. 3), the time when the navigation occurs (as shown in the sentence marked as b in fig. 3), and records the traveling of 53 small road segments included in the navigation track (as shown in the sentence marked as c in fig. 3), and the positioning information of each time point included in each small road segment (as shown in the sentence marked as d in fig. 3), and as can be seen from the sentence marked as d in fig. 3, the positioning information is recorded 17 times in the first small road segment.

In a possible implementation manner of the embodiment of the application, the acquired navigation data set may further include time information, where the time information may refer to a time when the vehicle reaches different positions when passing through a path around the target interest point. In this embodiment, when the navigation data associated with the target interest point is acquired, the navigation data set associated with the target interest point in the current acquisition period may be acquired according to the time information in the navigation data set. That is, the acquired navigation data set may be filtered to filter out the navigation data set whose time information matches the current sampling period. For example, if the sampling period is 5 hours and the current time is 17:00, data with time information falling within the range of 12: 00-17: 00 can be screened from the navigation data set to form a new navigation data set for the presence judgment.

For a point of interest, when the point of interest is a destination navigated by a user, if the user drives into the point of interest, it may be said that the point of interest is normal, and if the user continues to drive after reaching the point of interest, it may be said that the point of interest is abnormal. Therefore, the navigation trajectory data around the point of interest as the navigation destination can reflect the current state of the point of interest. Therefore, in a possible implementation manner of the embodiment of the present application, for a target interest point of a to-be-analyzed situational state, a navigation data set with a navigation destination as the target interest point may be obtained for use in the situational judgment.

And 102, performing statistical analysis on each navigation data in the navigation data set based on the mapping relation between the preset matrix elements and the area positions, and determining the current corresponding numerical value of each element in the preset matrix.

Fig. 4 is an exemplary diagram of the correspondence between the tracks of the numbers and the matrix elements. As shown in fig. 4, when the handwritten number "7" is shown in the picture, actually controlled by the underlying pixels, the trajectory of the number "7" may be mapped into a matrix, and represented by the difference of the element values in the matrix, as shown in the right diagram in fig. 4. Similarly, for a range of navigation tracks, the navigation track may also be represented by an element in a matrix.

In this embodiment, a mapping relationship between a preset matrix element and an area position may be pre-established, where the area position is an area where a target interest point and a peripheral path thereof are located, and is obtained by dividing the area into a plurality of small areas, and one area position represents one small area and corresponds to one element in the preset matrix, and the area position may be represented by latitude and longitude information corresponding to a boundary of the small area. In this embodiment, the area position and the preset matrix element may be preset, and a mapping relationship between the area position and the preset matrix element may be preset and stored.

Furthermore, in this embodiment, after the navigation data set associated with the target interest point is obtained, statistical analysis may be performed on each navigation data in the obtained navigation data set based on a mapping relationship between a preset matrix element and an area position, so as to determine a current corresponding value of each element in the preset matrix.

As an example, the number of times that the position of the vehicle in the navigation data set is located in the area position corresponding to the element may be counted for each element in the preset matrix based on the mapping relationship between the preset matrix element and the area position, and the obtained number is used as the current corresponding value of the element.

Further, in a possible implementation manner of the embodiment of the present application, before performing statistical analysis on each navigation data in the navigation data set, an element value corresponding to the target interest point may be set as a preset value based on a preset mapping relationship between a matrix element and an area position. Specifically, the area where the target interest point is located may be determined, the longitude and latitude coordinates of the area may be obtained, the corresponding matrix element may be determined according to the longitude and latitude coordinates of the target interest point based on the mapping relationship between the preset matrix element and the area position, and the value of the matrix element may be set as the preset value. For example, the preset value is a maximum value, and the preset value may be set to 999, 9999, or the like. The target interest points are represented by setting preset values, and the target interest points are favorably identified.

For example, fig. 5(a) is a first example of matrix transformation of the navigation track, and fig. 5(b) is a second example of matrix transformation of the navigation track. The left diagrams in fig. 5(a) and 5(b) are simple navigation maps, which can be roughly determined according to the path around the target interest point. For example, as shown in the left diagram in fig. 5(a), when the target interest point is normal, the vehicle needs to pass through the trajectory 1 when entering the target interest point; as shown in fig. 5(b), when the target interest point is abnormal, the vehicle does not enter the target interest point, and the vehicle directly passes through the target interest point through the track 2. As shown in fig. 5(a) and 5(b), the value of the matrix element corresponding to the target interest point is 999, and the values of the other matrix elements are obtained by performing statistical analysis on the navigation data in the navigation data set and determined according to the number of times the vehicle appears at the area position corresponding to the matrix element. As can be seen from fig. 5(a) and 5(b), elements in the matrix having a value other than 0 may form a trajectory that coincides with the actual navigation trajectory.

And 103, determining the current map data of the target interest point according to the current corresponding numerical value of each element.

In this embodiment, after the current corresponding numerical value of each element in the preset matrix is determined, the current map data of the target interest point may be determined according to the current corresponding numerical value of each element.

As an example, a related visualization processing technology may be adopted to perform visualization processing on the matrix in which the element values are determined, and the visual map obtained after the visualization processing is used as a driving path, so as to determine the current map data of the target interest point according to whether the driving path reaches the target interest point. If the displayed driving path reaches the target interest point, determining that the situation state of the target interest point is normal; otherwise, determining the current state of the target interest point as abnormal.

As an example, the situational state of the target point of interest may be determined by a pre-trained machine learning model. And inputting the matrix with the determined element values into a trained machine learning model, so as to directly obtain whether the current state of the target interest point is normal or abnormal.

In the map presence detection method based on the navigation data, the navigation data set associated with the target interest point is obtained, statistical analysis is performed on each navigation data in the navigation data set based on the mapping relationship between the preset matrix elements and the area positions, the value currently corresponding to each element in the preset matrix is determined, and then the presence of the current map data of the target interest point is determined according to the value currently corresponding to each element. Therefore, the purpose of judging the situation by using navigation data with wide coverage and high real-time performance is achieved, the accuracy and timeliness of the situation detection of the target interest point are improved, the accuracy of the recommended route of the map is improved, and the user experience is improved.

In order to determine the current map data of the target interest point according to the current corresponding numerical values of the elements in the foregoing embodiments, the embodiments of the present application provide two possible implementation manners, one is to determine the current map data of the target interest point through a visualization processing technique, and the other is to determine the current map data of the target interest point through a pre-trained prediction model. Each implementation will be described in detail below.

Fig. 6 is a flowchart illustrating another map presence detection method based on navigation data according to an embodiment of the present disclosure, where in this embodiment, the presence of current map data of a target point of interest is determined by a visualization processing technique. As shown in fig. 6, the map presence detecting method based on navigation data may include the steps of:

step 201, a navigation data set associated with a target point of interest is obtained.

The navigation data set may include location information, where the location information may be longitude and latitude coordinates of the vehicle at different times.

Step 202, determining a target matrix element corresponding to the area position where the target interest point is located based on the mapping relationship between the preset matrix element and the area position.

For a determined target interest point, the position information of the area position of the target interest point is determined. In this embodiment, a mapping relationship between a preset matrix element and a region position may be pre-established, and then, according to the mapping relationship between the preset matrix element and the region position and the position information of the region position where the target interest point is located, a target matrix element corresponding to the region position where the target interest point is located may be determined in the preset matrix.

Step 203, determining a preset value according to the data volume included in the navigation data set associated with the target interest point.

And step 204, setting the numerical value of the target matrix element as a preset value.

For example, after the navigation data set associated with the target interest point is obtained, the data amount included in the target data set may be counted, and then the preset value may be determined according to the counted data amount. For example, if 500 navigation data associated with the target interest point are acquired to form a navigation data set, the preset value may be determined to be any value greater than 500, such as 600, 800, 900, and the like.

In this embodiment, after determining the target matrix element corresponding to the area location where the target interest point is located and the preset value, the value of the target matrix element may be set as the preset value, that is, the element value of the matrix element corresponding to the area location where the target interest point is located in the preset matrix is set as the preset value, so that the matrix element corresponding to the target interest point is distinguished from other matrix elements, thereby facilitating identification of the target interest point.

Step 205, based on the mapping relationship between the preset matrix elements and the region positions, performing statistical analysis on each navigation data in the navigation data set, and determining the current corresponding value of each element in the preset matrix.

The navigation data set comprises position information, and the position information can be used for indicating the area positions of the vehicle at different moments, so that in the embodiment, based on the mapping relation between the preset matrix elements and the area positions, the element values corresponding to the elements in the preset matrix can be counted by performing statistical analysis on the position information in the navigation data set.

As an example, when determining a current corresponding value of each element in the preset matrix, the corresponding relationship between each position in the navigation data set and each element in the preset matrix may be sequentially determined based on a mapping relationship between a preset matrix element and a region position, and if it is determined that the ith position in the navigation data set is located in the region corresponding to the jth element, the value corresponding to the jth element is added by 1. Wherein i and j are both positive integers. For example, when determining whether the ith position is located in the area corresponding to the jth element, it may be determined whether the longitude and latitude coordinates fall within the longitude and latitude range of the area position corresponding to the jth element according to the longitude and latitude coordinates of the ith position, and if so, it is determined that the ith position is located in the area corresponding to the jth element.

Step 206, determining a visual view corresponding to the current value of each element in the preset matrix based on the corresponding relationship between the preset value and the display style.

The display pattern may include, for example, a display color, a size of a display pattern, and the like, and the display pattern may be a dot, a square, a pentagram, and the like. In the correspondence between the numerical value and the display style, the numerical value may be a specific number or a ratio. For example, when the data amount in the navigation data set associated with the target interest point obtained each time is a fixed value and the corresponding relationship between the numerical value and the display style is established, the used numerical value may be a specific number or a ratio; when the data volume in the navigation data set associated with the target interest point acquired each time is an uncertain value, and when the corresponding relationship between the numerical value and the display style is established, the used numerical value may be a ratio.

The following explains the process of determining the visual map by taking the example of determining the visual map corresponding to the current numerical value of each element in the preset matrix based on the corresponding relationship between the preset ratio and the display style.

In an example, the display style is a color, and the preset ratio and the corresponding relationship of the display style are as follows: when the ratio is less than 0.4, the corresponding color is white; when the ratio is greater than or equal to 0.4, the corresponding color is red. In this example, for each element in the preset matrix, a ratio of a current numerical value of the element to a data amount of the navigation data set acquired this time is calculated, the obtained ratio is compared with 0.4, if the ratio is not less than 0.4, the color of the element is set to red, and otherwise, the color of the element is set to white. Therefore, the visualization processing of the matrix is realized, the matrix represented by the numerical value is converted into the image display, the corresponding visual graph is obtained, and in the visual graph, the red color represents that the traffic state is good.

Example two, the display style is color, and the preset ratio and the corresponding relationship of the display style are as follows: when the ratio is less than 0.2, the corresponding color is white; when the ratio is between 0.2 and 0.4 (including 0.2 but not including 0.4), the corresponding color is green; when the ratio is between 0.4 and 0.6 (including 0.4 but not including 0.6), the corresponding color is blue; when the ratio is between 0.6 and 0.8 (including 0.6 but not including 0.8), the corresponding color is orange; when the ratio is greater than or equal to 0.8, the corresponding color is red. Wherein, red represents the best traffic state, orange represents the second order, and green represents the worst traffic state. In this example, for each element in the preset matrix, a ratio between a current value of the element and a data amount of the navigation data set acquired this time is calculated, the obtained ratio is compared with each ratio range in the corresponding relationship, and a color corresponding to the matched ratio range is determined as the color of the element in the preset matrix. For example, if the ratio of the value of an element in the predetermined matrix to the data amount of the navigation data set is 0.35, the color of the element is set to green. And then, after the colors of all elements in the preset matrix are determined, displaying the preset matrix to obtain a visual image.

Example three, the display style is a dot, and the corresponding relationship between the preset ratio and the display style is as follows: when the ratio is less than 0.5, the dots are not displayed; when the ratio is greater than or equal to 0.5, the dots are displayed. In this example, for each element in the preset matrix, a ratio of a current numerical value of the element to a data amount of the navigation data set acquired this time is calculated, the obtained ratio is compared with 0.5, and if the ratio is not less than 0.5, the numerical value corresponding to the element is replaced with a dot; when the ratio is less than 0.5, no display is made on the element. And after the processing of all the elements in the preset matrix is finished, displaying a dot diagram corresponding to the preset matrix, namely a visual diagram, wherein roads connected by dots show good passing state.

Example four, the display style is a dot, and the corresponding relationship between the preset ratio and the display style is as follows: when the ratio is less than 0.1, the dots are not displayed; when the ratio is between 0.1-0.2 (including 0.1 but not including 0.2), displaying dots with a diameter of 1 mm; when the ratio is between 0.2 and 0.3 (including 0.2 but not including 0.3), dots with a diameter of 1.5mm are displayed; when the ratio is between 0.3 and 0.4 (including 0.3 but not including 0.4), dots with a diameter of 2mm are displayed; by analogy, when the ratio is greater than or equal to 0.9, a dot with a diameter of 5mm is displayed. The larger the diameter of the dot, the better the traffic state. In this example, for each element in the preset matrix, a ratio between a current numerical value of the element and a data amount of the navigation data set acquired this time is calculated, the obtained ratio is compared with each ratio range in the corresponding relationship, and a dot corresponding to the matched ratio range is determined as a dot of the element in the preset matrix. For example, if the ratio of the numerical value of an element in the predetermined matrix to the data amount of the navigation data set is 0.55, the numerical value of the element in the predetermined matrix is replaced with a dot having a diameter of 3 mm. And further, after the dots corresponding to all the elements in the preset matrix are determined, displaying the dots with different sizes in the preset matrix to obtain a visible view. According to the size of the dots, different traffic states can be determined.

And step 207, determining the current traffic state diagram of the target interest point associated road section according to the visible view.

It can be understood that after the display style of each element in the preset matrix is determined according to the corresponding relationship between the preset numerical value and the display style, the elements respectively corresponding to each region in the same road need to be communicated to form the passing state diagram of the road.

In this embodiment, after the visual map corresponding to the numerical value of each matrix element in the preset matrix is determined, the current traffic state map of the road section associated with the target interest point may be determined according to the determined visual map.

In the corresponding relationship between the preset values and the display style, when the values are between 80 and 90 (including 80 but not 90), dots with the diameter of 5mm are displayed, when the values are between 50 and 60 (including 50 but not 60), dots with the diameter of 2mm are displayed, when the values are less than 10, the dots are not displayed, and when the values are 999, dots with the diameter of 6mm are displayed to represent the target interest point. Therefore, when the determined values of the matrix elements in the preset matrix are as shown in the right diagram in fig. 5(a), the display style corresponding to the values of the elements can be determined according to the corresponding relationship between the preset values and the display styles, and the elements respectively corresponding to different areas of the same road are communicated, so that the visual diagram as shown in the left side of fig. 5a can be formed. From this visual map, it may be determined that the road segment associated with target point of interest a is in a transit state.

And step 208, determining the current state information of the target interest point according to the current passing state diagram of the associated road section.

In this embodiment, after the current traffic state diagram of the road segment associated with the target interest point is determined, the current state information of the target interest point may be determined according to the determined traffic state diagram.

As an example, the state information of the target interest point corresponding to the current traffic state diagram of the associated road segment may be determined based on a mapping relationship between preset state information of the target interest point and traffic state diagrams of the associated road segments.

In this embodiment, for the target interest point, it may be assumed that the state information of the target interest point is normal and closed respectively, the traffic state diagrams of each associated road segment of the target interest point are obtained in advance, and a mapping relationship between the state information of the target interest point and the traffic state diagrams of each associated road segment is established. Furthermore, after the current passing state diagram of the target interest point associated road section is determined, the state information of the corresponding target interest point can be determined by inquiring the mapping relation established in advance.

Step 209, determining the present situation of the current map data of the target interest point according to the current state information of the target interest point.

In this embodiment, according to the determined current state information of the target interest point, the present situation of the current map data of the target interest point may be determined. For example, when the current state information of the target interest point is normal, determining that the current map data of the target interest point has a normal current state; and when the current state information of the target interest point is closed, determining that the current map data of the target interest point has abnormal current state.

The map presence detection method based on navigation data of this embodiment is implemented by obtaining a navigation data set associated with a target interest point, performing statistical analysis on each navigation data in the navigation data set based on a mapping relationship between a preset matrix element and an area position, determining a value currently corresponding to each element in a preset matrix, determining a target matrix element corresponding to an area position where the target interest point is located based on the corresponding relationship, determining a preset value according to a data amount included in the navigation data set associated with the target interest point, setting the value of the target matrix element as the preset value, determining a visual map corresponding to the current value of each element in the preset matrix based on the corresponding relationship between the preset value and a display style, determining a current traffic state map of a road segment associated with the target interest point according to the visual map, and further determining the current traffic state map of the associated road segment according to the current traffic state map of the associated road segment, and determining the current state information of the target interest point, and finally determining the current map data situation of the target interest point according to the current state information of the target interest point, so that the situation of the interest point is accurately judged and timely updated, and the navigation accuracy of the electronic map is improved.

Fig. 7 is a flowchart illustrating a map presence detection method based on navigation data according to an embodiment of the present disclosure, and in this embodiment, a specific implementation process of determining the presence of current map data of a target point of interest through a pre-trained prediction model is mainly described. As shown in fig. 7, on the basis of the embodiment shown in fig. 1, step 103 may include the following steps:

step 301, analyzing a matrix including a current corresponding numerical value of each element by using a state prediction network generated by training to obtain current state information of the target interest point.

In order to obtain the current state information of the target interest point by using the state prediction network generated by training, the state prediction network needs to be trained in advance.

When network training is performed, a large number of training samples are acquired, and each training sample comprises a matrix with known values of matrix elements and state information of corresponding interest points. When a training sample is obtained, aiming at an interest point, a plurality of navigation tracks related to the interest point can be obtained, the corresponding navigation tracks when the state information of the interest point is normal or abnormal are marked in a manual marking or machine marking mode, a navigation data set is obtained from an electronic map, and a matrix corresponding to each navigation track is determined in a statistical analysis mode. And then, the labeled navigation track and the corresponding matrix are used as training samples for network training. Further, each matrix in the training sample is input as an initial network model (e.g., a convolutional neural network model, a deep neural network model, etc.), state information of the labeled interest points is output, and the initial network model is trained to obtain a state prediction network.

In this embodiment, after the values of the matrix elements are determined, the matrix after the values of the matrix elements are determined may be input to a trained state prediction network, and the state prediction network performs analysis processing on the matrix to output current state information of the target interest point.

Step 302, determining the present situation of the current map data of the target interest point according to the current state information of the target interest point.

In this embodiment, after the current state information of the target interest point is obtained, the current map data of the target interest point can be determined according to the state information.

According to the map situation detection method based on the navigation data, the matrix comprising the current corresponding numerical values of all the elements is analyzed through the state prediction network generated by training, so that the current state information of the target interest point is obtained, and the situation of the current map data of the target interest point is determined according to the obtained state information, so that the situation of the interest point is accurately judged and timely updated, and the navigation accuracy of the electronic map is improved. In addition, the interest point state prediction network obtained through training can be used for online prediction of interest point state information, real-time monitoring of the state of the interest point is achieved, and timely judgment of the situation of the interest point is facilitated.

The path trajectories of the periphery of different points of interest are not necessarily the same. Taking the gas stations as an example, if some gas stations are arranged on the side of a straight road, a straight and circular track can appear when a vehicle enters the gas stations; in some gas stations, such as a three-way intersection or a five-way intersection, the trajectory of the vehicle entering the gas station may be a curved trajectory, which is different from the trajectory of the gas station entering the road of the straight road. In addition, the route distribution around different points of interest is also different, there is only one route around some points of interest, and there may be three routes, five routes, etc. around some points of interest.

For an interest point, when routes distributed around the interest point are dense, if the position of an area corresponding to a preset matrix element is large, there may be more than one road divided in the same area, which may cause an inaccuracy of the value of each element in the preset matrix determined according to the navigation data. Therefore, the embodiment of the present application provides another map situation detection method based on navigation data, so as to determine corresponding area division granularity according to the distribution of routes around an interest point, and ensure that any two routes around the interest point do not fall into the same area position as much as possible.

Fig. 8 is a flowchart illustrating a map presence detection method based on navigation data according to an embodiment of the present application. As shown in fig. 8, on the basis of the embodiment shown in fig. 1, before step 102, the following steps may also be included:

step 401, determining an area division granularity corresponding to the target interest point according to the position relationship between the target interest point and the associated road section.

The position relationship between the target interest point and the associated road segment may be a distribution of the associated road segment of the target interest point.

In this embodiment, the region division granularity corresponding to the target interest point may be determined according to the position relationship between the target interest point and the associated road segment. For example, the correspondence between the number of the target interest point associated road segments and the region partition granularity may be stored in advance, where the greater the number of the associated road segments, the smaller the corresponding region partition granularity. For example, the correspondence relationship between the region division granularity and the number of associated links is shown in fig. 9. And determining the corresponding region division granularity by inquiring the corresponding relation according to the number of the associated road sections of the target interest points.

Step 402, determining a mapping relationship between a preset matrix element and a region position according to the region partition granularity.

In this embodiment, after the region partition granularity corresponding to the target interest point is determined, the mapping relationship between the preset matrix element and the region position may be determined according to the determined region partition granularity. The smaller the region division granularity is, the smaller the range of the region position corresponding to the matrix element is, and the larger the matrix dimension is. For example, assuming that an area 1.2 km around the target interest point is divided into examples, when the area division granularity is 8 m × 6m, the longitude span and the latitude span of the area position corresponding to the matrix element are about 0.2 s, and the corresponding matrix latitude is about 150 × 200; when the region partition granularity is 4 m × 3m, the longitude span and the latitude span of the region position corresponding to the matrix element are about 0.1 second, and the corresponding matrix dimension is about 300 × 400.

According to the map situational detection method based on the navigation data, the area division granularity corresponding to the target interest point is determined according to the position relationship between the target interest point and the associated road section, and then the mapping relationship between the preset matrix element and the area position is determined according to the area division granularity, so that any two routes around the target interest point can be ensured to be not in the same area position as much as possible, and conditions are provided for improving the accuracy of the numerical value of each element in the preset matrix determined according to the navigation data.

In order to implement the above embodiments, the present application further provides a map presence detection apparatus based on navigation data.

Fig. 10 is a schematic structural diagram of a map presence detection apparatus based on navigation data according to an embodiment of the present application.

As shown in fig. 10, the map presence detecting apparatus 50 based on navigation data includes: an acquisition module 510, an analysis module 520, and a determination module 530.

The obtaining module 510 is configured to obtain a navigation data set associated with the target point of interest.

In one possible implementation manner of the embodiment of the present application, the navigation data set may include time information. Thus, the obtaining module 510 is specifically configured to obtain the navigation data set associated with the target interest point in the current acquisition period according to the time information in the navigation data set.

In a possible implementation manner of the embodiment of the present application, the obtaining module 510 is specifically configured to obtain a navigation data set with a navigation destination being a target point of interest.

The analysis module 520 is configured to perform statistical analysis on each navigation data in the navigation data set based on a mapping relationship between a preset matrix element and an area position, and determine a current corresponding value of each element in the preset matrix.

In a possible implementation manner of the embodiment of the present application, the navigation data set acquired by the acquiring module 510 may include location information. Therefore, in this embodiment, the analysis module 520 is specifically configured to sequentially determine a corresponding relationship between each position in the navigation data set and each element in the preset matrix based on a mapping relationship between a preset matrix element and a region position, and add 1 to a numerical value corresponding to a jth element when it is determined that an ith position in the navigation data set is located in a region corresponding to the jth element; wherein i and j are both positive integers.

The determining module 530 is configured to determine, according to the current corresponding numerical value of each element, a current map data availability of the target point of interest.

Further, in a possible implementation manner of the embodiment of the present application, as shown in fig. 11, on the basis of the embodiment shown in fig. 10, the map presence detecting apparatus 50 based on navigation data further includes:

a preset value determining module 500, configured to determine a preset value according to a data amount included in the navigation data set associated with the target interest point.

A setting module 501, configured to determine, based on a mapping relationship between preset matrix elements and area positions, a target matrix element corresponding to an area position where a target interest point is located; and setting the numerical value of the target matrix element as a preset value.

The determining module 530 may include:

the state diagram determining unit 5301 is configured to determine, based on a corresponding relationship between a preset numerical value and a display style, a visual diagram corresponding to a current numerical value of each element in a preset matrix; and determining the current traffic state diagram of the target interest point associated road section according to the visible view.

The state information determining unit 5302 is configured to determine current state information of the target interest point according to the current traffic state diagram of the associated road segment.

Specifically, the state information determining unit 5302 is configured to determine, based on a mapping relationship between state information of a preset target interest point and a traffic state diagram of each associated link, state information of the target interest point corresponding to a current traffic state diagram of the associated link.

The situational determination unit 5303 is configured to determine the situational of the current map data of the target point of interest according to the current state information of the target point of interest.

The method comprises the steps of carrying out statistical analysis on navigation data in a navigation data set based on the mapping relation between preset matrix elements and area positions, determining the current corresponding numerical value of each element in a preset matrix, determining a target matrix element corresponding to the area position of a target interest point based on the corresponding relation, determining a preset value according to the data quantity contained in the navigation data set associated with the target interest point, setting the numerical value of the target matrix element as the preset value, further determining a visual map corresponding to the current numerical value of each element in the preset matrix based on the corresponding relation between the preset numerical value and a display style, determining the current traffic state map of a road section associated with the target interest point according to the visual map, further determining the current state information of the target interest point according to the current traffic state map of the associated road section, and finally determining the current map data availability of the target interest point according to the current state information of the target interest point, therefore, the situation of the interest point is accurately judged and timely updated, and the navigation accuracy of the electronic map is improved.

In a possible implementation manner of this embodiment of the application, as shown in fig. 12, on the basis of the embodiment shown in fig. 10, the determining module 530 may include:

the analyzing unit 5311 is configured to analyze a matrix including a current corresponding numerical value of each element by using the state prediction network generated by training, so as to obtain current state information of the target interest point.

The determining unit 5312 is configured to determine, according to the current state information of the target interest point, a current map data of the target interest point.

The matrix comprising the current corresponding numerical values of all the elements is analyzed by utilizing the state prediction network generated by training to obtain the current state information of the target interest point, and the current map data of the target interest point is determined according to the obtained state information, so that the current situation of the interest point is accurately judged and timely updated, and the navigation accuracy of the electronic map is improved. In addition, the state prediction network obtained through training can be used for online prediction of state information of the interest points, real-time monitoring of the states of the interest points is achieved, and timely judgment of the situations of the interest points is facilitated.

In a possible implementation manner of the embodiment of the present application, as shown in fig. 13, on the basis of the embodiment shown in fig. 10, the map presence detecting apparatus 50 based on navigation data may further include:

and a division granularity determining module 540, configured to determine, according to the position relationship between the target interest point and the associated road segment, an area division granularity corresponding to the target interest point.

And a mapping relation determining module 550, configured to determine a mapping relation between a preset matrix element and a region position according to the region partition granularity.

The area division granularity corresponding to the target interest point is determined according to the position relation between the target interest point and the associated road section, and then the mapping relation between the preset matrix element and the area position is determined according to the area division granularity, so that any two routes around the target interest point can be ensured to be prevented from falling into the same area position as much as possible, and conditions are provided for improving the accuracy of the numerical value of each element in the preset matrix determined according to the navigation data.

It should be noted that the foregoing explanation of the embodiment of the map presence detection method based on navigation data is also applicable to the map presence detection apparatus based on navigation data of the embodiment, and the implementation principle thereof is similar, and is not repeated here.

The map presence detection apparatus based on navigation data of this embodiment performs statistical analysis on each navigation data in the navigation data set by acquiring the navigation data set associated with the target interest point and based on the mapping relationship between the preset matrix element and the area position, determines the current corresponding value of each element in the preset matrix, and further determines the presence of the current map data of the target interest point according to the current corresponding value of each element. Therefore, the purpose of judging the situation by using navigation data with wide coverage and high real-time performance is achieved, the accuracy and timeliness of the situation detection of the target interest point are improved, the accuracy of the recommended route of the map is improved, and the user experience is improved.

In order to implement the above embodiments, the present application also provides a computer device.

As shown in fig. 14, the computer device 90 includes: a processor 910 and a memory 920. Wherein, the processor 910 runs a program corresponding to the executable program code by reading the executable program code stored in the memory 920 for implementing the map presence detecting method based on the navigation data as described in the foregoing embodiments.

In order to implement the above embodiments, the present application also proposes a computer-readable storage medium on which a computer program is stored, which when executed by a processor implements the map presence detection method based on navigation data as described in the foregoing embodiments.

In order to implement the above embodiments, the present application also proposes a computer program product, in which instructions, when executed by a processor, implement the map presence detection method based on navigation data as described in the foregoing embodiments.

In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.

Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.

Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present application.

The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.

It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.

It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.

In addition, functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.

The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.

Claims

1. A map presence detection method based on navigation data is characterized by comprising the following steps:

acquiring a navigation data set associated with a target interest point;

determining the current map data of the target interest point according to the current corresponding numerical value of each element, specifically: determining a visual graph corresponding to the current numerical value of each element in the preset matrix based on the corresponding relation between the preset numerical value and the display style; determining a current passing state diagram of the target interest point associated road section according to the visible view; determining the current state information of the target interest point according to the current passing state diagram of the associated road section; and determining the current map data of the target interest point according to the current state information of the target interest point.

2. The method of claim 1, wherein the navigation dataset includes location information;

the determining of the current corresponding numerical value of each element in the preset matrix includes:

sequentially determining the corresponding relation between each position in the navigation data set and each element in the preset matrix based on the mapping relation between the preset matrix element and the area position;

if the ith position in the navigation data set is determined to be located in the area corresponding to the jth element, adding 1 to the numerical value corresponding to the jth element;

wherein i and j are both positive integers.

3. The method of claim 1, wherein said determining the presence of the current map data of the target point of interest based on the current corresponding values of the elements comprises:

analyzing a matrix comprising the current corresponding numerical values of the elements by using a state prediction network generated by training to obtain the current state information of the target interest point;

and determining the current map data of the target interest point according to the current state information of the target interest point.

4. The method of claim 1, wherein prior to performing the statistical analysis on each navigation data in the navigation data set, further comprising:

determining a target matrix element corresponding to the area position of the target interest point based on the mapping relation between the preset matrix element and the area position;

and setting the numerical value of the target matrix element as a preset value.

5. The method of claim 4, wherein before setting the value of the target matrix element to the preset value, further comprising:

and determining the preset value according to the data volume included in the navigation data set associated with the target interest point.

6. The method of any of claims 1-5, wherein prior to performing the statistical analysis on each navigation data in the set of navigation data, further comprising:

determining the region division granularity corresponding to the target interest point according to the position relation between the target interest point and the associated road section;

and determining the mapping relation between the preset matrix elements and the region positions according to the region division granularity.

7. The method of any of claims 1-5, wherein the navigation dataset includes time information;

the acquiring of the navigation data set associated with the target point of interest includes:

and acquiring the navigation data set associated with the target interest point in the current acquisition period according to the time information in the navigation data set.

8. The method of any one of claims 1-5, wherein said obtaining a navigation data set associated with a target point of interest comprises:

and acquiring a navigation data set with a navigation destination of the target interest point.

9. A map presence detecting apparatus based on navigation data, comprising:

a determining module, configured to determine, according to the current corresponding numerical value of each element, a current map data availability of the target point of interest, where the current map data availability of the target point of interest is specifically: determining a visual graph corresponding to the current numerical value of each element in the preset matrix based on the corresponding relation between the preset numerical value and the display style; determining a current passing state diagram of the target interest point associated road section according to the visible view; determining the current state information of the target interest point according to the current passing state diagram of the associated road section; and determining the current map data of the target interest point according to the current state information of the target interest point.

10. A computer device comprising a processor and a memory;

wherein the processor executes a program corresponding to the executable program code by reading the executable program code stored in the memory for implementing the map presence detecting method based on navigation data according to any one of claims 1 to 8.

11. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out a map presence detection method based on navigation data according to any one of claims 1 to 8.