CN114625884B

CN114625884B - Track data processing method, device, equipment, storage medium and program product

Info

Publication number: CN114625884B
Application number: CN202210190779.2A
Authority: CN
Inventors: 杨宁; 陈建国; 王亦乐
Original assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Current assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Priority date: 2022-02-28
Filing date: 2022-02-28
Publication date: 2024-09-03
Anticipated expiration: 2042-02-28
Also published as: CN114625884A

Abstract

The disclosure provides a track data processing method, a track data processing device, a track data storage medium and a track data storage program product, and relates to the field of artificial intelligence, in particular to the fields of big data, knowledge maps and intelligent traffic. The specific implementation scheme is as follows: the method comprises the steps of constructing a knowledge graph according to track data of each user device by acquiring track data of the user devices with various different data sources; searching for a first user vertex whose attribute information satisfies a first condition based on the knowledge graph, and marking the first user vertex as being applied to the construction vehicle; according to the attribute information marked as the user vertex applied to the construction vehicle, the user equipment information applied to the construction vehicle of each data source is determined, the user equipment applied to the construction vehicle can be automatically mined without knowing which vehicle the user equipment is specifically applied to, the track of the construction vehicle can be further mined on the basis, and the method can be applied to various application scenes.

Description

Track data processing method, device, equipment, storage medium and program product

Technical Field

The present disclosure relates to the field of big data, knowledge graph, intelligent traffic, etc. in artificial intelligence, and in particular, to a method, apparatus, device, storage medium, and program product for processing trajectory data.

Background

With the development of artificial intelligence technology, in the field of intelligent transportation, products with track tracking and recording functions such as network vehicle platform, intelligent delivery application (such as takeaway delivery takeaway platform, logistics platform and the like), automobile data recorder, navigation map and the like are presented, and devices for acquiring track data in different types of products are called user equipment, such as a smart phone provided with network vehicle platform client application, a smart phone provided with takeaway delivery client application, automobile data recorder and the like.

The track data of the user equipment with various different data sources in a certain region can be summarized, the complete track data in the region can be known, and a lot of useful information can be obtained by carrying out data mining on the track data. Under many scenes in practical application, track data of construction vehicles need to be mined from the track data, which roads are construction roads can be mined based on the track data of the construction vehicles, which roads can normally pass, and a data basis is provided for applications such as map navigation, intelligent opening of the roads and the like. Under the condition that the specific application of the user equipment to the vehicle is not known, a technical scheme capable of mining which user equipment is applied to the construction vehicle based on the track data does not exist at present.

Disclosure of Invention

The present disclosure provides a method, apparatus, device, storage medium, and program product for processing trajectory data.

According to a first aspect of the present disclosure, there is provided a method for processing trajectory data, including:

Responding to a user equipment information mining request applied to a construction vehicle, and acquiring track data of user equipment of various different data sources;

Constructing a knowledge graph according to the track data of the user equipment, wherein the knowledge graph comprises user vertexes corresponding to each user equipment and data source vertexes corresponding to each data source, each user vertex is connected with the data source vertexes corresponding to the data sources to which the corresponding user equipment belongs through edges, the attribute information of each user vertex comprises track characteristic information of the corresponding user equipment, and the attribute information of each data source vertex is determined according to the attribute information of all connected user vertexes;

Searching a first user vertex of which attribute information meets a first condition in the knowledge graph, and marking the first user vertex as being applied to a construction vehicle;

User equipment information for each data source that is applied to the construction vehicle is determined based on attribute information labeled as user vertices for application to the construction vehicle.

According to a second aspect of the present disclosure, there is provided a track data processing apparatus, including:

The track data acquisition module is used for responding to the user equipment information mining request applied to the construction vehicle and acquiring track data of user equipment of various different data sources;

The knowledge graph construction module is used for constructing a knowledge graph according to the track data of the user equipment, wherein the knowledge graph comprises user vertexes corresponding to each user equipment and data source vertexes corresponding to each data source, each user vertex is connected with the data source vertexes corresponding to the data sources to which the corresponding user equipment belongs through edges, the attribute information of each user vertex comprises track characteristic information of the corresponding user equipment, and the attribute information of each data source vertex is determined according to the attribute information of all connected user vertexes;

The first mining processing module is used for searching a first user vertex with attribute information meeting a first condition in the knowledge graph and marking the first user vertex as being applied to a construction vehicle;

And the information determining module is used for determining the user equipment information applied to the construction vehicle of each data source according to the attribute information marked as the user vertex applied to the construction vehicle.

According to a third aspect of the present disclosure, there is provided an electronic device comprising:

At least one processor; and

A memory communicatively coupled to the at least one processor; wherein,

The memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of the first aspect.

According to a fourth aspect of the present disclosure, there is provided a non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of the first aspect.

According to a fifth aspect of the present disclosure, there is provided a computer program product comprising: a computer program stored in a readable storage medium, from which it can be read by at least one processor of an electronic device, the at least one processor executing the computer program causing the electronic device to perform the method of the first aspect.

The technology disclosed by the invention can automatically mine out the user equipment applied to the construction vehicle based on the track data of the user equipment.

It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.

Drawings

The drawings are for a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:

FIG. 1 is a flow chart of a method of processing trajectory data according to a first embodiment of the present disclosure;

FIG. 2 is a schematic diagram of a graph structure model of a knowledge graph, in accordance with an embodiment of the disclosure;

FIG. 3 is a schematic diagram of a mapping relationship of a number of data source vertices to user vertices according to an embodiment of the present disclosure;

FIG. 4 is a flow chart of a method of processing trace data according to a second embodiment of the present disclosure;

Fig. 5 is a schematic structural view of a track data processing apparatus according to a third embodiment of the present disclosure;

fig. 6 is a schematic structural view of a processing apparatus of trajectory data according to a fourth embodiment of the present disclosure;

Fig. 7 is a block diagram of an electronic device for implementing a method of processing trajectory data of an embodiment of the present disclosure.

Detailed Description

Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and should be considered as merely exemplary. Accordingly, one of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

The terms "first," "second," "third," and the like, as used in this disclosure, 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. In the following description of the embodiments, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.

The present disclosure provides a method, an apparatus, a device, a storage medium, and a program product for processing trajectory data, which are applied to fields of big data, a knowledge graph, intelligent traffic, etc. in artificial intelligence, and which user devices are applied to construction vehicles can be mined based on trajectory data of the user devices without knowing which vehicle the user devices are specifically applied to, so as to provide a data base for further mining construction trajectories and applications.

The track data processing method can be particularly used for various application scenes such as an electronic map and an intelligent traffic control system, track data of user equipment can be obtained from various different data sources, and a knowledge graph is constructed according to the track data of each user equipment; searching for a first user vertex whose attribute information satisfies a first condition based on the knowledge graph, and marking the first user vertex as being applied to the construction vehicle; according to the attribute information marked as the user vertex applied to the construction vehicle, the user equipment information applied to the construction vehicle of each data source is determined, so that the user equipment applied to the construction vehicle is excavated, and on the basis, the track of the construction vehicle can be further excavated and can be applied to a plurality of different application scenes.

The knowledge graph comprises user vertexes corresponding to each user device and data source vertexes corresponding to each data source, wherein each user vertex is connected with the data source vertexes corresponding to the data sources to which the corresponding user device belongs through edges, the attribute information of each user vertex comprises track characteristic information of the corresponding user device, and the attribute information of each data source vertex is determined according to the attribute information of all connected user vertexes.

The following describes the technical scheme of the present application and how the technical scheme of the present application solves the above technical problems in detail with specific embodiments. The following embodiments may be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments. Embodiments of the present application will be described below with reference to the accompanying drawings.

Fig. 1 is a flowchart of a method of processing trajectory data according to a first embodiment of the present disclosure. The track data processing method provided by the embodiment can be particularly applied to electronic equipment, and the electronic equipment can realize different functions according to different practical application scenes, such as electronic map, intelligent traffic control and the like.

As shown in fig. 1, the method specifically comprises the following steps:

Step S101, track data of user equipment of a plurality of different data sources are acquired in response to a user equipment information mining request applied to a construction vehicle.

In this embodiment, when the user equipment applied to the construction vehicle needs to be mined, the user equipment information mining request applied to the construction vehicle may be submitted to the electronic device. The electronic device obtains trajectory data of the user device for a plurality of different data sources in response to the request.

In different application scenes, according to the requirements of actual application scenes, the time range and the geographic range for data mining can be specified, the track data in the specified time range and geographic range are processed in a mining mode, and the user equipment applied to the construction vehicle is determined.

Step S102, constructing a knowledge graph according to the track data of the user equipment, wherein the knowledge graph comprises user vertexes corresponding to each user equipment and data source vertexes corresponding to each data source, each user vertex is connected with the data source vertexes corresponding to the data sources to which the corresponding user equipment belongs through edges, the attribute information of each user vertex comprises track characteristic information of the corresponding user equipment, and the attribute information of each data source vertex is determined according to the attribute information of all connected user vertexes.

After the track data of the user equipment of various different data sources are acquired, a knowledge graph is constructed according to the acquired track data of all the user equipment.

Illustratively, the graph structure of the knowledge graph adopts a model as shown in fig. 2, and includes the following two types of vertices: a user vertex and a data source vertex. The user vertex is a vertex corresponding to the user equipment, the data source vertex is a vertex corresponding to the data source, and an edge is arranged between the user vertex of the user equipment and the data source vertex of the data source to which the user equipment belongs. The user vertex has attribute information, and the attribute information of the user vertex is track characteristic information of the user equipment. The data source vertices also have attribute information that may be obtained by aggregating attribute information of user vertices connected to the data source vertices.

The edge between the data source vertex and the user vertex can be represented by a one-to-one key, and the edge enables the data source vertex and the user vertex to form a one-to-many mapping relation.

For example, taking an example that one data source has a plurality of user devices, the mapping relationship between the data source vertex a corresponding to the data source and the user vertex B corresponding to the plurality of user devices is shown in fig. 3, a circle with grid filling in the middle represents one data source vertex a, and a non-filled circle around a and connected with the data source vertex a through edges represents the user vertex B, as shown in fig. 3, the user vertex B has a plurality of user vertices.

After the knowledge graph is constructed according to the track data of the user equipment, searching related to the user vertex and the data source vertex can be realized based on the knowledge graph.

Step S103, searching the knowledge graph for a first user vertex with attribute information meeting a first condition, and marking the first user vertex as being applied to the construction vehicle.

Wherein the first condition is set by analyzing and extracting characteristics specific to the track of the construction vehicle based on the history track data of the large data amount and according to the characteristics possessed by the track of the construction vehicle. For any user vertex, if the attribute information of the user vertex satisfies the first condition, it may be determined that the user vertex is a user vertex applied to the construction vehicle, that is, the user vertex corresponds to user equipment applied to the construction vehicle, for example, a vehicle recorder installed on the construction vehicle.

After the knowledge graph is constructed, determining a first user vertex satisfying the first condition by searching for the first user vertex whose attribute information satisfies the first condition in the knowledge graph, taking the first user vertex as a user vertex applied to the construction vehicle, and marking the first user vertex as the user vertex applied to the construction vehicle.

Step S104, determining user equipment information applied to the construction vehicle of each data source according to the attribute information marked as the user vertex applied to the construction vehicle.

After the user vertex applied to the construction vehicle is marked in the knowledge graph, user equipment information applied to the construction vehicle for each data source can be searched for according to the attribute information marked as the user vertex applied to the construction vehicle.

Based on the user equipment information of each data source applied to the construction vehicle, the track data of the construction vehicle can be further mined, and a data base is provided for downstream tasks.

In the embodiment, the track data of the user equipment are obtained from a plurality of different data sources, and a knowledge graph is constructed according to the track data of each user equipment; searching for a first user vertex whose attribute information satisfies a first condition based on the knowledge graph, and marking the first user vertex as being applied to the construction vehicle; according to the attribute information marked as the user vertex applied to the construction vehicle, the user equipment information applied to the construction vehicle of each data source is determined, the user equipment applied to the construction vehicle can be automatically mined without knowing which vehicle the user equipment is specifically applied to, the track of the construction vehicle can be further mined on the basis, and the method can be applied to various application scenes.

Fig. 4 is a flowchart of a method of processing trajectory data according to a second embodiment of the present disclosure. On the basis of the first embodiment described above, in this embodiment,

As shown in fig. 4, the method specifically comprises the following steps:

Step S401, track data of user equipment of a plurality of different data sources are acquired in response to a user equipment information mining request applied to a construction vehicle.

Step S402, constructing a knowledge graph according to the track data of the user equipment.

Alternatively, the edge between the data source vertex and the user vertex may be a directed edge that points from the data source vertex to the user vertex (as shown in FIG. 2), the start vertex of the directed edge being the data source vertex, and the end vertex of the directed edge being the user vertex.

Alternatively, the edge between the data source vertex and the user vertex may be an undirected edge, indicating that there is an association between the user vertex and the data source vertex, and the user vertex and the data source vertex may be distinguished by the attribute of the vertex.

For example, taking an example that one data source has a plurality of user devices, the mapping relationship between the data source vertex a corresponding to the data source and the user vertex B corresponding to the plurality of user devices is shown in fig. 3, the middle dot represents one data source vertex a, and the user vertex B connected to the data source vertex a by an edge has a plurality of user vertices B. Assuming that there are 100 user devices of the data source, 100 user vertices B corresponding to the 100 user devices are connected to the data source vertex a corresponding to the data source through one edge. Therefore, the track characteristic information of each user equipment can be poured into the attribute information of the user vertex in the knowledge graph, and then each item of attribute information of the data source vertex can be determined through graph reasoning, so that the calculation efficiency can be improved.

Optionally, the trajectory characteristic information of the user device includes at least one of:

the method comprises the steps of data source identification, data source name, location information, average mileage of tracks, average interval mileage between adjacent track points, average duration of tracks, average interval duration between adjacent track points, total mileage of all tracks of all user equipment of the data source, total duration of all tracks of all user equipment of the data source, average confidence of tracks, average vehicle probability of track points, average emission probability of track points, average projection distance of track points, average speed of track points and maximum speed of track points.

Each piece of track data of the user equipment comprises track information such as total mileage, total duration, track confidence and the like, information of the user equipment and the like.

The data source identification refers to identification information of a data source to which the user vertex corresponds to the user equipment.

The data source name is the name of the data source to which the user vertex corresponds to the user device.

The location information is the area information where the user vertex corresponds to the track data of the user equipment, such as the province, the city, the country and the like.

The average mileage of a trace refers to: the user vertices correspond to an average of the total mileage of different trajectory data of the user device.

Average distance mileage between adjacent track points refers to: the user vertex corresponds to the average value of interval duration between adjacent track points in all track data of the user equipment.

The average length of the trace refers to: the user vertices correspond to an average of the total duration of the different trajectory data of the user device.

Average distance mileage between adjacent track points refers to: the user vertex corresponds to the average value of the interval mileage between adjacent track points in all track data of the user equipment.

The total mileage of all trajectories of all user devices of the data source refers to: the user vertex corresponds to the sum of the total mileage of all trajectory data of all user devices of the data source to which the user device belongs.

The total duration of all tracks of all user devices of the data source is referred to as: the user vertex corresponds to the sum of the total duration of all trajectory data of all user devices of the data source to which the user device belongs.

The average confidence of a trace refers to: the user vertices correspond to averages of track confidence levels of different track data of the user device.

The average vehicle probability of the track points means: the user vertex corresponds to the average value of probabilities that track points belong to the vehicle track in all track data of the user equipment.

The average emission probability of a trace point refers to: the user vertices correspond to the average of the emission probabilities of the trace points in all trace data of the user device.

The average projection distance of the track points means: the user vertex corresponds to the average value of the projection distances of the track points in all track data of the user equipment.

The average speed of the trace points means: the user vertex corresponds to the average of the velocities at the trace points in all trace data of the user device.

The maximum speed of a trace point refers to: the user vertex corresponds to the maximum value of the velocity at the track point in all track data of the user device.

Whether applied to a construction vehicle refers to: the user vertex corresponds to whether the user device is applied to the construction vehicle.

In practical application, when the knowledge graph is constructed, the attribute information of the user equipment corresponding to the user vertex can comprise multiple pieces of track characteristic information in the track characteristic information of the user equipment, wherein each piece of track characteristic information is used as one piece of attribute information, and the more the attribute information contained in the user vertex is, the more the accuracy of the user equipment which is determined based on knowledge graph mining and applied to the construction vehicle can be improved.

By extracting rich track characteristic information of the user equipment and constructing a knowledge graph as attribute information of corresponding user vertexes, the knowledge graph is known to contain the rich track characteristic information of each user equipment, and a data basis is provided for accurately mining out the user equipment applied to the construction vehicle.

Optionally, the attribute information of the user vertex may further include the following two items: the confidence level of whether to be applied to the construction vehicle or not is used as label information of whether to be applied to the construction vehicle or not as the user vertex so as to realize the marking of the user vertex applied to the construction vehicle.

The confidence applied to the construction vehicle is as follows: the user vertices correspond to the confidence that the user device applies to the construction vehicle.

The confidence level as to whether or not the application to the construction vehicle and the application to the construction vehicle is attribute information that needs to be determined by data mining through the subsequent steps.

Alternatively, attribute information of the data source vertices in the knowledge graph may be set corresponding to attribute information of the user vertices.

Illustratively, the user vertices in the knowledge graph may include the following attribute information: the method comprises the steps of data source identification, data source name, location information, track average mileage, average interval mileage between adjacent track points, average duration of tracks, average interval duration between adjacent track points, total mileage of all tracks of all user equipment of a data source, total duration of all tracks of all user equipment of the data source, average confidence of tracks, average vehicle running probability of track points, average emission probability of track points, average projection distance of track points, average speed of track points, maximum speed of track points, whether the method is applied to a construction vehicle or not, and confidence of application to the construction vehicle.

Accordingly, the data source vertices in the knowledge graph may include the following attribute information: the method comprises the steps of data source identification, data source name, location information, track average mileage, average interval mileage between adjacent track points, average duration of tracks, average interval duration between adjacent track points, total mileage of all tracks of all user equipment, total duration of all tracks of all user equipment, average confidence of tracks, average vehicle probability of track points, average emission probability of track points, average projection distance of track points, average speed of track points, maximum speed of track points, application of the track points to construction vehicles or application of the track points to the confidence of the construction vehicles. The attribute information, namely whether the data source vertex is applied to the construction vehicle or not and the confidence degree of the data source vertex is applied to the construction vehicle, is used as label information of whether the data source vertex is applied to the construction vehicle or not, so that the marking of the data source vertex applied to the construction vehicle is realized.

For each item of numerical attribute information of a data source vertex, it can be determined by integrating the values of the same attribute information of all user vertices connected to the data source vertex.

For example, the average mileage of the trajectories of the data source vertices is equal to the average of the average mileage of the trajectories of all user vertices connected. The total mileage of all trajectories of all user devices of the data source vertex is equal to the total mileage of all trajectories of all user devices of the data source to which any user vertex is connected. The maximum speed of the trace points of the data source vertices is equal to the maximum of the maximum speeds of the trace points of all the user vertices connected. Other attribute information for the vertices of the data source may be determined in a similar manner and are not listed here.

Specifically, this step may be implemented as follows:

Extracting track characteristic information of each user equipment according to the track data of each user equipment; constructing a knowledge graph according to the track characteristic information of each user equipment, wherein the attribute information of each user vertex in the knowledge graph comprises the track characteristic information of the corresponding user equipment, and the attribute information of each data source vertex is initialized to a preset attribute value; and determining the attribute information of the data source vertexes according to the attribute information of all the user vertexes connected with each data source vertex, and updating the attribute information of each data source vertex in the knowledge graph.

The preset attribute value may be determined according to a possible value of the track feature information of the user equipment, where the preset attribute value is not in the possible values of all track feature information. For example, the preset attribute value may be a negative number, such as-1.

Therefore, the track characteristic information of each user equipment is poured into the attribute information of the user vertex in the knowledge graph, and then all the attribute information of the data source vertex can be determined through graph reasoning, so that the calculation efficiency can be improved. The constructed knowledge graph contains rich track characteristic information of each user equipment and total track characteristic information of each data source, and provides a data basis for mining the user equipment and the data sources applied to the construction vehicle.

Step S403, searching the knowledge graph for a first user vertex whose attribute information satisfies the first condition, and marking the first user vertex as being applied to the construction vehicle.

In a practical application scenario, based on analysis of a large amount of historical trajectory data, it can be determined that if one user device is applied to a construction vehicle, the user device typically has the following characteristics:

the long track appears on the normal road, and whether the construction vehicle is a crane, a crane or a road pressing vehicle, the construction vehicle is necessary to travel on the normal road. A proportion of the trajectory occurs in a construction area, which may be a road network missing area or a gray-scale road network area (i.e., a road network is made but no outside line is drawn).

In combination with these two characteristics, the trajectory data of the user equipment applied to the construction vehicle generally has the following characteristic information:

1) The trajectory data of this user device should be sufficiently mileage rather than very short.

2) The mileage of each track data should be averaged to a certain level.

3) The average confidence of the track should be in a certain interval (not the best and not the worst), and the average confidence of the track is an evaluation of the quality of the track.

4) The probability of the track point going should be in a certain interval.

5) The probability of emission of the trace point should be in a certain interval.

6) The average projection distance of the track points should be within a certain interval.

7) The total number of track points should reach a certain scale.

8) The number of matching failure points should be up to a certain level, where the matching failure points refer to trace points that are not matched to the road network (link).

9) The number of far, middle and near points that match the determination should be of a certain order of magnitude. The far point is a track point with the shortest distance to link being greater than a first distance threshold. The midpoint refers to a locus point where the shortest distance to link is less than or equal to a first distance threshold and the shortest distance to link is greater than a second distance threshold. The near point refers to a locus point with the shortest distance to link less than or equal to the second distance threshold. The first distance threshold and the second distance threshold can be set and adjusted according to actual application scenes.

User equipment applied to the construction vehicle may be mined based on the characteristic information described above. Whether the track data of the user equipment has the characteristic information can be determined according to the attribute information of the corresponding user vertex of the user equipment.

By performing the summary analysis of the characteristic information of 1) to 9) above, it is possible to determine whether or not the user equipment is the first condition applied to the construction vehicle.

Illustratively, the first condition may be the trajectory data of the user device satisfying the following conditions simultaneously:

(1) The sum of the number of failed match points and the number of far points is greater than or equal to a first conditional threshold, and the proportion of the sum of the number of failed match points and the number of far points is greater than a second conditional threshold.

(2) The sum of the number of successful match points and the number of near points is greater than or equal to a third conditional threshold. The successful matching point refers to a trace point successfully matched to a road network (link).

(3) The total mileage of all tracks is greater than or equal to a fourth condition threshold, the average mileage of different tracks is greater than or equal to a fifth condition threshold, and the total number of track points of all tracks is greater than or equal to a sixth condition threshold.

(4) The average confidence of the track is in the first value interval, the vehicle probability of the track is in the second value interval, and the emission probability of the track is in the third value interval.

The first condition threshold, the second condition threshold, the third condition threshold, the fourth condition threshold, the fifth condition threshold, the sixth condition threshold, the first value interval, the second value interval and the third value interval in the above conditions can be all determined according to a large amount of historical data of an actual application scene through analysis, and are not limited specifically herein.

Alternatively, the first condition may be superimposed with other conditions besides satisfying the conditions (1) to (4) above, and specifically may be determined by performing summary analysis on the characteristic information of the conditions (1) to (9) above, and the specific content of the first condition is not specifically limited herein.

Optionally, after searching the first user vertex whose attribute information meets the first condition in the knowledge graph, determining a confidence level of the first user vertex corresponding to the user equipment applied to the construction vehicle according to the attribute information of the first user vertex; and updating the attribute information of the confidence degree of the first user vertex applied to the construction vehicle according to the confidence degree of the first user vertex applied to the construction vehicle corresponding to the user equipment.

Therefore, not only the first user vertex applied to the construction vehicle can be excavated, but also the confidence of the first user vertex applied to the construction vehicle can be given, and the reliability of the excavation result can be reflected.

In addition, the first user vertex is screened based on the confidence coefficient of the first user vertex applied to the construction vehicle, the user vertex with lower confidence coefficient applied to the construction vehicle is filtered, and only the first user vertex with the confidence coefficient applied to the construction vehicle reaching the confidence coefficient threshold value is marked as the user vertex applied to the construction vehicle, so that the accuracy of the mining result is improved.

In an alternative embodiment, after the first user vertex is marked as being applied to the construction vehicle in this step, step S406 may be directly performed, user equipment information applied to the construction vehicle for each data source is determined according to the attribute information marked as being applied to the user vertex of the construction vehicle, and subsequent applications are performed.

In practical applications, if a large percentage of the user devices in one data source are applied to a construction vehicle, then there is a greater likelihood that other user devices within the same data source will be applied to the construction vehicle.

In an alternative embodiment, after the first user vertex is marked as being applied to the construction vehicle, step S406 may be directly performed, and user equipment information applied to the construction vehicle for each data source is determined according to the attribute information marked as the user vertex applied to the construction vehicle, and subsequent applications are performed.

In another alternative embodiment, after the first user vertex is marked as being applied to the construction vehicle, for a data source that occupies a relatively small area for user equipment that is applied to the construction vehicle, it may be considered that a small number of the user equipment that is applied to the construction vehicle in the data source are error data, the attribute of the user vertex marked as being applied to the construction vehicle in the data source is updated, and whether the attribute of the user vertex is applied to the construction vehicle is updated to be negative. For example, the value of the attribute of whether or not these user vertices are applied to the construction vehicle is set to 0,0 indicating no, and 1 indicating yes.

In another alternative embodiment, after the first user vertex is marked as being applied to the construction vehicle, the second mining is performed by using the user equipment applied to the construction vehicle to occupy a relatively large data source in steps S404-S405, and more user equipment applied to the construction vehicle is mined from the data source, so that the recall rate of recalling the construction track based on the user equipment applied to the construction vehicle obtained by mining can be improved.

Step S404, determining a first data source vertex to be mined for the second time.

After the first mining is performed in step S403 to determine the first user vertex applied to the construction vehicle, the first data source vertex that needs to be mined for the second time may also be determined according to the first ratio value occupied by the first user vertex in the user vertices connected by the data source vertices.

Specifically, this step may be implemented as follows:

Determining the number of first user vertexes connected with each data source vertex according to the attribute information of the first user vertexes marked as being applied to the construction vehicle in the knowledge graph; determining a first proportion value corresponding to each data source vertex according to the number of the first user vertices connected with each data source vertex, wherein the first proportion value is the proportion of the first user vertices in all user nodes connected with the data source vertex; and determining a first data source vertex of which the corresponding first proportion value is larger than a first threshold value, and obtaining a first data source vertex to be subjected to second mining.

The first threshold may be set and adjusted according to an actual application scenario and an empirical value, which is not specifically limited herein.

And step S405, searching for a second user vertex of which the attribute information meets the second condition and does not meet the first condition from all user vertices connected with the first data source vertex in the knowledge graph, and marking the second user vertex as being applied to the construction vehicle.

The second condition is looser than the first condition, and the attribute of the user vertex meeting the first condition must meet the second condition, but the attribute information of the user vertex meeting the second condition does not necessarily meet the first condition. More user vertexes meeting the conditions can be excavated based on the second conditions, so that more user vertexes applied to the construction vehicle are excavated, and recall rate of the excavation result in actual application is improved.

After determining the first data source vertices to be mined for the second time, in this step, more second user vertices applied to the construction vehicle, connected to the first data source vertices, are mined by performing the second mining based on a second condition that is relatively looser than the first condition, searching the knowledge graph for second user vertices whose attribute information satisfies the second condition and does not satisfy the first condition, and marking the second user vertices as applied to the construction vehicle.

Optionally, after searching for a second user vertex whose attribute information satisfies the second condition and does not satisfy the first condition from all user vertices connected to the first data source vertex in the knowledge graph, determining a confidence level of the second user vertex corresponding to the user equipment applied to the construction vehicle according to the attribute information of the second user vertex; and updating the attribute information of the confidence degree of the second user vertex applied to the construction vehicle according to the confidence degree of the second user vertex applied to the construction vehicle corresponding to the user equipment. Thus, not only the second user vertex applied to the construction vehicle can be excavated, but also the confidence of the second user vertex applied to the construction vehicle can be given, and the reliability of the excavation result can be reflected.

Optionally, before updating the attribute information, which is the confidence coefficient of the second user vertex applied to the construction vehicle, the confidence coefficient of the second user vertex corresponding to the user equipment applied to the construction vehicle may be multiplied by a preset coefficient, where the preset coefficient is greater than 0 and the preset coefficient is less than 1, so that the confidence coefficient of the second user vertex obtained by the second mining applied to the construction vehicle is reduced, and the confidence coefficient of the user vertex is more reasonable and more accurate.

The preset coefficient may be set and adjusted according to an actual application scenario, which is not specifically limited herein.

Step S406, determining user equipment information applied to the construction vehicle for each data source according to the attribute information marked as the user vertex applied to the construction vehicle.

After all user vertexes applied to the construction vehicle in the knowledge graph are excavated, and the user vertexes are marked as being applied to the construction vehicle, user equipment information applied to the construction vehicle of each data source is easily inquired and obtained based on the knowledge graph.

Optionally, in this step, at least one of the following information of any data source may be determined and outputted according to attribute information marked as a user vertex applied to the construction vehicle:

The number of user devices in the data source that are applied to the construction vehicle;

the proportion of user equipment applied to the construction vehicle in the data source;

identification information of user equipment applied to the construction vehicle in the data source.

In response to a query request of a user, the number, the proportion, the user equipment identification information and the like of user equipment applied to a construction vehicle in any data source can be easily obtained through querying a knowledge graph, so that a data basis is provided for track and the like of the construction vehicle to be excavated later, and the method can be applied to various application scenes.

Alternatively, information of user equipment of each data source applied to the construction vehicle may be formed into a report form, and the report form is output for easy viewing.

In a possible application scenario, for a construction road which is not yet repaired, a plurality of tracks of construction vehicles on the construction road are arranged, if the construction tracks are not distinguished from other tracks, the construction road can be automatically judged to be opened in advance, and the construction road is opened by mistake.

Alternatively, after determining the user equipment information applied to the construction vehicle for each data source according to the attribute information marked as the user vertex applied to the construction vehicle, the automatic opening function of the construction road may be also implemented based on the user equipment information applied to the construction vehicle. The method can be realized by the following steps:

Removing construction track data from track data on a preset construction road according to user equipment information of each data source, wherein the construction track data are track data of user equipment applied to the construction vehicle; and judging whether the preset construction road meets the opening condition according to the residual track data on the preset construction road after the construction track data are removed.

Therefore, after the track data of the construction vehicles on the construction road are removed based on the user equipment information of each data source, the track data of the construction vehicles on the construction road are the track data of other non-construction vehicles, whether the construction road meets the opening condition is judged based on the track data of the other non-construction vehicles, and whether the construction road can be opened or not can be judged more accurately according to the actual situation, so that the situation that the construction road cannot be opened by mistake when the construction road is opened due to the fact that the construction road is covered by the track of the construction vehicles is avoided.

Optionally, the construction track belonging to the user equipment applied to the construction vehicle can be determined according to the excavated information of the user equipment applied to the construction vehicle; if the construction road is covered by the construction track, it is determined that the construction road cannot be opened temporarily. If the construction road is not covered by the construction track in the last time period, the construction road is determined to be opened.

Optionally, according to the excavated information of the user equipment applied to the construction vehicle, determining the construction track belonging to the user equipment applied to the construction vehicle, and determining the road covered by the construction track as the construction road, and performing special treatment on the unopened construction road when performing map matching and map navigation.

The user equipment which is obtained through excavation by the track data processing method and applied to the construction vehicle in the embodiment has the following two aspects of application, on one hand, the real-time task flow is transmitted, the user equipment which is applied to the construction vehicle is added with the label, and the data base is provided for the subsequent task flow. On the other hand, the method is provided for an offline mining end, user equipment applied to a construction vehicle can be removed at the offline mining end, and corresponding scene processing is performed based on track data of other user equipment. The embodiment is not limited to a specific application scenario.

In an alternative embodiment, the graph database service can be built by using an open-source graph database, and the open-source graph database service is used for realizing the functions of constructing and searching the knowledge graph. The functions of calculating data and importing knowledge patterns are realized by writing requests locally. For example, the open source graph database may be hugegraph or the like.

When the method is applied, the electronic equipment responds to a user equipment information mining request applied to a construction vehicle, acquires track data of user equipment of various different data sources, and extracts track characteristic information of each user equipment according to the track data of each user equipment; and submitting a request to the database service, so that the database service constructs a knowledge graph according to the track characteristic information of each user equipment, wherein the attribute information of each user vertex in the knowledge graph comprises the track characteristic information of the corresponding user equipment, and the attribute information of each data source vertex is initialized to a preset attribute value. And then based on graph reasoning, determining the attribute information of the data source vertexes according to the attribute information of all the user vertexes connected with each data source vertex, and updating the attribute information of each data source vertex in the knowledge graph.

The subsequent updating of the knowledge graph, searching, inquiring and other functions based on the knowledge graph are performed by the database service, and result data is fed back to the electronic equipment.

When user equipment information of an application construction vehicle is needed to be acquired, the electronic equipment submits a request to a database service, and the database service inquires attribute information, which is connected with data source vertexes corresponding to each data source and marked as the user vertexes applied to the construction vehicle, in a knowledge graph according to the data source information and counts the number, the proportion and the identification information of the user vertexes applied to the construction vehicle in each data source; and feeding back the query result to the electronic device. And the electronic equipment generates a report according to the query result and displays the report.

Optionally, visual display of the knowledge graph can be realized, the overall structure of the knowledge graph can be displayed through a front-end page, a substructure related to any data source is supported, and query and display of information of any data source and any user equipment are supported.

For example, the overall structure of the knowledge graph can be displayed through a front-end page, and when a user clicks one of the data source vertices, the mapping relationship between the data source vertex and the user vertex is unfolded and displayed to display the sub-structure related to the data source. In addition, for any vertex in the illustrated graph structure, attribute information of the vertex may also be displayed based on a user request.

In this embodiment, after the first user vertex is marked as being applied to the construction vehicle, the user equipment applied to the construction vehicle occupies a relatively large data source, and the second excavation is performed, so that more user equipment applied to the construction vehicle is excavated from the data source, and the recall rate of recalling the construction track based on the user equipment applied to the construction vehicle obtained by excavation can be improved.

Fig. 5 is a schematic structural view of a track data processing apparatus according to a third embodiment of the present disclosure. The track data processing device provided by the embodiment of the disclosure can execute the processing flow provided by the track data processing method embodiment. As shown in fig. 5, the track data processing device 50 includes: the system comprises a track data acquisition module 51, a knowledge graph construction module 52, a first mining processing module 53 and an information determination module 54.

Specifically, the track data obtaining module 51 is configured to obtain track data of user equipment of a plurality of different data sources in response to a user equipment information mining request applied to a construction vehicle.

The knowledge graph construction module 52 is configured to construct a knowledge graph according to trajectory data of the user device, where the knowledge graph includes a user vertex corresponding to each user device and a data source vertex corresponding to each data source, each user vertex is connected to a data source vertex corresponding to a data source to which the corresponding user device belongs by an edge, and attribute information of each user vertex includes trajectory feature information of the corresponding user device, and attribute information of each data source vertex is determined according to attribute information of all connected user vertices.

The first mining processing module 53 is configured to search the knowledge graph for a first user vertex whose attribute information satisfies a first condition, and mark the first user vertex as being applied to the construction vehicle.

An information determination module 54 for determining user equipment information for each data source to be applied to the construction vehicle based on the attribute information labeled as user vertices to be applied to the construction vehicle.

The apparatus provided in the embodiment of the present disclosure may be specifically configured to perform the method embodiment provided in the first embodiment, and specific functions and technical effects that are achieved are not described herein.

Fig. 6 is a schematic structural diagram of a processing apparatus of trajectory data according to a fourth embodiment of the present disclosure. The track data processing device provided by the embodiment of the disclosure can execute the processing flow provided by the track data processing method embodiment. As shown in fig. 6, the track data processing device 60 includes: a trajectory data acquisition module 61, a knowledge graph construction module 62, a first mining processing module 63, and an information determination module 64.

Specifically, the track data obtaining module 61 is configured to obtain track data of user equipment of a plurality of different data sources in response to a user equipment information mining request applied to a construction vehicle.

The knowledge graph construction module 62 is configured to construct a knowledge graph according to trajectory data of the user device, where the knowledge graph includes a user vertex corresponding to each user device and a data source vertex corresponding to each data source, each user vertex is connected to a data source vertex corresponding to a data source to which the corresponding user device belongs by an edge, and attribute information of each user vertex includes trajectory feature information of the corresponding user device, and attribute information of each data source vertex is determined according to attribute information of all connected user vertices.

The first mining processing module 63 is configured to search the knowledge graph for a first user vertex whose attribute information satisfies a first condition, and mark the first user vertex as being applied to the construction vehicle.

An information determination module 64 for determining user equipment information for each data source for application to the construction vehicle based on the attribute information labeled as user vertices for application to the construction vehicle.

Alternatively, as shown in fig. 6, the knowledge graph construction module 62 includes:

the track feature extraction unit 621 is configured to extract track feature information of each user device according to track data of each user device.

The knowledge graph construction unit 622 is configured to construct a knowledge graph according to the trajectory feature information of each user device, where the attribute information of each user vertex in the knowledge graph includes the trajectory feature information of the corresponding user device, and the attribute information of each data source vertex is initialized to a preset attribute value.

The attribute updating unit 623 is configured to determine attribute information of the data source vertices according to attribute information of all user vertices connected to each data source vertex, and update attribute information of each data source vertex in the knowledge graph.

Optionally, the first excavation processing module is further configured to:

determining the confidence coefficient of the first user vertex applied to the construction vehicle corresponding to the user equipment according to the attribute information of the first user vertex; and updating the attribute information of the confidence degree of the first user vertex applied to the construction vehicle according to the confidence degree of the first user vertex applied to the construction vehicle corresponding to the user equipment.

Optionally, as shown in fig. 6, the track data processing device 60 further includes:

a second excavation processing module 65 for:

Determining the number of first user vertexes connected with each data source vertex according to the attribute information of the first user vertexes marked as being applied to the construction vehicle in the knowledge graph; determining a first proportion value corresponding to each data source vertex according to the number of the first user vertices connected with each data source vertex, wherein the first proportion value is the proportion of the first user vertices in all user nodes connected with the data source vertex; determining a first data source vertex with a corresponding first proportion value larger than a first threshold value; searching for second user vertexes of which attribute information meets a second condition and does not meet the first condition from all user vertexes connected with the first data source vertexes in the knowledge graph; the second user vertex is marked as applied to the construction vehicle.

Optionally, the second excavation processing module is further configured to:

Determining the confidence coefficient of the second user vertex applied to the construction vehicle corresponding to the user equipment according to the attribute information of the second user vertex; and updating the attribute information of the confidence degree of the second user vertex applied to the construction vehicle according to the confidence degree of the second user vertex applied to the construction vehicle corresponding to the user equipment.

Optionally, the second excavation processing module is further configured to:

And multiplying the confidence coefficient of the second user vertex corresponding to the user equipment applied to the construction vehicle by a preset coefficient before updating the attribute information of the confidence coefficient of the second user vertex corresponding to the user equipment applied to the construction vehicle, wherein the preset coefficient is larger than 0 and smaller than 1.

Optionally, the information determining module is further configured to:

determining and outputting at least one of the following information of any data source according to attribute information marked as user vertex applied to the construction vehicle:

The road opening processing module 66 is configured to:

The apparatus provided in the embodiment of the present disclosure may be specifically configured to perform the method embodiment provided in the second embodiment, and specific functions and technical effects that are achieved are not described herein.

Note that, the track data of the user device in this embodiment is not track data for a specific user, and cannot reflect personal information of a specific user.

In the technical scheme of the disclosure, the related processes of collecting, storing, using, processing, transmitting, providing, disclosing and the like of the personal information of the user accord with the regulations of related laws and regulations, and the public order colloquial is not violated.

According to embodiments of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

According to an embodiment of the present disclosure, the present disclosure also provides a computer program product comprising: a computer program stored in a readable storage medium, from which at least one processor of an electronic device can read, the at least one processor executing the computer program causing the electronic device to perform the solution provided by any one of the embodiments described above.

Fig. 7 illustrates a schematic block diagram of an example electronic device 700 that may be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.

As shown in fig. 7, the apparatus 700 includes a computing unit 701 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM) 702 or a computer program loaded from a storage unit 708 into a Random Access Memory (RAM) 703. In the RAM 703, various programs and data required for the operation of the device 700 may also be stored. The computing unit 701, the ROM 702, and the RAM 703 are connected to each other through a bus 704. An input/output (I/O) interface 705 is also connected to bus 704.

Various components in device 700 are connected to I/O interface 705, including: an input unit 706 such as a keyboard, a mouse, etc.; an output unit 707 such as various types of displays, speakers, and the like; a storage unit 708 such as a magnetic disk, an optical disk, or the like; and a communication unit 709 such as a network card, modem, wireless communication transceiver, etc. The communication unit 709 allows the device 700 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.

The computing unit 701 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 701 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc. The calculation unit 701 performs the respective methods and processes described above, for example, a processing method of trajectory data. For example, in some embodiments, the method of processing trajectory data may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as storage unit 708. In some embodiments, part or all of the computer program may be loaded and/or installed onto device 700 via ROM 702 and/or communication unit 709. When a computer program is loaded into the RAM 703 and executed by the computing unit 701, one or more steps of the above-described processing method of trajectory data may be performed. Alternatively, in other embodiments, the computing unit 701 may be configured to perform the processing method of the trajectory data in any other suitable way (e.g. by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), complex Programmable Logic Devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.

Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the internet.

The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service ("Virtual PRIVATE SERVER" or simply "VPS") are overcome. The server may also be a server of a distributed system or a server that incorporates a blockchain.

It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel or sequentially or in a different order, provided that the desired results of the technical solutions of the present disclosure are achieved, and are not limited herein.

The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present disclosure are intended to be included within the scope of the present disclosure.

Claims

1. A method of processing trajectory data, comprising:

And determining user equipment information applied to the construction vehicle for each data source according to the attribute information marked as the user vertex applied to the construction vehicle, wherein the user equipment information is used for judging whether the construction road is opened or not.

2. The method of claim 1, wherein the constructing a knowledge-graph from the trajectory data of the user device comprises:

extracting track characteristic information of each user equipment according to the track data of each user equipment;

Constructing a knowledge graph according to the track characteristic information of each user equipment, wherein the attribute information of each user vertex in the knowledge graph comprises the track characteristic information of the corresponding user equipment, and the attribute information of each data source vertex is initialized to a preset attribute value;

And determining the attribute information of the data source vertexes according to the attribute information of all the user vertexes connected with each data source vertex, and updating the attribute information of each data source vertex in the knowledge graph.

3. The method of claim 1, wherein the searching the knowledge-graph for the first user vertex whose attribute information satisfies the first condition further comprises:

Determining the confidence level of the user equipment corresponding to the first user vertex applied to the construction vehicle according to the attribute information of the first user vertex;

And updating attribute information of the confidence degree of the first user vertex applied to the construction vehicle according to the confidence degree of the first user vertex applied to the construction vehicle corresponding to the user equipment.

4. The method of any of claims 1-3, wherein the searching the knowledge-graph for a first user vertex whose attribute information satisfies a first condition, the first user vertex being marked as applied to a construction vehicle, further comprises:

Determining the number of first user vertexes connected with each data source vertex according to the attribute information of the first user vertexes marked as being applied to the construction vehicle in the knowledge graph;

Determining a first proportion value corresponding to each data source vertex according to the number of first user vertices connected with each data source vertex, wherein the first proportion value is the proportion of the first user vertices in all user nodes connected with the data source vertex;

Determining a first data source vertex corresponding to the first ratio value larger than a first threshold value;

Searching for second user vertexes of which attribute information meets a second condition and does not meet the first condition from all user vertexes connected with the first data source vertexes in the knowledge graph;

The second user vertex is marked as being applied to a construction vehicle, wherein the attribute of the user vertex satisfying the first condition must satisfy the second condition, but the attribute information of the user vertex satisfying the second condition does not necessarily satisfy the first condition.

5. The method of claim 4, wherein the searching for the second user vertex whose attribute information satisfies the second condition and does not satisfy the first condition among all user vertices connected to the first data source vertex in the knowledge graph further comprises:

Determining the confidence level of the second user vertex corresponding to the user equipment applied to the construction vehicle according to the attribute information of the second user vertex;

And updating attribute information of the confidence degree of the second user vertex applied to the construction vehicle according to the confidence degree of the second user vertex applied to the construction vehicle corresponding to the user equipment.

6. The method of claim 5, wherein before updating the attribute information of the confidence level of the second user vertex applied to the construction vehicle according to the confidence level of the second user vertex applied to the construction vehicle by the corresponding user device, further comprises:

and multiplying the confidence level of the second user vertex corresponding to the user equipment applied to the construction vehicle by a preset coefficient, wherein the preset coefficient is greater than 0 and the preset coefficient is less than 1.

7. The method of claim 5 or 6, wherein the determining user equipment information for each data source for application to the construction vehicle based on attribute information labeled as user vertices for application to the construction vehicle comprises:

the proportion of the user equipment applied to the construction vehicle in the data source;

And the data source is used for identifying information of user equipment applied to the construction vehicle.

8. The method of claim 5 or 6, wherein after determining the user equipment information for each data source for application to the construction vehicle based on the attribute information labeled as user vertex for application to the construction vehicle, further comprising:

Removing construction track data from track data on a preset construction road according to user equipment information of each data source, wherein the construction track data are track data of user equipment applied to the construction vehicle;

And judging whether the preset construction road meets the opening condition according to the residual track data on the preset construction road after the construction track data are removed.

9. A track data processing apparatus, comprising:

And the information determining module is used for determining the user equipment information applied to the construction vehicle of each data source according to the attribute information marked as the user vertex applied to the construction vehicle, wherein the user equipment information is used for judging whether the construction road is opened or not.

10. The apparatus of claim 9, wherein the knowledge-graph construction module comprises:

the track feature extraction unit is used for extracting track feature information of each user device according to the track data of each user device;

The knowledge graph construction unit is used for constructing a knowledge graph according to the track characteristic information of each user equipment, wherein the attribute information of each user vertex in the knowledge graph comprises the track characteristic information of the corresponding user equipment, and the attribute information of each data source vertex is initialized to a preset attribute value;

And the attribute updating unit is used for determining the attribute information of the data source vertexes according to the attribute information of all the user vertexes connected with each data source vertex and updating the attribute information of each data source vertex in the knowledge graph.

11. The apparatus of claim 9, wherein the first mining processing module is further to:

12. The apparatus of any of claims 9-11, further comprising:

the second excavation processing module is used for:

13. The apparatus of claim 12, the second mining processing module further to:

14. The apparatus of claim 13, the second mining processing module further to:

And multiplying the confidence level of the second user vertex corresponding to the user equipment applied to the construction vehicle by a preset coefficient before updating the attribute information of the confidence level of the second user vertex corresponding to the user equipment applied to the construction vehicle, wherein the preset coefficient is larger than 0 and smaller than 1.

15. The apparatus of claim 13 or 14, wherein the information determination module is further configured to:

16. The apparatus of claim 13 or 14, further comprising:

the road opening processing module is used for:

17. An electronic device, comprising:

At least one processor; and

A memory communicatively coupled to the at least one processor; wherein,

The memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-8.

18. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1-8.

19. A computer program product comprising a computer program which, when executed by a processor, implements the steps of the method of any of claims 1-8.