CN-121998223-A - Data processing method, apparatus, storage medium, device and program product

Abstract

The embodiment of the application relates to the technical field of computers and discloses a data processing method, a device, a storage medium, equipment and a program product, wherein the method comprises the steps of obtaining road data from a plurality of track data sources and determining road data corresponding to a change road from the road data of each track data source; the method comprises the steps of obtaining a track data source, carrying out data change prediction on road data corresponding to a change road to obtain a predicted change time point of a road element in the change road, obtaining a change prediction contribution degree of each track data source to the road element based on an actual change time point and a predicted change time point of the road element in the change road, selecting a target data source from a plurality of track data sources based on the change prediction contribution degree corresponding to each track data source, and taking the target data source as a business data source of a road business associated with the road element. By adopting the embodiment of the application, the data sources which are helpful to business analysis can be accurately screened out.

Inventors

• YE YAN

Assignees

• 腾讯科技（深圳）有限公司

Dates

Publication Date

20260508

Application Date

20241104

Claims (14)

1. 1. A method of data processing, the method comprising: Acquiring road data from a plurality of track data sources, and determining road data corresponding to a change road from the road data of each track data source, wherein the change road refers to a road with a change of road elements in a historical time period; Carrying out data change prediction on the road data corresponding to the change road to obtain a predicted change time point of the road elements in the change road; obtaining the change prediction contribution degree of each track data source to the road element based on the actual change time point and the prediction change time point of the road element in the change road; and predicting contribution degree based on the change corresponding to each track data source, selecting a target data source from the plurality of track data sources, and taking the target data source as a service data source of the road service associated with the road element.
2. 2. The method according to claim 1, wherein the obtaining the predicted contribution of each track data source to the change of the road element based on the actual change time point of the road element in the change road and the predicted change time point includes: calculating a time difference between the actual change time point and the predicted change time point; Acquiring a first traffic flow of the change road in a first preset time period before the actual change time point and a second traffic flow of the change road in a second preset time period after the actual change time point; And obtaining the predicted contribution degree of each track data source to the change of the road element according to the time difference value, the first traffic flow and the second traffic flow.
3. 3. The method of claim 2, wherein the deriving a predicted contribution of each of the trajectory data sources to the variation of the road element based on the time difference, the first traffic flow, and the second traffic flow comprises; calculating a flow difference between the first traffic flow and the second traffic flow; obtaining an initial prediction contribution degree according to the flow difference value and the time difference value; And acquiring the data confidence coefficient of each track data source, and obtaining the change prediction contribution degree of each track data source to the road element according to the data confidence coefficient of each track data source and the initial prediction contribution degree.
4. 4. The method according to claim 1, wherein the predicting the data change of the road data corresponding to the change road to obtain the predicted change time point of the road element in the change road includes: Acquiring traffic flow time sequence data of the change road from road data corresponding to the change road; and detecting the change point of the traffic flow time sequence data to obtain the predicted change time point of the road element in the change road.
5. 5. The method of claim 4, wherein the traffic flow time series data comprises traffic flow data corresponding to a plurality of dates, and the performing the variable point detection on the traffic flow time series data to obtain the predicted change time point of the road element in the change road comprises: Dividing traffic flow data corresponding to the same date based on a plurality of preset time intervals to obtain traffic flow data corresponding to the same date in each preset time interval; constructing interval flow time sequence data corresponding to each preset time interval according to traffic flow data corresponding to different dates in the same preset time interval; And detecting the change point of the time sequence data of the flow of each interval to obtain a predicted change time point corresponding to the road element in each preset time interval.
6. 6. The method according to claim 5, wherein the obtaining the predicted contribution of each track data source to the change of the road element based on the actual change time point of the road element in the change road and the predicted change time point includes: Obtaining a change prediction contribution degree corresponding to each interval flow time sequence data based on a prediction change time point corresponding to each interval flow time sequence data and the actual change time point; and predicting contribution degree according to the change corresponding to the time sequence data of each interval flow, and obtaining the change prediction contribution degree of each track data source to the road element.
7. 7. The method according to claim 6, wherein the predicting the contribution according to the change corresponding to the time series data of each interval flow to obtain the predicted contribution of each track data source to the change of the road element includes: Acquiring a time weight corresponding to each preset time interval; Based on the time weight corresponding to each preset time interval, carrying out weighting processing on the change prediction contribution corresponding to the corresponding interval flow time sequence data to obtain the weighted prediction contribution corresponding to each interval flow time sequence data; and obtaining the change prediction contribution degree of each track data source to the road element according to the weighted prediction contribution degree corresponding to each interval flow time sequence data.
8. 8. The method of claim 4, wherein the performing the change point detection on the traffic flow time sequence data to obtain the predicted change time point of the road element in the change road comprises: Aiming at each time point in the traffic flow time sequence data, acquiring prior probability and likelihood of each time point; calculating posterior probability of each time point based on the prior probability and the likelihood; And taking the time point with the maximum posterior probability in the traffic flow time sequence data as a predicted change time point of the road elements in the change road.
9. 9. The method of any one of claims 1 to 8, wherein the acquiring road data from a plurality of trajectory data sources comprises: acquiring driving track data acquired by each candidate track data source in a plurality of candidate track data sources; selecting a plurality of track data sources which accord with a preset magnitude standard from the plurality of candidate track data sources based on the driving track data corresponding to each candidate track data source, wherein the preset magnitude standard is set based on the data requirement of the road service; And obtaining road data of the plurality of track data sources based on the travel track data corresponding to the plurality of track data sources.
10. 10. The method according to any one of claims 1 to 8, wherein the selecting a target data source from the plurality of trace data sources based on the predicted contribution of the change corresponding to each trace data source comprises: comparing the change prediction contribution degree corresponding to each track data source with a preset contribution degree to obtain a comparison result; And if the comparison result represents that the change prediction contribution degree corresponding to each track data source is greater than or equal to the preset contribution degree, determining each track data source as the target data source.
11. 11. A data processing apparatus, the apparatus comprising an acquisition unit, a prediction unit, a processing unit, and a screening unit, wherein: The acquisition unit is used for acquiring road data from a plurality of track data sources and determining road data corresponding to a change road from the road data of each track data source, wherein the change road is a road with changed road elements in a historical time period; The prediction unit is used for predicting the data change of the road data corresponding to the change road to obtain a predicted change time point of the road elements in the change road; the processing unit is used for obtaining the change prediction contribution degree of each track data source to the road element based on the actual change time point and the prediction change time point of the road element in the change road; The screening unit is configured to predict a contribution degree based on the change corresponding to each track data source, select a target data source from the plurality of track data sources, and use the target data source as a traffic data source of a road traffic associated with the road element.
12. 12. A computer readable medium on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the data processing method according to any one of claims 1 to 10.
13. 13. An electronic device, comprising: a processor adapted to implement one or more instructions, and A computer storage medium storing one or more instructions adapted to be loaded by the processor and to perform the data processing method of any one of claims 1 to 10.
14. 14. A computer program product, characterized in that the computer program product comprises a computer program adapted to be loaded by a processor and to perform the data processing method according to any of claims 1 to 10.

Description

Data processing method, apparatus, storage medium, device and program product Technical Field The present application relates to the field of computer technology, and in particular, to a data processing method, apparatus, storage medium, device, and program product. Background With the rise of the Internet of vehicles, the application of the fields of maps, navigation and the like is increasing, and correspondingly, the data sources capable of providing the running track data of the objects such as vehicles and the like are also increasing. Currently, some business of the internet of vehicles can be assisted in analyzing through data in the data sources. But not all data in the data source has a positive, positive aid in the analysis of the traffic. Therefore, how to accurately screen out data sources helpful to business analysis is a current urgent problem to be solved. Disclosure of Invention The embodiment of the application provides a data processing method, a data processing device and a storage medium, which can accurately screen out data sources helpful to business analysis. In one aspect, an embodiment of the present application provides a data processing method, including: Acquiring road data from a plurality of track data sources, and determining road data corresponding to a change road from the road data of each track data source, wherein the change road refers to a road with a change of road elements in a historical time period; carrying out data change prediction on the road data corresponding to the change road to obtain a predicted change time point of the road elements in the change road; Obtaining the change prediction contribution degree of each track data source to the road elements based on the actual change time point and the prediction change time point of the road elements in the change road; And predicting contribution degree based on the change corresponding to each track data source, selecting a target data source from the plurality of track data sources, and taking the target data source as a business data source of a road business associated with the road element. In one aspect, an embodiment of the present application provides a data processing apparatus, where the data processing apparatus includes an acquisition unit, a prediction unit, a processing unit, and a screening unit, where: An acquisition unit, configured to acquire road data from a plurality of track data sources, and determine road data corresponding to a change road from the road data of each track data source, where the change road refers to a road where a road element changes in a historical time period; the prediction unit is used for predicting the data change of the road data corresponding to the changed road to obtain a predicted change time point of the road elements in the changed road; The processing unit is used for obtaining the change prediction contribution degree of each track data source to the road elements based on the actual change time point and the predicted change time point of the road elements in the changed road; And the screening unit is used for predicting contribution degree based on the change corresponding to each track data source, selecting a target data source from the track data sources, and taking the target data source as a service data source of the road service associated with the road element. In one embodiment of the application, based on the scheme, the processing unit is particularly used for executing the calculation of the time difference between the actual change time point and the predicted change time point when the predicted contribution degree of each track data source to the change of the road element is obtained based on the actual change time point and the predicted change time point of the road element in the change road, the acquisition of the first traffic flow of the change road in a first preset time period before the actual change time point and the second traffic flow of the change road in a second preset time period after the actual change time point, and the obtaining of the predicted contribution degree of each track data source to the change of the road element according to the time difference, the first traffic flow and the second traffic flow. In one embodiment of the application, based on the scheme, when the processing unit obtains the variation prediction contribution degree of each track data source to the road element according to the time difference value, the first traffic flow and the second traffic flow, the processing unit is particularly used for executing the steps of calculating the flow difference value between the first traffic flow and the second traffic flow, obtaining the initial prediction contribution degree according to the flow difference value and the time difference value, obtaining the data confidence coefficient of each track data source, and obtaining the variation prediction contribution degree of each track data source to