阅读说明：本技术 交通数据的分析方法、装置、设备、车辆及存储介质 (Traffic data analysis method, device, equipment, vehicle and storage medium ) 是由 廖瑞华 黄育瀑 林树龙 刘彦武 于 2020-12-16 设计创作，主要内容包括：本申请公开了一种交通数据的分析方法、装置、设备、车辆及存储介质,涉及人工智能技术领域,具体为大数据处理、自动驾驶、车联网领域,可应用于AI导航。具体实现方案包括：获取多个初始交通数据；确定各初始交通数据的类别；接收AI分析模型的搜索指令,搜索指令中包括目标类别信息；根据各初始交通数据的类别,从各初始交通数据中确定出与目标类别信息对应的目标交通数据；向AI分析模型发送目标交通数据,以使AI分析模型根据目标交通数据进行AI分析。本申请实施例的技术方案可以提高AI分析的效率和AI分析的多样性。(The application discloses a traffic data analysis method, a traffic data analysis device, traffic data analysis equipment, a traffic data analysis vehicle and a traffic data analysis storage medium, relates to the technical field of artificial intelligence, specifically relates to the fields of big data processing, automatic driving and Internet of vehicles, and can be applied to AI navigation. The specific implementation scheme comprises the following steps: acquiring a plurality of initial traffic data; determining the category of each initial traffic data; receiving a search instruction of the AI analysis model, wherein the search instruction comprises target category information; determining target traffic data corresponding to the target category information from each initial traffic data according to the category of each initial traffic data; and sending the target traffic data to the AI analysis model so that the AI analysis model carries out AI analysis according to the target traffic data. The technical scheme of the embodiment of the application can improve the efficiency of AI analysis and the diversity of AI analysis.)

1. A method of analyzing traffic data, comprising:

acquiring a plurality of initial traffic data;

determining a category of each of the initial traffic data;

receiving a search instruction of an AI analysis model, wherein the search instruction comprises target category information;

determining target traffic data corresponding to the target category information from each initial traffic data according to the category of each initial traffic data;

and sending the target traffic data to the AI analysis model so that the AI analysis model carries out AI analysis according to the target traffic data.

2. The method of claim 1, wherein before determining the target traffic data corresponding to the target category information from each of the initial traffic data according to the category of each of the initial traffic data, further comprising:

and carrying out error correction processing on the initial traffic data.

3. The method of claim 2, wherein error correcting the initial traffic data comprises:

determining first associated traffic data of the initial traffic data under the condition that the initial traffic data is abnormal traffic data;

and performing error correction processing on the initial traffic data according to the incidence relation between the initial traffic data and the first associated traffic data.

4. The method of claim 1, wherein the initial traffic data comprises first initial traffic data and second initial traffic data, and determining a category for each of the initial traffic data comprises:

determining whether the second initial traffic data is second associated traffic data of the first initial traffic data according to the position relation between the first initial traffic data and the second initial traffic data;

and if the second initial traffic data is second associated traffic data of the first initial traffic data, setting the category of the second initial traffic data as the category of the first initial traffic data.

5. The method of claim 1, wherein sending the target traffic data to the AI analytics model comprises:

packaging the target traffic data;

and sending the packaged target traffic data to the AI analysis model.

6. The method of any of claims 1 to 5, wherein acquiring a plurality of initial traffic data comprises:

determining a target object meeting a preset distance condition with the position of the vehicle;

and acquiring traffic data corresponding to the target object to obtain the initial traffic data.

7. A method of analyzing traffic data, comprising:

sending a search instruction to a traffic data processor, wherein the search instruction comprises target category information;

receiving target traffic data sent by the traffic data processor, wherein the target traffic data is data corresponding to the target category information determined by the traffic data processor from each initial traffic data according to the category of each initial traffic data;

and carrying out AI analysis according to the target traffic data.

8. An analysis device of traffic data, comprising:

the acquisition module is used for acquiring a plurality of initial traffic data;

a first determining module for determining a category of each of the initial traffic data;

the receiving module is used for receiving a search instruction of the AI analysis model, wherein the search instruction comprises target category information;

the second determining module is used for determining target traffic data corresponding to the target category information from each initial traffic data according to the category of each initial traffic data;

and the sending module is used for sending the target traffic data to the AI analysis model so as to enable the AI analysis model to carry out AI analysis according to the target traffic data.

9. The apparatus of claim 8, further comprising:

and the data processing module is used for carrying out error correction processing on the initial traffic data.

10. The apparatus of claim 9, wherein the data processing module comprises:

the first determining submodule is used for determining first associated traffic data of the initial traffic data under the condition that the initial traffic data is abnormal traffic data;

and the error correction processing submodule is used for carrying out error correction processing on the initial traffic data according to the incidence relation between the initial traffic data and the first associated traffic data.

11. The apparatus of claim 8, wherein the initial traffic data comprises first initial traffic data and second initial traffic data, the first determining module comprising:

a second determining submodule, configured to determine whether the second initial traffic data is second associated traffic data of the first initial traffic data according to a position relationship between the first initial traffic data and the second initial traffic data;

and the category setting submodule is used for setting the category of the second initial traffic data as the category of the first initial traffic data under the condition that the second initial traffic data is the second associated traffic data of the first initial traffic data.

12. The apparatus of claim 8, wherein the means for transmitting comprises:

the packaging sub-module is used for packaging the target traffic data;

and the sending submodule is used for sending the packaged target traffic data to the AI analysis model.

13. The apparatus of any of claims 8 to 12, wherein the obtaining means comprises:

the third determining submodule is used for determining a target object meeting the preset distance condition with the position of the vehicle;

and the acquisition submodule is used for acquiring the traffic data corresponding to the target object to obtain the initial traffic data.

14. An analysis device of traffic data, comprising:

the sending module is used for sending a searching instruction to the traffic data processor, wherein the searching instruction comprises target category information;

the receiving module is used for receiving target traffic data sent by the traffic data processor, wherein the target traffic data is data corresponding to the target category information determined by the traffic data processor from each initial traffic data according to the category of each initial traffic data;

and the analysis module is used for carrying out AI analysis according to the target traffic data.

15. An electronic device, comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,

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 1-7.

16. A vehicle, comprising:

the device of any one of claims 8-13 and the device of claim 14.

17. A non-transitory computer readable storage medium having stored thereon computer instructions for causing a computer to perform the method of any one of claims 1-7.

18. A computer program product comprising computer instructions which, when executed by a processor, implement the method of any one of claims 1-7.

Technical Field

The application relates to the field of artificial intelligence, in particular to the field of big data processing, automatic driving and the field of Internet of vehicles.

Background

At present, vehicles can basically acquire a small amount of traffic data from a server to perform an AI (Artificial Intelligence) analysis, for example, a road congestion state is analyzed by using road condition data. Therefore, the traffic data utilization efficiency is low and the analysis function is single.

Disclosure of Invention

The application provides a method, a device, equipment, a vehicle and a storage medium for analyzing traffic data.

According to a first aspect of the present application, there is provided a traffic data analysis method, including:

acquiring a plurality of initial traffic data;

determining the category of each initial traffic data;

receiving a search instruction of the AI analysis model, wherein the search instruction comprises target category information;

determining target traffic data corresponding to the target category information from each initial traffic data according to the category of each initial traffic data;

and sending the target traffic data to the AI analysis model so that the AI analysis model carries out AI analysis according to the target traffic data.

According to a second aspect of the present application, there is provided a traffic data analysis method, including:

sending a search instruction to a traffic data processor, wherein the search instruction comprises target category information;

receiving target traffic data sent by a traffic data processor, wherein the target traffic data is data corresponding to target category information determined by the traffic data processor from each initial traffic data according to the category of each initial traffic data;

and performing AI analysis according to the target traffic data.

According to a third aspect of the present application, there is provided an analysis apparatus of traffic data, comprising:

the acquisition module is used for acquiring a plurality of initial traffic data;

the first determining module is used for determining the category of each initial traffic data;

the receiving module is used for receiving a search instruction of the AI analysis model, wherein the search instruction comprises target category information;

the second determining module is used for determining target traffic data corresponding to the target category information from the initial traffic data according to the category of the initial traffic data;

and the sending module is used for sending the target traffic data to the AI analysis model so that the AI analysis model carries out AI analysis according to the target traffic data.

According to a fourth aspect of the present application, there is provided an analysis apparatus of traffic data, comprising:

the sending module is used for sending a searching instruction to the traffic data processor, and the searching instruction comprises target category information;

the receiving module is used for receiving target traffic data sent by the traffic data processor, wherein the target traffic data is data corresponding to target category information determined by the traffic data processor from each initial traffic data according to the category of each initial traffic data;

and the analysis module is used for carrying out AI analysis according to the target traffic data.

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

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,

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

According to a sixth aspect of the present application, there is provided a vehicle comprising: the apparatus provided by the second aspect above and the apparatus provided by the fourth aspect above.

According to a seventh aspect of the present application, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing a computer to perform the method provided by the first or third aspect above.

According to an eighth aspect of embodiments herein, there is provided a computer program product comprising computer instructions which, when executed by a processor, implement the method provided in the first or third aspect above.

According to the analysis method of the embodiment, the classification of each initial traffic data is determined by classifying the acquired initial traffic data. Furthermore, when a search instruction of the AI analysis model is received, corresponding target traffic data can be quickly determined from each initial traffic data based on the target category information in the search instruction, and the target traffic data is quickly pushed to the AI analysis model, so that the AI analysis model can conveniently perform AI analysis based on the target traffic data, and the AI analysis efficiency can be improved. Moreover, since the target traffic data can be accurately provided to the AI analysis model through the correspondence between the category of the initial traffic data and the AI analysis model, the analysis method is suitable for large-scale AI analysis with a plurality of AI analysis models, thereby facilitating the improvement of diversity of AI analysis.

It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present application, nor do they limit the scope of the present application. Other features of the present application will become apparent from the following description.

Drawings

The drawings are included to provide a better understanding of the present solution and are not intended to limit the present application. Wherein:

fig. 1 is a first flowchart illustrating a method for analyzing traffic data according to an embodiment of the present disclosure;

FIG. 2 is a second schematic flow chart illustrating a method for analyzing traffic data according to an embodiment of the present disclosure;

FIG. 3 is a schematic flow chart of step S102 according to an embodiment of the present application;

FIG. 4 is a flowchart illustrating step S105 according to an embodiment of the present application;

FIG. 5 is a flow diagram of a method of analyzing traffic data according to another embodiment of the present application;

FIG. 6 is a first schematic diagram of an apparatus for analyzing traffic data according to an embodiment of the present application;

FIG. 7 is a second schematic diagram of an apparatus for analyzing traffic data according to an embodiment of the present application;

FIG. 8 is a schematic view of an apparatus for analyzing traffic data according to another embodiment of the present application;

FIG. 9 is a schematic illustration of a vehicle according to an embodiment of the present application;

fig. 10 is a block diagram of an electronic device for implementing the traffic data analysis method according to the embodiment of the present application.

Detailed Description

The following description of the exemplary embodiments of the present application, taken in conjunction with the accompanying drawings, includes various details of the embodiments of the application for the understanding of the same, which are to be considered exemplary only. Accordingly, those 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 application. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

Fig. 1 is a first flowchart illustrating a traffic data analysis method according to an embodiment of the present disclosure. As shown in fig. 1, the analysis method may include:

s101, acquiring a plurality of initial traffic data.

S102, determining the category of each initial traffic data;

s103, receiving a search instruction of the AI analysis model, wherein the search instruction comprises target category information;

s104, determining target traffic data corresponding to the target category information from the initial traffic data according to the category of the initial traffic data;

and S105, sending the target traffic data to the AI analysis model so that the AI analysis model carries out AI analysis according to the target traffic data.

According to the analysis method of the embodiment, the classification of each initial traffic data is determined by classifying the acquired initial traffic data. Furthermore, when a search instruction of the AI analysis model is received, corresponding target traffic data can be quickly determined from each initial traffic data based on the target category information in the search instruction, and the target traffic data is quickly pushed to the AI analysis model, so that the AI analysis model can conveniently perform AI analysis based on the target traffic data, and the AI analysis efficiency can be improved.

Moreover, since the target traffic data can be accurately provided to the AI analysis model through the correspondence between the category of the initial traffic data and the AI analysis model, the analysis method is suitable for large-scale AI analysis with a plurality of AI analysis models, thereby facilitating the improvement of diversity of AI analysis.

It should be noted that the traffic data analysis method in the embodiment of the present application is suitable for being applied to the fields of big data processing, automatic driving, and car networking. For example, a classification process of mass traffic data, AI navigation required for automatic driving using the traffic data, AI analysis required for a field of internet of vehicles using the traffic data, and the like.

In one example, the initial traffic data may be obtained from a server and/or vehicle. The server may be a traffic information system, and the initial data obtained from the server includes, but is not limited to, road data, road network data, and shop data from the vehicle. The initial data acquired from the vehicle includes acquiring travel data of the vehicle from the vehicle.

Illustratively, the road data includes, but is not limited to, position information, name, length information, number of lanes, and distance information of the next road from the position where the vehicle is located. The road network data includes, but is not limited to, position information, names, length information, the number of lanes, steering information, and zebra crossing information of surrounding roads within a preset range from the position of the vehicle. The store data includes, but is not limited to, the name of the store, location information, price information, and rating information. The running data of the vehicle includes, but is not limited to, vehicle speed information, remaining oil amount, and remaining power amount of the vehicle.

In another example, the type of the initial traffic data may be multiple, and the type and the number of the initial traffic data may be selected and adjusted according to actual needs, which is not limited in this embodiment of the application as long as the initial traffic data can be classified according to the required functions.

For example, each category of the initial traffic data may include a vehicle information category, an in-case information category, and a surrounding road network information category. Determining the category of each initial traffic data may include: marking the type of the vehicle information aiming at the driving data of the vehicle; aiming at the information category in the road data marking case; and marking the peripheral road network intelligence categories according to the road network data and the shop data.

For example, the initial traffic data belonging to the same category may be divided into the same data group such that the initial traffic data of the same data group has the same category. When the AI analysis model needs to call target traffic data of a certain category, all initial traffic data in the data group with the category may be sent to the AI analysis model.

In one example, the AI analysis model may be a plurality of AI analysis models having different analysis functions, each AI analysis model having a correspondence relationship with one or more categories of initial traffic data. For example, the AI analysis models may include a first AI analysis model having a correspondence relationship with vehicle speed information of the vehicle in the own vehicle alert category, a second AI analysis model having a correspondence relationship with remaining fuel amount of the vehicle in the own vehicle alert category and position information of a next road in the in-case information category, and a third AI analysis model having a correspondence relationship with vehicle speed information of the vehicle in the own vehicle alert category, position information of a next road in the in-case information category, and position information of a peripheral road and position information of a shop in the peripheral road network information category.

Therefore, when each AI analysis model responds to the analysis instruction, the search instruction with the target category information can be generated based on the corresponding relation between each AI analysis model and the initial traffic data in each category, and further when the search instruction of the AI analysis model is received, the target traffic data can be determined from each initial traffic data according to the category of each initial traffic data, so that the accurate target traffic data can be quickly provided for the AI analysis model.

Illustratively, the search instruction for the AI analysis model can be generated for the AI analysis model in response to the voice control information. For example, when the AI analysis model receives the voice control information "find the nearest shop," then the AI analysis model generates a corresponding search instruction.

In one example, the AI analytical model can be of various types, depending on the application scenario. For example, the AI analysis models include, but are not limited to, a yaw analysis model, a fuel volume analysis model, an avoidance congestion analysis model, a shop analysis model, and the like.

For example, the business analysis model may be a gas station analysis model or a restaurant analysis model. The AI analysis functions of the gas station analysis model may include: and determining target traffic data corresponding to the gas station analysis model from the initial traffic data, and analyzing the gas station with the lowest price from the position of the vehicle. The AI analysis functions of the restaurant analysis model may include: and analyzing the restaurant with the best evaluation from the position of the vehicle by using the target traffic data corresponding to the restaurant analysis model determined from the initial traffic data.

It can be understood that the AI analysis function of the AI analysis model can be selected and adjusted according to actual needs, and the specific AI analysis function of the AI analysis model is not limited in the embodiments of the present application.

In one embodiment, before step S104, the method may further include: and carrying out error correction processing on the initial traffic data.

Wherein, the error correction processing on the initial traffic data may include: and inputting the initial traffic data of the same category into the corresponding error correction model so that the error correction model carries out error correction processing on the corresponding initial traffic data.

For example, the error correction model may include a self-vehicle-report error correction model, an in-case-report error correction model, and a peripheral road network information error correction model, and the initial traffic data with the type of self-vehicle information may be input to the self-vehicle-report error correction model, the initial traffic data with the type of in-case information may be input to the in-case-report error correction model, and the initial traffic data with the type of peripheral road network information may be input to the peripheral road network information error correction model for error correction processing, respectively.

For example, the corresponding relationship between the evaluation level of the shop and the average score is: and when the evaluation level of the shop is input into the peripheral road network information error correction model, if the evaluation level of the shop is A level and the average score determined in the peripheral road network information error correction model is 3 points, the evaluation level of the shop is output as B level.

Based on this, the accuracy of the initial traffic data can be improved by performing error correction processing on the initial traffic data, and further, the accuracy of the AI analysis is improved. In addition, the initial traffic data of the same category is input into the corresponding error correction model for error correction processing, so that the parallel error correction processing of the initial traffic data of each category is facilitated, and the efficiency of the error correction processing can be improved.

In one embodiment, before step S104, the method may further include: and carrying out verification processing on the initial traffic data. Therefore, the validity and the integrity of the initial traffic data transmission can be verified, and the accuracy of AI analysis is improved.

In one embodiment, as shown in fig. 2, the performing the error correction process on the initial traffic data may include:

s201, under the condition that the initial traffic data are abnormal traffic data, determining first associated traffic data of the initial traffic data;

s202, performing error correction processing on the initial traffic data according to the incidence relation between the initial traffic data and the first associated traffic data.

In one example, step S201 may include: determining the initial traffic data as abnormal traffic data under the condition that the original value of the initial traffic data exceeds the threshold range; wherein, the threshold range can be obtained by machine learning for a plurality of initial traffic data.

For example, the initial traffic data may include speed limit information of a road, wherein the speed limit range is 110km/h to 120km/h, and when the speed limit information of the road is located outside the speed limit range, the speed limit information is determined to be abnormal traffic data; the speed limit range is obtained by machine learning of speed limit information samples of multiple roads.

In yet another example, step S202 may include: determining a normal value of the initial traffic data according to the incidence relation between the initial traffic data and the first associated traffic data; the original value is replaced with the normal value of the initial traffic data.

For example, in the case where the speed limit information of the road is located outside 110km/h to 120km/h, the level information of the road is determined to be level 5; and according to the incidence relation between the speed limit information and the grade information, determining that the corresponding speed limit information is 120km/h, and replacing the original numerical value of the speed limit information by 120 km/h.

Based on the method, more accurate initial traffic data can be provided for the AI analysis model, and the accuracy of AI analysis is improved. It should be noted that, because the initial traffic data may be transmitted from the server to the vehicle, the initial traffic data may also generate abnormal traffic data in the transmission process, and the influence of the network transmission performance on the accuracy of the AI analysis may also be reduced by performing error correction processing on the abnormal traffic data generated in the transmission process.

In one embodiment, the initial traffic data includes first initial traffic data and second initial traffic data, and determining a category for each of the initial traffic data includes:

s301, determining whether the second initial traffic data is second associated traffic data of the first initial traffic data according to the position relation between the first initial traffic data and the second initial traffic data;

s302, under the condition that the second initial traffic data is the second associated traffic data of the first initial traffic data, setting the category of the second initial traffic data as the category of the first initial traffic data.

In one example, step S301 may include:

inputting the first initial traffic data and the second initial traffic data into a position relation identification model to identify a position relation between the first initial traffic data and the second initial traffic data; the position relation model is obtained by training a deep learning network model based on a plurality of first initial traffic data samples and a plurality of second initial traffic data samples; and determining whether the second initial traffic data is second associated traffic data of the first initial traffic data according to the identification result.

For example, the first initial traffic data may be shop location information and the second initial traffic data may be road location information. Inputting shop position information and road position information into a position relation identification model, and identifying whether shops are located in a distance threshold range of a road; and determining whether the shop position information is second associated traffic data of the road position information according to the identification result. For example, when the shop is located within the distance threshold range of the road, determining that the shop location information is second associated traffic data of the road location information; otherwise, the shop location information is not the second associated traffic data of the road.

For example, in step S302, in the case that the shop location information is the second related traffic data of the road, the category of the road location information is set as the category of the shop location information, that is, the road location information and the shop location information are divided into data groups of the same category. The shop position information is associated with the road position information, and the position relation of the shop relative to the road can be determined according to the road position information.

In the embodiment, the first initial traffic data and the second initial traffic data are associated through the position relationship between the first initial traffic data and the second initial traffic data, so that on one hand, the calculation amount during the classification of the initial traffic data can be reduced, the calculation amount of an AI analysis model can be reduced, and the AI analysis efficiency can be improved.

In one embodiment, as shown in fig. 4, the sending of the target traffic data to the AI analysis model in step S105 may include:

s401, packaging the target traffic data;

s402, sending the packaged target traffic data to the AI analysis model.

In one example, the target traffic data may be packaged by category such that target traffic data of the same category is packaged together to send the target traffic data to the AI analytics model by category.

In one example, all of the target traffic data associated with the AI analytics model may be packaged for sending the target traffic data to the AI analytics model at one time.

In one embodiment, step S101 may include:

determining a target object meeting a preset distance condition with the position of the vehicle;

and acquiring traffic data corresponding to the target object to obtain initial traffic data.

The distance condition may be in an area where the position of the vehicle is a center of a circle and the radius is a preset distance. The preset distance can be 1km, 2km, 3km, 5km and the like. The preset distance condition and the preset distance can be selected and adjusted according to actual needs, and the preset distance condition and the preset distance are not limited in the embodiment of the application.

The target objects include, but are not limited to, roads and shops, the target objects can be selected and adjusted according to actual needs, and the target objects are not limited in the embodiments of the application.

In one example, the target object which is determined to satisfy the preset distance condition with the position of the vehicle may be a next road, a surrounding road and a shop within a circular area which is determined to be 1km away from the position of the vehicle, wherein the distance between the surrounding road and the position of the vehicle is greater than the distance between the next road and the position of the vehicle.

In yet another example, the obtaining of the traffic data corresponding to the target object, the obtaining of the initial traffic data, may be obtaining position information, name, length information, number of lanes and distance information of a next road, name, length information, number of lanes, steering information and zebra crossing information of surrounding roads, name, position information, price information and score information of shops, and the like.

The method of the embodiment of the application can be executed by a vehicle end and also can be executed by a server. In one embodiment, before step S101, the method may further include: and establishing a communication long connection between the vehicle and the server so as to receive the initial traffic data pushed by the server or actively request the initial traffic data from the server according to a preset time interval.

In one example, receiving the initial traffic data pushed by the server may include: receiving a push request sent by a server; based on the pushing request, sending the position information of the position of the vehicle to a server; and receiving traffic data which is sent by the server and corresponds to the target object.

Correspondingly, the server sends a pushing request according to a preset time interval; when the server receives the position information of the position of the vehicle, determining a target object meeting a preset distance condition with the position of the vehicle based on the position information; and pushing the traffic data corresponding to the target object to the vehicle.

In yet another example, actively requesting initial traffic data from the server at preset time intervals may include: sending the position information of the position of the vehicle to a server according to a preset time interval; traffic data corresponding to the target object is received.

Correspondingly, when the server receives the position information of the position of the vehicle, the server determines a target object meeting a preset distance condition with the position of the vehicle based on the position information; and pushing the traffic data corresponding to the target object to the vehicle.

The server determines, based on the location information, that the target object meeting the preset distance condition with the location of the vehicle may be: according to the vehicle identification of the vehicle, determining an initial object corresponding to the vehicle identification from a preset object list; and determining target objects meeting a preset distance condition with the position of the vehicle from the initial objects based on the position information. Therefore, the determination efficiency of the target object is improved, so that the initial traffic data can be rapidly acquired, and the AI analysis efficiency is improved.

Fig. 5 is a flowchart illustrating an analysis method of intermodulation data according to another embodiment of the present application. When the traffic data processor on the vehicle side performs the analysis method of traffic data of the above-described embodiment, the analysis method of intermodulation data of the other embodiment is adapted to be performed by an AI analysis model. As shown in fig. 5, the method for analyzing intermodulation data of the another embodiment may include:

s501, sending a search instruction to a traffic data processor, wherein the search instruction comprises target category information;

s502, receiving target traffic data sent by a traffic data processor, wherein the target traffic data is data corresponding to target category information determined by the traffic data processor from each initial traffic data according to the category of each initial traffic data;

and S503, carrying out AI analysis according to the target traffic data.

In this embodiment, by sending the search instruction to the traffic data processor, the traffic data processor can quickly determine the target traffic data from the initial traffic data based on the target category information, so as to facilitate quick reception and AI analysis based on the target traffic data, thereby improving the efficiency of AI analysis.

Moreover, since the target traffic data can be accurately searched through the correspondence between the AI analysis and the category of the initial traffic data, the analysis method is suitable for large-scale AI analysis with a plurality of AI analysis models, thereby facilitating the improvement of diversity of the AI analysis.

Fig. 6 is a first schematic diagram of a traffic data analysis device 600 according to an embodiment of the present application. As shown in fig. 6, the traffic data analysis device 600 may include:

an obtaining module 610, configured to obtain a plurality of initial traffic data;

a first determining module 620, configured to determine a category of each initial traffic data;

a receiving module 630, configured to receive a search instruction of the AI analysis model, where the search instruction includes target category information;

a second determining module 640, configured to determine, according to the category of each initial traffic data, target traffic data corresponding to the target category information from each initial traffic data;

the sending module 650 is configured to send the target traffic data to the AI analysis model, so that the AI analysis model performs AI analysis according to the target traffic data.

Fig. 7 is a second schematic diagram of an analysis apparatus 600 for traffic data according to an embodiment of the present application. As shown in fig. 7, the traffic data analysis device 600 may further include:

and the data processing module 710 is configured to perform error correction processing on the initial traffic data.

In one embodiment, the data processing module 710 includes:

the first determining submodule is used for determining first associated traffic data of the initial traffic data under the condition that the initial traffic data is abnormal traffic data;

and the error correction processing submodule is used for carrying out error correction processing on the initial traffic data according to the incidence relation between the initial traffic data and the first associated traffic data.

In one embodiment, the initial traffic data may include first initial traffic data and second initial traffic data, and the first determining module 620 may include:

the second determining submodule is used for determining whether the second initial traffic data is second associated traffic data of the first initial traffic data according to the position relation between the first initial traffic data and the second initial traffic data;

and the category setting submodule is used for setting the category of the second initial traffic data as the category of the first initial traffic data under the condition that the second initial traffic data is the second associated traffic data of the first initial traffic data.

In one embodiment, the sending module 650 may include:

the packaging submodule is used for packaging the target traffic data;

and the sending submodule is used for sending the encapsulated target traffic data to the AI analysis model.

In one embodiment, the obtaining module 610 may include:

the third determining submodule is used for determining a target object meeting the preset distance condition with the position of the vehicle;

and the acquisition submodule is used for acquiring the traffic data corresponding to the target object to obtain initial traffic data.

Fig. 8 is a schematic diagram of an apparatus 800 for analyzing traffic data according to another embodiment of the present application. As shown in fig. 8, the traffic data analysis apparatus 800 may include:

a sending module 810, configured to send a search instruction to the traffic data processor, where the search instruction includes target category information;

a receiving module 820, configured to receive target traffic data sent by the traffic data processor, where the target traffic data is data determined by the traffic data processor according to the category of each initial traffic data, and corresponds to target category information from each initial traffic data;

and the analysis module 830 is configured to perform AI analysis according to the target traffic data.

Fig. 9 is a schematic diagram of a vehicle 900 according to an embodiment of the present application. As shown in fig. 9, the vehicle 900 may include: the traffic data analysis device 600 according to any of the above embodiments and the traffic data analysis device 800 according to another embodiment.

The functions of each module in each apparatus in the embodiment of the present application may refer to corresponding descriptions in the above method, and are not described herein again.

There is also provided, in accordance with an embodiment of the present application, an electronic device, a readable storage medium, and a computer program product.

As shown in fig. 10, the embodiment of the present application is a block diagram of an electronic device of a traffic data analysis method. 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 phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present application that are described and/or claimed herein.

As shown in fig. 10, the electronic apparatus includes: one or more processors 1001, memory 1002, and interfaces for connecting the various components, including high-speed interfaces and low-speed interfaces. The various components are interconnected using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions for execution within the electronic device, including instructions stored in or on the memory to display graphical information of a GUI on an external input/output apparatus (such as a display device coupled to the interface). In other embodiments, multiple processors and/or multiple buses may be used, along with multiple memories and multiple memories, as desired. Also, multiple electronic devices may be connected, with each device providing portions of the necessary operations (e.g., as a server array, a group of blade servers, or a multi-processor system). Fig. 10 illustrates an example of one processor 1001.

The memory 1002 is a non-transitory computer readable storage medium provided herein. Wherein the memory stores instructions executable by at least one processor to cause the at least one processor to perform the method of analyzing traffic data provided herein. The non-transitory computer-readable storage medium of the present application stores computer instructions for causing a computer to perform the method of analyzing traffic data provided herein.

The memory 1002, as a non-transitory computer-readable storage medium, may be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as program instructions/modules corresponding to the analysis method of traffic data in the embodiment of the present application (for example, the obtaining module 610, the receiving module 620, the first determining module 630, the second determining module 640, and the transmitting module 650 shown in fig. 6). The processor 1001 executes various functional applications of the server and data processing, i.e., implements the analysis method of traffic data in the above-described method embodiments, by running non-transitory software programs, instructions, and modules stored in the memory 1002.

The memory 1002 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the electronic device of the analysis method of the traffic data, and the like. Further, the memory 1002 may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 1002 may optionally include memory located remotely from the processor 1001, which may be connected to the electronics of the method of analyzing traffic data via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The electronic device of the method of analyzing traffic data may further include: an input device 1003 and an output device 1004. The processor 1001, the memory 1002, the input device 1003, and the output device 1004 may be connected by a bus or other means, and the bus connection is exemplified in fig. 10.

The input device 1003 may receive input numeric or character information and generate key signal inputs related to user settings and function control of the electronic apparatus of the analysis method of traffic data, such as an input device of a touch screen, a keypad, a mouse, a track pad, a touch pad, a pointing stick, one or more mouse buttons, a track ball, a joystick, or the like. The output devices 1004 may include a display device, auxiliary lighting devices (e.g., LEDs), and tactile feedback devices (e.g., vibrating motors), among others. The display device may include, but is not limited to, a Liquid Crystal Display (LCD), a Light Emitting Diode (LED) display, and a plasma display. In some implementations, the display device can be a touch screen.

Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, application specific ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.

These computer programs (also known as programs, software applications, or code) include machine instructions for a programmable processor, and may be implemented using high-level procedural and/or object-oriented programming languages, and/or assembly/machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor.

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 a pointing device (e.g., a mouse or a 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 can 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, speech, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a back-end 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 back-end, 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 clients and servers. A client and server are generally 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 as to solve the defects of high management difficulty and weak service expansibility in the traditional physical host and Virtual Private Server (VPS) service. The server may also be a server of a distributed system, or a server incorporating a blockchain.

It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present application may be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solutions disclosed in the present application can be achieved, and the present invention is not limited herein.

The above-described embodiments should not be construed as limiting the scope of the present application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.