CN112669609A

CN112669609A - Data processing method based on big data and edge calculation and big data server

Info

Publication number: CN112669609A
Application number: CN202011569380.2A
Authority: CN
Inventors: 顾小菊
Original assignee: Individual
Current assignee: Individual
Priority date: 2020-12-26
Filing date: 2020-12-26
Publication date: 2021-04-16

Abstract

The embodiment of the application discloses a data processing method based on big data and edge calculation and a big data server, wherein the data processing method based on the big data and the edge calculation comprises the following steps: and carrying out track analysis on a traffic flow data track in target traffic flow data of a target traffic block to be subjected to traffic scheduling, and carrying out local correction and global correction to realize traffic scheduling processing so as to obtain a corresponding driving track of the target traffic flow data corresponding to the target track in the target traffic flow data within a set time period. The method has the advantages that the interference of local traffic flow data and global traffic flow data is considered when the corresponding driving track of the target traffic flow data in the set time period is determined, so that the driving track of the target traffic flow data in the set time period can be determined quickly and accurately, more time is not needed to be consumed to determine the expected driving track, the traffic scheduling efficiency of the target traffic block can be improved as much as possible, and traffic jam can be relieved in time.

Description

Data processing method based on big data and edge calculation and big data server

Technical Field

The application relates to the technical field of big data and edge calculation, in particular to a data processing method based on big data and edge calculation and a big data server.

Background

With the rapid development of big data and internet related technologies, people's production and living styles have changed greatly. Applications for big data involve aspects such as blockchain payments, industrial braking, online cloud education, online cloud office, artificial intelligence, smart parks, smart medicine, and smart cities, to name a few. Wherein, the smart city is closely related to the life of people. With the improvement of living standard of people, most families already have private cars, which brings about urban diseases such as traffic jam and the like while bringing convenience to the outgoing of people, so that how to improve the urban diseases such as traffic jam is a problem to be considered at present.

Disclosure of Invention

One of the embodiments of the present application provides a data processing method based on big data and edge calculation, where the method includes: acquiring target traffic flow data of a target traffic block to be subjected to traffic scheduling; respectively carrying out travel deceleration type track analysis and travel lane change type track analysis on a plurality of traffic flow data tracks in the target traffic flow data to obtain a travel deceleration type track analysis result and a travel lane change type track analysis result; performing local correction processing on the track analysis result of the driving and decelerating type through a preset local correction model aiming at the track analysis result to obtain a driving track corresponding to local traffic flow data comprising the driving and decelerating type track in a set time period; performing global correction processing on the track analysis result of the driving lane change class through a preset global correction model aiming at the track analysis result to obtain a driving track corresponding to global traffic flow data comprising the driving lane change class track in a set time period; performing traffic scheduling processing on the basis of the corresponding running track of the local traffic flow data in a set time period and the corresponding running track of the global traffic flow data in the set time period to obtain the corresponding running track of target traffic flow data corresponding to a target track in the target traffic flow data in the set time period; the target track comprises at least one of a deceleration type track and a lane change type track, and the corresponding travel track of the target traffic flow data in a set period of time is used for carrying out traffic scheduling on the target traffic block.

One of the embodiments of the present application provides a big data server, which includes a processing engine, a network module, and a memory; the processing engine and the memory communicate through the network module, and the processing engine reads the computer program from the memory and operates to perform the above-described method.

One of the embodiments of the present application provides a computer storage medium, on which a computer program is stored, where the computer program is executed to implement the method described above.

In the description that follows, additional features will be set forth, in part, in the description. These features will be in part apparent to those skilled in the art upon examination of the following and the accompanying drawings, or may be learned by production or use. The features of the present application may be realized and attained by practice or use of various aspects of the methodologies, instrumentalities and combinations particularly pointed out in the detailed examples that follow.

Drawings

The present application will be further explained by way of exemplary embodiments, which will be described in detail by way of the accompanying drawings. These embodiments are not intended to be limiting, and in these embodiments like numerals are used to indicate like structures, wherein:

FIG. 1 is a flow diagram illustrating an exemplary big data and edge computation based data processing method and/or process according to some embodiments of the invention;

FIG. 2 is a block diagram of an exemplary big data and edge computation based data processing apparatus, according to some embodiments of the invention;

FIG. 3 is a block diagram of an exemplary big data and edge computation based data processing system, according to some embodiments of the invention; and

FIG. 4 is a diagram illustrating the hardware and software components of an exemplary big data server, according to some embodiments of the invention.

Detailed Description

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings used in the description of the embodiments will be briefly introduced below. It is obvious that the drawings in the following description are only examples or embodiments of the application, from which the application can also be applied to other similar scenarios without inventive effort for a person skilled in the art. Unless otherwise apparent from the context, or otherwise indicated, like reference numbers in the figures refer to the same structure or operation.

It should be understood that "system", "device", "unit" and/or "module" as used herein is a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, other words may be substituted by other expressions if they accomplish the same purpose.

As used in this application and the appended claims, the terms "a," "an," "the," and/or "the" are not intended to be inclusive in the singular, but rather are intended to be inclusive in the plural unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that steps and elements are included which are explicitly identified, that the steps and elements do not form an exclusive list, and that a method or apparatus may include other steps or elements.

Flow charts are used herein to illustrate operations performed by systems according to embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in the exact order in which they are performed. Rather, the various steps may be processed in reverse order or simultaneously. Meanwhile, other operations may be added to the processes, or a certain step or several steps of operations may be removed from the processes.

The inventor finds that most of common scheduling methods for intelligent traffic are manually guided or drivers adjust driving routes by themselves, which may cause secondary congestion in traffic scheduling (i.e., congestion cannot be relieved and may be further aggravated in the scheduling process). The inventor further researches and discovers that the main reason for causing the problems is that mutual interference among different vehicle driving tracks is not considered, namely, a certain block is not scheduled from a global level, so that excessive time is consumed for performing track scheduling of different vehicles in a form, the efficiency of traffic scheduling is reduced, and traffic jam is difficult to relieve in time.

In view of the above problems, the inventor has purposefully proposed a data processing method and a big data server based on big data and edge calculation, which can analyze two types of trajectory analysis including driving deceleration and driving lane change for target traffic flow data of a target traffic block, and respectively perform local correction processing on the trajectory analysis result of the driving deceleration type and global correction processing on the trajectory analysis result of the driving lane change type through a local correction model and a global correction model, so as to take mutual traffic interference into consideration, thereby implementing traffic scheduling processing, so as to determine the corresponding driving trajectory of the target traffic flow data in a set time period, thereby implementing traffic scheduling for the target traffic block, improving traffic scheduling efficiency, and timely alleviating traffic congestion.

First, an exemplary data processing method based on big data and edge calculation is described, please refer to fig. 1, which is a flowchart of an exemplary data processing method and/or process based on big data and edge calculation according to some embodiments of the present invention, and the data processing method based on big data and edge calculation may include the technical solutions described in the following steps S1-step 4.

Step S1, acquiring target traffic flow data of a target traffic block to be subjected to traffic scheduling; and respectively carrying out travel deceleration type track analysis and travel lane change type track analysis on a plurality of traffic flow data tracks in the target traffic flow data to obtain a travel deceleration type track analysis result and a travel lane change type track analysis result.

In an exemplary illustration, the target traffic block may be a block with severe congestion after big data analysis or citizen feedback, such as a traffic block corresponding to a downtown area, a city center, or a shopping mall. Traffic flow data, which may be understood as road traffic flow data and may include movement trajectory data (speed, acceleration) of vehicles and/or pedestrians and the corresponding chronological order, is used for the analysis of traffic conditions and road network conditions, so that intelligent traffic handling is achieved. The traffic flow data track may be used to describe an activity track of a vehicle and/or a pedestrian, and the traffic flow data track may be curve data in an electronic two-dimensional map, or may be curve data in a two-dimensional coordinate plane, which is not limited herein. Further, the running deceleration type trajectory analysis and the running lane change type trajectory analysis may be performed by operation data of tail lamps and turn lamps of the vehicle. It is understood that the big data server may communicate with an onboard controller of a vehicle to obtain the state of the lights of the corresponding vehicle in real time. In practical application, deceleration and lane change may be performed simultaneously, where the driving deceleration type trajectory analysis refers to a deceleration driving behavior without lane change, and the deceleration driving behavior with lane change may be classified as the driving lane change type trajectory analysis.

And step S2, carrying out local correction processing on the track analysis result of the driving and decelerating type through a preset local correction model aiming at the track analysis result to obtain a driving track corresponding to local traffic flow data including the driving and decelerating type track in a set time period.

In an exemplary illustration, the local correction model may be a space-time graph convolutional neural network model, and the set time period may be set in advance according to the traffic flow density of the target traffic block, for example, if the traffic flow density is large, the set time period may be set to be relatively small, and if the traffic flow density is small, the set time period may be set to be relatively large, which is not limited herein. Further, the local traffic flow data may be traffic flow data corresponding to a part of streets in the target traffic block, for example, 20 streets exist in the target traffic block, and the local traffic flow data may be 2 or 4 streets therein, which is not limited herein.

And step S3, carrying out global correction processing on the track analysis result of the lane change type through a preset global correction model aiming at the track analysis result to obtain the corresponding running track of the global traffic flow data including the lane change type track in a set time period.

In an exemplary description, the global correction model may also be a space-time graph convolutional neural network model, further, the local correction model and the global correction model may adopt different training sets during training, or may perform different model parameter adjustments during later-stage model use to distinguish between the two models, and a specific implementation manner is determined according to actual business requirements, and is not limited herein. Further, the global traffic flow data may be traffic flow data corresponding to all streets in the target traffic block.

Step S4, traffic scheduling processing is carried out on the basis of the corresponding driving track of the local traffic flow data in the set time period and the corresponding driving track of the global traffic flow data in the set time period, and the driving track of the target traffic flow data corresponding to the target track in the target traffic flow data in the set time period is obtained; the target track comprises at least one of a deceleration type track and a lane change type track, and the corresponding travel track of the target traffic flow data in a set period of time is used for carrying out traffic scheduling on the target traffic block.

In an exemplary illustration, the traffic scheduling process may be a global adjustment process for the driving tracks of different vehicles, for example, the prediction of the driving tracks of different vehicles may be realized by combining traffic light scheduling and genetic algorithm, so as to realize the driving track scheduling for as many vehicles as possible in the target traffic block. Furthermore, the big data server can generate the running track scheduling instructions corresponding to different vehicles according to the running tracks of the target traffic flow data in the set time period and send the running track scheduling instructions to the vehicle-mounted controllers of the corresponding vehicles, so that the vehicles in the target traffic block can adjust the running tracks according to the corresponding running track scheduling instructions, and it can be understood that the running tracks of the target traffic flow data in the set time period can be understood as the tracks after the completion of scheduling, that is, the big data server can realize feedback scheduling of the running tracks of different vehicles according to the expected running tracks analyzed in advance, and the running tracks corresponding to the target traffic flow data in the set time period can be quickly and accurately determined because the interference of local traffic flow data and global traffic flow data is considered in determining the running tracks of the target traffic flow data in the set time period, the expected driving track is determined without consuming more time, so that the traffic scheduling efficiency of the target traffic block can be improved as much as possible, and the traffic jam can be relieved in time.

In the following, some alternative embodiments will be described, which should be understood as examples and not as technical features essential for implementing the present solution.

For some possible embodiments, the performing, by step S1, a driving deceleration type trajectory analysis and a driving lane change type trajectory analysis on the plurality of traffic flow data trajectories in the target traffic flow data respectively to obtain a driving deceleration type trajectory analysis result and a driving lane change type trajectory analysis result may include the following steps S11-S13.

Step S11, performing travel deceleration type trajectory analysis on each of the plurality of traffic flow data trajectories in the target traffic flow data to obtain a travel deceleration type trajectory analysis indicator in each of the traffic flow data trajectories and an initial travel trajectory type corresponding to each of the travel deceleration type trajectory analysis indicators. For example, the driving deceleration type trajectory analysis index may be performed by a pedestrian walking situation of a road or a weather situation of a corresponding block, and the initial driving trajectory type may include a trajectory type adjusted for the pedestrian walking or a trajectory type adjusted for the weather situation.

And step S12, determining the track analysis result of the driving and decelerating class based on the driving and decelerating class track analysis index in each traffic flow data track and the corresponding initial driving track type.

Step S13, performing lane change type lane analysis on each of the plurality of traffic flow data lanes in the target traffic flow data to obtain a lane change type lane analysis result.

It can be understood that by implementing the above steps S11-S13, different driving deceleration type trajectory analysis indexes and corresponding initial driving trajectory types can be considered when determining the driving deceleration type trajectory analysis result, so as to ensure that the driving deceleration type trajectory analysis result can take into account the real-time road condition and weather condition, thereby ensuring the integrity and accuracy of the trajectory analysis result.

For some possible embodiments, the performing of the trajectory analysis of the lane change class on the plurality of traffic flow data trajectories in the target traffic flow data in step S13 to obtain the trajectory analysis result of the lane change class may further include the following steps S131 to S134.

Step S131, vehicle posture recognition is carried out on a plurality of traffic flow data tracks in the target traffic flow data respectively, and vehicle posture recognition results corresponding to the traffic flow data tracks are obtained. For example, vehicle attitude recognition may be performed in conjunction with a gyro sensor of the vehicle.

Step S132, vehicle speed recognition is carried out on the plurality of traffic flow data tracks in the target traffic flow data respectively, and vehicle speed recognition results corresponding to the traffic flow data tracks are obtained.

Step S133 associates the vehicle attitude recognition result and the vehicle speed recognition result corresponding to the same vehicle object.

Step S134, carrying out travel lane change type track analysis processing based on a vehicle speed recognition result associated with a target vehicle posture recognition result in the target traffic flow data to obtain a track analysis result of the travel lane change type; wherein the target vehicle attitude recognition result is a vehicle attitude recognition result corresponding to the marked vehicle object.

In this way, when the contents described in steps S131 to S134 are applied, the vehicle posture recognition result and the vehicle speed recognition result can be subjected to correlation analysis, so that the obtained track analysis result of the lane change class matches the actual vehicle driving state, and thus the lane change class track analysis can be realized from the global level.

For some possible embodiments, the step S2 may include performing a local correction process on the trajectory analysis result of the driving and decelerating category through a preset local correction model for the trajectory analysis result to obtain a corresponding driving trajectory of the local traffic flow data including the trajectory of the driving and decelerating category within a set time period, which may include the contents described in the following steps S21-S24.

And step S21, performing track type matching on each traffic flow data track in the track analysis results of the driving deceleration classes respectively to obtain the unique driving track type corresponding to each traffic flow data track.

And step S22, updating the trajectory analysis indexes respectively based on the analysis index updating records of the driving and decelerating trajectory analysis indexes corresponding to the corresponding unique driving trajectory type in each traffic flow data trajectory to obtain updated driving and decelerating trajectory analysis results.

And step S23, continuously updating the updated track analysis result of the driving deceleration type to obtain a plurality of corresponding driving tracks of the local candidate traffic flow data including the driving deceleration type tracks in a set time period.

Step S24, according to the driving deceleration type corresponding to the driving trajectory corresponding to each local candidate traffic flow data in the set time period, performing local trajectory correction processing on the driving trajectory corresponding to the local candidate traffic flow data belonging to the same driving deceleration type in the set time period, to obtain the driving trajectory corresponding to the local traffic flow data including the driving deceleration type trajectory in the set time period.

By designing in this way, based on the above steps S21-S24, the driving deceleration type can be considered, so that the local trajectory correction processing is performed on the corresponding driving trajectory of the local candidate traffic flow data belonging to the same driving deceleration type within the set time period, thus eliminating the interference and influence of the local traffic flow data on the global traffic flow data as much as possible, and facilitating the subsequent faster determination of the corresponding driving trajectory of the target traffic flow data within the set time period.

Further, in step S21, performing trajectory type matching on each traffic flow data trajectory in the trajectory analysis result of the driving deceleration class to obtain a unique driving trajectory type corresponding to each traffic flow data trajectory, which may include the following steps S211 to S213.

Step S211, aiming at each traffic flow data track in the track analysis results of the driving deceleration class, when the number of the types of the initial driving track of the traffic flow data track is not less than two, obtaining the traffic congestion evaluation information of each initial driving track type. For example, traffic congestion evaluation information may be used to characterize the cause of traffic congestion occurring.

And step S212, when the initial travel track type with the highest traffic congestion coefficient corresponding to the traffic congestion evaluation information is one, taking the initial travel track type with the highest traffic congestion coefficient corresponding to the traffic congestion evaluation information as the unique travel track type of the corresponding traffic flow data track. For example, the traffic congestion coefficient is used to characterize the degree of traffic congestion, and a higher traffic congestion coefficient indicates a more congested road.

Step S213, when the initial travel track type with the highest traffic congestion coefficient corresponding to the traffic congestion evaluation information is not less than two, acquiring a track analysis index update frequency of a corresponding travel deceleration type track analysis index for the initial travel track type with the highest traffic congestion coefficient corresponding to each piece of traffic congestion evaluation information; and determining the only driving track type corresponding to the corresponding traffic flow data track according to the initial driving track type corresponding to the highest track analysis index updating frequency. For example, the track analysis index updating frequency is used for representing the real-time change situation of the driving deceleration type track analysis index, it can be understood that the traffic situation is changed in real time, and the one-to-one corresponding relation between the corresponding traffic flow data track and the driving track type can be accurately and reliably determined by considering the track analysis index updating frequency.

It is understood that when the contents described in the above-described steps S211 to S213 are applied, the traffic situation that changes in real time can be taken into account when performing the trajectory type matching, so that the one-to-one correspondence relationship of the corresponding traffic flow data trajectory and the travel trajectory type is accurately and reliably determined.

In another embodiment, the updating of the analysis index of step S22 based on the trajectory analysis index of the driving deceleration type corresponding to the corresponding unique driving trajectory type in each traffic flow data trajectory may include the following steps S221-S224.

Step S221, for each traffic flow data track, index heat information of a driving deceleration type track analysis index corresponding to the corresponding unique driving track type in each traffic flow data track is obtained. For example, the index heat information may be used to represent the heat of use of the driving deceleration type track analysis index, and the higher the index heat, the more frequently the corresponding driving deceleration type track analysis index is used.

Step S222, when the current index heat corresponding to the index heat information is within a preset index heat interval, maintaining a corresponding driving and decelerating trajectory analysis result, where the maintained driving and decelerating trajectory analysis result includes a driving and decelerating trajectory analysis index and a unique driving trajectory type corresponding to the driving and decelerating trajectory analysis index. For example, the preset index heat interval may be adaptively adjusted according to actual conditions, and will not be further described herein.

And step S223, deleting the running deceleration type track analysis result of the corresponding traffic flow data track when the current index heat corresponding to the index heat information is not in the preset index heat interval.

Step S224, obtaining the updated track analysis result of the driving and decelerating class based on the driving and decelerating class track analysis result corresponding to each traffic flow data track.

It can be understood that, by executing the above steps S221 to S224, the index heat information of the trajectory analysis index of the driving deceleration class can be analyzed when determining the updated trajectory analysis result of the driving deceleration class, so as to take the historical usage of the trajectory analysis index of the driving deceleration class into account, which can ensure that the updated trajectory analysis result of the driving deceleration class matches the analysis result of the main stream as much as possible, and further provide a reliable decision basis for the subsequent traffic scheduling.

For a further embodiment, the step S23 of continuously updating the updated trajectory analysis result of the driving and decelerating category to obtain a plurality of corresponding driving trajectories of the local candidate traffic flow data including the trajectory of the driving and decelerating category within a set time period may include the following steps S231 to S235.

And S231, continuously updating the updated track analysis result of the running deceleration class to obtain multiple groups of automatic driving tracks and non-automatic driving tracks.

Step S232, determining the corresponding running track comparison result of the traffic flow data between each group of automatic driving track and non-automatic driving track in the set time period.

Step S233, when the similarity corresponding to the comparison result of the driving trajectories corresponding to the traffic flow data in the set time period is greater than or equal to the preset similarity, taking the driving trajectory corresponding to the traffic flow data in the set time period, which is formed by the corresponding set of the automatic driving trajectory and the non-automatic driving trajectory, as the driving trajectory corresponding to the local candidate traffic flow data in the set time period. For example, the preset similarity may be adjusted according to actual situations, and is not limited herein.

Step S234, for the corresponding running track of each local candidate traffic flow data in the set time period, determining the target running deceleration type with the largest number of times of statistics according to the updated unique running track type corresponding to each traffic flow data track in the corresponding running tracks of the local candidate traffic flow data in the set time period.

Step S235, regarding the target driving deceleration type as the driving deceleration type corresponding to the driving deceleration type included in the driving trajectory corresponding to the corresponding local candidate traffic flow data in the set time period.

In this way, when the updated trajectory analysis result of the driving deceleration type is continuously updated, the automatic driving trajectory and the non-automatic driving trajectory can be distinguished and analyzed, so that the automatic driving trajectory and the non-automatic driving trajectory are not mixed in the updating process of the trajectory analysis result, and further, the deviation occurs in the updating process of convenience for people.

For some possible embodiments, the driving deceleration type trajectory analysis result in the driving deceleration type trajectory analysis results includes an active deceleration analysis result and a passive deceleration analysis result, based on which the continuously updating process is performed on the updated driving deceleration type trajectory analysis result described in step S231 to obtain multiple sets of automatic driving trajectories and non-automatic driving trajectories, which may include the following steps S2311-S2314.

Step S2311, the traffic flow data trajectory corresponding to the first passive deceleration analysis result in the current update process among the updated trajectory analysis results of the driving deceleration classes is used as the automatic driving trajectory of the current group.

Step S2312, traversing the traffic flow data trajectory subsequent to the current set of autodrive trajectories.

Step S2313, when the traversed current traffic flow data track corresponds to an active deceleration analysis result and the driving deceleration type track analysis results corresponding to the traffic flow data track within the global preset time period from the current traffic flow data track are all active deceleration analysis results, taking the current traffic flow data track as the current group of non-automatic driving tracks.

Step S2314, taking the traffic flow data trajectory corresponding to the first passive deceleration analysis result after the non-automatic driving trajectory of the current group as the automatic driving trajectory of the current group for the next update processing, and returning to the step of traversing the traffic flow data trajectory after the automatic driving trajectory of the current group to continue the execution until multiple groups of automatic driving trajectories and non-automatic driving trajectories are obtained.

Thus, when the automatic driving track and the non-automatic driving track are distinguished by applying the steps S2311 to S2314, the active deceleration analysis result and the passive deceleration analysis result can be combined for comprehensive consideration, so that the accurate distinction between the automatic driving track and the non-automatic driving track can be ensured as much as possible, and the crossing and confusion between the automatic driving track and the non-automatic driving track can be avoided.

For an optional embodiment, before the current traffic flow data trajectory is taken as the non-automatic driving trajectory of the current group when the traversed current traffic flow data trajectory corresponds to the active deceleration analysis result and the driving deceleration type trajectory analysis results corresponding to the traffic flow data trajectory within the global preset time period from the current traffic flow data trajectory are all the active deceleration analysis results described in step 2313, the method may further include the following technical solutions described in steps a to c.

Step a, when the continuous updating time length of the driving track corresponding to the traversed current traffic flow data track and the automatic driving track of the current group in the set time period is smaller than a set updating time length threshold value, determining whether the driving deceleration type track analysis result corresponding to the current traffic flow data track is the active deceleration analysis result.

And b, when the current traffic flow data track corresponds to a passive deceleration analysis result, taking the current traffic flow data track as one of the traffic flow data tracks in the corresponding driving tracks of the traffic flow data corresponding to the current group in a set time period.

Step c, when the current traffic flow data track corresponds to an active deceleration analysis result and a driving deceleration type track analysis result within the global preset time from the current traffic flow data track comprises a passive deceleration analysis result, taking the traffic flow data track corresponding to the first passive deceleration analysis result in the global preset time length from the current traffic flow data track as the traversed next traffic flow data track, and returning to the step when the continuous updating time length of the corresponding running track of the traversed current traffic flow data track and the current set of automatic driving tracks in the set time period is less than the set updating time length threshold value, and determining whether the driving deceleration type track analysis result corresponding to the current traffic flow data track is the active deceleration analysis result or not.

Further, the step S2311 of using, as the current group of automatic driving trajectories, the traffic flow data trajectory corresponding to the first passive deceleration analysis result in the current update process in the updated trajectory analysis results of the driving deceleration classes may include: determining target traffic flow data corresponding to the first passive deceleration analysis result in the current updating process in the updated track analysis results of the driving deceleration class; when the analysis result of the driving deceleration type track corresponding to the next traffic flow data track of the target traffic flow data is the analysis result of the active deceleration, deleting the analysis result of the driving deceleration type track corresponding to the target traffic flow data; and when the analysis result of the driving deceleration type track corresponding to the next traffic flow data track of the target traffic flow data is the passive deceleration analysis result, taking the target traffic flow data as the automatic driving track of the current group.

For some possible embodiments, the step S24 may include performing the local trajectory correction process on the corresponding travel trajectories of the local candidate traffic flow data belonging to the same travel deceleration type within the set time period according to the travel deceleration type corresponding to the corresponding travel trajectory of each of the local candidate traffic flow data within the set time period, so as to obtain the corresponding travel trajectories of the local traffic flow data including the travel deceleration type trajectories within the set time period, and the following steps S241 and S242 may be included.

Step S241, determining the driving deceleration type corresponding to the driving track corresponding to each local candidate traffic flow data in the set time period.

Step S242, when the corresponding travel tracks of more than one local candidate traffic flow data that are adjacent in time sequence within the set time period all belong to the same travel deceleration type, performing travel track fusion on the corresponding travel tracks of the more than one local candidate traffic flow data within the set time period to obtain the corresponding travel tracks of the local traffic flow data corresponding to the same travel deceleration type within the set time period. By the design, when local track correction is carried out, track fusion is carried out through the time sequence, mutual interference and influence among different local traffic flow data can be improved as much as possible, and therefore accurate decision basis is provided for subsequent global traffic scheduling.

For some possible embodiments, the step S3 may include performing global correction processing on the trajectory analysis result of the lane change class through a preset global correction model for the trajectory analysis result to obtain a corresponding travel trajectory of the global traffic flow data including the trajectory of the lane change class in a set time period, which includes the following contents described in the steps S31 and S32.

And step S31, continuously updating the track analysis result of the driving lane change type to obtain a plurality of corresponding driving tracks of the global candidate traffic flow data including the driving lane change type track in a set time period.

Step S32, according to the lane change type corresponding to the driving track corresponding to each global candidate traffic flow data in the set time period, performing global track correction processing on the driving track corresponding to the global candidate traffic flow data belonging to the same lane change type in the set time period, to obtain the driving track corresponding to the global traffic flow data including the lane change type of driving track in the set time period.

It is understood that, through the above steps S31 and S32, the lane change type can be taken into account, so as to take into account the traffic interference (such as congestion or traffic accident caused by lane change) between the global candidate traffic flow data, which can ensure that the above traffic interference is eliminated as much as possible when the global trajectory correction processing is performed, thereby ensuring that the corresponding travel trajectory of the global traffic flow data including the lane change type trajectory is not interfered by the local traffic flow data as much as possible in the set time period.

In an alternative implementation, the traffic scheduling process performed in step S4 based on the corresponding travel track of the local traffic flow data in the set period and the corresponding travel track of the global traffic flow data in the set period to obtain the corresponding travel track of the target traffic flow data corresponding to the target track in the target traffic flow data in the set period may include the following steps S41-S43.

Step S41, when the corresponding travel track of the global traffic flow data in the set time period is completely within the corresponding travel track of the local traffic flow data in the set time period, or the corresponding travel track of the local traffic flow data in the set time period is completely within the corresponding travel track of the global traffic flow data in the set time period, updating the corresponding travel track of the global traffic flow data in the set time period and maintaining the corresponding travel track of the local traffic flow data in the set time period, so as to obtain the corresponding travel track of the target traffic flow data corresponding to the deceleration type track in the set time period.

Step S42, when the following traffic flow data track in the corresponding travel tracks of the local traffic flow data in the set time interval is crossed with the preceding traffic flow data track in the corresponding travel tracks of the global traffic flow data in the set time interval, the corresponding travel track of the local traffic flow data in the set time interval is maintained as the corresponding travel track of the target traffic flow data corresponding to the deceleration type track in the set time interval, the non-automatic driving track in the corresponding travel tracks of the local traffic flow data in the set time interval is used as the automatic driving track of the corresponding travel tracks of the global traffic flow data in the set time interval, the travel track corresponding to the updated global traffic flow data in the set time interval is obtained, and the corresponding travel track of the updated global traffic flow data in the set time interval is used as the corresponding travel track of the target traffic flow data corresponding to the lane change type track in the set time interval A trajectory.

Step S43, when the following traffic flow data track in the corresponding travel tracks of the global traffic flow data in the set time interval is crossed with the preceding traffic flow data track in the corresponding travel tracks of the local traffic flow data in the set time interval, the corresponding travel track of the local traffic flow data in the set time interval is maintained as the corresponding travel track of the target traffic flow data corresponding to the deceleration type track in the set time interval, the automatic driving track in the corresponding travel track of the local traffic flow data in the set time interval is used as the non-automatic driving track of the corresponding travel track of the global traffic flow data in the set time interval, the travel track corresponding to the updated global traffic flow data in the set time interval is obtained, and the corresponding travel track of the updated global traffic flow data in the set time interval is used as the corresponding travel track of the target traffic flow data corresponding to the lane change type track in the set time interval .

It is understood that by implementing the contents described in the above steps S41-S43, the inclusion relation of the form tracks corresponding to the global traffic flow data and the local traffic flow data can be taken into account, and determines a corresponding driving track of the target traffic flow data in a set time period by combining the automatic driving track and the non-automatic driving track, so that, when determining the corresponding running track (expected running track) of the target traffic flow data in the set time period, the mutual traffic interference between the global traffic flow data and the local traffic flow data can be weakened as much as possible, thereby reducing the time consumption for determining the expected driving track, which can improve the traffic scheduling efficiency for the target traffic block, the big data server can rapidly generate and send the indication information related to the driving strategy based on the expected driving track, so that the congestion condition of the target traffic block is rapidly improved.

In an alternative embodiment, after step S4, the method may further include the following step S5. Step S5: and generating a running track scheduling instruction according to the running track corresponding to the target traffic flow data corresponding to the target track in a set time period, and sending the running track scheduling instruction to the vehicle-mounted controller in the target traffic block. By the design, the vehicles in the target traffic block can be globally scheduled, so that the traffic jam condition is quickly relieved.

Secondly, for the data processing method based on big data and edge calculation, the embodiment of the present invention further provides an exemplary data processing apparatus 200 based on big data and edge calculation, as shown in fig. 2, the data processing apparatus 200 based on big data and edge calculation may include the following functional modules.

The data acquisition module 210 is configured to acquire target traffic flow data of a target traffic block to be subjected to traffic scheduling; and respectively carrying out travel deceleration type track analysis and travel lane change type track analysis on a plurality of traffic flow data tracks in the target traffic flow data to obtain a travel deceleration type track analysis result and a travel lane change type track analysis result.

And the local correction module 220 is configured to perform local correction processing on the trajectory analysis result of the driving and decelerating class through a preset local correction model for the trajectory analysis result, so as to obtain a corresponding driving trajectory of the local traffic flow data including the driving and decelerating class trajectory in a set time period.

And the global correction module 230 is configured to perform global correction processing on the track analysis result of the lane change class through a preset global correction model for the track analysis result, so as to obtain a corresponding driving track of global traffic flow data including the lane change class track in a set time period.

The data processing module 240 is configured to perform traffic scheduling processing based on a corresponding travel track of the local traffic flow data in a set time period and a corresponding travel track of the global traffic flow data in the set time period, so as to obtain a corresponding travel track of target traffic flow data corresponding to a target track in the target traffic flow data in the set time period; the target track comprises at least one of a deceleration type track and a lane change type track, and the corresponding travel track of the target traffic flow data in a set period of time is used for carrying out traffic scheduling on the target traffic block.

Then, based on the above method embodiment and apparatus embodiment, the embodiment of the present invention further provides a system embodiment, that is, a data processing system based on big data and edge calculation, please refer to fig. 3 in combination, and the data processing system 30 based on big data and edge calculation may include a big data server 10 and an onboard controller 20. Where the big data server 10 and the onboard controller 20 communicate to implement the above method, further, the functionality of the data processing system 30 based on big data and edge calculations is described below.

A data processing system based on big data and edge calculation comprises a big data server and an on-board controller which are communicated with each other, wherein the on-board controller is located in a target traffic block;

wherein the big data server is configured to:

acquiring target traffic flow data of a target traffic block to be subjected to traffic scheduling; respectively carrying out travel deceleration type track analysis and travel lane change type track analysis on a plurality of traffic flow data tracks in the target traffic flow data to obtain a travel deceleration type track analysis result and a travel lane change type track analysis result;

performing local correction processing on the track analysis result of the driving and decelerating type through a preset local correction model aiming at the track analysis result to obtain a driving track corresponding to local traffic flow data comprising the driving and decelerating type track in a set time period;

performing global correction processing on the track analysis result of the driving lane change class through a preset global correction model aiming at the track analysis result to obtain a driving track corresponding to global traffic flow data comprising the driving lane change class track in a set time period;

performing traffic scheduling processing on the basis of the corresponding running track of the local traffic flow data in a set time period and the corresponding running track of the global traffic flow data in the set time period to obtain the corresponding running track of target traffic flow data corresponding to a target track in the target traffic flow data in the set time period; the target track comprises at least one of a deceleration type track and a lane change type track, and the corresponding travel track of the target traffic flow data in a set period of time is used for carrying out traffic scheduling on the target traffic block.

Further, referring to fig. 4 in conjunction, the big data server 10 may include a processing engine 110, a network module 120, and a memory 130, the processing engine 110 and the memory 130 communicating through the network module 120.

Processing engine 110 may process the relevant information and/or data to perform one or more of the functions described herein. For example, in some embodiments, processing engine 110 may include at least one processing engine (e.g., a single core processing engine or a multi-core processor). By way of example only, the Processing engine 110 may include a Central Processing Unit (CPU), an Application-Specific Integrated Circuit (ASIC), an Application-Specific Instruction Set Processor (ASIP), a Graphics Processing Unit (GPU), a Physical Processing Unit (PPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), a controller, a microcontroller Unit, a Reduced Instruction Set Computer (RISC), a microprocessor, or the like, or any combination thereof.

Network module 120 may facilitate the exchange of information and/or data. In some embodiments, the network module 120 may be any type of wired or wireless network or combination thereof. Merely by way of example, the Network module 120 may include a cable Network, a wired Network, a fiber optic Network, a telecommunications Network, an intranet, the internet, a Local Area Network (LAN), a Wide Area Network (WAN), a Wireless Local Area Network (WLAN), a Metropolitan Area Network (MAN), a Public Switched Telephone Network (PSTN), a bluetooth Network, a Wireless personal Area Network, a Near Field Communication (NFC) Network, and the like, or any combination thereof. In some embodiments, the network module 120 may include at least one network access point. For example, the network module 120 may include wired or wireless network access points, such as base stations and/or network access points.

The Memory 130 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like. The memory 130 is used for storing a program, and the processing engine 110 executes the program after receiving the execution instruction.

It will be appreciated that the configuration shown in fig. 4 is merely illustrative and that the big data server 10 may also include more or fewer components than shown in fig. 2, or have a different configuration than shown in fig. 4. The components shown in fig. 4 may be implemented in hardware, software, or a combination thereof.

In summary, the data processing method and the big data server based on big data and edge calculation respectively perform travel deceleration type trajectory analysis and travel lane change type trajectory analysis on a plurality of traffic flow data trajectories of target traffic flow data to be subjected to traffic scheduling, so as to obtain a travel deceleration type trajectory analysis result and a travel lane change type trajectory analysis result, that is, the traffic flow data trajectories in the target traffic flow data are classified into a travel deceleration type and a travel lane change type; and because the traffic congestion influence degrees of the running deceleration type track and the running lane change type track are different, and various track analysis results may be inaccurate in the track analysis results, respectively correcting the track analysis results of the running deceleration type and the track analysis results of the running lane change type through different correction models to obtain a running track corresponding to local traffic flow data containing the running deceleration type track in a set time period and a running track corresponding to global traffic flow data containing the running lane change type track in the set time period.

Because the corresponding travel track of the local traffic flow data in the set time period and the corresponding travel track of the global traffic flow data in the set time period may have mutual traffic interference, and the mutual traffic interference affects the scheduling efficiency, the corresponding travel track of the local traffic flow data in the set time period and the corresponding travel track of the global traffic flow data in the set time period are subjected to traffic scheduling processing to obtain the corresponding travel track of the target traffic flow data corresponding to the target track in the set time period, the corresponding travel track of the traffic flow data of the target track in the set time period can be quickly and accurately determined, and the corresponding travel track of the traffic flow data corresponding to the target track in the set time period does not need to consume much time to determine the corresponding travel track of the traffic flow data of the target track, so that the traffic scheduling efficiency for the target traffic block is improved as much as possible.

It should be understood that, for the above, a person skilled in the art can deduce from the above disclosure to determine the meaning of the related technical term without doubt, for example, for some values, coefficients, weights, indexes, factors, and other terms, a person skilled in the art can deduce and determine from the logical relationship between the above and the following, and the value range of these values can be selected according to the actual situation, for example, 0 to 1, for example, 1 to 10, and for example, 50 to 100, which are not limited herein.

The skilled person can unambiguously determine some preset, reference, predetermined, set and target technical features/terms, such as threshold values, threshold intervals, threshold ranges, etc., from the above disclosure. For some technical characteristic terms which are not explained, the technical solution can be clearly and completely implemented by those skilled in the art by reasonably and unambiguously deriving the technical solution based on the logical relations in the previous and following paragraphs. Prefixes of unexplained technical feature terms, such as "first", "second", "previous", "next", "current", "history", "latest", "best", "target", "specified", and "real-time", etc., can be unambiguously derived and determined from the context. Suffixes of technical feature terms not to be explained, such as "list", "feature", "sequence", "set", "matrix", "unit", "element", "track", and "list", etc., can also be derived and determined unambiguously from the foregoing and the following.

The foregoing disclosure of embodiments of the present invention will be apparent to those skilled in the art. It should be understood that the process of deriving and analyzing technical terms, which are not explained, by those skilled in the art based on the above disclosure is based on the contents described in the present application, and thus the above contents are not an inventive judgment of the overall scheme.

It should be appreciated that the system and its modules shown above may be implemented in a variety of ways. For example, in some embodiments, the system and its modules may be implemented in hardware, software, or a combination of software and hardware. Wherein the hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory for execution by a suitable instruction execution system, such as a microprocessor or specially designed hardware. Those skilled in the art will appreciate that the methods and systems described above may be implemented using computer executable instructions and/or embodied in processor control code, such code being provided, for example, on a carrier medium such as a diskette, CD-or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The system and its modules of the present application may be implemented not only by hardware circuits such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., but also by software executed by various types of processors, for example, or by a combination of the above hardware circuits and software (e.g., firmware).

The beneficial effects that may be brought by the embodiments of the present application include, but are not limited to: (1) the method comprises the steps of (1) forecasting the driving tracks of different vehicles by combining traffic light dispatching and a genetic algorithm, so that the driving tracks of vehicles in a target traffic block are dispatched as many as possible, (2) feedback dispatching of the driving tracks of different vehicles is realized according to an expected driving track analyzed in advance, (3) the driving track corresponding to target traffic flow data in a set time interval is determined by considering the interference of local traffic flow data and global traffic flow data, so that the driving track corresponding to the target traffic flow data in the set time interval can be rapidly and accurately determined, and the expected driving track is determined without consuming much time, so that the traffic dispatching efficiency of the target traffic block can be improved as much as possible, and traffic jam can be relieved in time.

It is to be noted that different embodiments may produce different advantages, and in different embodiments, any one or combination of the above advantages may be produced, or any other advantages may be obtained.

Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be considered merely illustrative and not restrictive of the broad application. Various modifications, improvements and adaptations to the present application may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present application and thus fall within the spirit and scope of the exemplary embodiments of the present application.

Also, this application uses specific language to describe embodiments of the application. Reference throughout this specification to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the present application is included in at least one embodiment of the present application. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the present application may be combined as appropriate.

Moreover, those skilled in the art will appreciate that aspects of the present application may be illustrated and described in terms of several patentable species or situations, including any new and useful combination of processes, machines, manufacture, or materials, or any new and useful improvement thereon. Accordingly, various aspects of the present application may be embodied entirely in hardware, entirely in software (including firmware, resident software, micro-code, etc.) or in a combination of hardware and software. The above hardware or software may be referred to as "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the present application may be represented as a computer product, including computer readable program code, embodied in one or more computer readable media.

The computer storage medium may comprise a propagated data signal with the computer program code embodied therewith, for example, on baseband or as part of a carrier wave. The propagated signal may take any of a variety of forms, including electromagnetic, optical, etc., or any suitable combination. A computer storage medium may be any computer-readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code located on a computer storage medium may be propagated over any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or any combination of the preceding.

Computer program code required for the operation of various portions of the present application may be written in any one or more programming languages, including an object oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C + +, C #, VB.NET, Python, and the like, a conventional programming language such as C, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, a dynamic programming language such as Python, Ruby, and Groovy, or other programming languages, and the like. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any network format, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or in a cloud computing environment, or as a service, such as a software as a service (SaaS).

Additionally, the order in which elements and sequences of the processes described herein are processed, the use of alphanumeric characters, or the use of other designations, is not intended to limit the order of the processes and methods described herein, unless explicitly claimed. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.

Similarly, it should be noted that in the preceding description of embodiments of the application, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to require more features than are expressly recited in the claims. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.

Numerals describing the number of components, attributes, etc. are used in some embodiments, it being understood that such numerals used in the description of the embodiments are modified in some instances by the use of the modifier "about", "approximately" or "substantially". Unless otherwise indicated, "about", "approximately" or "substantially" indicates that the numbers allow for adaptive variation. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximations that may vary depending upon the desired properties of the individual embodiments. In some embodiments, the numerical parameter should take into account the specified significant digits and employ a general digit preserving approach. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of the range are approximations, in the specific examples, such numerical values are set forth as precisely as possible within the scope of the application.

The entire contents of each patent, patent application publication, and other material cited in this application, such as articles, books, specifications, publications, documents, and the like, are hereby incorporated by reference into this application. Except where the application is filed in a manner inconsistent or contrary to the present disclosure, and except where the claim is filed in its broadest scope (whether present or later appended to the application) as well. It is noted that the descriptions, definitions and/or use of terms in this application shall control if they are inconsistent or contrary to the statements and/or uses of the present application in the material attached to this application.

Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present application. Other variations are also possible within the scope of the present application. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the present application can be viewed as being consistent with the teachings of the present application. Accordingly, the embodiments of the present application are not limited to only those embodiments explicitly described and depicted herein.

Claims

1. A data processing method based on big data and edge calculation, the method comprising:

2. The method according to claim 1, wherein the performing a travel deceleration type trajectory analysis and a travel lane change type trajectory analysis on a plurality of traffic flow data trajectories in the target traffic flow data to obtain a travel deceleration type trajectory analysis result and a travel lane change type trajectory analysis result respectively comprises:

respectively analyzing the driving and decelerating type tracks of a plurality of traffic flow data tracks in the target traffic flow data to obtain driving and decelerating type track analysis indexes in the traffic flow data tracks and initial driving track types corresponding to the driving and decelerating type track analysis indexes;

determining a track analysis result of the driving and decelerating type based on the driving and decelerating type track analysis index and the corresponding initial driving track type in each traffic flow data track;

and respectively carrying out travel lane change type trajectory analysis on a plurality of traffic flow data trajectories in the target traffic flow data to obtain a trajectory analysis result of the travel lane change type.

3. The method according to claim 2, wherein the performing the trajectory analysis of the lane change driving class on the plurality of traffic flow data trajectories in the target traffic flow data respectively to obtain the trajectory analysis result of the lane change driving class comprises:

respectively carrying out vehicle attitude identification on a plurality of traffic flow data tracks in the target traffic flow data to obtain vehicle attitude identification results corresponding to the traffic flow data tracks;

respectively identifying the vehicle speed of a plurality of traffic flow data tracks in the target traffic flow data to obtain vehicle speed identification results corresponding to the traffic flow data tracks;

associating the vehicle attitude recognition results and the vehicle speed recognition results corresponding to the same vehicle object;

performing driving lane change type track analysis processing based on a vehicle speed recognition result associated with a target vehicle posture recognition result in the target traffic flow data to obtain a track analysis result of the driving lane change type; wherein the target vehicle attitude recognition result is a vehicle attitude recognition result corresponding to the marked vehicle object.

4. The method according to any one of claims 1 to 3, wherein the local correction processing is performed on the trajectory analysis result of the driving and decelerating class through a preset local correction model for the trajectory analysis result to obtain a corresponding driving trajectory of the local traffic flow data including the trajectory of the driving and decelerating class in a set time period, and the method comprises the following steps:

respectively carrying out track type matching on each traffic flow data track in the track analysis result of the driving deceleration class to obtain a unique driving track type corresponding to each traffic flow data track;

updating the track analysis indexes respectively based on the analysis index updating records of the running deceleration type track analysis indexes corresponding to the corresponding unique running track type in each traffic flow data track to obtain updated running deceleration type track analysis results;

continuously updating the updated track analysis result of the running and decelerating type to obtain a plurality of running tracks corresponding to the local candidate traffic flow data comprising the running and decelerating type tracks in a set time period;

according to the driving deceleration types corresponding to the driving tracks corresponding to the local candidate traffic flow data in the set time period, carrying out local track correction processing on the driving tracks corresponding to the local candidate traffic flow data belonging to the same driving deceleration type in the set time period to obtain the driving tracks corresponding to the local traffic flow data comprising the driving deceleration type tracks in the set time period;

the track type matching is performed on each traffic flow data track in the track analysis result of the driving deceleration class, so as to obtain a unique driving track type corresponding to each traffic flow data track, and the method comprises the following steps:

aiming at each traffic flow data track in the track analysis results of the driving and decelerating classes, when the number of the types of the initial driving track of the traffic flow data track is not less than two, obtaining the traffic congestion evaluation information of each initial driving track type;

when the initial travel track type with the highest traffic congestion coefficient corresponding to the traffic congestion evaluation information is one, taking the initial travel track type with the highest traffic congestion coefficient corresponding to the traffic congestion evaluation information as the unique travel track type of the corresponding traffic flow data track;

when the initial travel track type with the highest traffic congestion coefficient corresponding to the traffic congestion evaluation information is not less than two, acquiring the track analysis index updating frequency of the corresponding travel deceleration type track analysis index aiming at the initial travel track type with the highest traffic congestion coefficient corresponding to each piece of traffic congestion evaluation information; determining the only driving track type corresponding to the corresponding traffic flow data track according to the initial driving track type corresponding to the highest track analysis index updating frequency;

wherein, the updating record of the analysis index based on the driving deceleration type track analysis index corresponding to the corresponding unique driving track type in each traffic flow data track is respectively updated by the track analysis index to obtain the updated driving deceleration type track analysis result, and the updating record comprises:

for each traffic flow data track, acquiring index heat information of a driving deceleration track analysis index corresponding to the corresponding unique driving track type in each traffic flow data track;

when the current index heat degree corresponding to the index heat degree information is within a preset index heat degree interval, maintaining a corresponding driving and decelerating track analysis result, wherein the maintained driving and decelerating track analysis result comprises a driving and decelerating track analysis index and a unique driving track type corresponding to the driving and decelerating track analysis index;

when the current index heat corresponding to the index heat information is not in the preset index heat interval, deleting the driving deceleration type track analysis result of the corresponding traffic flow data track;

and obtaining an updated track analysis result of the driving and decelerating type based on the driving and decelerating type track analysis result corresponding to each traffic flow data track.

5. The method according to claim 4, wherein the continuously updating the updated trajectory analysis result of the driving and decelerating type to obtain a plurality of corresponding driving trajectories of the local candidate traffic flow data including the trajectory of the driving and decelerating type within a set time period comprises:

continuously updating the updated track analysis result of the running deceleration class to obtain a plurality of groups of automatic driving tracks and non-automatic driving tracks;

determining a corresponding running track comparison result of traffic flow data between each group of automatic driving tracks and non-automatic driving tracks in a set time period;

when the similarity corresponding to the comparison result of the running tracks corresponding to the traffic flow data in the set time interval is greater than or equal to the preset similarity, taking the running track corresponding to the traffic flow data consisting of the automatic driving track and the non-automatic driving track in the set time interval as the running track corresponding to the local candidate traffic flow data in the set time interval;

for the corresponding running track of each local candidate traffic flow data in a set time period, determining a target running deceleration type with the largest number of statistics according to the updated unique running track type corresponding to each traffic flow data track in the corresponding running tracks of the local candidate traffic flow data in the set time period;

taking the target running deceleration type as a running deceleration type corresponding to a running deceleration type track included in a running track corresponding to the corresponding local candidate traffic flow data in a set time period;

wherein, the driving deceleration type track analysis result in the driving deceleration type track analysis result comprises an active deceleration analysis result and a passive deceleration analysis result, and the track analysis result of the updated driving deceleration type is continuously updated to obtain a plurality of groups of automatic driving tracks and non-automatic driving tracks, and the method comprises the following steps:

taking the traffic flow data track corresponding to the first passive deceleration analysis result in the current updating process in the updated track analysis results of the driving deceleration classes as the automatic driving track of the current group;

traversing a traffic flow data trajectory subsequent to the current set of autodrive trajectories;

when the traversed current traffic flow data track corresponds to an active deceleration analysis result and the driving deceleration type track analysis results corresponding to the traffic flow data track within the global preset time from the current traffic flow data track are all active deceleration analysis results, taking the current traffic flow data track as the current group of non-automatic driving tracks;

taking the traffic flow data track corresponding to the first passive deceleration analysis result after the non-automatic driving track of the current group as the automatic driving track of the current group which is updated and processed next time, and returning to the step of traversing the traffic flow data track after the automatic driving track of the current group to continue executing until obtaining multiple groups of automatic driving tracks and non-automatic driving tracks;

when the traversed current traffic flow data track corresponds to an active deceleration analysis result and the driving deceleration type track analysis results corresponding to the traffic flow data track within the global preset time period from the current traffic flow data track are all active deceleration analysis results, the method further comprises the following step of taking the current traffic flow data track as the non-automatic driving track of the current group before:

when the continuous updating time length of the driving track corresponding to the traffic flow data determined by the traversed current traffic flow data track and the automatic driving track of the current group in a set time period is less than a set updating time length threshold value, determining whether the driving deceleration type track analysis result corresponding to the current traffic flow data track is an active deceleration analysis result;

when the current traffic flow data track corresponds to a passive deceleration analysis result, taking the current traffic flow data track as one of traffic flow data tracks in corresponding driving tracks of the current group of corresponding traffic flow data within a set time period;

and when the current traffic flow data track corresponds to an active deceleration analysis result and a driving deceleration type track analysis result in a global preset time length from the current traffic flow data track comprises a passive deceleration analysis result, taking the traffic flow data track corresponding to the first passive deceleration analysis result in the global preset time length from the current traffic flow data track as a traversed next traffic flow data track, and returning to the step of determining whether the driving deceleration type track analysis result corresponding to the current traffic flow data track is the active deceleration analysis result or not when the continuous updating time length of the driving track corresponding to the traversed current traffic flow data track and the determined traffic flow data of the current group of automatic driving tracks in a set time length is less than a set updating time length threshold value.

6. The method according to claim 5, wherein the step of using the traffic flow data trajectory corresponding to the first passive deceleration analysis result in the current update process among the updated trajectory analysis results of the driving deceleration classes as the automatic driving trajectory of the current group comprises:

determining target traffic flow data corresponding to the first passive deceleration analysis result in the current updating process in the updated track analysis results of the driving deceleration class;

when the analysis result of the driving deceleration type track corresponding to the next traffic flow data track of the target traffic flow data is the analysis result of the active deceleration, deleting the analysis result of the driving deceleration type track corresponding to the target traffic flow data;

and when the analysis result of the driving deceleration type track corresponding to the next traffic flow data track of the target traffic flow data is the passive deceleration analysis result, taking the target traffic flow data as the automatic driving track of the current group.

7. The method of claim 4, wherein the step of performing the local track correction processing on the corresponding travel track of the local candidate traffic flow data belonging to the same travel deceleration type within the set time period according to the travel deceleration type corresponding to the corresponding travel track of each local candidate traffic flow data within the set time period to obtain the corresponding travel track of the local traffic flow data including the travel deceleration type within the set time period comprises the steps of:

determining the driving deceleration type corresponding to the driving track corresponding to each local candidate traffic flow data in a set time period;

when the corresponding running tracks of more than one local candidate traffic flow data which are adjacent in time sequence in the set time period all belong to the same running deceleration type, the running tracks corresponding to the more than one local candidate traffic flow data in the set time period are subjected to running track fusion to obtain the corresponding running tracks of the local traffic flow data corresponding to the same running deceleration type in the set time period.

8. The method according to any one of claims 1 to 7, wherein the global correction processing is performed on the track analysis result of the lane change class through a preset global correction model for the track analysis result to obtain a corresponding driving track of the global traffic flow data including the lane change class track in a set time period, and the method comprises the following steps:

continuously updating the track analysis result of the driving lane change type to obtain a plurality of corresponding driving tracks of global candidate traffic flow data comprising the driving lane change type track in a set time period;

and according to the driving lane change type corresponding to the driving track corresponding to each global candidate traffic flow data in the set time period, carrying out global track correction processing on the driving track corresponding to the global candidate traffic flow data belonging to the same driving lane change type in the set time period to obtain the driving track corresponding to the global traffic flow data comprising the driving lane change type track in the set time period.

9. A big data server is characterized by comprising a processing engine, a network module and a memory; the processing engine and the memory communicate through the network module, the processing engine reading a computer program from the memory and operating to perform the method of any of claims 1-8.

10. A computer storage medium, having stored thereon a computer program which, when executed, implements the method of any one of claims 1-8.