CN112738209A

CN112738209A - Data analysis method based on big data and artificial intelligence and cloud computing server

Info

Publication number: CN112738209A
Application number: CN202011569391.0A
Authority: CN
Inventors: 顾小菊
Original assignee: Individual
Current assignee: Individual
Priority date: 2020-12-26
Filing date: 2020-12-26
Publication date: 2021-04-30

Abstract

The embodiment of the application discloses a data analysis method and a cloud computing server based on big data and artificial intelligence, congestion state analysis is carried out on motor vehicle traffic flow data and non-motor vehicle traffic flow data in a traffic section to be scheduled by considering road network interference information between the motor vehicle traffic flow data and the non-motor vehicle traffic flow data, a traffic congestion state analysis result can be obtained, a traffic guidance scheduling strategy corresponding to the non-motor vehicle traffic flow data to be scheduled can be determined, and the congestion state analysis is further realized to determine a target congestion state analysis result aiming at the traffic guidance scheduling strategy. The motor vehicle scheduling strategy in the traffic road section to be scheduled and the scheduling execution indication information corresponding to the motor vehicle scheduling strategy can be determined. When the motor vehicles are indicated to carry out traffic scheduling based on the scheduling execution indication information, traffic flow data of the non-motor vehicles can be taken into account, the phenomenon of traffic jam of the non-motor vehicles when the motor vehicles are scheduled is avoided, and further traffic congestion or traffic accidents are avoided.

Description

Data analysis method based on big data and artificial intelligence and cloud computing server

Technical Field

The application relates to the technical field of big data and artificial intelligence, in particular to a data analysis method based on big data and artificial intelligence and a cloud computing server.

Background

Along with the development of science and technology, the living standard of people is continuously improved. The continuous progress of urbanization leads the keeping quantity of motor vehicles and non-motor vehicles in large cities to be in a trend of increasing year by year, thus causing an increasingly serious traffic jam problem. Traffic congestion in urban streets not only affects daily trips of people, but also can cause a series of traffic accidents. In recent years, how to manage traffic congestion in a large city is currently a major consideration.

Disclosure of Invention

One of the embodiments of the present application provides a data analysis method based on big data and artificial intelligence, including:

acquiring motor vehicle traffic flow data and non-motor vehicle traffic flow data in a traffic section to be scheduled;

analyzing the congestion state of the motor vehicle traffic flow data and the non-motor vehicle traffic flow data in the traffic section to be scheduled based on road network interference information of the traffic flow data between the motor vehicle traffic flow data and the non-motor vehicle traffic flow data in the traffic section to be scheduled to obtain a traffic congestion state analysis result;

determining non-motor vehicle traffic flow data with a congestion tag in a congestion state analysis process as non-motor vehicle traffic flow data to be scheduled, and determining a traffic guidance scheduling strategy corresponding to the non-motor vehicle traffic flow data to be scheduled according to the traffic route track correlation information between the non-motor vehicle traffic flow data and the non-motor vehicle traffic flow data to be scheduled in the traffic congestion state analysis result;

carrying out congestion state analysis on the traffic guidance scheduling strategy corresponding to the traffic flow data of the non-motor vehicles to be scheduled and the traffic flow data of the non-motor vehicles to be scheduled to obtain a target congestion state analysis result aiming at the traffic guidance scheduling strategy;

and determining the motor vehicle scheduling strategy in the traffic section to be scheduled and scheduling execution indication information corresponding to the motor vehicle scheduling strategy according to the target congestion state analysis result aiming at the traffic guidance scheduling strategy and the traffic congestion state analysis result.

One of the embodiments of the present application provides a cloud computing server, which includes a data analysis device based on big data and artificial intelligence, and a functional module in the data analysis device implements the method when running.

One of the embodiments of the present application provides a cloud computing server, including 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 of an exemplary big data and artificial intelligence based data analysis method and/or process, according to some embodiments of the invention;

FIG. 2 is a block diagram of an exemplary big data and artificial intelligence based data analysis apparatus, according to some embodiments of the invention;

FIG. 3 is a block diagram of an exemplary big data and artificial intelligence based data analysis system, shown in accordance with some embodiments of the present invention, an

Fig. 4 is a schematic diagram illustrating hardware and software components in an exemplary cloud computing 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 through research and analysis that it is difficult for the conventional method for managing traffic congestion to quickly implement traffic scheduling and dredging, because vehicles and non-vehicles are not distinguished during traffic scheduling and dredging, which may cause secondary congestion during traffic scheduling, for example, when vehicles are scheduled, the traffic congestion of non-vehicles may cause further traffic congestion or traffic accidents.

In order to solve the problems, the inventor provides a data analysis method and a cloud computing server based on big data and artificial intelligence, motor vehicle traffic flow data and non-motor vehicle traffic flow data in a traffic section to be scheduled are respectively analyzed, road network interference information between the motor vehicle traffic flow data and the non-motor vehicle traffic flow data is considered, and scheduling execution indicating information corresponding to a motor vehicle scheduling strategy and a motor vehicle scheduling strategy in the traffic section to be scheduled can be quickly and accurately determined.

First, an exemplary big data and artificial intelligence based data analysis method is described, referring to fig. 1, which is a flowchart illustrating an exemplary big data and artificial intelligence based data analysis method and/or process according to some embodiments of the present invention, and the big data and artificial intelligence based data analysis method may include the following steps S11-S15.

And step S11, acquiring motor vehicle traffic flow data and non-motor vehicle traffic flow data in the traffic section to be dispatched.

For example, the traffic segment to be scheduled may be a traffic segment corresponding to a certain block in a city, such as a city center segment, a shopping mall segment, a maintenance segment, or other traffic segments where congestion often occurs. The motor vehicle traffic flow data can be understood as traffic flow data corresponding to private cars and buses, and the non-motor vehicle traffic flow data can be understood as traffic flow data corresponding to two-wheel battery cars, three-wheel battery cars, bicycles and scooters. Further, the traffic flow data may include trajectory data, longitude and latitude data, and time data, and may also be understood as spatio-temporal data, and the traffic flow data may be used for analyzing traffic conditions (e.g., road conditions).

Step S12, based on road network interference information of traffic flow data between motor vehicle traffic flow data and non-motor vehicle traffic flow data in the traffic section to be dispatched, carrying out congestion state analysis on the motor vehicle traffic flow data and the non-motor vehicle traffic flow data in the traffic section to be dispatched to obtain a traffic congestion state analysis result.

For example, the road network interference information may be understood as interference between different vehicles during the road traveling process, and such interference may be spatial interference, for example, spatial congestion and occupation between the vehicles. The road network interference information is closely related to the congestion of the traffic road, so that the state difference of the motor vehicles and the non-motor vehicles in the driving process can be distinguished by taking the road network interference information into account when the congestion state analysis is carried out, and the global congestion state analysis is realized.

Step S13, determining the non-motor vehicle traffic flow data with the congestion label in the congestion state analysis process as the non-motor vehicle traffic flow data to be scheduled, and determining the traffic guidance scheduling strategy corresponding to the non-motor vehicle traffic flow data to be scheduled according to the correlation information of the traffic route track between the non-motor vehicle traffic flow data and the non-motor vehicle traffic flow data to be scheduled in the traffic congestion state analysis result.

For example, the congestion tag may be obtained after being analyzed and processed in advance according to a historical congestion record, and the congestion tag is used for representing that the non-motor vehicle traffic flow data may generate congestion influence on the traffic section to be scheduled, so that the traffic congestion of the traffic section to be scheduled is caused indirectly or directly. The traffic track can be understood as a track corresponding to the electronic map when different vehicles are driving, and it can be understood that the electronic map can be a unified digital map generated based on a global positioning system. The correlation information may be used to characterize the similarity between different traffic lane traces. And the traffic guidance scheduling strategy corresponding to the traffic flow data of the non-motor vehicles to be scheduled can be used for indicating the corresponding non-motor vehicles to carry out adjustment on the driving routes. The traffic guidance dispatch strategy may include a route adjustment indication for the non-motorized vehicle, such as indicating a change of the non-motorized vehicle from a motorized lane to a non-motorized lane, such as indicating a deceleration of the non-motorized vehicle, and the like.

Step S14, carrying out congestion state analysis on the traffic guidance scheduling strategy corresponding to the traffic flow data of the non-motor vehicles to be scheduled and the traffic flow data of the non-motor vehicles to be scheduled, and obtaining a target congestion state analysis result aiming at the traffic guidance scheduling strategy.

For example, the target congestion state analysis result can take the randomness in the non-motor driving process into consideration, for example, the non-motor vehicles do not adjust the driving route according to the corresponding traffic guidance scheduling strategy and perform random congestion adding, running red light and the like, if the non-motor vehicles are scheduled and monitored in real time, the scheduling cost is increased greatly, therefore, the traffic scheduling of the motor vehicles can be indicated in reverse by determining the target congestion state analysis result, so that the traffic scheduling efficiency is improved as much as possible, and secondary congestion and potential traffic accident potential caused by illegal driving behaviors of the non-motor vehicles are avoided.

Step S15, according to the target congestion state analysis result aiming at the traffic guidance scheduling strategy and the traffic congestion state analysis result, determining a motor vehicle scheduling strategy in the traffic section to be scheduled and scheduling execution indication information corresponding to the motor vehicle scheduling strategy.

For example, the scheduling execution indication information corresponding to the vehicle scheduling policy may include a plurality of driving indications for the vehicle, including but not limited to a driving speed indication, a lane change time indication, a driving route indication, and the like. It can be understood that the difficulty of traffic scheduling of the motor vehicles is lower than that of traffic scheduling of the non-motor vehicles, so that the difficulty and uncertainty of scheduling can be reduced by traffic scheduling of the motor vehicles, traffic flow data of the non-motor vehicles can be taken into account, the phenomenon of traffic jam of the non-motor vehicles during scheduling of the motor vehicles is avoided, and further traffic congestion or traffic accidents are avoided.

In summary, based on steps S11-S15, congestion state analysis is performed on the motor vehicle traffic flow data and the non-motor vehicle traffic flow data in the traffic section to be scheduled, and road network interference information between the motor vehicle traffic flow data and the non-motor vehicle traffic flow data is considered, so that a traffic congestion state analysis result can be obtained, a traffic guidance scheduling policy corresponding to the non-motor vehicle traffic flow data to be scheduled can be determined, and congestion state analysis is further performed to determine a target congestion state analysis result for the traffic guidance scheduling policy. Therefore, the motor vehicle scheduling strategy in the traffic section to be scheduled and the scheduling execution indication information corresponding to the motor vehicle scheduling strategy can be determined. Therefore, when the motor vehicles are instructed to carry out traffic scheduling based on the scheduling execution instruction information, traffic flow data of the non-motor vehicles can be taken into account, the phenomenon of traffic jam of the non-motor vehicles when the motor vehicles are scheduled is avoided, and further traffic congestion or traffic accidents are avoided.

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 acquiring of the motor vehicle traffic flow data and the non-motor vehicle traffic flow data in the traffic section to be scheduled, which is described in step S11, may include the following steps S111-S114.

And step S111, acquiring at least two groups of motor vehicle driving track data and at least two groups of non-motor vehicle driving track data in the traffic road section to be scheduled.

Step S112, obtaining the driving track change information of the motor vehicle driving track data and the road traffic-wrong data of the motor vehicle driving track data between the at least two sets of motor vehicle driving track data, and obtaining the driving track change information of the non-motor vehicle driving track data and the road traffic-wrong data of the non-motor vehicle driving track data between the at least two sets of non-motor vehicle driving track data. For example, the travel track change information may be calculated according to a positioning track curve of the corresponding vehicle on the electronic map at different time periods. The road traffic-crossing data may be traffic state data corresponding to vehicles traveling in opposite directions when a traffic crossing is made, for example, traffic jam data based on deceleration that may be performed when a traffic crossing is made.

Step S113, fusing the at least two groups of motor vehicle running track data according to the running track change information of the motor vehicle running track data and the road traffic error data of the motor vehicle running track data to obtain motor vehicle traffic flow data in the traffic road section to be scheduled; the set of motor vehicle traffic flow data includes at least one set of motor vehicle travel track data. For example, the driving track data fusion may splice or fuse the driving track curves to obtain corresponding traffic flow data.

Step S114, fusing the at least two groups of non-motor vehicle running track data according to the running track change information of the non-motor vehicle running track data and the road traffic-wrong data of the non-motor vehicle running track data to obtain non-motor vehicle traffic flow data in the traffic road section to be scheduled; the set of non-motor vehicle traffic flow data includes at least one set of non-motor vehicle travel trajectory data.

Thus, by implementing the contents described in the above steps S111-S114, the driving track change information and the data of the wrong way to the road can be taken into account, thereby ensuring that the motor vehicle traffic flow data and the non-motor vehicle traffic flow data can completely reflect the actual traffic condition of the traffic section to be dispatched.

In some embodiments, the road network interference information of the traffic flow data between the motor vehicle traffic flow data and the non-motor vehicle traffic flow data in the traffic section to be scheduled, which is described in step S12, is used to perform congestion status analysis on the motor vehicle traffic flow data and the non-motor vehicle traffic flow data in the traffic section to be scheduled, so as to obtain a traffic congestion status analysis result, which may include the contents described in the following steps S121 to S123.

Step S121, determining the non-motor vehicle traffic flow data in the traffic section to be dispatched as non-motor vehicle traffic flow data analyzed according to the congestion state, and determining the motor vehicle traffic flow data in the traffic section to be dispatched as motor vehicle traffic flow data analyzed according to the congestion state; and the non-motor vehicle running track data in the non-motor vehicle traffic flow data analyzed aiming at the congestion state is determined from the running track data corresponding to the monitoring video of the traffic section to be dispatched. For example, the surveillance video may be an image video captured by a corresponding electronic camera, and the travel track data may be obtained by performing image analysis based on the surveillance video, which is not described herein again since the related technology of image analysis is a conventional technical means.

Step S122, obtaining motor vehicle running track data in the running track data corresponding to the monitoring video; and determining running track change information of running track data between the motor vehicle running track data in the running track data corresponding to the monitoring video and the motor vehicle running track data in the motor vehicle traffic flow data analyzed aiming at the congestion state as road network interference information of the traffic flow data between the non-motor vehicle traffic flow data analyzed aiming at the congestion state and the motor vehicle traffic flow data analyzed aiming at the congestion state.

Step S123, when the road network interference index of the road network interference information of the traffic flow data is greater than or equal to the road network interference index threshold, performing congestion state analysis on the non-motor vehicle traffic flow data for congestion state analysis and the motor vehicle traffic flow data for congestion state analysis to obtain a traffic congestion state analysis result. For example, the road network interference index is used for representing the degree of interference between the motor vehicle and the non-motor vehicle, and the larger the road network interference index is, the larger the degree of interference between the motor vehicle and the non-motor vehicle is. The road network interference index threshold may be adjusted according to actual situations, and is not limited herein.

In this way, when the contents described in the above steps S121 to S123 are applied, the determination of traffic flow data for congestion state analysis may be implemented based on the monitoring video, so as to accurately determine road network interference information between traffic flow data of different vehicles through the travel track change information, and implement congestion state analysis in real time in combination with the road network interference index, thereby ensuring that the traffic congestion state analysis result takes into account mutual interference of the motor vehicle and the non-motor vehicle on the road, and providing a reliable decision basis for subsequent scheduling policy generation.

In some embodiments, the non-motor traffic flow data to be scheduled comprises non-motor travel trajectory data for violations in the traffic segment to be scheduled; the number of the traffic jam state analysis results is at least two; the non-motor vehicle traffic flow data in each traffic congestion state analysis result respectively includes non-motor vehicle running track data which is not violated in the traffic section to be scheduled, and on the basis of the above, the determining, by step S13, a traffic guidance scheduling policy corresponding to the non-motor vehicle traffic flow data to be scheduled according to the information about the correlation of the traffic route track between the non-motor vehicle traffic flow data and the non-motor vehicle traffic flow data to be scheduled in the traffic congestion state analysis result may include the following steps S131 to S135.

Step S131, obtaining a first traffic flow data congestion label of the traffic flow data of the non-motor vehicles to be dispatched according to the illegal non-motor vehicle running track data.

Step S132, according to the non-motor vehicle running track data which is not violated and included in each traffic jam state analysis result, respectively obtaining a second traffic flow data jam label of the non-motor vehicle traffic flow data in each traffic jam state analysis result.

Step S133, obtaining a tag similarity between the first traffic flow data congestion tag and the second traffic flow data congestion tag corresponding to each traffic congestion state analysis result. For example, the label similarity can be obtained by calculating label attribute values between different traffic flow data congestion labels, the label attribute values can be used for distinguishing different traffic flow data congestion labels, and the value of the label similarity can be 0-1, that is, the higher the value of the label similarity is, the higher the correlation between different traffic flow data congestion labels is, or the higher the influence degree between different traffic flow data congestion labels is, for example, a running track which is violated may influence a running track which is not violated.

Step S134, according to the label similarity corresponding to each traffic jam state analysis result, determining the correlation information of the traffic route track between the non-motor vehicle traffic flow data in each traffic jam state analysis result and the non-motor vehicle traffic flow data to be scheduled.

Step S135, when the number of the target traffic jam state analysis results is larger than a first preset number threshold and smaller than or equal to a second preset number threshold, determining the traffic guidance scheduling strategy corresponding to the motor vehicle traffic flow data in the target traffic jam state analysis results as the traffic guidance scheduling strategy corresponding to the to-be-scheduled non-motor vehicle traffic flow data; the target traffic jam state analysis result refers to a traffic jam state analysis result in which the correlation coefficient of the correlation information of the corresponding traffic route track is greater than or equal to the correlation coefficient threshold of the traffic route track. For example, the correlation coefficient may also take a value of 0 to 1.

In this way, by implementing the steps S131 to S135, when determining the traffic guidance scheduling policy corresponding to the traffic flow data of the non-motor vehicles to be scheduled, the influence of the violation behaviors of the non-motor vehicles on the driving states of the motor vehicles can be considered, so that the traffic guidance scheduling policy corresponding to the traffic flow data of the non-motor vehicles to be scheduled can comprehensively consider the driving states of the motor vehicles and the driving states of the non-motor vehicles.

For some possible embodiments, the number of the types of travel track variation of the violating non-motor vehicle travel track data is at least two, and based on this, the obtaining the first traffic flow data congestion label of the non-motor vehicle traffic flow data to be scheduled according to the violating non-motor vehicle travel track data described in step S131 includes: acquiring a running track data congestion label corresponding to each non-motor vehicle running track data in at least two non-motor vehicle running track data violating regulations; acquiring a first associated congestion label corresponding to the at least two violation non-motor vehicle running track data according to the running track data congestion label corresponding to each violation non-motor vehicle running track data; and determining the first associated congestion tag as the first traffic flow data congestion tag.

In some possible embodiments, the at least two traffic congestion status analysis results include an ith traffic congestion status analysis result, i being a positive integer less than or equal to the total number of the at least two traffic congestion status analysis results; the number of the travel track variation types of the non-violated non-motor vehicle travel track data included in the ith traffic congestion status analysis result is at least two, and based on this, the step S132 of obtaining the second traffic flow data congestion label of the non-motor vehicle traffic flow data in each traffic congestion status analysis result according to the non-violated non-motor vehicle travel track data included in each traffic congestion status analysis result respectively may include: acquiring a running track data congestion label corresponding to each non-motor vehicle running track data in at least two non-motor vehicle running track data which are not violated and included in the ith traffic congestion state analysis result; acquiring a second associated congestion label corresponding to the at least two non-motor vehicle running track data which are not violated according to the running track data congestion label corresponding to each non-motor vehicle running track data which are not violated; and determining the second associated congestion tag as a second traffic flow data congestion tag of the non-motor vehicle traffic flow data in the ith traffic congestion state analysis result.

For some possible embodiments, the number of the non-motor traffic flow data to be scheduled is at least two, and further, the method may further include the following steps S21-S24.

Step S21, when the number of the target traffic congestion state analysis results is less than or equal to the first preset number threshold, determining the traffic congestion state analysis result where the non-motor vehicle traffic flow data with the largest correlation coefficient corresponding to the correlation information of the traffic route track between each piece of non-motor vehicle traffic flow data to be scheduled is located as the to-be-determined congestion state analysis result combination corresponding to each piece of non-motor vehicle traffic flow data to be scheduled, respectively.

Step S22, determining the traffic guidance scheduling policy corresponding to the motor vehicle traffic flow data in the analysis result combination of the to-be-determined congestion state corresponding to each non-motor vehicle traffic flow data to be scheduled, respectively, as the to-be-determined traffic guidance scheduling policy corresponding to each non-motor vehicle traffic flow data to be scheduled.

Step S23, determining the accumulated use times of at least two scheduling strategies corresponding to the scheduling strategies to be determined according to the traffic guidance scheduling strategies to be determined corresponding to the traffic flow data of each non-motor vehicle to be scheduled; acquiring first use statistical information of the accumulated use times of the at least two scheduling strategies in traffic guidance scheduling strategies corresponding to motor vehicle traffic flow data of the at least two traffic jam state analysis results; and determining the accumulated use times of each non-motor vehicle traffic flow data to be scheduled aiming at a first target scheduling strategy of the scheduling strategies to be determined according to the first use statistical information.

Step S24, determining the scheduling strategy to be determined respectively with the accumulated use times of the first target scheduling strategy corresponding to each non-motor vehicle traffic flow data to be scheduled as the traffic guidance scheduling strategy corresponding to each non-motor vehicle traffic flow data to be scheduled; and the second use statistical information of the accumulated use times of the at least two scheduling strategies in the traffic guidance scheduling strategy corresponding to each non-motor vehicle traffic flow data to be scheduled is matched with the first use statistical information.

In this way, by implementing the steps S21-S24, the traffic guidance scheduling policy can be determined from the cumulative usage level of the scheduling policy, so as to ensure that the traffic guidance scheduling policy corresponding to each non-motor vehicle traffic flow data to be scheduled matches the actual road traffic condition.

On the basis of the above, the following contents described in steps S25 to S27 may be further included.

Step S25, when the number of the target traffic jam state analysis results is larger than the second preset number threshold, counting scheduling strategy matching information of the accumulated use times of at least two scheduling strategies of the scheduling strategies to be determined in the traffic guidance scheduling strategies corresponding to the motor vehicle driving track data of the target traffic jam state analysis results; and the accumulated use times of the at least two scheduling strategies are determined according to the traffic guidance scheduling strategy corresponding to the motor vehicle traffic flow data in the target traffic congestion state analysis result.

Step S26, determining, according to the traffic route track correlation information between the non-motor vehicle traffic flow data to be scheduled and the target traffic congestion state analysis result and the scheduling policy matching information, the cumulative number of times of use of the non-motor vehicle traffic flow data to be scheduled for a second target scheduling policy of the scheduling policy to be determined from the cumulative number of times of use of the at least two scheduling policies.

And step S27, determining the scheduling strategy to be determined with the accumulated use times of the second target scheduling strategy as a traffic guidance scheduling strategy corresponding to the traffic flow data of the non-motor vehicles to be scheduled.

In an actual implementation process, the traffic condition changes in real time, and in order to realize accurate prediction of subsequent traffic conditions as much as possible to determine a corresponding scheduling policy, the scheduling policy needs to be updated iteratively, and to achieve the purpose, the scheme may further include the following contents: determining a traffic guidance scheduling strategy corresponding to the motor vehicle traffic flow data in the traffic congestion state analysis result as a traffic guidance scheduling strategy corresponding to the traffic congestion state analysis result; determining the traffic jam state analysis result and the target jam state analysis result aiming at the traffic guidance scheduling strategy as a jam state analysis result combination in the traffic section to be scheduled; combining the congestion state analysis results with the corresponding traffic guidance scheduling strategy to determine the congestion state analysis results as a target traffic guidance scheduling strategy; adding the same non-motor vehicle road network label to the non-motor vehicle traffic flow data in the combination of the target traffic guidance scheduling strategy and the congestion state analysis result; and respectively mapping the target traffic guidance scheduling strategy with the non-motor vehicle road network label to a neural network model based on a genetic algorithm, a convolutional neural network model based on a space-time diagram and a neural network model based on forward feedback.

In the actual implementation process, the iteration times of strategy iterative updating of the target traffic guidance scheduling strategy through the neural network model based on the genetic algorithm are greater than the iteration times of strategy iterative updating of the target traffic guidance scheduling strategy through the convolutional neural network model based on the space-time diagram; the iteration times of strategy iterative updating of the target traffic guidance scheduling strategy through the convolutional neural network model based on the space-time diagram are greater than the iteration times of strategy iterative updating of the target traffic guidance scheduling strategy through the neural network model based on the forward feedback.

Further, the data mapping defect ratio of the neural network model based on the genetic algorithm for the target traffic guidance scheduling strategy is smaller than that of the convolutional neural network model based on the space-time diagram for the target traffic guidance scheduling strategy; the data mapping defect ratio of the convolutional neural network model based on the space-time diagram aiming at the target traffic guidance scheduling strategy is smaller than that of the neural network model based on the forward feedback aiming at the target traffic guidance scheduling strategy.

It is understood that the training process and the parameter tuning process of the different types of neural network models described above are prior art and will not be further described here.

On the basis of the above, the determining of the motor vehicle scheduling policy in the traffic section to be scheduled and the scheduling execution indication information corresponding to the motor vehicle scheduling policy according to the target congestion state analysis result for the traffic guidance scheduling policy and the traffic congestion state analysis result described in step S15 may include the following steps S151 and S152.

And step S151, determining the motor vehicle dispatching strategy in the traffic section to be dispatched according to the non-motor vehicle traffic flow data in the congestion state analysis result combination.

Step S152, according to the non-motor vehicle traffic flow data in the congestion state analysis result combination having the non-motor vehicle road network label, obtaining the target traffic guidance scheduling policy having the non-motor vehicle road network label from the neural network model based on the genetic algorithm, the convolutional neural network model based on the space-time diagram, or the neural network model based on the forward feedback, and determining the obtained target traffic guidance scheduling policy as scheduling execution instruction information corresponding to the motor vehicle scheduling policy.

In this way, the target traffic guidance scheduling policy with the non-motor vehicle road network labels can be determined based on different types of neural networks, so as to realize iterative updating of the traffic guidance scheduling policy, so as to realize accurate prediction of subsequent traffic conditions as much as possible to determine the corresponding scheduling policy.

In an alternative embodiment, the step S152 of obtaining the target traffic guidance scheduling policy with the non-motor vehicle road network label from the genetic algorithm-based neural network model, the space-time diagram-based convolutional neural network model or the feedforward-based neural network model according to the non-motor vehicle traffic flow data in the congestion status analysis result combination may include the following steps S1521-S1523.

Step S1521, generating, according to the non-motor vehicle road network label included in the non-motor vehicle traffic flow data in the congestion state analysis result combination, first policy iteration indication information for performing policy iteration update on the target traffic guidance scheduling policy in the neural network model based on the genetic algorithm, and when iteration convergence of the target traffic guidance scheduling policy is not realized from the neural network model based on the genetic algorithm according to the first policy iteration indication information, generating, according to the first policy iteration indication information, second policy iteration indication information for performing policy iteration update on the target traffic guidance scheduling policy in the convolutional neural network model based on the space-time diagram.

Step S1522, when the iterative convergence of the target traffic guidance scheduling policy is not realized from the convolutional neural network model based on the space-time diagram according to the second policy iteration indication information, generating third policy iteration indication information for performing policy iteration updating on the target traffic guidance scheduling policy in the neural network model based on the forward feedback according to the second policy iteration indication information.

Step S1523, according to the third policy iteration indication information, performing policy iteration update on the target traffic guidance scheduling policy from the neural network model based on forward feedback, and obtaining the target traffic guidance scheduling policy that satisfies a set convergence condition and is output by the neural network model based on forward feedback. For example, meeting the set convergence condition may be that the scheduling elapsed time corresponding to the target traffic guidance scheduling policy is less than the set elapsed time, or that the traffic flow change rate (road smoothness) corresponding to the target traffic guidance scheduling policy is less than the set change rate.

Therefore, the iteration of the scheduling strategy can be carried out through different neural network models, so that the obtained target traffic guidance scheduling strategy is ensured to meet the set convergence condition, the target traffic guidance scheduling strategy can be ensured to be matched with the traffic section to be scheduled as far as possible, and the traffic congestion dredging can be quickly and reliably realized.

In an alternative embodiment, on the basis of the above-mentioned steps S1521 to S1523, the target traffic guidance scheduling policy mapped by the space-time graph-based convolutional neural network model includes a local guidance scheduling policy and a global guidance scheduling policy, based on which the method further includes: configuring a local scheduling timeliness index for the local guiding scheduling strategy and configuring a global scheduling timeliness index for the global guiding scheduling strategy; the local scheduling timeliness index is different from the global scheduling timeliness index; when the local traffic scheduling time corresponding to the local guidance scheduling strategy does not meet the local scheduling aging index, clearing the local guidance scheduling strategy from the convolutional neural network model based on the space-time diagram at the local traffic scheduling time corresponding to the local guidance scheduling strategy, and when the global traffic scheduling time corresponding to the global guidance scheduling strategy does not meet the global scheduling aging index, clearing the global guidance scheduling strategy from the convolutional neural network model based on the space-time diagram at the global traffic scheduling time corresponding to the global guidance scheduling strategy.

In an alternative embodiment, after the step of determining the motor vehicle dispatching strategy in the traffic section to be dispatched and the dispatching execution indication information corresponding to the motor vehicle dispatching strategy described in the step S15, the method may further include the following step S16. And step S16, generating a route guidance prompt aiming at the target motor vehicle in the traffic road section to be dispatched based on the motor vehicle dispatching strategy and the dispatching execution indication information, and issuing the route guidance prompt to the target motor vehicle. By means of the design, the target motor vehicles in the traffic section to be dispatched are subjected to line guidance, and the traffic jam dredging of the traffic section to be dispatched can be achieved as quickly as possible, so that the time consumed by traffic jam dredging is reduced, and the motor vehicles and the non-motor vehicles are prevented from being scratched or other traffic accidents in the dredging process.

Next, for the data analysis method based on big data and artificial intelligence, an exemplary data analysis apparatus based on big data and artificial intelligence is further provided in the embodiment of the present invention, as shown in fig. 2, the data analysis apparatus 200 based on big data and artificial intelligence may include the following functional modules.

The data acquiring module 210 is configured to acquire motor vehicle traffic flow data and non-motor vehicle traffic flow data in a traffic road segment to be scheduled.

The state analysis module 220 is configured to perform congestion state analysis on the motor vehicle traffic flow data and the non-motor vehicle traffic flow data in the traffic section to be scheduled based on road network interference information of the traffic flow data between the motor vehicle traffic flow data and the non-motor vehicle traffic flow data in the traffic section to be scheduled, so as to obtain a traffic congestion state analysis result.

The policy determining module 230 is configured to determine non-motor vehicle traffic flow data with a congestion tag in a congestion state analysis process as non-motor vehicle traffic flow data to be scheduled, and determine a traffic guidance scheduling policy corresponding to the non-motor vehicle traffic flow data to be scheduled according to traffic route track correlation information between the non-motor vehicle traffic flow data and the non-motor vehicle traffic flow data to be scheduled in the traffic congestion state analysis result.

And a congestion analysis module 240, configured to perform congestion state analysis on the traffic guidance scheduling policy corresponding to the to-be-scheduled non-motor vehicle traffic flow data and the to-be-scheduled non-motor vehicle traffic flow data, so as to obtain a target congestion state analysis result for the traffic guidance scheduling policy.

And the scheduling indication module 250 is configured to determine a motor vehicle scheduling policy in the traffic section to be scheduled and scheduling execution indication information corresponding to the motor vehicle scheduling policy according to the target congestion state analysis result for the traffic guidance scheduling policy and the traffic congestion state analysis result.

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 analysis system based on big data and artificial intelligence, please refer to fig. 3 in combination, and the data analysis system 30 based on big data and artificial intelligence may include the cloud computing server 10 and the vehicle-mounted control device 20. Wherein the cloud computing server 10 and the in-vehicle control device 20 communicate to implement the above method, further, the functionality of the big data and artificial intelligence based data analysis system 30 is described as follows.

Exemplarily, a data analysis system based on big data and artificial intelligence comprises a cloud computing server and an on-board control device, which are in communication with each other, wherein the on-board control device is disposed in a motor vehicle located in a traffic section to be scheduled, the on-board control device is in one-to-one correspondence with the motor vehicle, and further, the cloud computing server is configured to:

determining a motor vehicle scheduling strategy in the traffic section to be scheduled and scheduling execution indication information corresponding to the motor vehicle scheduling strategy according to the target congestion state analysis result and the traffic congestion state analysis result aiming at the traffic guidance scheduling strategy;

and generating a route guidance prompt aiming at the target motor vehicle in the traffic road section to be dispatched based on the motor vehicle dispatching strategy and the dispatching execution indication information, and issuing the route guidance prompt to the vehicle-mounted controller corresponding to the target motor vehicle.

Further, referring to fig. 4 in combination, the cloud computing server 10 may include a processing engine 110, a network module 120, and a memory 130, wherein the processing engine 110 and the memory 130 communicate 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 is to be understood that the configuration shown in fig. 4 is merely illustrative, and that cloud computing server 10 may 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.

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) when the congestion state analysis is carried out, the road network interference information is taken into account, the state difference of motor vehicles and non-motor vehicles in the driving process can be distinguished, so that the global congestion state analysis is realized, (2) the traffic scheduling of the motor vehicles can be indicated in reverse by determining the target congestion state analysis result, so that the efficiency of the traffic scheduling is improved as much as possible, and simultaneously secondary congestion and potential traffic accident hidden dangers caused by illegal driving behaviors of the non-motor vehicles are avoided, (3) the difficulty of the traffic scheduling of the motor vehicles is lower than that of the traffic scheduling of the non-motor vehicles, so that the difficulty and uncertainty of the scheduling can be reduced by carrying out the traffic scheduling on the motor vehicles, the traffic flow data of the non-motor vehicles can be taken into account, and the congestion phenomenon of the non-motor vehicles when the motor vehicles are scheduled is avoided, thereby avoiding causing further traffic congestion or traffic accidents.

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 analysis method based on big data and artificial intelligence is characterized by comprising the following steps:

acquiring motor vehicle traffic flow data and non-motor vehicle traffic flow data in the traffic road section to be scheduled;

2. The method of claim 1, wherein obtaining automotive traffic flow data and non-automotive traffic flow data in a traffic segment to be scheduled comprises:

acquiring at least two groups of motor vehicle driving track data and at least two groups of non-motor vehicle driving track data in the traffic road section to be scheduled;

acquiring the running track change information of the motor vehicle running track data and the road wrong-way data of the motor vehicle running track data between the at least two groups of motor vehicle running track data, and acquiring the running track change information of the non-motor vehicle running track data and the road wrong-way data of the non-motor vehicle running track data between the at least two groups of non-motor vehicle running track data;

fusing the at least two groups of motor vehicle running track data according to the running track change information of the motor vehicle running track data and the road traffic error data of the motor vehicle running track data to obtain motor vehicle traffic flow data in the traffic section to be scheduled; the group of motor vehicle traffic flow data comprises at least one group of motor vehicle driving track data;

fusing the at least two groups of non-motor vehicle running track data according to the running track change information of the non-motor vehicle running track data and the road traffic-crossing data of the non-motor vehicle running track data to obtain non-motor vehicle traffic flow data in the traffic section to be scheduled; the set of non-motor vehicle traffic flow data includes at least one set of non-motor vehicle travel trajectory data.

3. The method as claimed in claim 2, wherein the analyzing congestion status of the motor vehicle traffic flow data and the non-motor vehicle traffic flow data in the traffic section to be scheduled based on the road network interference information of the traffic flow data between the motor vehicle traffic flow data and the non-motor vehicle traffic flow data in the traffic section to be scheduled to obtain the traffic congestion status analysis result comprises:

determining the non-motor vehicle traffic flow data in the traffic section to be scheduled as non-motor vehicle traffic flow data analyzed according to the congestion state, and determining the motor vehicle traffic flow data in the traffic section to be scheduled as motor vehicle traffic flow data analyzed according to the congestion state; the non-motor vehicle running track data in the non-motor vehicle traffic flow data for the congestion state analysis is determined from running track data corresponding to the monitoring video of the traffic section to be scheduled;

acquiring motor vehicle running track data in running track data corresponding to the monitoring video; determining running track change information of running track data between motor vehicle running track data in running track data corresponding to the monitoring video and motor vehicle running track data in motor vehicle traffic flow data analyzed for the congestion state as road network interference information of the traffic flow data between non-motor vehicle traffic flow data analyzed for the congestion state and motor vehicle traffic flow data analyzed for the congestion state;

and when the road network interference index of the road network interference information of the traffic flow data is greater than or equal to a road network interference index threshold value, performing congestion state analysis on the non-motor vehicle traffic flow data aiming at the congestion state analysis and the motor vehicle traffic flow data aiming at the congestion state analysis to obtain a traffic congestion state analysis result.

4. The method of claim 1 wherein the non-motor traffic flow data to be scheduled comprises non-motor travel trajectory data for violations in the traffic segment to be scheduled; the number of the traffic jam state analysis results is at least two; the non-motor vehicle traffic flow data in each traffic jam state analysis result respectively comprise non-motor vehicle running track data which are not violated in the traffic section to be dispatched; the determining a traffic guidance scheduling strategy corresponding to the non-motor vehicle traffic flow data to be scheduled according to the traffic route track correlation information between the non-motor vehicle traffic flow data and the non-motor vehicle traffic flow data to be scheduled in the traffic congestion state analysis result comprises the following steps:

acquiring a first traffic flow data congestion label of the traffic flow data of the non-motor vehicles to be scheduled according to the illegal non-motor vehicle running track data;

respectively acquiring a second traffic flow data congestion label of non-motor vehicle traffic flow data in each traffic congestion state analysis result according to non-violation non-motor vehicle running track data in each traffic congestion state analysis result;

acquiring label similarity between the first traffic congestion data congestion label and a second traffic congestion data congestion label corresponding to each traffic congestion state analysis result;

determining the correlation information of the traffic route track between the non-motor vehicle traffic flow data in each traffic jam state analysis result and the non-motor vehicle traffic flow data to be scheduled according to the label similarity corresponding to each traffic jam state analysis result;

when the number of the target traffic jam state analysis results is larger than a first preset number threshold and smaller than or equal to a second preset number threshold, determining a traffic guidance scheduling strategy corresponding to motor vehicle traffic flow data in the target traffic jam state analysis results as a traffic guidance scheduling strategy corresponding to the to-be-scheduled non-motor vehicle traffic flow data; the target traffic jam state analysis result refers to a traffic jam state analysis result of which the correlation coefficient of the correlation information of the corresponding traffic route track is greater than or equal to the correlation coefficient threshold of the traffic route track;

the number of the types of the running track change of the illegal non-motor vehicle running track data is at least two; the step of obtaining a first traffic flow data congestion label of the traffic flow data of the non-motor vehicles to be scheduled according to the illegal non-motor vehicle running track data comprises the following steps:

acquiring a running track data congestion label corresponding to each non-motor vehicle running track data in at least two non-motor vehicle running track data violating regulations;

acquiring a first associated congestion label corresponding to the at least two violation non-motor vehicle running track data according to the running track data congestion label corresponding to each violation non-motor vehicle running track data; determining the first associated congestion tag as the first traffic flow data congestion tag;

the at least two traffic congestion state analysis results comprise an ith traffic congestion state analysis result, wherein i is a positive integer less than or equal to the total number of the at least two traffic congestion state analysis results; the number of the running track change types of the non-motor vehicle running track data which are not violated and are included in the ith traffic congestion state analysis result is at least two; the step of respectively obtaining a second traffic flow data congestion label of non-motor vehicle traffic flow data in each traffic congestion state analysis result according to non-violation non-motor vehicle running track data included in each traffic congestion state analysis result comprises the following steps:

acquiring a running track data congestion label corresponding to each non-motor vehicle running track data in at least two non-motor vehicle running track data which are not violated and included in the ith traffic congestion state analysis result;

acquiring a second associated congestion label corresponding to the at least two non-motor vehicle running track data which are not violated according to the running track data congestion label corresponding to each non-motor vehicle running track data which are not violated;

and determining the second associated congestion tag as a second traffic flow data congestion tag of the non-motor vehicle traffic flow data in the ith traffic congestion state analysis result.

5. The method of claim 4, wherein the number of non-motor traffic flow data to be scheduled is at least two; the method further comprises the following steps:

when the number of the target traffic jam state analysis results is smaller than or equal to the first preset number threshold, respectively determining the traffic jam state analysis result where the non-motor vehicle traffic flow data with the largest correlation coefficient corresponding to the correlation information of the traffic route track between each piece of non-motor vehicle traffic flow data to be scheduled is located as a to-be-determined traffic jam state analysis result combination corresponding to each piece of non-motor vehicle traffic flow data to be scheduled;

respectively determining traffic guidance scheduling strategies corresponding to the motor vehicle traffic flow data in the analysis result combination of the to-be-determined congestion states corresponding to each non-motor vehicle traffic flow data to be scheduled as the to-be-determined traffic guidance scheduling strategies corresponding to each non-motor vehicle traffic flow data to be scheduled;

determining the accumulated use times of at least two scheduling strategies corresponding to the to-be-scheduled scheduling strategies according to the to-be-scheduled traffic guidance scheduling strategies corresponding to the traffic flow data of each to-be-scheduled non-motor vehicle;

acquiring first use statistical information of the accumulated use times of the at least two scheduling strategies in traffic guidance scheduling strategies corresponding to motor vehicle traffic flow data of the at least two traffic jam state analysis results;

determining the accumulated use times of each non-motor vehicle traffic flow data to be scheduled aiming at a first target scheduling strategy of the scheduling strategies to be determined according to the first use statistical information;

determining the dispatching strategies to be determined respectively with the accumulated use times of the first target dispatching strategies corresponding to the non-motor vehicle traffic flow data to be dispatched as traffic guidance dispatching strategies corresponding to the non-motor vehicle traffic flow data to be dispatched; and the second use statistical information of the accumulated use times of the at least two scheduling strategies in the traffic guidance scheduling strategy corresponding to each non-motor vehicle traffic flow data to be scheduled is matched with the first use statistical information.

6. The method of claim 4, further comprising:

when the number of the target traffic jam state analysis results is larger than the second preset number threshold, counting scheduling strategy matching information of the accumulated use times of at least two scheduling strategies of the scheduling strategies to be determined in the traffic guidance scheduling strategies corresponding to the motor vehicle running track data of the target traffic jam state analysis results; the accumulated use times of the at least two scheduling strategies are determined according to the traffic guidance scheduling strategy corresponding to the motor vehicle traffic flow data in the target traffic congestion state analysis result;

determining the cumulative use times of the traffic flow data of the non-motor vehicles to be scheduled aiming at a second target scheduling strategy of the scheduling strategies to be determined from the cumulative use times of the at least two scheduling strategies according to the correlation information of the traffic line track between the traffic flow data of the non-motor vehicles to be scheduled and the analysis result of the target traffic jam state and the matching information of the scheduling strategies;

and determining the scheduling strategy to be determined with the accumulated use times of the second target scheduling strategy as a traffic guidance scheduling strategy corresponding to the traffic flow data of the non-motor vehicles to be scheduled.

7. The method of claim 1, further comprising:

determining a traffic guidance scheduling strategy corresponding to the motor vehicle traffic flow data in the traffic congestion state analysis result as a traffic guidance scheduling strategy corresponding to the traffic congestion state analysis result; determining the traffic jam state analysis result and the target jam state analysis result aiming at the traffic guidance scheduling strategy as a jam state analysis result combination in the traffic section to be scheduled; combining the congestion state analysis results with the corresponding traffic guidance scheduling strategy to determine the congestion state analysis results as a target traffic guidance scheduling strategy; adding the same non-motor vehicle road network label to the non-motor vehicle traffic flow data in the combination of the target traffic guidance scheduling strategy and the congestion state analysis result; mapping the target traffic guidance scheduling strategy with the non-motor vehicle road network label to a neural network model based on a genetic algorithm, a convolutional neural network model based on a space-time diagram and a neural network model based on forward feedback respectively;

the iteration times of strategy iterative updating of the target traffic guidance scheduling strategy through the neural network model based on the genetic algorithm are greater than the iteration times of strategy iterative updating of the target traffic guidance scheduling strategy through the convolutional neural network model based on the space-time diagram; the iteration times of strategy iterative updating of the target traffic guidance scheduling strategy through the convolutional neural network model based on the space-time diagram are greater than the iteration times of strategy iterative updating of the target traffic guidance scheduling strategy through the neural network model based on the forward feedback;

and the number of the first and second electrodes,

the data mapping defect ratio of the neural network model based on the genetic algorithm aiming at the target traffic guidance scheduling strategy is smaller than that of the convolutional neural network model based on the space-time diagram aiming at the target traffic guidance scheduling strategy; the data mapping defect ratio of the convolutional neural network model based on the space-time diagram aiming at the target traffic guidance scheduling strategy is smaller than that of the neural network model based on the forward feedback aiming at the target traffic guidance scheduling strategy;

the determining the motor vehicle scheduling policy in the traffic section to be scheduled and the scheduling execution indication information corresponding to the motor vehicle scheduling policy according to the target congestion state analysis result and the traffic congestion state analysis result aiming at the traffic guidance scheduling policy comprises:

determining the motor vehicle dispatching strategy in the traffic section to be dispatched according to the non-motor vehicle traffic flow data in the congestion state analysis result combination;

according to the non-motor vehicle traffic flow data in the congestion state analysis result combination, the target traffic guidance scheduling strategy with the non-motor vehicle road network label is obtained from the neural network model based on the genetic algorithm, the convolutional neural network model based on the space-time diagram or the neural network model based on the forward feedback, and the obtained target traffic guidance scheduling strategy is determined as scheduling execution indication information corresponding to the motor vehicle scheduling strategy.

8. A cloud computing server, comprising big data and artificial intelligence based data analysis means, functional modules in the data analysis means implementing the method of any one of claims 1 to 7 when run.

9. A cloud computing server 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 1-7.

10. A computer storage medium, having stored thereon a computer program which, when executed, implements the method of any one of claims 1-7.