CN111476192B

CN111476192B - Intercepted image synthesis method based on intelligent traffic and big data cloud server

Info

Publication number: CN111476192B
Application number: CN202010292578.4A
Authority: CN
Inventors: 陈建
Original assignee: Baweitong Technology Co ltd
Current assignee: BAWEITONG TECHNOLOGY Co.,Ltd.
Priority date: 2020-04-15
Filing date: 2020-04-15
Publication date: 2021-03-12
Anticipated expiration: 2040-04-15
Also published as: CN112270239A; CN112270243A; CN111476192A

Abstract

The embodiment of the disclosure provides an intercepted image synthesis method based on intelligent traffic and a big data cloud server, wherein a traffic linkage behavior node generating traffic linkage behavior is determined from each target synthetic image after image synthesis of a corresponding intercepted image set through related traffic behaviors related to traffic abnormal behaviors of a target object in a first traffic behavior sequence and a second traffic behavior sequence based on a traffic linkage monitoring relation, the linkage process of the node is traced, and a tracing animation special effect of each target synthetic image is generated according to a tracing process expression vector. Therefore, the user can conveniently and uniformly check the tracing process condition of the linked abnormal traffic behaviors without repeatedly switching the pictures, and the monitoring and troubleshooting efficiency is improved.

Description

Intercepted image synthesis method based on intelligent traffic and big data cloud server

Technical Field

The disclosure relates to the technical field of Internet of things and artificial intelligence, in particular to an intercepted image synthesis method based on intelligent traffic and a big data cloud server.

Background

In the traffic monitoring process of the urban intelligent traffic system, a large number of linkage control operations are usually involved, for example, after a certain gate camera monitors abnormal traffic behaviors of a target object, the position of the abnormal event is reported to the server immediately, so that the server controls other gate cameras in related traffic areas to perform linkage monitoring according to the position of the abnormal traffic behaviors, and comprehensive judgment is performed by combining traffic information in the whole linkage process to determine road conditions of some complex events. Therefore, how to facilitate a user to uniformly check the tracing process condition of the linked abnormal traffic behaviors without repeatedly switching pictures is a technical problem to be solved urgently in the field.

Disclosure of Invention

In order to overcome at least the above deficiencies in the prior art, the present disclosure aims to provide an intercepted image synthesis method and a big data cloud server based on smart traffic, which can facilitate a user to uniformly check the tracing process condition of linked traffic abnormal behaviors without repeatedly switching pictures, thereby improving the monitoring and troubleshooting efficiency.

In a first aspect, the present disclosure provides an intercepted image synthesis method based on smart traffic, which is applied to a big data cloud server, where the big data cloud server is in communication connection with smart traffic terminals in a plurality of smart traffic management areas, and the method includes:

acquiring a first intercepted image set corresponding to a first traffic behavior sequence and a second intercepted image set corresponding to a second traffic behavior sequence, wherein the first traffic behavior sequence and the second traffic behavior sequence are respectively a first traffic image data stream and a second traffic image data stream which have a traffic linkage monitoring relationship, and the traffic linkage monitoring relationship is used for representing a linkage monitoring action generated aiming at traffic abnormal behaviors of a target object;

according to the first traffic behavior sequence and the second traffic behavior sequence, the associated traffic behavior related to the abnormal traffic behavior of the target object is established with the associated relation between the associated traffic behavior and the target object, the image synthesis operation between the first intercepted image set and the second intercepted image set is subjected to synthesis control according to the associated relation between the associated traffic behavior and the target object, and each target synthesis image subjected to synthesis control is obtained;

determining traffic linkage behavior nodes generating traffic linkage behaviors from each target synthetic image, acquiring linkage track information among the traffic linkage behavior nodes according to each target synthetic image, and generating a traffic linkage behavior node linkage sequence according to the linkage track information among the traffic linkage behavior nodes, wherein adjacent traffic linkage behavior nodes in the traffic linkage behavior node linkage sequence are traversed by at least one traffic linkage behavior;

obtaining the characteristic expression vector of the traffic linkage behavior node, tracing the linkage process of the traffic linkage behavior node in the traffic linkage behavior node linkage sequence according to the characteristic expression vector of the traffic linkage behavior node to obtain a tracing process expression vector corresponding to the traffic linkage behavior node, and generating a tracing animation special effect of each target synthetic image according to the tracing process expression vector of the traffic linkage behavior node, wherein the tracing process expression vector is used for expressing the association of the corresponding traffic linkage behavior node and other traffic linkage behavior nodes in the traffic linkage process.

In a possible implementation manner of the first aspect, the step of performing synthesis control on image synthesis operations between the first captured image set and the second captured image set according to an association relationship between the associated traffic behavior and the target object, and obtaining each target synthesis image after synthesis control includes:

adding the traffic behavior prediction areas corresponding to the first intercepted image set and the second intercepted image set to an image synthesis scene;

simulating related traffic behaviors corresponding to the traffic behavior prediction region in the first intercepted image set and the second intercepted image set according to traffic behavior information of the traffic behavior prediction region corresponding to the requested synthesis node in the image synthesis scene to obtain synthesis strategy information of a synthesis editing interface of each related traffic behavior in the image synthesis scene, and respectively extracting synthesis control results of the corresponding related traffic behaviors under each image synthesis service from the synthesis strategy information corresponding to each related traffic behavior;

and according to the incidence relation between the associated traffic behaviors and the target object aiming at each image synthesis service, performing synthesis control on a synthesis control result of the corresponding associated traffic behaviors under the corresponding image synthesis service, so that the synthesis control result under the image synthesis service after the synthesis control is completed can complete the synthesis control in the image synthesis operation between the first intercepted image set and the second intercepted image set, and obtaining each target synthesis image after the synthesis control.

In a possible implementation manner of the first aspect, the step of simulating, in the image synthesis scene, the relevant traffic behaviors corresponding to the traffic behavior prediction area in the first and second captured image sets according to the traffic behavior information of the traffic behavior prediction area corresponding to the requested synthesis node to obtain synthesis policy information of a synthesis editing interface of each relevant traffic behavior in the image synthesis scene includes:

according to the item editing information of the intercepted image set aiming at the synthesis control item of the traffic behavior prediction area, establishing behavior associated information of the traffic behavior prediction area, wherein the behavior associated information is used for reflecting the behavior associated information when synthesis control is carried out in the traffic behavior prediction area;

behavior crawling is carried out on behavior associated information of a synthesis control item of the traffic behavior prediction area according to a behavior crawling model corresponding to the requested synthesis node, and traffic behavior information of the traffic behavior prediction area corresponding to the requested synthesis node is obtained;

and determining a synthetic editing process curve of the traffic behavior prediction area according to the traffic behavior information of the traffic behavior prediction area, and simulating each related traffic behavior according to the synthetic editing process curve to obtain synthetic strategy information of a synthetic editing interface of each related traffic behavior in an image synthetic scene.

In a possible implementation manner of the first aspect, the step of establishing behavior association information of the traffic behavior prediction area according to item edit information of a composite control item of the intercepted image set for the traffic behavior prediction area includes:

acquiring a first project editing operation vector of each project editing node in the project editing information, wherein the first project editing operation vector is used for representing an image synthesis characteristic vector interval of the project editing node;

performing feature recognition on the first project editing operation vector to obtain first behavior associated information and synthetic control feature information corresponding to the first behavior associated information;

acquiring first project queue simulation information and project editing information of the project editing node, and extracting a project queue calling interface of the first project queue simulation information, wherein the project queue calling interface of the first project queue simulation information comprises a specified project queue calling code;

acquiring appointed item queue calling codes of preset historical item editing nodes, and adjusting the appointed item queue calling codes of the first item queue simulation information according to the appointed item queue calling codes to enable a calling script between the appointed item queue calling codes in the first item queue simulation information to be matched with a calling script between the appointed item queue calling codes in the preset historical item editing nodes;

obtaining an item queue calling interface of second item queue simulation information according to each adjusted designated item queue calling code in the first item queue simulation information, and generating second item queue simulation information according to the item queue calling interface of the second item queue simulation information;

searching and obtaining synthesis control characteristic information matched with the project editing information and first behavior associated information corresponding to the synthesis control characteristic information according to project queue calling interfaces of the project editing information and the second project queue simulation information, and adjusting the first behavior associated information corresponding to the synthesis control characteristic information according to the project queue calling interface of the second project queue simulation information to obtain second behavior associated information;

and mapping and associating the second behavior associated information with the second item queue simulation information to establish behavior associated information of the traffic behavior prediction area.

In a possible implementation manner of the first aspect, the step of operating each relevant traffic behavior according to the composite editing process curve to obtain composite strategy information of a composite editing interface of each relevant traffic behavior in an image composite scene includes:

calling the synthesis editing interface to access a corresponding image synthesis editing thread according to the synthesis editing process curve, and simulating each related traffic behavior through the image synthesis editing thread;

determining a composite control decision node corresponding to the composite control attribute object of each relevant traffic behavior according to composite control attribute objects of different composite control types called for each relevant traffic behavior, wherein the composite control attribute objects of different composite control types respectively correspond to different composite control decision nodes;

determining object data of different composite control attribute objects of each related traffic behavior, and acquiring a first image matching object set of at least two same composite control image matching objects included in the corresponding multiple composite control image matching objects and at least one second image matching object set of which the calling times are greater than preset times in the remaining composite control image matching objects according to the object data;

generating a synthesis control thread for determining synthesis strategy information of the synthesis editing interface according to an image matching object set which is selected from the first image matching object set and has the calling times larger than a set time and serves as a target image matching object set and the at least one second image matching object set, wherein the synthesis control thread comprises the target image matching object set and the at least one second image matching object set;

and respectively determining the synthesis strategy information of each relevant traffic behavior in the image synthesis scene according to the synthesis control thread.

In a possible implementation manner of the first aspect, the step of determining, according to the composition control thread, composition policy information of a composition editing interface of each relevant traffic behavior in the image composition scene respectively includes:

determining a synthesis control attribute object corresponding to each target synthesis control image matching object in the target image matching object set and the at least one second image matching object set according to the synthesis control thread;

and determining the synthesis strategy information of each relevant traffic behavior synthesis editing interface in the image synthesis scene according to the synthesis control attribute object corresponding to each target synthesis control image matching object.

In a possible implementation manner of the first aspect, the step of obtaining the feature expression vector of the traffic linkage behavior node includes:

acquiring real-time traffic state data of the traffic linkage behavior nodes from each target synthetic image, and performing semantic analysis on the real-time traffic state data of the traffic linkage behavior nodes to obtain feature expression vectors for describing semantics of the real-time traffic state data, wherein the feature expression vectors are the same in length;

and averaging all the characteristic representation vectors of the same traffic linkage behavior node to obtain an average vector as the characteristic representation vector of the corresponding traffic linkage behavior node.

In a possible implementation manner of the first aspect, the step of tracing the linkage process of the traffic linkage behavior node in the traffic linkage behavior node linkage sequence to obtain a tracing process expression vector corresponding to the traffic linkage behavior node includes:

tracing the linkage process of the traffic linkage behavior nodes in the traffic linkage behavior node linkage sequence according to the characteristic expression vector of the traffic linkage behavior nodes through a linkage process analysis model to obtain a tracing process expression vector corresponding to the traffic linkage behavior nodes;

the method comprises the following steps of tracing the linkage process of the traffic linkage behavior nodes in the traffic linkage behavior node linkage sequence through the linkage process analysis model according to the characteristic expression vectors of the traffic linkage behavior nodes, and obtaining the tracing process expression vectors corresponding to the traffic linkage behavior nodes, and further comprises the following steps of:

acquiring coded representations of the traffic linkage behavior nodes, and arranging the coded representations of the traffic linkage behavior nodes according to the arrangement sequence of the traffic linkage behavior nodes in the traffic linkage behavior node linkage sequence to obtain a coded representation sequence corresponding to the traffic linkage behavior nodes;

and inputting the linkage process analysis model through the coding representation sequence so that the linkage process analysis model traces the linkage process of the traffic linkage behavior nodes in the traffic linkage behavior node linkage sequence.

In a possible implementation manner of the first aspect, the step of obtaining linkage track information between the traffic linkage behavior nodes according to each target composite image includes:

determining the direction of a behavior vector between every two traffic linkage behavior nodes and the corresponding trend strength of the direction of the behavior vector according to each target synthetic image;

generating decision nodes of a decision tree according to the traffic linkage behavior nodes, and generating decision vectors among the decision nodes according to the behavior vector direction among the traffic linkage behavior nodes;

calculating the weight corresponding to a first decision vector pointing to other traffic linkage behavior nodes from the same traffic linkage behavior node in the decision tree, wherein the decision node in the decision tree represents the traffic linkage behavior node, the decision vector in the decision tree represents the direction of the behavior vector between the two traffic linkage behavior nodes, and the weight of the decision vector is obtained according to the magnitude of the trend strength corresponding to the direction of the behavior vector;

generating a traffic linkage behavior node linkage sequence according to the linkage track information among the traffic linkage behavior nodes, wherein the sequence comprises the following steps:

selecting a part of decision nodes from the decision tree as initial nodes of a traffic linkage behavior node linkage sequence;

and according to the weight of each decision vector in the decision tree, starting wandering from each initial node in the decision tree, and generating a traffic linkage behavior node linkage sequence according to nodes passing through a wandering path, wherein the length of the traffic linkage behavior node linkage sequence is a preset length, and adjacent nodes of the same node are different in the wandering path.

In a second aspect, the disclosed embodiment further provides a captured image synthesizing apparatus based on smart traffic, which is applied to a big data cloud server, where the big data cloud server is in communication connection with smart traffic terminals in a plurality of smart traffic management areas, and the apparatus includes:

the system comprises an acquisition module, a processing module and a display module, wherein the acquisition module is used for acquiring a first intercepted image set corresponding to a first traffic behavior sequence and a second intercepted image set corresponding to a second traffic behavior sequence, the first traffic behavior sequence and the second traffic behavior sequence are respectively a traffic behavior sequence corresponding to a first traffic image data stream and a second traffic image data stream with a traffic linkage monitoring relationship, and the traffic linkage monitoring relationship is used for representing a linkage monitoring action generated aiming at the abnormal traffic behavior of a target object;

the synthesis control module is used for establishing an association relationship between the associated traffic behavior and the target object according to the associated traffic behavior related to the traffic abnormal behavior of the target object in the first traffic behavior sequence and the second traffic behavior sequence, performing synthesis control on image synthesis operation between the first intercepted image set and the second intercepted image set according to the association relationship between the associated traffic behavior and the target object, and obtaining each target synthesis image after synthesis control;

the first generation module is used for determining traffic linkage behavior nodes for generating traffic linkage behaviors from each target synthetic image, acquiring linkage track information among the traffic linkage behavior nodes according to each target synthetic image, and generating a traffic linkage behavior node linkage sequence according to the linkage track information among the traffic linkage behavior nodes, wherein adjacent traffic linkage behavior nodes in the traffic linkage behavior node linkage sequence are traversed by at least one traffic linkage behavior;

and the second generation module is used for acquiring the characteristic expression vector of the traffic linkage behavior node, tracing the linkage process of the traffic linkage behavior node in the traffic linkage behavior node linkage sequence according to the characteristic expression vector of the traffic linkage behavior node to obtain a tracing process expression vector corresponding to the traffic linkage behavior node, and generating a tracing animation special effect of each target synthetic image according to the tracing process expression vector of the traffic linkage behavior node, wherein the tracing process expression vector is used for expressing the association of the corresponding traffic linkage behavior node and other traffic linkage behavior nodes in the traffic linkage process.

In a third aspect, the disclosed embodiment further provides an intercepted image synthesis system based on smart traffic, where the intercepted image synthesis system based on smart traffic includes a big data cloud server and smart traffic terminals in a plurality of smart traffic management areas, and the smart traffic terminals are in communication connection with the big data cloud server;

the big data cloud server is used for acquiring a first intercepted image set corresponding to a first traffic behavior sequence and a second intercepted image set corresponding to a second traffic behavior sequence, wherein the first traffic behavior sequence and the second traffic behavior sequence are respectively a first traffic image data stream and a second traffic image data stream which have a traffic linkage monitoring relationship, and the traffic linkage monitoring relationship is used for representing a linkage monitoring action generated aiming at a traffic abnormal behavior of a target object;

the big data cloud server is used for establishing an association relationship between the associated traffic behavior and the target object according to the associated traffic behavior related to the traffic abnormal behavior of the target object in the first traffic behavior sequence and the second traffic behavior sequence, performing synthesis control on image synthesis operation between the first intercepted image set and the second intercepted image set according to the association relationship between the associated traffic behavior and the target object, and obtaining each target synthesis image after synthesis control;

the big data cloud server is used for determining traffic linkage behavior nodes generating traffic linkage behaviors from each target synthetic image, acquiring linkage track information among the traffic linkage behavior nodes according to each target synthetic image, and generating a traffic linkage behavior node linkage sequence according to the linkage track information among the traffic linkage behavior nodes, wherein adjacent traffic linkage behavior nodes in the traffic linkage behavior node linkage sequence are traversed by at least one traffic linkage behavior;

the big data cloud server is used for obtaining the characteristic expression vector of the traffic linkage behavior node, tracing the linkage process of the traffic linkage behavior node in the traffic linkage behavior node linkage sequence according to the characteristic expression vector of the traffic linkage behavior node to obtain a tracing process expression vector corresponding to the traffic linkage behavior node, and generating a tracing animation special effect of each target synthetic image according to the tracing process expression vector of the traffic linkage behavior node, wherein the tracing process expression vector is used for expressing the association of the corresponding traffic linkage behavior node and other traffic linkage behavior nodes in the traffic linkage process.

In a fourth aspect, the disclosed embodiment further provides a big data cloud server, where the big data cloud server includes a processor, a machine-readable storage medium, and a network interface, where the machine-readable storage medium, the network interface, and the processor are connected through a bus system, the network interface is configured to be communicatively connected with at least one smart traffic terminal, the machine-readable storage medium is configured to store a program, an instruction, or a code, and the processor is configured to execute the program, the instruction, or the code in the machine-readable storage medium to perform the intercepted image synthesis method based on smart traffic in any one of the possible designs of the first aspect or the first aspect.

In a fifth aspect, an embodiment of the present disclosure provides a computer-readable storage medium, in which instructions are stored, and when executed, cause a computer to execute the method for synthesizing a cut image based on intelligent traffic in the first aspect or any one of the possible designs of the first aspect.

According to any one of the aspects, the method determines traffic linkage behavior nodes generating traffic linkage behaviors from each target synthetic image after image synthesis of a corresponding intercepted image set based on the associated traffic behaviors related to the abnormal traffic behaviors of the target object in the first traffic behavior sequence and the second traffic behavior sequence with the traffic linkage monitoring relationship, traces back the linkage process of the nodes, and generates the tracing back animation special effect of each target synthetic image according to the tracing back process expression vector. Therefore, the user can conveniently and uniformly check the tracing process condition of the linked abnormal traffic behaviors without repeatedly switching the pictures, and the monitoring and troubleshooting efficiency is improved.

Drawings

To more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present disclosure and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings may be obtained from the drawings without inventive effort.

Fig. 1 is a schematic view of an application scenario of a captured image synthesis system based on intelligent transportation according to an embodiment of the present disclosure;

fig. 2 is a schematic flowchart of a captured image synthesis method based on intelligent traffic according to an embodiment of the present disclosure;

fig. 3 is a schematic functional block diagram of an intelligent traffic-based captured image synthesis apparatus according to an embodiment of the present disclosure;

fig. 4 is a block diagram illustrating a structure of a big data cloud server for implementing the intelligent traffic-based intercepted image synthesis method according to the embodiment of the present disclosure.

Detailed Description

The present disclosure is described in detail below with reference to the drawings, and the specific operation methods in the method embodiments can also be applied to the device embodiments or the system embodiments.

Fig. 1 is an interactive schematic diagram of a smart traffic-based captured image synthesis system 10 according to an embodiment of the present disclosure. The intercepted image synthesis system 10 based on smart transportation may include a big data cloud server 100 and a smart transportation terminal 200 communicatively connected to the big data cloud server 100. The intelligent traffic-based truncated image composition system 10 shown in fig. 1 is only one possible example, and in other possible embodiments, the intelligent traffic-based truncated image composition system 10 may include only a portion of the components shown in fig. 1 or may include other components.

Artificial Intelligence (AI) is a theory, method, technique and application system that uses a digital computer or a machine controlled by a digital computer to simulate, extend and expand human Intelligence, perceive the environment, acquire knowledge and use the knowledge to obtain the best results. In other words, artificial intelligence is a comprehensive technique of computer science that attempts to understand the essence of intelligence and produce a new intelligent machine that can react in a manner similar to human intelligence. Artificial intelligence is the research of the design principle and the realization method of various intelligent machines, so that the machines have the functions of perception, reasoning and decision making.

The artificial intelligence technology is a comprehensive subject and relates to the field of extensive technology, namely the technology of a hardware level and the technology of a software level. The artificial intelligence infrastructure generally includes technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and the like.

In this embodiment, the intelligent transportation terminal 200 may include a series of traffic monitoring devices, such as a traffic gate camera, and the like, which is not limited herein.

In this embodiment, the big data cloud server 100 and the smart traffic terminal 200 in the captured image synthesis system 10 based on smart traffic may cooperatively perform the captured image synthesis method based on smart traffic described in the following method embodiments, and the following detailed description of the method embodiments may be referred to for the specific steps performed by the big data cloud server 100 and the smart traffic terminal 200.

To solve the technical problem in the background art, fig. 2 is a schematic flowchart of a smart traffic-based captured image synthesis method according to an embodiment of the present disclosure, which can be executed by the big data cloud server 100 shown in fig. 1, and the smart traffic-based captured image synthesis method is described in detail below.

Step S110, a first intercepted image set corresponding to the first traffic behavior sequence and a second intercepted image set corresponding to the second traffic behavior sequence are obtained.

And step S120, establishing an association relationship between the associated traffic behaviors and the target object according to the associated traffic behaviors related to the abnormal traffic behaviors of the target object in the first traffic behavior sequence and the second traffic behavior sequence, performing synthesis control on image synthesis operations between the first intercepted image set and the second intercepted image set according to the association relationship between the associated traffic behaviors and the target object, and obtaining each target synthesis image after synthesis control.

Step S130, determining traffic linkage behavior nodes generating traffic linkage behaviors from each target synthetic image, acquiring linkage track information among the traffic linkage behavior nodes according to each target synthetic image, and generating a traffic linkage behavior node linkage sequence according to the linkage track information among the traffic linkage behavior nodes.

And step S140, obtaining the characteristic expression vector of the traffic linkage behavior node, tracing the linkage process of the traffic linkage behavior node in the traffic linkage behavior node linkage sequence according to the characteristic expression vector of the traffic linkage behavior node to obtain a tracing process expression vector corresponding to the traffic linkage behavior node, and generating a tracing animation special effect of each target synthetic image according to the tracing process expression vector of the traffic linkage behavior node.

In this embodiment, the first traffic behavior sequence and the second traffic behavior sequence are traffic behavior sequences corresponding to a first traffic image data stream and a second traffic image data stream having a traffic linkage monitoring relationship, respectively.

In this embodiment, the adjacent traffic linkage behavior nodes in the traffic linkage behavior node linkage sequence are traversed by at least one traffic linkage behavior in sequence.

In this embodiment, the tracing process expression vector may be used to express the association between the corresponding traffic linkage behavior node and other traffic linkage behavior nodes in the traffic linkage process, and the traffic linkage monitoring relationship may be used to express a linkage monitoring action generated for the traffic abnormal behavior of the target object.

In this embodiment, the tracing animation special effect of each target synthetic image may be used to represent a traffic tracing process of the associated traffic behavior related to the abnormal traffic behavior in the target synthetic image in the traffic monitoring area of the target synthetic image, so that a user may conveniently and uniformly view the tracing process condition of the linked abnormal traffic behavior without repeatedly switching pictures.

Therefore, based on the above steps, in this embodiment, based on the associated traffic behaviors related to the traffic abnormal behavior of the target object in the first traffic behavior sequence and the second traffic behavior sequence having the traffic linkage monitoring relationship, a traffic linkage behavior node generating the traffic linkage behavior is determined from each target synthetic image after image synthesis of the corresponding intercepted image set, the linkage process is traced, and the tracing animation special effect of each target synthetic image is generated according to the tracing process expression vector. Therefore, the user can conveniently and uniformly check the tracing process condition of the linked abnormal traffic behaviors without repeatedly switching the pictures, and the monitoring and troubleshooting efficiency is improved.

In a possible implementation manner, step S110 may be further implemented by the following sub-steps, which are described in detail below.

In the substep S111, a related intelligent traffic region sequence corresponding to the first image collecting position corresponding to the intelligent traffic terminal 200 in the first traffic display direction is obtained.

Substep S112, obtaining each second image capture location from at least one second image capture location of the sequence of associated intelligent traffic zones, and monitoring a first traffic image data stream associated with the first image capture location at a first traffic display orientation and a second traffic image data stream associated with the second image capture location at a corresponding second traffic display orientation for a set duration.

And a substep S113, predicting the traffic behavior in the first traffic image data stream and the traffic behavior in the second traffic image data stream respectively based on a preconfigured artificial intelligence model, and respectively obtaining a first traffic behavior sequence in the first traffic image data stream and a second traffic behavior sequence in the second traffic image data stream.

And a substep S114, acquiring a first intercepted image set corresponding to the first traffic behavior sequence and a second intercepted image set corresponding to the second traffic behavior sequence.

In this embodiment, the first traffic display orientation may be a traffic display orientation when the intelligent traffic terminal 200 captures a target object with a traffic anomaly behavior at the first image collecting position, the associated intelligent traffic region sequence may include at least one second image collecting position, each second image collecting position has a traffic linkage monitoring relationship between the corresponding second traffic display orientation and the corresponding first image collecting position, and the second traffic display orientation is a linkage traffic display orientation of the first traffic display orientation.

In detail, for each intelligent transportation terminal 200, there may be a plurality of transportation exhibition orientations during the monitoring process, such as right front, right back, oblique upper front, oblique lower front, oblique upper back, oblique lower back, and so on. The image capturing position may be understood as a position where the intelligent transportation terminal 200 specifically captures the image. In specific implementation, after a certain intelligent transportation terminal 200 monitors that a target object with abnormal traffic behavior exists, the big data cloud server 100 may control other intelligent transportation terminals 200 in the related traffic area to perform monitoring and monitoring in a linkage manner according to the position of the abnormal traffic behavior, so that a first traffic image data stream associated with a first image acquisition position in a first traffic display orientation and a second traffic image data stream associated with a second image acquisition position in a corresponding second traffic display orientation may be continuously monitored in a set duration period.

In a possible implementation manner, step S113 can be further implemented by the following sub-steps, which are described in detail below.

And a substep S1131, predicting the traffic behavior in the first traffic image data stream and the traffic behavior in the second traffic image data stream respectively based on a preconfigured artificial intelligence model, so as to obtain a traffic behavior prediction result in the first traffic image data stream and a traffic behavior prediction result in the second traffic image data stream.

In this embodiment, the traffic behavior prediction result may include the confidence of each candidate traffic behavior in different image unit regions.

And a sub-step S1132 of selecting, according to the traffic behavior prediction result in the first traffic image data stream and the traffic behavior prediction result in the second traffic image data stream, a sequence formed by corresponding candidate traffic behaviors having a confidence greater than a set confidence as the first traffic behavior sequence in the first traffic image data stream and the second traffic behavior sequence in the second traffic image data stream, respectively.

As a possible example, the artificial intelligence model may be trained as follows:

(1) and acquiring a training sample set, wherein the training sample set comprises a plurality of training sample images and traffic behavior labels in each image unit area corresponding to each training sample image. For example, the traffic behavior tag may be used to represent a corresponding traffic behavior category in the image cell area.

(2) And extracting an image unit feature map in each image unit area in each training sample image in the training sample set based on a preset convolutional neural network model, and inputting the image unit feature maps into a classification layer to obtain prediction classification label information corresponding to the image unit feature maps.

It should be noted that, in order to implement the feature one-to-one association relationship, the preset convolutional neural network model may include a plurality of convolution extraction units corresponding to each image unit region one to one.

(3) And adjusting model parameters of a preset convolutional neural network model according to the loss function value between the predicted classification label information corresponding to the image unit feature map of each image unit region in each training sample image in the training sample set and the traffic behavior label, then carrying out iterative training, and outputting the artificial intelligent model obtained by training when the preset convolutional neural network model reaches the training end condition.

For example, the artificial intelligence model obtained by training may be output when the loss function value is lower than the set function value, or the artificial intelligence model obtained by training may be output when the loss function value does not decrease any more, or the artificial intelligence model obtained by training may be output when the number of iterative training reaches the set number.

Therefore, the traffic behavior in the first traffic image data stream and the traffic behavior in the second traffic image data stream are predicted by adopting the artificial intelligence model obtained by the training, so that the traffic behavior sequence with a traffic linkage monitoring relation can be quickly and accurately analyzed, and the monitoring and troubleshooting efficiency is improved.

On this basis, in a possible implementation manner, for the step S120, the following sub-steps may be further implemented, which are described in detail below.

And a substep S121, obtaining a traffic behavior prediction region associated with the time dimension and/or the space dimension of the abnormal traffic behavior of the target object in the first traffic behavior sequence and the second traffic behavior sequence, and constructing a first association model corresponding to the first traffic behavior sequence and a second association model corresponding to the second traffic behavior sequence according to the traffic behavior prediction region.

For example, the first association model and the second association model may each include association zone binding nodes for a plurality of different association zone labels. It should be noted that the associated area flag may be used to indicate a frequent traffic behavior tag corresponding to the associated traffic behavior prediction area (for example, a traffic behavior whose traffic behavior repetition rate exceeds a set repetition rate), and the associated area binding node may be used to indicate one or more unit prediction areas in the traffic behavior prediction area related to the frequent traffic behavior tag, for those skilled in the art, the unit prediction area may determine a corresponding area size according to an actual requirement, which is not limited in detail in this embodiment.

And a substep S122, extracting an abnormal behavior unit of the first traffic behavior sequence in each association region binding node of the first association model, determining an association region binding node of a related association region mark corresponding to the traffic behavior prediction region in the second association model as a target association region binding node, mapping the abnormal behavior unit to the target association region binding node according to a preset mapping model and a behavior characteristic vector set of the second traffic behavior sequence, obtaining a target abnormal association node in the target association region binding node, and generating abnormal traffic track point data between the first traffic behavior sequence and the second traffic behavior sequence according to matching characteristics between the abnormal behavior unit and the target abnormal association node.

And step S123, acquiring a first abnormal binding region from the binding nodes of the target association region by taking the target abnormal association node as a target, mapping the first abnormal binding region to the binding nodes of the association region where the abnormal behavior unit is located according to the reverse abnormal traffic track point data corresponding to the abnormal traffic track point data, obtaining a second abnormal binding region corresponding to the first abnormal binding region from the binding nodes of the association region where the abnormal behavior unit is located, and summarizing the first abnormal binding region and the second abnormal binding region into the target abnormal binding region.

Substep S124, obtaining the abnormal behavior region mapped by the abnormal behavior unit to the target associated region binding node, and according to the coverage between the target abnormal binding region and the positioning regions corresponding to the plurality of unit regions to be matched on the abnormal behavior region, sequentially acquiring target association areas corresponding to the target abnormal binding areas in the second association model until the area coordinates of the binding nodes of the association areas where the acquired target association areas are located are consistent with the area coordinates of the target abnormal binding areas in the first association model, stopping acquiring the target association areas in the binding nodes of the next association area, and determining common traffic behaviors between the target abnormal binding region and the target associated region acquired each time as associated traffic behaviors related to the traffic abnormal behaviors of the target object, and establishing an association relationship between the associated traffic behaviors and the target object.

As such, the present embodiment provides for a user to select a first image capture location for display by monitoring a first traffic image data stream associated with the first image capture location at a first traffic display orientation and a second image data stream associated with the second image capture location at a corresponding second traffic display orientation for a set duration of time (e.g., within 3 minutes), then respectively predicting the traffic behaviors in the first traffic image data stream and the second traffic image data stream based on a pre-configured artificial intelligence model, thereby determining the related traffic behaviors related to the abnormal traffic behaviors of the target object by analyzing the traffic behavior sequence with the traffic linkage monitoring relation through artificial intelligence, and the associated traffic behaviors and the target objects are established into an associated relationship, so that the traffic investigation efficiency is extremely low, and the condition of manual misjudgment can be improved, and the accuracy of the final analysis result is improved.

In a possible implementation manner, still referring to step S120, the following sub-steps can be further implemented, which are described in detail below.

And a substep S125 of adding the traffic behavior prediction regions corresponding to the first and second sets of clipped images to the image synthesis scene.

And a substep S126, simulating related traffic behaviors corresponding to the traffic behavior prediction region in the first intercepted image set and the second intercepted image set according to the traffic behavior information of the traffic behavior prediction region corresponding to the requested synthesis node in the image synthesis scene to obtain synthesis strategy information of each related traffic behavior synthesis editing interface in the image synthesis scene, and respectively extracting synthesis control results of the corresponding related traffic behaviors in each image synthesis service from the synthesis strategy information corresponding to each related traffic behavior.

And a substep S127 of performing synthesis control on the synthesis control result of the corresponding related traffic behavior under the corresponding image synthesis service according to the incidence relation between the related traffic behavior and the target object for each image synthesis service, so that the synthesis control result under the image synthesis service after the synthesis control is completed can complete the synthesis control in the image synthesis operation between the first intercepted image set and the second intercepted image set, and each target synthesis image after the synthesis control is obtained.

In a possible implementation manner, regarding step S126, a possible example will be given below to set forth a non-limiting description on a specific implementation thereof.

In the substep S1261, behavior related information of the traffic behavior prediction region is established according to the item edit information of the composite control item of the intercepted image set for the traffic behavior prediction region.

For example, the behavior related information may be used to reflect behavior related information when performing composite control in a traffic behavior prediction area. As an example, this sub-step S1261 may be implemented by:

(1) and acquiring a first project editing operation vector of each project editing node in the project editing information, wherein the first project editing operation vector is used for representing an image synthesis characteristic vector interval of the project editing node.

(2) And performing feature recognition on the first project editing operation vector to obtain first behavior associated information and synthetic control feature information corresponding to the first behavior associated information.

(3) And acquiring first project queue simulation information and project editing information of the project editing node, and extracting a project queue calling interface of the first project queue simulation information, wherein the project queue calling interface of the first project queue simulation information comprises a specified project queue calling code.

(4) And acquiring appointed item queue calling codes of preset historical item editing nodes, and adjusting the appointed item queue calling codes of the first item queue simulation information according to the appointed item queue calling codes to enable a calling script between each appointed item queue calling code in the first item queue simulation information to be matched with a calling script between each appointed item queue calling code in the preset historical item editing nodes.

(5) And obtaining an item queue calling interface of the second item queue simulation information according to each adjusted appointed item queue calling code in the first item queue simulation information, and generating the second item queue simulation information according to the item queue calling interface of the second item queue simulation information.

(6) And searching to obtain the synthesis control characteristic information matched with the project editing information and the first behavior associated information corresponding to the synthesis control characteristic information according to the project queue calling interface of the project editing information and the second project queue simulation information, and adjusting the first behavior associated information corresponding to the synthesis control characteristic information according to the project queue calling interface of the second project queue simulation information to obtain the second behavior associated information.

(7) And mapping and associating the second behavior associated information with the second item queue simulation information to establish behavior associated information of the traffic behavior prediction area.

And a substep S1262 of behavior crawling the behavior associated information of the synthesized control item of the traffic behavior prediction region according to the behavior crawling model corresponding to the requested synthesized node to obtain the traffic behavior information of the traffic behavior prediction region corresponding to the requested synthesized node.

And a substep S1263, determining a synthetic editing process curve of the traffic behavior prediction region according to the traffic behavior information of the traffic behavior prediction region, and simulating each related traffic behavior according to the synthetic editing process curve to acquire synthetic strategy information of a synthetic editing interface of each related traffic behavior in an image synthetic scene.

As an example, this sub-step S1263 may be implemented by:

(1) and calling a synthesis editing interface to access the corresponding image synthesis editing thread according to the synthesis editing process curve, and simulating each related traffic behavior through the image synthesis editing thread.

(2) And determining a composite control decision node corresponding to the composite control attribute object of each relevant traffic behavior according to the composite control attribute objects of different composite control types called for each relevant traffic behavior, wherein the composite control attribute objects of different composite control types respectively correspond to different composite control decision nodes.

(3) The method comprises the steps of determining object data of different composite control attribute objects of each related traffic behavior, and acquiring a first image matching object set of at least two same composite control image matching objects included in a plurality of corresponding composite control image matching objects and at least one second image matching object set of which the calling times are greater than preset times in the rest composite control image matching objects according to the object data.

(4) And generating a synthesis control thread for determining synthesis strategy information of a synthesis editing interface according to an image matching object set which is selected from the first image matching object set and has the calling times larger than the set times and serves as a target image matching object set and at least one second image matching object set, wherein the synthesis control thread comprises the target image matching object set and the at least one second image matching object set.

(5) And respectively determining the synthesis strategy information of each relevant traffic behavior in the image synthesis scene according to the synthesis control thread.

For example, a target image matching object set and a composite control attribute object corresponding to each target composite control image matching object in the at least one second image matching object set may be determined according to the composite control thread, and composite policy information of a composite editing interface of each relevant traffic behavior in the image composite scene may be determined according to the composite control attribute object corresponding to each target composite control image matching object.

In a possible implementation manner, step S140 may be further implemented by the following sub-steps, which are described in detail below.

And a substep S141 of obtaining real-time traffic state data of the traffic linkage behavior nodes from each target synthetic image, and performing semantic analysis on the real-time traffic state data of the traffic linkage behavior nodes to obtain feature expression vectors for describing semantics of the real-time traffic state data, wherein the feature expression vectors are the same in length.

And a substep S142, averaging all the characteristic representation vectors of the same traffic linkage behavior node to obtain an average vector as the characteristic representation vector of the corresponding traffic linkage behavior node.

In a possible implementation manner, still referring to step S140, the following sub-steps can be further implemented, which are described in detail below.

And a substep S143, tracing the linkage process of the traffic linkage behavior nodes in the traffic linkage behavior node linkage sequence according to the characteristic expression vector of the traffic linkage behavior nodes through the linkage process analysis model to obtain the tracing process expression vector corresponding to the traffic linkage behavior nodes.

Exemplarily, before the substep S143, the embodiment may specifically obtain the coded representation of the traffic linkage behavior node, and arrange the coded representation of the traffic linkage behavior node according to the arrangement order of the traffic linkage behavior node in the traffic linkage behavior node linkage sequence to obtain the coded representation sequence corresponding to the traffic linkage behavior node. And then, inputting the linkage process analysis model through the coding representation sequence so that the linkage process analysis model traces the linkage process of the traffic linkage behavior nodes in the traffic linkage behavior node linkage sequence.

In a possible implementation manner, step S130 may be further implemented by the following sub-steps, which are described in detail below.

And a substep S131 of determining the behavior vector direction between every two traffic linkage behavior nodes and the corresponding trend strength of the behavior vector direction according to each target synthetic image.

And a substep S132, generating decision nodes of the decision tree according to the traffic linkage behavior nodes, and generating decision vectors among the decision nodes according to the behavior vector directions among the traffic linkage behavior nodes.

In the substep S133, in the calculation of the decision tree, weights corresponding to first decision vectors pointing from the same traffic linkage behavior node to other traffic linkage behavior nodes are calculated.

It should be noted that the decision nodes in the decision tree in this embodiment may specifically represent traffic linkage behavior nodes, the decision vector in the decision tree represents a behavior vector direction between two traffic linkage behavior nodes, and the weight of the decision vector is obtained according to the magnitude of the trend strength corresponding to the behavior vector direction.

In a possible implementation manner, still referring to step S130, the following sub-steps can be further implemented, which are described in detail below.

And a substep S134, selecting a part of decision nodes from the decision tree as initial nodes of the traffic linkage behavior node linkage sequence.

And a substep S135, starting to walk from each initial node in the decision tree according to the weight of each decision vector in the decision tree, and generating a traffic linkage behavior node linkage sequence according to nodes passing through a walking path, wherein the length of the traffic linkage behavior node linkage sequence is a preset length, and adjacent nodes of the same node are different in the walking path.

Fig. 3 is a schematic diagram illustrating functional modules of a smart traffic-based captured image synthesizing apparatus 300 according to an embodiment of the present disclosure, which can divide the smart traffic-based captured image synthesizing apparatus 300 according to the method embodiment executed by the big data cloud server 100, that is, the following functional modules corresponding to the smart traffic-based captured image synthesizing apparatus 300 can be used to execute the method embodiments executed by the big data cloud server 100. The apparatus 300 for synthesizing the smart traffic-based captured image may include an obtaining module 310, a synthesizing control module 320, a first generating module 330, and a second identifying module 340, wherein the functions of the functional modules of the apparatus 300 for synthesizing the smart traffic-based captured image are described in detail below.

The obtaining module 310 is configured to obtain a first captured image set corresponding to a first traffic behavior sequence and a second captured image set corresponding to a second traffic behavior sequence, where the first traffic behavior sequence and the second traffic behavior sequence are traffic behavior sequences corresponding to a first traffic image data stream and a second traffic image data stream, respectively, which have a traffic linkage monitoring relationship, and the traffic linkage monitoring relationship is used to represent a linkage monitoring action generated for a traffic abnormal behavior of a target object. The obtaining module 310 may be configured to perform the step S110, and the detailed implementation of the obtaining module 310 may refer to the detailed description of the step S110.

And the synthesis control module 320 is configured to establish an association relationship between the associated traffic behavior and the target object according to the associated traffic behavior related to the abnormal traffic behavior of the target object in the first traffic behavior sequence and the second traffic behavior sequence, perform synthesis control on image synthesis operations between the first captured image set and the second captured image set according to the association relationship between the associated traffic behavior and the target object, and obtain each target synthesis image after the synthesis control. The composition control module 320 may be configured to execute the step S120, and as for the detailed implementation of the composition control module 320, reference may be made to the detailed description of the step S120.

The first generating module 330 is configured to determine traffic linkage behavior nodes that generate traffic linkage behaviors from each target synthetic image, acquire linkage trajectory information between the traffic linkage behavior nodes according to each target synthetic image, and generate a traffic linkage behavior node linkage sequence according to the linkage trajectory information between the traffic linkage behavior nodes, where adjacent traffic linkage behavior nodes in the traffic linkage behavior node linkage sequence are traversed by at least one traffic linkage behavior. The first generating module 330 may be configured to execute the step S130, and the detailed implementation of the first generating module 330 may refer to the detailed description of the step S130.

The second generating module 340 is configured to obtain a feature expression vector of a traffic linkage behavior node, trace back a linkage process of the traffic linkage behavior node in a traffic linkage behavior node linkage sequence according to the feature expression vector of the traffic linkage behavior node to obtain a tracing back process expression vector corresponding to the traffic linkage behavior node, and generate a tracing back animation special effect of each target synthetic image according to the tracing back process expression vector of the traffic linkage behavior node, where the tracing back process expression vector is used to represent an association between the corresponding traffic linkage behavior node and other traffic linkage behavior nodes in a traffic linkage process. The second generating module 340 may be configured to execute the step S140, and for a detailed implementation of the second generating module 340, reference may be made to the detailed description of the step S140.

It should be noted that the division of the modules of the above apparatus is only a logical division, and the actual implementation may be wholly or partially integrated into one physical entity, or may be physically separated. And these modules can be realized in the form of software called by processing element; or may be implemented entirely in hardware; and part of the modules can be realized in the form of calling software by the processing element, and part of the modules can be realized in the form of hardware. For example, the obtaining module 310 may be a processing element separately set up, or may be implemented by being integrated into a chip of the apparatus, or may be stored in a memory of the apparatus in the form of program code, and the processing element of the apparatus calls and executes the functions of the obtaining module 310. Other modules are implemented similarly. In addition, all or part of the modules can be integrated together or can be independently realized. The processing element described herein may be an integrated circuit having signal processing capabilities. In implementation, each step of the above method or each module above may be implemented by an integrated logic circuit of hardware in a processor element or an instruction in the form of software.

For example, the above modules may be one or more integrated circuits configured to implement the above methods, such as: one or more Application Specific Integrated Circuits (ASICs), or one or more microprocessors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs), among others. For another example, when some of the above modules are implemented in the form of a processing element scheduler code, the processing element may be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor that can call program code. As another example, these modules may be integrated together, implemented in the form of a system-on-a-chip (SOC).

Fig. 4 shows a hardware structure diagram of a big data cloud server 100 for implementing the above control device according to an embodiment of the present disclosure, and as shown in fig. 4, the big data cloud server 100 may include a processor 110, a machine-readable storage medium 120, a bus 130, and a transceiver 140.

In a specific implementation process, the at least one processor 110 executes computer-executable instructions stored in the machine-readable storage medium 120 (for example, the obtaining module 310, the synthesis control module 320, the first generating module 330, and the second identifying module 340 included in the intelligent traffic-based intercepted image synthesis apparatus 300 shown in fig. 3), so that the processor 110 may execute the intelligent traffic-based intercepted image synthesis method according to the above method embodiment, where the processor 110, the machine-readable storage medium 120, and the transceiver 140 are connected through the bus 130, and the processor 110 may be configured to control the transceiving action of the transceiver 140, so as to perform data transceiving with the intelligent traffic terminal 200.

For a specific implementation process of the processor 110, reference may be made to the above-mentioned method embodiments executed by the big data cloud server 100, and implementation principles and technical effects thereof are similar, and details of this embodiment are not described herein again.

In the embodiment shown in fig. 4, it should be understood that the Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the present invention may be embodied directly in a hardware processor, or in a combination of the hardware and software modules within the processor.

The machine-readable storage medium 120 may comprise high-speed RAM memory and may also include non-volatile storage NVM, such as at least one disk memory.

The bus 130 may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (EISA) bus, or the like. The bus 130 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, the buses in the figures of the present application are not limited to only one bus or one type of bus.

In addition, the embodiment of the disclosure also provides a readable storage medium, in which a computer executing instruction is stored, and when a processor executes the computer executing instruction, the intelligent traffic-based intercepted image synthesis method is implemented.

The readable storage medium described above may be implemented by any type of volatile or non-volatile memory device or combination thereof, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disk. Readable storage media can be any available media that can be accessed by a general purpose or special purpose computer.

Finally, it should be noted that: the above embodiments are only used for illustrating the technical solutions of the present disclosure, and not for limiting the same; while the present disclosure has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art will understand that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present disclosure.

Claims

1. A method for synthesizing intercepted images based on smart traffic is applied to a big data cloud server, the big data cloud server is in communication connection with smart traffic terminals of a plurality of smart traffic management areas, and the method comprises the following steps:

obtaining a characteristic expression vector of the traffic linkage behavior node, tracing the linkage process of the traffic linkage behavior node in a traffic linkage behavior node linkage sequence according to the characteristic expression vector of the traffic linkage behavior node to obtain a tracing process expression vector corresponding to the traffic linkage behavior node, and generating a tracing animation special effect of each target synthetic image according to the tracing process expression vector of the traffic linkage behavior node, wherein the tracing process expression vector is used for expressing the association of the corresponding traffic linkage behavior node and other traffic linkage behavior nodes in the traffic linkage process;

the step of acquiring linkage track information between the traffic linkage behavior nodes according to each target composite image comprises the following steps:

2. The intelligent traffic-based intercepted image combining method according to claim 1, wherein the step of performing combination control of the image combining operation between the first intercepted image set and the second intercepted image set according to the association relationship between the associated traffic behavior and the target object and obtaining each target combined image after the combination control comprises:

according to the incidence relation between the associated traffic behaviors and the target object aiming at each image synthesis service, synthesizing and controlling the synthesis control result of the corresponding associated traffic behaviors under the corresponding image synthesis service, so that the synthesis control result under the image synthesis service after the synthesis control can complete the synthesis control in the image synthesis operation between the first intercepted image set and the second intercepted image set, and each target synthesis image after the synthesis control is obtained;

the step of simulating the related traffic behaviors corresponding to the traffic behavior prediction area in the first and second captured image sets according to the traffic behavior information of the traffic behavior prediction area corresponding to the requested synthesis node in the image synthesis scene to obtain the synthesis strategy information of the synthesis editing interface of each related traffic behavior in the image synthesis scene includes:

3. The intelligent traffic-based intercepted image combining method according to claim 2, wherein the step of creating behavior related information of the traffic behavior prediction region based on item edit information of the intercepted image set with respect to a combination control item of the traffic behavior prediction region includes:

4. The intelligent traffic-based intercepted image synthesizing method according to claim 2, wherein the step of operating each relevant traffic behavior according to the synthesized edit process curve to obtain synthesized strategy information of a synthesized edit interface of each relevant traffic behavior in the image synthesis scene comprises:

5. The intelligent traffic-based intercepted image combining method according to claim 4, wherein the step of determining the combining strategy information of the combining and editing interface in the image combining scene for each relevant traffic behavior according to the combining control thread comprises:

6. The intelligent traffic-based intercepted image combining method according to claim 1, wherein the step of obtaining the feature expression vector of the traffic linkage behavior node comprises:

7. The intelligent traffic-based intercepted image synthesizing method according to claim 1, wherein the step of tracing back the linkage process of the traffic linkage behavior nodes in the traffic linkage behavior node linkage sequence to obtain the tracing back process expression vectors corresponding to the traffic linkage behavior nodes comprises:

8. A big data cloud server, wherein the big data cloud server comprises a processor, a machine-readable storage medium, and a network interface, the machine-readable storage medium, the network interface, and the processor are connected via a bus system, the network interface is configured to be communicatively connected to at least one smart transportation terminal, the machine-readable storage medium is configured to store a program, an instruction, or code, and the processor is configured to execute the program, the instruction, or the code in the machine-readable storage medium to perform the intercepted image synthesis method according to any one of claims 1 to 7.