CN115659154B

CN115659154B - Data transmission method, device, server and computer readable medium

Info

Publication number: CN115659154B
Application number: CN202211592260.3A
Authority: CN
Inventors: 龙文; 李敏; 齐新迎; 翁元祥; 陶武康; 艾永军; 黄家琪; 刘智睿; 王倩
Original assignee: GAC Aion New Energy Automobile Co Ltd
Current assignee: GAC Aion New Energy Automobile Co Ltd
Priority date: 2022-12-13
Filing date: 2022-12-13
Publication date: 2023-06-27
Anticipated expiration: 2042-12-13
Also published as: CN115659154A

Abstract

Embodiments of the present disclosure disclose a data transmission method, apparatus, server, and computer readable medium. One embodiment of the method comprises the following steps: receiving at least one real-time vehicle travel data set; filtering each real-time vehicle running data set to obtain at least one filtered vehicle running data set; for each filtered vehicle travel data set, the following determination steps are performed: splicing the filtered vehicle running data set with the historical vehicle running data set sequence to obtain a spliced vehicle running data set sequence; determining whether to continue; determining the number of spliced vehicle running data sets in the spliced vehicle running data set sequence as a first number; packaging the spliced vehicle driving data group sequence to obtain packaged data; performing behavior recognition on the packed data to obtain a recognition result; the real-time vehicle travel data set is transmitted to a database. According to the method and the device, the calculated amount of behavior recognition can be reduced, and the accuracy of behavior recognition is improved.

Description

Data transmission method, device, server and computer readable medium

Technical Field

Embodiments of the present disclosure relate to the field of computer technology, and in particular, to a data transmission method, apparatus, server, and computer readable medium.

Background

The data transmission method may be a method of performing behavior recognition on a plurality of traveling data corresponding to a plurality of traveling vehicles. In the case of behavior recognition of a plurality of traveling data, the following methods are generally adopted: first, a plurality of real-time vehicle travel data sets corresponding to a plurality of traveling vehicles are acquired. And secondly, for each of the plurality of traveling vehicles, transmitting the real-time vehicle traveling data set of the traveling vehicle to a corresponding behavior recognition server so that the behavior recognition server performs behavior recognition on at least one real-time vehicle traveling data set.

However, the inventors found that when behavior recognition is performed on a plurality of traveling data in the above manner, there are often the following technical problems:

firstly, at least one real-time vehicle running data set which contains a plurality of behavior features at the current moment is identified, so that the calculation amount of behavior identification is large, and the accuracy of behavior identification is low.

Second, by determining a plurality of vehicle running data, the plurality of vehicle running data influence the determination result, and thus the accuracy of vehicle behavior recognition is low.

Thirdly, performing behavior recognition on at least one real-time vehicle running data set, wherein the at least one real-time vehicle running data set contains more useless behavior characteristics, so that the accuracy of behavior recognition is low.

Disclosure of Invention

The disclosure is in part intended to introduce concepts in a simplified form that are further described below in the detailed description. The disclosure is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.

Some embodiments of the present disclosure propose data transmission methods, apparatuses, servers, and computer-readable media to solve one or more of the technical problems mentioned in the background section above.

In a first aspect, some embodiments of the present disclosure provide a data transmission method, the method including: receiving at least one real-time vehicle driving data set for at least one vehicle, which is transmitted by a data dividing server; filtering each real-time vehicle running data set in the at least one real-time vehicle running data set by using data filtering equipment corresponding to preset vehicle behavior characteristic information to obtain at least one filtered vehicle running data set; for each of the at least one filtered vehicle travel data set, performing the determining step of: splicing the filtered vehicle running data set with the corresponding historical vehicle running data set sequence in the cache layer to obtain a spliced vehicle running data set sequence; determining whether the time sequence corresponding to the spliced vehicle running data set sequence is continuous or not; in response to determining continuity, determining a number of spliced vehicle travel data sets in the sequence of spliced vehicle travel data sets as a first number; in response to determining that the first number is greater than or equal to a preset value, performing data packaging on the spliced vehicle driving data group sequence to obtain packaged data; performing behavior recognition on the packed data by using recognition equipment corresponding to the data filtering equipment to obtain a recognition result; and transmitting the real-time vehicle running data set corresponding to the filtered vehicle running data set to a database corresponding to the data filtering equipment in response to the fact that the identification result is the result representing the behavior information corresponding to the preset vehicle behavior characteristic information.

In a second aspect, some embodiments of the present disclosure provide a data transmission apparatus, the apparatus including: a receiving unit configured to receive at least one real-time vehicle travel data set for at least one vehicle transmitted by the data dividing server; the filtering processing unit is configured to utilize data filtering equipment corresponding to preset vehicle behavior characteristic information to filter each real-time vehicle running data set in the at least one real-time vehicle running data set so as to obtain at least one filtered vehicle running data set; an execution unit configured to execute, for each of the at least one filtered vehicle travel data set, the following determination steps: splicing the filtered vehicle running data set with the corresponding historical vehicle running data set sequence in the cache layer to obtain a spliced vehicle running data set sequence; determining whether the time sequence corresponding to the spliced vehicle running data set sequence is continuous or not; in response to determining continuity, determining a number of spliced vehicle travel data sets in the sequence of spliced vehicle travel data sets as a first number; in response to determining that the first number is greater than or equal to a preset value, performing data packaging on the spliced vehicle driving data group sequence to obtain packaged data; performing behavior recognition on the packed data by using recognition equipment corresponding to the data filtering equipment to obtain a recognition result; and transmitting the real-time vehicle running data set corresponding to the filtered vehicle running data set to a database corresponding to the data filtering equipment in response to the fact that the identification result is the result representing the behavior information corresponding to the preset vehicle behavior characteristic information.

In a third aspect, some embodiments of the present disclosure provide an electronic device comprising: one or more processors; a storage device having one or more programs stored thereon, which when executed by one or more processors causes the one or more processors to implement the method described in any of the implementations of the first aspect above.

In a fourth aspect, some embodiments of the present disclosure provide a computer readable medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method described in any of the implementations of the first aspect.

The above embodiments of the present disclosure have the following advantageous effects: according to the data transmission method of some embodiments of the present disclosure, the calculation amount of behavior feature recognition can be reduced, and the accuracy of behavior feature recognition is improved. Specifically, the reason why the calculation amount of behavior feature recognition is large and the accuracy of behavior feature recognition is low is that: at least one real-time vehicle running data set containing a plurality of behavior features at the current moment is identified, so that the calculation amount of behavior feature identification is large, and the accuracy of behavior feature identification is low. Based on this, the data transmission method of some embodiments of the present disclosure receives at least one real-time vehicle travel data set for at least one vehicle transmitted by the data dividing server; filtering each real-time vehicle running data set in the at least one real-time vehicle running data set by using data filtering equipment corresponding to preset vehicle behavior characteristic information to obtain at least one filtered vehicle running data set; the real-time vehicle running data set meeting the preset vehicle behavior characteristic information is filtered from the real-time vehicle running data set, the filtered vehicle running data set can show the behavior characteristics of the vehicle, and the data quantity is small relative to the real-time vehicle running data set, so that the calculation quantity of the follow-up behavior characteristic identification can be reduced. For each of the at least one filtered vehicle travel data set, performing the determining step of: splicing the filtered vehicle running data set with the corresponding historical vehicle running data set sequence in the cache layer to obtain a spliced vehicle running data set sequence; determining whether the time sequence corresponding to the spliced vehicle running data set sequence is continuous or not; the spliced vehicle running data set sequence indicates that the vehicle behavior features are rich by determining whether the spliced vehicle running data set sequence is continuous or not. So that the utility of the above-described sequence of spliced vehicle travel data sets can be determined. In response to determining continuity, determining a number of spliced vehicle travel data sets in the sequence of spliced vehicle travel data sets as a first number; in response to determining that the first number is greater than or equal to a preset value, performing data packaging on the spliced vehicle driving data group sequence to obtain packaged data; performing behavior recognition on the packed data by using recognition equipment corresponding to the data filtering equipment to obtain a recognition result; the behavior characteristics contained in the packed data are rich, so that the accuracy of behavior characteristic identification can be improved by carrying out behavior identification on the packed data. And transmitting the real-time vehicle running data set corresponding to the filtered vehicle running data set to a database corresponding to the data filtering equipment in response to the fact that the identification result is the result representing the behavior information corresponding to the preset vehicle behavior characteristic information. And filtering the real-time vehicle running data set meeting the preset vehicle behavior characteristic information from the at least one real-time vehicle running data set, and carrying out behavior characteristic recognition on the packed data to reduce the calculated amount of behavior recognition and improve the accuracy of behavior recognition.

Drawings

The above and other features, advantages, and aspects of embodiments of the present disclosure will become more apparent by reference to the following detailed description when taken in conjunction with the accompanying drawings. The same or similar reference numbers will be used throughout the drawings to refer to the same or like elements. It should be understood that the figures are schematic and that elements and components are not necessarily drawn to scale.

FIG. 1 is a flow chart of some embodiments of a data transmission method according to the present disclosure;

fig. 2 is a schematic structural diagram of some embodiments of a data transmission apparatus according to the present disclosure;

fig. 3 is a schematic structural diagram of an electronic device suitable for use in implementing some embodiments of the present disclosure.

Detailed Description

Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete. It should be understood that the drawings and embodiments of the present disclosure are for illustration purposes only and are not intended to limit the scope of the present disclosure.

It should be noted that, for convenience of description, only the portions related to the present invention are shown in the drawings. Embodiments of the present disclosure and features of embodiments may be combined with each other without conflict.

It should be noted that the terms "first," "second," and the like in this disclosure are merely used to distinguish between different devices, modules, or units and are not used to define an order or interdependence of functions performed by the devices, modules, or units.

It should be noted that references to "one", "a plurality" and "a plurality" in this disclosure are intended to be illustrative rather than limiting, and those of ordinary skill in the art will appreciate that "one or more" is intended to be understood as "one or more" unless the context clearly indicates otherwise.

The names of messages or information interacted between the various devices in the embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.

Fig. 1 illustrates a flow 100 of some embodiments of a data transmission method according to the present disclosure. The flow 100 of the data transmission method includes the following steps:

Step 101, at least one real-time vehicle driving data set for at least one vehicle transmitted by a data dividing server is received.

In some embodiments, an executing subject (e.g., behavior recognition server) of the data transmission method may receive at least one real-time vehicle travel data set for at least one vehicle transmitted by the data dividing server. The data dividing server may be used for determining corresponding behavior recognition server information of the running vehicle, so as to send the real-time vehicle running data set of the running vehicle to the server of the corresponding behavior recognition server. The real-time vehicle travel data in the real-time vehicle travel data group may be travel data of the vehicle while traveling in real-time. For example, the real-time vehicle travel data may be vehicle travel speed data or vehicle travel acceleration data. The behavior recognition server may be a server for performing behavior recognition on each of the at least one real-time vehicle travel data set.

In some optional implementations of some embodiments, each of the at least one real-time vehicle travel data set for the at least one vehicle transmitted by the data partitioning server is obtained by:

First, for each of a plurality of traveling vehicles, the following steps are performed:

and a first sub-step of acquiring a real-time full-quantity vehicle running data set and vehicle identification code data corresponding to the running vehicle. The real-time panoramic vehicle running data in the real-time panoramic vehicle running data set may be running data of the vehicle during real-time running. Wherein the at least one vehicle may be part of the plurality of traveling vehicles. The real-time panoramic vehicle traveling data in the panoramic real-time vehicle traveling data set may be traveling data of the vehicle when traveling in real time. For example, the real-time panoramic vehicle travel data may be vehicle travel speed data or vehicle travel acceleration data. Wherein, the vehicle identification code data can represent the data of the vehicle identification code.

And a second sub-step of determining behavior recognition server information corresponding to the running vehicle according to the vehicle identification code data. Wherein the behavior recognition server may be a server for performing behavior recognition on the received at least one real-time vehicle driving data set.

In practice, the determining the behavior recognition server information corresponding to the running vehicle according to the vehicle identification code data includes:

Step one, determining a hash value corresponding to the vehicle identification code data by utilizing a hash function.

And step two, determining the remainder corresponding to the hash value by utilizing a Ha Xiqu remainder algorithm.

And thirdly, determining the behavior recognition server information corresponding to the remainder as the behavior recognition server information corresponding to the running vehicle. The behavior recognition server information may be a server for performing behavior recognition on a real-time panoramic running data set of the running vehicle. For example, the behavior recognition server information may be a serial number corresponding to the behavior recognition server.

And a third sub-step of determining the real-time full-volume vehicle running data set as a real-time vehicle running data set to be transmitted to a behavior recognition server corresponding to the behavior recognition server information.

Step 102, filtering each real-time vehicle running data set in at least one real-time vehicle running data set by using data filtering equipment corresponding to preset vehicle behavior characteristic information to obtain at least one filtered vehicle running data set.

In some embodiments, the executing body may perform filtering processing on each real-time vehicle running data set in the at least one real-time vehicle running data set by using a data filtering device corresponding to the preset vehicle behavior feature information, so as to obtain at least one filtered vehicle running data set. The preset vehicle behavior feature information may represent information of behavior features of the vehicle when the vehicle is running. For example, the behavioral characteristics may be characteristics of automatic braking. The data filtering device may be a filtering device for filtering out real-time vehicle running data required for the behavior information corresponding to the preset vehicle behavior feature information in the at least one real-time vehicle running data set. For example, the behavior information may be indicative of automatic braking information, and may be indicative of vehicle steering information.

In some optional implementations of some embodiments, the preset vehicle behavior feature information includes, but is not limited to, at least one of:

the characteristic information of the automatic braking behavior of the vehicle and the characteristic information of the steering behavior of the vehicle. The characteristic information of the automatic braking behavior of the vehicle can represent information of a real-time vehicle running data set of the vehicle during automatic braking. The vehicle steering behavior characteristic information may be information characterizing a real-time vehicle travel data set of the vehicle while steering.

Step 103, for each of the at least one filtered vehicle travel data set, performing the following determination steps:

step 1031, splicing the filtered vehicle running data set with the corresponding historical vehicle running data set sequence in the buffer layer to obtain a spliced vehicle running data set sequence.

In some embodiments, the executing body may splice the filtered vehicle running data set with a corresponding historical vehicle running data set sequence in the buffer layer to obtain a spliced vehicle running data set sequence. The caching layer can be used for caching a server of a historical vehicle driving data set sequence. The vehicle running data set in the historical vehicle running data set sequence may be a filtered vehicle running data set of the historical time corresponding to the filtered vehicle running data set. The spliced vehicle running data set sequence may be a matrix type spliced vehicle running data set sequence. For example, the sequence of the spliced vehicle traveling data sets may be [ [ vehicle traveling speed at the present time, vehicle traveling acceleration at the present time, … …, vehicle deceleration at the present time ], [ vehicle traveling speed at the last time, vehicle traveling acceleration at the last time, … …, vehicle deceleration at the last time ], … …, [ vehicle traveling speed at the nth time, vehicle traveling acceleration at the nth time, … …, and vehicle deceleration at the nth time ] of history.

In practice, the execution body may splice the filtered vehicle running data set with a corresponding sequence of historical vehicle running data sets in the cache layer, including

And adding the filtered vehicle running data set to the corresponding historical vehicle running data set sequence in the buffer layer.

Step 1032 determines whether the time series corresponding to the sequence of spliced vehicle travel data sets is consecutive.

In some embodiments, the executing body may determine whether the time series corresponding to the spliced vehicle running data set sequence is continuous. The time sequence may be a sequence formed by corresponding times of the spliced vehicle running data sets in the spliced vehicle running data set sequence.

Step 1033, in response to determining the succession, determines a number of spliced vehicle travel data sets in the sequence of spliced vehicle travel data sets as the first number.

In some embodiments, in response to determining to continue, the executing entity may determine a number of spliced vehicle travel data sets in the sequence of spliced vehicle travel data sets as the first number.

Optionally, the executing body may determine, in response to determining the connection, a number of spliced vehicle running data sets in the spliced vehicle running data set sequence as the first number, further include:

And a first step of deleting the spliced vehicle running data set meeting the preset position condition in the spliced vehicle running data set sequence in response to determining that the spliced vehicle running data set is not connected, and obtaining a deleted vehicle running data set sequence. For example, the preset position condition may be a spliced vehicle running data set before the interruption of the continuity.

And a second step of determining the number of the deleted vehicle running data sets in the deleted vehicle running data set sequence as a second number.

And thirdly, in response to determining that the second number is greater than or equal to the preset value, data packaging is conducted on the deleted vehicle driving data group sequence, and packaged data are obtained.

And a fourth step of determining the deleted vehicle running data set sequence as a historical vehicle running data set sequence to store in the buffer layer in response to determining that the second number is smaller than the preset value.

Step 1034, in response to determining that the first number is greater than or equal to the preset value, data packaging the spliced vehicle running data set sequence to obtain packaged data.

In some embodiments, in response to determining that the first number is greater than or equal to a preset number, the sequence of spliced vehicle drive data sets is data packed to obtain packed data. For example, the predetermined value may be 30. The step of data packaging the spliced vehicle running data set sequence may be to combine the spliced vehicle running data set sequence. The packed data may be the combined data of the sequence of spliced vehicle drive data sets.

Optionally, the executing body may further include, after performing data packaging on the spliced vehicle running data set sequence in response to determining that the first number is greater than or equal to a preset value, obtaining the packaged data:

and in response to determining that the first number is less than the preset value, determining the spliced vehicle running data set sequence as a historical vehicle running data set sequence to store in the cache layer.

And 1035, performing behavior recognition on the packed data by using recognition equipment corresponding to the data filtering equipment to obtain a recognition result.

In some embodiments, the executing body may perform behavior recognition on the packed data by using a recognition device corresponding to the data filtering device, to obtain a recognition result. The identifying device may be used for identifying whether the packed data represents data of behavior information corresponding to the preset vehicle behavior feature information, and obtaining an identifying result. The identification result comprises: and representing the result of the behavior information corresponding to the preset vehicle behavior characteristic information and representing the result of the behavior information not corresponding to the preset vehicle behavior characteristic information.

In practice, the performing behavior recognition on the packed data to obtain a recognition result includes:

And performing behavior recognition processing on the packed data by using a support vector machine to obtain a recognition result.

In some optional implementations of some embodiments, performing behavior recognition on the packed data to obtain a recognition result includes:

first, screening out preset quantity of vehicle steering data meeting preset vehicle steering conditions from the packed data. The preset vehicle steering condition may be a condition that the steering angle of the vehicle steering data is larger than a preset angle. For example, the predetermined angle may be 30 degrees. For example, the preset number may be 5.

Second, for each of the preset number of vehicle steering data, the following behavior recognition step is performed:

a first sub-step of determining vehicle speed data, vehicle acceleration data and data representing whether the vehicle turns on a turn signal at a time corresponding to the vehicle steering data.

And a second sub-step of performing behavior recognition on the vehicle speed data, the vehicle acceleration data and the data representing whether the vehicle turns on a turn signal or not to obtain candidate recognition results.

In practice, the behavior recognition is performed on the vehicle speed data, the vehicle acceleration data and the data representing whether the vehicle turns on a turn signal, and the behavior recognition comprises the following steps;

Step one, in response to determining that the corresponding speed of the vehicle speed data is smaller than or equal to a preset speed, the corresponding acceleration of the vehicle acceleration data is smaller than or equal to a preset acceleration, the data representing whether the vehicle turns on a steering lamp is data representing whether the vehicle turns on the steering lamp, and the result representing the steering behavior information of the vehicle is determined to be a candidate recognition result. For example, the preset speed may be 10 km/hour. For example, the preset acceleration may be m/s.

And step two, determining a result of which the representation is not vehicle steering behavior information as a candidate recognition result in response to the fact that the corresponding speed of the vehicle speed data is greater than the preset speed, the corresponding acceleration of the vehicle acceleration data is smaller than or equal to the preset acceleration, the data representing whether the vehicle turns on a steering lamp is the data representing whether the vehicle turns on the steering lamp, and the result of which the representation is not the vehicle steering behavior information.

And step three, determining a result of which the representation is not vehicle steering behavior information as a candidate recognition result in response to the fact that the corresponding speed of the vehicle speed data is less than or equal to the preset speed, the corresponding acceleration of the vehicle acceleration data is greater than the preset acceleration, and the data representing whether the vehicle turns on a steering lamp is the data representing whether the vehicle turns on the steering lamp.

And step four, in response to determining that the speed corresponding to the vehicle speed data is less than or equal to the preset speed, the acceleration corresponding to the vehicle acceleration data is less than or equal to the preset acceleration, the data representing whether the vehicle turns on the turn signal is data representing that the vehicle does not turn on the turn signal, and determining a result representing that the vehicle does not turn on the turn signal as a candidate recognition result.

And thirdly, determining a result representing the behavior information corresponding to the preset vehicle behavior characteristic information as a recognition result in response to determining that each candidate recognition result in the preset number of candidate recognition results represents the vehicle steering behavior information.

And fourthly, determining a result which is characterized as not being corresponding to the preset vehicle behavior characteristic information as a recognition result in response to determining that the obtained candidate recognition result in the preset number of candidate recognition results is not characterized as the vehicle steering behavior information.

The first to fourth steps described above are an invention point of the embodiments of the present disclosure, and solve the second technical problem mentioned in the background art, namely "by determining a plurality of vehicle running data, the plurality of vehicle running data affects the determination result, so that the accuracy of vehicle behavior recognition is low. Factors that cause a plurality of vehicle running data to affect the judgment result, and thus the accuracy of vehicle behavior recognition is low tend to be as follows: by judging the plurality of vehicle running data, the plurality of vehicle running data influence the judgment result, so that the accuracy of vehicle behavior identification is low. If the above factors are solved, the accuracy of vehicle behavior recognition is improved. To achieve this, first, a predetermined number of vehicle steering data satisfying a predetermined vehicle steering condition is selected from the above-described package data. The preset vehicle steering condition may be a condition that the steering angle of the vehicle steering data is larger than a preset angle. Each vehicle steering data in the preset number of vehicle steering data meets preset vehicle steering conditions, and overtaking behavior information or steering information of the vehicle at a plurality of moments can be determined. Next, for each of the above-described preset number of vehicle steering data, the following behavior recognition step is performed: a first sub-step of determining vehicle speed data, vehicle acceleration data and data representing whether the vehicle turns on a turn signal at a time corresponding to the vehicle steering data. And a second sub-step of performing behavior recognition on the vehicle speed data, the vehicle acceleration data and the data representing whether the vehicle turns on a turn signal or not to obtain candidate recognition results. In practice, the behavior recognition is performed on the vehicle speed data, the vehicle acceleration data and the data representing whether the vehicle turns on a turn signal, and the behavior recognition comprises the following steps; step one, in response to determining that the corresponding speed of the vehicle speed data is smaller than or equal to a preset speed, the corresponding acceleration of the vehicle acceleration data is smaller than or equal to a preset acceleration, the data representing whether the vehicle turns on a steering lamp is data representing whether the vehicle turns on the steering lamp, and the result representing the steering behavior information of the vehicle is determined to be a candidate recognition result. For example, the preset speed may be 10 km/hour. For example, the preset acceleration may be m/s. And step two, determining a result of which the representation is not vehicle steering behavior information as a candidate recognition result in response to the fact that the corresponding speed of the vehicle speed data is greater than the preset speed, the corresponding acceleration of the vehicle acceleration data is smaller than or equal to the preset acceleration, the data representing whether the vehicle turns on a steering lamp is the data representing whether the vehicle turns on the steering lamp, and the result of which the representation is not the vehicle steering behavior information. And step three, determining a result of which the representation is not vehicle steering behavior information as a candidate recognition result in response to the fact that the corresponding speed of the vehicle speed data is less than or equal to the preset speed, the corresponding acceleration of the vehicle acceleration data is greater than the preset acceleration, and the data representing whether the vehicle turns on a steering lamp is the data representing whether the vehicle turns on the steering lamp. And step four, in response to determining that the speed corresponding to the vehicle speed data is less than or equal to the preset speed, the acceleration corresponding to the vehicle acceleration data is less than or equal to the preset acceleration, the data representing whether the vehicle turns on the turn signal is data representing that the vehicle does not turn on the turn signal, and determining a result representing that the vehicle does not turn on the turn signal as a candidate recognition result. Under the same moment, the corresponding speed of the vehicle speed data is smaller than or equal to the preset speed, the corresponding acceleration of the vehicle acceleration data is smaller than or equal to the preset acceleration, the data representing whether the vehicle turns on the steering lamp is the data representing whether the vehicle turns on the steering lamp, and the judgment result can be prevented from being influenced by a plurality of vehicle running data by judging whether the vehicle steering behavior information is the vehicle steering behavior information or not according to a small amount of vehicle running data, so that the accuracy of vehicle behavior identification can be improved. Then, in response to determining that each candidate recognition result of the preset number of candidate recognition results represents the result of the vehicle steering behavior information, determining the result representing the behavior information corresponding to the preset vehicle behavior characteristic information as a recognition result. And finally, determining a result which is characterized as not being the behavior information corresponding to the preset vehicle behavior characteristic information as a recognition result in response to determining that the obtained candidate recognition result in the preset number of candidate recognition results is not characterized as the vehicle steering behavior information. The vehicle steering behavior information is determined whether to be the vehicle steering behavior information or not by determining a small amount of vehicle running data, so that influence of a plurality of vehicle running data on a determination result can be avoided, and the accuracy of vehicle behavior recognition can be improved.

the first step is to send the packed data to a first feature extraction layer of a preset vehicle behavior recognition model to obtain a first behavior feature vector. The preset vehicle behavior recognition model can be used for a behavior recognition model. The input of the preset vehicle behavior recognition model may be the package data. The output of the preset vehicle behavior recognition model may represent whether the vehicle steering behavior information is a result. The preset vehicle behavior recognition model includes: the device comprises a first feature extraction layer, a second feature extraction layer, a third feature extraction layer, a fourth feature extraction layer, a fifth feature extraction layer and a behavior recognition layer. For example, the first feature extraction layer may be a recurrent neural network (Recurrent Neural Network, RNN). For example, the second feature extraction layer may be a recurrent neural network. For example, the third feature extraction layer may be a recurrent neural network. For example, the fourth feature extraction layer may be a recurrent neural network. The first feature extraction layer may be a network layer for extracting vehicle behavior features. The second feature extraction layer may be a network layer for extracting vehicle behavior features. The third feature extraction layer may be a network layer for extracting vehicle behavior features. The fourth feature extraction layer may be a network layer for extracting vehicle behavior features. The behavior recognition layer may be used to identify a network layer that characterizes whether it is the result of vehicle steering behavior information.

And a second step of combining the packed data and the first behavior feature vector to input the packed data and the first behavior feature vector to the second feature extraction layer to obtain a second behavior feature vector.

And thirdly, combining the packed data and the second behavior feature vector to input the packed data and the second behavior feature vector into the third feature extraction layer to obtain a third behavior feature vector.

And a fourth step of combining the first behavior feature vector and the second behavior feature vector to input the first behavior feature vector and the second behavior feature vector into the fourth feature extraction layer to obtain a fourth behavior feature vector.

And fifthly, combining the third behavior feature vector and the fourth behavior feature vector to obtain a combined behavior feature vector. The combined behavior feature vector may be a feature vector obtained by extracting features of the packed data.

And sixthly, inputting the combined behavior feature vector into the behavior recognition layer to obtain a candidate recognition result. The candidate recognition result may be a result of whether the candidate recognition result is the vehicle steering behavior information.

In practice, the behavior recognition layer may be obtained by:

the first substep, using a linear kernel function, determines an inner product corresponding to the combined behavior feature vector as a first inner product.

And a second sub-step of determining an inner product corresponding to the combined behavior feature vector by using a polynomial kernel function as a second inner product.

And a third sub-step of determining an inner product corresponding to the combined behavior feature vector by using a Gaussian kernel function as a third inner product.

And a fourth sub-step of determining an inner product corresponding to the combined behavior feature vector by using a radial basis function as a fourth inner product.

And a fifth substep of performing a loss processing on the first inner product, the second inner product, the third inner product and the fourth inner product by using a cross entropy loss function to obtain a first loss value, a second loss value, a third loss value and a fourth loss value. The first loss value may be a loss value corresponding to the first inner product. The second loss value may be a loss value corresponding to the second inner product. The third loss value may be a loss value corresponding to the third inner product. The fourth loss value may be a loss value corresponding to the fourth inner product.

And a sixth sub-step of combining the first loss value, the second loss value, the third loss value, and the fourth loss value to obtain a processed loss value set.

A seventh substep of, for each of the set of post-processing loss values, performing the following comparison value determination step:

and step one, removing the processed loss value from the processed loss value set to obtain a removed loss value set.

And step two, determining the sum of each removed loss value in the removed loss value set and the processed loss value as a first target loss value, and obtaining a first target loss value set.

And thirdly, determining the sum of the first target loss values in the first target loss value set as a comparison value corresponding to the processed loss value. Wherein the comparison value may be used to compare the magnitudes to determine the value of the corresponding kernel function. The corresponding kernel function may be the linear kernel function, the gaussian kernel function, the polynomial kernel function, or the radial basis kernel function.

And an eighth substep of determining a comparison value in the obtained comparison value set satisfying a preset comparison value condition. The preset comparison value condition may be a minimum comparison value in the comparison value set.

And a ninth substep, determining a function corresponding to the comparison value as a kernel function of a preset behavior recognition model, and obtaining an updated behavior recognition model serving as a behavior recognition layer. The preset behavior recognition model may be used as a model for behavior recognition. The input of the preset behavior recognition model may be a feature vector obtained by extracting features of the packed data. The output of the preset behavior recognition model may represent whether the output is the result of the vehicle steering behavior information. For example, the preset behavior recognition model may be a support vector machine (Support Vector Machine, SVM).

Seventh, in response to determining that the candidate recognition result represents the result of the vehicle steering behavior information, determining the result representing the behavior information corresponding to the preset vehicle behavior characteristic information as the recognition result.

And eighth, in response to determining that the candidate recognition result represents the result which is not the vehicle steering behavior information, determining the result which represents the behavior information corresponding to the preset vehicle behavior characteristic information as the recognition result.

The first to eighth steps are taken as an invention point of the embodiments of the present disclosure, and solve the third technical problem mentioned in the background art, namely, performing behavior recognition on at least one real-time vehicle driving data set, where the behavior recognition includes more useless behavior features, so that the accuracy of behavior recognition is low. Factors that lead to low accuracy of behavior recognition tend to be as follows: and carrying out behavior recognition on at least one real-time vehicle driving data set, wherein the at least one real-time vehicle driving data set contains more useless behavior characteristics, so that the accuracy of behavior recognition is low. If the above factors are solved, the accuracy of vehicle behavior recognition is improved. In order to achieve the effect, firstly, the packed data is sent to a first feature extraction layer of a preset vehicle behavior recognition model to obtain a first behavior feature vector. And secondly, carrying out combination processing on the packed data and the first behavior feature vector so as to input the packed data and the first behavior feature vector into the second feature extraction layer to obtain a second behavior feature vector. And thirdly, carrying out combination processing on the packed data and the two behavior feature vectors to input the packed data and the two behavior feature vectors into the third feature extraction layer to obtain a third behavior feature vector. Fourth, the first behavior feature vector and the second behavior feature vector are combined and processed to be input into the fourth feature extraction layer, and a fourth behavior feature vector is obtained. Fifth, the third behavior feature vector and the fourth behavior feature vector are combined to obtain a combined behavior feature vector. The combined behavior feature vector may be a feature vector obtained by extracting features of the packed data. The third behavior feature vector and the fourth behavior feature vector are combined, so that the situation that useful vehicle behavior features are not extracted during feature extraction can be avoided, and the richness of representing vehicle steering behavior features can be improved. And sixthly, inputting the combined behavior feature vector into the behavior recognition layer to obtain a candidate recognition result. In practice, the behavior recognition layer may be obtained by: and determining an inner product corresponding to the combined behavior feature vector by using a linear kernel function as a first inner product. And determining an inner product corresponding to the combined behavior feature vector by using a polynomial kernel function as a second inner product. And determining an inner product corresponding to the combined behavior feature vector by using a Gaussian kernel function as a third inner product. And determining an inner product corresponding to the combined behavior feature vector by using a radial basis function as a fourth inner product. And performing loss processing on the first inner product, the second inner product, the third inner product and the fourth inner product by using a cross entropy loss function to obtain a first loss value, a second loss value, a third loss value and a fourth loss value. And combining the first loss value, the second loss value, the third loss value and the fourth loss value to obtain a processed loss value set. For each post-processing loss value in the set of post-processing loss values, the following comparison value determination step is performed: and removing the processed loss value from the processed loss value set to obtain a removed loss value set. And determining the sum of each removed loss value in the removed loss value set and the processed loss value as a first target loss value to obtain a first target loss value set. And determining the sum of the first target loss values in the first target loss value set as a comparison value corresponding to the processed loss value. Wherein the comparison value may be used to compare the magnitudes to determine the value of the corresponding kernel function. The corresponding function may be the linear kernel function, the gaussian kernel function, the polynomial kernel function, or the radial basis kernel function. And determining the comparison value which meets the preset comparison value condition in the obtained comparison value set. The preset comparison value condition may be a minimum comparison value in the comparison value set. And determining a function corresponding to the comparison value as a kernel function of a preset behavior recognition model, and obtaining an updated behavior recognition model serving as a behavior recognition layer. The preset behavior recognition model may be used as a model for behavior recognition. The input of the preset behavior recognition model may be a feature vector obtained by extracting features of the packed data. The output of the preset behavior recognition model may represent whether the output is the result of the vehicle steering behavior information. The comparison value may be a score value corresponding to each kernel function, and the lower the score, the higher the accuracy, so that the feature vector after feature extraction is performed on the packed data by using the kernel function corresponding to the smallest comparison value is used for performing behavior recognition, and the accuracy of behavior recognition may be improved. Seventh, in response to determining that the candidate recognition result represents the result of the vehicle steering behavior information, determining the result representing the behavior information corresponding to the preset vehicle behavior feature information as the recognition result. Eighth, in response to determining that the candidate recognition result characterizes not the vehicle steering behavior information, determining a result characterizing not the behavior information corresponding to the preset vehicle behavior feature information as a recognition result. The comparison value can be a score value corresponding to each kernel function, and the lower the score is, the higher the accuracy is, so that the feature of the steering behavior of the characterization vehicle with high richness is identified by utilizing the kernel function corresponding to the smallest comparison value, and the accuracy of behavior identification can be improved.

Step 1036, in response to determining that the identification result is a result representing the behavior information corresponding to the preset vehicle behavior feature information, transmitting the real-time vehicle running data set corresponding to the filtered vehicle running data set to the database corresponding to the data filtering device.

In some embodiments, in response to determining that the identification result is a result representing behavior information corresponding to the preset vehicle behavior feature information, the real-time vehicle running data set corresponding to the filtered vehicle running data set is sent to the database corresponding to the data filtering device. The database may be used to store a database of real-time vehicle travel data sets.

In some optional implementations of some embodiments, the identification device corresponding to the automatic braking behavior feature information of the vehicle is an automatic braking data identification device, and the identification device corresponding to the steering behavior feature information of the vehicle is a steering data identification device of the vehicle. The automatic braking data identifying device may be a device for identifying whether the packed data is data of automatic braking behavior characteristic information of the vehicle. The vehicle steering data identifying device may be a device for identifying whether the package data is data of vehicle steering behavior characteristic information.

The above embodiments of the present disclosure have the following advantageous effects: according to the data transmission method of some embodiments of the present disclosure, the calculation amount of behavior recognition can be reduced, and the accuracy of behavior feature recognition can be improved. Specifically, the reason why the calculation amount of behavior recognition is large and the accuracy of behavior recognition is low is that: at least one real-time vehicle running data set which is at the current moment and contains a plurality of behavior features is identified, so that the calculation amount of behavior identification is large, and the accuracy of behavior identification is low. Based on this, the data transmission method of some embodiments of the present disclosure receives at least one real-time vehicle travel data set for at least one vehicle transmitted by the data dividing server; filtering each real-time vehicle running data set in the at least one real-time vehicle running data set by using data filtering equipment corresponding to preset vehicle behavior characteristic information to obtain at least one filtered vehicle running data set; the real-time vehicle running data set meeting the preset vehicle behavior characteristic information is filtered from the real-time vehicle running data set, the filtered vehicle running data set can show the behavior characteristics of the vehicle, and the data quantity is small relative to the real-time vehicle running data set, so that the calculation quantity of the follow-up behavior characteristic identification can be reduced. For each of the at least one filtered vehicle travel data set, performing the determining step of: splicing the filtered vehicle running data set with the corresponding historical vehicle running data set sequence in the cache layer to obtain a spliced vehicle running data set sequence; determining whether the time sequence corresponding to the spliced vehicle running data set sequence is continuous or not; the spliced vehicle running data set sequence indicates that the vehicle behavior features are rich by determining whether the spliced vehicle running data set sequence is continuous or not. So that the utility of the above-described sequence of spliced vehicle travel data sets can be determined. In response to determining continuity, determining a number of spliced vehicle travel data sets in the sequence of spliced vehicle travel data sets as a first number; in response to determining that the first number is greater than or equal to a preset value, performing data packaging on the spliced vehicle driving data group sequence to obtain packaged data; performing behavior recognition on the packed data by using recognition equipment corresponding to the data filtering equipment to obtain a recognition result; the behavior characteristics contained in the packed data are rich, so that the accuracy of behavior characteristic identification can be improved by carrying out behavior identification on the packed data. And transmitting the real-time vehicle running data set corresponding to the filtered vehicle running data set to a database corresponding to the data filtering equipment in response to the fact that the identification result is the result representing the behavior information corresponding to the preset vehicle behavior characteristic information. And filtering the real-time vehicle running data set meeting the preset vehicle behavior characteristic information from the at least one real-time vehicle running data set, and carrying out behavior characteristic recognition on the packed data to reduce the calculated amount of behavior recognition and improve the accuracy of behavior recognition.

With further reference to fig. 2, as an implementation of the method shown in the above figures, the present disclosure provides some embodiments of a data transmission apparatus, which correspond to those method embodiments shown in fig. 1, and which are particularly applicable in various electronic devices.

As shown in fig. 2, the data transmission apparatus 200 of some embodiments includes: a receiving unit 201, a filtering processing unit 202, and an executing unit 203, wherein the receiving unit 201 is configured to receive at least one real-time vehicle travel data set for at least one vehicle, which is transmitted by the data dividing server; a filtering processing unit 202, configured to perform filtering processing on each real-time vehicle running data set in the at least one real-time vehicle running data set by using a data filtering device corresponding to preset vehicle behavior feature information, so as to obtain at least one filtered vehicle running data set; an execution unit 203 configured to execute, for each of the at least one filtered vehicle travel data set, the following determination steps: splicing the filtered vehicle running data set with the corresponding historical vehicle running data set sequence in the cache layer to obtain a spliced vehicle running data set sequence; determining whether the time sequence corresponding to the spliced vehicle running data set sequence is continuous or not; in response to determining continuity, determining a number of spliced vehicle travel data sets in the sequence of spliced vehicle travel data sets as a first number; in response to determining that the first number is greater than or equal to a preset value, performing data packaging on the spliced vehicle driving data group sequence to obtain packaged data; performing behavior recognition on the packed data by using recognition equipment corresponding to the data filtering equipment to obtain a recognition result; and transmitting the real-time vehicle running data set corresponding to the filtered vehicle running data set to a database corresponding to the data filtering equipment in response to the fact that the identification result is the result representing the behavior information corresponding to the preset vehicle behavior characteristic information.

It will be appreciated that the elements described in the data transmission apparatus 200 correspond to the respective steps in the method described with reference to fig. 1. Thus, the operations, features and resulting benefits described above for the method are equally applicable to the apparatus 200 and the units contained therein, and are not described in detail herein.

Referring now to fig. 3, a schematic diagram of an electronic device 300 (e.g., the behavior recognition server of fig. 1) suitable for use in implementing some embodiments of the present disclosure is shown. The server illustrated in fig. 3 is merely an example, and should not be construed as limiting the functionality and scope of use of embodiments of the present disclosure in any way.

As shown in fig. 3, the electronic device 300 may include a processing means 301 (e.g., a central processing unit, a graphics processor, etc.) that may perform various suitable actions and processes in accordance with a program stored in a Read Only Memory (ROM) 302 or a program loaded from a storage means 308 into a Random Access Memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the electronic apparatus 300 are also stored. The processing device 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input/output (I/O) interface 305 is also connected to bus 304.

In general, the following devices may be connected to the I/O interface 305: input devices 306 including, for example, a touch screen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; an output device 307 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 308 including, for example, magnetic tape, hard disk, etc.; and communication means 309. The communication means 309 may allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. While fig. 3 shows an electronic device 300 having various means, it is to be understood that not all of the illustrated means are required to be implemented or provided. More or fewer devices may be implemented or provided instead. Each block shown in fig. 3 may represent one device or a plurality of devices as needed.

In particular, according to some embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such embodiments, the computer program may be downloaded and installed from a network via communications device 309, or from storage device 308, or from ROM 302. The above-described functions defined in the methods of some embodiments of the present disclosure are performed when the computer program is executed by the processing means 301.

It should be noted that, in some embodiments of the present disclosure, the computer readable medium may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In some embodiments of the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In some embodiments of the present disclosure, however, the computer-readable signal medium may comprise a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.

In some implementations, the clients, servers may communicate using any currently known or future developed network protocol, such as HTTP (Hyper Text Transfer Protocol ), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the internet (e.g., the internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed networks.

The computer readable medium may be embodied in the apparatus; or may exist alone without being incorporated into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: receiving at least one real-time vehicle driving data set for at least one vehicle, which is transmitted by a data dividing server; filtering each real-time vehicle running data set in the at least one real-time vehicle running data set by using data filtering equipment corresponding to preset vehicle behavior characteristic information to obtain at least one filtered vehicle running data set; for each of the at least one filtered vehicle travel data set, performing the determining step of: splicing the filtered vehicle running data set with the corresponding historical vehicle running data set sequence in the cache layer to obtain a spliced vehicle running data set sequence; determining whether the time sequence corresponding to the spliced vehicle running data set sequence is continuous or not; in response to determining continuity, determining a number of spliced vehicle travel data sets in the sequence of spliced vehicle travel data sets as a first number; in response to determining that the first number is greater than or equal to a preset value, performing data packaging on the spliced vehicle driving data group sequence to obtain packaged data; performing behavior recognition on the packed data by using recognition equipment corresponding to the data filtering equipment to obtain a recognition result; and transmitting the real-time vehicle running data set corresponding to the filtered vehicle running data set to a database corresponding to the data filtering equipment in response to the fact that the identification result is the result representing the behavior information corresponding to the preset vehicle behavior characteristic information.

Computer program code for carrying out operations for some embodiments of the present disclosure may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. 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 case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The units described in some embodiments of the present disclosure may be implemented by means of software, or may be implemented by means of hardware. The described units may also be provided in a processor, for example, described as: a processor includes a receiving unit, a filtering processing unit, and an executing unit. The names of these units do not in any way limit the unit itself, for example, the receiving unit can also be described as "a unit which receives at least one real-time vehicle travel data set for at least one vehicle" transmitted by the data dividing server.

The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a Complex Programmable Logic Device (CPLD), and the like.

The foregoing description is only of the preferred embodiments of the present disclosure and description of the principles of the technology being employed. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments of the present disclosure is not limited to the specific combination of the above technical features, but encompasses other technical features formed by any combination of the above technical features or their equivalents without departing from the spirit of the invention. Such as the above-described features, are mutually substituted with (but not limited to) the features having similar functions disclosed in the embodiments of the present disclosure.

Claims

1. A data transmission method, comprising:

receiving at least one real-time vehicle driving data set for at least one vehicle, which is transmitted by a data dividing server;

filtering each real-time vehicle running data set in the at least one real-time vehicle running data set by using data filtering equipment corresponding to preset vehicle behavior characteristic information to obtain at least one filtered vehicle running data set;

for each of the at least one filtered vehicle travel data set, performing the following determining step:

splicing the filtered vehicle running data set with the corresponding historical vehicle running data set sequence in the cache layer to obtain a spliced vehicle running data set sequence;

determining whether the time sequence corresponding to the spliced vehicle driving data set sequence is continuous or not;

in response to determining continuity, determining a number of spliced vehicle travel data sets in the sequence of spliced vehicle travel data sets as a first number;

in response to determining that the first number is greater than or equal to a preset value, performing data packaging on the spliced vehicle driving data group sequence to obtain packaged data;

performing behavior recognition on the packed data by using recognition equipment corresponding to the data filtering equipment to obtain a recognition result;

And in response to determining that the identification result is a result representing the behavior information corresponding to the preset vehicle behavior characteristic information, sending the real-time vehicle running data set corresponding to the filtered vehicle running data set to a database corresponding to the data filtering equipment.

2. The method of claim 1, wherein each of the at least one real-time vehicle travel data set for the at least one vehicle transmitted by the data partitioning server is obtained by:

for each of the plurality of traveling vehicles, performing the steps of:

acquiring a real-time full-quantity vehicle running data set and vehicle identification code data corresponding to the running vehicle;

determining behavior recognition server information corresponding to the running vehicle according to the vehicle identification code data;

and determining the real-time full-quantity vehicle driving data set as a real-time vehicle driving data set, and sending the real-time full-quantity vehicle driving data set to a behavior recognition server corresponding to the behavior recognition server information.

3. The method of claim 1, wherein, after said determining the number of spliced vehicle travel data sets in the sequence of spliced vehicle travel data sets as the first number in response to determining continuity, further comprising:

Deleting the spliced vehicle running data sets meeting the preset position conditions in the spliced vehicle running data set sequence in response to determining the discontinuity, so as to obtain a deleted vehicle running data set sequence;

determining the number of the deleted vehicle running data sets in the deleted vehicle running data set sequence as a second number;

in response to determining that the second number is greater than or equal to the preset value, data packaging is conducted on the deleted vehicle driving data group sequence to obtain packaged data;

and in response to determining that the second number is less than the preset value, determining the deleted vehicle travel data set sequence as a historical vehicle travel data set sequence for saving to the cache layer.

4. The method of claim 1, wherein after data packaging the sequence of spliced vehicle drive data sets in response to determining that the first number is greater than or equal to a preset number, further comprising:

and in response to determining that the first number is less than the preset value, determining the spliced vehicle running data set sequence as a historical vehicle running data set sequence to save to the cache layer.

5. The method of claim 1, wherein the preset vehicle behavior feature information comprises at least one of:

the characteristic information of the automatic braking behavior of the vehicle and the characteristic information of the steering behavior of the vehicle.

6. The method of claim 5, wherein the identification device corresponding to the vehicle automatic braking behavior feature information is an automatic braking data identification device and the identification device corresponding to the vehicle steering behavior feature information is a vehicle steering data identification device.

7. A data transmission apparatus comprising:

a receiving unit configured to receive at least one real-time vehicle travel data set for at least one vehicle transmitted by the data dividing server;

the filtering processing unit is configured to utilize data filtering equipment corresponding to preset vehicle behavior characteristic information to filter each real-time vehicle running data set in the at least one real-time vehicle running data set to obtain at least one filtered vehicle running data set;

an execution unit configured to execute, for each of the at least one filtered vehicle travel data set, the following determination steps: splicing the filtered vehicle running data set with the corresponding historical vehicle running data set sequence in the cache layer to obtain a spliced vehicle running data set sequence; determining whether the time sequence corresponding to the spliced vehicle driving data set sequence is continuous or not; in response to determining continuity, determining a number of spliced vehicle travel data sets in the sequence of spliced vehicle travel data sets as a first number; in response to determining that the first number is greater than or equal to a preset value, performing data packaging on the spliced vehicle driving data group sequence to obtain packaged data; performing behavior recognition on the packed data by using recognition equipment corresponding to the data filtering equipment to obtain a recognition result; and in response to determining that the identification result is a result representing the behavior information corresponding to the preset vehicle behavior characteristic information, sending the real-time vehicle running data set corresponding to the filtered vehicle running data set to a database corresponding to the data filtering equipment.

8. A server, comprising:

one or more processors;

a storage device having one or more programs stored thereon,

when executed by the one or more processors, causes the one or more processors to implement the method of any of claims 1-6.

9. A computer readable medium having stored thereon a computer program, wherein the program when executed by a processor implements the method of any of claims 1-6.