CN112581760B

CN112581760B - Traffic data matching method and device for intelligent traffic

Info

Publication number: CN112581760B
Application number: CN202011428875.3A
Authority: CN
Inventors: 张兴莉; 冯丽琴; 张涛
Original assignee: Wuhan Suzhi Intelligent Transportation Service Co ltd
Current assignee: Wuhan Suzhi Intelligent Transportation Service Co ltd
Priority date: 2020-12-09
Filing date: 2020-12-09
Publication date: 2022-06-24
Anticipated expiration: 2040-12-09
Also published as: CN112581760A

Abstract

The invention discloses a traffic data matching method and device for intelligent traffic. In the method, in order to ensure the correctness of the driving direction of the target vehicle, firstly, the driving direction data generated on the current driving road when the target vehicle drives is obtained, secondly, acquiring the driving direction data read by the vehicle-mounted terminal of the target vehicle from each driving distance, thus, the correctness of the driving direction can be determined again, the error of the driving line in the driving process is avoided, on the basis, when the vehicle-mounted terminal of the target vehicle touches the circuit to adjust the prompt information, judging whether the driving direction of the target vehicle is changed or not according to the driving direction data, if so, the target travel route data corresponding to the vehicle-mounted terminal of the target vehicle can be quickly determined, and thus, when the target vehicle meets an emergency, the intelligent adjustment can be carried out on the driving route in advance.

Description

Traffic data matching method and device for intelligent traffic

Technical Field

The disclosure relates to the technical field of traffic data processing of intelligent traffic, in particular to a traffic data matching method of intelligent traffic.

Background

With the rapid development of intelligent traffic, the driving routes of current vehicles are gradually increased, however, in the driving process, when traffic jam occurs, the vehicles can only slowly drive in situ, or can be adjusted by workers, so that the target vehicles cannot intelligently adjust the driving routes in advance according to emergency.

Disclosure of Invention

In order to solve the technical problems in the related art, the present disclosure provides a traffic data matching method and device for intelligent traffic.

The invention provides a traffic data matching method of intelligent traffic, which is applied to an intelligent traffic data processing terminal and comprises the following steps:

acquiring real-time driving direction data generated on a current driving road when a target vehicle drives to obtain a first group of driving direction data sets;

acquiring real-time driving direction data read by a vehicle-mounted terminal of the target vehicle from each driving distance to obtain a second group of driving direction data sets;

when detecting that the vehicle-mounted terminal of the target vehicle triggers the line adjustment prompt information, determining whether the real-time driving direction is changed according to the real-time driving direction data in the first group of driving direction data sets and the second group of driving direction data sets; and if the real-time driving direction is changed, determining target driving route data corresponding to the vehicle-mounted terminal of the target vehicle.

Preferably, after detecting that the vehicle-mounted terminal of the target vehicle triggers the route adjustment prompt message, determining whether the real-time driving direction is changed according to the real-time driving direction data in the first group of driving direction data sets and the second group of driving direction data sets, includes:

determining whether a first set of anomalies exists based on a first set of real-time travel direction data in the first set of travel direction data, the first set of anomalies being changes in real-time travel direction without changes in travel of the target vehicle;

determining whether a second group of abnormalities exist according to a second group of real-time driving direction data in the second group of driving direction data set, wherein the second group of abnormalities are changes of real-time driving directions in two adjacent driving routes;

determining whether a third group of abnormity exists according to the first group of driving direction data set and the real-time driving direction data in the same time period in the second group of driving direction data set, wherein the third group of abnormity is that the real-time driving direction generated by the driving of the target vehicle and the real-time driving direction read by the vehicle-mounted terminal of the target vehicle are changed;

and when the first group of abnormity, the second group of abnormity and the third group of abnormity exist at the same time, determining that the real-time driving direction changes.

Preferably, the first and second electrodes are formed of a metal,

determining whether a first set of anomalies exists based on a first set of real-time driving direction data in the first set of driving direction data, comprising: calculating a first group of driving direction data difference between first group of real-time driving direction data acquired at any two adjacent moments in the first group of driving direction data sets, and determining that a first group of abnormalities exist if the first group of driving direction data difference is larger than a first preset difference value;

determining whether a second set of anomalies exists based on a second set of real-time driving direction data in the second set of driving direction data, comprising: calculating a second group of driving direction data difference between second group of real-time driving direction data corresponding to two adjacent driving routes, and determining that a second group of abnormity exists if the second group of driving direction data difference is larger than a second preset difference value;

determining whether a third set of anomalies exists based on the real-time driving direction data in the first set of driving direction data and the second set of driving direction data, including: and calculating a third group of driving direction data difference between the first group of real-time driving direction data and the second group of real-time driving direction data obtained in the same time period, and determining that a third group of abnormity exists if the third group of driving direction data difference is larger than a third preset difference value.

Preferably, the first and second electrodes are formed of a metal,

the first group of real-time driving direction data and the second group of real-time driving direction data are stored in the same queue according to the data acquisition priority; determining whether a first set of anomalies exists based on a first set of real-time driving direction data in the first set of driving direction data, comprising: calculating a driving direction data difference between two adjacent first groups of real-time driving direction data stored in the queue to obtain a first group of driving direction data differences, and if the first group of driving direction data differences are larger than a first preset difference value, determining that a first group of abnormity exists;

determining whether a second set of anomalies exists based on a second set of real-time driving direction data in the second set of driving direction data, comprising: calculating a driving direction data difference between two adjacent second real-time driving direction data in the queue to obtain a second group of driving direction data differences, and if the second group of driving direction data differences are larger than a second preset difference value, determining that a second group of exceptions exist;

determining whether a third set of anomalies exists based on the real-time driving direction data in the first set of driving direction data and the second set of driving direction data, including: and calculating the driving direction data difference between the first group of real-time driving direction data and the second group of real-time driving direction data which are adjacent in the queue to obtain a third group of driving direction data difference, and if the third group of driving direction data difference is larger than a third preset difference value, determining that a third group of abnormality exists.

Preferably, obtaining real-time driving direction data generated on the current driving road when the target vehicle drives to obtain a first group of driving direction data sets includes:

when the target vehicle is detected to run, acquiring a current real-time running direction which is obtained by a GPS positioning module in the target vehicle and corresponds to the running target vehicle after running;

and converting the current real-time driving direction into driving direction data on a current driving road to obtain a first group of real-time driving direction data, and storing the first group of real-time driving direction data.

Preferably, the obtaining real-time driving direction data read by the vehicle-mounted terminal of the target vehicle from each driving distance to obtain a second group of driving direction data sets includes: acquiring current real-time driving direction data read by a vehicle-mounted terminal of the target vehicle from each driving distance;

and converting the current real-time driving direction data into driving direction data on a current driving road to obtain a second group of real-time driving direction data, and storing the second group of real-time driving direction data.

Preferably, the process of determining the target travel route data corresponding to the vehicle-mounted terminal of the target vehicle includes: counting the times of the change of the real-time driving direction of the vehicle-mounted terminal of the target vehicle in a specified time period; and if the times reach preset times, determining target driving route data corresponding to the vehicle-mounted terminal of the target vehicle.

Preferably, the method further comprises: and determining target driving route data corresponding to a vehicle-mounted terminal of the target vehicle, and judging whether a target monitoring block corresponding to the target driving route data has a traffic safety risk or not.

The invention also provides a traffic data matching device of intelligent traffic, which is applied to an intelligent traffic data processing terminal, and the device comprises:

the first data acquisition module is used for acquiring real-time driving direction data generated on a current driving road when the target vehicle drives to obtain a first group of driving direction data sets;

the second data acquisition module is used for acquiring real-time driving direction data read by the vehicle-mounted terminal of the target vehicle from each driving distance to obtain a second group of driving direction data sets;

the driving direction judging module is used for determining whether the real-time driving direction is changed or not according to the real-time driving direction data in the first group of driving direction data sets and the second group of driving direction data sets after detecting that the vehicle-mounted terminal of the target vehicle triggers the line adjustment prompt information; and if the real-time driving direction is changed, determining target driving route data corresponding to the vehicle-mounted terminal of the target vehicle.

Preferably, the apparatus further comprises: and the safety risk judgment module is used for determining target driving route data corresponding to the vehicle-mounted terminal of the target vehicle and judging whether a target monitoring block corresponding to the target driving route data has a traffic safety risk or not.

The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects.

The invention provides a traffic data matching method and a device of intelligent traffic, in order to ensure the correctness of the traveling direction of a target vehicle, firstly, the traveling direction data generated on the current traveling road when the target vehicle travels are obtained, secondly, the traveling direction data read by a vehicle-mounted terminal of the target vehicle from each traveling distance are obtained, thus the correctness of the traveling direction can be determined again, the traveling route error in the traveling process is avoided, on the basis, after the vehicle-mounted terminal of the target vehicle touches the route adjustment prompt information, whether the traveling direction of the target vehicle is changed or not is judged according to the traveling direction data, if the traveling direction is judged to be changed, the target traveling route data corresponding to the vehicle-mounted terminal of the target vehicle can be rapidly determined, thus, when the target vehicle encounters an emergency, the intelligent adjustment can be carried out to the route of traveling in advance.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.

Fig. 1 is a flowchart of a traffic data matching method for intelligent traffic according to an embodiment of the present invention.

Fig. 2 is a block diagram of a traffic data matching device for intelligent traffic according to an embodiment of the present invention.

Fig. 3 is a schematic diagram of a hardware structure of an intelligent traffic data processing terminal according to an embodiment of the present invention.

Detailed Description

Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

Referring to fig. 1, the present invention provides a flow chart of a traffic data matching method for intelligent traffic, which can be applied to an intelligent traffic data processing terminal, and the intelligent traffic data processing terminal specifically executes the contents described in the following steps S11-S13 when implementing the method.

Step S11, real-time driving direction data generated on the current driving road when the target vehicle drives are obtained, and a first group of driving direction data sets are obtained.

And step S12, acquiring real-time driving direction data read by the vehicle-mounted terminal of the target vehicle from each driving distance to obtain a second group of driving direction data sets.

Step S13, when detecting that the vehicle-mounted terminal of the target vehicle triggers the line adjustment prompt message, determining whether the real-time driving direction is changed according to the real-time driving direction data in the first group of driving direction data sets and the second group of driving direction data sets; and if the real-time driving direction is changed, determining target driving route data corresponding to the vehicle-mounted terminal of the target vehicle.

The following advantageous effects can be achieved when the method described in the above steps S11-S13 is performed:

in order to ensure the correctness of the driving direction of the target vehicle, firstly, the driving direction data generated on the current driving road when the target vehicle drives is obtained, secondly, acquiring the driving direction data read by the vehicle-mounted terminal of the target vehicle from each driving distance, thus, the correctness of the driving direction can be determined again, the error of the driving line in the driving process is avoided, on the basis, when the vehicle-mounted terminal of the target vehicle touches the circuit to adjust the prompt information, judging whether the driving direction of the target vehicle is changed or not according to the driving direction data, if so, the target travel route data corresponding to the vehicle-mounted terminal of the target vehicle can be quickly determined, and thus, when the target vehicle meets an emergency, the intelligent adjustment can be carried out on the driving route in advance.

In specific implementation, in order to quickly determine whether there is a change in the real-time driving direction of the target vehicle during driving, the step S13 is executed to determine whether there is a change in the real-time driving direction according to the real-time driving direction data in the first group of driving direction data sets and the second group of driving direction data sets after detecting that the on-board terminal of the target vehicle triggers the route adjustment prompting message.

In a first embodiment, it is determined whether a first set of anomalies exists based on a first set of real-time driving direction data in the first set of driving direction data, the first set of anomalies being changes in real-time driving direction without changes in driving of the target vehicle.

In a second embodiment, it is determined whether a second group of anomalies is present, which is a change in the real-time driving direction in two adjacent driving routes, on the basis of a second group of real-time driving direction data from the second group of driving direction data sets.

In a third embodiment, whether a third group of abnormalities exists is determined according to the real-time driving direction data in the same time period in the first group of driving direction data sets and the second group of driving direction data sets, and the third group of abnormalities is that the real-time driving direction generated by the driving of the target vehicle and the real-time driving direction read by the vehicle-mounted terminal of the target vehicle change.

In a fourth embodiment, when the first, second, and third group abnormalities are present at the same time, it is determined that there is a change in the real-time travel direction.

Through the four embodiments, whether the real-time driving direction of the target vehicle is changed or not can be quickly judged when the target vehicle drives.

It is to be understood that the step S13 of the first embodiment for determining whether there is a first group of abnormality according to the first group of real-time driving direction data in the first group of driving direction data set specifically includes: and calculating a first group of driving direction data difference between first group of real-time driving direction data acquired at any two adjacent moments in the first group of driving direction data sets, and determining that a first group of abnormalities exist if the first group of driving direction data difference is larger than a first preset difference value.

It is to be understood that the determining whether there is a second group of abnormality according to the second group of real-time driving direction data in the second group of driving direction data set in the second embodiment of step S13 specifically includes: calculating a second group of driving direction data difference between second group of real-time driving direction data corresponding to two adjacent driving routes, and determining that a second group of abnormity exists if the second group of driving direction data difference is larger than a second preset difference value;

it is understood that the determining whether the third group of abnormality exists according to the real-time driving direction data in the first group of driving direction data set and the second group of driving direction data set in the third embodiment of step S13 specifically includes: and calculating a third group of driving direction data difference between the first group of real-time driving direction data and the second group of real-time driving direction data obtained in the same time period, and determining that a third group of abnormity exists if the third group of driving direction data difference is larger than a third preset difference value.

Further, the first group of real-time driving direction data and the second group of real-time driving direction data are stored in the same queue according to the acquisition priority of the acquired data; based on the above description, the step S13 of determining whether there is a first group of abnormality according to the first group of real-time driving direction data in the first group of driving direction data set according to the first embodiment may further include: calculating a driving direction data difference between two adjacent first groups of real-time driving direction data stored in the queue to obtain a first group of driving direction data differences, and if the first group of driving direction data differences are larger than a first preset difference value, determining that a first group of abnormity exists;

determining whether a second set of anomalies exist according to a second set of real-time driving direction data in the second set of driving direction data as described in the second embodiment of step S13 may further include: calculating a driving direction data difference between two adjacent second real-time driving direction data in the queue to obtain a second group of driving direction data differences, and if the second group of driving direction data differences are larger than a second preset difference value, determining that a second group of exceptions exist;

in step S13, the determining whether there is a third group of abnormality according to the real-time driving direction data in the first group of driving direction data set and the second group of driving direction data set may specifically include: and calculating the driving direction data difference between the first group of real-time driving direction data and the second group of real-time driving direction data which are adjacent in the queue to obtain a third group of driving direction data difference, and if the third group of driving direction data difference is larger than a third preset difference value, determining that a third group of abnormality exists.

In a specific implementation, in order to accurately determine the driving direction of the target vehicle, and to ensure that the driving direction is consistent with the planned route, and to avoid driving errors, the step S11 obtains real-time driving direction data generated on the current driving road when the target vehicle is driving, to obtain the first set of driving direction data sets, and may specifically include the contents described in the following substeps 111-substep S112.

And a substep S111, when the target vehicle is detected to run, acquiring the current real-time running direction corresponding to the running target vehicle, which is acquired by a GPS positioning module in the target vehicle after the target vehicle runs.

And a substep S112, converting the current real-time driving direction into driving direction data on the current driving road to obtain a first group of real-time driving direction data, and storing the first group of real-time driving direction data.

By executing the following steps from step S111 to step S112, the driving direction of the target vehicle can be accurately determined based on the GPS positioning module, so as to ensure consistency with the planned route and avoid driving errors.

In a specific implementation, in order to know the correctness of the driving direction in real time and further search the driving data of the distance in the following step, the step S12 describes obtaining the real-time driving direction data read by the vehicle-mounted terminal of the target vehicle from each distance to obtain a second set of driving direction data sets, and may specifically include the contents described in the following substeps 121 to substep S122.

And a substep S121, acquiring the current real-time driving direction data read by the vehicle-mounted terminal of the target vehicle from each driving distance.

And a substep S122, converting the current real-time driving direction data into driving direction data on the current driving road to obtain a second group of real-time driving direction data, and storing the second group of real-time driving direction data.

Therefore, in the running process of the target vehicle, the correctness of the running direction can be known in real time through the current real-time running direction data read from each running distance by the vehicle-mounted terminal of the target vehicle, and the real-time running direction data obtained by running is stored, so that the running data of the running distance can be conveniently searched subsequently.

It can be understood that the process of determining the target travel route data corresponding to the vehicle-mounted terminal of the target vehicle includes: counting the times of changing of the real-time driving direction of the vehicle-mounted terminal of the target vehicle in a specified time period; and if the times reach preset times, determining target driving route data corresponding to the vehicle-mounted terminal of the target vehicle.

Based on the above, the present invention may further include step S14: and determining target driving route data corresponding to a vehicle-mounted terminal of the target vehicle, and judging whether a target monitoring block corresponding to the target driving route data has a traffic safety risk or not.

Further, the step S14 may include determining the target travel route data corresponding to the vehicle-mounted terminal of the target vehicle, and determining whether a traffic safety risk exists in the target monitoring block corresponding to the target travel route data, which is described in the following description.

Step S141, first block traffic road information and second block traffic road information for the target monitoring block are acquired.

For example, the traffic congestion weight of the second neighborhood traffic road information is smaller than the traffic congestion weight of the first neighborhood traffic road information.

Step S142, determining target traffic flow information of the target monitoring block according to the traffic time interval sequence of the second block traffic road information, and acquiring real-time monitoring information of the target monitoring block from the first block traffic road information according to the target traffic flow information; and determining the difference value between the target monitoring information identification degree of the real-time monitoring information and each candidate monitoring information identification degree in a preset information identification degree queue.

For example, the preset information identification degree queue includes a plurality of candidate monitoring information identification degrees, each candidate monitoring information identification degree is correspondingly provided with a traffic safety tag, and the traffic safety tags indicate that traffic safety risks exist or do not exist in the target monitoring block.

Step S143, selecting m candidate monitoring information identification degrees from the preset information identification degree queue based on a difference value between the target monitoring information identification degree and each candidate monitoring information identification degree; and judging whether the target monitoring block has traffic safety risks or not based on the m traffic safety labels with the candidate monitoring information identification degrees.

For example, traffic safety tags are used to determine the safety status of a target surveillance neighborhood. m is a positive integer greater than or equal to 1.

It can be understood that, by executing the above steps S141 to S143, first obtaining first block traffic road information and second block traffic road information, then determining target traffic flow information of a target monitoring block according to a traffic time period sequence of the second block traffic road information, further obtaining real-time monitoring information of the target monitoring block from the first block traffic road information, then determining a difference between a target monitoring information identification degree of the real-time monitoring information and each candidate monitoring information identification degree in a preset information identification degree queue, and finally determining whether there is a traffic safety risk in the target monitoring block based on m traffic safety tags of the candidate monitoring information identification degrees selected from the preset information identification degree queue.

Therefore, the street traffic road information with different traffic jam weights can be analyzed, so that the traffic time interval sequence and the real-time monitoring information can be determined relatively independently based on different street traffic road information, the influence deviation between the traffic time interval sequence and the real-time monitoring information can be ensured not to be overlarge, the reliability of the real-time monitoring information is improved, and the accuracy of the difference value of the target monitoring information identification degree and each candidate monitoring information identification degree in the preset information identification degree queue is ensured. Therefore, when a plurality of candidate monitoring information identification degrees are selected, the candidate monitoring information identification degrees corresponding to the traffic safety labels related to the target monitoring block can be selected as much as possible, so that when the traffic safety risk judgment of the target monitoring block is carried out based on the traffic safety labels, different safety characteristics identified by the target monitoring block can be comprehensively considered, the reliability of the traffic safety risk identification is improved, the traffic safety of the target monitoring block is ensured, and the problem of mistakenly judging the safety of the target monitoring block due to inaccurate identification is avoided.

In some examples, the selecting m candidate monitoring information recognizability from the preset information recognition degree queue based on the difference between the target monitoring information recognizability and each candidate monitoring information recognizability described in step S143 may include: and selecting m candidate monitoring information identification degrees with the largest difference from the preset information identification degree queue based on the difference between the target monitoring information identification degree and each candidate monitoring information identification degree in the preset information identification degree queue.

In practical application, in order to comprehensively consider different safety features identified by a target monitoring block to improve the reliability of traffic safety risk identification, the safety feature similarity corresponding to nodes in different monitoring times needs to be considered, so that the instant variability of the safety features is considered. To achieve this, in step S143, it is determined whether there is a traffic safety risk in the target monitoring block based on the m traffic safety tags identified by the candidate monitoring information, which may illustratively include the contents described in steps S1431 to S1436 below.

Step S1431, based on the tag similarity between every two adjacent traffic safety tags in the traffic safety tags of the m candidate monitoring information recognizability, determines a current state information set used for calculating the comprehensive information recognizability corresponding to the m candidate monitoring information recognizability.

Step S1432, based on the current state information set, obtaining a to-be-monitored block state information set corresponding to each block monitoring time node in a first set monitoring block time period of the target monitoring block, where the first set monitoring block time period includes at least two block monitoring time nodes, and the to-be-monitored block state information set corresponding to each block monitoring time node includes monitoring safety parameters of the monitoring block collected or calculated by a safety state verification unit in the target monitoring block in the corresponding block monitoring time node.

Step S1433, determining a security feature similarity rate between the to-be-monitored block status information sets corresponding to each block monitoring time node in the first set monitoring block time period.

Step S1434, determining a block picture record set of the target monitoring block in the first set monitoring block time period according to a security feature similarity between to-be-monitored block state information sets corresponding to each block monitoring time node in the first set monitoring block time period.

Step S1435, determining the security level index of the target monitoring block in the first set monitoring block time period according to the block picture record set.

Step S1436, calculating the comprehensive information identification degrees corresponding to the m candidate monitoring information identification degrees through the safety level index; judging whether the identification degree of the comprehensive information is greater than the identification degree of set information; determining that the target monitoring block has no traffic safety risk when the comprehensive information identification degree is determined to be greater than or equal to the set information identification degree; and when the comprehensive information identification degree is judged to be smaller than the set information identification degree, determining that the traffic safety risk exists in the target monitoring block, and locking the safety accident event information of the target monitoring block when the traffic safety risk exists in the target monitoring block.

Thus, by applying the contents described in the above steps S1431 to S1436, the safety feature similarity between the to-be-monitored block status information sets corresponding to each block monitoring time node in the first set monitoring block time period can be determined, the block picture record set of the target monitoring block in the first set monitoring block time period can be determined, the safety level index of the target monitoring block in the first set monitoring block time period can be determined according to the block picture record set, and the comprehensive information identification degree can be calculated based on the safety level index, so that the safety feature similarity corresponding to different monitoring time nodes can be considered, the instant variability of the safety feature can be considered, and the different safety features monitored by the target monitoring block can be comprehensively considered. It can be understood that whether the traffic safety risk exists in the target monitoring block or not is monitored through the comprehensive information identification degree, and the reliability of traffic safety risk identification can be improved.

Further, the obtaining of the to-be-monitored block state information set corresponding to each block monitoring time node of the target monitoring block in the first set monitoring block time period described in step S1432 may be implemented by the following contents described in steps S14321 to S14324.

Step S14321, obtaining monitoring security parameters of the monitored neighborhood collected by the security status verification unit in the target monitored neighborhood within the set time duration after the first neighborhood monitoring time node starts, and determining a set of to-be-monitored neighborhood status information corresponding to the first neighborhood monitoring time node according to the monitoring security parameters of the monitored neighborhood collected by the security status verification unit in the target monitored neighborhood within the set time duration after the first neighborhood monitoring time node starts, where the first neighborhood monitoring time node is any one of the neighborhood monitoring time nodes within the first set monitored neighborhood time duration.

Step S14322, in a case that the monitoring security parameter of the monitored neighborhood is not acquired within a set time duration after the security status verification unit in the target monitored neighborhood starts at a second neighborhood monitoring time node, determining a set of to-be-monitored neighborhood status information corresponding to the second neighborhood monitoring time node according to the monitoring security parameter of the monitored neighborhood calculated by the security status verification unit in the target monitored neighborhood, where the second neighborhood monitoring time node is any neighborhood monitoring time node other than the first neighborhood monitoring time node within the first set monitored neighborhood time period.

Step S14323, the safety state verification unit in the target monitoring block does not collect the monitoring safety parameters of the monitoring block within the set time interval after the monitoring time node of the third block is started, and the monitored block state information sets corresponding to the continuous first set number of block monitoring time nodes before the third block monitoring time node are all determined according to the monitoring safety parameters of the monitored blocks calculated by the safety state verification unit, sending a monitoring block acquisition instruction to the safety state verification unit, so that the security status verification unit collects the monitoring security parameters of the monitoring neighborhood in response to the monitoring neighborhood collection instruction, the third neighborhood monitoring time node is any neighborhood monitoring time node except the first neighborhood monitoring time node and the second neighborhood monitoring time node in the first set monitoring neighborhood time period.

Step S14324, obtaining the monitoring safety parameters of the monitoring neighborhood collected by the safety status verifying unit in response to the monitoring neighborhood collecting instruction, and determining the neighborhood status information set to be monitored corresponding to the third neighborhood monitoring time node according to the monitoring safety parameters of the monitoring neighborhood collected by the safety status verifying unit in response to the monitoring neighborhood collecting instruction.

It can be understood that by executing the steps S14321 to S14324, the set of the to-be-monitored block status information corresponding to the different block monitoring time nodes can be completely determined, so that sufficient data basis is provided for the subsequent calculation of the comprehensive information identification degree, and the reliability of the subsequent calculation of the comprehensive information identification degree is ensured.

Further, the determination of the security feature similarity between the to-be-monitored neighborhood state information sets corresponding to the neighborhood monitoring time nodes in the first set monitoring neighborhood time period described in step S1433 may be implemented by the following two implementation manners.

In the first implementation mode, a dynamic monitoring security parameter set is determined from a to-be-monitored block state information set corresponding to each block monitoring time node in a first set monitoring block time period; and respectively determining each to-be-monitored block state information set except the dynamic monitoring safety parameter set in the to-be-monitored block state information set corresponding to each block monitoring time node in the first set monitoring block time period, and the safety feature similarity between the to-be-monitored block state information set and the dynamic monitoring safety parameter set.

In a second implementation manner, the security feature similarity rates between the to-be-monitored neighborhood state information sets corresponding to every two adjacent neighborhood monitoring time nodes in the first set monitoring neighborhood time period are respectively determined.

It will be appreciated that the above described embodiments of determining a security feature similarity ratio may alternatively be used, thereby allowing flexible and fast calculation of the security feature similarity ratio.

On the basis of the above step S1431 to step S1436, the to-be-monitored neighborhood state information set corresponding to each neighborhood monitoring time node in the first set monitoring neighborhood time period includes an updatable state data set and a non-updatable state data set, and the neighborhood picture record set includes a first neighborhood picture record set determined according to the security feature similarity rate corresponding to the updatable state data set of each specified neighborhood monitoring time node in the first set monitoring neighborhood time period, and a second neighborhood picture record set determined according to the security feature similarity rate corresponding to the non-updatable state data set of each specified neighborhood monitoring time node in the first set monitoring neighborhood time period. Based on this, the step S1435 of determining the safety level index of the target monitoring block in the first set monitoring block time period according to the block picture record set includes the step S14350: and determining the safety level index of the target monitoring block in the first set monitoring block time period according to the first block picture record set and the second block picture record set.

Further, the step S14350 of determining the safety level index of the target monitoring block in the first set monitoring block time period according to the first block picture record set and the second block picture record set may further include the following steps S14351 to S14353.

Step S14351, determining that the security level index of the target monitoring block in the first set monitoring block time period is the first target level index, when the block picture variation coefficient corresponding to the first block picture record set is not smaller than the preset first variation coefficient threshold and the block picture variation coefficient corresponding to the second block picture record set is not smaller than the preset second variation coefficient threshold.

Step S14352 is executed to determine that the safety level index of the target monitored block in the first set monitored block time period is a second target level index, when the block picture variation coefficient corresponding to the first block picture record set is not smaller than the first variation coefficient threshold and the block picture variation coefficient corresponding to the second block picture record set is smaller than the second variation coefficient threshold.

Step S14353 is executed to determine that the security level index of the target monitored block in the first set monitored block time period is a third target level index, when the block picture variation coefficient corresponding to the first block picture record set is smaller than the first variation coefficient threshold and the block picture variation coefficient corresponding to the second block picture record set is smaller than the second variation coefficient threshold.

Therefore, different third target grade indexes can be determined according to different street picture change coefficients, and therefore the third target grade indexes are ensured to be matched with picture records monitored by actual target monitoring streets.

Further, the step S1434 determines, according to the security feature similarity between the to-be-monitored block status information sets corresponding to each block monitoring time node in the first set monitoring block time period, a block picture record set of the target monitoring block in the first set monitoring block time period, including the contents described in the following steps S14341 and S14342.

Step S14341 is to determine at least one target updatable status data set in which the monitoring block credibility weight is higher than the first set credibility weight threshold and at least one target non-updatable status data set in which the monitoring block credibility weight is higher than the second set credibility weight threshold from the to-be-monitored block status information sets corresponding to each block monitoring time node in the first set monitoring block time period.

Step S14342 is to determine the first street view record set according to the security feature similarity corresponding to the at least one target updatable status data set, and determine the second street view record set according to the security feature similarity corresponding to the at least one target non-updatable status data set.

In addition, the determining, according to the security feature similarity between the to-be-monitored neighborhood state information sets corresponding to the respective neighborhood monitoring time nodes in the first set monitored neighborhood time period and described in step S1434, a neighborhood picture record set of the target monitored neighborhood in the first set monitored neighborhood time period may also be implemented by: determining relevance parameters of the safety feature similarity rates according to the quantity of the to-be-monitored block state information contained in the to-be-monitored block state information set corresponding to each block monitoring time node in the first set monitoring block time period; and determining a block picture record set of the target monitoring block in the first set monitoring block time period according to the safety feature similarity between the block state information sets to be monitored corresponding to the block monitoring time nodes in the first set monitoring block time period and the relevance parameters of the safety feature similarity.

It can be understood that the two further implementation manners described above with respect to step S1434 are performed according to the reliability weight of the monitored neighborhood and the relevance parameter, respectively, so that an implementation manner that is easy to implement can be flexibly selected according to the target monitored neighborhood for implementation.

It is to be understood that the determining of the difference between the target monitoring information identification degree of the real-time monitoring information and each candidate monitoring information identification degree in the preset information identification degree queue described in step S142 may be implemented by any one of the following three embodiments.

In the first embodiment, the difference between the target monitoring information identification degree and the candidate monitoring information identification degree is determined based on the monitoring timing sequence identification coefficient of the target monitoring information identification degree and the candidate monitoring information identification degree.

In a second embodiment, the difference between the target monitoring information identification degree and the candidate monitoring information identification degree is determined based on the monitoring event identification coefficient between the target monitoring information identification degree and the candidate monitoring information identification degree.

In a third embodiment, a difference between the target monitoring information identification degree and the candidate monitoring information identification degree is determined based on a monitoring risk identification coefficient between the target monitoring information identification degree and the candidate monitoring information identification degree.

In one possible embodiment, in order to ensure that the target traffic flow information of the target monitoring block can cover the target traffic flow information identified by the target monitoring block, the step S142 determines the target traffic flow information of the target monitoring block according to the traffic time interval sequence of the second block traffic road information, and further includes the following steps S1421-S1426.

Step S1421, multiple traffic restriction information combinations corresponding to the traffic time period sequence of the second block traffic road information and a traffic mode information set corresponding to each traffic restriction information combination are obtained, where each traffic restriction information combination includes multiple different traffic information labels.

Step S1422, determine a first traffic restriction identifier sequence corresponding to the traffic restriction information combination in the traffic manner information set corresponding to the traffic restriction information combination.

Step S1423, the first traffic restriction mark sequence corresponding to the traffic restriction information combination is adopted to correct the speed limit sign information, and the speed limit sign information correction result of each traffic information label in the traffic restriction information combination is obtained.

Step S1424, based on the speed limit sign information correction result of each traffic information tag in multiple traffic restriction information combinations, performing traffic rate update on the first traffic restriction identifier sequence corresponding to the traffic restriction information combination to obtain a first updated traffic rate corresponding to the traffic restriction information combination.

Step S1425, add the first updated traffic rate corresponding to the traffic restriction information combination to the traffic mode information set corresponding to the traffic restriction information combination.

Step S1426, returning to and executing the step of determining a first traffic restriction identifier sequence corresponding to the traffic restriction information combination in the traffic mode information set corresponding to the traffic restriction information combination until the safety traffic coefficient corresponding to the multiple traffic restriction information combinations reaches a set coefficient; and when the safety traffic coefficient corresponding to the multiple traffic restriction information combinations reaches the set coefficient, determining the target traffic flow information of the target monitoring block based on the safety traffic coefficient and the multiple traffic restriction information combinations.

Thus, by applying the steps S1421 to S1426, the first traffic restriction identifier sequence can be determined iteratively, so that the safe traffic coefficients corresponding to various traffic restriction information combinations are ensured to reach the set coefficient, and thus the target traffic flow information of the target monitoring block can be determined based on the safe traffic coefficients and the various traffic restriction information combinations. Since the safe traffic coefficient reaches the set coefficient, and the set coefficient is configured based on the target traffic flow information identified by the target monitoring block, the method can ensure that the target traffic flow information of the target monitoring block can cover the target traffic flow information identified by the target monitoring block.

Further, the determining of the first traffic restriction identification sequence corresponding to the traffic restriction information combination in the traffic manner information set corresponding to the traffic restriction information combination described in step S1422 may be exemplarily explained as the following steps S14221 to S14224.

Step S14221, determine a second traffic restriction identifier sequence and a first static traffic rate corresponding to the traffic restriction information combination, and a first static traffic rate corresponding to the target traffic restriction information combination.

Step S14222, obtaining a first comparison result of the first static traffic rate corresponding to the traffic restriction information combination by performing bit-by-bit comparison on the first static traffic rate corresponding to the traffic restriction information combination and the first static traffic rate corresponding to the target traffic restriction information combination, where the target traffic restriction information combination is all traffic restriction information combinations including the traffic restriction information combination in the multiple traffic restriction information combinations.

Step S14223, obtaining a second comparison result of the first static traffic rate of the traffic restriction information combination by performing bit-by-bit comparison between the first static traffic rate corresponding to the traffic restriction information combination and the second traffic restriction identifier sequence corresponding to the traffic restriction information combination.

Step S14224, based on the second comparison result and the first comparison result, determining the second traffic restriction identifier sequence corresponding to the traffic restriction information combination or the first static traffic rate corresponding to the traffic restriction information combination as the first traffic restriction identifier sequence corresponding to the traffic restriction information combination.

Further, in the step S14221, the first static traffic rate corresponding to the target traffic restriction information combination is determined, which includes the following steps: step S142211, acquiring a restricted schedule set of the target traffic restriction information combination, and determining a traffic restriction operation record corresponding to the target traffic restriction information combination; step S142212, according to the restriction schedule set of the target traffic restriction information combination, a first static traffic rate corresponding to the target traffic restriction information combination is determined in the traffic restriction operation record corresponding to the target traffic restriction information combination.

In a further embodiment, the step S142211 of determining the traffic restriction operation record corresponding to the combination of the target traffic restriction information can be implemented by the following steps a-d.

Step a, determining a second comparison result and a first comparison result of each passing mode information set in the passing mode information sets corresponding to the target passing limitation information combination.

And b, calculating the queue continuity weight of each correction safety factor queue in the traffic mode information set corresponding to the target traffic limitation information combination based on the second comparison result and the first comparison result.

C, sequencing each correction safety factor queue in the traffic mode information set corresponding to the target traffic restriction information combination according to the queue continuity weight, determining the first sequenced correction safety factor queue as a main correction safety factor queue, and integrating the correction safety factor queues sequenced in a set value interval into a secondary correction safety factor queue; and determining the interval difference value of the sequencing serial numbers of the set value interval and the main correction safety coefficient queue according to the average value of the queue continuity weight of each correction safety coefficient queue.

And d, determining a traffic restriction operation record corresponding to the target traffic restriction information combination according to the secondary correction safety factor queue.

In an alternative embodiment, the step S142 of obtaining the real-time monitoring information of the target monitoring block from the first block traffic road information according to the target traffic flow information may further include the following steps (1) to (4).

(1) And acquiring safety feature change data from the first block traffic road information according to the target traffic flow information.

(2) Carrying out feature clustering on the security feature change data to obtain a security feature data set; the feature evaluation of each feature data in the security feature data set is a first feature evaluation or a second feature evaluation, and the feature data corresponding to all the first feature evaluations are the marked feature data of the security feature data set.

(3) And determining a real-time information sequence matched with the marked feature data from the first block traffic road information.

(4) And determining the real-time monitoring information of the target monitoring block according to the real-time information sequence.

In step (1), the acquiring safety feature change data from the first block traffic road information according to the target traffic flow information includes: determining safety feature description information according to the feature variable division record of the second block traffic road information and the feature variable division record of the first block traffic road information; and acquiring safety feature change data from the first block traffic road information according to the safety feature description information and the target traffic flow information.

By the design, based on the content described in the steps (1) to (4), the real-time information sequence can be determined in real time based on the safety feature change data, so that the determined real-time monitoring information of the target monitoring block has better timeliness.

In another alternative embodiment, the step S141 of obtaining the first block traffic road information and the second block traffic road information for the target monitoring block may include the following steps S1411 to S1414.

Step S1411, determining the current thread state information of the event monitoring thread corresponding to the target monitoring block; and determining a safety state characteristic from the current thread state information.

In step S1412, it is determined whether the operable state in the current thread state information is changed from the operable state in the previous thread state information of the current thread state information.

Step S1413, if yes, the safety state feature determined from the current thread state information is determined as the effective safety state feature of the current thread state information; otherwise, fusing the safety state features determined from the current thread state information with the effective safety state features at the corresponding positions in the previous thread state information to obtain a fusion result, and determining the fusion result as the effective safety state features of the current thread state information.

Step S1414, obtaining the first block traffic road information and the second block traffic road information according to different information extraction manners based on the effective safety status feature of the current thread status information.

In this way, by applying the above steps S1411 to S1414, the validity of the security features between the acquired different blocks of traffic road information can be ensured.

Based on the same inventive concept, please refer to fig. 2, the invention further provides a block diagram of a traffic data matching device 20 for intelligent traffic, which is applied to an intelligent traffic data processing terminal, and the device may further include the following functional modules:

the first data acquisition module 21 is configured to acquire real-time driving direction data generated on a current driving road when the target vehicle is driving, so as to obtain a first group of driving direction data sets;

the second data acquisition module 22 is configured to acquire real-time driving direction data read by the vehicle-mounted terminal of the target vehicle from each driving route to obtain a second group of driving direction data sets;

the driving direction judging module 23 is configured to determine whether a real-time driving direction is changed according to real-time driving direction data in the first group of driving direction data sets and the second group of driving direction data sets after detecting that the vehicle-mounted terminal of the target vehicle triggers the line adjustment prompt information; and if the real-time driving direction is changed, determining target driving route data corresponding to the vehicle-mounted terminal of the target vehicle.

Further, the apparatus further comprises: and the safety risk judgment module 24 is configured to determine target driving route data corresponding to the vehicle-mounted terminal of the target vehicle, and judge whether a traffic safety risk exists in a target monitoring block corresponding to the target driving route data.

On the basis, please refer to fig. 3 in combination, which provides a smart traffic data processing terminal 110, including a processor 111, and a memory 112 and a bus 113 connected to the processor 111; wherein, the processor 111 and the memory 112 complete the communication with each other through the bus 113; the processor 111 is used to call program instructions in the memory 112 to perform the above-described method.

Further, a readable storage medium is provided, on which a program is stored, which when executed by a processor implements the method described above.

It should be understood that, for technical terms that are not noun explanations to the above-mentioned contents, a person skilled in the art can deduce and unambiguously determine the meaning of the present invention according to the above-mentioned disclosure, for example, for some values, coefficients, weights and other terms, a person skilled in the art can deduce and determine according to the logical relationship before and after, the value range of these values can be selected according to the actual situation, for example, 0 to 1, for example, 1 to 10, for example, 50 to 100, but not limited thereto, and a person skilled in the art can unambiguously determine some preset, reference, predetermined, set and target technical features/technical terms according to the above-mentioned disclosure. For some technical characteristic terms which are not explained, the skilled person is fully capable of reasonably and unambiguously deriving the technical solution based on the logical relations between the preceding and following terms, so as to clearly and completely implement the technical solution. The foregoing will therefore be clear and complete to those skilled in the art. It should be understood that the process of deriving and analyzing technical terms, which are not explained, by those skilled in the art based on the above disclosure is based on the contents described in the present application, and thus the above contents are not an inventive judgment of the overall scheme.

It will be understood that the invention is not limited to the precise arrangements that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the invention is limited only by the appended claims.

Claims

1. A traffic data matching method of intelligent traffic is characterized in that the method is applied to an intelligent traffic data processing terminal, and comprises the following steps:

when detecting that the vehicle-mounted terminal of the target vehicle triggers the line adjustment prompt information, determining whether the real-time driving direction is changed according to the real-time driving direction data in the first group of driving direction data sets and the second group of driving direction data sets; if the real-time driving direction is changed, determining target driving route data corresponding to a vehicle-mounted terminal of the target vehicle;

wherein the method further comprises: determining target driving route data corresponding to a vehicle-mounted terminal of the target vehicle, and judging whether a target monitoring block corresponding to the target driving route data has a traffic safety risk or not;

the determining of the target driving route data corresponding to the vehicle-mounted terminal of the target vehicle and the judging whether a traffic safety risk exists in a target monitoring block corresponding to the target driving route data includes:

acquiring first street traffic road information and second street traffic road information aiming at a target monitoring street; the traffic jam weight of the second block traffic road information is smaller than that of the first block traffic road information;

determining target traffic flow information of the target monitoring block according to the traffic time interval sequence of the second block traffic road information, and acquiring real-time monitoring information of the target monitoring block from the first block traffic road information according to the target traffic flow information; determining a difference value between the target monitoring information identification degree of the real-time monitoring information and each candidate monitoring information identification degree in a preset information identification degree queue; the preset information identification degree queue comprises a plurality of candidate monitoring information identification degrees, wherein each candidate monitoring information identification degree is correspondingly provided with a traffic safety label, and the traffic safety label represents that the target monitoring block has traffic safety risk or does not have traffic safety risk;

selecting m candidate monitoring information identification degrees from the preset information identification degree queue based on the difference value between the target monitoring information identification degree and each candidate monitoring information identification degree; judging whether traffic safety risks exist in the target monitoring block or not based on m traffic safety labels with candidate monitoring information identification degrees; wherein m is a positive integer greater than or equal to 1;

the step of judging whether the target monitoring block has traffic safety risk or not based on the traffic safety tags of the m candidate monitoring information identification degrees comprises the following steps:

determining a current state information set used for calculating comprehensive information identification degrees corresponding to the m candidate monitoring information identification degrees based on the label similarity between every two adjacent traffic safety labels in the m candidate monitoring information identification degrees of traffic safety labels;

acquiring a to-be-monitored block state information set corresponding to each block monitoring time node of the target monitoring block in a first set monitoring block time period based on the current state information set, wherein the first set monitoring block time period comprises at least two block monitoring time nodes, and the to-be-monitored block state information set corresponding to each block monitoring time node comprises monitoring safety parameters of the monitored block, which are acquired or calculated by a safety state verification unit in the target monitoring block in the corresponding block monitoring time node;

determining the security feature similarity between the to-be-monitored block state information sets corresponding to each block monitoring time node in the first set monitoring block time period;

determining a block picture record set of the target monitoring block in the first set monitoring block time period according to the safety feature similarity between the block state information sets to be monitored corresponding to each block monitoring time node in the first set monitoring block time period;

determining the safety level index of the target monitoring block in the first set monitoring block time period according to the block picture record set;

calculating the comprehensive information identification degrees corresponding to the m candidate monitoring information identification degrees through the safety level index; judging whether the identification degree of the comprehensive information is greater than the identification degree of set information; determining that the target monitoring block has no traffic safety risk when the comprehensive information identification degree is judged to be greater than or equal to the set information identification degree; determining that the traffic safety risk exists in the target monitoring block when the comprehensive information identification degree is judged to be smaller than the set information identification degree, and locking safety accident event information of the target monitoring block when the traffic safety risk exists in the target monitoring block;

the acquiring of the to-be-monitored block state information set corresponding to each block monitoring time node of the target monitoring block in the first set monitoring block time period comprises:

acquiring monitoring safety parameters of a monitoring block acquired by a safety state verification unit in the target monitoring block in a set time interval after a first block monitoring time node starts, and determining a to-be-monitored block state information set corresponding to the first block monitoring time node according to the monitoring safety parameters of the monitoring block acquired by the safety state verification unit in the target monitoring block in the set time interval after the first block monitoring time node starts, wherein the first block monitoring time node is any block monitoring time node in the first set monitoring block time interval;

under the condition that a safety state verification unit in the target monitoring neighborhood does not acquire monitoring safety parameters of the monitoring neighborhood within a set time interval after a second neighborhood monitoring time node starts, determining a to-be-monitored neighborhood state information set corresponding to the second neighborhood monitoring time node according to the monitoring safety parameters of the monitoring neighborhood calculated by the safety state verification unit in the target monitoring neighborhood, wherein the second neighborhood monitoring time node is any neighborhood monitoring time node except the first neighborhood monitoring time node within the first set monitoring neighborhood time period;

wherein the method further comprises:

the safety state verification unit in the target monitoring neighborhood does not collect the monitoring safety parameters of the monitoring neighborhood within a set time interval after the monitoring time node of the third neighborhood starts, and the monitored block state information sets corresponding to the continuous first set number of block monitoring time nodes before the third block monitoring time node are all determined according to the monitoring safety parameters of the monitored blocks calculated by the safety state verification unit, sending a monitoring block acquisition instruction to the safety state verification unit, so that the security status verification unit collects the monitoring security parameters of the monitoring neighborhood in response to the monitoring neighborhood collection instruction, the third neighborhood monitoring time node is any neighborhood monitoring time node except the first neighborhood monitoring time node and the second neighborhood monitoring time node in the first set monitoring neighborhood time period;

and acquiring monitoring safety parameters of the monitoring blocks acquired by the safety state verification unit in response to the monitoring block acquisition instructions, and determining a to-be-monitored block state information set corresponding to the third block monitoring time node according to the monitoring safety parameters of the monitoring blocks acquired by the safety state verification unit in response to the monitoring block acquisition instructions.

2. The method of claim 1, wherein determining whether there is a change in the real-time driving direction according to the real-time driving direction data in the first set of driving direction data and the second set of driving direction data after detecting that the on-board terminal of the target vehicle triggers the route adjustment prompt message comprises:

determining whether a third group of abnormalities exist according to the real-time driving direction data in the same time period in the first group of driving direction data sets and the second group of driving direction data sets, wherein the third group of abnormalities are changes of the real-time driving direction generated by the driving of the target vehicle and the real-time driving direction read by the vehicle-mounted terminal of the target vehicle;

3. The method of claim 2,

4. The method of claim 2,

5. The method of claim 1, wherein obtaining real-time driving direction data generated on a current driving road while a target vehicle is driving to obtain a first set of driving direction data comprises:

6. The method of claim 1, wherein the obtaining real-time driving direction data read by the vehicle-mounted terminal of the target vehicle from each driving distance obtains a second set of driving direction data sets, and comprises: acquiring current real-time driving direction data read by a vehicle-mounted terminal of the target vehicle from each driving distance;

7. The method of claim 1, wherein determining the target travel route data corresponding to the vehicle-mounted terminal of the target vehicle comprises: counting the times of the change of the real-time driving direction of the vehicle-mounted terminal of the target vehicle in a specified time period; and if the times reach preset times, determining target driving route data corresponding to the vehicle-mounted terminal of the target vehicle.

8. A traffic data matching device of intelligent traffic is characterized in that the device is applied to an intelligent traffic data processing terminal, and the device comprises:

the driving direction judging module is used for determining whether the real-time driving direction changes or not according to the real-time driving direction data in the first group of driving direction data sets and the second group of driving direction data sets after detecting that the vehicle-mounted terminal of the target vehicle triggers the line adjustment prompt information; if the real-time driving direction is changed, determining target driving route data corresponding to a vehicle-mounted terminal of the target vehicle;

wherein the apparatus further comprises: the safety risk judgment module is used for determining target driving route data corresponding to a vehicle-mounted terminal of the target vehicle and judging whether a target monitoring block corresponding to the target driving route data has a traffic safety risk or not;

acquiring first block traffic road information and second block traffic road information aiming at a target monitoring block; the traffic jam weight of the second block traffic road information is smaller than that of the first block traffic road information;

determining target traffic flow information of the target monitoring block according to the traffic time interval sequence of the second block traffic road information, and acquiring real-time monitoring information of the target monitoring block from the first block traffic road information according to the target traffic flow information; determining a difference value between the target monitoring information identification degree of the real-time monitoring information and each candidate monitoring information identification degree in a preset information identification degree queue; the preset information identification degree queue comprises a plurality of candidate monitoring information identification degrees, each candidate monitoring information identification degree is correspondingly provided with a traffic safety label, and the traffic safety labels indicate that traffic safety risks exist or do not exist in the target monitoring block;

selecting m candidate monitoring information identification degrees from the preset information identification degree queue based on the difference value between the target monitoring information identification degree and each candidate monitoring information identification degree; judging whether the target monitoring block has traffic safety risks or not based on m traffic safety labels with candidate monitoring information identification degrees; wherein m is a positive integer greater than or equal to 1;

based on the current state information set, acquiring a to-be-monitored block state information set corresponding to each block monitoring time node of the target monitoring block in a first set monitoring block time period, wherein the first set monitoring block time period comprises at least two block monitoring time nodes, and the to-be-monitored block state information set corresponding to each block monitoring time node comprises monitoring safety parameters of the monitored block, which are collected or calculated in the corresponding block monitoring time node by a safety state verification unit in the target monitoring block;

determining the safety feature similarity rate between the to-be-monitored block state information sets corresponding to each block monitoring time node in the first set monitoring block time period;

determining a safety level index of the target monitoring block in the first set monitoring block time period according to the block picture record set;

calculating the comprehensive information identification degrees corresponding to the m candidate monitoring information identification degrees through the safety level index; judging whether the identification degree of the comprehensive information is greater than the identification degree of set information; determining that the target monitoring block has no traffic safety risk when the comprehensive information identification degree is judged to be greater than or equal to the set information identification degree; determining that the target monitoring block has traffic safety risk when the comprehensive information identification degree is judged to be smaller than the set information identification degree, and locking safety accident event information of the target monitoring block when the target monitoring block is judged to have traffic safety risk;

wherein the method further comprises:

the safety state verification unit in the target monitoring neighborhood does not collect the monitoring safety parameters of the monitoring neighborhood within a set time interval after the monitoring time node of the third neighborhood starts, and the monitored block state information sets corresponding to the continuous first set number of block monitoring time nodes before the third block monitoring time node are all determined according to the monitoring safety parameters of the monitored blocks calculated by the safety state verification unit, sending a monitoring block acquisition instruction to the safety state verification unit, so that the security status verification unit collects the monitoring security parameters of the monitoring neighborhood in response to the monitoring neighborhood collection instruction, the third neighborhood monitoring time node is any neighborhood monitoring time node except the first neighborhood monitoring time node and the second neighborhood monitoring time node in the first set neighborhood monitoring time period;