CN112581759B

CN112581759B - Cloud computing method and system based on smart traffic

Info

Publication number: CN112581759B
Application number: CN202011428872.XA
Authority: CN
Inventors: 张兴莉; 冯丽琴; 张涛
Original assignee: Shanghai Bitshare Software Co ltd
Current assignee: SHANGHAI BITSHARE SOFTWARE Co.,Ltd.
Priority date: 2020-12-09
Filing date: 2020-12-09
Publication date: 2021-11-09
Anticipated expiration: 2040-12-09
Also published as: CN112581759A

Abstract

The invention relates to a cloud computing method and a cloud computing system based on intelligent traffic, which can analyze street traffic road information with different traffic jam weights, so that influence deviation between a traffic time period sequence and 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 a difference value between target monitoring information identification and each candidate monitoring information identification is ensured. 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 situation that the safety of the target monitoring block is judged by mistake due to inaccurate identification is avoided.

Description

Cloud computing method and system based on smart traffic

Technical Field

The application relates to the technical field of intelligent traffic and cloud computing, in particular to a cloud computing method and system based on intelligent traffic.

Background

With the development of intelligent traffic technology, the traffic safety problem in cities is more and more concerned, and safety monitoring for intelligent traffic is very necessary.

However, the common intelligent traffic safety monitoring technology is difficult to realize all-around monitoring and consideration of the monitored block, so that reliable monitoring is difficult to realize, and the problem that the safety condition of the monitored block is judged by mistake due to inaccurate identification and monitoring often exists.

Disclosure of Invention

A first aspect of the present application discloses a smart traffic-based cloud computing method, the method including:

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, 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 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.

Preferably, 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 includes:

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.

Preferably, the determining, by the traffic safety tag based on m candidate monitoring information identification degrees, whether the target monitoring block has a traffic safety risk includes:

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 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 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 the following steps:

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.

Preferably, the 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 includes:

determining a dynamic monitoring safety parameter set from a to-be-monitored block state information set corresponding to each block monitoring time node in a first set block monitoring time period; respectively determining each to-be-monitored block state information set except the dynamic monitoring safety parameter set in a 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;

or

And respectively determining the security feature similarity between the to-be-monitored block state information sets corresponding to every two adjacent block monitoring time nodes in the first set monitoring block time period.

Preferably, the to-be-monitored neighborhood state information set corresponding to each neighborhood monitoring time node in the first set monitoring neighborhood time period comprises an updatable state data set and a non-updatable state data set, and the neighborhood picture record set comprises a first neighborhood picture record set determined according to the security feature similarity corresponding to the updatable state data set of each designated 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 corresponding to the non-updatable state data set of each designated neighborhood monitoring time node in the first set monitoring neighborhood time period; the 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 comprises: determining a security 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;

determining, according to 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, a block picture record set of the target monitoring block in the first set monitoring block time period includes:

determining at least one target updatable state data set with the monitoring block credibility weight higher than a first set credibility weight threshold and at least one target non-updatable state data set with the monitoring block credibility weight higher than a second set credibility weight threshold from the to-be-monitored block state information sets corresponding to all block monitoring time nodes in the first set monitoring block time period;

determining the first neighborhood picture record set according to the security feature similarity corresponding to the at least one target updatable status data set, and determining the second neighborhood picture record set according to the security feature similarity corresponding to the at least one target non-updatable status data set;

determining the security level index of the target monitoring neighborhood within the first set monitoring neighborhood time period according to the first neighborhood picture record set and the second neighborhood picture record set comprises:

determining the safety level index of the target monitoring block in the first set monitoring block time period as a first target level index under the condition that the block picture change coefficient corresponding to the first block picture record set is not smaller than a preset first change coefficient threshold value and the block picture change coefficient corresponding to the second block picture record set is not smaller than a preset second change coefficient threshold value;

determining the safety level index of the target monitoring block in the first set monitoring block time period as a second target level index under the condition that the block picture change coefficient corresponding to the first block picture record set is not smaller than the first change coefficient threshold and the block picture change coefficient corresponding to the second block picture record set is smaller than the second change coefficient threshold;

determining that the safety level index of the target monitoring block in the first set monitoring block time period is a third target level index under the condition that the block picture change coefficient corresponding to the first block picture record set is smaller than the first change coefficient threshold and the block picture change coefficient corresponding to the second block picture record set is smaller than the second change coefficient threshold;

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.

Preferably, the determining a 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 includes:

determining a difference value between the target monitoring information identification degree and the candidate monitoring information identification degree based on a monitoring time sequence identification coefficient between the target monitoring information identification degree and the candidate monitoring information identification degree; alternatively, the first and second electrodes may be,

determining a difference value between the target monitoring information identification degree and the candidate monitoring information identification degree based on a monitoring event identification coefficient of the target monitoring information identification degree and the candidate monitoring information identification degree; alternatively, the first and second electrodes may be,

and determining the difference value of the target monitoring information identification degree and the candidate monitoring information identification degree based on the monitoring risk identification coefficient of the target monitoring information identification degree and the candidate monitoring information identification degree.

Preferably, the determining the target traffic flow information of the target monitoring block according to the traffic time interval sequence of the second block traffic road information includes:

acquiring multiple traffic restriction information combinations corresponding to the traffic time interval sequence of the second block traffic road information and a traffic mode information set corresponding to each traffic restriction information combination, wherein each traffic restriction information combination comprises multiple different traffic information labels;

determining a first traffic restriction identification sequence corresponding to the traffic restriction information combination in a traffic mode information set corresponding to the traffic restriction information combination;

correcting speed limit sign information by adopting a first traffic limit identification sequence corresponding to the traffic limit information combination to obtain a speed limit sign information correction result of each traffic information label in the traffic limit information combination;

based on the speed limit sign information correction result of each traffic information label in multiple traffic limit information combinations, updating the traffic rate of a first traffic limit identifier sequence corresponding to the traffic limit information combination to obtain a first updated traffic rate corresponding to the traffic limit information combination;

adding a first updated traffic rate corresponding to the traffic restriction information combination into a traffic mode information set corresponding to the traffic restriction information combination;

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

wherein, the determining 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 includes:

determining a second traffic restriction identification 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;

obtaining a first comparison result of the first static traffic rate corresponding to the traffic restriction information combination by comparing the first static traffic rate corresponding to the traffic restriction information combination and the first static traffic rate corresponding to a target traffic restriction information combination bit by bit, wherein the target traffic restriction information combination is all traffic restriction information combinations including the traffic restriction information combination in a plurality of traffic restriction information combinations;

obtaining a second comparison result of the first static traffic speed of the traffic restriction information combination by comparing the first static traffic speed corresponding to the traffic restriction information combination and the second traffic restriction identification sequence corresponding to the traffic restriction information combination bit by bit;

determining a second traffic restriction identification sequence corresponding to the traffic restriction information combination or a first static traffic rate corresponding to the traffic restriction information combination as a first traffic restriction identification sequence corresponding to the traffic restriction information combination based on the second comparison result and the first comparison result;

the determining a first static traffic rate corresponding to the target traffic restriction information combination comprises:

acquiring a restriction schedule set of the target traffic restriction information combination, and determining a traffic restriction operation record corresponding to the target traffic restriction information combination; determining a first static traffic rate corresponding to the target traffic restriction information combination in a traffic restriction operation record corresponding to the target traffic restriction information combination according to the restriction schedule set of the target traffic restriction information combination;

the determining of the traffic restriction operation record corresponding to the target traffic restriction information combination includes:

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;

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

sorting 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 sorted correction safety factor queue as a main correction safety factor queue, and integrating the correction safety factor queues sorted in a set numerical interval into a secondary correction safety factor queue; the interval difference value of the sequencing serial numbers of the set value interval and the main correction safety coefficient queue is determined according to the average value of the queue continuity weight of each correction safety coefficient queue;

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

A second aspect of the present application provides a cloud computing system based on smart transportation, wherein the cloud platform is in communication with a target monitoring neighborhood, and is configured to:

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

Compared with the prior art, the cloud computing method and system based on the intelligent traffic provided by the embodiment of the invention have the following technical effects: 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 can be 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 can be 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 situation that the safety of the target monitoring block is judged by mistake due to inaccurate identification is avoided.

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

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.

The methods, systems, and/or processes of the figures are further described in accordance with the exemplary embodiments. These exemplary embodiments will be described in detail with reference to the drawings. These exemplary embodiments are non-limiting exemplary embodiments in which reference numerals represent similar mechanisms throughout the various views of the drawings.

Fig. 1 is a schematic diagram of a communication architecture of a cloud computing system based on smart transportation according to the present invention.

Fig. 2 is a schematic diagram of hardware and software components of a cloud platform provided in accordance with the present invention.

Fig. 3 is a flowchart of a smart transportation based cloud computing method and/or process according to the present invention.

Fig. 4 is a block diagram of a cloud computing device based on smart transportation according to the present invention.

Detailed Description

In order to better understand the technical solutions of the present invention, the following detailed descriptions of the technical solutions of the present invention are provided with the accompanying drawings and the specific embodiments, and it should be understood that the specific features in the embodiments and the examples of the present invention are the detailed descriptions of the technical solutions of the present invention, and are not limitations of the technical solutions of the present invention, and the technical features in the embodiments and the examples of the present invention may be combined with each other without conflict.

In the following detailed description, numerous specific details are set forth by way of examples in order to provide a thorough understanding of the relevant guidance. It will be apparent, however, to one skilled in the art that the present invention may be practiced without these specific details. In other instances, well-known methods, procedures, systems, compositions, and/or circuits have been described at a relatively high-level, without detail, in order to avoid unnecessarily obscuring aspects of the invention.

These and other features, functions, methods of execution, and combination of functions and elements of related elements in the structure and economies of manufacture disclosed in the present application may become more apparent upon consideration of the following description with reference to the accompanying drawings, all of which form a part of this application. It is to be expressly understood, however, that the drawings are for the purpose of illustration and description only and are not intended as a definition of the limits of the application. It should be understood that the drawings are not to scale. It is to be expressly understood, however, that the drawings are for the purpose of illustration and description only and are not intended as a definition of the limits of the invention. It should be understood that the drawings are not to scale.

Flowcharts are used herein to illustrate the implementations performed by systems according to embodiments of the present application. It should be expressly understood that the processes performed by the flowcharts may be performed out of order. Rather, these implementations may be performed in the reverse order or simultaneously. In addition, at least one other implementation may be added to the flowchart. One or more implementations may be deleted from the flowchart.

Fig. 1 is a communication architecture diagram illustrating an exemplary smart transportation based cloud computing system 100 according to some embodiments of the invention, the smart transportation based cloud computing system 100 may include a cloud platform 200 and a traffic monitoring device 300. Wherein the cloud platform 200 may be a cloud server.

In some embodiments, as shown in fig. 2, the cloud platform 200 may include a processing engine 210, a network module 220, and a memory 230, the processing engine 210 and the memory 230 communicating through the network module 220.

Processing engine 210 may process the relevant information and/or data to perform one or more of the functions described herein. For example, in some embodiments, processing engine 210 may include at least one processing engine (e.g., a single core processing engine or a multi-core processor). By way of example only, Processing engine 210 may include a Central Processing Unit (CPU), an Application-Specific Integrated Circuit (ASIC), an Application-Specific Instruction Set Processor (ASIP), a Graphics Processing Unit (GPU), a Physical Processing Unit (PPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), a controller, a microcontroller Unit, a Reduced Instruction Set Computer (RISC), a microprocessor, or the like, or any combination thereof.

The network module 220 may facilitate the exchange of information and/or data. In some embodiments, the network module 220 may be any type of wired or wireless network or combination thereof. Merely by way of example, the Network module 220 may include a cable Network, a wired Network, a fiber optic Network, a telecommunications Network, an intranet, the internet, a Local Area Network (LAN), a Wide Area Network (WAN), a Wireless Local Area Network (WLAN), a Metropolitan Area Network (MAN), a Public Switched Telephone Network (PSTN), a bluetooth Network, a Wireless personal Area Network, a Near Field Communication (NFC) Network, and the like, or any combination thereof. In some embodiments, the network module 220 may include at least one network access point. For example, the network module 220 may include a wired or wireless network access point, such as a base station and/or a network access point.

The Memory 230 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like. The memory 230 is used for storing a program, and the processing engine 210 executes the program after receiving an execution instruction.

It is to be understood that the configuration shown in fig. 2 is merely illustrative and that cloud platform 200 may also include more or fewer components than shown in fig. 2 or have a different configuration than shown in fig. 2. The components shown in fig. 2 may be implemented in hardware, software, or a combination thereof.

Fig. 3 is a flowchart illustrating a smart transportation based cloud computing method and/or process according to some embodiments of the present invention, where the smart transportation based cloud computing method is applied to the cloud platform 200 in fig. 1, and may specifically include the contents described in the following steps S31 to S33.

And step S31, acquiring first block traffic road information and second block traffic road information aiming at the target monitoring block.

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

Step S32, 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 S33, 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; 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 monitored neighborhood of objects. m is a positive integer greater than or equal to 1.

It can be understood that, by executing the above steps S31-S33, 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 interval 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 value 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 the target monitoring block has a traffic safety risk 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 identification degrees 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 described in step S33 may include the following steps: 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 rates corresponding to nodes in different monitoring times need to be considered, so that the instantaneous variability of the safety features is considered. To achieve this, in step S33, it is determined whether the target monitoring block has a traffic safety risk based on m traffic safety tags identified by the candidate monitoring information, which may include the following steps S331 to S336.

Step S331, determining a current state information set for calculating the 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 traffic safety labels of the m candidate monitoring information identification degrees.

Step S332, acquiring 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 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 monitoring block collected or calculated by a safety state verification unit in the target monitoring block in the corresponding block monitoring time node.

Step S333, determining a security 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.

Step S334 is to determine a block picture record set of the target monitoring block in the first set monitoring block time period according to 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.

Step S335, 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.

Step S336, calculating the comprehensive information identification degrees corresponding to the m candidate monitoring information identification degrees through the safety grade 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; 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 S331 to S336, the safety feature similarity between the to-be-monitored block state information sets corresponding to the respective block monitoring time nodes 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 the different monitoring time nodes can be considered, the instantaneous 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 S332 may be implemented by the following contents described in steps S3321 to S3324.

Step S3321, acquiring monitoring safety parameters of the monitored blocks, which are acquired by a safety state verification unit in the target monitored block within 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 monitored blocks, which are acquired by the safety state verification unit in the target monitored block within 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 within the first set monitored block time period.

Step S3322, when the monitoring security parameters of the monitored block are not collected within a set time interval after the security status verification unit in the target monitored block starts at the second block monitoring time node, determining a to-be-monitored block status information set corresponding to the second block monitoring time node according to the monitoring security parameters of the monitored block calculated by the security status verification unit in the target monitored block, where the second block monitoring time node is any block monitoring time node except the first block monitoring time node within the first set monitored block time period.

Step S3323, 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 S3324, acquiring the monitoring safety parameters of the monitoring blocks acquired by the safety state verification unit responding to the monitoring block acquisition instruction, 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 responding to the monitoring block acquisition instruction.

It can be understood that by executing the steps S3321 to S3324, the to-be-monitored block status information sets corresponding to different block monitoring time nodes can be completely determined, so as to provide sufficient data basis for the subsequent calculation of the comprehensive information identification degree, and ensure the reliability of the subsequent calculation of the comprehensive information identification degree.

Further, the determining of the security feature similarity between the to-be-monitored block state information sets corresponding to the block monitoring time nodes in the first set monitoring block time period in step S333 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, security feature similarity rates between to-be-monitored block status information sets corresponding to every two adjacent block monitoring time nodes in the first set monitoring block 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 steps S331 to S336, the to-be-monitored block status information set corresponding to each block monitoring time node in the first set monitoring block time period includes an updatable status data set and a non-updatable status data set, and the block picture record set includes a first block picture record set determined according to the security feature similarity rate corresponding to the updatable status data set of each block monitoring time node specified in the first set monitoring block time period and a second block picture record set determined according to the security feature similarity rate corresponding to the non-updatable status data set of each block monitoring time node specified in the first set monitoring block time period. Based on this, the step S335 of determining the security level index of the target monitoring block within the first set monitoring block time period according to the block picture record set includes the step S3350: 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 S3350 of determining the security level index of the target monitoring block within 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 S3351 to S3353.

Step S3351, determining that the safety 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 S3352, determining 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 S3353, determining that the safety 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 determining, by the method described in step S334, a block picture record set of the target monitoring block in the first set monitoring block time period according to 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 includes the following contents described in steps S3341 and S3342.

Step S3341, determining at least one target updatable status data set with the monitoring block credibility weight higher than a first set credibility weight threshold and at least one target non-updatable status data set with the monitoring block credibility weight higher than a 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 S3342, determining the first street picture record set according to the security feature similarity corresponding to the at least one target updatable status data set, and determining the second street picture 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 block state information sets corresponding to each block monitoring time node in the first set monitoring block time period and described in step S334, a block picture record set of the target monitoring block in the first set monitoring block time period may also be implemented by the following implementation manners: 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 for step S334 are implemented according to the reliability weight of the monitored neighborhood and the relevance parameter, so that an implementation manner that is easy to implement can be flexibly selected according to the target monitored neighborhood.

It is to be understood that the determination 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 S32 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 S32 of determining the target traffic flow information of the target monitoring block according to the sequence of the transit periods of the second block traffic road information further includes the following steps S321-S326.

Step S321, acquiring multiple traffic restriction information combinations corresponding to the traffic time interval sequence of the second block traffic road information and a traffic mode information set corresponding to each traffic restriction information combination, wherein each traffic restriction information combination comprises multiple different traffic information labels.

Step S322, determining 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 S323, the first traffic restriction mark sequence corresponding to the traffic restriction information combination is adopted to carry out speed restriction mark information correction, and the speed restriction mark information correction result of each traffic information label in the traffic restriction information combination is obtained.

Step S324, based on the speed limit sign information correction result of each traffic information label in various traffic restriction information combinations, performing traffic rate updating on the first traffic restriction identification sequence corresponding to the traffic restriction information combination to obtain a first updated traffic rate corresponding to the traffic restriction information combination.

Step S325, 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 S326, returning and executing the step to determine a first traffic restriction identification sequence corresponding to the traffic restriction information combination in a 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.

In this way, by applying the above steps S321 to S326, the first traffic restriction identifier sequence can be determined iteratively, so as to ensure that the safe traffic coefficients corresponding to multiple traffic restriction information combinations 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 multiple 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 S322 may be exemplarily interpreted as the following step S3221-step S3224.

Step S3221, determining 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 S3222, 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 S3223, performing bit-by-bit comparison on 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 to obtain a second comparison result of the first static traffic rate of the traffic restriction information combination.

Step S3224, based on the second comparison result and the first comparison result, determining a second traffic restriction identifier sequence corresponding to the traffic restriction information combination or a first static traffic rate corresponding to the traffic restriction information combination as the traffic restriction information combination first traffic restriction identifier sequence.

Further, in the above step S3221, a first static traffic rate corresponding to the target traffic limitation information combination is determined, which includes the following contents: step S32211, acquiring a restriction 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 S32212, according to the restriction schedule set of the target traffic restriction information combination, determines a first static traffic rate corresponding to the target traffic restriction information combination in the traffic restriction operation record corresponding to the target traffic restriction information combination.

In a further embodiment, the determination of the target traffic restriction information combined with the corresponding traffic restriction operation record described in step S32211 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 S32 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 S31 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 S311-S314.

Step S311, 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.

Step S312, determine whether the operable state in the current thread state information changes relative to the operable state in the previous thread state information of the current thread state information.

Step S313, 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 S314, obtaining the first block traffic road information and the second block traffic road information according to different information extraction methods based on the effective safety state feature of the current thread state information.

In this way, by applying the above steps S311 to S314, the validity of the security feature between the acquired different block traffic road information can be ensured.

Fig. 4 is a block diagram illustrating an exemplary smart transportation-based cloud computing device 140 according to some embodiments of the present invention, wherein the smart transportation-based cloud computing device 140 includes the following functional modules.

The road information acquiring module 141 is configured to acquire first block traffic road information and second block traffic road information for a target monitoring block; and the traffic jam weight of the second block traffic road information is smaller than that of the first block traffic road information.

An information identification degree calculation module 142, configured to determine target traffic flow information of the target monitoring block according to the traffic time period sequence of the second block traffic road information, and obtain 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 labels represent that traffic safety risks exist in the target monitoring block or do not exist in the target monitoring block.

The safety risk judgment module 143 is configured to select m candidate monitoring information identification degrees from the preset information identification degree queue based on a difference 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.

For a description of the above-described device embodiment, reference is made to the description of the method embodiment described in fig. 3.

Further, based on the same inventive concept, a corresponding system embodiment is also provided, which is described as follows.

A cloud computing system based on intelligent traffic comprises a cloud platform and traffic monitoring equipment which are communicated with each other; wherein the cloud platform is to:

For a description of the above system embodiment, reference is made to the description of the method embodiment described in fig. 3.

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

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

In addition, those skilled in the art will recognize that the various aspects of the application may be illustrated and described in terms of several patentable species or contexts, including any new and useful combination of procedures, machines, articles, or materials, or any new and useful modifications thereof. Accordingly, various aspects of the present application may be embodied entirely in hardware, entirely in software (including firmware, resident software, micro-code, etc.) or in a combination of hardware and software. The above hardware or software may be referred to as a "unit", "component", or "system". Furthermore, aspects of the present application may be represented as a computer product, including computer readable program code, embodied in at least one computer readable medium.

A computer readable signal medium may comprise a propagated data signal with computer program code embodied therein, for example, on a baseband or as part of a carrier wave. The propagated signal may take any of a variety of forms, including electromagnetic, optical, and the like, or any suitable combination. A computer readable signal medium may 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 on a computer readable signal medium may be propagated over any suitable medium, including radio, electrical cable, fiber optic cable, RF, or the like, or any combination of the preceding.

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

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

It should also be appreciated that in the foregoing description of embodiments of the present application, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of at least one embodiment of the invention. However, this method of disclosure is not intended to require more features than are expressly recited in the claims. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.

Claims

1. A cloud computing method based on smart transportation, the method comprising:

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;

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:

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:

wherein the method further comprises:

2. The method of claim 1, wherein selecting m candidate monitoring information recognizability levels from the preset information recognition level queue based on a difference between a target monitoring information recognition level and each candidate monitoring information recognition level comprises:

3. The method of claim 1, wherein the determining the security feature similarity between the to-be-monitored neighborhood status information sets corresponding to the respective neighborhood monitoring time nodes within the first set monitoring neighborhood time period comprises:

or

4. The method of claim 1, wherein the to-be-monitored block status information set corresponding to each block monitoring time node in the first set monitoring block time period comprises an updatable status data set and a non-updatable status data set, and the block picture record set comprises a first block picture record set determined according to the security feature similarity rate corresponding to the updatable status data set of each block monitoring time node specified in the first set monitoring block time period and a second block picture record set determined according to the security feature similarity rate corresponding to the non-updatable status data set of each block monitoring time node specified in the first set monitoring block time period; the 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 comprises: determining a security 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;

5. The method according to any one of claims 1 to 4, wherein the determining the difference 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 comprises:

6. The method of claim 5, wherein the determining the target traffic flow information of the target monitoring neighborhood according to the sequence of transit periods of the second neighborhood traffic road information comprises:

determining a first static traffic rate corresponding to the target traffic restriction information combination, including:

7. A cloud computing system based on intelligent transportation is characterized by comprising a cloud platform and a traffic monitoring device which are communicated with each other, wherein the cloud platform is used for:

wherein the safety state verification unit in the target monitoring block does not acquire the monitoring safety parameters of the monitoring block within a 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;