CN112927498A

CN112927498A - Data analysis method and device based on intelligent traffic monitoring

Info

Publication number: CN112927498A
Application number: CN202110074736.3A
Authority: CN
Inventors: 蔡家斌; 张�杰; 杨艳
Original assignee: Individual
Current assignee: Guangzhou Xinliuxiang Electronics Science And Technology Co ltd
Priority date: 2021-01-20
Filing date: 2021-01-20
Publication date: 2021-06-08
Anticipated expiration: 2041-01-20
Also published as: CN112927498B

Abstract

The invention discloses a data analysis method and device based on intelligent traffic monitoring. The method comprises the steps of identifying accident road sections by determining reference data, real-time traffic flow density data to be processed and current real-time pedestrian flow density data from acquired urban traffic state data to be analyzed, selecting marked real-time traffic flow density data by using obtained target accident road section information, selecting more accurate real-time traffic flow density data, determining adjusted road section information by using the marked real-time traffic flow density data and the current real-time pedestrian flow density data in order to obtain more accurate adjusted road section information during each iteration, and analyzing the real-time urban traffic state when safe driving conditions are met. Therefore, the problem of inaccurate urban traffic safety state generated when the real-time urban traffic state is analyzed can be avoided, and the analyzed target urban traffic safety state is more accurate.

Description

Data analysis method and device based on intelligent traffic monitoring

Technical Field

The present disclosure relates to the field of intelligent traffic and data analysis technologies, and in particular, to a data analysis method and apparatus based on intelligent traffic monitoring.

Background

With the rapid development of the internet and intelligent traffic, urban traffic jam, increasingly serious traffic pollution and frequent traffic accidents occur. The most common means is to analyze monitored data and then adopt a corresponding improvement strategy for corresponding urban traffic problems according to the analyzed result in order to solve various problems in urban traffic under the condition of passing through the intelligent traffic. However, when analyzing the monitored data, the analysis is often too comprehensive, which results in an inaccurate urban traffic safety state, and thus the urban traffic safety state cannot be accurately analyzed.

Disclosure of Invention

In order to solve the technical problems in the related art, the present disclosure provides a data analysis method and device based on intelligent traffic monitoring.

The invention provides a data analysis method based on intelligent traffic monitoring, which comprises the following steps:

acquiring urban traffic state data to be analyzed, and determining benchmark reference data of the urban traffic state data to be analyzed; the benchmark reference data comprises benchmark people flow density data and benchmark traffic flow density data;

selecting current real-time pedestrian flow density data from the current real-time urban traffic state corresponding to the urban traffic state data to be analyzed, and acquiring corresponding real-time traffic flow density data to be processed based on the urban traffic state data to be analyzed;

performing accident road section identification based on the current real-time people flow density data, the to-be-processed real-time traffic flow density data and the reference data to obtain target accident road section information;

selecting marked real-time traffic flow density data from the current real-time urban traffic state according to target accident road section information, and determining adjusted road section information corresponding to the current real-time urban traffic state according to the marked real-time traffic flow density data and the current real-time people flow density data;

performing density data analysis on the marked real-time traffic flow density data and the current real-time traffic flow density data based on the target accident road section information to obtain a reference data analysis result, adjusting the current real-time traffic flow density data and the to-be-processed real-time traffic flow density data according to a first deviation data information of the reference data analysis result and the reference data, and returning to the step of accident road section identification until a first safe driving condition is met;

and carrying out real-time urban traffic state analysis based on the adjusted road section information meeting the first safe driving condition and the target accident road section information to obtain a target urban traffic safety state corresponding to the urban traffic state data to be analyzed.

Optionally, the adjusting the current real-time people flow density data and the to-be-processed real-time traffic flow density data according to the first deviation data information of the reference data analysis result and the reference data, and returning to the step of identifying the accident road section until a first safe driving condition is met includes:

determining first deviation data information based on the reference data analysis result and the reference data, and when the first deviation data information does not meet a first safe driving condition, adjusting the current real-time urban traffic state based on the adjusted road section information to obtain a marked real-time urban traffic state;

and selecting marked real-time traffic density data from the marked real-time urban traffic state to obtain adjusted current real-time traffic density data, taking the marked real-time traffic density data as adjusted to-be-processed real-time traffic density data, and returning to the step of performing accident road section identification based on the current real-time traffic density data, the to-be-processed real-time traffic density data and the reference data to obtain target accident road section information until a first safe driving condition is met.

The urban traffic state data to be analyzed is a real-time urban traffic state index, and the analysis result of the reference data comprises reference dynamic pedestrian flow density data and reference dynamic traffic flow density data; determining first deviation data information based on the baseline data analysis result and the baseline reference data, comprising:

determining deviation data information corresponding to the people flow density data based on the reference dynamic people flow density data and the reference people flow density data, and determining deviation data information corresponding to the traffic flow density data based on the reference dynamic traffic flow density data and the reference traffic flow density data;

and obtaining first deviation data information of the reference data analysis result and the reference data based on deviation data information corresponding to the traffic flow density data and deviation data information corresponding to the people flow density data.

The urban traffic state data to be analyzed is a delayed urban traffic state index, and the reference data analysis result comprises reference dynamic pedestrian flow density data and reference dynamic traffic flow density data; determining first deviation data information based on the baseline data analysis result and the baseline reference data, comprising:

acquiring next road section information corresponding to the next urban traffic state index of the delayed urban traffic state indexes; the next road section information is road section information used by the next urban traffic state index in real-time urban traffic state analysis;

determining road section deviation data information of the next road section information and the adjusted road section information, and obtaining a first deviation data information of the reference data analysis result and the reference data based on the deviation data information corresponding to the traffic flow density data, the deviation data information corresponding to the people flow density data and the road section deviation data information.

Optionally, the determining the reference pedestrian flow density data and the reference traffic flow density data corresponding to the urban traffic state data to be analyzed includes:

analyzing the urban traffic state based on the urban traffic state data to be analyzed to obtain the urban traffic state change trend;

detecting road condition safety information corresponding to the urban traffic state in the urban traffic state change trend to obtain road condition safety information corresponding to the urban traffic state data to be analyzed;

and determining reference people flow density data and reference traffic flow density data from the road condition safety information corresponding to the urban traffic state.

Optionally, the urban traffic state data to be analyzed is a real-time urban traffic state index; the method for acquiring the corresponding to-be-processed real-time traffic flow density data based on the urban traffic state indexes comprises the following steps:

acquiring urban traffic jam amount, loading the current real-time pedestrian flow density data into a reference density data set according to the urban traffic jam amount to obtain current pedestrian flow density data, and identifying an accident road section based on the current pedestrian flow density data and the reference pedestrian flow density data to obtain updated road section pedestrian flow density data;

and selecting to-be-processed real-time traffic flow density data corresponding to the real-time urban traffic state index from the traffic flow density data change trend corresponding to the real-time urban traffic state of the current real-time urban traffic state according to the updated road section traffic flow density data.

The acquiring of the urban traffic jam amount comprises the following steps:

acquiring congestion indexes corresponding to all congested road sections, and selecting a current congested road section from the congestion indexes corresponding to all congested road sections;

loading the current real-time pedestrian flow density data into a reference density data set according to the current congested road section to obtain current pedestrian flow density data of the congested road section, and identifying an accident road section based on the current pedestrian flow density data of the congested road section and the reference pedestrian flow density data to obtain road section pedestrian flow density data corresponding to the congested road section;

selecting real-time traffic flow density data of the congested road section from traffic flow density data variation trends corresponding to real-time urban traffic states of the current real-time urban traffic states according to the road section pedestrian flow density data corresponding to the congested road section;

performing congestion road section accident identification based on the real-time traffic flow density data of the congestion road section, the current real-time people flow density data and the reference data to obtain target accident road section information corresponding to the congestion road section;

selecting marked real-time traffic flow density data of the congested road section from the traffic flow density data change trend corresponding to the real-time urban traffic state according to the target accident road section information corresponding to the congested road section;

determining the adjusted road section information corresponding to the congestion road section corresponding to the current real-time urban traffic state according to the marked real-time traffic flow density data and the current real-time people flow density data of the congestion road section;

performing density data analysis on the marked real-time traffic flow density data and the current real-time pedestrian flow density data of the congested road section based on the target accident road section information corresponding to the congested road section to obtain a reference data analysis result of the congested road section, adjusting the real-time traffic flow density data and the current real-time pedestrian flow density data of the congested road section according to the reference data analysis result of the congested road section and second deviation data information of the reference data, and returning to the step of congestion road section accident identification until a second safe driving condition is met to obtain current second deviation data information corresponding to the current congested road section;

traversing the congestion indexes corresponding to the congested road sections to obtain current second deviation data information corresponding to the congestion indexes corresponding to the congested road sections, comparing the current second deviation data information to obtain target second deviation data information, and taking the congestion indexes corresponding to the congested road sections corresponding to the target second deviation data information as the urban traffic congestion amount.

The step of adjusting real-time traffic flow density data and current real-time people flow density data of the congested road section according to the reference data analysis result of the congested road section and second deviation data information of the reference data, and returning to the step of identifying the accident of the congested road section until a second safe driving condition is met includes:

when the second deviation data information does not meet a second safe driving condition, adjusting the current real-time urban traffic state based on the adjusted road section information corresponding to the congested road section to obtain a marked real-time urban traffic state of the congested road section;

selecting marked real-time pedestrian flow density data of the congested road section from the marked real-time urban traffic state of the congested road section, taking the marked real-time pedestrian flow density data of the congested road section as current real-time pedestrian flow density data, taking the marked real-time traffic flow density data of the congested road section as real-time traffic flow density data of the congested road section, and returning the real-time traffic flow density data of the congested road section, the current real-time pedestrian flow density data and the reference data to perform congestion road section accident identification to obtain target accident road section information corresponding to the congested road section until a second safe driving condition is met.

Optionally, the identifying the accident road section based on the current people stream density data and the reference people stream density data to obtain updated road section people stream density data includes:

acquiring first initial road section pedestrian flow density data corresponding to the real-time urban traffic state index, and loading the current real-time pedestrian flow density data into a reference density data set based on the first initial road section pedestrian flow density data to obtain first updated current pedestrian flow density data;

determining third deviation data information based on the first updated current people stream density data and the reference people stream density data;

adjusting the people stream density data of the first initial road section according to the third deviation data information, and returning to the step of loading the current real-time people stream density data into a reference density data set based on the people stream density data of the first initial road section to obtain first updated current people stream density data until the third deviation data information meets a third safe driving condition;

and taking the first initial road section stream density data meeting the third safe driving condition as the updated road section stream density data.

Optionally, the urban traffic state data to be analyzed is a delayed urban traffic state index; the method for acquiring the corresponding to-be-processed real-time traffic flow density data based on the to-be-analyzed urban traffic state data comprises the following steps:

acquiring next real-time traffic flow density data corresponding to a next urban traffic state index of the delayed urban traffic state indexes; the next real-time traffic flow density data is real-time traffic flow density data in a real-time urban traffic state corresponding to the next urban traffic state index;

and taking the next real-time traffic flow density data as the to-be-processed real-time traffic flow density data.

Optionally, the urban traffic state data to be analyzed is a real-time urban traffic state index; the method for identifying the accident road section based on the current real-time people flow density data, the to-be-processed real-time traffic flow density data and the reference data to obtain the target accident road section information comprises the following steps:

acquiring second initial road section pedestrian flow density data corresponding to the real-time urban traffic state index, and loading the current real-time pedestrian flow density data and the to-be-processed real-time traffic flow density data into a reference density data set based on the second initial road section pedestrian flow density data to obtain a real-time quasi data analysis result;

determining fourth deviation data information based on the real-time quasi data analysis result and the benchmark reference data;

adjusting the second initial road section pedestrian flow density data according to the fourth deviation data information, and returning to the step of loading the current real-time pedestrian flow density data and the to-be-processed real-time traffic flow density data into a reference density data set based on the second initial road section pedestrian flow density data to obtain a real-time quasi data analysis result until the fourth deviation data information meets a fourth safe driving condition;

and taking the second initial road section people stream density data meeting the fourth safe driving condition as the target accident road section information corresponding to the real-time urban traffic state index.

Optionally, the urban traffic state data to be analyzed is a delayed urban traffic state index; the method for identifying the accident road section based on the current real-time people flow density data, the to-be-processed real-time traffic flow density data and the reference data to obtain the target accident road section information comprises the following steps:

acquiring third initial road section pedestrian flow density data corresponding to the delayed urban traffic state index, and loading the current real-time pedestrian flow density data and the to-be-processed real-time traffic flow density data into a reference density data set according to the third initial road section pedestrian flow density data to obtain a delayed reference data analysis result;

determining fifth deviation data information based on the delayed reference data analysis result and the reference data, and acquiring next road segment pedestrian flow density data corresponding to a next urban traffic state index of the delayed urban traffic state index, wherein the next road segment pedestrian flow density data is road segment pedestrian flow density data of a real-time urban traffic state corresponding to the next urban traffic state index;

determining attitude deviation data information of the pedestrian flow density data of the next road section and the pedestrian flow density data of the third initial road section, and obtaining target fifth deviation data information according to the fifth deviation data information and the attitude deviation data information;

adjusting third initial road section pedestrian flow density data corresponding to the delayed urban traffic state index according to the target fifth deviation data information, and returning to the step of loading the current real-time pedestrian flow density data and the to-be-processed real-time traffic flow density data into a reference density data set according to the third initial road section pedestrian flow density data to obtain a delayed reference data analysis result until the target fifth deviation data information meets a fifth safe driving condition;

and taking the third initial road section people stream density data meeting the fifth safe driving condition as the target accident road section information corresponding to the delayed urban traffic state index.

Optionally, the selecting marked real-time traffic flow density data from the current real-time urban traffic state according to the target accident road section information includes:

acquiring the fluctuation track of traffic flow acceleration data in the traffic flow density data variation trend corresponding to the real-time urban traffic state of the current real-time urban traffic state, acquiring urban traffic environment information, and selecting corresponding real-time track segments from the fluctuation track of the traffic flow acceleration data according to the urban traffic environment information;

loading each real-time track segment into a reference density data set according to the target accident road section information to obtain each segment data analysis result;

determining sixth deviation data information based on each fragment data analysis result and the reference traffic flow density data, comparing the sixth deviation data information corresponding to each fragment data analysis result to obtain target sixth deviation data information, and taking the real-time track fragment corresponding to the target sixth deviation data information as marked real-time traffic flow density data corresponding to the reference traffic flow density data.

The invention also provides a data analysis device based on intelligent traffic monitoring, which comprises:

the system comprises a reference data acquisition module, a data analysis module and a data analysis module, wherein the reference data acquisition module is used for acquiring urban traffic state data to be analyzed and determining benchmark reference data of the urban traffic state data to be analyzed; the benchmark reference data comprises benchmark people flow density data and benchmark traffic flow density data;

the density data determining module is used for selecting current real-time pedestrian flow density data from the current real-time urban traffic state corresponding to the urban traffic state data to be analyzed and acquiring corresponding real-time traffic flow density data to be processed based on the urban traffic state data to be analyzed;

the accident road section identification module is used for identifying an accident road section based on the current real-time pedestrian flow density data, the to-be-processed real-time traffic flow density data and the reference data to obtain target accident road section information;

the road section information determining module is used for selecting marked real-time traffic flow density data from the current real-time urban traffic state according to target accident road section information, and determining adjusted road section information corresponding to the current real-time urban traffic state according to the marked real-time traffic flow density data and the current real-time pedestrian flow density data;

the density data analysis module is used for carrying out density data analysis on the marked real-time traffic flow density data and the current real-time traffic flow density data based on the target accident road section information to obtain a reference data analysis result, adjusting the current real-time traffic flow density data and the to-be-processed real-time traffic flow density data according to first deviation data information of the reference data analysis result and the reference data, and returning to the step of accident road section identification until a first safe driving condition is met;

and the traffic state analysis module is used for carrying out real-time urban traffic state analysis based on the adjusted road section information and the target accident road section information which meet the first safe driving condition to obtain a target urban traffic safety state corresponding to the urban traffic state data to be analyzed.

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

The invention provides a data analysis method and a device based on intelligent traffic monitoring, which determine reference data, real-time traffic flow density data to be processed and current real-time traffic flow density data from the acquired urban traffic state data to be analyzed, then identify accident road sections according to the current real-time traffic flow density data, the real-time traffic flow density data to be processed and the reference data, select marked real-time traffic flow density data from the real-time urban traffic state by using the obtained target accident road section information, thus selecting more accurate real-time traffic flow density data, further determine adjustment road section information corresponding to the real-time urban traffic state by using the marked real-time traffic flow density data and the current real-time traffic flow density data, and ensure the adjustment road section information by using the marked real-time traffic flow density data and the current real-time traffic flow density data during each iteration, the more accurate adjusted road section information can be obtained, and then when the safe driving condition is met, the adjusted road section information and the target accident road section information are used for carrying out real-time urban traffic state analysis. Therefore, the problem of inaccurate urban traffic safety state generated when the real-time urban traffic state is analyzed can be avoided, and the analyzed target urban traffic safety state is more accurate.

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 data analysis method based on intelligent traffic monitoring according to an embodiment of the present invention.

Fig. 2 is a block diagram of a data analysis device based on intelligent traffic monitoring according to an embodiment of the present invention.

Fig. 3 is a schematic diagram of a hardware structure of a data analysis 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.

To solve the technical problem in the background art, please refer to fig. 1, which provides a flow chart of a data analysis method based on intelligent traffic monitoring, and the following steps 11 to 16 are specifically executed when the method is implemented.

And 11, acquiring urban traffic state data to be analyzed, and determining benchmark reference data of the urban traffic state data to be analyzed.

In this embodiment, the reference data includes reference traffic density data and reference traffic density data.

And 12, selecting current real-time pedestrian flow density data from the current real-time urban traffic state corresponding to the urban traffic state data to be analyzed, and acquiring corresponding real-time traffic flow density data to be processed based on the urban traffic state data to be analyzed.

And step 13, performing accident road section identification based on the current real-time people flow density data, the to-be-processed real-time traffic flow density data and the reference data to obtain target accident road section information.

And 14, selecting marked real-time traffic flow density data from the current real-time urban traffic state according to the target accident road section information, and determining the adjustment road section information corresponding to the current real-time urban traffic state according to the marked real-time traffic flow density data and the current real-time people flow density data.

And step 15, performing density data analysis on the marked real-time traffic flow density data and the current real-time traffic flow density data based on the target accident road section information to obtain a reference data analysis result, adjusting the current real-time traffic flow density data and the to-be-processed real-time traffic flow density data according to first deviation data information of the reference data analysis result and the reference data, and returning to the step of recognizing the accident road section until a first safe driving condition is met.

And step 16, carrying out real-time urban traffic state analysis based on the adjusted road section information meeting the first safe driving condition and the target accident road section information to obtain a target urban traffic safety state corresponding to the urban traffic state data to be analyzed.

The following advantageous technical effects can be achieved when the method described in the above steps 11 to 16 is performed: determining reference data, real-time traffic flow density data to be processed and current real-time traffic flow density data from the acquired urban traffic state data to be analyzed, selecting the current real-time traffic flow density data from the corresponding real-time urban traffic state, then performing accident road section identification according to the current real-time traffic flow density data, the real-time traffic flow density data to be processed and the reference data, selecting marked real-time traffic flow density data from the real-time urban traffic state by using the obtained target accident road section information, thus more accurate real-time traffic flow density data can be selected, further determining adjusted road section information corresponding to the real-time urban traffic state by using the marked real-time traffic flow density data and the current real-time traffic flow density data, and determining the adjusted road section information by using the marked real-time traffic flow density data and the current real-time traffic flow density data during each iteration, the more accurate adjusted road section information can be obtained, and then when the safe driving condition is met, the adjusted road section information and the target accident road section information are used for carrying out real-time urban traffic state analysis. Therefore, the problem of inaccurate urban traffic safety state generated when the real-time urban traffic state is analyzed can be avoided, and the analyzed target urban traffic safety state is more accurate.

In specific implementation, in order to quickly determine the urban traffic state meeting the first safe driving condition, the step 15 of adjusting the current real-time traffic density data and the to-be-processed real-time traffic density data according to the first deviation data information between the analysis result of the reference data and returning to the step of identifying the accident road section until the first safe driving condition is met specifically includes steps 151 and 152.

Step 151, determining first deviation data information based on the reference data analysis result and the reference data, and when the first deviation data information does not meet a first safe driving condition, adjusting the current real-time urban traffic state based on the adjusted road section information to obtain a marked real-time urban traffic state;

and 152, selecting marked real-time traffic density data from the marked real-time urban traffic state to obtain adjusted current real-time traffic density data, taking the marked real-time traffic density data as adjusted to-be-processed real-time traffic density data, and returning to the step of performing accident road section identification based on the current real-time traffic density data, the to-be-processed real-time traffic density data and the reference data to obtain target accident road section information until a first safe driving condition is met.

Thus, by performing the operations described in step 151 and step 152, first deviation data information of the analysis result of the reference data and the reference data is determined, further judging whether the first deviation data information meets a first safe driving condition, if not, adjusting the current real-time urban traffic state according to the adjusted road section information, thus, the current real-time urban traffic state can be adjusted in a targeted manner, and the marked real-time urban traffic state can be accurately obtained, further selecting marked real-time people stream density data in the marked real-time urban traffic state, therefore, the real-time people flow density data in the marked real-time urban traffic state can be rapidly judged in real time, and then, identifying the accident road section based on the current real-time people flow density data, the to-be-processed real-time traffic flow density data and the reference data. Therefore, the urban traffic state meeting the first safe driving condition can be quickly determined by carrying out multiple iterations through the description contents.

In this embodiment, the urban traffic state data to be analyzed is a real-time urban traffic state index, and the reference data analysis result includes reference dynamic traffic density data and reference dynamic traffic density data. Further, the determining the first deviation data information based on the baseline data analysis result and the baseline reference data as described in step 151 may include, in a first embodiment: determining deviation data information corresponding to the people flow density data based on the reference dynamic people flow density data and the reference people flow density data, and determining deviation data information corresponding to the traffic flow density data based on the reference dynamic traffic flow density data and the reference traffic flow density data; and obtaining first deviation data information of the reference data analysis result and the reference data based on deviation data information corresponding to the traffic flow density data and deviation data information corresponding to the people flow density data.

In some alternative embodiments, the urban traffic state data to be analyzed is a delayed urban traffic state index, and the reference data analysis result includes reference dynamic traffic density data and reference dynamic traffic density data; further, the determining the first deviation data information based on the baseline data analysis result and the baseline reference data described in step 151 may further include, in the second embodiment: determining deviation data information corresponding to the people flow density data based on the reference dynamic people flow density data and the reference people flow density data, and determining deviation data information corresponding to the traffic flow density data based on the reference dynamic traffic flow density data and the reference traffic flow density data; acquiring next road section information corresponding to the next urban traffic state index of the delayed urban traffic state indexes; the next road section information is road section information used by the next urban traffic state index in real-time urban traffic state analysis; determining road section deviation data information of the next road section information and the adjusted road section information, and obtaining a first deviation data information of the reference data analysis result and the reference data based on the deviation data information corresponding to the traffic flow density data, the deviation data information corresponding to the people flow density data and the road section deviation data information.

In either of the above two embodiments, the first deviation data information can be accurately determined.

In specific implementation, in order to determine the reference pedestrian flow density data and the reference traffic flow density data from the traffic safety information quickly and in real time, the problem of deviation of detection of the traffic safety information due to a wrong urban traffic state can be avoided, the condition that the determined reference pedestrian flow density data and the determined reference traffic flow density data are not accurate is avoided, the reference pedestrian flow density data and the reference traffic flow density data corresponding to the to-be-analyzed urban traffic state data are determined in step 11, and the method specifically includes the contents described in steps 111 to 113.

And 111, analyzing the urban traffic state based on the urban traffic state data to be analyzed to obtain the urban traffic state change trend.

And 112, detecting the road condition safety information corresponding to the urban traffic state in the urban traffic state change trend to obtain the road condition safety information corresponding to the urban traffic state data to be analyzed.

And 113, determining reference people flow density data and reference traffic flow density data from the road condition safety information corresponding to the urban traffic state.

Thus, the contents described in steps 111 to 113 are executed, the urban traffic state is firstly analyzed, and then the change trend of the urban traffic state is determined, and then the traffic safety information corresponding to the urban traffic state is detected according to the change trend of the urban traffic state, so as to detect the safe traffic information.

In some alternative embodiments, the urban traffic status data to be analyzed is a real-time urban traffic status indicator; further, in order to ensure the real-time performance of the selected to-be-processed real-time traffic density data, the step 12 of obtaining the corresponding to-be-processed real-time traffic density data based on the urban traffic state index may specifically include the steps 121 and 122.

Step 121, acquiring urban traffic jam amount, loading the current real-time pedestrian flow density data into a reference density data set according to the urban traffic jam amount to obtain current pedestrian flow density data, and performing accident road section identification based on the current pedestrian flow density data and the reference pedestrian flow density data to obtain updated road section pedestrian flow density data;

and step 122, selecting to-be-processed real-time traffic flow density data corresponding to the real-time urban traffic state index from the traffic flow density data change trend corresponding to the real-time urban traffic state of the current real-time urban traffic state according to the updated road section traffic flow density data.

The description contents of the step 121 and the step 122 are that firstly, the accident road section is identified according to the current pedestrian flow density data and the reference pedestrian flow density data, then the updated road section pedestrian flow density data is obtained in real time, and then the to-be-processed real-time traffic flow density data corresponding to the real-time urban traffic state index is selected from the traffic flow density data change trend on the basis of obtaining the updated road section pedestrian flow density data. Therefore, the accuracy of the selected to-be-processed real-time traffic density data can be ensured.

Further, the step 121 of obtaining the amount of urban traffic congestion includes:

step 1211, obtaining congestion indexes corresponding to all congested road sections, and selecting a current congested road section from the congestion indexes corresponding to all congested road sections;

step 1212, loading the current real-time traffic density data into a reference density data set according to the current congested section to obtain current traffic density data of the congested section, and performing accident section identification based on the current traffic density data of the congested section and the reference traffic density data to obtain section traffic density data corresponding to the congested section;

step 1213, selecting the real-time traffic flow density data of the congested road section from the traffic flow density data change trend corresponding to the real-time urban traffic state of the current real-time urban traffic state according to the road section pedestrian flow density data corresponding to the congested road section;

step 1214, performing congestion road section accident identification based on the real-time traffic flow density data of the congestion road section, the current real-time pedestrian flow density data and the reference data to obtain target accident road section information corresponding to the congestion road section;

step 1215 of selecting marked real-time traffic flow density data of the congested road section from the traffic flow density data change trend corresponding to the real-time urban traffic state according to the target accident road section information corresponding to the congested road section;

step 1216, determining adjusted road section information corresponding to the congested road section corresponding to the current real-time urban traffic state according to the marked real-time traffic flow density data of the congested road section and the current real-time people flow density data;

step 1217, performing density data analysis on the marked real-time traffic flow density data and the current real-time pedestrian flow density data of the congested road section based on the target accident road section information corresponding to the congested road section to obtain a reference data analysis result of the congested road section, adjusting the real-time traffic flow density data and the current real-time pedestrian flow density data of the congested road section according to the reference data analysis result of the congested road section and second deviation data information of the reference data, and returning to the step of accident identification of the congested road section until a second safe driving condition is met to obtain current second deviation data information corresponding to the current congested road section;

step 1218, traversing the congestion indexes corresponding to the congested road segments to obtain current second deviation data information corresponding to the congestion indexes corresponding to the congested road segments, comparing the current second deviation data information to obtain target second deviation data information, and taking the congestion index corresponding to the congested road segment corresponding to the target second deviation data information as the urban traffic congestion amount.

Further, the step 1217 of adjusting the real-time traffic density data and the current real-time traffic density data of the congested road segment according to the analysis result of the reference data of the congested road segment and the second deviation data information of the reference data, and returning to the step of identifying the accident of the congested road segment until a second safe driving condition is met, includes: when the second deviation data information does not meet a second safe driving condition, adjusting the current real-time urban traffic state based on the adjusted road section information corresponding to the congested road section to obtain a marked real-time urban traffic state of the congested road section; selecting marked real-time pedestrian flow density data of the congested road section from the marked real-time urban traffic state of the congested road section, taking the marked real-time pedestrian flow density data of the congested road section as current real-time pedestrian flow density data, taking the marked real-time traffic flow density data of the congested road section as real-time traffic flow density data of the congested road section, and returning the real-time traffic flow density data of the congested road section, the current real-time pedestrian flow density data and the reference data to perform congestion road section accident identification to obtain target accident road section information corresponding to the congested road section until a second safe driving condition is met.

It is understood that the identifying of the accident road segment based on the current people stream density data and the reference people stream density data, which is described in step 121, to obtain the people stream density data of the updated road segment, includes: acquiring first initial road section pedestrian flow density data corresponding to the real-time urban traffic state index, and loading the current real-time pedestrian flow density data into a reference density data set based on the first initial road section pedestrian flow density data to obtain first updated current pedestrian flow density data; determining third deviation data information based on the first updated current people stream density data and the reference people stream density data; adjusting the people stream density data of the first initial road section according to the third deviation data information, and returning to the step of loading the current real-time people stream density data into a reference density data set based on the people stream density data of the first initial road section to obtain first updated current people stream density data until the third deviation data information meets a third safe driving condition; and taking the first initial road section stream density data meeting the third safe driving condition as the updated road section stream density data.

In some alternative embodiments, the urban traffic status data to be analyzed is a delayed urban traffic status indicator. In a specific implementation, the step 12 of obtaining the corresponding to-be-processed real-time traffic density data based on the to-be-analyzed urban traffic state data includes steps 123 and 124.

Step 123, acquiring next real-time traffic density data corresponding to the next urban traffic state index of the delayed urban traffic state indexes; and the next real-time traffic flow density data is the real-time traffic flow density data in the real-time urban traffic state corresponding to the next urban traffic state index.

And step 124, taking the next real-time traffic density data as the to-be-processed real-time traffic density data.

In some alternative embodiments, the urban traffic status data to be analyzed is a real-time urban traffic status indicator; it can be understood that, in the first embodiment, the accident section identification based on the current real-time people flow density data, the to-be-processed real-time traffic flow density data, and the reference data, which is described in step 13, to obtain the target accident section information may specifically include the contents described in steps 131 to 134.

Step 131, obtaining second initial road section pedestrian flow density data corresponding to the real-time urban traffic state index, and loading the current real-time pedestrian flow density data and the to-be-processed real-time traffic flow density data into a reference density data set based on the second initial road section pedestrian flow density data to obtain a real-time quasi data analysis result.

Step 132, determining fourth deviation data information based on the real-time quasi data analysis result and the benchmark reference data.

Step 133, adjusting the second initial road section traffic density data according to the fourth deviation data information, and returning to the step of loading the current real-time traffic density data and the to-be-processed real-time traffic density data into a reference density data set based on the second initial road section traffic density data to obtain a real-time quasi data analysis result until the fourth deviation data information meets a fourth safe driving condition.

And 134, taking the second initial road section pedestrian flow density data meeting the fourth safe driving condition as the target accident road section information corresponding to the real-time urban traffic state index.

In some alternative embodiments, the urban traffic status data to be analyzed is a delayed urban traffic status indicator. Further, in the second embodiment, the identifying of the accident section based on the current real-time people flow density data, the to-be-processed real-time traffic flow density data and the reference data, which is described in step 13, to obtain the target accident section information may specifically include the contents described in steps 135 to 139.

And 135, acquiring third initial road section pedestrian flow density data corresponding to the delayed urban traffic state index, and loading the current real-time pedestrian flow density data and the to-be-processed real-time traffic flow density data into a reference density data set according to the third initial road section pedestrian flow density data to obtain a delayed reference data analysis result.

And 136, determining fifth deviation data information based on the delayed reference data analysis result and the reference data, and acquiring next road segment pedestrian flow density data corresponding to a next urban traffic state index of the delayed urban traffic state index, wherein the next road segment pedestrian flow density data is road segment pedestrian flow density data of a real-time urban traffic state corresponding to the next urban traffic state index.

And 137, determining attitude deviation data information of the pedestrian flow density data of the next road section and the pedestrian flow density data of the third initial road section, and obtaining target fifth deviation data information according to the fifth deviation data information and the attitude deviation data information.

And 138, adjusting third initial road section pedestrian flow density data corresponding to the delayed urban traffic state index according to the target fifth deviation data information, and returning to the step of loading the current real-time pedestrian flow density data and the to-be-processed real-time traffic flow density data into a reference density data set according to the third initial road section pedestrian flow density data to obtain a delayed reference data analysis result until the target fifth deviation data information meets a fifth safe driving condition.

And 139, taking the third initial road section pedestrian flow density data meeting the fifth safe driving condition as the target accident road section information corresponding to the delayed urban traffic state index.

In this way, by implementing either of the two embodiments, the target accident road section information corresponding to the real-time urban traffic state index can be accurately determined.

In specific implementation, in order to accurately select the marked real-time traffic density data from the current real-time urban traffic state, avoid the problem of marking errors, and improve the working efficiency, the step 14 of selecting the marked real-time traffic density data from the current real-time urban traffic state according to the target accident road section information may specifically include the contents described in the steps 141 to 143.

Step 141, obtaining a fluctuation track of the traffic flow acceleration data in the traffic flow density data change trend corresponding to the real-time urban traffic state of the current real-time urban traffic state, obtaining urban traffic environment information, and selecting corresponding real-time track segments from the fluctuation track of the traffic flow acceleration data according to the urban traffic environment information.

And 142, loading each real-time track segment into a reference density data set according to the target accident road section information to obtain each segment data analysis result.

Step 143, determining sixth deviation data information based on each fragment data analysis result and the reference traffic flow density data, comparing the sixth deviation data information corresponding to each fragment data analysis result to obtain target sixth deviation data information, and taking the real-time track fragment corresponding to the target sixth deviation data information as the marked real-time traffic flow density data corresponding to the reference traffic flow density data.

Executing the content described in the above steps 141 to 143, first determining the fluctuation track of the traffic flow acceleration data in the traffic flow density data variation trend to know the safety problem of the current real-time urban traffic state in real time, and then selecting corresponding real-time track segments from the fluctuation track of the traffic flow acceleration data according to the obtained urban traffic environment information, so that the deviation data information between the reference traffic flow density data and the data analysis results of the segments can be analyzed through the real-time track segments, and then the marked real-time traffic flow density data can be accurately selected from the current real-time urban traffic state according to the deviation data information, thereby avoiding the problem of marking errors and improving the working efficiency.

On the basis of the above, please refer to fig. 2, the present invention further provides a block diagram of a data analysis device 20 based on intelligent traffic monitoring, which includes the following functional modules.

The reference data acquisition module 21 is configured to acquire urban traffic state data to be analyzed and determine reference data of the urban traffic state data to be analyzed; the benchmark reference data comprises benchmark people flow density data and benchmark traffic flow density data.

And the density data determining module 22 is configured to select current real-time pedestrian flow density data from current real-time urban traffic states corresponding to the urban traffic state data to be analyzed, and obtain corresponding to-be-processed real-time traffic flow density data based on the urban traffic state data to be analyzed.

And the accident road section identification module 23 is configured to identify an accident road section based on the current real-time pedestrian flow density data, the to-be-processed real-time traffic flow density data, and the reference data, so as to obtain target accident road section information.

And the road section information determining module 24 is configured to select marked real-time traffic flow density data from the current real-time urban traffic state according to the target accident road section information, and determine adjusted road section information corresponding to the current real-time urban traffic state according to the marked real-time traffic flow density data and the current real-time pedestrian flow density data.

And the density data analysis module 25 is configured to perform density data analysis on the marked real-time traffic density data and the current real-time traffic density data based on the target accident road section information to obtain a reference data analysis result, adjust the current real-time traffic density data and the to-be-processed real-time traffic density data according to a first deviation data information between the reference data analysis result and the reference data, and return to the step of identifying the accident road section until a first safe driving condition is met.

And the traffic state analysis module 26 is configured to perform real-time urban traffic state analysis based on the adjusted road segment information and the target accident road segment information that satisfy the first safe driving condition, so as to obtain a target urban traffic safety state corresponding to the to-be-analyzed urban traffic state data.

On the basis, please refer to fig. 3 in combination, a data analysis terminal 110 is provided, which includes 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 will be understood that the invention is not limited to the precise arrangements 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 data analysis method based on intelligent traffic monitoring is characterized by comprising the following steps:

2. The method according to claim 1, wherein the step of returning to the accident road section identification after adjusting the current real-time traffic density data and the to-be-processed real-time traffic density data according to the first deviation data information of the reference data analysis result and the reference data until the first safe driving condition is met comprises:

selecting marked real-time traffic density data from the marked real-time urban traffic state to obtain adjusted current real-time traffic density data, taking the marked real-time traffic density data as adjusted to-be-processed real-time traffic density data, and returning to perform accident road section identification based on the current real-time traffic density data, the to-be-processed real-time traffic density data and the reference data to obtain target accident road section information until a first safe driving condition is met;

obtaining first deviation data information of the reference data analysis result and the reference data based on deviation data information corresponding to the traffic flow density data and deviation data information corresponding to the people flow density data;

3. The method according to claim 1, wherein the determining of the reference pedestrian flow density data and the reference traffic flow density data corresponding to the urban traffic state data to be analyzed comprises:

4. The method according to claim 1, wherein the urban traffic status data to be analyzed is a real-time urban traffic status indicator; the method for acquiring the corresponding to-be-processed real-time traffic flow density data based on the urban traffic state indexes comprises the following steps:

selecting to-be-processed real-time traffic flow density data corresponding to the real-time urban traffic state index from the traffic flow density data change trend corresponding to the real-time urban traffic state of the current real-time urban traffic state according to the updated road section traffic flow density data;

the acquiring of the urban traffic jam amount comprises the following steps:

traversing the congestion indexes corresponding to the congested road sections to obtain current second deviation data information corresponding to the congestion indexes corresponding to the congested road sections, comparing the current second deviation data information to obtain target second deviation data information, and taking the congestion indexes corresponding to the congested road sections corresponding to the target second deviation data information as the urban traffic congestion amount;

5. The method of claim 4, wherein the identifying of the accident road segment based on the current traffic density data and the reference traffic density data to obtain updated road segment traffic density data comprises:

6. The method according to claim 1, wherein the urban traffic status data to be analyzed is a delayed urban traffic status indicator; the method for acquiring the corresponding to-be-processed real-time traffic flow density data based on the to-be-analyzed urban traffic state data comprises the following steps:

7. The method according to claim 1, wherein the urban traffic status data to be analyzed is a real-time urban traffic status indicator; the method for identifying the accident road section based on the current real-time people flow density data, the to-be-processed real-time traffic flow density data and the reference data to obtain the target accident road section information comprises the following steps:

8. The method according to claim 1, wherein the urban traffic status data to be analyzed is a delayed urban traffic status indicator; the method for identifying the accident road section based on the current real-time people flow density data, the to-be-processed real-time traffic flow density data and the reference data to obtain the target accident road section information comprises the following steps:

9. The method of claim 1, wherein the selecting marked real-time traffic density data from the current real-time urban traffic state according to the target accident road segment information comprises:

10. A data analysis device based on intelligent traffic monitoring, the device comprising: