Traffic situation analysis system based on Internet data acquisition

Abstract

The invention discloses a traffic situation analysis system based on Internet data acquisition, relates to the technical field of traffic situation analysis, and solves the technical problems that traffic situation analysis cannot be performed according to data characteristics and real-time traffic stage division cannot be performed according to traffic situation types in the prior art. Specifically, a data acquisition terminal performs data acquisition according to road monitoring; the data characteristic screening unit performs characteristic screening on the collected data of the data collection terminal, and after the characteristic screening is completed, the types of the collected data of the data collection terminal are sent to the traffic intelligent management and control platform and transmitted to the data storage library; the real-time situation analysis unit analyzes and processes the real-time collected data in the data storage library to obtain a different growth data type, and deduces the traffic situation of the current road condition according to data analysis; and the situation type analysis unit performs road traffic situation analysis according to the collected different growth data.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic situation analysis, and specifically to a traffic situation analysis system based on Internet data collection. Background Art

[0002] A traffic situation analysis system based on Internet data collection is an intelligent system that uses Internet technology and big data analysis means to collect, process, analyze, and visually display traffic-related data to achieve real-time monitoring, prediction, and evaluation of traffic situations. This system can provide decision-making support and information services for traffic management departments, travelers, etc., and helps to optimize traffic resource allocation, improve traffic efficiency, and relieve traffic congestion.

[0003] However, in the prior art, based on a large amount of data of the same type, although it is possible to evaluate the road traffic conditions, the data screening intensity is high, and a large amount of data also needs to be screened even when there is no abnormality in the road traffic situation. In addition, it is impossible to analyze the traffic situation according to the data characteristics, and it is not possible to divide the real-time traffic stages according to the traffic situation types, so that it is impossible to conduct targeted traffic situation analysis and control, reducing the control efficiency of the road network.

[0004] In view of the above technical deficiencies, a solution is now proposed. Summary of the Invention

[0005] The purpose of the present invention is to solve the above-mentioned problems and propose a traffic situation analysis system based on Internet data collection.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] A traffic situation analysis system based on Internet data collection includes a traffic intelligent control platform, which is communicatively connected to:

[0008] A data collection terminal collects data according to road monitoring;

[0009] A data characteristic screening unit screens the collected data of the data collection terminal according to characteristics. After completing the characteristic screening, the data collection types of the data collected by the data collection terminal are sent to the traffic intelligent control platform together and transferred to the data storage library;

[0010] A real-time situation analysis unit analyzes and processes the real-time collected data in the data storage library to obtain abnormal growth data types, and infers the traffic situation of the current road conditions based on the data analysis;

[0011] A situation type analysis unit conducts traffic situation analysis of the road traffic according to the collected abnormal growth data analysis.

[0012] As a preferred embodiment of the present invention, the process of the data characteristic screening unit is as follows:

[0013] Integrate the collected data of the data acquisition terminal, and perform characteristic analysis on the collected data of each tag, where the tag indicates the generation location and generation time of the data;

[0014] Obtain the consistency frequency of the same type of traffic data values corresponding to different tags in the real-time collected data, and when in the same historical traffic state, collect the maximum deviation value of the same type of traffic data corresponding to different tags, and analyze the consistency frequency of the same type of traffic data values corresponding to different tags in the real-time collected data and the maximum deviation value of the same type of traffic data corresponding to different tags.

[0015] As a preferred embodiment of the present invention, if the consistency frequency of the same type of traffic data values corresponding to different tags in the real-time collected data exceeds the frequency threshold, then mark the current traffic data type as a long-time-effect type;

[0016] If the consistency frequency of the same type of traffic data values corresponding to different tags in the real-time collected data does not exceed the frequency threshold, then mark the current traffic data type as a short-time-effect type;

[0017] If the maximum deviation value of the same type of traffic data corresponding to different tags exceeds the maximum deviation threshold, then mark the current traffic type data as a high-span type; if the maximum deviation value of the same type of traffic data corresponding to different tags does not exceed the maximum deviation threshold, then mark the current traffic type data as a low-span type.

[0018] As a preferred embodiment of the present invention, the process of the real-time situation analysis unit is as follows:

[0019] Set the trend influence for each type of stored data, that is, if the stored data grows and the road state drops, then mark it as abnormal growth data, otherwise mark it as positive growth data;

[0020] Set the situation analysis period, obtain the duration of the same growth trend fluctuation of the abnormal growth data of the long-time-effect type within the situation analysis period and the timeliness duration threshold of the current abnormal growth data type, and calculate the duration ratio according to the duration ratio, and mark it as the abnormal timeliness ratio of the long-time-effect type data; collect the numerical peak values of each data timeliness cycle of the abnormal growth data of the short-time-effect type within the situation analysis period, and obtain the increasing span of the abnormal growth data corresponding to the continuous increase of the data timeliness cycle according to the numerical peak values, and calculate the average increasing span through ratio calculation; the timeliness cycle is expressed as the timeliness duration of the short-time-effect type data.

[0021] As a preferred embodiment of the present invention, if the abnormal timeliness ratio of the abnormal growth data during the situation analysis period exceeds the set timeliness ratio threshold, or the average value of the increasing span continuously increases during the situation analysis period, the current type of abnormal growth data will be sent to the situation type analysis unit together; if the abnormal timeliness ratio of the abnormal growth data during the situation analysis period does not exceed the set timeliness ratio threshold, and the average value of the increasing span does not continuously increase during the situation analysis period, it is inferred that the road traffic situation corresponding to the collected data during the current situation analysis period is normal, and the current abnormal growth data will be continuously monitored.

[0022] As a preferred embodiment of the present invention, the process of the situation type analysis unit is as follows:

[0023] Obtain the road where the abnormal growth data is collected and mark it as an abnormal situation road, and analyze the abnormal situation road;

[0024] When the abnormal growth data is high-span type data, if the numerical span of the high-span type data in the abnormal situation road continuously rises, it indicates that the current abnormal situation road is in the stage of abnormal situation trend, and the warning level is increased according to the increasing speed of the increasing span of the data value;

[0025] When the abnormal growth data is low-span type data, when the low-span type data in the abnormal situation road fluctuates and no longer grows, it indicates that the current abnormal situation road is in the stage of abnormal situation generation; and the decreasing speed of the vehicle flow passing volume per minute in the abnormal situation road is used as the judgment standard for the influence level signal;

[0026] Send the stage of abnormal situation trend or the stage of abnormal situation generation and the corresponding level signal to the traffic intelligent control platform.

[0027] As a preferred embodiment of the present invention, after the traffic intelligent control platform receives it, if it is in the stage of abnormal situation trend, a comprehensive road traffic control will be carried out on the abnormal situation road. If the current warning signal level exceeds the set level threshold, the timeliness duration threshold of the long-timeliness data processed by the analysis of the abnormal situation trend stage will be lengthened, and the data fluctuation span threshold of the short-timeliness data will be decreased;

[0028] If it is in the stage of abnormal situation generation, a local road traffic control will be carried out on the abnormal situation road. If the current influence level signal exceeds the set level threshold, the control rate at any position of the current abnormal situation road will be adjusted, and when an abnormal position appears on the abnormal situation road, the control buffer duration will be shortened, and the control duration will be controlled according to the real-time influence level.

[0029] Compared with the prior art, the beneficial effects of the present invention are:

[0030] 1. In the present invention, the collected data of the data acquisition terminal is screened by characteristics, which improves the pertinence of data processing through data characteristics, reduces the processing intensity of massive data, and can perform data processing specifically according to characteristics. Facing complex traffic conditions, it can accurately and quickly analyze the traffic situation to improve the accuracy of traffic situation analysis and facilitate timely regulation based on the situation.

[0031] 2. In the present invention, the real-time collected data in the data repository is analyzed and processed to infer the traffic situation of the current road conditions, so as to facilitate timely road traffic planning, reduce the road traffic pressure, and infer the traffic states of each road to ensure the smooth operation of the entire traffic network; according to the traffic situation analysis, the road state type can be inferred, and targeted road planning can be carried out according to the road state type. For example, when an accident or vehicle failure occurs on the road, resulting in slow traffic flow, but this type of traffic situation will be alleviated within a certain period of time. If traffic planning is carried out only based on this type, it will increase the traffic pressure on adjacent roads and cause unqualified road resource planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] For the convenience of those skilled in the art to understand, the present invention will be further described below with reference to the accompanying drawings.

[0033] Figure 1 It is a principle block diagram of a traffic situation analysis system based on Internet data collection according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0034] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts belong to the scope of protection of the present invention.

[0035] Reference to "embodiment" in this text means that the specific features, structures or characteristics described in connection with the embodiment may be included in at least one embodiment of the present invention. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0036] Embodiment 1

[0037] Please refer to Figure 1As shown, a traffic situation analysis system based on Internet data collection includes a traffic intelligent control platform, wherein the traffic intelligent control platform is connected to a data collection terminal; it should be explained that the data collection terminal is a data collection terminal for road traffic, such as networked cameras, aerial photography terminals and other equipment, which is used to collect road data and upload it to the Internet;

[0038] The data collection terminal collects data based on road monitoring and generates massive data. In the prior art, although the road traffic conditions can be evaluated based on the same type of massive data, the data screening intensity is large, and the massive data also needs to be screened when there is no abnormality in the road traffic situation. Therefore, the present application adds a data characteristic screening unit to screen the massive data to reduce the processing intensity of Internet data, and at the same time builds a repository for data storage, and performs data storage control according to the data life cycle for further screening;

[0039] The data characteristic screening unit performs characteristic screening on the data collected by the data collection terminal, improves the pertinence of data processing through data characteristics, reduces the processing intensity of massive data, and can process data pertinently according to the characteristics. In the face of complex traffic conditions, it can accurately and quickly analyze traffic situation, so as to improve the accuracy of traffic situation analysis and facilitate timely regulation according to the situation;

[0040] The collected data of the data collection terminal is integrated, and the characteristics of the collected data of each tag are analyzed, where the tag indicates the location and time of data generation; the collected data contains traffic data of the same location at different times, and also stores traffic data of different locations at the same time. The collected traffic data actually includes the number of vehicles, vehicle speed, and traffic flow, etc.

[0041] Obtain the consistent frequency of the same type of traffic data values ​​corresponding to different tags in real-time data collection, and under the same traffic conditions in history, collect the maximum deviation value of the same type of traffic data corresponding to different tags, and analyze the consistent frequency of the same type of traffic data values ​​corresponding to different tags in real-time data collection and the maximum deviation value of the same type of traffic data corresponding to different tags:

[0042] If the consistent frequency of the same type of traffic data values ​​corresponding to different labels of real-time collected data exceeds the frequency threshold, it is inferred that the current type of traffic data has long-term validity, that is, this type of data can be used for continuous monitoring of traffic situation assessment in the current continuous stage, and the current traffic data type is marked as a long-term type;

[0043] If the consistency frequency of the same type of traffic data values corresponding to different tags in real-time data collection does not exceed the frequency threshold, it is inferred that the current type of traffic data has short timeliness, that is, for the traffic situation assessment in the current short stage, this type of data can be used for continuous monitoring, and the current traffic data type is marked as a short-timeliness type;

[0044] If the maximum deviation value of the same type of traffic data corresponding to different tags exceeds the maximum deviation threshold, it is inferred that the impact of the current type of traffic data situation has a high range span, that is, when continuously monitoring the situation, this type of data is used as the data type for continuous monitoring, and it can be used as a prediction analysis parameter according to the continuous fluctuation of this type of data, and the current traffic type data is marked as a high-span type;

[0045] If the maximum deviation value of the same type of traffic data corresponding to different tags does not exceed the maximum deviation threshold, it is inferred that the impact of the current type of traffic data situation has a low range span, that is, when short-term monitoring the situation, this type of data is used as the data type for continuous monitoring, and it can be used as a traceability analysis parameter according to the instantaneous fluctuation of this type of data, and the current traffic type data is marked as a low-span type;

[0046] The data collection types collected by the data collection terminal are sent to the traffic intelligent control platform together and transferred to the data storage repository;

[0047] It should be noted that after each type of data is classified and stored, when the traffic intelligent control platform conducts situation analysis, for the long-timeliness type and short-timeliness type of the collected data, the data exceeding the time limit type is deleted. At the same time, if the situation assessment is not carried out in the current stage and the real-time long-timeliness type data is continuously updated, the storage volume of the long-timeliness data is reduced; for example, the average speed of the traffic flow on the road has long timeliness, while the driving speed of a vehicle at a single moment has short timeliness;

[0048] If there is an instantaneous change in the road traffic situation, the low-span type data is analyzed and evaluated. For example, the maximum buffer duration when the vehicle speed instantaneously drops to zero is used for vehicle accident detection; the reciprocating adjustment speed of the vehicle driving speed is used for vehicle driving state detection;

[0049] Embodiment 2

[0050] The traffic intelligent control platform conducts road traffic situation assessment according to each type of data, generates a real-time situation analysis signal and sends it to the real-time situation analysis unit;

[0051] The real-time situation analysis unit is used to analyze and process the real-time collected data in the data storage repository to infer the traffic situation of the current road conditions, so as to timely conduct road traffic planning, reduce the road traffic pressure, and infer each road traffic state to ensure the smooth operation of the entire traffic network;

[0052] Set trend impacts for various types of stored data. That is, if the stored data grows, the road condition deteriorates, and it is marked as abnormally growing data; conversely, it is marked as positively growing data. It should be noted that this solution takes data growth as an example, and data reduction also has the characteristic of affecting the road condition;

[0053] Set the period for situation analysis, obtain the duration of the floating of the abnormally growing data of the long-duration type corresponding to the same growth trend within the situation analysis period and the timeliness duration threshold of the current type of abnormally growing data, and calculate the duration ratio based on the duration ratio, and mark it as the abnormal timeliness ratio of the long-duration type data. It can be understood that the timeliness duration threshold is the duration manifestation of data timeliness, and the effective duration of each type of data can be obtained according to historical data collection;

[0054] Collect the numerical peaks of each data timeliness cycle of the abnormally growing data of the short-duration type within the situation analysis period, and obtain the increasing span of the abnormally growing data corresponding to the continuous increase of the data timeliness cycle based on the numerical peaks, and calculate the average increasing span through ratio calculation; The timeliness cycle represents the timeliness duration of the short-duration type data;

[0055] If the abnormal timeliness ratio of the abnormally growing data within the situation analysis period exceeds the set timeliness ratio threshold, or the average increasing span within the situation analysis period continues to increase, it is inferred that the road traffic situation corresponding to the collected data within the current situation analysis period is abnormal, and the current type of abnormally growing data is sent to the situation type analysis unit together;

[0056] If the abnormal timeliness ratio of the abnormally growing data within the situation analysis period does not exceed the set timeliness ratio threshold, and the average increasing span within the situation analysis period does not continue to increase, it is inferred that the road traffic situation corresponding to the collected data within the current situation analysis period is normal, and the current abnormally growing data is continuously monitored;

[0057] The situation type analysis unit conducts road traffic situation analysis based on the collected abnormally growing data. According to the traffic situation analysis, the road condition type can be inferred, and targeted road planning is carried out according to the road condition type. For example, if there is an accident or vehicle failure on the road, resulting in slow traffic flow, but this type of traffic situation will be alleviated within a certain period of time. If traffic planning is carried out solely based on this type, it will increase the traffic pressure on adjacent roads and cause unqualified road resource planning;

[0058] Obtain the road where the abnormally growing data is collected and mark it as an abnormally situation road, and analyze the abnormally situation road;

[0059] When the abnormally growing data is high-span type data, if the numerical span of the high-span type data in the abnormally situation road continues to rise, it indicates that the current abnormally situation road is in the stage of abnormal situation trend, and the warning level is increased according to the increasing speed of the rising span of the data value, such as level one, level two, etc.;

[0060] When the abnormal growth data is of the low-span type, when the low-span type data in the abnormal situation road fluctuates and no longer grows, it indicates that the current abnormal situation road is in the stage of abnormal situation generation; and the decline speed of the vehicle flow passing through per minute in the abnormal situation road is used as the judgment criterion for the influence level signal;

[0061] Send the abnormal situation trend stage or the abnormal situation generation stage and the corresponding level signal to the traffic intelligent control platform;

[0062] After the traffic intelligent control platform receives it, if it is in the abnormal situation trend stage, it will conduct a comprehensive road traffic control on the abnormal situation road. If the current early warning signal level exceeds the set level threshold, it will increase the time limit threshold of the long-term data processed by the analysis of the abnormal situation trend stage, and decrease the data floating span threshold of the short-term data;

[0063] If it is in the abnormal situation generation stage, it will conduct a partial road traffic control on the abnormal situation road. If the current influence level signal exceeds the set level threshold, it will regulate the control rate at any position of the current abnormal situation road to ensure that vehicle accidents can be processed immediately at each position. And when an abnormal position appears on the abnormal situation road, it will shorten the control buffer duration and control the control duration according to the real-time influence level.

[0064] When the present invention is in use, the data acquisition terminal collects data according to road monitoring; the data characteristic screening unit screens the collected data of the data acquisition terminal. After the characteristic screening is completed, it sends the data acquisition types of the data acquisition terminal to the traffic intelligent control platform together and transfers them to the data storage library; the real-time situation analysis unit analyzes and processes the real-time collected data in the data storage library to obtain the abnormal growth data type, and infers the traffic situation of the current road condition according to the data analysis; the situation type analysis unit conducts a road traffic situation analysis according to the collected abnormal growth data analysis.

[0065] The setting of the threshold or the preset value, preset range, etc. is for result comparison and analysis to determine whether it is good or bad. Regarding the size value of it, it is set and stored by combining the large model analysis of sample data and manual experience, and can also be adjusted appropriately according to seasonal or regular influence conditions;

[0066] And regarding the setting of the weight ratio coefficient, influence factor, etc., specific values are allocated according to the influence degree of each parameter on the result, and finally reflect the influence situation on the result. It is also set and stored by combining the large model analysis of sample data and manual experience, and can also be adjusted appropriately according to seasonal or regular influence conditions.

[0067] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only the specific embodiments. Obviously, according to the content of this specification, many modifications and variations can be made. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can well understand and utilize the present invention. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A traffic situation analysis system based on Internet data collection, characterized in that, Including a traffic intelligent control platform, which is communicatively connected to: The data acquisition terminal collects data according to road monitoring. The data characteristic screening unit screens the characteristics of the collected data of the data acquisition terminal. After completing the characteristic screening, it sends the data types collected by the data acquisition terminal to the traffic intelligent control platform together and transfers them to the data storage library. The real-time situation analysis unit analyzes and processes the real-time collected data in the data storage library to obtain the data types of abnormal growth, and infers the traffic situation of the current road condition based on the data analysis. The situation type analysis unit analyzes the road traffic situation based on the analysis of the collected data of abnormal growth.

2. The traffic situation analysis system based on Internet data collection according to claim 1, wherein The process of the data characteristic screening unit is as follows: Integrate the collected data of the data acquisition terminal, and perform characteristic analysis on the collected data of each label, where the label indicates the generation location and generation time of the data. Obtain the consistency frequency of the same type of traffic data values corresponding to different labels of the real-time collected data, and when in the same traffic state historically, obtain the maximum deviation value of the same type of traffic data corresponding to different labels, and analyze the consistency frequency of the same type of traffic data values corresponding to different labels of the real-time collected data and the maximum deviation value of the same type of traffic data corresponding to different labels.

3. The traffic situation analysis system based on Internet data collection according to claim 2, characterized in that, If the consistency frequency of the same type of traffic data values corresponding to different labels of the real-time collected data exceeds the frequency threshold, mark the current traffic data type as the long-time effect type. If the consistency frequency of the same type of traffic data values corresponding to different labels of the real-time collected data does not exceed the frequency threshold, mark the current traffic data type as the short-time effect type. If the maximum deviation value of the same type of traffic data corresponding to different labels exceeds the maximum deviation threshold, mark the current traffic type data as the high-span type; if the maximum deviation value of the same type of traffic data corresponding to different labels does not exceed the maximum deviation threshold, mark the current traffic type data as the low-span type.

4. A traffic situation analysis system based on Internet data collection according to claim 3, characterized in that, The process of the real-time situation analysis unit is as follows: Set the trend influence for each type of stored data, that is, if the stored data grows, the road state drops, then mark it as abnormal growth data, otherwise mark it as positive growth data. Set the situation analysis period, obtain the floating duration of the same growth trend corresponding to the abnormal growth data of the long-time effect type within the situation analysis period and the timeliness duration threshold of the current abnormal growth data type, and calculate the duration ratio based on the duration ratio, and mark it as the abnormal timeliness ratio of the long-time effect type data; obtain the numerical peak value of each data timeliness period of the abnormal growth data of the short-time effect type within the situation analysis period, and obtain the increasing span of the corresponding abnormal growth data when the data timeliness period continuously increases based on the numerical peak value, and calculate the average increasing span through the ratio; the timeliness period is expressed as the timeliness duration of the short-time effect type data.

5. The traffic situation analysis system based on Internet data collection according to claim 4, wherein If the abnormal aging ratio of the abnormal growth data during the situation analysis period exceeds the set aging ratio threshold, or the average increasing span continuously increases during the situation analysis period, the current type of abnormal growth data will be sent to the situation type analysis unit together; if the abnormal aging ratio of the abnormal growth data during the situation analysis period does not exceed the set aging ratio threshold, and the average increasing span does not continuously increase during the situation analysis period, it is inferred that the road traffic situation corresponding to the collected data during the current situation analysis period is normal, and the current abnormal growth data will be continuously monitored.

6. The traffic situation analysis system based on Internet data collection according to claim 5, characterized in that, The process of the situation type analysis unit is as follows: Obtain the road where the abnormal growth data is collected and mark it as an abnormal situation road, and analyze the abnormal situation road; When the abnormal growth data is high-span type data, if the numerical span of the high-span type data in the abnormal situation road continues to rise, it indicates that the current abnormal situation road is in the stage of abnormal situation trend, and the warning level is increased according to the increasing speed of the increasing span of the data value; When the abnormal growth data is low-span type data, when the low-span type data in the abnormal situation road fluctuates and no longer grows, it indicates that the current abnormal situation road is in the stage of abnormal situation generation; and the decreasing speed of the vehicle flow passing volume per minute in the abnormal situation road is used as the judgment standard for the influence level signal; Send the stage of abnormal situation trend or abnormal situation generation and the corresponding level signal to the traffic intelligent control platform.

7. An intelligent transportation system based on Internet of Vehicles data acquisition, characterized in that, After receiving it, if it is in the stage of abnormal situation trend, the traffic intelligent control platform will conduct a comprehensive road traffic control on the abnormal situation road. If the current warning signal level exceeds the set level threshold, the aging duration threshold of the long-aging data processed by the analysis of the abnormal situation trend stage will be lengthened, and the data floating span threshold of the short-aging data will be decreased; If it is in the stage of abnormal situation generation, the traffic intelligent control platform will conduct a partial road traffic control on the abnormal situation road. If the current influence level signal exceeds the set level threshold, the control rate at any position of the current abnormal situation road will be adjusted, and when an abnormal position appears on the abnormal situation road, the control buffer duration will be shortened, and the control duration will be controlled according to the real-time influence level.

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