Intelligent traffic monitoring management system and method based on multi-element data fusion analysis
By adopting multi-factor data fusion analysis technology in the smart traffic monitoring and management system, the problem of insufficient data processing capabilities of existing systems when processing large amounts of traffic data is solved, and a more efficient traffic scheduling and travel experience is achieved.
Patent Information
- Application Number
- CN202510142987.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-02-10
- Publication Date
- 2025-05-13
AI Technical Summary
When the existing smart traffic monitoring and management system processes a large amount of traffic data, the data processing capacity is insufficient, resulting in insufficient decision support and inability to reflect traffic conditions in real time and accurately, affecting the efficiency and accuracy of traffic scheduling.
The intelligent traffic monitoring and management system based on multi-factor data fusion analysis is adopted, including data collection, preprocessing, storage, analysis, intelligent scheduling, monitoring and alarm, visual display and system control modules, and real-time analysis and scheduling are carried out through machine learning algorithms and data mining technology.
It improves data processing capabilities, optimizes traffic scheduling, reduces traffic congestion and accidents, and improves travel experience and traffic operation efficiency.
Smart Images
Figure CN119992831A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of traffic monitoring and management, and in particular to an intelligent traffic monitoring and management system and method based on multi-factor data fusion analysis. Background Art
[0002] Smart traffic monitoring and management refers to the comprehensive application of advanced information technology, data communication transmission technology, electronic sensor technology, control technology and computer technology to the entire transportation management system to achieve effective monitoring, real-time analysis, dynamic scheduling and comprehensive management of the transportation system. Its core lies in the use of big data, Internet of Things, artificial intelligence and other technologies to collect, analyze, process and intelligently schedule traffic network information, thereby improving traffic operation efficiency, improving traffic congestion problems, reducing the incidence of traffic accidents and enhancing people's travel experience.
[0003] In the existing technology, the intelligent traffic monitoring and management system has the problem of insufficient data processing capacity. Traditional systems are difficult to process a large amount of traffic data in a short period of time, resulting in insufficient decision support, unable to reflect traffic conditions in real time and accurately, and affecting the efficiency and accuracy of traffic dispatch.
[0004] In order to solve the above problems, this application proposes an intelligent traffic monitoring and management system and method based on multi-factor data fusion analysis. Summary of the invention
[0005] 1. Purpose of the invention In order to solve the technical problems existing in the background technology, the present invention proposes an intelligent traffic monitoring and management system and method based on multi-factor data fusion analysis. The present invention improves data processing capabilities, optimizes traffic scheduling, and enhances travel experience.
[0006] (II) Technical solution To solve the above problems, the present invention provides a smart traffic monitoring and management system based on multi-factor data fusion analysis, comprising: A data collection module, used for collecting traffic data in real time from a traffic data source; Data preprocessing module, used to clean, convert and preprocess the collected data; A data storage module, used to store the processed data in a distributed database; Data analysis module, used for real-time analysis and mining of stored data; Intelligent dispatching module, used to intelligently dispatch traffic and optimize traffic flow; Monitoring and alarm module, used to monitor traffic conditions in real time and promptly alarm when abnormalities are found; Visual display module, used to display the analysis results to managers in a visual way; The system control module is responsible for the coordination and control between modules.
[0007] Preferably, the data acquisition module includes: A camera acquisition unit, used to capture real-time video streams from traffic monitoring cameras and extract traffic parameters such as vehicle flow, speed, and density; Sensor acquisition unit: including geomagnetic sensor, infrared sensor, radar sensor, used to detect the presence, speed, and type information of the vehicle; Vehicle GPS collection unit, used to collect the vehicle's real-time location, speed, and driving trajectory data; The data interface unit is connected to the traffic data source to obtain data.
[0008] Preferably, the data preprocessing module includes: Data cleaning unit, used to remove duplicate data, erroneous data, and invalid data; Data conversion unit, used to convert data in different formats and from different sources into a unified format and standard; The data preprocessing algorithm unit is used to apply data smoothing, data normalization, and data dimension reduction algorithms.
[0009] Preferably, the data storage module includes: Distributed database unit for storing massive traffic data; Data index unit, which establishes data indexes to improve the efficiency of data query and retrieval; Data security unit, used to adopt encryption technology and access control measures.
[0010] Preferably, the data analysis module includes: Data mining algorithm unit, used to extract valuable information and patterns from data; Machine learning model unit, which builds and trains machine learning models to predict traffic conditions and identify traffic anomalies; The real-time analysis unit is used to quickly analyze the data collected in real time.
[0011] Preferably, the intelligent scheduling module includes: Traffic flow optimization algorithm unit, used to optimize traffic flow; The dispatching strategy generation unit generates traffic signal control strategies and bus dispatching strategies based on the analysis results; The scheduling execution unit is used to send the generated scheduling strategy to related devices and systems to execute scheduling tasks.
[0012] Preferably, the monitoring and alarm module includes: Real-time monitoring unit, which displays traffic conditions in real time through charts and maps; Anomaly detection unit, which applies anomaly detection algorithms to identify abnormal traffic events; The alarm notification unit is used to send alarm information to management personnel and relevant departments in a timely manner.
[0013] Preferably, the visual display module includes: Data visualization tool unit, used to display analysis results in the form of charts and maps; Interactive interface unit, used to support managers in querying, filtering and analyzing data; The report generation unit is used to automatically generate traffic status reports to provide decision support for managers.
[0014] Preferably, the system control module includes: Module coordination unit, responsible for data exchange and coordination between modules; System monitoring unit, used to detect and solve potential problems in a timely manner; The configuration management unit is used to provide system configuration management functions.
[0015] The intelligent traffic monitoring and management method based on multi-factor data fusion analysis is characterized by comprising the following steps: S1. Data collection: Use cameras and sensor devices to collect traffic data in real time, including vehicle location, speed, and traffic flow; S2. Data preprocessing: Clean, convert and preprocess the collected data to remove noise and abnormal data to ensure data quality; S3, Data Storage: Store the preprocessed data in a distributed database for subsequent analysis and query; S4. Data analysis: Use machine learning algorithms and data mining techniques to analyze and mine stored data in real time to extract useful information; S5, Intelligent Scheduling: Based on the analysis results, intelligently dispatch traffic to optimize traffic flow and reduce congestion and accidents; S6. Monitoring and alarm: Monitor traffic conditions in real time, promptly issue alarms when abnormalities are detected, and send reminders to drivers through LED display devices; S7, Visual display: The analysis results are presented to managers in a visual way, including the changing trends of traffic flow, speed, and accident rate data, to facilitate decision-making; S8, system control: Responsible for the coordination and control between modules to ensure stable operation of the system.
[0016] The above technical solution of the present invention has the following beneficial technical effects: Improve data processing capabilities: Utilize distributed computing and machine learning algorithms to achieve efficient processing and analysis of large amounts of traffic data, and improve the accuracy and real-time nature of decision support.
[0017] Optimize traffic scheduling: Conduct intelligent scheduling based on real-time traffic data to reduce traffic congestion and accidents and improve traffic operation efficiency.
[0018] Improve travel experience: Through real-time monitoring and alarm, prompts are given to drivers in a timely manner to improve travel safety and convenience. BRIEF DESCRIPTION OF THE DRAWINGS
[0019] Figure 1 This is a structural schematic diagram of an intelligent traffic monitoring and management system and method based on multi-factor data fusion analysis proposed by the present invention.
[0020] Figure 2 This is a flow chart of an intelligent traffic monitoring and management method based on multi-factor data fusion analysis proposed by the present invention. DETAILED DESCRIPTION
[0021] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings. It should be understood that these descriptions are only exemplary and are not intended to limit the scope of the present invention. In addition, in the following description, the description of well-known structures and technologies is omitted to avoid unnecessary confusion of the concept of the present invention.
[0022] like Figure 1-2 As shown, the present invention proposes a smart traffic monitoring and management system based on multi-factor data fusion analysis, including: Data collection module, used to collect traffic data in real time from traffic data sources (such as cameras, sensors, vehicle GPS); The data acquisition module includes: A camera acquisition unit, used to capture real-time video streams from traffic monitoring cameras and extract traffic parameters such as vehicle flow, speed, and density; Sensor acquisition unit: including geomagnetic sensor, infrared sensor, radar sensor, used to detect the presence, speed, and type information of the vehicle; Vehicle GPS collection unit, which is used to collect the vehicle's real-time location, speed, and driving trajectory data through the GPS device installed on the vehicle; The data interface unit is connected to the traffic data source (such as traffic light control system, bus system, taxi company) to obtain data.
[0023] Data preprocessing module, used to clean, convert and preprocess the collected data for subsequent analysis; The data preprocessing module includes: Data cleaning unit, used to remove duplicate data, erroneous data, and invalid data to ensure data accuracy and consistency; Data conversion unit, used to convert data in different formats and from different sources into a unified format and standard for subsequent processing and analysis; The data preprocessing algorithm unit is used to apply data smoothing, data normalization, and data dimension reduction algorithms to preprocess the data and improve data quality and analysis efficiency.
[0024] Data storage module, used to store processed data in a distributed database for long-term management and query; The data storage module includes: Distributed database unit, using Hadoop and Spark distributed storage technologies to store massive traffic data; Data index unit, which establishes data indexes to improve the efficiency of data query and retrieval; The data security unit is used to ensure the security and privacy of data by adopting encryption technology and access control measures.
[0025] Data analysis module, which uses machine learning algorithms and data mining techniques to perform real-time analysis and mining of stored data; Data analysis modules include: Data mining algorithm unit, which applies association rule mining, cluster analysis, and classification prediction data mining algorithms to extract valuable information and patterns from data; The machine learning model unit builds and trains machine learning models, such as neural networks, support vector machines, and random forests, to predict traffic conditions and identify traffic anomalies.
[0026] The real-time analysis unit is used to quickly analyze the data collected in real time and provide real-time traffic condition monitoring and early warning.
[0027] Intelligent dispatching module, which is used to intelligently dispatch traffic and optimize traffic flow based on the analysis results; Monitoring and alarm module, used to monitor traffic conditions in real time and promptly alarm when abnormalities are found; Visual display module, used to display the analysis results to managers in a visual way to facilitate decision-making; The system control module is responsible for the coordination and control between modules to ensure the stable operation of the system.
[0028] Preferably, the intelligent scheduling module includes: Traffic flow optimization algorithm unit, which applies traffic flow theory and optimization algorithms to optimize traffic flow and reduce congestion and delays; The dispatching strategy generation unit generates traffic signal control strategies and bus dispatching strategies based on the analysis results to improve traffic operation efficiency; The scheduling execution unit is used to send the generated scheduling strategy to related devices and systems to execute scheduling tasks.
[0029] Preferably, the monitoring and alarm module includes: The real-time monitoring unit displays traffic conditions in real time, including vehicle flow, speed, and congestion, through charts and maps; Anomaly detection unit, which applies anomaly detection algorithms to identify traffic anomalies, such as traffic accidents and road construction; The alarm notification unit is used to send alarm information to management personnel and relevant departments in a timely manner through SMS, email, and APP push.
[0030] Preferably, the visual display module includes: The data visualization tool unit uses ECharts and Tableau data visualization tools to display the analysis results in the form of charts and maps; Interactive interface unit, which designs user-friendly interactive interfaces to support managers in querying, filtering, and analyzing data; The report generation unit automatically generates traffic status reports based on the analysis results to provide decision support for managers.
[0031] Preferably, the system control module includes: The module coordination unit is responsible for data exchange and coordination between modules to ensure the stability and efficiency of the overall operation of the system; The system monitoring unit monitors the operating status of the system, including hardware resources, network conditions, and data processing speed, in order to promptly identify and resolve potential problems.
[0032] The configuration management unit is used to provide system configuration management functions and support managers to adjust and optimize system parameters and module configurations.
[0033] A smart traffic monitoring and management method based on multi-factor data fusion analysis includes the following steps: S1. Data collection: Use cameras and sensor devices to collect traffic data in real time, including vehicle location, speed, and traffic flow; S2. Data preprocessing: Clean, convert and preprocess the collected data to remove noise and abnormal data to ensure data quality; S3, Data Storage: Store the preprocessed data in a distributed database for subsequent analysis and query; S4. Data analysis: Use machine learning algorithms (such as clustering algorithms, classification algorithms) and data mining techniques to analyze and mine stored data in real time to extract useful information; Clustering algorithm: used to group similar traffic data into one category for subsequent analysis. Formula: ; in: D(x, y) represents the distance between data points x and y, x i and i Represent the i-th feature of data points x and y respectively.
[0034] Classification algorithm: used to classify traffic data into different categories (such as congestion, smooth flow). Formula: , where P(y|x) represents the probability of belonging to category y given a data point x.
[0035] S5, Intelligent Scheduling: Based on the analysis results, intelligently dispatch traffic to optimize traffic flow and reduce congestion and accidents; S6. Monitoring and alarm: Monitor traffic conditions in real time, promptly report any abnormalities (such as traffic accidents, congestion), and send reminders to drivers through LED display devices; S7, Visual display: The analysis results are presented to managers in a visual way, including the changing trends of traffic flow, speed, and accident rate data, to facilitate decision-making; S8, system control: Responsible for the coordination and control between modules to ensure stable operation of the system.
[0036] Prediction model: used to predict future traffic conditions. Formula: y=β0+β1x1+β2x2+⋯+β n x n +ϵ, where y represents the predicted values x1, x2, ⋯, x n Denotes factors affecting traffic conditions, β0, β1, ⋯, β n represents the coefficient and ϵ represents the error term.
[0037] In the present invention, the data collected by the camera acquisition unit, the sensor acquisition unit, the vehicle GPS acquisition unit and the data interface unit are all transmitted to the data cleaning unit of the data preprocessing module.
[0038] The data processed by the data cleaning unit will be passed to the data conversion unit for format unification.
[0039] The data converted by the data conversion unit is then passed to the data preprocessing algorithm unit for further processing, such as data smoothing, normalization, and dimensionality reduction.
[0040] The processed data will eventually be stored in the distributed database unit of the data storage module.
[0041] The data stored in the distributed database unit can be read and analyzed by the data mining algorithm unit, machine learning model unit and real-time analysis unit of the data analysis module.
[0042] At the same time, the data index unit will create an index to improve data query efficiency.
[0043] The analysis results of the data mining algorithm unit, machine learning model unit and real-time analysis unit will be passed to the traffic flow optimization algorithm unit and scheduling strategy generation unit of the intelligent scheduling module.
[0044] These analysis results may also be used by the real-time monitoring unit and anomaly detection unit of the monitoring and alarm module to display real-time traffic conditions and detect anomalies.
[0045] The scheduling strategies generated by the traffic flow optimization algorithm unit and the scheduling strategy generation unit will be passed to the scheduling execution unit for execution.
[0046] The results of scheduling execution are fed back to related equipment and systems, such as traffic light control systems, through the data interface unit.
[0047] The monitoring and detection results of the real-time monitoring unit and the anomaly detection unit will be transmitted to the alarm notification unit for alarm notification.
[0048] The data visualization tool unit obtains the analysis results from the data analysis module and displays them visually.
[0049] The interactive interface unit will interact with the data visualization tool unit to provide a user-friendly query, filtering and analysis interface.
[0050] The report generation unit will also obtain the analysis results from the data analysis module and automatically generate a traffic condition report.
[0051] It should be understood that the above specific embodiments of the present invention are only used to illustrate or explain the principles of the present invention, and do not constitute a limitation of the present invention. Therefore, any modifications, equivalent substitutions, improvements, etc. made without departing from the spirit and scope of the present invention should be included in the protection scope of the present invention. In addition, the appended claims of the present invention are intended to cover all changes and modifications that fall within the scope and boundaries of the appended claims, or the equivalent forms of such scope and boundaries.
Claims
1. A smart traffic monitoring and management system based on multi-factor data fusion analysis, characterized in that: include: A data collection module, used for collecting traffic data in real time from a traffic data source; Data preprocessing module, used to clean, convert and preprocess the collected data; A data storage module, used to store the processed data in a distributed database; Data analysis module, used for real-time analysis and mining of stored data; Intelligent dispatching module, used to intelligently dispatch traffic and optimize traffic flow; Monitoring and alarm module, used to monitor traffic conditions in real time and promptly alarm when abnormalities are found; Visual display module, used to display the analysis results to managers in a visual way; The system control module is responsible for the coordination and control between modules.
2. The intelligent traffic monitoring and management system based on multi-factor data fusion analysis according to claim 1 is characterized in that: The data acquisition module includes: A camera acquisition unit, used to capture real-time video streams from traffic monitoring cameras and extract traffic parameters such as vehicle flow, speed, and density; Sensor acquisition unit: including geomagnetic sensor, infrared sensor, radar sensor, used to detect the presence, speed, and type information of the vehicle; Vehicle GPS collection unit, used to collect the vehicle's real-time location, speed, and driving trajectory data; The data interface unit is connected to the traffic data source to obtain data.
3. The intelligent traffic monitoring and management system based on multi-factor data fusion analysis according to claim 2 is characterized in that: The data preprocessing module includes: Data cleaning unit, used to remove duplicate data, erroneous data, and invalid data; Data conversion unit, used to convert data in different formats and from different sources into a unified format and standard; The data preprocessing algorithm unit is used to apply data smoothing, data normalization, and data dimension reduction algorithms.
4. The intelligent traffic monitoring and management system based on multi-factor data fusion analysis according to claim 3 is characterized in that: The data storage module includes: Distributed database unit for storing massive traffic data; Data index unit, which establishes data indexes to improve the efficiency of data query and retrieval; Data security unit, used to adopt encryption technology and access control measures.
5. The intelligent traffic monitoring and management system based on multi-factor data fusion analysis according to claim 4 is characterized in that: Data analysis modules include: Data mining algorithm unit, used to extract valuable information and patterns from data; Machine learning model unit, which builds and trains machine learning models to predict traffic conditions and identify traffic anomalies; The real-time analysis unit is used to quickly analyze the data collected in real time.
6. The intelligent traffic monitoring and management system based on multi-factor data fusion analysis according to claim 5 is characterized in that: The intelligent scheduling module includes: Traffic flow optimization algorithm unit, used to optimize traffic flow; The dispatching strategy generation unit generates traffic signal control strategies and bus dispatching strategies based on the analysis results; The scheduling execution unit is used to send the generated scheduling strategy to related devices and systems to execute scheduling tasks.
7. The intelligent traffic monitoring and management system based on multi-factor data fusion analysis according to claim 6 is characterized in that: The monitoring and alarm modules include: Real-time monitoring unit, which displays traffic conditions in real time through charts and maps; Anomaly detection unit, which applies anomaly detection algorithms to identify abnormal traffic events; The alarm notification unit is used to send alarm information to management personnel and relevant departments in a timely manner.
8. The intelligent traffic monitoring and management system based on multi-factor data fusion analysis according to claim 7 is characterized in that: The visualization module includes: Data visualization tool unit, used to display analysis results in the form of charts and maps; Interactive interface unit, used to support managers in querying, filtering and analyzing data; The report generation unit is used to automatically generate traffic status reports to provide decision support for managers.
9. The intelligent traffic monitoring and management system based on multi-factor data fusion analysis according to claim 8 is characterized in that: The system control module includes: Module coordination unit, responsible for data exchange and coordination between modules; System monitoring unit, used to detect and solve potential problems in a timely manner; Configuration management unit, used to provide system configuration management functions.
10. The intelligent traffic monitoring and management method based on multi-factor data fusion analysis is characterized by: The following steps are involved: S1. Data collection: Use cameras and sensor devices to collect traffic data in real time, including vehicle location, speed, and traffic flow; S2. Data preprocessing: Clean, convert and preprocess the collected data to remove noise and abnormal data to ensure data quality; S3, Data Storage: Store the preprocessed data in a distributed database for subsequent analysis and query; S4. Data analysis: Use machine learning algorithms and data mining techniques to analyze and mine stored data in real time to extract useful information; S5, Intelligent Scheduling: Based on the analysis results, intelligently dispatch traffic to optimize traffic flow and reduce congestion and accidents; S6. Monitoring and alarm: Monitor traffic conditions in real time, promptly issue alarms when abnormalities are detected, and send reminders to drivers through LED display devices; S7, Visual display: The analysis results are presented to managers in a visual way, including the changing trends of traffic flow, speed, and accident rate data, to facilitate decision-making; S8, system control: Responsible for the coordination and control between modules to ensure stable operation of the system.