Intelligent traffic network monitoring method and system based on data management technology
By building a topological node network and linkage sensors to collect data, perform data cleaning and timing-series linkage monitoring, the problems of insufficient traffic monitoring accuracy and limitations of early warning mechanism are solved, and efficient monitoring and intelligent early warning of the intelligent traffic road network are realized.
Patent Information
- Application Number
- CN202411390665.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Priority Date
- 2024-04-10
- Filing Date
- 2024-10-08
- Publication Date
- 2025-01-07
AI Technical Summary
The existing technology has insufficient monitoring accuracy and limitations in traffic monitoring, making it difficult to meet the efficiency and intelligence needs of modern urban traffic management.
By building a topology node network, using linkage sensors to collect traffic data, perform data cleaning, fusion and timing alignment, generate linkage warning signs, and perform timing linkage monitoring at associated topology nodes, accumulate early warning results, synchronize to the cloud for section status analysis, and generate Road section warning information.
The accuracy and intelligent early warning capabilities of traffic road network monitoring have been improved, personalized early warning prompts have been realized, and the efficiency and intelligence of traffic management have been enhanced.
Smart Images
Figure CN119274343A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of intelligent road network, and in particular to an intelligent traffic road network monitoring method and system based on data management technology. Background Art
[0002] In today's society, with the acceleration of urbanization and the continuous growth of transportation demand, intelligent traffic network monitoring has become a key issue in improving traffic efficiency, alleviating traffic congestion, and ensuring traffic safety. However, with the expansion of urban scale and the complexity of transportation networks, traditional traffic monitoring methods and systems have gradually revealed problems such as insufficient monitoring accuracy and limitations in early warning mechanisms, making it difficult to meet the efficiency and intelligence requirements of modern urban traffic management. Summary of the invention
[0003] The present application provides an intelligent traffic network monitoring method and system based on data management technology, which is used to solve the technical problems that the existing technology has insufficient monitoring accuracy, limitations in early warning mechanism, and difficulty in meeting the high efficiency and intelligence of modern urban traffic management.
[0004] In view of the above problems, the present application provides an intelligent transportation network monitoring method and system based on data management technology.
[0005] In a first aspect of the present application, a method for monitoring an intelligent transportation network based on data management technology is provided, the method comprising:
[0006] Establish a topological node for traffic network monitoring, the topological node covers the target monitoring area, and the topological node has a node association identifier; obtain a monitoring data set of the topological node, the monitoring data set is collected and acquired by a linkage sensor set at the topological node, and the monitoring data set is aligned by time series; perform data calculation of the node monitoring data set at each topological node, wherein the data calculation includes data cleaning and data fusion, and establish a linkage warning identifier based on the data calculation result, and synchronize the linkage warning identifier to the associated topological node with the association identifier; perform time series linkage monitoring at the associated topological node, and perform warning accumulation on the time series linkage monitoring result to generate a warning accumulation result; synchronize the node monitoring data set to the cloud, and perform a section status analysis based on the topological node in the cloud to generate a section status analysis result; generate section warning information based on the warning accumulation result and the section status analysis result, and complete intelligent traffic network monitoring based on the section warning information.
[0007] The second aspect of the present application provides an intelligent traffic network monitoring system based on data management technology, the system comprising:
[0008] A topological node establishment module, wherein the topological node establishment module establishes a topological node for traffic network monitoring, wherein the topological node covers a target monitoring area, and the topological node carries a node association identifier; a monitoring data set acquisition module, wherein the monitoring data set acquisition module acquires a monitoring data set of the topological node, wherein the monitoring data set is acquired through the linkage sensor set at the topological node, and the monitoring data set is aligned by time series; a data calculation module, wherein the data calculation module performs data calculation of the node monitoring data set at each topological node, wherein the data calculation includes data cleaning and data fusion, and a linkage warning identifier is established according to the data calculation result, and the linkage warning identifier is used to identify the linkage warning data set. The warning mark is synchronized to the associated topological node; the warning accumulation result generation module performs time-series linkage monitoring on the associated topological node, and accumulates the time-series linkage monitoring results for warning to generate a warning accumulation result; the section status analysis result generation module synchronizes the node monitoring data set to the cloud, and performs section status analysis based on the topological node in the cloud to generate a section status analysis result; the intelligent transportation network monitoring module generates section warning information according to the warning accumulation result and the section status analysis result, and completes the intelligent transportation network monitoring based on the section warning information.
[0009] In a third aspect, the present application provides an electronic device comprising: a processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the steps of any one of the methods described in the first aspect above.
[0010] One or more technical solutions provided in this application have at least the following technical effects or advantages:
[0011] The present application establishes a topological node for traffic network monitoring; obtains a monitoring data set of the topological node, and the monitoring data set is collected and obtained by a linkage sensor set at the topological node; performs data calculation of the node monitoring data set at each topological node, and establishes a linkage warning mark according to the data calculation result, and synchronizes the linkage warning mark to the associated topological node; performs time-series linkage monitoring at the associated topological node, accumulates the time-series linkage monitoring result for early warning, and generates an accumulated early warning result; synchronizes the node monitoring data set to the cloud, performs a section status analysis in the cloud, and generates a section status analysis result; generates section warning information according to the accumulated early warning result and the section status analysis result, and completes intelligent traffic network monitoring based on the section warning information. The present invention solves the technical problems of the prior art for solving the existing technology that the monitoring accuracy is insufficient and the early warning mechanism has limitations, and improves the monitoring accuracy and intelligent early warning technology effect by constructing a topological node network and calling vehicle information for personalized early warning prompts. BRIEF DESCRIPTION OF THE DRAWINGS
[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.
[0013] Figure 1 A schematic diagram of a flow chart of an intelligent transportation network monitoring method based on data management technology provided in an embodiment of the present application;
[0014] Figure 2 A schematic diagram of the structure of an intelligent transportation network monitoring system based on data management technology provided in an embodiment of the present application;
[0015] Figure 3 It is a schematic diagram of the structure of an exemplary electronic device of the present application.
[0016] Explanation of the accompanying drawings: topological node establishment module 11, monitoring data set acquisition module 12, data calculation module 13, early warning accumulation result generation module 14, road section status analysis result generation module 15, intelligent transportation network monitoring module 16, electronic device 300, memory 301, processor 302, communication interface 303, bus architecture 304. DETAILED DESCRIPTION
[0017] The present application provides an intelligent traffic network monitoring method and system based on data management technology, which is used to solve the technical problems that the existing technology has insufficient monitoring accuracy, limitations in early warning mechanism, and difficulty in meeting the high efficiency and intelligence of modern urban traffic management. By constructing a topological node network and calling vehicle information for personalized early warning prompts, the monitoring accuracy and intelligent early warning technology effect can be improved.
[0018] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.
[0019] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products or devices.
[0020] Embodiment 1
[0021] like Figure 1 As shown, the present application provides an intelligent traffic network monitoring method based on data management technology, the method comprising:
[0022] Step S100: establishing a topological node for traffic network monitoring, wherein the topological node covers a target monitoring area and has a node association identifier;
[0023] In the embodiment of the present application, firstly, a comprehensive investigation and analysis is conducted on the target monitoring area to understand the road layout, traffic flow, traffic characteristics and other information in the area. Based on this information, a node layout planning scheme is formulated to determine the number, location and coverage of the nodes. According to the layout planning scheme, suitable sensor devices are selected, which have functions such as data acquisition, transmission and storage. At the same time, the sensor devices are configured, and parameters such as data acquisition frequency and transmission mode are set to ensure that the nodes can operate stably and reliably. Finally, the selected sensor devices are installed in the planned location, and necessary debugging is performed to ensure that the nodes can work normally. Through these steps, the topological nodes for traffic network monitoring are established.
[0024] Each topological node has a unique association identifier, which can be a number, letter, or other form of code. The role of the association identifier is to accurately identify and manage each node in subsequent data processing and analysis.
[0025] By establishing topological nodes for traffic network monitoring, real-time monitoring and analysis of traffic conditions in the target monitoring area can be achieved.
[0026] Step S200: Acquire a monitoring data set of the topological node, wherein the monitoring data set is acquired by a linkage sensor arranged at the topological node, and the monitoring data set is aligned by time series;
[0027] In the embodiment of the present application, the linkage sensors set at the topological nodes include traffic flow sensors, vehicle speed sensors, road condition sensors, etc., which are used to monitor traffic conditions in real time. The sensors collect traffic-related data in real time according to a preset collection frequency. These data include the number of vehicles passing, vehicle speed, road congestion, etc.
[0028] Since different sensors have slight time deviations, the data collected by each sensor is timestamped to ensure that all data is based on a unified timeline. According to the calibrated timestamps, the data collected by different sensors are synchronized to ensure that the data from different sensors at the same time can accurately correspond. The synchronized data is integrated in chronological order to obtain a continuous and time-consistent monitoring data set.
[0029] Step S300: performing data calculation of the node monitoring data set at each topological node, wherein the data calculation includes data cleaning and data fusion, and establishing a linkage warning mark according to the data calculation result, and synchronizing the linkage warning mark to the associated topological node with the associated mark;
[0030] In the embodiment of the present application, the data calculation of the node monitoring data set first performs data cleaning to eliminate noise and outliers in the data and improve the accuracy of the data. Data cleaning includes removing invalid data, processing outliers, and data smoothing. After that, data fusion is performed. According to the type and characteristics of the data, a suitable data fusion method is selected, such as weighted average, maximum value selection, Bayesian fusion, etc., to fuse the information of multiple data sources into a unified data set.
[0031] Based on the results of data cleaning and fusion, the status of the traffic network is further analyzed and a linkage warning mark is established. First, according to the node monitoring data set, the changing trends of key indicators such as traffic flow, speed, density, etc. are analyzed to determine the traffic status, such as smooth, congested, and accidents. According to actual needs, a series of warning rules are set, such as triggering a congestion warning when the traffic flow exceeds a certain threshold, or triggering an accident warning when an abnormal event is detected. Finally, according to the warning rules and data analysis results, a corresponding linkage warning mark is generated for each topological node. These marks include information such as warning type, warning level, and occurrence location.
[0032] In order to realize the linkage monitoring and early warning of the entire traffic network, the linkage early warning mark of each node is synchronized to its associated topological node. The synchronization process is to match the associated mark, and find the target node that needs to synchronize the early warning mark through the associated mark of the node. Then the early warning mark is transmitted, and the generated linkage early warning mark is transmitted to the target node through the communication network to ensure the timely transmission of information. Finally, the target node receives and processes it, such as updating the local database, triggering the local early warning mechanism, etc. Through this process, it can be ensured that each topological node in the entire traffic network can obtain the early warning information of the relevant nodes in real time and accurately, thereby realizing the linkage monitoring and early warning of the entire road network.
[0033] Step S400: performing time-series linkage monitoring on the associated topological nodes, and accumulating early warnings on the time-series linkage monitoring results to generate early warning accumulation results;
[0034] In the embodiment of the present application, time-series linkage monitoring refers to establishing a collaborative monitoring mechanism between related topological nodes, and realizing real-time monitoring and prediction of road network traffic conditions by performing time series analysis on the monitoring data of each node. When performing time-series linkage monitoring, first ensure data synchronization and sharing between related topological nodes. This includes real-time transmission of monitoring data, updating of node status information, etc., to ensure that each node can obtain the latest traffic data. Then, time series analysis is performed on the synchronized monitoring data, including data trend identification, periodicity analysis, anomaly detection, etc.
[0035] Early warning accumulation is to summarize and accumulate early warning information in the process of time-series linkage monitoring to generate global and continuous early warning results. When accumulating early warnings, the early warning information generated by each associated topological node is first summarized. These early warning information include congestion warnings, accident warnings, abnormal event warnings, etc. Then, according to the severity and urgency of the early warning information, the warning level is divided. Finally, the summarized early warning information is processed in time series. This includes statistics on the number of warnings, duration, and impact range.
[0036] Through time-series linkage monitoring and early warning accumulation processing, the early warning accumulation results are finally generated.
[0037] Step S500: synchronizing the node monitoring data set to the cloud, and performing a road section status analysis based on topological nodes in the cloud to generate a road section status analysis result;
[0038] In the embodiment of the present application, the node monitoring data set is transmitted to the cloud through a secure network channel. This process involves data encryption and the selection of transmission protocols to ensure the security and integrity of the data during transmission. At the same time, the cloud has a corresponding database or data storage system for receiving and storing the monitoring data set transmitted from each topological node.
[0039] According to the association identification of the topological nodes, the monitoring data of different nodes are analyzed in association. The mutual influence and association relationship between road sections are identified to provide comprehensive data support for the analysis of road section status. Based on the preprocessed monitoring data set, the road section status is identified using machine learning, data mining and other algorithms. This includes identifying congested sections, accident-prone sections, sections with abnormal status, etc. Various factors are considered in the identification process, such as traffic volume, vehicle speed, road occupancy rate, etc. Through the time series analysis of historical monitoring data, the changing trend and periodic law of the road section status are revealed. At the same time, the prediction algorithm is used to predict the future status of the road section to provide forward-looking guidance for traffic management and decision-making.
[0040] Based on the above analysis, detailed road section status analysis results are generated, including road section status diagrams, statistical reports, early warning information, etc.
[0041] Step S600: Generate road section warning information according to the warning accumulation result and the road section status analysis result, and complete intelligent transportation network monitoring based on the road section warning information.
[0042] In the embodiment of the present application, the warning level is first determined, and the warning level of the road section is determined by combining the number of warnings, duration, and impact range in the warning accumulation results. For example, the warning level of a road section with a large number of warnings, a long duration, and a wide impact range should be increased accordingly. At the same time, the real-time traffic data in the road section status analysis results, such as vehicle flow and speed changes, are considered to dynamically adjust the warning level.
[0043] According to the determined warning level, formulate corresponding warning content, which should include key information such as the specific location of the warning section, warning level, expected impact time, and recommended measures.
[0044] The generated road section warning information will be released through the platform of the intelligent transportation network monitoring system or related channels.
[0045] After issuing the road section warning information, the intelligent traffic network monitoring system should continue to monitor the relevant road sections in real time. Collect real-time traffic data through sensors and other equipment, compare and analyze it with the warning information. According to the real-time monitoring results, dynamically adjust or update the warning information.
[0046] Furthermore, step S500 in the method provided in the application embodiment also includes:
[0047] Acquire weather forecast data, use the weather forecast data as input data, input into a road state prediction model associated with each topological node, and generate a time series road state prediction result, wherein the road state prediction model is constructed by using the weather road state data of each topological node;
[0048] The time series road state prediction result is corrected according to the node monitoring data set to generate a road environment state correction result, and the road environment state correction result is added to the road section state analysis result.
[0049] In the embodiment of the present application, weather forecast data is one of the important inputs for road state prediction, and contains information about weather conditions in the future, such as rainfall, wind speed, visibility, etc. These data are provided by meteorological departments or professional weather forecasting agencies. According to the needs of the road state prediction model, the acquired weather forecast data is converted into an appropriate format so that the model can read and process it correctly. The weather forecast data is preprocessed as necessary, such as removing outliers, filling missing values, etc., to improve the quality and availability of the data.
[0050] The road state prediction model is a machine learning model built based on historical weather and road state data to predict future road states. Select the road state prediction model associated with each topological node and load it into the computing environment. Use the processed weather forecast data as input data and input it into the road state prediction model. The model calculates and analyzes the input weather forecast data in combination with its internal learning rules and patterns to generate a time series road state prediction result.
[0051] After the model calculation is completed, the time series road state prediction results are output. These results are presented in the form of time series, showing the changing trend of road state over time.
[0052] When correcting the time-series road state prediction results based on the node monitoring data set, synchronize and match the node monitoring data set with the prediction results to ensure that they are consistent in time and space. Formulate a correction strategy based on the actual situation of the node monitoring data set. For example, when there is a large difference between the actual road state and the prediction result, make appropriate adjustments to the prediction result based on the monitoring data. Apply the correction strategy to correct the time-series road state prediction results and generate road environment state correction results. These results are closer to the actual road state and improve the accuracy of the prediction.
[0053] Finally, the road environment status correction results are integrated with the original road section status analysis results to form a more comprehensive road section status analysis report.
[0054] Furthermore, the method further comprises:
[0055] Parsing the node monitoring data set to obtain real-time traffic flow data;
[0056] Performing road congestion evaluation based on the real-time traffic flow data to generate road vehicle status analysis results;
[0057] The road vehicle status analysis result is added to the road segment status analysis result.
[0058] In an embodiment of the present application, image processing technology is used to perform real-time processing on the node monitoring data set to extract real-time traffic data, including the number of vehicles, speed distribution, etc.
[0059] According to the actual needs of the traffic management department, set appropriate congestion evaluation indicators, such as vehicle density, average speed, congestion index, etc. Based on real-time traffic data and set congestion indicators, identify congested sections and congestion levels. According to the congestion level and duration, divide the congested sections into different levels, such as slight congestion, moderate congestion, severe congestion, etc. Organize the results of congestion evaluation into structured data to generate road vehicle status analysis results, which include the location of the congested section, congestion level, duration and other information.
[0060] Finally, the road vehicle status analysis results are integrated with the road section status analysis results to form a comprehensive and integrated road section status report.
[0061] Furthermore, the method also includes:
[0062] Get the sensor monitoring space under each topological node;
[0063] After the timing alignment is performed, the same monitoring target authentication of the node monitoring data set is performed according to the sensor monitoring space;
[0064] The measurement results are fused and corrected according to the certification results of the same monitoring target to complete the data calculation.
[0065] In the embodiment of the present application, the monitoring range of each sensor is determined according to the distribution and configuration of the topological nodes. Based on the monitoring range of the sensor, a monitoring space model of each topological node can be established.
[0066] Since different sensors may have problems with data collection frequency and clock synchronization, it is necessary to perform time alignment on the collected node monitoring data. After time alignment, target recognition is performed on the data collected by different sensors. For example, the camera recognizes the vehicle license plate, and the vehicle detector detects the time and speed of the vehicle passing. By comparing this information, it is confirmed which data belongs to the same monitoring target. For the same monitoring target, cross-validation is used to confirm its identity consistency in the monitoring space of different sensors.
[0067] For the same monitoring target, multiple sensors provide different measurement results, such as speed, position, etc. At this time, these results are fused according to factors such as sensor type, accuracy and reliability to obtain a more accurate and comprehensive measurement result. During the data fusion process, it may be found that the measurement results of some sensors are significantly different from those of other sensors. This is caused by sensor failure, environmental interference, etc. For these abnormal data, correction or modification is performed to improve data quality.
[0068] After the above steps, a node monitoring dataset that has been fused and corrected is obtained.
[0069] Furthermore, the method further comprises:
[0070] Performing data integrity verification on the monitoring data set, and generating a first cleaning constraint based on the integrity verification result;
[0071] Performing data consistency authentication based on the same monitoring target authentication result, and generating a second cleaning constraint based on the data consistency authentication result;
[0072] The monitoring data set is updated by using the first cleaning constraint and the second cleaning constraint, and fusion correction is completed according to the update result.
[0073] In the embodiment of the present application, the purpose of data integrity verification is to check whether there is missing, duplicate or abnormal data in the monitoring data set. When performing data integrity verification, a missingness check is performed to check whether there are fields in the data set that are empty or missing, especially key fields such as vehicle identification information, timestamps, etc. Duplicate data identification is then performed to identify and delete or merge identical records. Finally, outlier detection is performed to identify outliers using statistical methods.
[0074] Based on the results of data integrity verification, the first cleaning constraints are generated. For example, for missing key fields, fill rules are set, such as using the mean, median or interpolation method to fill; for duplicate data, specific conditions and methods for merging or deleting duplicate data are set; according to the type and degree of abnormal values, processing rules for deletion, correction or marking as abnormal are set.
[0075] Data consistency certification is based on the certification results of the same monitoring target, and aims to ensure that the data provided by different sensors or data sources for the same monitoring target are consistent. Data consistency certification is carried out through target matching verification, data comparison analysis, and threshold setting and judgment.
[0076] Based on the result of data consistency authentication, the second cleaning constraints are generated. These constraints are mainly used to deal with conflicts and inconsistencies between data.
[0077] According to the first cleaning constraint and the second cleaning constraint, the monitoring data set is cleaned and corrected. Based on data cleaning, data fusion correction is further performed. This involves fusing data provided by multiple data sources to obtain a more comprehensive and accurate data set. Fusion correction involves a variety of methods such as weighted averaging, maximum or minimum value selection, etc.
[0078] Furthermore, the method further comprises:
[0079] Retrieving vehicle information according to the accumulated warning results, and querying the owner of the vehicle using the vehicle information;
[0080] If the query result is the result of the vehicle owner's authorization, the accumulated warning result will be sent to the vehicle owner to execute the warning prompt.
[0081] In the embodiment of the present application, when the accumulated warning result of a certain vehicle reaches a preset threshold, the vehicle information calling mechanism is automatically triggered. Detailed information related to the vehicle is retrieved from the database, including the license plate number, vehicle model, vehicle color, registration time, etc. After obtaining the vehicle information, the information is further used to query the owner.
[0082] If the query result is the result of the car owner's authorization, the accumulated warning result will be sent to the car owner to execute the warning prompt. The warning prompt includes the warning content, occurrence time, occurrence location, detailed situation, etc. The system will send the warning prompt content to the car owner through the contact information reserved by the car owner.
[0083] In summary, the embodiments of the present application have at least the following technical effects:
[0084] The present application establishes a topological node for traffic network monitoring; obtains a monitoring data set of the topological node, and the monitoring data set is collected and obtained by a linkage sensor set at the topological node; performs data calculation of the node monitoring data set at each topological node, and establishes a linkage warning mark according to the data calculation result, and synchronizes the linkage warning mark to the associated topological node; performs time-series linkage monitoring at the associated topological node, accumulates the time-series linkage monitoring result for early warning, and generates an accumulated early warning result; synchronizes the node monitoring data set to the cloud, performs a section status analysis in the cloud, and generates a section status analysis result; generates section warning information according to the accumulated early warning result and the section status analysis result, and completes intelligent traffic network monitoring based on the section warning information. The present invention solves the technical problems of the prior art for solving the existing technology that the monitoring accuracy is insufficient and the early warning mechanism has limitations, and improves the monitoring accuracy and intelligent early warning technology effect by constructing a topological node network and calling vehicle information for personalized early warning prompts.
[0085] Embodiment 2
[0086] Based on the same inventive concept as the intelligent traffic network monitoring method based on data management technology in the aforementioned embodiment, Figure 2 As shown, the present application provides an intelligent traffic network monitoring system based on data management technology, and the system and method embodiments in the present application embodiments are based on the same inventive concept. Among them, the system includes:
[0087] A topological node establishment module 11, wherein the topological node establishment module 11 establishes a topological node for traffic network monitoring, wherein the topological node covers a target monitoring area and has a node association identifier;
[0088] A monitoring data set acquisition module 12, wherein the monitoring data set acquisition module 12 acquires the monitoring data set of the topological node, wherein the monitoring data set is acquired by collecting the linkage sensor set at the topological node, and the monitoring data set is aligned by time series;
[0089] A data calculation module 13, wherein the data calculation module 13 performs data calculation of a node monitoring data set at each topological node, wherein the data calculation includes data cleaning and data fusion, and establishes a linkage warning mark according to the data calculation result, and synchronizes the linkage warning mark to the associated topological node with the associated mark;
[0090] An early warning accumulation result generating module 14, wherein the early warning accumulation result generating module 14 performs time series linkage monitoring on the associated topological nodes, and accumulates the time series linkage monitoring results for early warning to generate an early warning accumulation result;
[0091] A road section status analysis result generation module 15, wherein the road section status analysis result generation module 15 synchronizes the node monitoring data set to the cloud, and performs a road section status analysis based on topological nodes in the cloud to generate a road section status analysis result;
[0092] The intelligent traffic network monitoring module 16 generates road section warning information according to the warning accumulation result and the road section status analysis result, and completes the intelligent traffic network monitoring based on the road section warning information.
[0093] Furthermore, the system also includes:
[0094] Acquire weather forecast data, use the weather forecast data as input data, input into a road state prediction model associated with each topological node, and generate a time series road state prediction result, wherein the road state prediction model is constructed by using the weather road state data of each topological node;
[0095] The time series road state prediction result is corrected according to the node monitoring data set to generate a road environment state correction result, and the road environment state correction result is added to the road section state analysis result.
[0096] Furthermore, the system also includes:
[0097] Parsing the node monitoring data set to obtain real-time traffic flow data;
[0098] Performing road congestion evaluation based on the real-time traffic flow data to generate road vehicle status analysis results;
[0099] The road vehicle status analysis result is added to the road segment status analysis result.
[0100] Furthermore, the system also includes:
[0101] Get the sensor monitoring space under each topological node;
[0102] After the timing alignment is performed, the same monitoring target authentication of the node monitoring data set is performed according to the sensor monitoring space;
[0103] The measurement results are fused and corrected according to the certification results of the same monitoring target to complete the data calculation.
[0104] Furthermore, the system also includes:
[0105] Performing data integrity verification on the monitoring data set, and generating a first cleaning constraint based on the integrity verification result;
[0106] Performing data consistency authentication based on the same monitoring target authentication result, and generating a second cleaning constraint based on the data consistency authentication result;
[0107] The monitoring data set is updated by using the first cleaning constraint and the second cleaning constraint, and fusion correction is completed according to the update result.
[0108] Furthermore, the system also includes:
[0109] Retrieving vehicle information according to the accumulated warning results, and querying the owner of the vehicle using the vehicle information;
[0110] If the query result is the result of the vehicle owner's authorization, the accumulated warning result will be sent to the vehicle owner to execute the warning prompt.
[0111] Reference below Figure 3 To describe the electronic device of the embodiment of the present application:
[0112] The electronic device 300 includes: a processor 302, a communication interface 303, and a memory 301. Optionally, the electronic device 300 may also include a bus architecture 304. The communication interface 303, the processor 302, and the memory 301 may be interconnected via the bus architecture 304; the bus architecture 304 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus architecture 304 may be divided into an address bus, a data bus, a control bus, etc.
[0113] The processor 302 may be a CPU, a microprocessor, an ASIC, or one or more integrated circuits for controlling the execution of the program of the present application.
[0114] The communication interface 303 uses any transceiver-like device for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), wired access network, etc.
[0115] The memory 301 may be a ROM or other type of static storage device that can store static information and instructions, a RAM or other type of dynamic storage device that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory may exist independently and be connected to the processor via a bus architecture 304. The memory may also be integrated with the processor, wherein the memory 301 is used to store computer-executable instructions for executing the solution of the present application, and the execution is controlled by the processor 302. The processor 302 is used to execute the computer-executable instructions stored in the memory 301.
[0116] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. Other embodiments are within the scope of the attached claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
[0117] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.
[0118] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.
Claims
1. An intelligent traffic network monitoring method based on data management technology, characterized in that: The method comprises: Establishing a topological node for traffic network monitoring, wherein the topological node covers a target monitoring area and has a node association identifier; Acquire a monitoring data set of the topological node, wherein the monitoring data set is acquired by collecting and acquiring the linkage sensor set at the topological node, and the monitoring data set is aligned by time series; Perform data calculation of the node monitoring data set at each topological node, wherein the data calculation includes data cleaning and data fusion, and establish a linkage warning mark according to the data calculation result, and synchronize the linkage warning mark to the associated topological node with the associated mark; Performing time-series linkage monitoring on the associated topological nodes, and accumulating early warnings on the time-series linkage monitoring results to generate early warning accumulation results; Synchronizing the node monitoring data set to the cloud, and performing a road section status analysis based on topological nodes in the cloud to generate a road section status analysis result; The road section warning information is generated according to the warning accumulation result and the road section status analysis result, and the intelligent transportation network monitoring is completed based on the road section warning information.
2. The method according to claim 1, characterized in that The step of synchronizing the node monitoring data set to the cloud, and performing a road section status analysis based on topological nodes in the cloud to generate a road section status analysis result includes: Acquire weather forecast data, use the weather forecast data as input data, input into a road state prediction model associated with each topological node, and generate a time series road state prediction result, wherein the road state prediction model is constructed by using the weather road state data of each topological node; The time series road state prediction result is corrected according to the node monitoring data set to generate a road environment state correction result, and the road environment state correction result is added to the road section state analysis result.
3. The method according to claim 2, characterized in that The method comprises: Parsing the node monitoring data set to obtain real-time traffic flow data; Performing road congestion evaluation based on the real-time traffic flow data to generate road vehicle status analysis results; The road vehicle status analysis result is added to the road segment status analysis result.
4. The method according to claim 1, characterized in that The method comprises: Get the sensor monitoring space under each topological node; After the timing alignment is performed, the same monitoring target authentication of the node monitoring data set is performed according to the sensor monitoring space; The measurement results are fused and corrected according to the certification results of the same monitoring target to complete the data calculation.
5. The method according to claim 4, characterized in that The method further comprises: Performing data integrity verification on the monitoring data set, and generating a first cleaning constraint based on the integrity verification result; Performing data consistency authentication based on the same monitoring target authentication result, and generating a second cleaning constraint based on the data consistency authentication result; The monitoring data set is updated by using the first cleaning constraint and the second cleaning constraint, and fusion correction is completed according to the update result.
6. The method according to claim 1, characterized in that The method comprises: Retrieving vehicle information according to the accumulated warning results, and querying the owner of the vehicle using the vehicle information; If the query result is the result of the vehicle owner's authorization, the accumulated warning result will be sent to the vehicle owner to execute the warning prompt.
7. Intelligent traffic network monitoring system based on data management technology, characterized by: The system comprises: A topological node establishment module, wherein the topological node establishment module establishes a topological node for traffic network monitoring, wherein the topological node covers a target monitoring area and has a node association identifier; A monitoring data set acquisition module, wherein the monitoring data set acquisition module acquires the monitoring data set of the topological node, wherein the monitoring data set is acquired by collecting the linkage sensor set at the topological node, and the monitoring data set is aligned by time series; A data calculation module, wherein the data calculation module performs data calculation of a node monitoring data set at each topological node, wherein the data calculation includes data cleaning and data fusion, and establishes a linkage warning mark according to the data calculation result, and synchronizes the linkage warning mark to the associated topological node with the associated mark; An early warning accumulation result generation module, wherein the early warning accumulation result generation module performs time-series linkage monitoring on the associated topological nodes, and accumulates the time-series linkage monitoring results for early warning to generate an early warning accumulation result; A road section status analysis result generation module, wherein the road section status analysis result generation module synchronizes the node monitoring data set to the cloud, and performs a road section status analysis based on topological nodes in the cloud to generate a road section status analysis result; An intelligent traffic network monitoring module generates road section warning information according to the warning accumulation result and the road section status analysis result, and completes intelligent traffic network monitoring based on the road section warning information.
8. An electronic device, characterized in that: The electronic device comprises: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the intelligent traffic network monitoring method based on data management technology as described in any one of claims 1-6.