Dynamic road traffic optimization control method and system based on perceptual intelligence

Abstract

The invention discloses a dynamic road traffic optimization control method and system based on perceptual intelligence, and relates to the technical field of traffic optimization control, and the method comprises the steps: laying an intelligent perceptual network, and collecting and obtaining a multi-source road traffic operation data flow; acquiring a traffic influence factor index set; setting traffic data analysis multiple channels based on the traffic influence factor index set; performing correlation analysis to obtain a traffic multi-dimensional operation state parameter set; and constructing a traffic optimization control strategy space, carrying out optimization analysis on the traffic multi-dimensional operation state parameter set, determining traffic control strategy optimization parameters, and carrying out road traffic dynamic control through the traffic control strategy optimization parameters. The technical problems of aggravated traffic congestion and low traffic efficiency caused by insufficient traffic data acquisition and analysis capability and lack of dynamic adjustment of a control strategy in the prior art are solved, and the technical effects of dynamically controlling road traffic, improving traffic operation efficiency and relieving congestion are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic optimization control, and particularly to a dynamic road traffic optimization control method and system based on perception intelligence. Background Art

[0002] With the acceleration of urbanization and the sharp increase in the number of motor vehicles, the problem of road traffic congestion has become increasingly serious. In the existing traffic management system, traditional traffic monitoring means rely on limited fixed devices, with a narrow data collection range and low accuracy, making it difficult to comprehensively and real-time grasp the traffic conditions. At the same time, traffic control strategies often rely on fixed timing plans and cannot be dynamically adjusted according to real-time traffic flow, weather changes, and sudden special events. This leads to a decline in road capacity during peak traffic periods or in case of special situations, with vehicles waiting for a long time, not only causing waste of energy but also greatly reducing the travel experience of residents. In addition, due to the lack of comprehensive analysis and utilization of multi-source traffic data, it is impossible to accurately locate the root causes of traffic congestion and difficult to formulate effective optimization measures, making the traffic congestion problem even more severe.

[0003] The prior art has technical problems such as insufficient traffic data collection and analysis capabilities and lack of dynamic adjustment of control strategies, resulting in aggravated traffic congestion and low traffic efficiency. Summary of the Invention

[0004] This application provides a dynamic road traffic optimization control method and system based on perception intelligence, aiming to solve the technical problems in the prior art, such as insufficient traffic data collection and analysis capabilities and lack of dynamic adjustment of control strategies, resulting in aggravated traffic congestion and low traffic efficiency.

[0005] In view of the above problems, this application provides a dynamic road traffic optimization control method and system based on perception intelligence.

[0006] In the first aspect of this application, a dynamic road traffic optimization control method based on perception intelligence is provided. The method includes:

[0007] Deploy an intelligent perception network, and based on the intelligent perception network, conduct real-time monitoring of the target traffic network to collect and obtain multi-source road traffic operation data streams; obtain a set of traffic impact factor indicators, where the set of traffic impact factor indicators includes traffic volume, traffic density, traffic facilities, weather conditions, and special events; based on the set of traffic impact factor indicators, set up multi-channels for traffic data analysis; map the multi-source road traffic operation data streams into the multi-channels for traffic data analysis for correlation analysis to obtain a set of traffic multi-dimensional operation state parameters; construct a traffic optimization control strategy space, and based on the traffic optimization control strategy space, conduct optimization analysis on the set of traffic multi-dimensional operation state parameters to determine traffic control strategy optimization parameters, and perform dynamic control of road traffic through the traffic control strategy optimization parameters.

[0008] In the second aspect of this application, a dynamic road traffic optimization control system based on perception intelligence is provided. The system includes:

[0009] A traffic operation data stream collection module, which is used to deploy an intelligent perception network, and based on the intelligent perception network, conduct real-time monitoring of the target traffic network to collect and obtain multi-source road traffic operation data streams; a traffic impact factor indicator set acquisition module, which is used to obtain a set of traffic impact factor indicators, where the set of traffic impact factor indicators includes traffic volume, traffic density, traffic facilities, weather conditions, and special events; a traffic data analysis multi-channel setting module, which is used to set up multi-channels for traffic data analysis based on the set of traffic impact factor indicators; a traffic multi-dimensional operation state parameter set acquisition module, which is used to map the multi-source road traffic operation data streams into the multi-channels for traffic data analysis for correlation analysis to obtain a set of traffic multi-dimensional operation state parameters; a traffic dynamic control module, which is used to construct a traffic optimization control strategy space, conduct optimization analysis on the set of traffic multi-dimensional operation state parameters based on the traffic optimization control strategy space to determine traffic control strategy optimization parameters, and perform dynamic control of road traffic through the traffic control strategy optimization parameters.

[0010] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0011] Deploy an intelligent perception network, and based on the intelligent perception network, conduct real-time monitoring on the target traffic network to collect and obtain multi-source road traffic operation data streams; obtain a set of traffic impact factor indicators, where the set of traffic impact factor indicators includes traffic volume, traffic density, traffic facilities, weather conditions, and special events; based on the set of traffic impact factor indicators, set multiple channels for traffic data analysis; map the multi-source road traffic operation data streams into the multiple channels for traffic data analysis for correlation analysis to obtain a set of traffic multi-dimensional operation state parameters; construct a traffic optimization control strategy space, and based on the traffic optimization control strategy space, conduct optimization analysis on the set of traffic multi-dimensional operation state parameters to determine traffic control strategy optimization parameters, and perform dynamic control of road traffic through the traffic control strategy optimization parameters. It achieves the dynamic control of road traffic and improves the technical effects of traffic operation efficiency and congestion alleviation. Description of the Drawings

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0013] Figure 1 Schematic flowchart of a dynamic road traffic optimization control method based on perception intelligence provided by an embodiment of the present application;

[0014] Figure 2 Schematic structural diagram of a dynamic road traffic optimization control system based on perception intelligence provided by an embodiment of the present application.

[0015] Description of the reference numerals: Traffic operation data stream collection module 10, traffic impact factor indicator set acquisition module 20, traffic data analysis multi-channel setting module 30, traffic multi-dimensional operation state parameter set acquisition module 40, traffic dynamic control module 50. Detailed Embodiments

[0016] The present application provides a dynamic road traffic optimization control method and system based on perception intelligence, which is used to solve the technical problems in the prior art such as insufficient traffic data collection and analysis capabilities and lack of dynamic adjustment of control strategies, resulting in aggravated traffic congestion and low traffic efficiency.

[0017] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts shall fall within the protection scope of the present application.

[0018] Embodiment 1, as Figure 1 shown, the present application provides a dynamic road traffic optimization control method based on perceptual intelligence, and the method includes:

[0019] Step S100: Deploy an intelligent perception network, and based on the intelligent perception network, perform real-time monitoring on the target traffic network, and collect and obtain multi-source road traffic operation data streams.

[0020] Specifically, first, according to the actual situation and monitoring requirements of the target traffic network, elaborate on the deployment plan of the intelligent perception network. This involves determining the key areas and nodes to be monitored, such as the intersections of main roads, congested sections, etc. Subsequently, select appropriate perception devices, such as high-definition traffic cameras, which can clearly capture information such as the driving trajectories and quantities of vehicles; geomagnetic sensors, which can accurately detect data such as the presence and speed of vehicles. At the same time, configure corresponding data transmission devices, such as wireless communication modules, fiber optic networks, etc., to ensure that data can be transmitted stably and quickly. Install these traffic monitoring devices and data transmission devices at various positions of the target traffic network according to the established deployment plan, and conduct comprehensive debugging to make them form an organic intelligent perception network. Thereafter, the network can continuously and real-time monitor the target traffic network, continuously collect multi-source road traffic operation data streams including vehicle flow, driving speed, road occupancy rate, vehicle type, etc., providing rich and accurate data support for subsequent in-depth analysis and optimization of traffic control strategies.

[0021] Step S200: Obtain a set of traffic impact factor indicators, and the set of traffic impact factor indicators includes traffic volume, traffic density, traffic facilities, weather conditions, and special events.

[0022] Specifically, in order to comprehensively and accurately grasp the traffic operation status, it is necessary to obtain a set of traffic impact factor indicators covering multiple aspects. Traffic volume, that is, the number of vehicles passing through a certain section of the road within a specific time period, is a basic indicator for measuring the busyness of the road, and it directly reflects the bearing pressure of the road. Traffic density reflects the number of vehicles on a unit length of the road, intuitively showing the degree of congestion of the road, and plays a key role in judging whether the traffic is smooth. In terms of traffic facilities, factors such as the signal timing, the width of the road, and the presence or absence of toll stations will significantly affect the traffic efficiency of vehicles and the distribution of traffic flow. Weather conditions cannot be ignored either. Severe weather such as heavy rain, heavy snow, and fog will reduce the visibility of drivers, affect the braking performance of vehicles, and thus change the traffic operation state. Special events, such as traffic accidents, large-scale sports events or concerts and other large-scale activities, will suddenly increase the traffic flow in a local area and seriously disrupt the normal traffic order. Incorporating these factors such as traffic volume, traffic density, traffic facilities, weather conditions, and special events into the set of traffic impact factor indicators can provide a comprehensive and crucial basis for more in-depth analysis of traffic conditions and optimization of traffic control strategies in the future.

[0023] Step S300: Based on the set of traffic impact factor indicators, set up multiple channels for traffic data analysis.

[0024] Specifically, based on the obtained set of traffic impact factor indicators including traffic volume, traffic density, traffic facilities, weather conditions, and special events, various machine learning algorithms can be used to set up multiple channels for traffic data analysis. For traffic volume and traffic density, time series analysis algorithms such as ARIMA (Autoregressive Integrated Moving Average Model) can be adopted. This algorithm can fit the historical traffic volume and density data and predict the numerical changes in a specific future time period, thereby setting up a traffic flow trend analysis channel. Regarding the impact of traffic facilities on traffic, a decision tree algorithm is used to construct a facility evaluation channel. The decision tree algorithm classifies and predicts the traffic conditions based on the characteristics and parameters of different traffic facilities, such as signal duration, number of lanes, etc., and judges the traffic smoothness under different facility combinations. For the correlation between weather conditions and traffic, a support vector machine (SVM) algorithm is used to establish a weather impact analysis channel. The SVM can find the optimal classification hyperplane between complex weather data and traffic data and analyze the traffic change rules under different weather types. For special events, a neural network algorithm is adopted to build an event impact analysis channel. The powerful non-linear mapping ability of the neural network can handle the complexity and uncertainty of special event data and accurately predict the impact range and degree of special events on the surrounding traffic. Through the multi-channel analysis set up by these machine learning algorithms, the rules behind traffic data can be more comprehensively and deeply explored, providing a reliable basis for traffic optimization control.

[0025] Step S400: Map the multi-source road traffic operation data stream to the traffic data analysis multi-channels for correlation analysis to obtain a traffic multi-dimensional operation state parameter set.

[0026] Specifically, the multi-source road traffic operation data stream containing information such as vehicle speed, flow, and occupancy collected previously is accurately mapped into each traffic data analysis channel. With the help of the multiple regression analysis algorithm, the quantitative relationships between factors such as traffic volume and traffic density can be explored in depth; using the clustering analysis algorithm, data with similar traffic operation patterns are classified to reveal the characteristics of different traffic operation states. By conducting correlation analysis in each channel, the internal connections between different traffic influencing factors and the road traffic operation conditions can be mined. For example, analyze the correlation between traffic volume, traffic density, and road congestion degree under bad weather conditions. Finally, by synthesizing the analysis results of each channel, a comprehensive and detailed traffic multi-dimensional operation state parameter set is obtained, which covers various aspects of information such as the dynamic changes, stability, and potential risks of traffic, providing solid data support for formulating scientific and reasonable traffic control strategies in the future.

[0027] Step S500: Construct a traffic optimization control strategy space, perform optimization analysis on the traffic multi-dimensional operation state parameter set based on the traffic optimization control strategy space, determine the traffic control strategy optimization parameters, and perform dynamic control of road traffic through the traffic control strategy optimization parameters.

[0028] Specifically, construct a traffic optimization control strategy space. Use the traffic multi-dimensional operation state parameter set as constraint information to match with the traffic optimization control strategy space to obtain the traffic optimization control strategy threshold. After clarifying the road traffic control target, construct a road traffic control effect fitness function, and based on this, perform global optimization on the traffic optimization control strategy threshold. During the optimization process, the traffic optimization control strategy threshold can be initialized first to obtain an initial parameter population, and then iterative evaluation and optimization are carried out. When the preset termination condition is reached, the traffic control strategy optimization parameters are determined. These optimization parameters can be used for dynamic control of road traffic to realize real-time adjustment of traffic signal duration, road traffic rules, etc., thereby effectively alleviating traffic congestion, improving road traffic efficiency, and ensuring the safe and smooth operation of road traffic.

[0029] In a possible implementation manner, step S100 further includes:

[0030] Step S110: Obtain the road traffic monitoring requirements, where the road traffic monitoring requirements include monitoring targets, monitoring scopes, and monitoring accuracies.

[0031] Step S120: Select sensing devices based on the road traffic monitoring requirements to determine a traffic monitoring device set and a data transmission device set.

[0032] Step S130: Perform sensing device layout analysis based on the distribution operation information of the target traffic network to obtain sensing device layout parameters.

[0033] Step S140: Debug and deploy the traffic monitoring device set and data transmission device set according to the sensing device layout parameters to obtain the intelligent sensing network.

[0034] Specifically, obtaining the road traffic monitoring requirements, elements such as monitoring targets, monitoring scopes, and monitoring accuracies is the basis and guidance for all subsequent work. For example, the monitoring target is the traffic flow change and speed anomaly in a specific section; the monitoring scope may cover the main urban roads, areas around transportation hubs, etc.; the monitoring accuracy determines the detail level of the collected data. For instance, whether the traffic flow statistics are accurate to per minute or per hour. Only by clarifying these requirements can the subsequent device selection and layout be more targeted and reasonable.

[0035] Based on the obtained road traffic monitoring requirements, select sensing devices. According to the different requirements of monitoring targets, scopes, and accuracies, select appropriate traffic monitoring devices and data transmission devices from numerous sensing devices to determine the traffic monitoring device set and data transmission device set. For example, if the monitoring target is to accurately obtain the driving trajectory of vehicles, high-precision cameras or radars need to be selected as traffic monitoring devices; if the monitoring scope is large, data transmission devices with a long transmission distance and good stability need to be selected to ensure that the collected data can be accurately transmitted to the subsequent processing system.

[0036] Based on the distribution operation information of the target traffic network, conduct layout analysis on the sensing devices. The distribution operation information of the traffic network includes the road layout, traffic flow patterns in different sections, traffic congestion-prone areas, etc. Through in-depth analysis of this information, determine the sensing device layout parameters required for each area, including specific layout positions and reasonable layout quantities. For example, in sections with large traffic flow and prone to congestion, monitoring devices need to be densely arranged at multiple key nodes to comprehensively and timely grasp the traffic conditions; while in sections with relatively small traffic flow, the number of devices can be appropriately reduced to avoid resource waste.

[0037] Debug and deploy the traffic monitoring device set and data transmission device set. During the deployment process, not only install the devices at the designated positions, but also debug the devices to ensure that they can operate normally, accurately collect data, and stably transmit. After this series of operations, finally successfully construct the intelligent sensing network, providing a strong guarantee for subsequent real-time and accurate monitoring of road traffic operation conditions.

[0038] In a possible implementation manner, step S130 further includes:

[0039] Step S131: Obtain traffic network distribution data and traffic network operation data according to the distribution operation information of the target traffic network.

[0040] Step S132: Identify key positions for the traffic network distribution data and traffic network operation data to obtain a traffic network key position set.

[0041] Step S133: Analyze the layout of sensing devices for each key position in the traffic network key position set to obtain sensing device layout parameters, where the sensing device layout parameters include layout positions and layout quantities.

[0042] Specifically, the distribution operation information of the target traffic network is an important basis for obtaining key data. By deeply mining this information, traffic network distribution data and traffic network operation data can be obtained. The traffic network distribution data covers information such as the topological structure of roads, the connection relationships of each road section, the grade classification of roads, and the functional layout of the surrounding areas. These data depict the static architecture of the traffic network. The traffic network operation data, on the other hand, reflects the dynamic situation of the traffic network, such as the traffic flow, vehicle speed, driving direction of vehicles, and the frequency and duration of congestion at each road section during different time periods.

[0043] Identify key positions by deeply analyzing the traffic network distribution data and traffic network operation data. Using data mining techniques, key nodes such as road intersections and the starting and ending points of important road sections are found from the traffic network distribution data. At the same time, combining indicators such as traffic flow, vehicle speed, and congestion frequency in the traffic network operation data, analyze the traffic pressure of each road section and its impact on the overall traffic flow. For example, those road sections with traffic flow far exceeding the average level, frequent congestion, and located at the core of the traffic hub, or road nodes connecting multiple important areas and where traffic flows converge, are determined as key positions. Summarize these key positions selected through comprehensive analysis to obtain a traffic network key position set, providing an important basis for the subsequent precise deployment of sensing devices.

[0044] Analyze the layout of sensing devices for each key position. For each key position, many factors need to be comprehensively considered to determine the sensing device layout parameters. For example, according to the traffic flow volume at this position, if the flow is large, more devices are needed for comprehensive monitoring; according to the complexity of the traffic flow, such as places with many road intersections and changing vehicle driving directions, layout positions that can cover different directions should be selected; combined with the required monitoring accuracy requirements, the number of devices should be reasonably increased in high-precision monitoring areas. By carefully analyzing the traffic conditions, environmental conditions, etc. of each key position, the specific layout position of the sensing device and the most suitable layout quantity at this position are determined, forming complete sensing device layout parameters, providing key support for the precise construction of the intelligent sensing network.

[0045] In a possible implementation manner, step S400 further includes:

[0046] Step S410: Shunt the multi-source road traffic operation data stream according to the traffic impact factor index set to obtain a traffic operation index data set.

[0047] Step S420: Based on the traffic data analysis multi-channel, map and match the traffic operation index data set to determine a traffic index data matching channel set.

[0048] Step S430: Analyze the status of the traffic operation index data set according to the traffic index data matching channel set to obtain a traffic multi-dimensional operation status parameter set.

[0049] Specifically, the multi-source road traffic operation data stream contains a large amount of data from different channels and different types, such as vehicle sensor data, road monitoring camera data, weather station data, etc. The traffic impact factor index set covers elements such as traffic volume, traffic density, traffic facilities, weather conditions, and special events. At this time, shunt the multi-source road traffic operation data stream according to these traffic impact factor index sets, and classify and screen the data according to different impact factors. For example, classify the data related to traffic volume into one category, and the data related to weather conditions into another category, and so on. Finally, a traffic operation index data set is obtained.

[0050] According to the set traffic data analysis multi-channel, each channel is set for a specific type of data or analysis purpose. Based on these channels, map and match the traffic operation index data set. During the matching process, compare the data in the traffic operation index data set with the functions and applicable scopes of each analysis channel to determine which channel each data subset is suitable for analysis. For example, traffic volume data may be more suitable for entering a channel that specifically analyzes the trend of traffic flow changes, while weather condition data enters a channel that studies the impact of weather on traffic. Through such matching, a traffic index data matching channel set is determined, laying a foundation for accurate data analysis.

[0051] Based on the determined traffic index data matching channel set, analyze the status of the traffic operation index data set. Different channels will process and analyze the data from their respective perspectives. For example, the traffic flow analysis channel will calculate indicators such as the change rate of vehicle flow and peak and trough periods, and the traffic density analysis channel will evaluate the degree of road congestion, etc. Each channel deeply mines and analyzes the corresponding data, reflects the traffic operation status from multiple dimensions, and integrates the results obtained from different dimensions to form a traffic multi-dimensional operation status parameter set, comprehensively and meticulously showing the real-time operation status of road traffic, providing strong data support for subsequent traffic optimization control.

[0052] In a possible implementation manner, step S430 further includes:

[0053] Step S431: Match a channel set according to the traffic index data to determine a traffic data preprocessing channel set and a traffic state analysis channel set.

[0054] Step S432: Preprocess the traffic operation index data set by using the traffic data preprocessing channel set to obtain a standard traffic operation index data set.

[0055] Step S433: Perform state analysis on the standard traffic operation index data set based on the traffic state analysis channel set to obtain a traffic multi-dimensional operation state parameter set.

[0056] Specifically, after obtaining the traffic index data matching channel set, in order to more efficiently and accurately extract valuable information from the traffic operation index data set, it is necessary to further classify these channels. According to the sequence and logical relationship of data processing, the traffic index data matching channel set is divided into a traffic data preprocessing channel set and a traffic state analysis channel set. The traffic data preprocessing channel set is mainly responsible for handling quality problems in the original data, such as clearing error data, filling missing values, standardizing data, etc., laying a good data foundation for subsequent analysis; while the traffic state analysis channel set focuses on using various analysis models and algorithms to deeply mine the preprocessed data from multiple dimensions to reveal the actual state of traffic operation. The two cooperate with each other to jointly serve the goal of obtaining accurate traffic state information.

[0057] Preprocess the traffic operation index data set by using the determined traffic data preprocessing channel set. The traffic data preprocessing channel set usually includes operation processes such as data cleaning, data standardization, and data filling. In the data cleaning link, noise data and error data in the data set will be removed; data standardization is to unify data of different magnitudes and different units to the same scale for subsequent analysis and comparison; for data with missing values, appropriate algorithms will be used to fill them. After this series of preprocessing operations, the traffic operation index data set is transformed into a standard traffic operation index data set, making the data quality higher and more accurately reflecting the actual traffic conditions.

[0058] Perform state analysis on the standard traffic operation index data set based on the traffic state analysis channel set. The traffic state analysis channel set uses various data analysis models and algorithms to mine and interpret data from multiple dimensions. For example, analyze the changing trend of traffic flow to predict the traffic volume in a future period; evaluate the traffic density to determine the degree of road congestion; study the distribution of traffic speed to find areas with abnormal vehicle speeds, etc. Integrate the results obtained from different-dimensional analyses to finally form a traffic multi-dimensional operation state parameter set, providing comprehensive and accurate data support for the traffic management department to formulate reasonable traffic control strategies.

[0059] In a possible implementation manner, step S500 further includes:

[0060] Step S510: Match the traffic multi-dimensional operation state parameter set with the traffic optimization control strategy space as constraint information to obtain traffic optimization control strategy thresholds.

[0061] Step S520: Obtain the road traffic control target, and construct a road traffic control effect fitness function according to the road traffic control target.

[0062] Step S530: Perform global optimization on the traffic optimization control strategy thresholds based on the road traffic control effect fitness function to determine traffic control strategy optimization parameters.

[0063] Specifically, the traffic multi-dimensional operation state parameter set is a comprehensive quantitative manifestation of the current actual traffic situation. Taking it as constraint information, it is matched with the pre-constructed traffic optimization control strategy space. The traffic optimization control strategy space contains a variety of possible control strategies and parameter combinations. Through matching, a series of strategy thresholds that conform to the current traffic situation are screened out. These thresholds provide a feasible value range for subsequent optimization, ensuring that the control strategy can be effectively formulated based on the current actual traffic situation.

[0064] By analyzing information such as the current situation of traffic congestion, historical traffic flow data, and urban development plans, determine road traffic control targets such as reducing the average waiting time of vehicles at intersections and improving the traffic efficiency of road sections. When constructing a fitness function with the goal of reducing the average waiting time of vehicles at intersections, a weighted average algorithm is used. First, collect the vehicle waiting time data of different lanes at different intersections at different time periods, denoted as T ij , where i represents the intersection number and j represents the lane number. Calculate the average waiting time of each intersection nj is the number of lanes at this intersection. Then assign weights wi according to the importance of the intersection. The weight of important intersections is high, and vice versa. Construct the fitness function By taking the reciprocal of the weighted average of the waiting times at different intersections, the algorithm makes it so that the shorter the waiting time, the larger the fitness function value, thus effectively measuring the advantages and disadvantages of traffic control strategies in reducing vehicle waiting times and providing a quantitative basis for subsequent optimization.

[0065] Using the previously constructed fitness function for the effect of road traffic control, start the global optimization of the traffic optimization control strategy threshold to determine the traffic control strategy optimization parameters. First, randomly select multiple initial parameter combinations from the value range defined by the traffic optimization control strategy threshold, and these combinations form the initial population. For each parameter combination, substitute it into the fitness function of the road traffic control effect for calculation to obtain the corresponding fitness value, which is used to measure the degree to which the traffic control strategy achieves the goal under this parameter combination. Then, use the genetic algorithm global optimization algorithm to simulate natural evolution or swarm intelligence behavior and perform iterative operations on the initial population. In each iteration, generate a new population of parameter combinations through operators such as selection, crossover, and mutation, and continuously explore a better solution space. As the iteration progresses, the parameter combinations with higher fitness values have a greater probability of being retained and inherited to the next generation, while the poorer combinations are gradually eliminated. Continue this process until the preset termination conditions are met, such as the number of iterations reaching the upper limit, the increase in the fitness value being less than the set threshold, etc. At this time, the parameter combination with the highest fitness value is the determined traffic control strategy optimization parameter, and these parameters will be used for the actual dynamic control of road traffic to improve the traffic conditions and enhance the traffic operation efficiency.

[0066] In a possible implementation manner, step S530 further includes:

[0067] Step S531: Initialize the traffic optimization control strategy threshold to obtain an initial parameter population.

[0068] Step S532: Iteratively evaluate and optimize the initial parameter population based on the fitness function of the road traffic control effect. When the preset termination condition is reached, determine the traffic control strategy optimization parameters, and the traffic control strategy optimization parameters are the parameter group with the maximum fitness.

[0069] Specifically, the traffic optimization control strategy threshold defines the value range of the control strategy parameters. To find the optimal parameters, an initialization operation needs to be performed on these thresholds. In this process, multiple different parameter combinations are randomly selected from the threshold range according to certain rules, and these combinations together form the initial parameter population. Each parameter combination represents a potential traffic control strategy, and by initializing, initial samples of multiple different strategies are obtained, providing a basis for subsequent optimization.

[0070] Using the constructed fitness function for road traffic control effect, each parameter combination in the initial parameter population is evaluated. Each parameter combination is substituted into the fitness function to calculate the corresponding fitness value, which reflects the effectiveness of the traffic control strategy corresponding to this parameter combination in achieving the road traffic control goal. Then, iterative evaluation and optimization are carried out based on these fitness values. By continuously repeating the iterative process and applying operations such as selection, crossover, and mutation in the genetic algorithm, the parameter combinations in the current population are adjusted to generate new parameter combinations. In each iteration, parameter combinations with high fitness values have a greater probability of being retained and participating in the generation of the next generation, while combinations with low fitness values are gradually eliminated. This iterative process continues until the preset termination conditions are met, such as reaching the predetermined maximum number of iterations, or in consecutive multiple iterations, the increase in the fitness value is extremely small, less than the preset threshold. When the termination conditions are met, the parameter group with the maximum fitness value that appears in all iterative processes is the optimized parameter of the finally determined traffic control strategy, and these parameters will be applied to actual road traffic control to achieve more efficient and reasonable traffic operation management.

[0071] Embodiment 2, based on the same inventive concept as the method for optimizing dynamic road traffic control based on perception intelligence in the foregoing embodiment, as Figure 2 shown, the present application provides a system for optimizing dynamic road traffic control based on perception intelligence. The system in the embodiment of the present application and the method embodiment are based on the same inventive concept. Among them, the system includes:

[0072] A traffic operation data stream acquisition module 10, configured to deploy an intelligent perception network, and based on the intelligent perception network, perform real-time monitoring on a target traffic network to collect and obtain multi-source road traffic operation data streams.

[0073] A traffic impact factor index set acquisition module 20, configured to acquire a traffic impact factor index set, where the traffic impact factor index set includes traffic volume, traffic density, traffic facilities, weather conditions, and special events.

[0074] A traffic data analysis multi-channel setting module 30, configured to set multi-channels for traffic data analysis based on the traffic impact factor index set.

[0075] A traffic multi-dimensional operation state parameter set acquisition module 40, configured to map the multi-source road traffic operation data streams into the traffic data analysis multi-channels for correlation analysis to obtain a traffic multi-dimensional operation state parameter set.

[0076] The traffic dynamic control module 50 is used to construct a traffic optimization control strategy space, optimize and analyze the traffic multi-dimensional operation state parameter set based on the traffic optimization control strategy space, determine traffic control strategy optimization parameters, and perform dynamic control of road traffic through the traffic control strategy optimization parameters.

[0077] Furthermore, the system is also used to implement the following functions:

[0078] Obtain the road traffic monitoring requirements, where the road traffic monitoring requirements include monitoring targets, monitoring scopes, and monitoring accuracies; select sensing devices based on the road traffic monitoring requirements to determine a traffic monitoring device set and a data transmission device set; perform sensing device layout analysis based on the distribution operation information of the target traffic network to obtain sensing device layout parameters; commission and layout the traffic monitoring device set and the data transmission device set according to the sensing device layout parameters to obtain the intelligent sensing network.

[0079] Furthermore, the system is also used to implement the following functions:

[0080] According to the distribution operation information of the target traffic network, obtain traffic network distribution data and traffic network operation data; identify key positions in the traffic network distribution data and traffic network operation data to obtain a traffic network key position set; perform sensing device layout analysis on each key position in the traffic network key position set respectively to obtain sensing device layout parameters, where the sensing device layout parameters include layout positions and layout quantities.

[0081] Furthermore, the system is also used to implement the following functions:

[0082] Divert the multi-source road traffic operation data stream according to the traffic impact factor index set to obtain a traffic operation index data set; perform mapping and matching on the traffic operation index data set through multiple channels based on the traffic data analysis to determine a traffic index data matching channel set; perform state analysis on the traffic operation index data set according to the traffic index data matching channel set to obtain a traffic multi-dimensional operation state parameter set.

[0083] Furthermore, the system is also used to implement the following functions:

[0084] According to the traffic index data matching channel set, determine a traffic data preprocessing channel set and a traffic state analysis channel set; preprocess the traffic operation index data set by using the traffic data preprocessing channel set to obtain a standard traffic operation index data set; perform state analysis on the standard traffic operation index data set based on the traffic state analysis channel set to obtain a traffic multi-dimensional operation state parameter set.

[0085] Further, the system is also used to implement the following functions:

[0086] Match the traffic multi-dimensional operation state parameter set as constraint information with the traffic optimization control strategy space to obtain a traffic optimization control strategy threshold; obtain a road traffic control target, and construct a road traffic control effect fitness function according to the road traffic control target; globally optimize the traffic optimization control strategy threshold based on the road traffic control effect fitness function to determine traffic control strategy optimization parameters.

[0087] Further, the system is also used to implement the following functions:

[0088] Initialize the traffic optimization control strategy threshold to obtain an initial parameter population; iteratively evaluate and optimize the initial parameter population based on the road traffic control effect fitness function, and when a preset termination condition is reached, determine traffic control strategy optimization parameters, where the traffic control strategy optimization parameters are the parameter group with the maximum fitness.

[0089] It should be noted that the above order of the embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. And the above specific embodiments of this specification have been described. 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.

[0090] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0091] This specification and the drawings are only exemplary descriptions of the present application and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications 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 equivalent technologies, the present application is intended to include these changes and modifications.

Claims

1. A dynamic road traffic optimization control method based on perception intelligence, characterized in that: The method comprises: Deploy an intelligent sensing network, monitor the target traffic network in real time based on the intelligent sensing network, and collect and obtain multi-source road traffic operation data streams; Acquire a traffic influencing factor index set, wherein the traffic influencing factor index set includes traffic volume, traffic density, traffic facilities, weather conditions and special events; Based on the traffic influencing factor indicator set, setting up multiple channels for traffic data analysis; Mapping the multi-source road traffic operation data stream to the traffic data analysis multi-channel for correlation analysis to obtain a traffic multi-dimensional operation status parameter set; A traffic optimization control strategy space is constructed, and based on the traffic optimization control strategy space, the traffic multi-dimensional operation status parameter set is optimized and analyzed to determine the traffic control strategy optimization parameters, and the road traffic is dynamically controlled by the traffic control strategy optimization parameters.

2. A method for dynamic road traffic optimization control based on perception intelligence as claimed in claim 1, characterized in that: The deployment of the intelligent sensing network includes: Obtaining road traffic monitoring requirements, wherein the road traffic monitoring requirements include monitoring targets, monitoring ranges, and monitoring accuracy; Selecting sensing equipment based on the road traffic monitoring requirements, and determining a traffic monitoring equipment set and a data transmission equipment set; Performing a sensing device deployment analysis based on the distribution operation information of the target traffic network to obtain sensing device deployment parameters; The traffic monitoring equipment set and the data transmission equipment set are debugged and deployed according to the sensing equipment deployment parameters to obtain the intelligent sensing network.

3. A method for dynamic road traffic optimization control based on perception intelligence as claimed in claim 2, characterized in that: The obtaining of the sensing device deployment parameters includes: Acquiring transportation network distribution data and transportation network operation data according to the distribution operation information of the target transportation network; Identifying key locations of the traffic network distribution data and the traffic network operation data to obtain a traffic network key location set; A sensing device deployment analysis is performed on each key position in the traffic network key position set to obtain sensing device deployment parameters, wherein the sensing device deployment parameters include deployment positions and deployment quantities.

4. The method for dynamic road traffic optimization control based on perception intelligence as claimed in claim 1, characterized in that: The method of obtaining a traffic multi-dimensional operation status parameter set includes: Dividing the multi-source road traffic operation data stream according to the traffic influencing factor indicator set to obtain a traffic operation indicator data set; Based on the traffic data analysis multi-channel, mapping and matching the traffic operation index data set is performed to determine a traffic index data matching channel set; The traffic operation index data set is subjected to state analysis according to the traffic index data matching channel set to obtain a traffic multi-dimensional operation state parameter set.

5. A method for dynamic road traffic optimization control based on perception intelligence as claimed in claim 4, characterized in that: The method of obtaining a traffic multi-dimensional operation status parameter set includes: Determine a traffic data preprocessing channel set and a traffic status analysis channel set according to the traffic index data matching channel set; Preprocessing the traffic operation index data set using the traffic data preprocessing channel set to obtain a standard traffic operation index data set; Based on the traffic status analysis channel set, the standard traffic operation index data set is subjected to status analysis to obtain a traffic multi-dimensional operation status parameter set.

6. A method for dynamic road traffic optimization control based on perception intelligence as claimed in claim 1, characterized in that: Determining the traffic control strategy optimization parameters includes: Matching the traffic multi-dimensional operation state parameter set as constraint information with the traffic optimization control strategy space to obtain a traffic optimization control strategy threshold; Acquire a road traffic control target, and construct a road traffic control effect fitness function according to the road traffic control target; Based on the road traffic control effect fitness function, the traffic optimization control strategy threshold is globally optimized to determine the traffic control strategy optimization parameters.

7. A method for dynamic road traffic optimization control based on perception intelligence as claimed in claim 6, characterized in that: Determining the traffic control strategy optimization parameters includes: Initializing the traffic optimization control strategy threshold to obtain an initial parameter population; Based on the road traffic control effect fitness function, the initial parameter population is iteratively evaluated and optimized, and when a preset termination condition is reached, the traffic control strategy optimization parameters are determined, and the traffic control strategy optimization parameters are the parameter group with the maximum fitness.

8. A dynamic road traffic optimization control system based on perception intelligence, characterized in that: The system is used to implement a dynamic road traffic optimization control method based on perception intelligence as described in any one of claims 1 to 7, and the system comprises: A traffic operation data flow collection module is used to deploy an intelligent perception network, monitor the target traffic network in real time based on the intelligent perception network, and collect and obtain multi-source road traffic operation data flows; A traffic influencing factor index set acquisition module, used to acquire a traffic influencing factor index set, wherein the traffic influencing factor index set includes traffic volume, traffic density, traffic facilities, weather conditions and special events; A traffic data analysis multi-channel setting module, used to set up traffic data analysis multi-channels based on the traffic influencing factor indicator set; A traffic multi-dimensional operation status parameter set acquisition module, used for mapping the multi-source road traffic operation data stream to the traffic data analysis multi-channel for correlation analysis to obtain a traffic multi-dimensional operation status parameter set; The traffic dynamic control module is used to construct a traffic optimization control strategy space, optimize and analyze the traffic multi-dimensional operating state parameter set based on the traffic optimization control strategy space, determine the traffic control strategy optimization parameters, and perform road traffic dynamic control through the traffic control strategy optimization parameters.

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