Scheduling method, system and equipment of intelligent transportation equipment and medium

Abstract

The invention relates to an intelligent traffic equipment scheduling method and system, equipment and a medium, and the method comprises the steps: obtaining road data and traffic management information of a region where intelligent traffic equipment is located, carrying out the region division processing of the road data based on the traffic management information, and obtaining the corresponding region road information; acquiring real-time equipment data of the intelligent traffic equipment, and performing association processing on the real-time equipment data and the regional road information to obtain a corresponding real-time regional traffic group; obtaining road vehicle data collected by the intelligent traffic equipment, and performing road prediction on the regional road information according to the road vehicle data to obtain a corresponding road congestion trend; performing road prediction on the emergency vehicle information based on the road congestion trend to obtain a corresponding emergency vehicle predicted passing road section; and comprehensively analyzing the initial equipment control scheme and the predicted emergency vehicle passing road section to obtain a corresponding global adjustment strategy. According to the invention, the equipment control scheme can be flexibly adjusted according to the passing demand of the emergency vehicle.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent traffic equipment, and in particular to a scheduling method, system, equipment and medium for intelligent traffic equipment. Background Art

[0002] With the acceleration of urbanization and the continuous increase in the number of motor vehicles, urban traffic congestion has become more and more serious. As an effective traffic management tool, intelligent traffic equipment can help alleviate traffic pressure and improve road traffic efficiency through real-time monitoring and data analysis. The dispatching method of intelligent traffic equipment plays a vital role in modern intelligent traffic systems, which can achieve effective control of traffic flow and reasonable allocation of road resources. However, the existing dispatching methods mainly rely on static road information and preset traffic rules, which are difficult to cope with dynamically changing traffic conditions and emergencies. In addition, the existing traffic dispatching methods usually only consider a single traffic factor, such as the number of vehicles, road capacity, etc., and ignore the combined influence of multiple factors, resulting in insufficient accuracy and effectiveness of the dispatching scheme. Summary of the invention

[0003] The main purpose of the present invention is to provide a dispatching method, system, device and medium for intelligent traffic equipment, which can flexibly adjust the equipment control scheme according to the traffic needs of emergency vehicles.

[0004] To achieve the above object, the present invention provides a scheduling method for intelligent transportation equipment, comprising: Acquire road data and traffic management information of the area where the intelligent traffic equipment is located, and perform regional division processing on the road data based on the traffic management information to obtain corresponding regional road information; Acquire real-time device data of the intelligent traffic device, associate the real-time device data with the regional road information, and obtain a corresponding real-time regional traffic group; Acquire the road vehicle data collected by the intelligent traffic equipment, perform road prediction on the regional road information according to the road vehicle data, and obtain the corresponding road congestion trend; Performing traffic planning analysis on the real-time regional traffic group and the road congestion trend to obtain a corresponding initial equipment control plan; Performing vehicle identification on the road vehicle data to obtain corresponding emergency vehicle information, and performing road prediction on the emergency vehicle information based on the road congestion trend to obtain a corresponding emergency vehicle predicted pass section; The initial equipment control scheme and the predicted emergency vehicle passage section are comprehensively analyzed to obtain a corresponding global adjustment strategy.

[0005] Furthermore, the acquiring of the road data and traffic management information of the area where the intelligent transportation device is located, and performing regional division processing on the road data based on the traffic management information to obtain the corresponding regional road information includes: Acquire electronic map data of the area where the intelligent transportation device is located, extract road feature information from the electronic map data, and generate corresponding initial road data based on the road feature information; Obtaining comprehensive traffic management information in the area based on the electronic map data; Clustering the initial road data according to the comprehensive traffic management information to obtain a plurality of traffic sub-areas with similar traffic characteristics; Performing topological processing on the roads in each of the traffic sub-areas to generate a corresponding regional feature vector, and performing functional division on the traffic sub-areas based on the regional feature vector to obtain a final traffic functional partition; Annotate the attributes of the roads in each of the traffic function zones, including road grade, number of lanes, speed limit and travel direction information, and generate detailed regional road attribute data; The traffic function zoning information is associated with the regional road attribute data to obtain complete regional road information.

[0006] Furthermore, the acquiring of the real-time device data of the intelligent traffic device, associating the real-time device data with the regional road information to obtain the corresponding real-time regional traffic group includes: Acquire real-time device data including device identification, geographic location and working status of the intelligent transportation device, perform feature extraction on the real-time device data, and obtain corresponding device features; Performing matrix conversion on the regional road information to obtain a corresponding regional feature matrix; Performing tensor operations on the device features and the region feature matrix to obtain corresponding device region associations; Performing regional matching on the intelligent transportation equipment according to the equipment area association degree to obtain a corresponding equipment area distribution map; Performing density clustering on the device area distribution map to obtain a corresponding device area distribution map; Performing spatiotemporal alignment of the regional road information and the real-time device data to obtain corresponding spatiotemporal alignment data; Performing multi-dimensional feature fusion on the spatiotemporal alignment data to obtain a corresponding fused feature set; The fused feature set is fused with the regional traffic feature to obtain the corresponding real-time regional traffic group.

[0007] Furthermore, the acquiring of the road vehicle data collected by the intelligent traffic device, and performing road prediction on the regional road information according to the road vehicle data to obtain a corresponding road congestion trend include: Predicting the time series changes of the road vehicle data to obtain the corresponding traffic flow change trend; Performing congestion clustering on the regional road information according to the traffic flow change trend to obtain a corresponding road congestion area; Perform road network topology on the regional road information according to the road congestion area to obtain a corresponding traffic node set; Performing road prediction on the regional road information according to the traffic node set to obtain corresponding short-term traffic prediction results; Performing a spatiotemporal correlation analysis on the regional road information according to the short-term prediction results to obtain corresponding traffic fluctuation information; Simulating the regional road information according to the traffic fluctuation information to obtain corresponding long-term traffic prediction results; Comprehensively predicting the short-term traffic prediction result and the long-term traffic flow prediction result to obtain a corresponding comprehensive traffic prediction result; The regional road information is subjected to congestion analysis according to the comprehensive traffic forecast result to obtain the road congestion trend.

[0008] Furthermore, the real-time regional traffic group and the road congestion trend are subjected to traffic planning analysis to obtain a corresponding initial equipment control scheme, including: Calculating the congestion index of the real-time regional traffic group according to the road congestion trend to obtain a corresponding comprehensive congestion index; The comprehensive congestion index is segmented according to a preset congestion threshold to obtain a corresponding congestion level; The intelligent traffic equipment is grouped according to the congestion level and the real-time equipment data to obtain a corresponding equipment dispatch group; Optimizing the signal timing of each of the equipment scheduling groups to obtain a corresponding signal timing plan; Matching the signal timing scheme with a preset traffic management strategy library to obtain a traffic control strategy; The traffic control strategy is integrated with the signal timing plan to obtain the initial equipment control plan.

[0009] Further, the performing vehicle identification on the road vehicle data to obtain corresponding emergency vehicle information, and performing road prediction on the emergency vehicle information based on the road congestion trend to obtain the corresponding emergency vehicle predicted pass section, includes: Performing image conversion processing on the road vehicle data to obtain a vehicle image; Performing vehicle edge detection, shape analysis and color region segmentation on the vehicle image in sequence to obtain vehicle fusion features, wherein the vehicle fusion features include vehicle contour features, vehicle type features and vehicle color features; According to a preset emergency vehicle feature template, the vehicle fusion feature is matched with the vehicle contour, type and color in sequence to obtain a corresponding matching score; Performing vehicle screening on the vehicle image based on the matching score to obtain the emergency vehicle information; Associating the emergency vehicle information with the regional road information to obtain corresponding vehicle association information; The path of the vehicle-related information is predicted based on the road congestion trend to obtain the predicted passage section of the emergency vehicle.

[0010] Furthermore, the initial equipment control scheme and the predicted emergency vehicle passage section are comprehensively analyzed to obtain a corresponding global adjustment strategy, including: Decision construction is performed according to the initial equipment control scheme and the predicted passage section of the emergency vehicle to obtain a corresponding initial multi-dimensional decision; Performing a scheme evaluation on the initial multi-dimensional decision according to a preset comprehensive evaluation rule to obtain a scheme evaluation result; Performing parameter adjustment analysis on the initial multi-dimensional decision according to the scheme evaluation results to obtain adjustment parameters including traffic signal timing, lane allocation and speed limit; Dynamically adjusting the initial multi-dimensional decision according to the adjustment parameters to obtain a dynamic adjustment strategy; Sending the dynamic adjustment strategy to the corresponding intelligent transportation device, and obtaining feedback data from the intelligent transportation device after executing the dynamic adjustment strategy; The dynamic adjustment strategy is optimized according to the feedback data to obtain the global adjustment strategy.

[0011] The present invention also provides a dispatching system for intelligent transportation equipment, which is applied to any one of the dispatching methods for intelligent transportation equipment described above, comprising: A collection module, the collection module is used to obtain road data and traffic management information of the area where the intelligent transportation equipment is located, and perform regional division processing on the road data based on the traffic management information to obtain corresponding regional road information; An analysis module, the analysis module is used to obtain real-time device data of the intelligent traffic device, associate the real-time device data with the regional road information, and obtain a corresponding real-time regional traffic group; An association module, the association module is used to obtain the road vehicle data collected by the intelligent traffic equipment, perform road prediction on the regional road information according to the road vehicle data, and obtain a corresponding road congestion trend; A processing module, the processing module is used to perform traffic planning analysis on the real-time regional traffic group and the road congestion trend to obtain a corresponding initial equipment control plan; A control module, the control module is used to perform vehicle identification on the road vehicle data to obtain corresponding emergency vehicle information, perform road prediction on the emergency vehicle information based on the road congestion trend to obtain a corresponding emergency vehicle predicted pass section; An execution module is used to comprehensively analyze the initial equipment control plan and the emergency vehicle predicted pass section to obtain a corresponding global adjustment strategy.

[0012] The present invention also provides a dispatching device for intelligent traffic equipment, comprising: Memory, used to store programs; The processor is used to execute the program to implement each step of the scheduling method of an intelligent transportation device described in any one of the above.

[0013] The present invention also provides a storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to execute any of the above methods.

[0014] The present invention provides a method, system, device and medium for dispatching intelligent traffic equipment, which has the following beneficial effects: By dividing the road data and traffic management information of the area where the intelligent traffic equipment is located, the road information of different areas can be obtained more accurately, thereby improving the accuracy of regional traffic condition assessment and providing a more reliable basis for traffic dispatch. By associating the real-time equipment data with the regional road information and predicting the road based on the road vehicle data, the refined management of the traffic conditions in different areas can be achieved, which is helpful to optimize the traffic flow and avoid the waste of road resources. The overall traffic demand analysis of the system based on the road vehicle data can ensure that the system can operate efficiently in different time periods and traffic conditions, reduce unnecessary traffic delays, and improve the overall operation efficiency of the system. By comprehensively analyzing the real-time regional traffic group and road congestion trends, a more reasonable initial equipment control plan can be formulated, and the optimized operation of the entire traffic system can be achieved through a comprehensive adjustment strategy, so as to effectively utilize road resources and reduce traffic congestion and environmental pollution. By identifying the road vehicle data, obtaining the emergency vehicle information, and analyzing the predicted passage section of the emergency vehicle based on the road congestion trend, the equipment control plan can be flexibly adjusted according to the passage demand of the emergency vehicle to ensure that the emergency vehicle can quickly pass through the congested section, making the system more adaptable to diverse traffic demand scenarios. This not only improves the efficiency of handling emergency situations, but also improves the overall traffic management level and enhances the system's emergency response capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a flow chart of a dispatching method of intelligent traffic equipment provided by the present invention; Figure 2 It is a structural diagram of a dispatching system of an intelligent transportation device provided by the present invention; Figure 3 It is a structural diagram of a dispatching device of an intelligent transportation device provided by the present invention.

[0016] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0018] The present invention is further described below in conjunction with the accompanying drawings and specific implementation methods.

[0019] Reference Figure 1 As shown, a scheduling method for intelligent transportation equipment includes: Step S1: Obtain road data and traffic management information of the area where the intelligent traffic equipment is located, and perform regional division processing on the road data based on the traffic management information to obtain corresponding regional road information; Step S2: acquiring real-time device data of intelligent traffic equipment, associating the real-time device data with regional road information, and obtaining a corresponding real-time regional traffic group; Step S3: Obtain road vehicle data collected by intelligent traffic equipment, perform road prediction on regional road information based on the road vehicle data, and obtain corresponding road congestion trends; Step S4: Perform traffic planning analysis on the real-time regional traffic group and road congestion trend to obtain the corresponding initial equipment control plan; Step S5: performing vehicle identification on the road vehicle data to obtain corresponding emergency vehicle information, and performing road prediction on the emergency vehicle information based on the road congestion trend to obtain the corresponding emergency vehicle predicted pass section; Step S6: Comprehensively analyze the initial equipment control plan and the predicted emergency vehicle pass section to obtain a corresponding global adjustment strategy.

[0020] Based on the above steps, the detailed process is as follows: Step S1: Obtain detailed road data from a geographic information system (GIS), a traffic management department, a map service provider, etc. The acquired road data includes the geographical coordinates, length, width, number of lanes, speed limit information, intersection location, road section slope, etc. of the road.

[0021] Obtain traffic management information from traffic management departments, traffic monitoring systems, traffic rules databases, etc. Traffic management information includes traffic light timing strategies, traffic signs, traffic rules (such as one-way streets, prohibited traffic periods, etc.), traffic control areas, and congestion charging areas.

[0022] The acquired road data and traffic management information are cleaned to remove erroneous and redundant data to ensure data consistency and accuracy.

[0023] Determine the division criteria based on the regional traffic function (such as commercial area, residential area, industrial area, etc.), road type (such as expressway, main road, secondary road, etc.) and traffic management strategy. Use the division criteria to divide the road data into regions and generate multiple sub-regions. Each sub-region has different traffic characteristics and management strategies.

[0024] The divided regions are integrated with the corresponding road data and traffic management information to generate regional road information. Each regional road information contains not only the basic attributes of the roads in the region, but also the traffic management strategy of the region.

[0025] Step S2: Collect device data in real time from intelligent traffic equipment (such as traffic cameras, traffic flow sensors, automatic vehicle identification systems, etc.). The real-time collected data includes real-time traffic data such as the number of vehicles, vehicle speed, traffic flow, and traffic accidents. The real-time data is transmitted to the data center through the sensor network and the communication network.

[0026] Clean the real-time equipment data, remove noise and redundant data, and ensure data consistency and accuracy. Convert the real-time equipment data into a format consistent with regional road information.

[0027] Fusion of real-time device data from different sources to generate comprehensive real-time traffic data. Location matching: Matching real-time data with corresponding regional road information based on the geographic location coordinates in real-time device data. For example, associating vehicle data collected by a traffic camera with the road area information where it is located. Sort and synchronize real-time data by timestamp to ensure data timing and consistency.

[0028] The real-time equipment data is associated with the regional road information to generate a real-time regional traffic group. Each real-time regional traffic group contains not only the basic road attributes and traffic management strategies of the region, but also the real-time traffic conditions of the region.

[0029] The associated processed data are integrated to generate real-time regional traffic groups, which are stored in an efficient time series database for quick query and processing in subsequent steps.

[0030] Step S3: Collect road vehicle data in real time from intelligent traffic equipment (such as traffic cameras, traffic flow sensors, etc.), including the number of vehicles, vehicle speed, lane occupancy, traffic accident conditions, etc. Transmit the road vehicle data to the data center through the sensor network and communication network. Clean the road vehicle data to remove noise data and redundant data to ensure data consistency and accuracy.

[0031] Convert road vehicle data into a format consistent with regional road information. Fuse road vehicle data from different sources to generate comprehensive road vehicle data.

[0032] Extract key features from road vehicle data and regional road information, such as vehicle flow change trends, vehicle speed distribution, lane occupancy, etc. Based on the above features, conduct trend analysis on road traffic conditions to determine changes in road traffic conditions in the future.

[0033] Using road vehicle data and regional road information, we can analyze the road traffic conditions in real time to determine whether traffic congestion will occur. Based on the analysis results, we can determine the road traffic conditions and the probability and severity of congestion in the future.

[0034] The analyzed road traffic condition information and congestion trend are combined to generate road congestion trend data. Road congestion trend data includes information such as the probability, severity and duration of congestion in various areas in the future.

[0035] Step S4: Obtain real-time regional traffic groups and road congestion trends.

[0036] Combine real-time regional traffic groups and road congestion trend data for comprehensive analysis. For example, analyze real-time traffic flow, vehicle speed distribution, and predicted congestion trends in a certain area.

[0037] Based on the comprehensive analysis results, potential traffic problem areas are identified, including excessive traffic flow, high lane occupancy, and low vehicle speed.

[0038] For the identified traffic problem areas, determine optimization goals, such as increasing vehicle speed, reducing traffic congestion, optimizing traffic flow, etc.

[0039] According to the optimization goal, the initial equipment control strategy is formulated. For example, traffic flow can be optimized by adjusting the traffic light timing strategy, changing lane allocation, setting temporary traffic control, etc.

[0040] Simulate the initial equipment control plan and evaluate its effect. Through simulation, estimate the impact of the plan on traffic flow, vehicle speed, congestion level, etc. Based on the simulation results, optimize and adjust the initial equipment control plan to ensure the effectiveness and feasibility of the plan. Integrate the formulated initial equipment control strategy and optimization results to generate the initial equipment control plan.

[0041] Step S5: Analyze the road vehicle data using license plate recognition technology and vehicle feature recognition technology to identify the information of emergency vehicles (such as ambulances, fire trucks, police cars, etc.). Emergency vehicle information includes the type of emergency vehicle, current location, driving direction, speed, etc.

[0042] The identified emergency vehicle information is integrated to generate an emergency vehicle information database.

[0043] Combine the current location and direction of the emergency vehicle, as well as the road congestion trend, to predict the possible passage sections of the emergency vehicle. According to the road congestion trend, the travel time of the emergency vehicle in different sections is estimated to determine the best passage path for the emergency vehicle.

[0044] The emergency vehicle information and the travel path prediction results are integrated to generate the emergency vehicle predicted travel section information. The generated emergency vehicle predicted travel section information is stored in an efficient database for quick query and processing in subsequent steps.

[0045] Step S6: Integrate the initial equipment control plan with the emergency vehicle predicted travel section information to form a comprehensive data set.

[0046] Analyze the integrated data to identify potential conflicts between the initial equipment control plan and the emergency vehicle's passage path. For example, a road section is set as temporarily closed in the initial equipment control plan, but it is also the best passage path for emergency vehicles.

[0047] Set priorities for different control strategies based on the traffic needs of emergency vehicles and the overall traffic optimization goals. The traffic needs of emergency vehicles usually have the highest priority. Based on the comprehensive analysis results, adjust the initial equipment control plan to ensure that the traffic path of emergency vehicles is unobstructed. For example, temporarily adjust the traffic light timing strategy, open emergency lanes, adjust traffic control measures, etc.

[0048] Evaluate the adjusted global strategy to ensure that it can maximize the overall traffic flow while meeting the needs of emergency vehicles. Design multiple global adjustment plans, conduct comparative analysis, and select the best plan.

[0049] The adjusted global strategies are integrated to form the final global adjustment strategy. The generated global adjustment strategy is stored in an efficient database for implementation and management by the traffic management department. The initial equipment control scheme is comprehensively analyzed with the predicted emergency vehicle traffic section to obtain the corresponding global adjustment strategy.

[0050] The present invention provides a dispatching method for intelligent traffic equipment. By performing regional division processing on the road data and traffic management information of the area where the intelligent traffic equipment is located, the road information of different areas can be obtained more accurately, thereby improving the accuracy of regional traffic condition assessment and providing a more reliable basis for traffic dispatching. By associating the real-time equipment data with the regional road information and performing road prediction based on the road vehicle data, it is possible to achieve refined management of traffic conditions in different areas, which helps to optimize traffic flow and avoid waste of road resources. By performing overall traffic demand analysis on the system based on road vehicle data, it can ensure that the system can operate efficiently in different time periods and traffic conditions, reduce unnecessary traffic delays, and improve the overall operation efficiency of the system. By comprehensively analyzing the real-time regional traffic groups and road congestion trends, a more reasonable initial equipment control plan is formulated, and the optimized operation of the entire traffic system is achieved through a comprehensive adjustment strategy, thereby effectively utilizing road resources and reducing traffic congestion and environmental pollution. By identifying vehicles on road vehicle data, obtaining emergency vehicle information, and analyzing the predicted passage sections for emergency vehicles based on road congestion trends, the system can flexibly adjust the equipment control plan according to the passage needs of emergency vehicles to ensure that emergency vehicles can quickly pass through congested sections, making the system more adaptable to diverse traffic demand scenarios. This not only improves the efficiency of emergency handling, but also improves the overall traffic management level and enhances the system's emergency response capabilities.

[0051] In one embodiment, road data and traffic management information of the area where the intelligent traffic device is located are obtained, and the road data is divided into regions based on the traffic management information to obtain corresponding regional road information, including: Obtain electronic map data of the area where the intelligent transportation equipment is located. Electronic map data contains information such as road networks, buildings, landmarks, etc. Extract road feature information from the electronic map data, such as road shape, length, width, intersection location, etc. Generate initial road data based on the road feature information. The initial road data is a structured representation of the road information in the original electronic map.

[0052] Obtain comprehensive traffic management information in the area based on electronic map data. Comprehensive traffic management information includes traffic flow data, signal light timing schemes, bus routes, parking lot distribution and other information. This information reflects the traffic operation status and management strategy in the area.

[0053] Based on the comprehensive traffic management information obtained, the initial road data is clustered and analyzed. The clustering process involves factors such as road traffic flow characteristics, signal control methods, and bus coverage. Roads with similar traffic characteristics are grouped together to form multiple traffic sub-areas, and areas with similar traffic characteristics are identified.

[0054] The roads in each traffic sub-area are topologically processed to generate vectors that reflect the overall characteristics of the area. Topological processing involves factors such as road connection relationships and intersection distribution. Based on the generated regional feature vectors, the traffic sub-areas are functionally divided, such as identifying traffic zones with different functions such as commercial areas, residential areas, and industrial areas.

[0055] Detailed attribute annotation is performed on the roads in each traffic function zone. The annotated attributes include road grade (such as main road, secondary road, branch road, etc.), number of lanes, speed limit and travel direction information. These attribute information are crucial for traffic management and scheduling, and can be used to formulate targeted traffic control strategies.

[0056] The traffic function zoning information is associated with the regional road attribute data to obtain complete regional road information. This association process combines the macro characteristics of the functional zoning with the micro attributes of the road to form a comprehensive regional traffic database.

[0057] This embodiment can generate initial road data by obtaining electronic map data of the area where the intelligent traffic equipment is located and extracting road feature information, providing a basis for subsequent traffic management. Cluster analysis is performed based on comprehensive traffic management information, and roads with similar traffic characteristics are grouped together to form traffic sub-areas, which helps to accurately identify traffic characteristics in the area. Topological processing and functional division of traffic sub-areas can distinguish traffic zones with different functions such as commercial areas and residential areas, thereby improving the pertinence of traffic management. Detailed road attribute annotation, including road grade, number of lanes, speed limit value and direction of travel information, makes traffic management more refined. Finally, the traffic function zoning information is associated with the regional road attribute data to generate complete regional road information, providing detailed data support for the dispatch of intelligent traffic equipment. This process not only improves the accuracy and completeness of traffic information, but also provides a reliable data basis for a variety of intelligent traffic applications, thereby improving the overall operating efficiency of regional traffic.

[0058] In one embodiment, real-time device data of intelligent traffic devices is obtained, and the real-time device data is associated with regional road information to obtain a corresponding real-time regional traffic group, including: Real-time device data is obtained through intelligent transportation equipment. The real-time device data includes device identification, geographic location and working status. The device identification is used to uniquely identify each device, the geographic location provides the current location of the device, and the working status describes the current operation of the device. Feature extraction is performed on these real-time device data. The extracted device features include a variety of information describing the device status and location.

[0059] Regional road information is an important data source for intelligent traffic dispatching. The regional road information is converted into a matrix, and the converted regional feature matrix contains various road attributes, such as road width, traffic flow, traffic light configuration information, etc. The device feature and regional feature matrix are subjected to tensor operations, and the device-region correlation between the device and the region is obtained through tensor operations. The device-region correlation reflects the strength of the correlation between each intelligent traffic device and different regions.

[0060] According to the regional correlation of equipment, the intelligent transportation equipment is regionally matched to obtain the distribution of equipment in different areas. The regional distribution map of equipment provides basic data for scheduling decisions. The distribution map distinguishes the density and distribution status of equipment in each area. The regional distribution map of equipment is subjected to density cluster analysis to identify the aggregation and sparseness of equipment in different areas, thereby optimizing the scheduling of equipment.

[0061] Regional road information is spatiotemporally aligned with real-time equipment data. The resulting spatiotemporally aligned data combines information in the time and space dimensions, which is more timely and spatially relevant. Multi-dimensional feature fusion is performed on the spatiotemporally aligned data. The multi-dimensional feature fusion process combines information in the equipment, regional, and time dimensions. The fused feature set contains richer feature information and can more comprehensively reflect the current traffic conditions.

[0062] The fusion feature set is fused with the regional traffic features to obtain a more accurate real-time regional traffic group. The real-time regional traffic group combines the real-time data of intelligent traffic equipment and regional road information, and can provide a more comprehensive and accurate reference for traffic dispatch. This group of data not only includes the real-time equipment status, but also the real-time road conditions and traffic flow information.

[0063] This implementation acquires the data of intelligent traffic equipment in real time and associates it with regional road information, which can not only reflect the status and location of the equipment in a timely manner, but also combine it with the actual situation of the road, so as to achieve more accurate scheduling. The feature extraction and matrix conversion steps ensure the efficiency and accuracy of data processing, and the tensor operation further refines the correlation between the equipment and the region, ensuring the accuracy of scheduling. The equipment regional distribution map and density cluster analysis can intuitively display the distribution of equipment in various regions, which helps to optimize the scheduling strategy. Spatiotemporal alignment and multi-dimensional feature fusion make the data more timely and spatially relevant, and the fused feature set provides more comprehensive traffic information. Through these processing steps, the final real-time regional traffic group can comprehensively reflect the current traffic conditions and provide strong data support for scheduling decisions. This method not only improves the accuracy and real-time performance of equipment scheduling, but also effectively responds to the dynamic changes in traffic flow, optimizes the allocation of traffic resources, and thus improves the efficiency and effectiveness of overall traffic management.

[0064] In one embodiment, the road vehicle data collected by the intelligent traffic equipment is obtained, and the road information of the region is predicted based on the road vehicle data to obtain the corresponding road congestion trend, including: Real-time road vehicle data is collected by acquiring intelligent traffic equipment (sensors, cameras, etc.) installed on the corresponding roads. Road vehicle data includes information such as the number of vehicles, speed, and driving direction. By analyzing these data, the time series changes of road vehicle data are obtained, and thus the change trend of traffic flow is obtained.

[0065] Analyze the trend of traffic flow and classify regional road information through clustering algorithms to identify road congestion areas. For example, during a peak period, the traffic flow on certain sections of the road increases significantly, and clustering analysis can identify these sections as congested areas.

[0066] Based on the identified road congestion area, the road network topology analysis is carried out to determine the traffic node set. The location and connection relationship of key nodes such as intersections and entrances and exits in the congestion area are analyzed to form a traffic node set that can reflect the current road conditions.

[0067] Based on the traffic node set, the regional road information is predicted to obtain short-term traffic prediction results. For example, the traffic conditions of certain sections of the road in the next period of time are predicted by combining the existing traffic flow data and road structure.

[0068] The short-term prediction results are analyzed for temporal and spatial correlation to obtain traffic fluctuation information. By analyzing traffic data in different time and space dimensions, the periodicity and sudden changes in traffic flow can be identified. For example, certain sections of road will have regular traffic peaks in a specific time period. This step can help us grasp these patterns more accurately.

[0069] Based on the traffic fluctuation information obtained, simulation is performed to obtain long-term traffic forecast results. In this step, traffic fluctuation information and other relevant factors (such as holidays, weather changes, etc.) are combined to predict traffic conditions in the long term in the future. This long-term forecast is helpful for long-term decision-making in urban planning and traffic management.

[0070] The short-term traffic forecast results and the long-term traffic forecast results are comprehensively analyzed to obtain the comprehensive traffic forecast results. Based on the comprehensive traffic forecast results, the regional road information is analyzed for congestion to obtain the road congestion trend. By analyzing the comprehensive forecast results of different time periods and different road sections, the possible congestion trends in the future can be identified.

[0071] This embodiment predicts the traffic flow trend through time series changes, and uses clustering algorithms to identify congested areas, so that traffic bottlenecks can be discovered and responded to in a timely manner. The formation of road network topology analysis and traffic node sets further enhances the accuracy of traffic forecasts, making short-term traffic forecast results more reliable. Through the acquisition of spatiotemporal correlation analysis and traffic fluctuation information, it is possible to identify the periodic and sudden changes in traffic flow and provide a comprehensive prediction of traffic conditions. Combining short-term and long-term traffic forecast results, a scientific scheduling plan can be formulated to effectively alleviate traffic congestion and improve road traffic efficiency. Finally, through congestion trend analysis, measures can be taken in advance to guide and manage traffic, ensuring the smooth operation of the urban transportation system. This method not only improves the decision-making ability of traffic management departments, but also significantly improves the urban traffic environment and reduces the socioeconomic losses caused by traffic congestion.

[0072] In one embodiment, a real-time regional traffic group and road congestion trend are analyzed for traffic planning to obtain a corresponding initial equipment control solution, including: Analyze the current road congestion trend. By comprehensively analyzing data such as vehicle speed, vehicle density and traffic flow, the congestion index of each area is calculated. The congestion index is a numerical value that reflects the degree of road congestion. By comparing historical data with real-time data, it accurately reflects the current traffic conditions.

[0073] These calculated comprehensive congestion indexes are classified using preset congestion thresholds to categorize roads into different congestion levels. For example, roads with a congestion index between 0-30 are classified as unblocked, roads between 31-60 are classified as lightly congested, roads between 61-90 are classified as moderately congested, and roads between 91-100 are classified as severely congested.

[0074] Intelligent traffic equipment is grouped according to different congestion levels and combined with real-time equipment data. For example, in areas with high congestion levels, traffic lights, electronic signs and surveillance cameras that need special attention are identified and grouped into a device dispatch group. These device dispatch groups can centrally handle specific traffic problems, thereby improving dispatch efficiency.

[0075] For each equipment dispatch group, the system performs signal timing optimization. Signal timing optimization refers to adjusting the duration of green and red lights of traffic lights to better guide traffic. For example, during peak hours, the system may extend the green light time on the main road and shorten the green light time on the branch road to speed up the vehicle passing speed on the main road. Such an optimization strategy can effectively alleviate local congestion and improve overall traffic flow.

[0076] After signal timing optimization, the generated signal timing scheme is matched with the preset traffic management strategy library. The traffic management strategy library stores a variety of strategies for dealing with different traffic conditions, such as diversion strategy, traffic restriction strategy and traffic guidance strategy. According to the current actual traffic conditions and signal timing scheme, the most appropriate traffic management strategy is selected. For example, in the case of severe congestion, a traffic restriction strategy is selected to prohibit certain vehicles from entering a specific area to reduce traffic pressure.

[0077] The selected traffic control strategy is integrated with the signal timing plan to obtain the initial equipment control plan. This initial equipment control plan includes specific signal timing adjustments and traffic control measures, and is executed in real time through intelligent traffic equipment. For example, electronic signs will display restricted traffic information, traffic lights will switch according to the optimized timing plan, and surveillance cameras will record changes in traffic flow.

[0078] This embodiment can effectively alleviate the problem of urban traffic congestion by conducting traffic planning analysis on real-time regional traffic groups and road congestion trends. First, by real-time monitoring and calculating the congestion index, the system can accurately evaluate the current traffic conditions and ensure the accuracy of scheduling decisions. Secondly, by grouping intelligent traffic equipment according to the congestion level, resource allocation is more reasonable, and the areas with severe congestion can be focused on, thereby improving scheduling efficiency. Signal timing optimization further enhances the ability to guide traffic flow. By reasonably adjusting the duration of signal lights, the waiting time of vehicles is effectively reduced, and the road traffic efficiency is improved. Matching the signal timing scheme with the traffic management strategy library ensures the scientificity and applicability of the strategy, making the traffic control measures more targeted. The final initial equipment control scheme integrates various information and strategies, and realizes dynamic adjustment and management of traffic flow through the real-time execution of intelligent traffic equipment. Overall, the embodiment not only improves the operating efficiency of the traffic system and reduces congestion time, but also enhances the flexibility and adaptability of traffic management, which helps to improve the travel experience and quality of life of urban residents.

[0079] In one embodiment, vehicle identification is performed on road vehicle data to obtain corresponding emergency vehicle information, and road prediction is performed on the emergency vehicle information based on the road congestion trend to obtain the corresponding emergency vehicle predicted pass section, including: Through image conversion processing, the vehicle data is converted into a vehicle image that can be analyzed. Vehicle edge detection, shape analysis and color region segmentation are performed on the vehicle image. Image processing technology can extract the outline, type and color characteristics of the vehicle. The outline of the vehicle is identified through edge detection, and shape analysis is used to determine the type of vehicle, such as a car, truck or motorcycle. Color region segmentation can determine the color characteristics of the vehicle. These steps comprehensively generate the fusion features of the vehicle, including vehicle outline features, vehicle type features and vehicle color features.

[0080] After acquiring the fusion features of the vehicle, the intelligent traffic device matches these features according to the preset emergency vehicle feature template. Specifically, the vehicle's outline, type and color are matched in turn, and the corresponding matching scores are calculated. These matching scores are used to measure the similarity between the current vehicle and the known emergency vehicle features.

[0081] Based on the calculated matching scores, the vehicle images are screened to identify vehicles with higher matching scores, thereby obtaining emergency vehicle information, which includes the vehicle identification results and the corresponding feature matching scores.

[0082] Correlate emergency vehicle information with regional road information to generate vehicle association information. This step involves integrating the identified emergency vehicle with its road location, direction of travel, and other information.

[0083] Based on the road congestion trend, the vehicle-related information is used to predict the path, thereby obtaining the predicted passage section for emergency vehicles. The road congestion trend can be obtained through real-time traffic data analysis, and intelligent traffic equipment predicts the best passage path for emergency vehicles under current road conditions.

[0084] In this embodiment, by performing image conversion processing on road vehicle data, the intelligent traffic equipment can efficiently obtain vehicle images, and further perform edge detection, shape analysis and color region segmentation on the image, so as to obtain accurate vehicle fusion features. This process ensures the accuracy and reliability of vehicle identification. The vehicle fusion features are matched using the preset emergency vehicle feature template, and the emergency vehicle information is screened out by calculating the matching score, which effectively improves the accuracy of emergency vehicle identification. The emergency vehicle information is associated with the regional road information, so that the system can grasp the location information and driving path of the emergency vehicle in real time. Path prediction is performed based on the road congestion trend, so as to provide the best route for emergency vehicles, improve the travel efficiency of emergency vehicles during peak traffic hours, and reduce delays caused by congestion. The overall embodiment realizes accurate identification and intelligent scheduling of emergency vehicles through a series of image processing and data analysis methods, and significantly improves the emergency response capability and operation efficiency of the traffic management system.

[0085] In one embodiment, the initial equipment control scheme and the predicted emergency vehicle pass section are comprehensively analyzed to obtain a corresponding global adjustment strategy, including: An initial equipment control plan is generated, which performs traffic planning analysis based on real-time regional traffic groups and road congestion trends. At the same time, the emergency vehicle's pass section is predicted, and the initial equipment control plan is comprehensively analyzed with the emergency vehicle's predicted pass section to formulate a global adjustment strategy.

[0086] The decision is constructed based on the initial equipment control plan and the predicted emergency vehicle traffic section to form an initial multi-dimensional decision. This decision includes a variety of traffic management measures, such as signal light timing adjustment, lane allocation and speed limit. The construction of multi-dimensional decision is to take into account the mutual influence and coordination between different traffic management measures, so that the system can make the best decision in a complex traffic environment.

[0087] The effectiveness of the scheme is evaluated based on the preset comprehensive evaluation rules. The comprehensive evaluation rules include indicators such as traffic flow, vehicle speed, congestion level, and emergency vehicle travel time. The scheme evaluation results are obtained by performing a multi-dimensional evaluation on the initial multi-dimensional decision, which reflects the performance of the current decision in the actual traffic environment.

[0088] Based on the results of the scheme evaluation, the initial multi-dimensional decision is analyzed for parameter adjustment. The parameter adjustment analysis aims to optimize specific parameters such as traffic signal timing, lane allocation and speed limit. For example, if the evaluation results show that the signal timing of a certain section of road is unreasonable, the green light time of the section will be adjusted; if the lane allocation of a certain section of road is unbalanced, the lanes will be reallocated to improve traffic efficiency. In addition, a higher speed limit will be set when emergency vehicles pass through the section to ensure that emergency vehicles can pass quickly.

[0089] After parameter adjustment analysis, the initial multi-dimensional decision is dynamically adjusted to generate a dynamic adjustment strategy. A dynamic adjustment strategy is a traffic management solution that is updated in real time and can be adjusted based on the latest traffic data to ensure smooth traffic and priority for emergency vehicles. The dynamic adjustment strategy is sent to the corresponding intelligent traffic devices, such as traffic lights, electronic signs, and surveillance cameras, which will immediately implement the new adjustment plan.

[0090] After executing the dynamic adjustment strategy, the intelligent traffic equipment will generate feedback data, which reflects the effect of the adjustment strategy in actual application. For example, whether the traffic flow has improved, whether the emergency vehicles can pass smoothly, etc. Based on these feedback data, the dynamic adjustment strategy is further optimized to ensure its continued effectiveness. After multiple cycles of optimization, a global adjustment strategy is finally generated.

[0091] This embodiment generates a global adjustment strategy by comprehensively analyzing the initial equipment control scheme and the predicted passage section of the emergency vehicle. This comprehensive analysis and dynamic adjustment method significantly improves the efficiency and accuracy of traffic management. Specifically, by using the initial multi-dimensional decision and the preset comprehensive evaluation rules, key parameters such as traffic signal timing, lane allocation and speed limit can be quickly evaluated and optimized, so that traffic flow can be effectively controlled and congestion can be reduced. At the same time, by obtaining feedback data in real time and making dynamic adjustments, it is ensured that emergency vehicles can have priority and smooth passage, and the emergency response capability is improved. In addition, the continuous optimization process of the system enables the global adjustment strategy to continuously adapt to changing traffic conditions, ensuring the long-term effectiveness and stability of traffic management. This not only improves the flexibility and reliability of the transportation system, but also provides higher safety and traffic efficiency for urban transportation.

[0092] Reference Figure 2 As shown, the present invention also provides a scheduling system for intelligent transportation equipment, which is applied to any of the above-mentioned scheduling methods for intelligent transportation equipment, including: The acquisition module is used to obtain the road data and traffic management information of the area where the intelligent transportation equipment is located, and to divide the road data into regions based on the traffic management information to obtain the corresponding regional road information; An analysis module, which is used to obtain real-time device data of intelligent traffic equipment, associate the real-time device data with regional road information, and obtain a corresponding real-time regional traffic group; The association module is used to obtain the road vehicle data collected by the intelligent traffic equipment, and make road predictions on the regional road information based on the road vehicle data to obtain the corresponding road congestion trend; A processing module, which is used to perform traffic planning analysis on real-time regional traffic groups and road congestion trends to obtain corresponding initial equipment control plans; A control module, the control module is used to perform vehicle identification on road vehicle data to obtain corresponding emergency vehicle information, perform road prediction on the emergency vehicle information based on the road congestion trend, and obtain the corresponding emergency vehicle predicted pass section; The execution module is used to comprehensively analyze the initial equipment control plan and the predicted emergency vehicle travel section to obtain the corresponding global adjustment strategy.

[0093] The present invention provides a dispatching system for intelligent traffic equipment. By performing regional division processing on the road data and traffic management information of the area where the intelligent traffic equipment is located, the road information of different areas can be obtained more accurately, thereby improving the accuracy of regional traffic condition assessment and providing a more reliable basis for traffic dispatching. By associating the real-time equipment data with the regional road information and performing road prediction based on the road vehicle data, it is possible to achieve refined management of traffic conditions in different areas, which helps to optimize traffic flow and avoid waste of road resources. By analyzing the overall traffic demand of the system based on the road vehicle data, it can ensure that the system can operate efficiently in different time periods and traffic conditions, reduce unnecessary traffic delays, and improve the overall operation efficiency of the system. By comprehensively analyzing the real-time regional traffic groups and road congestion trends, a more reasonable initial equipment control plan is formulated, and the optimized operation of the entire traffic system is achieved through a comprehensive adjustment strategy, thereby effectively utilizing road resources and reducing traffic congestion and environmental pollution. By identifying vehicles on road vehicle data, obtaining emergency vehicle information, and analyzing the predicted passage sections for emergency vehicles based on road congestion trends, the system can flexibly adjust the equipment control plan according to the passage needs of emergency vehicles to ensure that emergency vehicles can quickly pass through congested sections, making the system more adaptable to diverse traffic demand scenarios. This not only improves the efficiency of emergency handling, but also improves the overall traffic management level and enhances the system's emergency response capabilities.

[0094] Reference Figure 3 As shown, the present invention also provides a dispatching device for intelligent traffic equipment, including: Memory, used to store programs; The processor is used to execute the program to implement each step of any one of the above-mentioned scheduling methods for intelligent transportation equipment.

[0095] In this embodiment, the processor and the memory may be connected via a bus or other means. The memory may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a read-only memory, a flash memory, a hard disk, or a solid-state drive. The processor may be a general-purpose processor, such as a central processing unit, a digital signal processor, an application-specific integrated circuit, or one or more integrated circuits configured to implement the embodiments of the present invention.

[0096] The present invention also provides a storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to execute any of the above methods.

[0097] It should be noted that technicians in the relevant technical field can clearly understand that for the convenience and conciseness of description, the specific working process of the system and each module described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0098] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A dispatching method for intelligent traffic equipment, characterized in that: include: Acquire road data and traffic management information of the area where the intelligent traffic equipment is located, and perform regional division processing on the road data based on the traffic management information to obtain corresponding regional road information; Acquire real-time device data of the intelligent traffic device, associate the real-time device data with the regional road information, and obtain a corresponding real-time regional traffic group; Acquire the road vehicle data collected by the intelligent traffic equipment, perform road prediction on the regional road information according to the road vehicle data, and obtain the corresponding road congestion trend; Performing traffic planning analysis on the real-time regional traffic group and the road congestion trend to obtain a corresponding initial equipment control plan; Performing vehicle identification on the road vehicle data to obtain corresponding emergency vehicle information, and performing road prediction on the emergency vehicle information based on the road congestion trend to obtain a corresponding emergency vehicle predicted pass section; The initial equipment control scheme and the predicted emergency vehicle passage section are comprehensively analyzed to obtain a corresponding global adjustment strategy.

2. The dispatching method of intelligent traffic equipment according to claim 1, characterized in that: The step of acquiring road data and traffic management information of an area where the intelligent traffic device is located, and performing regional division processing on the road data based on the traffic management information to obtain corresponding regional road information includes: Acquire electronic map data of the area where the intelligent transportation device is located, extract road feature information from the electronic map data, and generate corresponding initial road data based on the road feature information; Obtaining comprehensive traffic management information in the area based on the electronic map data; Clustering the initial road data according to the comprehensive traffic management information to obtain a plurality of traffic sub-areas with similar traffic characteristics; Performing topological processing on the roads in each of the traffic sub-areas to generate a corresponding regional feature vector, and performing functional division on the traffic sub-areas based on the regional feature vector to obtain a final traffic functional partition; Annotate the attributes of the roads in each of the traffic function zones, including road grade, number of lanes, speed limit and travel direction information, and generate detailed regional road attribute data; The traffic function zoning information is associated with the regional road attribute data to obtain complete regional road information.

3. The dispatching method of intelligent traffic equipment according to claim 1, characterized in that: The step of acquiring the real-time device data of the intelligent traffic device and associating the real-time device data with the regional road information to obtain a corresponding real-time regional traffic group includes: Acquire real-time device data including device identification, geographic location and working status of the intelligent transportation device, perform feature extraction on the real-time device data, and obtain corresponding device features; Performing matrix conversion on the regional road information to obtain a corresponding regional feature matrix; Performing tensor operations on the device features and the region feature matrix to obtain corresponding device region associations; Performing regional matching on the intelligent transportation equipment according to the equipment area association degree to obtain a corresponding equipment area distribution map; Performing density clustering on the device area distribution map to obtain a corresponding device area distribution map; Performing spatiotemporal alignment of the regional road information and the real-time device data to obtain corresponding spatiotemporal alignment data; Performing multi-dimensional feature fusion on the spatiotemporal alignment data to obtain a corresponding fused feature set; The fused feature set is fused with the regional traffic feature to obtain the corresponding real-time regional traffic group.

4. The dispatching method of intelligent traffic equipment according to claim 1, characterized in that: The acquiring of the road vehicle data collected by the intelligent traffic device, and performing road prediction on the regional road information according to the road vehicle data to obtain a corresponding road congestion trend includes: Predicting the time series changes of the road vehicle data to obtain the corresponding traffic flow change trend; Performing congestion clustering on the regional road information according to the traffic flow change trend to obtain a corresponding road congestion area; Perform road network topology on the regional road information according to the road congestion area to obtain a corresponding traffic node set; Performing road prediction on the regional road information according to the traffic node set to obtain corresponding short-term traffic prediction results; Performing a spatiotemporal correlation analysis on the regional road information according to the short-term prediction results to obtain corresponding traffic fluctuation information; Simulating the regional road information according to the traffic fluctuation information to obtain corresponding long-term traffic prediction results; Comprehensively predicting the short-term traffic prediction result and the long-term traffic flow prediction result to obtain a corresponding comprehensive traffic prediction result; The regional road information is subjected to congestion analysis according to the comprehensive traffic forecast result to obtain the road congestion trend.

5. The dispatching method of intelligent traffic equipment according to claim 1, characterized in that: The real-time regional traffic group and the road congestion trend are subjected to traffic planning analysis to obtain a corresponding initial equipment control scheme, including: Calculating the congestion index of the real-time regional traffic group according to the road congestion trend to obtain a corresponding comprehensive congestion index; The comprehensive congestion index is segmented according to a preset congestion threshold to obtain a corresponding congestion level; The intelligent traffic equipment is grouped according to the congestion level and the real-time equipment data to obtain a corresponding equipment dispatch group; Optimizing the signal timing of each of the equipment scheduling groups to obtain a corresponding signal timing plan; Matching the signal timing scheme with a preset traffic management strategy library to obtain a traffic control strategy; The traffic control strategy is integrated with the signal timing plan to obtain the initial equipment control plan.

6. The dispatching method of intelligent traffic equipment according to claim 1, characterized in that: The performing vehicle identification on the road vehicle data to obtain corresponding emergency vehicle information, and performing road prediction on the emergency vehicle information based on the road congestion trend to obtain the corresponding emergency vehicle predicted pass section, includes: Performing image conversion processing on the road vehicle data to obtain a vehicle image; Performing vehicle edge detection, shape analysis and color region segmentation on the vehicle image in sequence to obtain vehicle fusion features, wherein the vehicle fusion features include vehicle contour features, vehicle type features and vehicle color features; According to a preset emergency vehicle feature template, the vehicle fusion feature is matched with the vehicle contour, type and color in sequence to obtain a corresponding matching score; Performing vehicle screening on the vehicle image based on the matching score to obtain the emergency vehicle information; Associating the emergency vehicle information with the regional road information to obtain corresponding vehicle association information; The path of the vehicle-related information is predicted based on the road congestion trend to obtain the predicted passage section of the emergency vehicle.

7. The dispatching method of intelligent traffic equipment according to claim 1, characterized in that: The comprehensive analysis of the initial equipment control scheme and the predicted emergency vehicle pass section to obtain a corresponding global adjustment strategy includes: Decision construction is performed according to the initial equipment control scheme and the predicted passage section of the emergency vehicle to obtain a corresponding initial multi-dimensional decision; Performing a scheme evaluation on the initial multi-dimensional decision according to a preset comprehensive evaluation rule to obtain a scheme evaluation result; Performing parameter adjustment analysis on the initial multi-dimensional decision according to the scheme evaluation results to obtain adjustment parameters including traffic signal timing, lane allocation and speed limit; Dynamically adjusting the initial multi-dimensional decision according to the adjustment parameters to obtain a dynamic adjustment strategy; Sending the dynamic adjustment strategy to the corresponding intelligent transportation device, and obtaining feedback data from the intelligent transportation device after executing the dynamic adjustment strategy; The dynamic adjustment strategy is optimized according to the feedback data to obtain the global adjustment strategy.

8. A dispatching system for intelligent traffic equipment, characterized in that: The scheduling method of the intelligent transportation equipment applied to any one of claims 1 to 7 above comprises: A collection module, the collection module is used to obtain road data and traffic management information of the area where the intelligent transportation equipment is located, and perform regional division processing on the road data based on the traffic management information to obtain corresponding regional road information; An analysis module, the analysis module is used to obtain real-time device data of the intelligent traffic device, associate the real-time device data with the regional road information, and obtain a corresponding real-time regional traffic group; An association module, the association module is used to obtain the road vehicle data collected by the intelligent traffic equipment, perform road prediction on the regional road information according to the road vehicle data, and obtain a corresponding road congestion trend; A processing module, the processing module is used to perform traffic planning analysis on the real-time regional traffic group and the road congestion trend to obtain a corresponding initial equipment control plan; A control module, the control module is used to perform vehicle identification on the road vehicle data to obtain corresponding emergency vehicle information, perform road prediction on the emergency vehicle information based on the road congestion trend to obtain a corresponding emergency vehicle predicted pass section; An execution module is used to comprehensively analyze the initial equipment control plan and the emergency vehicle predicted pass section to obtain a corresponding global adjustment strategy.

9. A dispatching device for intelligent traffic equipment, characterized in that: include: Memory, used to store programs; The processor is used to execute the program to implement each step of the scheduling method of an intelligent transportation device as described in any one of claims 1 to 7.

10. A storage medium, characterized in that: Computer instructions are stored, and the computer instructions are used to make a computer execute the method according to any one of claims 1 to 7.

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