Vehicle management and control system and method based on port traffic

Abstract

This invention discloses a vehicle management system and method based on port traffic, belonging to the field of vehicle management technology. The invention constructs a vehicle monitoring network to collect vehicle data both inside and outside the operational scenario area and to monitor vehicle anomalies. Based on the collected data of operational vehicles within the operational scenario area, it analyzes the congestion level of operational vehicles in the real-time operational scenario area. Based on the analysis results, it manages vehicles outside the operational scenario area and performs task association analysis on operational vehicles within the operational scenario area. Combining the real-time operational progress data of each operational vehicle within the corresponding operational scenario area, it analyzes the operational priority of each operational vehicle within the operational scenario area. Based on the priority analysis data, it schedules and processes each operational vehicle within the operational scenario. This invention effectively ensures traffic order and improves port operational efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of vehicle management and control, in particular to a vehicle management and control system and method based on port traffic. BACKGROUND

[0002] In port traffic, traditional vehicle management and control faces many challenges; the number of on-site operation vehicles is large, and multi-type operation vehicles are concentrated in the wharf berth area and other areas waiting for operation, which easily causes port traffic chaos; and the non-standard driving of external vehicles is more likely to cause frequent interaction of vehicles in the port, resulting in serious queuing, backlog and turning of wharf operation vehicles, frequent vehicle collision events, and the existing control means mainly depends on the driving quality of the driver, which cannot effectively guarantee the traffic order and port operation efficiency; therefore, there is an urgent need for a means of port vehicle traffic control. SUMMARY

[0003] The present application aims to provide a vehicle management and control system and method based on port traffic to solve the problems in the prior art.

[0004] To achieve the above-mentioned purpose, the present application provides the following technical scheme:

[0005] A vehicle management and control method based on port traffic, the method comprising the following steps:

[0006] A vehicle monitoring network is constructed to collect vehicle data inside and outside the operation scene area, and to monitor vehicle abnormalities;

[0007] Based on the collected data of the operation vehicles in the operation scene area, the congestion degree of the operation vehicles in the real-time operation scene area is analyzed, based on the analysis result, the vehicles outside the operation scene area are controlled and the operation task correlation analysis of the operation vehicles in the operation scene area is performed, the operation priority of each operation vehicle in the operation scene area is analyzed in combination with the real-time operation progress data of each operation vehicle in the corresponding operation scene area, and the operation vehicles in the operation scene area are dispatched based on the priority analysis data.

[0008] Further, a vehicle monitoring network is arranged in the operation scene area to monitor and collect vehicle data of the vehicles inside and outside the operation scene area; wherein the vehicle monitoring network comprises an intelligent camera and a GPS positioning system; the vehicle data includes vehicle speed data, driving distance data, driving time speed, etc.

[0009] The real-time driving speed of the vehicles inside and outside the operation scene area is monitored for abnormalities, the vehicles with a speed greater than the threshold value are marked as abnormal, and the license plate information of the corresponding abnormal vehicles is collected; the vehicles with a speed less than the threshold value are not processed; wherein the camera is used to collect the vehicle license plate information, and the GPS positioning is used to determine the vehicle position data.

[0010] Further, the congestion degree of the work vehicles in the work scene area is analyzed based on the real-time collection data of the work vehicles in the work scene area; the real-time driving speed data and the current work traveled distance of each work vehicle in the work scene area are obtained; the real-time average driving speed data of the work vehicles in the work scene area is obtained by mean calculation based on the real-time driving speed data of each work vehicle in the work scene area; the total work distance data of the work vehicles in the current work scene area is determined by summing the current work traveled distance data of each work vehicle in the work scene area; the overall work time data of the work vehicles in the current work scene area is obtained by ratio calculation based on the total work distance data of the work vehicles in the current work scene area and the average driving speed data of the work vehicles in the current work scene area.

[0011] The number of work vehicles in the current work scene area and the road area data of the work scene area are determined, and the real-time work vehicle traffic flow in the current work scene area is analyzed; the real-time work vehicle traffic flow data in the current work scene area is obtained by ratio calculation of the number of work vehicles in the current work scene area and the road area data of the work scene area; the traffic congestion index of the work vehicles in the current work scene area is determined by product calculation of the real-time work vehicle traffic flow data in the current work scene area and the overall work time data of the work vehicles in the current work scene area; the work vehicle traffic congestion threshold in the work scene area is set, the traffic congestion index of the work vehicles in the current work scene area is compared with the work vehicle traffic congestion threshold in the work scene area, and the work vehicles outside the work scene area are controlled and the work vehicles in the work scene area are analyzed according to the comparison result; if the traffic congestion index of the work vehicles in the current work scene area is greater than or equal to the threshold, it is judged that the work vehicles in the current work scene area are congested, the automatic release of the traffic gate outside the work scene area is stopped, and the work task association analysis of the work vehicles in the work scene area is performed; otherwise, the state is maintained, and no control and work task association analysis of the work vehicles are performed.

[0012] Furthermore, when the vehicles operating within the work scenario area are in a state of traffic congestion, a work task correlation analysis is performed on the vehicles within the work scenario area. This involves acquiring the work task data of each vehicle within the work scenario area, extracting the work feature data from the corresponding work task data, and constructing a work task feature set for each vehicle. The work feature data includes vehicle transport destination data, work time window data, and work route data. Correlation analysis is then performed on the work task feature sets of each vehicle within the work scenario area. Specifically, by selecting any two work task feature sets, the correlation values ​​of the corresponding work feature data in each set are analyzed. The analysis is as follows:

[0013] ;

[0014] Wherein, G[i(s),j(s)] corresponds to the correlation value of the operation feature data index s in any two operation task feature sets i and j; i(s) and j(s) respectively correspond to the data of the operation feature data index s in any two operation task feature sets i and j; based on the comprehensive analysis of the correlation values ​​of each operation feature data in any two operation task feature sets, the correlation coefficient of any two operation task feature sets is obtained; the analysis is as follows:

[0015] ;

[0016] Where R(i,j) is the correlation coefficient between any two sets of task features i and j of work vehicles; ws corresponds to the weight of the task feature data sequence s; n is the number of task feature data in the corresponding set of task features of work vehicles; where the weight of each task feature data is set by human experience based on the degree of correlation influence analysis of different task feature data.

[0017] The real-time work progress data of each work vehicle within the work scenario area is determined, and a dispatch index analysis is performed on the current work vehicle by combining the correlation coefficient between the current work progress data of the corresponding work vehicle and the work task feature set of other work vehicles within the work scenario area. The real-time work progress data of the work vehicle includes the planned travel distance, planned travel time, current travel distance, and current travel time. The dispatch index analysis of the current work vehicle is as follows:

[0018] ;

[0019] Where Pi is the dispatch index of the current working vehicle i; dq(i) is the distance traveled by the current working vehicle i; da(i) is the planned distance traveled by the current working vehicle i; tq(i) is the travel time of the current working vehicle i; ta(i) is the planned travel time of the current working vehicle i; m is the number of other working vehicles associated with working vehicle i in the current working scene area; based on the dispatch index of each vehicle in the current working scene area, the dispatch index is sorted in descending order, and the working vehicles are dispatched and controlled according to the priority from largest to smallest according to the sorting result, and the vehicle dispatch decision is executed; the working vehicle dispatch control has a retrieval area and a waiting area. According to the priority, when the number of working vehicles in the retrieval area reaches the upper limit, the remaining vehicles to be retrieved are planned to the waiting area. When the number of vehicles in the waiting area decreases, they are replenished according to the priority; the dispatch decision includes route dispatch, vehicle sorting dispatch, etc.

[0020] Furthermore, the monitoring port will report back vehicles with abnormal markings inside and outside the work area, and output the license plate information and abnormal data of the abnormal vehicles.

[0021] When the work area is in a state of traffic congestion, the system will manage and control vehicles outside the work area and make scheduling decisions for vehicles within the work area.

[0022] A vehicle management system based on port traffic, the system comprising a vehicle monitoring and acquisition module, a regional congestion analysis module, a regional vehicle dispatching and analysis module, and a feedback output module;

[0023] The vehicle monitoring and acquisition module constructs a vehicle monitoring network to collect vehicle data both inside and outside the work scenario area and performs vehicle anomaly monitoring. The regional congestion analysis module analyzes the real-time congestion level of work vehicles in the work scenario area based on the collected data, and manages vehicles outside the work scenario area based on the analysis results. The regional vehicle scheduling and analysis module performs work task association analysis on work vehicles within the work scenario area, and analyzes the work priority of each work vehicle within the work scenario area by combining the real-time work progress data of each work vehicle in the corresponding work scenario area, and schedules each work vehicle in the work scenario based on the priority analysis data. The feedback output module outputs abnormal vehicle data inside and outside the work scenario area and executes vehicle control and scheduling decisions.

[0024] Furthermore, the vehicle monitoring and acquisition module includes a scene monitoring and acquisition unit and an abnormal vehicle monitoring unit;

[0025] The scene monitoring and data acquisition unit monitors vehicles inside and outside the work scene area and collects vehicle data by installing a vehicle monitoring network in the work scene area.

[0026] The abnormal vehicle monitoring unit monitors the real-time driving speed of vehicles inside and outside the work scene area for abnormalities. By setting a safe vehicle driving speed threshold, vehicles exceeding the threshold are marked as abnormal, and the license plate information of the corresponding abnormal vehicles is collected.

[0027] Furthermore, the regional congestion analysis module includes a work vehicle data analysis unit and a regional congestion analysis unit;

[0028] The operational vehicle data analysis unit analyzes the congestion level of operational vehicles within the operational scenario area based on real-time collected data. It comprehensively acquires the real-time driving speed data and the distance already traveled for each operational vehicle within the operational scenario area; calculates the average real-time driving speed data of operational vehicles within the operational scenario area based on the average real-time driving speed data of each operational vehicle within the operational scenario area; sums the distance already traveled for each operational vehicle within the operational scenario area to determine the total operational distance data of operational vehicles within the current operational scenario area; and calculates the ratio between the total operational distance data and the average driving speed data of operational vehicles within the current operational scenario area to obtain the integrated operational time data of operational vehicles within the current operational scenario area.

[0029] The regional congestion analysis unit determines the number of operating vehicles and the road area data of the current operation scenario area, and analyzes the real-time traffic flow of operating vehicles in the current operation scenario area. It obtains real-time traffic flow data of operating vehicles in the current operation scenario area by calculating the ratio of the number of operating vehicles to the road area data. It then determines the traffic congestion index of operating vehicles in the current operation scenario area by multiplying the real-time traffic flow data with the integrated operation time data of operating vehicles in the current operation scenario area. Finally, it sets a traffic congestion threshold for operating vehicles in the operation scenario area, and compares the traffic congestion index with the threshold. Based on the comparison results, it manages vehicles outside the operation scenario area and performs task-related analysis on operating vehicles within the operation scenario area.

[0030] Furthermore, the regional vehicle dispatch analysis module includes a regional vehicle correlation analysis unit and a regional vehicle dispatch analysis unit;

[0031] When the vehicles in the work scenario area are in a state of traffic congestion, the regional vehicle association analysis unit performs work task association analysis on the vehicles in the work scenario area. By acquiring the work task data of each vehicle in the work scenario area, it extracts the work feature data from the work task data of the corresponding vehicles and constructs the work task feature set of the corresponding vehicles. It then performs association analysis on the work task feature sets of each vehicle in the work scenario area. By selecting any two work task feature sets, it analyzes the association values ​​of the corresponding work feature data in each set. Based on the comprehensive analysis of the association values ​​of each work feature data in the work task feature sets of any two work vehicles, it obtains the association coefficient of the work task feature sets of any two work vehicles.

[0032] The regional vehicle dispatch analysis unit determines the real-time operation progress data of each vehicle in the operation scenario area, and performs dispatch index analysis of the current vehicle by combining the correlation coefficient between the current operation progress data of the corresponding vehicle and the operation task feature set of other vehicles in the operation scenario area; according to the dispatch index of each vehicle in the current operation scenario area, the dispatch index is sorted in descending order, and the vehicles are dispatched and controlled according to the priority from large to small based on the sorting result, and vehicle dispatch decisions are executed.

[0033] Furthermore, the feedback output module includes an anomaly control feedback unit and a scheduling decision execution unit;

[0034] The anomaly control feedback unit will monitor and report vehicles with abnormal markings inside and outside the work scene area, and output the license plate information and abnormal data of the abnormal vehicles.

[0035] When the work scenario area is in a state of traffic congestion, the scheduling decision execution unit will perform the control and management of vehicles outside the work scenario area and the scheduling decision of work vehicles within the work scenario area.

[0036] Compared with the prior art, the beneficial effects of the present invention are:

[0037] This invention monitors abnormal vehicle movement both inside and outside the work area by constructing a monitoring network; it analyzes the data of working vehicles within the work area to determine the work status and congestion situation; based on the analysis results, it manages vehicles outside the work area and performs correlation analysis on working vehicles within the work area, and makes scheduling decisions based on the correlation analysis data; this invention effectively ensures traffic order and improves port operation efficiency. Attached Figure Description

[0038] Figure 1This is a schematic diagram of the structure of a vehicle control system based on port traffic according to the present invention;

[0039] Figure 2 This is a flowchart illustrating a vehicle management method based on port traffic according to the present invention. Detailed Implementation

[0040] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0041] Example: Figure 1 As shown, the present invention provides a technical solution:

[0042] A vehicle management system based on port traffic, the system comprising a vehicle monitoring and acquisition module, a regional congestion analysis module, a regional vehicle dispatching and analysis module, and a feedback output module;

[0043] The vehicle monitoring and acquisition module constructs a vehicle monitoring network to collect vehicle data both inside and outside the work scenario area and performs vehicle anomaly monitoring. The regional congestion analysis module analyzes the real-time congestion level of work vehicles in the work scenario area based on the collected data, and manages vehicles outside the work scenario area based on the analysis results. The regional vehicle scheduling and analysis module performs work task association analysis on work vehicles within the work scenario area, and analyzes the work priority of each work vehicle within the work scenario area by combining the real-time work progress data of each work vehicle in the corresponding work scenario area, and schedules each work vehicle in the work scenario based on the priority analysis data. The feedback output module outputs abnormal vehicle data inside and outside the work scenario area and executes vehicle control and scheduling decisions.

[0044] Furthermore, the vehicle monitoring and acquisition module includes a scene monitoring and acquisition unit and an abnormal vehicle monitoring unit;

[0045] The scene monitoring and data acquisition unit monitors vehicles inside and outside the work scene area and collects vehicle data by installing a vehicle monitoring network in the work scene area.

[0046] The abnormal vehicle monitoring unit monitors the real-time driving speed of vehicles inside and outside the work scene area for abnormalities. By setting a safe vehicle driving speed threshold, vehicles exceeding the threshold are marked as abnormal, and the license plate information of the corresponding abnormal vehicles is collected.

[0047] Furthermore, the regional congestion analysis module includes a work vehicle data analysis unit and a regional congestion analysis unit;

[0048] The operational vehicle data analysis unit analyzes the congestion level of operational vehicles within the operational scenario area based on real-time collected data. It comprehensively acquires the real-time driving speed data and the distance already traveled for each operational vehicle within the operational scenario area; calculates the average real-time driving speed data of operational vehicles within the operational scenario area based on the average real-time driving speed data of each operational vehicle within the operational scenario area; sums the distance already traveled for each operational vehicle within the operational scenario area to determine the total operational distance data of operational vehicles within the current operational scenario area; and calculates the ratio between the total operational distance data and the average driving speed data of operational vehicles within the current operational scenario area to obtain the integrated operational time data of operational vehicles within the current operational scenario area.

[0049] The regional congestion analysis unit determines the number of operating vehicles and the road area data of the current operation scenario area, and analyzes the real-time traffic flow of operating vehicles in the current operation scenario area. It obtains real-time traffic flow data of operating vehicles in the current operation scenario area by calculating the ratio of the number of operating vehicles to the road area data. It then determines the traffic congestion index of operating vehicles in the current operation scenario area by multiplying the real-time traffic flow data with the integrated operation time data of operating vehicles in the current operation scenario area. Finally, it sets a traffic congestion threshold for operating vehicles in the operation scenario area, and compares the traffic congestion index with the threshold. Based on the comparison results, it manages vehicles outside the operation scenario area and performs task-related analysis on operating vehicles within the operation scenario area.

[0050] Furthermore, the regional vehicle dispatch analysis module includes a regional vehicle correlation analysis unit and a regional vehicle dispatch analysis unit;

[0051] When the vehicles in the work scenario area are in a state of traffic congestion, the regional vehicle association analysis unit performs work task association analysis on the vehicles in the work scenario area. By acquiring the work task data of each vehicle in the work scenario area, it extracts the work feature data from the work task data of the corresponding vehicles and constructs the work task feature set of the corresponding vehicles. It then performs association analysis on the work task feature sets of each vehicle in the work scenario area. By selecting any two work task feature sets, it analyzes the association values ​​of the corresponding work feature data in each set. Based on the comprehensive analysis of the association values ​​of each work feature data in the work task feature sets of any two work vehicles, it obtains the association coefficient of the work task feature sets of any two work vehicles.

[0052] The regional vehicle dispatch analysis unit determines the real-time operation progress data of each vehicle in the operation scenario area, and performs dispatch index analysis of the current vehicle by combining the correlation coefficient between the current operation progress data of the corresponding vehicle and the operation task feature set of other vehicles in the operation scenario area; according to the dispatch index of each vehicle in the current operation scenario area, the dispatch index is sorted in descending order, and the vehicles are dispatched and controlled according to the priority from large to small based on the sorting result, and vehicle dispatch decisions are executed.

[0053] Furthermore, the feedback output module includes an anomaly control feedback unit and a scheduling decision execution unit;

[0054] The anomaly control feedback unit will monitor and report vehicles with abnormal markings inside and outside the work scene area, and output the license plate information and abnormal data of the abnormal vehicles.

[0055] When the work scenario area is in a state of traffic congestion, the scheduling decision execution unit will execute the control and processing of vehicles outside the work scenario area and the scheduling decision of work vehicles within the work scenario area.

[0056] like Figure 2 As shown, the present invention provides another technical solution:

[0057] A vehicle management method based on port traffic, the method comprising the following steps:

[0058] A vehicle monitoring network is constructed to collect vehicle data both inside and outside the work area and to monitor vehicle anomalies.

[0059] Based on the collected data of the vehicles in the work scenario area, the congestion level of the vehicles in the work scenario area is analyzed in real time. Based on the analysis results, vehicles outside the work scenario area are controlled and the work task association analysis is performed on the vehicles in the work scenario area. Combined with the real-time work progress data of each vehicle in the corresponding work scenario area, the work priority of each vehicle in the work scenario area is analyzed. Based on the priority analysis data, the scheduling and processing of each vehicle in the work scenario is carried out.

[0060] Furthermore, a vehicle monitoring network is installed in the work area to monitor vehicles inside and outside the work area and collect vehicle data; the vehicle monitoring network consists of smart cameras and a GPS positioning system; the vehicle data includes vehicle speed data, travel distance data, travel time and speed, etc.

[0061] By monitoring the real-time driving speed of vehicles inside and outside the work area for anomalies, and by setting a safe vehicle driving speed threshold, vehicles exceeding the threshold are marked as abnormal and their license plate information is collected; vehicles below the threshold are not processed; vehicle license plate information is collected by cameras and vehicle location data is determined by GPS positioning.

[0062] Furthermore, the congestion level of the work vehicles in the work scenario area is analyzed based on real-time data collected from the vehicles within the work scenario area. This is achieved by comprehensively acquiring the real-time driving speed data and the distance already traveled by each work vehicle in the work scenario area; calculating the average real-time driving speed data of each work vehicle in the work scenario area based on the average real-time driving speed data of each work vehicle in the work scenario area; summing the distance already traveled by each work vehicle in the work scenario area based on the average distance traveled by each work vehicle in the work scenario area based on the average driving speed data of each work vehicle in the work scenario area based on the sum; and finally, calculating the ratio between the total distance traveled by each work vehicle in the work scenario area and the average driving speed data of each work vehicle in the work scenario area based on the sum, to obtain the integrated work time data of each work vehicle in the work scenario area.

[0063] The system determines the number of vehicles and road area within the current work scenario area, and analyzes the real-time traffic flow of these vehicles. It obtains real-time traffic flow data by calculating the ratio of the number of vehicles to the road area. The system then calculates the traffic congestion index by multiplying the real-time traffic flow data with the integrated work time data of the vehicles within the current work scenario area. A traffic congestion threshold is set within the work scenario area. The system compares the traffic congestion index with this threshold, and based on the comparison results, it manages vehicles outside the work scenario area and performs task-related analysis on vehicles within the work scenario area. If the traffic congestion index is greater than or equal to the threshold, the system determines that the work scenario area is congested, stops the automatic passage of vehicles outside the work scenario area, and performs task-related analysis on vehicles within the work scenario area. Otherwise, the system remains unchanged, without management or task-related analysis.

[0064] Furthermore, when the vehicles operating within the work scenario area are in a state of traffic congestion, a work task correlation analysis is performed on the vehicles within the work scenario area. This involves acquiring the work task data of each vehicle within the work scenario area, extracting the work feature data from the corresponding work task data, and constructing a work task feature set for each vehicle. The work feature data includes vehicle transport destination data, work time window data, and work route data. Correlation analysis is then performed on the work task feature sets of each vehicle within the work scenario area. Specifically, by selecting any two work task feature sets, the correlation values ​​of the corresponding work feature data in each set are analyzed. The analysis is as follows:

[0065] ;

[0066] Wherein, G[i(s),j(s)] corresponds to the correlation value of the operation feature data index s in any two operation task feature sets i and j; i(s) and j(s) respectively correspond to the data of the operation feature data index s in any two operation task feature sets i and j; based on the comprehensive analysis of the correlation values ​​of each operation feature data in any two operation task feature sets, the correlation coefficient of any two operation task feature sets is obtained; the analysis is as follows:

[0067] ;

[0068] Where R(i,j) is the correlation coefficient between any two sets of task features i and j of work vehicles; ws corresponds to the weight of the task feature data sequence s; n is the number of task feature data in the corresponding set of task features of work vehicles; where the weight of each task feature data is set by human experience based on the degree of correlation influence analysis of different task feature data.

[0069] The real-time work progress data of each work vehicle within the work scenario area is determined, and a dispatch index analysis is performed on the current work vehicle by combining the correlation coefficient between the current work progress data of the corresponding work vehicle and the work task feature set of other work vehicles within the work scenario area. The real-time work progress data of the work vehicle includes the planned travel distance, planned travel time, current travel distance, and current travel time. The dispatch index analysis of the current work vehicle is as follows:

[0070] ;

[0071] Where Pi is the dispatch index of the current working vehicle i; dq(i) is the distance traveled by the current working vehicle i; da(i) is the planned distance traveled by the current working vehicle i; tq(i) is the travel time of the current working vehicle i; ta(i) is the planned travel time of the current working vehicle i; m is the number of other working vehicles associated with working vehicle i in the current working scene area; based on the dispatch index of each vehicle in the current working scene area, the dispatch index is sorted in descending order, and the working vehicles are dispatched and controlled according to the priority from largest to smallest according to the sorting result, and the vehicle dispatch decision is executed; the working vehicle dispatch control has a retrieval area and a waiting area. According to the priority, when the number of working vehicles in the retrieval area reaches the upper limit, the remaining vehicles to be retrieved are planned to the waiting area. When the number of vehicles in the waiting area decreases, they are replenished according to the priority; the dispatch decision includes route dispatch, vehicle sorting dispatch, etc.

[0072] Furthermore, the monitoring port will report back vehicles with abnormal markings inside and outside the work area, and output the license plate information and abnormal data of the abnormal vehicles.

[0073] When the work area is in a state of traffic congestion, the system will manage and control vehicles outside the work area and make scheduling decisions for vehicles within the work area.

[0074] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A vehicle management method based on port traffic, characterized in that: The method includes the following steps: A vehicle monitoring network is constructed to collect vehicle data both inside and outside the work area and to monitor vehicle anomalies. Based on the collected data of the vehicles in the work scene area, the congestion level of the vehicles in the work scene area is analyzed in real time. Based on the analysis results, vehicles outside the work scene area are controlled and the work task association analysis is performed on the vehicles in the work scene area. Combined with the real-time work progress data of each vehicle in the corresponding work scene area, the work priority of each vehicle in the work scene area is analyzed. Based on the priority analysis data, the scheduling and processing of each vehicle in the work scene is carried out. When the vehicles operating within the work scenario area are experiencing traffic congestion, a correlation analysis of their work tasks is performed. This involves acquiring work task data for each vehicle within the work scenario area, extracting corresponding work feature data, and constructing a set of work task features for each vehicle. These feature data include vehicle transport destination data, work time window data, and work route data. A correlation analysis is then performed on each set of work task features for each vehicle within the work scenario area. Specifically, by selecting any two sets of work task features, the correlation values ​​of the corresponding work feature data within each set are analyzed. The analysis is as follows: ； Wherein, G[i(s),j(s)] corresponds to the correlation value of the operation feature data index s in any two operation task feature sets i and j; i(s) and j(s) respectively correspond to the data of the operation feature data index s in any two operation task feature sets i and j; based on the comprehensive analysis of the correlation values ​​of each operation feature data in any two operation task feature sets, the correlation coefficient of any two operation task feature sets is obtained; the analysis is as follows: ； Where R(i,j) is the correlation coefficient between any two sets of task features i and j of work vehicles; ws corresponds to the weight of the task feature data sequence s; n is the number of task feature data in the corresponding set of task features of work vehicles; where the weight of each task feature data is set by human experience based on the degree of correlation influence analysis of different task feature data. The real-time work progress data of each work vehicle within the work scenario area is determined, and a dispatch index analysis is performed on the current work vehicle by combining the correlation coefficient between the current work progress data of the corresponding work vehicle and the work task feature set of other work vehicles within the work scenario area. The real-time work progress data of the work vehicle includes the planned travel distance, planned travel time, current travel distance, and current travel time. The dispatch index analysis of the current work vehicle is as follows: ； Where Pi is the dispatch index of the current working vehicle i; dq(i) is the distance traveled by the current working vehicle i; da(i) is the planned distance traveled by the current working vehicle i; tq(i) is the travel time of the current working vehicle i; ta(i) is the planned travel time of the current working vehicle i; m is the number of other working vehicles associated with working vehicle i in the current working scene area; based on the dispatch index of each vehicle in the current working scene area, the dispatch index is sorted in descending order, and the working vehicles are dispatched and controlled according to the priority from large to small according to the sorting result, and the vehicle dispatch decision is executed; the working vehicle dispatch control has a retrieval area and a waiting area. According to the priority, when the number of working vehicles in the retrieval area reaches the upper limit, the remaining vehicles to be retrieved are planned to the waiting area. When the number of vehicles in the waiting area decreases, they are replenished according to the priority; the dispatch decision includes route dispatch and vehicle sorting dispatch.

2. The vehicle control method based on port traffic according to claim 1, characterized in that: By installing a vehicle monitoring network in the work area, vehicles inside and outside the work area can be monitored and vehicle data can be collected. By monitoring the real-time driving speed of vehicles inside and outside the work area for anomalies, and by setting a safe vehicle driving speed threshold, vehicles exceeding the threshold are marked as abnormal, and the license plate information of the corresponding abnormal vehicles is collected.

3. The vehicle control method based on port traffic according to claim 2, characterized in that: The system analyzes the congestion level of work vehicles within the work scenario area based on real-time data collected from the vehicles. It obtains the real-time driving speed data and the distance traveled by each work vehicle within the work scenario area in a coordinated manner. The system calculates the average real-time driving speed data of the work vehicles within the work scenario area by averaging the real-time driving speed data of each work vehicle within the work scenario area. The total distance traveled by each vehicle in the current work scenario area is summed to determine the total distance traveled by the vehicles in the current work scenario area. The ratio between the total distance traveled by the vehicles in the current work scenario area and the average speed of the vehicles in the current work scenario area is calculated to obtain the integrated work time data of the vehicles in the current work scenario area. Determine the number of vehicles and the road area within the current work scenario area, and analyze the real-time traffic flow of vehicles within the current work scenario area; By calculating the ratio of the number of vehicles operating within the current work scenario area to the road area data of the work scenario area, real-time traffic flow data of vehicles operating within the current work scenario area is obtained. The traffic congestion index of vehicles operating within the current work scenario area is determined by multiplying the real-time traffic flow data of vehicles operating within the current work scenario area with the integrated work time data of vehicles operating within the current work scenario area. A traffic congestion threshold for vehicles operating within the work scenario area is set. By comparing the traffic congestion index of vehicles operating within the current work scenario area with the traffic congestion threshold for vehicles operating within the work scenario area, the system manages vehicles outside the work scenario area and performs work task correlation analysis on vehicles operating within the work scenario area based on the comparison results.

4. The vehicle control method based on port traffic according to claim 3, characterized in that: The system will monitor and report any vehicles with abnormal markings inside or outside the work area, and output the license plate information and abnormal data of the abnormal vehicles. When the work area is in a state of traffic congestion, the system will manage and control vehicles outside the work area and make scheduling decisions for vehicles within the work area.

5. A vehicle control system based on port traffic, used to implement the vehicle control method based on port traffic as described in claim 1, characterized in that: The system includes a vehicle monitoring and data acquisition module, a regional congestion analysis module, a regional vehicle dispatching and analysis module, and a feedback output module. The vehicle monitoring and acquisition module constructs a vehicle monitoring network to collect vehicle data both inside and outside the work scene area and to monitor vehicle anomalies. The area congestion analysis module analyzes the real-time congestion level of the work scene area based on the collected data of the work vehicles within the work scene area, and manages vehicles outside the work scene area based on the analysis results. The regional vehicle dispatching and analysis module performs task association analysis on the work vehicles in the work scenario area, and analyzes the work priority of each work vehicle in the work scenario area by combining the real-time work progress data of each work vehicle in the corresponding work scenario area. Based on the priority analysis data, the module dispatches and processes each work vehicle in the work scenario. The feedback output module outputs abnormal vehicle data inside and outside the work scenario area and executes vehicle control and dispatching decisions.

6. A vehicle control system based on port traffic according to claim 5, characterized in that: The vehicle monitoring and acquisition module includes a scene monitoring and acquisition unit and an abnormal vehicle monitoring unit; The scene monitoring and data acquisition unit monitors vehicles inside and outside the work scene area and collects vehicle data by installing a vehicle monitoring network in the work scene area. The abnormal vehicle monitoring unit monitors the real-time driving speed of vehicles inside and outside the work scene area for abnormalities. By setting a safe vehicle driving speed threshold, vehicles exceeding the threshold are marked as abnormal, and the license plate information of the corresponding abnormal vehicles is collected.

7. A vehicle control system based on port traffic according to claim 6, characterized in that: The regional congestion analysis module includes a work vehicle data analysis unit and a regional congestion analysis unit. The operation vehicle data analysis unit analyzes the congestion level of operation vehicles in the operation scene area based on real-time collected data of operation vehicles in the operation scene area. It obtains real-time driving speed data and current distance traveled by each vehicle in the work scene area in a coordinated manner; and calculates the average real-time driving speed data of each vehicle in the work scene area by averaging the real-time driving speed data of each vehicle in the work scene area. The total distance traveled by each vehicle in the current work scenario area is summed to determine the total distance traveled by the vehicles in the current work scenario area. The ratio between the total distance traveled by the vehicles in the current work scenario area and the average speed of the vehicles in the current work scenario area is calculated to obtain the integrated work time data of the vehicles in the current work scenario area. The regional congestion analysis unit determines the number of operating vehicles and the road area data of the current operation scenario area, and analyzes the real-time traffic flow of operating vehicles in the current operation scenario area. By calculating the ratio of the number of vehicles operating within the current work scenario area to the road area data of the work scenario area, real-time traffic flow data of vehicles operating within the current work scenario area is obtained. The traffic congestion index of vehicles operating within the current work scenario area is determined by multiplying the real-time traffic flow data of vehicles operating within the current work scenario area with the integrated work time data of vehicles operating within the current work scenario area. A traffic congestion threshold for vehicles operating within the work scenario area is set. By comparing the traffic congestion index of vehicles operating within the current work scenario area with the traffic congestion threshold for vehicles operating within the work scenario area, the system manages vehicles outside the work scenario area and performs work task correlation analysis on vehicles operating within the work scenario area based on the comparison results.

8. A vehicle control system based on port traffic according to claim 7, characterized in that: The regional vehicle dispatching analysis module includes a regional vehicle correlation analysis unit and a regional vehicle dispatching analysis unit. When the vehicles in the work scenario area are in a state of traffic congestion, the regional vehicle association analysis unit performs work task association analysis on the vehicles in the work scenario area. By acquiring the work task data of each vehicle in the work scenario area, it extracts the work feature data from the work task data of the corresponding vehicles and constructs the work task feature set of the corresponding vehicles. It then performs association analysis on the work task feature sets of each vehicle in the work scenario area. By selecting any two work task feature sets, it analyzes the association values ​​of the corresponding work feature data in the sets. Based on the comprehensive analysis of the association values ​​of each work feature data in the work task feature sets of any two work vehicles, it obtains the association coefficient of the work task feature sets of any two work vehicles. The regional vehicle dispatch analysis unit determines the real-time operation progress data of each vehicle in the operation scenario area, and performs dispatch index analysis of the current vehicle by combining the correlation coefficient between the current operation progress data of the corresponding vehicle and the operation task feature set of other vehicles in the operation scenario area; according to the dispatch index of each vehicle in the current operation scenario area, the dispatch index is sorted in descending order, and the vehicles are dispatched and controlled according to the priority from large to small based on the sorting result, and vehicle dispatch decisions are executed.

9. A vehicle control system based on port traffic according to claim 8, characterized in that: The feedback output module includes an anomaly control feedback unit and a scheduling decision execution unit; The anomaly control feedback unit will monitor and report vehicles with abnormal markings inside and outside the work scene area, and output the license plate information and abnormal data of the abnormal vehicles. When the work scenario area is in a state of traffic congestion, the scheduling decision execution unit will perform the control and management of vehicles outside the work scenario area and the scheduling decision of work vehicles within the work scenario area.

Images