Highway toll station management system

Abstract

The invention relates to an expressway toll station management system, which is applied to the technical field of expressway toll station management, and the method comprises a vehicle detection module which is used for detecting vehicle information, and the vehicle information comprises traffic flow information and vehicle identity information; the vehicle guiding module is used for guiding the vehicle to enter a target lane based on the vehicle identity information; the vehicle weighing module is used for weighing vehicles entering and exiting the toll station; the vehicle charging module is used for charging the vehicle according to a preset rule and the vehicle identity information; the lane management and control module is used for controlling opening and closing of a lane based on the traffic flow information; and the manual allocation module is used for allocating workers based on the traffic flow information. The method has the effect of improving the management efficiency of the highway toll station.

Description

Technical Field

[0001] This application relates to the technical field of highway toll station management, and particularly to a highway toll station management system. Background Art

[0002] In the current highway toll collection system, although technological innovation never stops, many toll stations are still deeply troubled by the low level of intelligence.

[0003] Most of the current highway toll collection systems still follow the traditional manual operation mode. Staff members are responsible for a series of tasks such as manual monitoring, handling of abnormal situations, and toll collection operations. First, staff members need to rely on visual observation and past experience to judge whether there are abnormal situations. Second, when there are abnormal situations, staff members need to promptly adopt appropriate strategies for abnormal handling based on past experience. Moreover, staff members need to perform operations such as vehicle identification, fee calculation, cash collection, or card swiping for each vehicle passing through the toll station. This process not only requires a large amount of labor costs, but also is extremely prone to errors or delays due to human factors. And during peak hours, vehicle congestion occurs frequently, that is, the management efficiency of current highway toll stations is relatively low. Summary of the Invention

[0004] In order to improve the management efficiency of highway toll stations, this application provides a highway toll station management system.

[0005] This application provides a highway toll station management system, adopting the following technical solutions: A highway toll station management system, comprising: A vehicle detection module, configured to detect vehicle information, where the vehicle information includes traffic flow information and vehicle identity information; A vehicle guidance module, configured to guide the vehicle into the target lane based on the vehicle identity information; A vehicle weighing module, configured to weigh the vehicles entering and leaving the toll station; A vehicle toll collection module, configured to collect tolls from the vehicle according to preset rules and the vehicle identity information; A lane control module, configured to control the opening and closing of the lane based on the traffic flow information; An artificial deployment module, configured to deploy staff members based on the traffic flow information.

[0006] By adopting the above technical solutions, the vehicle detection module performs automated vehicle identification and detection to obtain traffic flow information and vehicle identity information, improving the efficiency of identification and detection; according to the vehicle guidance module, the target lane suitable for vehicle passage can be quickly determined, and the vehicle can be guided to the target lane for passage, improving the vehicle passage efficiency; the vehicle weighing module completes the vehicle weighing process, effectively reducing the situation of overloaded vehicles on the road, thus enhancing road traffic safety; the vehicle toll collection module can quickly and accurately calculate the toll amount, reducing human operation errors; the lane control module can dynamically open or close lanes according to the traffic flow information to cope with the traffic flow changes during peak and off-peak hours, which can significantly reduce the vehicle queuing time and improve the overall passage efficiency; the manual allocation module can adjust the staff configuration in real time according to the traffic flow information, increasing the number of staff during peak hours to cope with the large traffic flow, and appropriately reducing the staff during off-peak hours, thus realizing the efficient utilization of human resources and cost control; in summary, through automated management, the management efficiency of highway toll stations is improved.

[0007] Optionally, the traffic flow information includes current traffic flow data and predicted traffic flow data, and the vehicle detection module is specifically used for: Obtain current traffic flow data and historical vehicle data; Analyze the historical vehicle data to determine the traffic flow correlation between different time periods; Divide the historical vehicle data into multiple historical data combinations according to a preset period; Train a machine learning algorithm based on the traffic flow correlation and the historical data combination to obtain a traffic flow prediction model; Predict the traffic flow based on the current traffic flow data and the traffic flow prediction model to obtain the predicted traffic flow data.

[0008] By adopting the above technical solutions, first analyze the historical vehicle data to determine the traffic flow correlation between different time periods, secondly divide the historical vehicle data into multiple historical data combinations, and finally train a machine learning algorithm through the traffic flow correlation and the historical data combination to obtain a traffic flow prediction model. Compared with directly training the machine learning algorithm with historical vehicle data, the training accuracy is improved, that is, the reliability of the traffic flow prediction model is improved. When the current traffic flow data is detected, the traffic flow is predicted based on the current traffic flow data and the traffic flow prediction model to obtain the predicted traffic flow data, so that appropriate traffic control strategies can be pre-formulated and adopted according to the predicted traffic flow data.

[0009] Optionally, the vehicle detection module is specifically used for: Collect vehicle image data; Identify the license plate number and vehicle type by recognizing the vehicle image data based on a preset image recognition model; Determine whether there is an on-vehicle device on the vehicle that can communicate wirelessly with the lane ETC antenna; Determine the vehicle payment type based on the judgment result; Determine the vehicle identity information based on the vehicle type, the license plate number, and the vehicle payment type.

[0010] By adopting the above technical solution, when detecting a vehicle, the vehicle image data is recognized through a preset image recognition model to obtain the license plate number and vehicle type, and the vehicle payment type is detected through the lane ETC antenna, improving the detection efficiency and accuracy.

[0011] Optionally, the vehicle identity information includes the vehicle type, license plate number, and vehicle payment type. The vehicle guidance module is specifically used for: Judge whether the vehicle is a non-passable vehicle based on the license plate number; If the vehicle is allowed to pass, determine the candidate lanes based on the vehicle type and the vehicle payment type; Obtain the queuing information of each of the candidate lanes; Determine the target lane based on the queuing information; Guide the vehicle into the target lane.

[0012] By adopting the above technical solution, when a vehicle enters the toll station, first judge whether the vehicle is allowed to pass, which can effectively reduce the probability of abnormal vehicle passing. For vehicles allowed to pass, determine the target lane based on the vehicle type, vehicle payment type, and the queuing information of the lane, and the vehicle passes through the target lane, improving the vehicle passing efficiency.

[0013] Optionally, the vehicle weighing module is specifically used for: Weigh the vehicle when it enters the toll station to obtain the first weight; Weigh the vehicle when it leaves the toll station to obtain the second weight; Calculate the difference between the first weight and the second weight; If the difference is not within the preset difference range, generate abnormal information.

[0014] By adopting the above technical solution, the vehicle is weighed both when entering and leaving the toll station, and the two weights are compared, reducing the possibility of weight errors, that is, improving the reliability of vehicle weight monitoring.

[0015] Optionally, the lane control module is specifically used for: Analyze the historical vehicle data to determine the vehicle proportions of different types of lanes; Calculate the predicted traffic sub - data of different types of lanes based on the predicted traffic volume data and the vehicle ratio. Determine the target opening quantity of various types of lanes based on the predicted traffic sub - data. Determine the lane opening and closing strategy based on the target opening quantity and the current opening quantity. Control the opening and closing of lanes based on the lane opening and closing strategy.

[0016] By adopting the above - mentioned technical solution, through analyzing historical vehicle data, the vehicle ratio of different types of lanes is obtained. Thus, combined with the predicted traffic volume data, the target opening quantity of various types of lanes can be predicted in advance, and then the work of lane opening and closing can be completed quickly, improving the efficiency of toll station management.

[0017] Optionally, the manual deployment module is specifically used for: Determine the number of toll collectors based on the target opening quantity of manual toll lanes. Obtain historical abnormal data. Analyze the historical abnormal data to determine the abnormal type and abnormal frequency. Determine the number of maintenance personnel based on the abnormal type and the abnormal frequency. Deploy the staff based on the number of toll collectors and the number of maintenance personnel.

[0018] By adopting the above - mentioned technical solution, the number of toll collectors can be determined according to the target opening quantity of manual toll lanes, and the number of maintenance personnel can be determined by analyzing historical abnormal data. Thus, the work of staff deployment can be completed quickly, improving the efficiency of staff deployment.

[0019] Optionally, the manual deployment module is specifically used for: Obtain the work records of the staff, where the work records include working hours, work type, and service satisfaction. Determine the work level based on the working hours and the service satisfaction. Determine the grade requirements for toll collectors based on the number of toll collectors; determine the grade requirements for maintenance personnel based on the number of maintenance personnel. Deploy the staff based on the work level, the work type, the grade requirements for toll collectors, and the grade requirements for maintenance personnel.

[0020] By adopting the above - mentioned technical solution, the work level is determined through the working hours and service satisfaction of the staff, improving the reliability of the work level. For different numbers of personnel, corresponding grade requirements for personnel are available, improving the reliability of staff deployment.

[0021] Optionally, the system further includes a vehicle control module, specifically for: Monitoring the number of vehicles currently waiting to pay tolls; Determining a vehicle congestion coefficient based on the number of vehicles and the number of available lanes; Determining a rear vehicle prompt message based on the vehicle congestion coefficient; Prompting the rear vehicles based on the rear vehicle prompt message.

[0022] By adopting the above technical solution, the congestion situation of the current toll station can be quickly determined by calculating the vehicle congestion coefficient. When the congestion coefficient is high, a rear vehicle prompt message is generated, so that the rear vehicles can timely take measures such as decelerating or taking a detour, reducing the impact of congestion on the rear vehicles, and at the same time reducing the possibility of further aggravating congestion.

[0023] Optionally, the system further includes an abnormal alarm module, specifically for: Obtaining vehicle monitoring data and toll collection records; Generating an abnormal alarm message based on the vehicle monitoring data and the toll collection records.

[0024] By adopting the above technical solution, when the vehicle monitoring data shows that there are vehicle abnormalities or toll collection abnormalities, an abnormal alarm message is generated, so that the staff can quickly understand the abnormal situation and take abnormal management measures in a timely manner.

[0025] In summary, the present application includes at least one of the following beneficial technical effects: 1. Through the vehicle detection module, automated vehicle identification and detection are carried out to obtain traffic flow information and vehicle identity information, improving the efficiency of identification and detection; according to the vehicle guidance module, the target lane suitable for vehicle passage can be quickly determined, and the vehicle can be guided to the target lane for passage, improving the vehicle passage efficiency; through the vehicle weighing module, the vehicle weighing process is completed, effectively reducing the situation of overloaded vehicles on the road, thus improving road traffic safety; through the vehicle toll collection module, the toll amount can be quickly and accurately calculated, reducing human operation errors; the lane control module can dynamically open or close lanes according to traffic flow information to cope with the traffic flow changes during peak and off-peak periods, which can significantly reduce the vehicle queuing waiting time and improve the overall passage efficiency; the manual allocation module can adjust the staff configuration in real time according to traffic flow information, increasing the number of staff during peak periods to cope with large traffic flows, and appropriately reducing the number of staff during off-peak periods, thus realizing the efficient utilization of human resources and cost control; in summary, through automated management, the management efficiency of highway toll stations is improved; 2. First, analyze the historical vehicle data to determine the traffic flow correlation between different time periods. Secondly, divide the historical vehicle data into multiple historical data combinations. Finally, train the machine learning algorithm through the traffic flow correlation and the historical data combinations to obtain a traffic flow prediction model. Compared with directly training the machine learning algorithm with historical vehicle data, the training accuracy is improved, that is, the reliability of the traffic flow prediction model is improved. When the current traffic flow data is detected, predict the traffic flow based on the current traffic flow data and the traffic flow prediction model to obtain the predicted traffic flow data, so that appropriate traffic control strategies can be formulated and taken in advance according to the predicted traffic flow data; 3. By calculating the vehicle congestion coefficient, the congestion situation of the current toll station can be quickly determined. When the congestion coefficient is high, generate a prompt message for the following vehicles, so that the following vehicles can take measures such as decelerating or taking a detour in time, reducing the impact of congestion on the following vehicles, and at the same time reducing the possibility of further aggravating congestion. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 is a structural block diagram of a highway toll station management system provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0027] The following further describes the present application in detail with reference to the accompanying drawings.

[0028] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Apparently, the described embodiments are some, but not all, of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0029] In addition, the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this article generally represents an "or" relationship between the associated objects before and after, unless otherwise specified.

[0030] As Figure 1 shown, a highway toll station management system includes (modules 101-108): A vehicle detection module 101 for detecting vehicle information.

[0031] Among them, the vehicle information includes traffic flow information and vehicle identity information. The traffic flow information includes current traffic flow data (i.e., the current number of vehicles at the toll station) and predicted traffic flow data (i.e., the future vehicle data at the toll station). The vehicle identity information includes, but is not limited to, vehicle type (e.g., sedan, bus, truck, etc.), license plate number, and vehicle payment type (e.g., ETC automatic payment, manual payment), etc.

[0032] The vehicle detection module 101 is specifically configured to: obtain the current traffic flow data and historical vehicle data; analyze the historical vehicle data to determine the traffic flow correlation between different time periods; divide the historical vehicle data into multiple historical data combinations according to a preset period; train a machine learning algorithm based on the traffic flow correlation and the historical data combinations to obtain a traffic flow prediction model; and predict the traffic flow based on the current traffic flow data and the traffic flow prediction model to obtain the predicted traffic flow data.

[0033] In this embodiment, a traffic flow statistical device for real-time traffic flow statistics is installed in the toll station. The traffic flow statistical device can be a camera, that is, the vehicle image data captured is recognized through a preset image recognition model in the camera to obtain the traffic flow data. The traffic flow statistical device can also be a coil vehicle detector. The current traffic flow data is obtained from the traffic flow statistical device, and the historical vehicle data is obtained from the database. The historical vehicle data is the traffic flow in each historical time period. The time interval of each time period (i.e., the preset period) is set in advance according to the actual situation and is not specifically limited here. The historical vehicle data is analyzed through a data analysis tool to obtain the traffic flow correlation between different time periods, that is, the correlation of the traffic flow in each time period is initially determined. For example, the average traffic flow in the time period from 10:00 to 11:00 is A vehicles more than the average traffic flow in the time period from 9:00 to 10:00, and the average traffic flow in the time period from 11:00 to 12:00 is B times that in the time period from 10:00 to 11:00. Among them, the data analysis tool can be excel, Python, or SQL. The preset image recognition model can be a convolutional neural network model (CNN) or a YOLO object detection model, and the specific model type can be selected according to needs.

[0034] The historical vehicle data is divided into multiple historical data combinations according to a preset period. A machine learning algorithm is trained through the traffic flow correlation and the multiple historical data combinations to obtain a traffic flow prediction model. The current traffic flow data is input into the traffic flow prediction model to predict the traffic flow and obtain the predicted traffic flow data. Among them, the machine learning algorithm can be a decision tree and random forest algorithm, a support vector machine, or a neural network and deep learning algorithm.

[0035] The vehicle detection module 101 is further specifically configured to: collect vehicle image data; identify the vehicle image data based on a preset image recognition model to obtain the license plate number and the vehicle type; determine whether there is an on-vehicle device on the vehicle that can communicate wirelessly with the lane ETC antenna; determine the vehicle payment type based on the judgment result; and determine the vehicle identity information based on the vehicle type, the license plate number, and the vehicle payment type.

[0036] In this embodiment, the camera at the toll station collects vehicle image data, and identifies the vehicle image data through a preset image recognition model to obtain the license plate number and the vehicle type. It is determined whether there is an on-vehicle device on the vehicle to communicate wirelessly with the lane ETC antenna through the lane ETC antenna installed at the toll station. If there is an on-vehicle device on the vehicle that can communicate wirelessly with the lane ETC antenna, the vehicle payment type is ETC automatic payment. If there is no on-vehicle device on the vehicle that can communicate wirelessly with the lane ETC antenna, the vehicle payment type is manual payment. The vehicle type, the license plate number, and the vehicle payment type are jointly determined as the vehicle identity information.

[0037] The vehicle guidance module 102 is used to guide the vehicle into the target lane based on the vehicle identity information.

[0038] The vehicle guidance module 102 is specifically configured to: determine whether the vehicle is a non-passable vehicle based on the license plate number; if the vehicle is allowed to pass, determine the candidate lanes based on the vehicle type and the vehicle payment type; obtain the queuing information of each candidate lane; determine the target lane based on the queuing information; and guide the vehicle into the target lane.

[0039] In this embodiment, the license plate number is compared with the license plate numbers of non-passable vehicles in the database to determine whether the vehicle is a non-passable vehicle. Non-passable vehicles include, for example, vehicles that need to be intercepted as instructed by the traffic management department. Each lane has a corresponding payment method and the vehicle types allowed to pass. If the vehicle is allowed to pass, the lanes with the same vehicle type allowed to pass as the current vehicle allowed to pass and the same payment method as the vehicle payment type are determined as candidate lanes. The vehicle image data of each lane is identified through a preset image recognition model to obtain the queuing information of each candidate lane. The queuing information includes the number of queuing vehicles and the vehicle identity information of the queuing vehicles. The candidate lane with the least number of queuing vehicles is determined as the target lane, and the vehicle is guided into the target lane through voice or in-vehicle navigation equipment.

[0040] The vehicle weighing module 103 is used to weigh the vehicles entering and leaving the toll station.

[0041] For vehicles such as trucks, it is usually necessary to determine whether the weight of the vehicle meets the requirements, so it is necessary to weigh the vehicle.

[0042] The vehicle weighing module 103 is specifically configured to: weigh the vehicle when it enters the toll station to obtain the first weight; weigh the vehicle when it leaves the toll station to obtain the second weight; calculate the difference between the first weight and the second weight; and generate an abnormal message if the difference is not within the preset difference range.

[0043] In this embodiment, weighing devices such as weighbridges are installed at both the entrance and the exit of the toll station. The vehicle needs to be weighed when it enters and leaves the toll station to obtain the first weight and the second weight. If the difference between the first weight and the second weight is not within the preset difference range (the pre-set error range), an abnormal message will be generated. The abnormal message includes vehicle identity information, the first weight, the second weight, and the difference. If the first weight and / or the second weight do not meet the preset weight threshold, an abnormal message will also be generated. Otherwise, there is no abnormality in the vehicle weight.

[0044] The vehicle toll collection module 104 is used to collect tolls from the vehicle according to the preset rules and the vehicle identity information.

[0045] When the vehicle arrives at the toll station, calculate the toll amount of the vehicle according to the preset rules (pre-set, not specifically defined here), and collect the toll by ETC or manual means.

[0046] The lane control module 105 is used to control the opening and closing of the lane based on the traffic flow information.

[0047] The lane control module 105 is specifically configured to: analyze the historical vehicle data to determine the vehicle proportions of different types of lanes; calculate the predicted vehicle flow sub-data of different types of lanes based on the predicted traffic flow data and the vehicle proportions; determine the target opening numbers of various types of lanes based on the predicted vehicle flow sub-data; determine the lane opening and closing strategy based on the target opening numbers and the current opening numbers; and control the opening and closing of the lane based on the lane opening and closing strategy.

[0048] In this embodiment, historical vehicle data of the toll station in the current time period (for example: 9:00 - 10:00) is obtained from the database, and the historical vehicle data is analyzed through a data analysis tool to obtain the vehicle proportion of different types of lanes. The predicted traffic volume sub - data of each type of lane = predicted traffic volume data × the vehicle proportion of this type of lane. The corresponding relationship between the predicted traffic volume sub - data of each type of lane and the target opening quantity is stored in the database. The target opening quantity of various types of lanes is found from the database according to the predicted traffic volume sub - data. The first quantity is the absolute value of the difference between the target opening quantity and the current opening quantity. If the target opening quantity is less than the current opening quantity, the lane opening and closing strategy is to close the first quantity of lanes. If the target opening quantity is greater than the current opening quantity, the lane opening and closing strategy is to open the first quantity of lanes. If the target opening quantity is equal to the current opening quantity, the lane opening and closing strategy is that there is no need to adjust the lane opening quantity. If the lane opening and closing need to be operated by staff, the lane opening and closing strategy is sent to the staff at this time, so that the staff can control the lane opening and closing. If the lane opening and closing do not need to be operated by staff, the lane opening and closing strategy is sent to the intelligent railing at this time to control the intelligent railing to complete the automatic opening and closing of the lane.

[0049] The manual deployment module 106 is used to deploy staff based on the traffic volume information.

[0050] Specifically, the manual deployment module 106 is used to: determine the number of toll collectors based on the target opening quantity of the manual toll lanes; obtain historical abnormal data; analyze the historical abnormal data to determine the abnormal type and the abnormal frequency; determine the number of maintenance staff based on the abnormal type and the abnormal frequency; and deploy staff based on the number of toll collectors and the number of maintenance staff.

[0051] In this embodiment, the number of unit staff required for a manual toll lane is obtained from the staff or the database. The number of toll collectors required for all manual toll lanes = the target opening quantity of the manual toll lanes × the number of unit staff. Historical abnormal data is obtained from the database, and the historical abnormal data is analyzed through a data analysis tool to determine the abnormal type and the abnormal frequency of each abnormal type. The target number of maintenance staff required for each type of abnormality during abnormality handling is different. The target number of maintenance staff required for each type of abnormality is obtained from the database according to the abnormal type. The candidate number of maintenance staff for each abnormal type = the target number of maintenance staff for this abnormal type × the abnormal frequency. The currently required number of maintenance staff is the largest candidate number of maintenance staff. When arranging the staff on duty, appropriate toll collectors are arranged according to the number of toll collectors, and appropriate maintenance staff are arranged according to the number of maintenance staff.

[0052] The manual deployment module 106 is specifically configured to: obtain the work records of the staff, where the work records include working hours, work types, and service satisfaction; determine the work levels based on the working hours and service satisfaction; determine the grade requirements for toll collectors based on the number of toll collectors; determine the grade requirements for maintenance personnel based on the number of maintenance personnel; and deploy the staff based on the work levels, work types, grade requirements for toll collectors, and grade requirements for maintenance personnel.

[0053] In this embodiment, the work records of the staff are obtained from the database, that is, the working hours, work types (such as toll collection types, maintenance types, etc.), and service satisfaction of each staff member. The database stores the corresponding relationship between working hours, service satisfaction, and work levels. The work levels of each staff member are obtained from the database according to the working hours and service satisfaction. The required work level combinations are different according to the different numbers of toll (maintenance) personnel in the same shift. For example, if the number of toll collectors is 3, the work level combination is one first work level, one second work level, and one third work level. The grade requirements for toll (maintenance) personnel, that is, the required work level combination, are obtained from the database according to the number of toll (maintenance) personnel. The staff are deployed according to the work levels, work types, grade requirements for toll collectors, and grade requirements for maintenance personnel of the staff.

[0054] The system further includes a vehicle control module 107, which is specifically configured to: monitor the number of vehicles waiting to pay tolls currently; determine the vehicle congestion coefficient based on the number of vehicles and the number of passable lanes; determine the rear vehicle prompt information based on the vehicle congestion coefficient; and prompt the rear vehicles based on the rear vehicle prompt information.

[0055] In this embodiment, the number of vehicles waiting to pay tolls currently is obtained from the traffic statistics device installed at the toll station. The vehicle congestion coefficient = the number of vehicles / the total number of passable lanes. If the vehicle congestion coefficient is greater than the preset congestion coefficient (pre-set, not specifically limited here), the rear vehicle prompt information is generated. The rear vehicle prompt information is, for example: "The toll station section ahead is congested. Please slow down." The rear vehicle prompt information is sent to the rear vehicles through voice or in-vehicle navigation equipment for prompting. If the vehicle congestion coefficient is less than or equal to the preset congestion coefficient, there is no need to generate the rear vehicle prompt information.

[0056] The system further includes an abnormal alarm module 108, which is specifically configured to: obtain vehicle monitoring data and toll collection records; and generate abnormal alarm information based on the vehicle monitoring data and toll collection records.

[0057] In this embodiment, vehicle image data of the toll station is obtained through a camera installed at the toll station, and the vehicle image data is recognized through a preset image recognition model to determine whether there are abnormal vehicles. Abnormal vehicles include, for example, vehicles with inconsistent vehicle types and license plate numbers, toll evasion vehicles, etc. The toll records of the vehicles are verified to determine whether there are abnormal situations such as failed payment and incorrect payment amounts. If there are abnormal vehicles and / or abnormal situations, abnormal alarm information is generated to prompt the staff to handle it in a timely manner.

[0058] The term "including", "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article or device including a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device.

[0059] The above description is only a preferred embodiment of the present application and an explanation of the applied technical principle. Those skilled in the art should understand that the scope of the application involved in the present application is not limited to the technical solution formed by the specific combination of the above technical features, and should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the foregoing application concept. For example, a technical solution formed by mutually replacing the above features with technical features having similar functions (but not limited to) applied in the present application.

Claims

1. A highway toll station management system, characterized in that, Including: A vehicle detection module, used to detect vehicle information, where the vehicle information includes traffic flow information and vehicle identity information; A vehicle guidance module, used to guide a vehicle into a target lane based on the vehicle identity information; A vehicle weighing module, used to weigh vehicles entering and leaving a toll station; A vehicle toll collection module, used to collect tolls from vehicles according to preset rules and the vehicle identity information; A lane control module, used to control the opening and closing of lanes based on the traffic flow information; An artificial deployment module, used to deploy staff based on the traffic flow information.

2. The system according to claim 1, characterized in that, The traffic flow information includes current traffic flow data and predicted traffic flow data, and the vehicle detection module is specifically used for: Obtaining current traffic flow data and historical vehicle data; Analyzing the historical vehicle data to determine the traffic flow correlation between different time periods; Dividing the historical vehicle data into multiple historical data combinations according to a preset period; Training a machine learning algorithm based on the traffic flow correlation and the historical data combinations to obtain a traffic flow prediction model; Predicting the traffic flow based on the current traffic flow data and the traffic flow prediction model to obtain the predicted traffic flow data.

3. The system according to claim 1, characterized in that The vehicle detection module is specifically used for: Collecting vehicle image data; Identifying the vehicle image data based on a preset image recognition model to obtain the license plate number and vehicle type; Judging whether there is an on-vehicle device on the vehicle that can communicate wirelessly with the lane ETC antenna; Determining the vehicle payment type based on the judgment result; Determining the vehicle identity information based on the vehicle type, the license plate number, and the vehicle payment type.

4. The system according to claim 1, wherein The vehicle identity information includes vehicle type, license plate number, and vehicle payment type, and the vehicle guidance module is specifically used for: Judging whether a vehicle is a non-passable vehicle based on the license plate number; If the vehicle is allowed to pass, determining candidate lanes based on the vehicle type and the vehicle payment type; Obtaining the queuing information of each candidate lane; Determining the target lane based on the queuing information; Guiding the vehicle into the target lane.

5. The system according to claim 1, wherein The vehicle weighing module is specifically used for: Weighing a vehicle when it enters a toll station to obtain a first weight; Weighing the vehicle when it leaves the toll station to obtain a second weight; Calculating the difference between the first weight and the second weight; If the difference is not within a preset difference range, generating abnormal information.

6. The system according to claim 2, wherein The lane control module is specifically used for: Analyzing the historical vehicle data to determine the vehicle proportion of different types of lanes; Calculating predicted traffic flow sub-data of different types of lanes based on the predicted traffic flow data and the vehicle proportion; Determining the target opening quantity of various types of lanes based on the predicted traffic flow sub-data; Determining a lane opening and closing strategy based on the target opening quantity and the current opening quantity; Controlling the opening and closing of lanes based on the lane opening and closing strategy.

7. The system according to claim 6, wherein The artificial deployment module is specifically used for: Determining the number of toll collectors based on the target opening quantity of manual toll lanes; Obtaining historical abnormal data; Analyzing the historical abnormal data to determine the abnormal type and abnormal frequency; Determine the number of maintenance personnel based on the abnormal type and the abnormal frequency; Dispatch the staff based on the number of toll collectors and the number of maintenance personnel.

8. The system according to claim 7, wherein The manual dispatching module is specifically used for: Obtain the work records of the staff, where the work records include working hours, work type, and service satisfaction; Determine the work level based on the working hours and the service satisfaction; Determine the level requirements for toll collectors based on the number of toll collectors; determine the level requirements for maintenance personnel based on the number of maintenance personnel; Dispatch the staff based on the work level, the work type, the level requirements for toll collectors, and the level requirements for maintenance personnel.

9. The system according to claim 1, characterized in that The system further includes a vehicle control module, which is specifically used for: Monitor the number of vehicles waiting to pay tolls currently; Determine the vehicle congestion coefficient based on the number of vehicles and the number of passable lanes; Determine the rear vehicle prompt information based on the vehicle congestion coefficient; Prompt the rear vehicles based on the rear vehicle prompt information.

10. The system according to claim 1, wherein, The system further includes an abnormal alarm module, which is specifically used for: Obtain vehicle monitoring data and toll collection records; Generate abnormal alarm information based on the vehicle monitoring data and the toll collection records.

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