Internet of Things-based transportation vehicle reservation management and scheduling system and method

Abstract

The present invention discloses a reservation management and scheduling system and method for transportation vehicles based on the Internet of Things, belonging to the technical field of transportation management. Through intelligent scheduling optimization and task matching, the present invention improves the scheduling efficiency of transportation vehicles, reduces the empty driving rate and resource waste. Based on the vehicle status information collected in real time, historical transportation data and current road traffic conditions, the optimal driving route and scheduling plan are generated, which can automatically match the most suitable vehicle to ensure that the transportation task can be completed efficiently within the shortest time. Users can accurately reserve transportation resources, reduce resource idleness, and improve the overall transportation efficiency. The running status of the vehicle is collected in real time through the Internet of Things technology, and intelligent matching is carried out in combination with task requirements. The most suitable vehicle for executing the task is selected based on transportation requirements, and the matching plan is optimized to ensure the accuracy and rationality of task allocation. It can achieve automated and precise task matching and improve the execution success rate of transportation tasks.

Description

Technical Field

[0001] The present invention relates to the technical field of transportation management, and particularly to a transportation vehicle reservation management and scheduling system and method based on the Internet of Things. Background Art

[0002] In the existing transportation industry, the scheduling management of transportation vehicles mostly relies on manual experience or a system based on simple logic rules for task allocation and route arrangement. This mode is stretched thin when facing a complex and changeable transportation environment. Specifically, most current scheduling systems lack the ability to perceive the entire transportation process in real time, and are unable to collect and process key parameters such as the location, status, running trajectory, and fuel consumption of transportation vehicles with high frequency and high precision, resulting in lagging scheduling instructions or untimely responses, and it is difficult to meet the high standards of the modern logistics system for transportation efficiency and reliability.

[0003] In addition, some existing scheduling platforms using information technology means can initially achieve vehicle reservation and basic route recommendation functions, but there are still obvious shortcomings in the level of intelligence. For example, the system fails to effectively integrate Internet of Things technology, cannot dynamically evaluate the comprehensive transportation performance of vehicles, lacks the ability to analyze the fusion of historical task data and real-time traffic conditions, and thus has great limitations in task matching and route optimization. Most platforms still mainly adopt the "task-driven" method, that is, first determine the task and then manually allocate vehicles, ignoring key factors such as the current running state, response ability, and historical service performance of vehicles, which in turn leads to unreasonable vehicle scheduling arrangements, low resource utilization efficiency, and even phenomena such as repeated vehicle dispatch and task conflicts.

[0004] In addition, existing technologies often rely on manual ad hoc processing when facing emergencies such as vehicle failures, traffic jams, and route deviations, and lack a systematic anomaly detection and response mechanism. Especially during peak hours or when multiple tasks are executed concurrently, the system cannot quickly make adjustments and reallocations, resulting in delays in transportation tasks, decreased customer satisfaction, and in severe cases, even transportation interruptions or safety risks. Summary of the Invention

[0005] The purpose of the present invention is to provide a transportation vehicle reservation management and scheduling system and method based on the Internet of Things to solve the problems raised in the above background art.

[0006] To achieve the above purpose, the present invention provides the following technical solution: A transportation vehicle reservation management and scheduling system based on the Internet of Things, including: a vehicle information collection module, a reservation management module, a scheduling optimization module, a task matching module, and an exception handling module;

[0007] A vehicle information collection module, configured to collect the operating status information of transport vehicles in real time; a reservation management module, configured to process users' vehicle reservation requests and allocate resources by combining vehicle status and reservation requests; a scheduling optimization module, configured to generate an optimal vehicle scheduling plan based on the real-time collected data and vehicle reservation situations; a task matching module, configured to perform intelligent matching based on vehicle status and task requirements; an exception handling module, configured to handle exception situations in case of emergencies.

[0008] Furthermore, the vehicle information collection module obtains the real-time position data of the vehicle through GPS and Internet of Things sensor devices, obtains the operating status parameters of the vehicle, the operating status parameters include load status, fuel level, driving speed and historical trip data, determines whether the vehicle is currently in a task execution state, and records the start time, estimated end time and task completion status of the task.

[0009] Furthermore, the reservation management module includes:

[0010] A reservation processing unit, configured to receive the vehicle reservation request submitted by the user, the reservation request includes reservation time, transportation requirements, departure location and destination, parse the reservation request information and pre-approve the reservation request;

[0011] A resource allocation unit, configured to combine the vehicle reservation request, based on the real-time operating status parameters provided by the vehicle information collection module and the task execution status of the vehicle, interact with the scheduling optimization module and the task matching module, obtain the optimal matching plan, lock the vehicle resources based on the optimal matching plan, and generate reservation confirmation information, the reservation confirmation information includes the basic information of the reserved vehicle, the task execution time and the scheduling arrangement, and send the reservation confirmation information to the user interaction module.

[0012] Furthermore, the reservation management module is also used for:

[0013] Obtain the set of historical operating status parameters of each vehicle in the vehicle information collection module during the effective operation cycle;

[0014] Integrate the real-time operating status parameters and the set of historical operating status parameters to obtain the comprehensive operating status set of each vehicle;

[0015] Classify the operating status parameters in the comprehensive operating status set according to different parameter types to obtain a classified operating status set;

[0016] Sort the classified operating status subsets corresponding to each type of parameter in the classified operating status set according to the time series characteristics, and input them into the same coordinate system based on the sorting results, and perform curve fitting to obtain the vehicle transportation status curve set of the current vehicle;

[0017] Analyze the curve volatility of each state curve in the vehicle transportation state curve set to obtain the comprehensive transportation performance of the current vehicle;

[0018] Combine the comprehensive transportation performance of each vehicle with the task execution status of the vehicle, and then interact with the scheduling optimization module and the task matching module to obtain an initial matching plan;

[0019] Obtain the user satisfaction of each vehicle during the historical transportation process, and then combine the user satisfaction to adjust the vehicle matching results in the initial matching plan to obtain an optimized matching plan;

[0020] Obtain the request response time and request response consent degree of each vehicle for vehicle reservation requests during the effective operation cycle to obtain the request impact factor of each vehicle;

[0021] Determine the request impact weight of the request impact factor based on the historical impact degree of the request impact factor of each vehicle on the vehicle matching plan during the effective operation cycle;

[0022] Combine the request impact factor of the vehicle and the corresponding request impact weight to optimize the optimized matching plan again to obtain the optimal matching plan.

[0023] Furthermore, the scheduling optimization module includes:

[0024] The data analysis unit is configured to obtain and store historical transportation task data. The historical transportation task data includes task execution time, vehicle driving route, transportation efficiency, and abnormal situation records, analyze the vehicle driving route, and combine road traffic information to evaluate the traffic conditions at different time periods and different routes, and identify efficient transportation routes;

[0025] Monitor the current task load situation, where the task load situation includes the number of tasks to be executed, the execution progress of the assigned tasks, and the usage of transportation resources;

[0026] The path optimization unit is configured to collect the current road traffic information. The road traffic information includes road congestion conditions, construction sections, and accident information, and combine the historical traffic information to evaluate the road traffic status of each route;

[0027] According to the current location, destination, task urgency, and road traffic status of the vehicle, calculate the optimal driving route. The optimal driving route is calculated based on the principles of the shortest path, optimal task matching, and minimum empty driving rate; generate the optimal vehicle scheduling plan based on the optimal driving route and send it to the relevant vehicles.

[0028] Furthermore, the task matching module includes:

[0029] A task analysis unit, configured to receive and analyze transportation requirements, where the transportation requirements include task type, departure location, destination location, cargo type, cargo weight, time requirements, and priority information, and set constraint conditions for matching tasks according to the time requirements and priority information of the transportation tasks;

[0030] A vehicle screening unit, configured to receive the transportation requirement data provided by the task analysis unit, interact with the vehicle information collection module to obtain the real-time operating status information of the vehicles, screen available vehicles that meet the load capacity, normal operating status, and are not in the task execution, failure, or maintenance status according to the transportation requirements, and combine the current location and historical driving trajectories of the vehicles to preliminarily screen candidate vehicles that are closer and suitable for task execution, and generate a list of candidate vehicles;

[0031] A matching calculation unit, configured to calculate optional matching schemes for tasks and vehicles based on the status information of the vehicles in the candidate vehicle list, based on the shortest path and the optimal task matching principle, and optimize the results of the optional matching schemes in combination with the optimal vehicle scheduling scheme provided by the scheduling optimization module;

[0032] In the optional matching schemes, select vehicles with short driving distances, high task execution efficiency, and low fuel consumption to generate an optimal matching scheme;

[0033] A matching confirmation unit, configured to receive the optimal matching scheme provided by the matching calculation unit, confirm the final task allocation scheme in combination with the real-time road traffic status and the urgency of the task, and record the task execution status; during the execution of the matching scheme, continuously monitor the real-time operating status information of the vehicles, and when vehicle failures, route deviations, or sudden traffic conditions occur, interact with the exception handling module to adjust the task matching scheme;

[0034] Send a task execution instruction to the vehicles that have successfully matched based on the final task allocation scheme, where the task execution instruction includes task details, departure location, destination location, optimal driving route, and estimated completion time, and generate a task matching result.

[0035] Furthermore, the matching confirmation unit is also used for:

[0036] Determine the importance of vehicle driving distance, task execution efficiency, and fuel consumption for the current task in combination with the task requirements, so as to determine the vehicle impact weights of vehicle driving distance, task execution efficiency, and fuel consumption;

[0037] Based on the vehicle impact weights of driving distance, task execution efficiency, and fuel consumption, combined with the vehicle driving distance, task execution efficiency, and fuel consumption, determine a first optimization factor for optimizing the optimal matching scheme;

[0038] Obtain the road surface condition when the vehicle arrives at the task departure location in the optimal matching plan, and determine the first vehicle driving condition corresponding to the task departure location of the vehicle by combining the real-time road traffic condition when the vehicle arrives at the task departure point;

[0039] Based on the preset departure time and estimated completion time when the vehicle travels from the task departure location to the destination location in the optimal matching plan, screen the historical road traffic conditions with the same time interval from the set of historical road traffic conditions to obtain the optimal set of historical road traffic conditions;

[0040] Input the real-time road traffic condition from the task departure location to the destination location and each historical road traffic condition in the optimal set of historical road traffic conditions into the preset road traffic prediction model, so as to predict the road traffic condition when the vehicle travels from the task departure point to the destination location and obtain the predicted road traffic condition;

[0041] Obtain the road surface condition of the optimal driving route when the vehicle travels from the task departure point to the destination location, and determine the second vehicle driving condition during the vehicle's task process by combining the predicted road traffic condition;

[0042] Based on the first vehicle driving condition and the second vehicle driving condition, and combining the vehicle weather influence coefficient, determine the second optimization factor for optimizing the optimal matching plan;

[0043] Obtain the task urgency of the current task, and optimize the optimal matching plan by combining the first optimization factor and the second optimization factor to determine the initial task allocation plan for the current vehicle to execute the task;

[0044] Simulate the initial task allocation plan to obtain the plan decision score of the simulated plan, and use the initial task allocation plan with a plan decision score higher than the preset minimum decision score as the final task allocation plan.

[0045] Furthermore, the exception handling module includes:

[0046] An exception detection unit configured to receive and analyze the real-time operation status data provided by the vehicle information collection module, and set exception situation judgment conditions, where the exception situations include vehicle failures, route deviations, sudden traffic conditions, and abnormal task executions;

[0047] When an exception situation is detected, record the exception event data, where the exception event data includes the exception occurrence time, exception type, and influence range;

[0048] An emergency scheduling unit configured to receive the exception event data and execute corresponding emergency handling strategies according to different types of exception situations;

[0049] When a vehicle breakdown occurs, determine whether the faulty vehicle can continue to perform the task. If it cannot, obtain alternative vehicle resources from the task matching module, select the optimal alternative vehicle, and send an emergency dispatch instruction.

[0050] When a route deviation occurs, calculate the deviation degree of the current vehicle and combine it with the road traffic status provided by the dispatch optimization module to generate an adjusted driving path, and send a path adjustment instruction to the abnormal vehicle.

[0051] When an unexpected traffic situation occurs, combine the real-time road traffic status provided by the dispatch optimization module, recalculate the task execution path, and generate a new optimal driving plan. Based on the optimal driving plan, send a new driving instruction to the affected vehicles.

[0052] When an abnormal task execution occurs, if the vehicle is detained for a long time or fails to complete the task as planned, send a task status confirmation instruction to the vehicle to confirm the reason for detention, and adjust the task execution plan in combination with the task matching module and the dispatch optimization module.

[0053] Record the abnormal task execution situation, and update the task execution status after the abnormal situation is handled.

[0054] Furthermore, when a route deviation occurs, calculate the deviation degree of the current vehicle and combine it with the road traffic status provided by the dispatch optimization module to generate an adjusted driving path, and send a path adjustment instruction to the abnormal vehicle, including:

[0055] When a route deviation occurs, obtain the real-time vehicle status parameters of the vehicle based on the preset vehicle sensors.

[0056] Among them, the real-time vehicle status parameters include the lateral position of the real-time vehicle position, the current heading angle, the real-time vehicle speed, and the lateral distance from the current vehicle position to the standard lane line.

[0057] Based on the lateral position and the current heading angle in the real-time vehicle position, combined with the lateral coordinate and the tangent direction angle of the optimal driving path at the vehicle position, determine the vehicle deviation index.

[0058] Based on the current heading angle, the real-time vehicle speed, and the lateral distance from the current vehicle position to the standard lane line, determine the time warning index.

[0059] Combine the vehicle deviation index and the time warning index to comprehensively calculate the deviation coefficient of the current vehicle, so as to determine the deviation degree of the current vehicle.

[0060] According to the deviation degree of the current vehicle and the road traffic status provided by the dispatch optimization module, generate an adjusted driving path, and send a path adjustment instruction to the abnormal vehicle.

[0061] Furthermore, the transportation vehicle reservation management and scheduling system based on the Internet of Things further includes a user interaction module configured to provide functions of reservation submission, modification, and cancellation, display vehicle status, task progress, and estimated arrival time, and receive and store user feedback information.

[0062] Furthermore, the transportation vehicle reservation management and scheduling method based on the Internet of Things is applied to the above-mentioned transportation vehicle reservation management and scheduling system based on the Internet of Things, and includes the following steps:

[0063] Step 1: Through the vehicle information collection module, use GPS and Internet of Things sensors to obtain real-time position data, load status, fuel level, driving speed, and historical itinerary data of the transportation vehicle, determine whether the vehicle is in a task execution state, and record the start time, estimated end time, and task completion status of the task;

[0064] Step 2: The user submits a vehicle reservation request through the user interaction module. The reservation management module parses the reservation request, pre-audits the reservation request in combination with the vehicle operation status parameters provided by the vehicle information collection module, combines the scheduling optimization module and the task matching module to obtain the optimal matching plan, locks the vehicle resources, and generates a reservation confirmation message and sends it to the user interaction module;

[0065] Step 3: The scheduling optimization module obtains and stores historical transportation task data, evaluates efficient transportation routes in combination with road traffic information, calculates the optimal driving route based on the vehicle's current position, destination, task urgency, and road traffic status, and generates an optimal vehicle scheduling plan based on the optimal driving route and sends a scheduling instruction to the relevant vehicle;

[0066] Step 4: The task matching module receives and analyzes the transportation demand, sets task matching constraint conditions, and filters available vehicles that meet the load capacity and normal operation status in combination with the vehicle operation status data, calculates the optimal matching plan, and sends a task execution instruction to the successfully matched vehicles;

[0067] Step 5: The exception handling module monitors the vehicle operation status in real time, identifies abnormal situations, executes emergency scheduling strategies according to the abnormal types, and sends an exception notification to the user interaction module. After completing the exception handling, update the task execution status.

[0068] Compared with the prior art, the beneficial effects of the present invention are:

[0069] 1. The present invention improves the dispatching efficiency of transport vehicles, reduces the empty driving rate and resource waste through intelligent dispatching optimization and task matching. It generates the optimal driving route and dispatching plan based on real-time collected vehicle status information, historical transportation data and current road traffic conditions. Compared with the traditional manual dispatching method, it can automatically match the most suitable vehicle to ensure that the transportation task can be completed efficiently in the shortest time, so that users can accurately reserve transportation resources, reduce idle resources and improve overall transportation efficiency.

[0070] 2. The present invention uses the Internet of Things technology to collect the operating status of vehicles in real time, and performs intelligent matching in combination with task requirements. Based on the real-time location, operating status and transportation requirements of the vehicles, the most suitable vehicles for performing the tasks are screened out, and the matching scheme is optimized to ensure the accuracy and rationality of task allocation. It can realize automated and precise task matching and improve the success rate of transportation task execution.

[0071] 3. The present invention can monitor the vehicle status in real time by automatically detecting abnormal situations such as vehicle failure, route deviation, sudden traffic conditions, and taking countermeasures quickly, and automatically adjust the scheduling plan when an abnormality occurs, thereby ensuring the stable execution of transportation tasks, reducing task delays, and improving the reliability and adaptability of the overall system. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 It is a schematic diagram of the transport vehicle reservation management and scheduling system module of the present invention;

[0073] Figure 2 It is a flow chart of the method for transport vehicle reservation management and scheduling of the present invention. DETAILED DESCRIPTION

[0074] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0075] See also Figure 1 , the present invention provides the following technical solutions:

[0076] The transportation vehicle reservation management and dispatching system based on the Internet of Things includes: vehicle information collection module, reservation management module, dispatch optimization module, user interaction module, task matching module and exception handling module;

[0077] A vehicle information collection module, configured to collect the operating status information of transport vehicles in real time; a reservation management module, configured to process users' vehicle reservation requests and allocate resources by combining vehicle status and reservation requests; a scheduling optimization module, configured to generate an optimal vehicle scheduling plan based on the real-time collected data and vehicle reservation situations; a task matching module, configured to perform intelligent matching based on vehicle status and task requirements; an exception handling module, configured to handle exceptions in case of emergencies; a user interaction module, configured to provide functions for reservation submission, modification, and cancellation, display vehicle status, task progress, and estimated arrival time, and receive and store user feedback information.

[0078] The vehicle information collection module obtains the real-time position data of the vehicle through GPS and Internet of Things sensor devices, obtains the operating status parameters of the vehicle, and the operating status parameters include load status, fuel level, driving speed, and historical trip data, determines whether the vehicle is currently in a task execution state, and records the start time, estimated end time, and task completion status of the task.

[0079] In the above embodiment, the introduction of the vehicle information collection module greatly improves the real-time monitoring ability of transport vehicles. Through the combination of GPS and Internet of Things sensors, the system can accurately obtain the real-time position, load status, fuel level, driving speed, and historical trip data of the vehicle. The collection of these information not only helps to evaluate the current operating status of the vehicle, but also effectively supports subsequent scheduling optimization and task matching. It can automatically determine whether the vehicle is in a task execution state, record the start time, estimated end time, and task completion status of the task, and realizes full-process automatic monitoring through Internet of Things technology, making the vehicle status information more reliable and efficient.

[0080] The reservation management module includes:

[0081] A reservation processing unit, configured to receive the vehicle reservation request submitted by the user. The reservation request includes reservation time, transportation requirements, departure location, and destination location, parse the reservation request information, and pre-approve the reservation request;

[0082] A resource allocation unit, configured to combine the vehicle reservation request, based on the real-time operating status parameters provided by the vehicle information collection module and the task execution status of the vehicle, interact with the scheduling optimization module and the task matching module, obtain the optimal matching plan, lock the vehicle resources based on the optimal matching plan, and generate reservation confirmation information. The reservation confirmation information includes the basic information of the reserved vehicle, task execution time, and scheduling arrangement, and send the reservation confirmation information to the user interaction module.

[0083] In the above embodiments, the introduction of the reservation management module optimizes the reservation process of transportation vehicles, enabling users to reserve transportation resources more efficiently. By automatically parsing users' reservation requests, including key information such as reservation time, transportation requirements, departure location, and destination location, it can automatically review the reasonableness of the reservation requests to ensure the integrity and accuracy of the reservation information. In addition, the system can also conduct preliminary screening for specific transportation requirements, such as recommending appropriate vehicle types based on the type of goods to avoid resource waste or mismatches in vehicle specifications. Based on the real-time collected vehicle operation status data for optimal matching, it interacts with the scheduling optimization module and the task matching module to ensure the rationality of resource allocation, significantly shortening the reservation processing time, improving the reservation response speed, and reducing the need for manual intervention. Meanwhile, the function of generating and automatically sending reservation confirmation information enables users to obtain reservation results in real time, enhancing the user experience.

[0084] The reservation management module is further configured to:

[0085] Obtain the set of historical operation status parameters of each vehicle in the vehicle information collection module during the effective operation cycle;

[0086] Integrate the real-time operation status parameters with the set of historical operation status parameters to obtain the comprehensive operation status set of each vehicle;

[0087] Classify the operation status parameters in the comprehensive operation status set according to different parameter types to obtain the classified operation status set;

[0088] Sort the classified operation status subsets corresponding to each type of parameter in the classified operation status set according to the time series characteristics, input them into the same coordinate system based on the sorting results, and perform curve fitting to obtain the set of vehicle transportation status curves of the current vehicle;

[0089] Analyze the curve volatility of each status curve in the set of vehicle transportation status curves to obtain the comprehensive transportation performance of the current vehicle;

[0090] Combine the comprehensive transportation performance of each vehicle with the task execution status of the vehicle, and thus interact with the scheduling optimization module and the task matching module to obtain an initial matching plan;

[0091] Obtain the user satisfaction of each vehicle during the historical transportation process, and thus adjust the vehicle matching results in the initial matching plan in combination with the user satisfaction to obtain an optimized matching plan;

[0092] Obtain the request response time and request response approval degree of each vehicle for the vehicle reservation request during the effective operation cycle to obtain the request impact factor of each vehicle;

[0093] Determine the request impact weight of the request impact factor based on the historical impact degree of the request impact factor of each vehicle on the vehicle matching scheme during the effective operation period;

[0094] Combine the request impact factor of the vehicle and the corresponding request impact weight to optimize the matching scheme again to obtain the optimal matching scheme.

[0095] In the above embodiment, the effective operation period refers to the effective time period during which the vehicle is performing tasks or operations.

[0096] In the above embodiment, the historical operation state parameter set refers to the set of all historical vehicle state data or parameters collected by the vehicle information collection module during the effective operation period. It can include speed, acceleration, position, fuel consumption rate, etc.

[0097] In the above embodiment, the real-time operation state parameter refers to the state data or parameters collected in real time by the vehicle information collection module when the vehicle is currently running, including speed, acceleration, position, fuel consumption rate, etc.

[0098] In the above embodiment, the comprehensive operation state set is the vehicle state data set obtained by combining the historical operation state parameters and the real-time operation state parameters.

[0099] In the above embodiment, the classified operation state set is the set obtained by classifying the parameters in the comprehensive operation state set according to the parameter type, where the parameter type includes speed parameters, fuel consumption parameters, etc.

[0100] In the above embodiment, the vehicle transportation state curve set is the curve set of the state curves obtained by sorting the parameters in the classified operation state set in time series, inputting them into the same coordinate system based on the sorting result, and performing curve fitting.

[0101] In the above embodiment, the comprehensive transportation performance is the evaluation of the overall transportation performance of the vehicle obtained by analyzing the volatility of the curves in the vehicle transportation state curve set. Among them, the volatility of each curve is different, and the corresponding performance is also different. The comprehensive transportation performance is obtained by synthesizing the volatility of each curve in the vehicle transportation state curve set. The value range of the curve volatility is (0,1). The larger the value of the curve volatility, the worse the corresponding performance. For example, if the curve volatilities of curves A, B, and C are 0.12, 0.09, and 0.15 respectively, then the comprehensive transportation performance is 1 - (0.12 + 0.09 + 0.15) / 3 = 0.88.

[0102] In the above embodiment, the task execution state includes information such as whether the vehicle is currently performing a task, the type of the task being performed, and the progress.

[0103] In the above embodiments, the initial matching scheme combines the comprehensive transportation performance of each vehicle with the task execution status of the vehicle, and after interacting with the scheduling optimization module and the task matching module, a matching scheme is initially generated based on vehicle and task information.

[0104] In the above embodiments, user satisfaction refers to the satisfaction evaluation of historical users on vehicle performance, transportation services, etc. after using vehicle transportation services. Among them, user satisfaction can be on a five-point scale, a ten-point scale, etc. For example, the user satisfaction is 4 points.

[0105] In the above embodiments, the optimized matching scheme refers to a matching improvement scheme obtained by adjusting the initial matching scheme in combination with user satisfaction. For example, the user satisfaction of vehicle A is 9 points, the user satisfaction of vehicle B is 8 points, and the matching priorities of vehicle A and vehicle B in the initial matching scheme are the same. Then, the matching priority of vehicle A is increased to adjust the initial matching scheme, and the matching priority of vehicle A after adjustment is higher than that of vehicle B.

[0106] In the above embodiments, the request response time refers to the time required for a vehicle to receive a reservation request and respond to the request. For example, the request response time is 1 min.

[0107] In the above embodiments, the request response consent degree is the acceptance degree or consent rate of a vehicle for a reservation request. For example, within the effective operation cycle of a vehicle, the number of times of issuing reservation confirmation information to vehicle A is 10 times, and the number of times vehicle A agrees to the reservation request is 8 times. Then, the request response consent degree of vehicle A is 0.8.

[0108] In the above embodiments, the request impact factor is calculated based on the request response time and the request response consent degree, and is an index used to reflect the ability or efficiency of a vehicle in processing reservation requests.

[0109] In the above embodiments, the request impact weight is a weight determined according to the historical impact degree of the request impact factor on the vehicle matching scheme within the effective operation cycle of the vehicle, and is used to consider the importance of the request impact factor when optimizing the matching scheme. The value range of the request impact weight is (0, 1). For example, the request impact weight can be 0.25.

[0110] In the above embodiments, the optimal matching scheme is the final matching scheme obtained by further adjusting the optimized matching scheme in combination with the request impact factor of the vehicle and the corresponding request impact weight.

[0111] The working principle of the above technical solution is as follows: First, obtain historical operation status parameters to get a set of historical operation status parameters, combine them with real-time operation status parameters for parameter integration, and classify the parameter integration results based on different parameter types. Then, sort the classified status parameters according to time series characteristics, input the sorted results into the same coordinate system for curve fitting to determine the comprehensive transportation performance of the vehicle, thereby determining the initial matching plan in combination with the task execution status. Then, optimize the plan according to the user satisfaction during the vehicle's historical transportation process, and optimize the plan again based on the influence of the reservation request of each vehicle to obtain the optimal matching plan.

[0112] The beneficial effects of the above technical solution are as follows: By combining historical operation status parameters and real-time operation status parameters to determine the comprehensive transportation performance of each vehicle during the task execution process, determining the initial matching plan, and optimizing the plan in combination with user satisfaction and the influence of the vehicle's reservation request, the optimal matching plan can be obtained, which can make the matching plan for vehicles and tasks more accurate, minimize transportation time and costs to the greatest extent, and improve the overall transportation efficiency.

[0113] The scheduling optimization module includes:

[0114] The data analysis unit is configured to obtain and store historical transportation task data. The historical transportation task data includes task execution time, vehicle driving route, transportation efficiency, and abnormal situation records, parse the vehicle driving route and combine it with road traffic information to evaluate the traffic conditions of different time periods and different routes, and identify efficient transportation routes;

[0115] Monitor the current task load situation, where the task load situation includes the number of tasks to be executed, the execution progress of the assigned tasks, and the usage of transportation resources;

[0116] The route optimization unit is configured to collect current road traffic information. The road traffic information includes road congestion conditions, construction sections, and accident information, and combine it with historical traffic information to evaluate the road traffic status of each route;

[0117] According to the vehicle's current location, destination, task urgency, and road traffic status, calculate the optimal driving route. The optimal driving route is calculated based on the principles of the shortest path, optimal task matching, and minimum empty driving rate; generate the optimal vehicle scheduling plan based on the optimal driving route and send it to the relevant vehicles.

[0118] In the above embodiments, through the collaborative work of the data analysis unit and the path optimization unit, the intelligent scheduling of transportation tasks is achieved. By storing and analyzing historical transportation task data, including information such as task execution time, vehicle driving route, transportation efficiency, and abnormal situations, it helps the system identify efficient transportation routes and can also provide optimization references for the scheduling of future tasks. For example, based on historical task data, the system can identify peak hours or congested sections during certain specific time periods, so as to avoid these adverse factors in future scheduling processes and improve transportation efficiency. By real-time collecting current road traffic information, including road congestion conditions, construction sections, and accident information, and combining historical traffic data to evaluate the traffic conditions of different routes. Based on this information, the system can calculate the optimal driving route and generate the optimal vehicle scheduling plan by adopting optimization principles such as the shortest path, optimal task matching, and minimum empty driving rate, which can minimize transportation time and costs and improve the overall transportation efficiency.

[0119] The task matching module includes:

[0120] The task parsing unit is configured to receive and parse transportation requirements, where the transportation requirements include task type, departure location, destination location, cargo type, cargo weight, time requirements, and priority information. According to the time requirements and priority information of the transportation task, set the constraint conditions for matching tasks;

[0121] The vehicle screening unit is configured to receive the transportation requirement data provided by the task parsing unit, interact with the vehicle information collection module to obtain the real-time operation status information of the vehicle, and based on the transportation requirements, screen available vehicles that meet the load capacity, normal operation status, and are not in the task execution, fault, or maintenance status. Combining the current location and historical driving trajectory of the vehicle, preliminarily screen candidate vehicles that are relatively close and suitable for executing the task, and generate a list of candidate vehicles;

[0122] The matching calculation unit is configured to calculate the optional matching solutions for the task and the vehicle based on the status information of the vehicles in the candidate vehicle list, based on the shortest path and the optimal task matching principle, and optimize the results of the optional matching solutions in combination with the optimal vehicle scheduling plan provided by the scheduling optimization module;

[0123] In the optional matching solutions, select vehicles with short driving distances, high task execution efficiency, and low fuel consumption to generate the optimal matching solution;

[0124] The matching confirmation unit is configured to receive the optimal matching solution provided by the matching calculation unit, combine the real-time road traffic conditions and the urgency of the task to confirm the final task allocation plan, and record the task execution status; during the execution of the matching plan, continuously monitor the real-time operation status information of the vehicle. When vehicle failures, route deviations, or sudden traffic conditions occur, interact with the exception handling module to adjust the task matching plan;

[0125] Send a task execution instruction to the successfully matched vehicle based on the final task allocation plan. The task execution instruction includes task details, departure location, destination location, optimal driving route, and estimated completion time, and generate a task matching result.

[0126] In the above embodiments, by analyzing the transportation demand, considering task type, departure location, destination location, cargo type, cargo weight, time requirements, and priority information, the rationality of task matching is ensured. By real-time monitoring the vehicle status, available vehicles that meet the task requirements are screened out, and combined with the historical driving trajectory and current position of the vehicle, the candidate vehicle list is optimized, which not only improves the accuracy of task matching, but also effectively reduces the empty driving rate and improves the utilization rate of transportation resources. Based on the shortest path and optimal task matching principle, the candidate vehicles are calculated and screened to select the vehicle with the highest task execution efficiency and the lowest fuel consumption. In addition, during the task execution process, the running status of the vehicle is continuously monitored, and when an abnormal situation occurs, it interacts with the abnormal handling module to ensure the stability and reliability of task execution, making the execution of transportation tasks smoother and reducing the situation of task delays and resource waste.

[0127] The matching confirmation unit is also used for:

[0128] Determine the importance of vehicle driving distance, task execution efficiency, and fuel consumption for the current task in combination with the task requirements, so as to determine the vehicle impact weights of vehicle driving distance, task execution efficiency, and fuel consumption;

[0129] Based on the vehicle impact weights of driving distance, task execution efficiency, and fuel consumption, combined with the vehicle's driving distance, task execution efficiency, and fuel consumption, determine the first optimization factor for optimizing the optimal matching plan;

[0130] Obtain the road surface status of the vehicle arriving at the task departure location in the optimal matching plan, and combine the real-time road traffic status of the vehicle arriving at the task departure point to determine the first vehicle driving condition corresponding to the vehicle arriving at the task departure point;

[0131] Based on the preset departure time and estimated completion time of the vehicle from the task departure location to the destination location in the optimal matching plan, screen the historical road traffic status with the same time interval from the set of historical road traffic status to obtain the optimal historical road traffic status set;

[0132] Input the real-time road traffic status from the task departure location to the destination location and each historical road traffic status in the optimal historical road traffic status set into a preset road traffic prediction model, so as to predict the road traffic status of the vehicle from the task departure point to the destination location and obtain the predicted road traffic status;

[0133] Obtain the road surface condition of the optimal driving route of the vehicle from the task starting point to the destination point, and combine with the predicted road traffic condition to determine the second vehicle driving condition during the vehicle's task process;

[0134] Based on the first vehicle driving condition and the second vehicle driving condition, combined with the vehicle weather influence coefficient, determine the second optimization factor for optimizing the optimal matching plan;

[0135] Obtain the urgency of the current task, and combine the first optimization factor and the second optimization factor to optimize the optimal matching plan, and determine the initial task allocation plan for the current vehicle to execute the task;

[0136] Conduct a simulation of the initial task allocation plan to obtain the plan decision score of the simulation plan, and use the initial task allocation plan with a plan decision score higher than the preset minimum decision score as the final task allocation plan.

[0137] In the above embodiment, the task requirement refers to the specific requirements of the task that needs to be executed currently, such as task type, task location, task time, etc. The task requirement will affect the importance of factors such as vehicle driving distance, task execution efficiency, and fuel consumption.

[0138] In the above embodiment, the vehicle driving distance refers to the total distance that the vehicle needs to travel from the starting point to the destination point.

[0139] In the above embodiment, the task execution efficiency is determined by the speed and quality of the vehicle to complete the task. It is usually related to factors such as the vehicle's driving speed and task completion time. The value range of the task execution efficiency is [0,1].

[0140] In the above embodiment, the fuel consumption refers to the amount of fuel consumed by the vehicle during driving.

[0141] In the above embodiment, the vehicle influence weight is determined according to the importance degree of factors such as vehicle driving distance, task execution efficiency, and fuel consumption for the current task. The vehicle influence weight is used to consider the importance of different factors in the optimal matching plan. Among them, the value range of the vehicle influence weight is (0,1).

[0142] In the above embodiment, the first optimization factor is an optimization reference situation of the optimal matching plan between the vehicle and the task determined by combining factors such as vehicle driving distance, task execution efficiency, and fuel consumption.

[0143] In the above embodiment, the road surface condition refers to the road surface condition of the road where the vehicle is driving. For example, whether the road surface is flat, whether there are potholes, and the road surface friction condition, etc. The road surface condition will affect the vehicle's driving condition and fuel consumption.

[0144] In the above embodiments, the real-time road traffic state refers to real-time information such as the real-time driving speed and traffic flow of vehicles on the current road. The real-time road traffic state reflects the congestion degree and traffic capacity of the road.

[0145] In the above embodiments, the first vehicle driving condition is the vehicle driving condition determined based on the road surface condition and the real-time road traffic state from the current position to the task departure location. It reflects the driving conditions of the vehicle before starting to execute the task.

[0146] In the above embodiments, the optimal historical road traffic state set is a state set obtained by screening from the historical road traffic state set the historical road traffic states having the same time interval as the preset departure time and the estimated completion time of the vehicle. For example, if the preset departure time is 10 o'clock and the estimated completion time is 6 hours, then the estimated arrival time is 16 o'clock. Then, the historical road traffic states corresponding to the time interval from 10 o'clock to 16 o'clock in the historical road traffic state set are extracted, so as to obtain the optimal historical road traffic state set.

[0147] In the above embodiments, the preset road traffic prediction model is a mathematical model used to predict the future road traffic state. Predictions can be made based on information such as the historical road traffic states in the optimal historical road traffic state set and the real-time road traffic state.

[0148] In the above embodiments, the predicted road traffic state is the result obtained by using the preset road traffic prediction model to predict the road traffic state from the task departure point to the destination point of the vehicle.

[0149] In the above embodiments, the second vehicle driving condition is the vehicle driving condition during the execution of the task determined based on the predicted road traffic state and the road surface condition.

[0150] In the above embodiments, the vehicle weather impact coefficient is a coefficient used to evaluate the impact of weather on the vehicle driving condition. The vehicle weather impact coefficient is adjusted according to weather conditions (such as rainy days, snowy days, etc.) to reflect the impact of weather on the vehicle driving speed and fuel consumption.

[0151] In the above embodiments, the second optimization factor is an optimization reference situation for the optimal matching plan of the vehicle and the task determined by combining the vehicle weather impact coefficient, the first vehicle driving condition, and the second vehicle driving condition.

[0152] In the above embodiments, the task urgency is the degree of importance and urgency of the current task. The task urgency will affect the formulation and optimization of the task assignment plan.

[0153] In the above embodiments, the initial task allocation plan is a preliminary task allocation plan for vehicles and tasks determined after adjusting the optimal matching plan based on the first optimization factor, the second optimization factor, and the task urgency.

[0154] In the above embodiments, the plan simulation is to input the initial task allocation plan into a virtual machine for simulated operation to evaluate the effect of the initial task allocation plan during actual execution, which helps to discover potential problems and optimize them.

[0155] In the above embodiments, the plan decision score is the score of the plan simulation result obtained after evaluating the simulation plan, which is used to reflect the execution effect of the simulation plan during actual execution. It can be evaluated according to multiple task completion indicators (such as task completion time, fuel consumption, etc.). Among them, the value of the plan decision score is .

[0156] In the above embodiments, the preset minimum decision score is the minimum score threshold used to evaluate whether the simulation plan is qualified. Among them, the value range of the preset minimum decision score is (5, 8), and only the initial task allocation plan with a plan decision score higher than the preset minimum decision score can be used as the final task allocation plan.

[0157] The working principle of the above technical solution is as follows: First, determine the vehicle impact weights of vehicle driving distance, task execution efficiency, and fuel consumption by combining the current task requirements to determine the first optimization factor of the optimal matching plan. Then, combine the vehicle weather impact coefficient with the first vehicle driving condition determined by the road surface condition and real-time road traffic condition when the vehicle travels from the current location to the departure location, and the second vehicle driving condition determined by the road surface condition and predicted road traffic condition when the vehicle travels from the departure location to the destination location to obtain the second optimization factor. Next, optimize the plan based on the task urgency, the first optimization factor, and the second optimization factor, and simulate based on the plan optimization result. Finally, determine the plan decision score based on the simulation result. When the plan decision score is higher than the preset minimum decision score, use the plan optimization result as the final task allocation plan to execute the task.

[0158] The beneficial effects of the above technical solution are as follows: By combining task requirements to determine vehicle impact weights, adjusting the optimal matching plan, combining the weather impact coefficient and vehicle driving conditions for plan optimization, and combining task urgency for secondary plan optimization, the obtained optimized plan can be made more consistent with the real-time vehicle conditions, determine the final task allocation plan, thereby improving the accuracy of task matching, making the execution of transportation tasks smoother, and reducing task delays and resource waste.

[0159] An exception handling module, including:

[0160] Anomaly detection unit, configured to receive and analyze real-time operation status data provided by the vehicle information collection module, and set anomaly situation judgment conditions, where the anomaly situations include vehicle failures, route deviations, sudden traffic conditions, and abnormal task executions;

[0161] When an anomaly situation is detected, record the anomaly event data, where the anomaly event data includes the anomaly occurrence time, anomaly type, and impact scope;

[0162] Emergency dispatch unit, configured to receive the anomaly event data and execute corresponding emergency handling strategies according to different types of anomaly situations;

[0163] When a vehicle failure occurs, determine whether the faulty vehicle can continue to execute the task. If it cannot, obtain alternative vehicle resources from the task matching module, select the optimal alternative vehicle, and send an emergency dispatch instruction;

[0164] When a route deviation occurs, calculate the deviation degree of the current vehicle combined with the road traffic status provided by the dispatch optimization module, generate an adjusted driving path, and send a path adjustment instruction to the abnormal vehicle;

[0165] When a sudden traffic condition occurs, recompute the task execution path combined with the real-time road traffic status provided by the dispatch optimization module, generate a new optimal driving plan, and send a new driving instruction to the affected vehicles based on the optimal driving plan;

[0166] When an abnormal task execution occurs, if the vehicle is detained for a long time or fails to complete the task as planned, send a task status confirmation instruction to the vehicle to confirm the detention reason, and adjust the task execution plan in combination with the task matching module and the dispatch optimization module;

[0167] Record the abnormal task execution situation, and update the task execution status after the anomaly handling is completed.

[0168] When a route deviation occurs, calculate the deviation degree of the current vehicle combined with the road traffic status provided by the dispatch optimization module, generate an adjusted driving path, and send a path adjustment instruction to the abnormal vehicle, including:

[0169] When a route deviation occurs, obtain the real-time vehicle state parameters of the vehicle based on the preset vehicle sensors;

[0170] Among them, the real-time vehicle state parameters include the lateral position of the real-time vehicle position, the current heading angle, the real-time vehicle speed, and the lateral distance from the current vehicle position to the standard lane line;

[0171] Based on the lateral position in the real-time vehicle position and the current heading angle, combined with the lateral coordinate and tangent direction angle of the optimal driving path at the vehicle position, determine the vehicle deviation index;

[0172] Determine the time warning index based on the current heading angle, the real-time vehicle speed, and the lateral distance from the current vehicle position to the standard lane line;

[0173] Combine the vehicle deviation index and the time warning index to comprehensively calculate the deviation coefficient of the current vehicle, thereby determining the deviation degree of the current vehicle;

[0174] According to the deviation degree of the current vehicle and the road traffic state provided by the dispatching optimization module, generate an adjusted driving path and send a path adjustment instruction to the abnormal vehicle.

[0175] In the above embodiment, the deviation coefficient of the current vehicle is T;

[0176] ;

[0177] Wherein, T is the deviation coefficient of the current vehicle, is the real-time lateral position of the current vehicle, is the lateral coordinate of the preset driving path corresponding to the final task allocation plan at the vehicle position, is the current heading angle, is the tangent direction angle of the preset driving path corresponding to the final task allocation plan at the vehicle position, is the lateral distance from the current vehicle position to the standard lane line, is the real-time vehicle speed of the current vehicle, is the weight coefficient of the distance deviation, is the weight coefficient of the heading deviation, is the deviation index, is the influence weight of the deviation index, is the time warning index, is the influence weight of the time warning index, e is the base of the natural logarithm, is the road anomaly factor corresponding to the current vehicle position. Among them, the road anomaly factor is related to the road congestion influence and the road construction influence. The value range of the road anomaly factor is (0, 1), c is a constant, and the value range of c is (0.1, 0.5). Among them, the sum of the weight coefficients of the distance deviation and the heading deviation is 1, the sum of the weights of the influence weight of the deviation index and the influence weight of the time warning index is 1. The value ranges of the weight coefficients of the distance deviation and the heading deviation are (0, 1), and the value ranges of the weights of the influence weight of the deviation index and the influence weight of the time warning index are (0, 1).

[0178] In the above embodiment, is to simulate the task execution path based on the final task allocation plan combined with the vehicle characteristics, thereby determining the tangent direction angle and the lateral coordinate of each vehicle position in the preset driving path corresponding to the final task allocation plan based on the simulation results.

[0179] In the above embodiments, based on the monotonicity of the square root function, the characteristics of smoothing data and dimensionless processing, the square root function is selected as the calculation function for the deviation index, and the distance deviation and the heading deviation are substituted into the square root function to construct the deviation index in the comprehensive formula; in addition, both the distance deviation and the heading deviation are relatively small values, and the square root function has good stability when dealing with small values, which can avoid the situation of the denominator being zero or the result tending to infinity, ensuring the stability and reliability of the driving assistance system.

[0180] In the above embodiments, the weight coefficients of the distance deviation, the heading deviation, the influence weight of the deviation index, and the influence weight of the time warning index are all obtained by solving the matrix constructed through pairwise comparison and scoring of importance using the analytic hierarchy process. For example, for the weight coefficients corresponding to the distance deviation D and the heading deviation H, a judgment is given according to the degree of importance using the 1-9 scale method to form an n-order judgment matrix A. , where aDH and aHD are the relative importance scale values to be determined. According to the 1-9 scale method, these values can be 1 (equally important), 3 (slightly important), 7 (strongly important), 9 (extremely important), and their reciprocals (indicating the opposite degree of importance), etc. For example, aDH is 1 / 4 and aHD is 4. If the judgment matrix passes the consistency test, the corresponding eigenvector of the judgment matrix is normalized to obtain the normalized matrix. , add up each row of the normalized matrix to obtain the weight vector, and normalize the weight vector to obtain 0.2, 0.8.

[0181] In the above embodiments, the minimum threshold of sinθ is 0.001. If sinθ is less than the minimum threshold, then sinθ is replaced with the minimum threshold for calculating the deviation coefficient.

[0182] The beneficial effects of the above technical solutions are as follows: By calculating the deviation between the driving situation of the current vehicle and the predicted driving situation of the vehicle corresponding to the current moment in the final task allocation plan, the deviation degree of the current vehicle is judged, and then the path is adjusted in combination with the road traffic state provided by the scheduling optimization module, which can make the abnormal judgment of the current vehicle more timely and accurate, and at the same time can also adjust the driving instruction in time, improving the accuracy of the transportation task.

[0183] In the above embodiments, through the collaborative work of the anomaly detection unit and the emergency dispatch unit, efficient anomaly monitoring and handling are achieved. The system analyzes the vehicle operation status data in real time, sets the judgment conditions for abnormal situations, and automatically records relevant data when an anomaly occurs. It can detect problems in a timely manner and trigger the corresponding emergency handling mechanism, and execute different emergency strategies according to the type of anomaly. For example, in the case of a vehicle failure, the system can automatically obtain alternative vehicle resources from the task matching module and quickly replace the faulty vehicle to ensure the smooth progress of the transportation task. When a route deviation or sudden traffic condition occurs, the system can recompute the optimal driving route by combining the road traffic status analysis of the dispatch optimization module and send a new driving instruction to the affected vehicles, greatly improving the reliability of the system and ensuring the timely completion of the transportation task.

[0184] Please refer to Figure 2 , a transportation vehicle reservation management and dispatching method based on the Internet of Things, which is applied to the above-mentioned transportation vehicle reservation management and dispatching system based on the Internet of Things, and includes the following steps:

[0185] Step 1: Through the vehicle information collection module, use GPS and Internet of Things sensors to obtain the real-time position data, load status, fuel level, driving speed, and historical itinerary data of the transportation vehicle, determine whether the vehicle is in the task execution state, and record the start time, estimated end time, and task completion status of the task;

[0186] Step 2: The user submits a vehicle reservation request through the user interaction module. The reservation management module parses the reservation request, pre-audits the reservation request in combination with the vehicle operation status parameters provided by the vehicle information collection module, obtains the optimal matching plan in combination with the dispatch optimization module and the task matching module, locks the vehicle resources, and generates a reservation confirmation message, which is sent to the user interaction module;

[0187] Step 3: The dispatch optimization module obtains and stores the historical transportation task data, evaluates the efficient transportation route in combination with the road traffic information, calculates the optimal driving route based on the vehicle's current location, destination, task urgency, and road traffic status, and generates the optimal vehicle dispatch plan based on the optimal driving route, and sends a dispatch instruction to the relevant vehicles;

[0188] Step 4: The task matching module receives and parses the transportation demand, sets the task matching constraint conditions, and screens the available vehicles that meet the load capacity and normal operation status in combination with the vehicle operation status data, calculates the optimal matching plan, and sends a task execution instruction to the successfully matched vehicles;

[0189] Step 5: The anomaly handling module monitors the vehicle operation status in real time, identifies abnormal situations, executes the emergency dispatch strategy according to the type of anomaly, and sends an anomaly notification to the user interaction module. After the anomaly handling is completed, the task execution status is updated.

[0190] As described above, it is only the preferred specific implementation manner of the present invention. However, the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, making equivalent substitutions or changes should be covered within the protection scope of the present invention.

Claims

1. A transportation vehicle reservation management and scheduling system based on the Internet of Things, characterized in that, Including: A vehicle information collection module configured to collect the operating status information of a transport vehicle in real time; A reservation management module configured to process a user's vehicle reservation request, allocate resources by combining the vehicle status and the reservation request, determine the comprehensive transportation performance of the vehicle during the task execution by combining the historical operating status parameters and the real-time operating status parameters, and optimize the plan by combining the user satisfaction and the vehicle reservation request; A scheduling optimization module configured to determine the vehicle impact weight based on the real-time collected data and the vehicle reservation situation, and optimize the plan by combining the task requirements, the weather impact coefficient, the vehicle driving condition, and the task urgency, and generate an optimal vehicle scheduling plan; A task matching module configured to perform intelligent matching based on the vehicle status and the task requirements; An exception handling module configured to handle exceptions in case of emergencies, calculate the deviation between the current vehicle driving condition and the predicted vehicle driving condition of the corresponding vehicle at the current moment in the final task allocation plan, judge the degree of vehicle deviation, and adjust the route; A user interaction module configured to provide functions of reservation submission, modification, and cancellation, and display the vehicle status and task progress; Among them, the reservation management module is used for: Obtaining the set of historical operating status parameters of each vehicle in the vehicle information collection module during the effective operation period; Combining the real-time operating status parameters with the set of historical operating status parameters to obtain the comprehensive operating status set of each vehicle; Classifying the operating status parameters in the comprehensive operating status set according to different parameter types to obtain a classified operating status set; Sorting the classified operating status subsets corresponding to each type of parameter in the classified operating status set according to the time series characteristics, inputting them into the same coordinate system based on the sorting result, and performing curve fitting to obtain the vehicle transportation status curve set of the current vehicle; Analyzing the curve volatility of each status curve in the vehicle transportation status curve set to obtain the comprehensive transportation performance of the current vehicle; Combining the comprehensive transportation performance of each vehicle with the task execution status of the vehicle, and thus interacting with the scheduling optimization module and the task matching module to obtain an initial matching plan; Obtaining the user satisfaction of each vehicle during the historical transportation process, and thus adjusting the vehicle matching result in the initial matching plan by combining the user satisfaction to obtain an optimized matching plan; Obtaining the request response time and request response consent degree of each vehicle for the vehicle reservation request during the effective operation period to obtain the request impact factor of each vehicle; Determining the request impact weight of the request impact factor based on the historical impact degree of the request impact factor of each vehicle on the vehicle matching plan during the effective operation period; Optimizing the optimized matching plan again by combining the request impact factor of the vehicle and the corresponding request impact weight to obtain an optimal matching plan.

2. The transportation vehicle reservation management and scheduling system based on the Internet of Things according to claim 1, wherein: The vehicle information collection module obtains the real-time position data of the vehicle through GPS and Internet of Things sensor devices, obtains the operating status parameters of the vehicle, the operating status parameters include the load status, fuel level, driving speed, and historical itinerary data, judges whether the vehicle is currently in the task execution state, and records the start time, estimated end time, and task completion status of the task.

3. The Internet of Things-based transportation vehicle reservation management and scheduling system according to claim 1, wherein: The reservation management module includes: A reservation processing unit configured to receive a vehicle reservation request submitted by a user. The reservation request includes a reservation time, transportation requirements, a departure location, and a destination location, parse the reservation request information, and perform a preliminary review of the reservation request. A resource allocation unit configured to combine the vehicle reservation request, interact with a scheduling optimization module and a task matching module based on the real-time operation status parameters provided by the vehicle information collection module and the task execution status of the vehicle, obtain an optimal matching solution, lock the vehicle resources based on the optimal matching solution, and generate a reservation confirmation message. The reservation confirmation message includes the basic information of the reserved vehicle, the task execution time, and the scheduling arrangement, and send the reservation confirmation message to the user interaction module.

4. The Internet of Things-based transportation vehicle reservation management and scheduling system according to claim 1, wherein: The scheduling optimization module includes: A data analysis unit configured to obtain and store historical transportation task data. The historical transportation task data includes task execution time, vehicle driving route, transportation efficiency, and abnormal situation records, parse the vehicle driving route, and combine the road traffic information to evaluate the traffic conditions at different time periods and on different routes, and identify efficient transportation routes. Monitor the current task load situation, where the task load situation includes the number of tasks to be executed, the execution progress of the assigned tasks, and the usage of transportation resources. A route optimization unit configured to collect the current road traffic information. The road traffic information includes road congestion conditions, construction sections, and accident information, and combine the historical traffic information to evaluate the road traffic status of each route. Calculate the optimal driving route based on the current location, destination, task urgency, and road traffic status of the vehicle. The optimal driving route is calculated based on the principles of the shortest path, optimal task matching, and minimum empty running rate; generate an optimal vehicle scheduling plan based on the optimal driving route and send it to the relevant vehicle.

5. The transportation vehicle reservation management and scheduling system based on the Internet of Things according to claim 1, wherein: The task matching module includes: A task parsing unit configured to receive and parse the transportation requirements. The transportation requirements include task type, departure location, destination location, cargo type, cargo weight, time requirements, and priority information, and set the constraint conditions for matching tasks according to the time requirements and priority information of the transportation tasks. A vehicle screening unit configured to receive the transportation requirement data provided by the task parsing unit, interact with the vehicle information collection module, obtain the real-time operation status information of the vehicle, screen the available vehicles that meet the load capacity, normal operation status, and are not in the task execution, failure, or maintenance status according to the transportation requirements, and combine the current location and historical driving trajectory of the vehicle to preliminarily screen the candidate vehicles that are relatively close and suitable for executing the task, and generate a candidate vehicle list. A matching calculation unit configured to calculate the optional matching solutions for the task and the vehicle based on the status information of the vehicles in the candidate vehicle list, based on the shortest path and the optimal task matching principle, and optimize the results of the optional matching solutions in combination with the optimal vehicle scheduling plan provided by the scheduling optimization module. Select the vehicle with a short driving distance, high task execution efficiency, and low fuel consumption in the optional matching solutions to generate an optimal matching solution. A matching confirmation unit, configured to receive the optimal matching scheme provided by the matching calculation unit, combine the real-time road traffic status and the task urgency, confirm the final task allocation scheme, and record the task execution status; during the execution of the matching scheme, continuously monitor the real-time operation status information of the vehicle, and when vehicle failures, route deviations or sudden traffic conditions occur, interact with the exception handling module to adjust the task matching scheme; Send task execution instructions to the vehicles that have successfully matched based on the final task allocation scheme, where the task execution instructions include task details, departure location, destination location, optimal driving route, and estimated completion time, and generate task matching results.

6. The Internet of Things-based transportation vehicle reservation management and scheduling system according to claim 5, wherein: The matching confirmation unit is further configured to: Determine the importance of vehicle driving distance, task execution efficiency, and fuel consumption for the current task in combination with the task requirements, so as to determine the vehicle impact weights of vehicle driving distance, task execution efficiency, and fuel consumption; Based on the vehicle impact weights of driving distance, task execution efficiency, and fuel consumption, combine the driving distance, task execution efficiency, and fuel consumption of the vehicle to determine a first optimization factor for optimizing the optimal matching scheme; Obtain the road surface status of the vehicle arriving at the task departure location in the optimal matching scheme, and combine the real-time road traffic status of the vehicle arriving at the task departure point to determine the first vehicle driving condition corresponding to the vehicle arriving at the task departure point; Based on the preset departure time and estimated completion time of the vehicle from the task departure location to the destination location in the optimal matching scheme, screen the historical road traffic status with the same time interval from the set of historical road traffic status to obtain the optimal set of historical road traffic status; Input the real-time road traffic status from the task departure location to the destination location and each historical road traffic status in the optimal set of historical road traffic status into a preset road traffic prediction model, so as to predict the road traffic status of the vehicle from the task departure point to the destination location and obtain the predicted road traffic status; Obtain the road surface status of the optimal driving route of the vehicle from the task departure point to the destination location, and combine the predicted road traffic status to determine the second vehicle driving condition during the vehicle's task process; Based on the first vehicle driving condition and the second vehicle driving condition, combine the vehicle weather impact coefficient to determine a second optimization factor for optimizing the optimal matching scheme; Obtain the task urgency of the current task, and combine the first optimization factor and the second optimization factor to optimize the optimal matching scheme to determine the initial task allocation scheme for the current vehicle to execute the task; Simulate the initial task allocation scheme to obtain the scheme decision score of the simulated scheme, and use the initial task allocation scheme with a scheme decision score higher than the preset minimum decision score as the final task allocation scheme.

7. The transportation vehicle reservation management and scheduling system based on the Internet of Things according to claim 1, characterized in that: The exception handling module includes: An exception detection unit, configured to receive and analyze the real-time operation status data provided by the vehicle information collection module, and set exception situation judgment conditions, where the exception situations include vehicle failures, route deviations, sudden traffic conditions, and abnormal task executions; When an exception situation is detected, record the exception event data, where the exception event data includes the exception occurrence time, exception type, and influence range; The emergency dispatch unit is configured to receive abnormal event data and execute corresponding emergency handling strategies according to different types of abnormal situations; When a vehicle breakdown occurs, it determines whether the faulty vehicle can continue to execute the task. If it cannot, it obtains alternative vehicle resources from the task matching module, selects the optimal alternative vehicle, and sends an emergency dispatch instruction; When a route deviation occurs, it calculates the deviation degree of the current vehicle and combines it with the road traffic status provided by the dispatch optimization module to generate an adjusted driving route and send a route adjustment instruction to the abnormal vehicle; When a sudden traffic condition occurs, it combines the real-time road traffic status provided by the dispatch optimization module, recalculates the task execution route, generates a new optimal driving plan, and sends a new driving instruction to the affected vehicles based on the optimal driving plan; When a task execution anomaly occurs, if the vehicle is detained for a long time or fails to complete the task as planned, it sends a task status confirmation instruction to the vehicle to confirm the reason for detention, and combines the task matching module and the dispatch optimization module to adjust the task execution plan; It records the task execution anomalies and updates the task execution status after the anomaly handling is completed.

8. The transportation vehicle reservation management and scheduling system based on the Internet of Things according to claim 7, wherein, When a route deviation occurs, it calculates the deviation degree of the current vehicle and combines it with the road traffic status provided by the dispatch optimization module to generate an adjusted driving route and send a route adjustment instruction to the abnormal vehicle, including: When a route deviation occurs, it obtains the real-time vehicle status parameters of the vehicle based on the preset vehicle sensors; Among them, the real-time vehicle status parameters include the lateral position of the real-time vehicle position, the current heading angle, the real-time vehicle speed, and the lateral distance from the current vehicle position to the standard lane line; Based on the lateral position and the current heading angle in the real-time vehicle position, it determines the vehicle deviation index in combination with the lateral coordinate and the tangent direction angle of the vehicle position on the optimal driving route; Based on the current heading angle, the real-time vehicle speed, and the lateral distance from the current vehicle position to the standard lane line, it determines the time warning index; It comprehensively calculates the deviation coefficient of the current vehicle by combining the vehicle deviation index and the time warning index, thereby determining the deviation degree of the current vehicle; According to the deviation degree of the current vehicle and the road traffic status provided by the dispatch optimization module, it generates an adjusted driving route and sends a route adjustment instruction to the abnormal vehicle.

9. A transportation vehicle reservation management and scheduling method based on the Internet of Things, which is applied to the transportation vehicle reservation management and scheduling system based on the Internet of Things according to any one of claims 1-8, characterized in that, It includes the following steps: Step 1: Through the vehicle information collection module, it uses GPS and Internet of Things sensors to obtain the real-time position data, load status, fuel level, driving speed, and historical trip data of the transport vehicle, determines whether the vehicle is in the task execution state, and records the start time, estimated end time, and task completion status of the task; Step 2: The user submits a vehicle reservation request through the user interaction module. The reservation management module parses the reservation request, pre-audits the reservation request in combination with the vehicle operation status parameters provided by the vehicle information collection module, obtains the optimal matching plan in combination with the dispatch optimization module and the task matching module, locks the vehicle resources, and generates a reservation confirmation message and sends it to the user interaction module; Step 3: The scheduling optimization module obtains and stores historical transportation task data, evaluates efficient transportation routes in combination with road traffic information, calculates the optimal driving route based on the vehicle's current location, destination, task urgency, and road traffic conditions, generates an optimal vehicle scheduling plan based on the optimal driving route, and sends a scheduling instruction to the relevant vehicles; Step 4: The task matching module receives and parses transportation demands, sets task matching constraint conditions, filters available vehicles that meet the load capacity and normal operating status in combination with vehicle operation status data, calculates the optimal matching plan, and sends a task execution instruction to the vehicles that succeed in matching; Step 5: The exception handling module monitors the vehicle operation status in real time, identifies abnormal situations, executes emergency scheduling strategies according to the types of exceptions, sends an exception notification to the user interaction module, and updates the task execution status after completing the exception handling.

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