Transport vehicle reservation management scheduling system and method based on Internet of Things

Abstract

The invention discloses a transport vehicle reservation management scheduling system and method based on the Internet of Things, and belongs to the technical field of transport management. Through intelligent scheduling optimization and task matching, the scheduling efficiency of transport vehicles is improved, the empty driving rate and the resource waste are reduced, the optimal driving path and the scheduling scheme are generated based on the vehicle state information, the historical transport data and the current road traffic condition which are collected in real time, the most suitable vehicle can be automatically matched, and the scheduling efficiency of the transport vehicles is improved. The method ensures that a transportation task can be efficiently completed in the shortest time, enables a user to accurately reserve transportation resources, reduces idle resources, improves the overall transportation efficiency, collects the operation state of vehicles in real time through the Internet of Things technology, carries out intelligent matching in combination with task demands, screens out the vehicle most suitable for task execution based on the transportation demands, and improves the transportation efficiency. And a matching scheme is optimized, so that the accuracy and rationality of task allocation are ensured, automatic and precise task matching can be realized, and the execution success rate of the transportation task is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of transportation management, and in particular 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, transportation vehicle dispatch management mostly relies on manual experience or systems based on simple logical rules to allocate tasks and arrange routes. This model is inadequate in the face of complex and changing transportation environments. Specifically, most current dispatch 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 at a high frequency and with high precision, resulting in delayed dispatch instructions or untimely responses, making it difficult to meet the high standards of transportation efficiency and reliability required by modern logistics systems.

[0003] In addition, although some of the existing dispatching platforms that use information technology can initially realize the functions of vehicle reservation and basic route recommendation, they still have obvious shortcomings in terms of intelligence. For example, the system has failed to effectively integrate the Internet of Things technology, cannot dynamically evaluate the comprehensive transportation performance of vehicles, and lacks the ability to integrate and analyze historical task data with real-time traffic status. Therefore, there are great limitations in task matching and route optimization. Most platforms are still based on the "task-driven" method, that is, first determine the task and then manually allocate the vehicle, ignoring key factors such as the vehicle's current operating status, response capabilities, and historical service performance, which leads to unreasonable vehicle scheduling arrangements, inefficient resource utilization, and even repeated vehicle dispatch and task conflicts.

[0004] In addition, existing technologies often rely on temporary manual processing when faced with emergencies such as vehicle breakdowns, traffic jams, route deviations, and other abnormal situations, and lack a systematic abnormality 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 transportation task delays, reduced customer satisfaction, and in serious cases, 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 technology.

[0006] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a transportation vehicle reservation management and dispatching system based on the Internet of Things, comprising: a vehicle information collection module, a reservation management module, a dispatching optimization module, a task matching module and an exception handling module; The vehicle information collection module is configured to collect the operating status information of the transport vehicle in real time; the reservation management module is configured to process the user's vehicle reservation request and allocate resources in combination with the vehicle status and reservation request; the scheduling optimization module is configured to generate the optimal vehicle scheduling plan based on the real-time collected data and vehicle reservation status; the task matching module is configured to perform intelligent matching based on the vehicle status and task requirements; the exception handling module is configured to handle exceptions in emergency situations.

[0007] Furthermore, the vehicle information collection module obtains the real-time location data of the vehicle through GPS and Internet of Things sensor devices, and obtains the vehicle's operating status parameters, which include load status, fuel level, driving speed and historical travel data, to determine whether the vehicle is currently in a task execution state, and record the start time, expected end time and task completion status of the task.

[0008] Furthermore, the reservation management module includes: A reservation processing unit is configured to receive a vehicle reservation request submitted by a user, the reservation request including a reservation time, transportation requirements, a departure point and a destination point, parse the reservation request information and pre-examine the reservation request; The resource allocation unit is configured to combine the vehicle reservation request, interact with the scheduling optimization module and the task matching module according to the real-time operating status parameters provided by the vehicle information collection module and the task execution status of the vehicle, obtain the optimal matching solution, lock the vehicle resources based on the optimal matching solution, and generate reservation confirmation information, which 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.

[0009] Furthermore, the reservation management module is also used to: Obtain a set of historical operating status parameters of each vehicle in the effective operating cycle in the vehicle information collection module; The real-time operating status parameters are combined with the historical operating status parameter set to obtain a comprehensive operating status set for 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 state subsets corresponding to each type of parameter in the classified operating state set according to the time series characteristics, inputting the sorting results into the same coordinate system, and performing curve fitting to obtain a vehicle transportation state curve set of the current vehicle; Analyze the curve fluctuation of each state curve in the vehicle transportation state curve set, so as to obtain the comprehensive transportation performance of the current vehicle; Combine the comprehensive transportation performance of each vehicle with the vehicle's task execution status, thereby interacting with the scheduling optimization module and the task matching module to obtain an initial matching solution; Obtain the user satisfaction of each vehicle in the historical transportation process, and then adjust the vehicle matching results in the initial matching plan based on the user satisfaction to obtain the optimized matching plan; Obtain the request response time and request response agreement of each vehicle to the vehicle reservation request within the effective operation cycle, and obtain the request impact factor of each vehicle; Determine the request influence weight of the request influence factor based on the historical influence degree of the request influence factor on the vehicle matching solution of each vehicle during the effective operation cycle; The optimized matching scheme is optimized again by combining the vehicle's request impact factor and the corresponding request impact weight to obtain the optimal matching scheme.

[0010] Furthermore, the scheduling optimization module includes: A data analysis unit is configured to obtain and store historical transport task data, wherein the historical transport task data includes task execution time, vehicle driving path, transport efficiency and abnormal situation records, analyze the vehicle driving path and combine it with road traffic information, evaluate the traffic conditions of different time periods and different routes, and identify efficient transport paths; Monitor the current task load, including the number of tasks to be performed, the progress of the assigned tasks, and the use of transportation resources; A route optimization unit is configured to collect current road traffic information, including road congestion, construction sections, and accident information, and evaluate the road traffic status of each route in combination with historical traffic information; The optimal driving path is calculated based on the vehicle's current location, destination, task urgency and road traffic conditions. The optimal driving path is calculated based on the principles of shortest path, optimal task matching and minimum empty driving rate. The optimal vehicle dispatch plan is generated based on the optimal driving path and sent to the relevant vehicles.

[0011] Furthermore, the task matching module includes: A task parsing unit is configured to receive and parse transportation requirements, wherein the transportation requirements include task type, departure point, destination point, cargo type, cargo weight, time requirement and priority information, and set constraint conditions for matching tasks according to the time requirement and priority information of the transportation task; The vehicle screening unit is configured to receive the transportation demand data provided by the task analysis unit, and interact with the vehicle information collection module to obtain the real-time operation status information of the vehicle, and screen available vehicles that meet the load capacity, are in normal operation, and are not in the task execution, fault or maintenance state according to the transportation demand, and preliminarily screen candidate vehicles that are close and suitable for executing the task in combination with the current location and historical driving trajectory of the vehicle, and generate a candidate vehicle list; A matching calculation unit is configured to calculate optional matching schemes between tasks and vehicles based on the state information of the vehicles in the candidate vehicle list, based on the shortest path and the optimal task matching principle, and optimize the optional matching scheme results in combination with the optimal vehicle scheduling scheme provided by the scheduling optimization module; Among the optional matching solutions, a vehicle with a short driving distance, high mission efficiency and low fuel consumption is selected to generate the optimal matching solution; The matching confirmation unit is configured to receive the optimal matching solution provided by the matching calculation unit, confirm the final task allocation solution 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 solution, the real-time operation status information of the vehicle is continuously monitored, and when a vehicle failure, route deviation or sudden traffic situation occurs, the task matching solution is adjusted interactively with the exception handling module; Based on the final task allocation plan, a task execution instruction is sent to the successfully matched vehicle, and the task execution instruction includes task details, departure location, destination point, optimal driving path and estimated completion time to generate a task matching result.

[0012] Furthermore, the matching confirmation unit is also used for: Determine the importance of vehicle driving distance, mission execution efficiency, and fuel consumption to the current task in combination with task requirements, and thus determine the vehicle impact weights of vehicle driving distance, mission execution efficiency, and fuel consumption; The vehicle influence weight based on the driving distance, task execution efficiency, and fuel consumption is combined with the vehicle's driving distance, task execution efficiency, and fuel consumption to determine a first optimization factor for optimizing the optimal matching solution; Obtain the road condition of the vehicle arriving at the mission departure point in the optimal matching solution, and determine the driving condition of the first vehicle corresponding to the vehicle arriving at the mission departure point in combination with the real-time road traffic condition of the vehicle arriving at the mission departure point; Based on the preset departure time and estimated completion time of the vehicle from the mission departure point to the destination point in the optimal matching solution, the historical road traffic states of the same time interval are selected from the historical road traffic state set to obtain the optimal historical road traffic state set; The real-time road traffic status from the mission departure point to the destination point and each historical road traffic status in the optimal historical road traffic status set are input into a preset road traffic prediction model, so as to predict the road traffic status of the vehicle from the mission departure point to the destination point and obtain the predicted road traffic status; Obtain the road condition of the vehicle's optimal driving path from the mission starting point to the destination point, and determine the second vehicle driving condition of the vehicle during the mission process in combination with the predicted road traffic condition; Determine a second optimization factor for optimizing the optimal matching solution based on the first vehicle driving condition and the second vehicle driving condition combined with the vehicle weather influence coefficient; Obtaining the task urgency of the current task, and optimizing the optimal matching solution in combination with the first optimization factor and the second optimization factor, to determine the initial task allocation solution for the current vehicle to perform the task; The initial task allocation plan is simulated to obtain the plan decision score of the simulated plan, and the initial task allocation plan with a plan decision score higher than the preset minimum decision score is taken as the final task allocation plan.

[0013] Furthermore, the exception handling module includes: An abnormality detection unit is configured to receive and analyze the real-time operating status data provided by the vehicle information collection module, and set abnormal situation judgment conditions, wherein the abnormal situation includes vehicle failure, route deviation, sudden traffic conditions, and abnormal task execution; When an abnormal situation is detected, the abnormal event data is recorded, and the abnormal event data includes the abnormal occurrence time, abnormal type and impact range; An 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 failure occurs, determine whether the faulty vehicle can continue to perform the task. If not, obtain alternative vehicle resources from the task matching module, select the best alternative vehicle and send an emergency dispatch instruction; When a route deviation occurs, the deviation degree of the current vehicle is calculated and combined with the road traffic status provided by the scheduling optimization module to generate an adjusted driving path and send a path adjustment instruction to the abnormal vehicle; When an unexpected traffic situation occurs, the task execution path is recalculated based on the real-time road traffic status provided by the scheduling optimization module, and a new optimal driving plan is generated. Based on the optimal driving plan, new driving instructions are sent to the affected vehicles. When an abnormal task execution occurs, such as if the vehicle is stranded for a long time or fails to complete the task as planned, a task status confirmation instruction is sent to the vehicle to confirm the reason for the delay, and the task execution plan is adjusted in combination with the task matching module and the scheduling optimization module; Record task execution exceptions and update the task execution status after exception handling is completed.

[0014] Furthermore, when a route deviation occurs, the deviation degree of the current vehicle is calculated and combined with the road traffic status provided by the scheduling optimization module to generate an adjusted driving path, and a path adjustment instruction is sent to the abnormal vehicle, including: When a route deviation occurs, real-time vehicle status parameters of the vehicle are obtained based on 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; Determine the vehicle deviation index based on the lateral position and current heading angle in the real-time vehicle position combined with the lateral coordinates and tangent direction angle of the optimal driving path at the vehicle position; Determine the time warning index based on the current heading angle, real-time vehicle speed and the lateral distance from the current vehicle position to the standard lane line; The deviation coefficient of the current vehicle is calculated by combining the vehicle deviation index and the time warning index, so as to determine the deviation degree of the current vehicle; According to the current vehicle's deviation degree and the road traffic status provided by the scheduling optimization module, an adjusted driving path is generated and a path adjustment instruction is sent to the abnormal vehicle.

[0015] Furthermore, the transportation vehicle reservation management and scheduling system based on the Internet of Things also includes a user interaction module, which is configured to provide reservation submission, modification and cancellation functions, display vehicle status, task progress and estimated arrival time, and receive and store user feedback information.

[0016] 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: Step 1: Through the vehicle information collection module, use GPS and IoT sensors to obtain the real-time location data, load status, fuel level, driving speed and historical travel data of the transport vehicle, determine whether the vehicle is in the task execution state, and record the start time, expected 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 and pre-examines the reservation request in combination with the vehicle operation status parameters provided by the vehicle information collection module. In combination with the scheduling optimization module and the task matching module, the optimal matching solution is obtained, the vehicle resources are locked, and the reservation confirmation information is generated and sent to the user interaction module. Step 3: The dispatch optimization module obtains and stores historical transportation task data, evaluates efficient transportation routes based on road traffic information, calculates the optimal driving route based on the vehicle's current location, destination, task urgency, and road traffic conditions, generates the optimal vehicle dispatch plan based on the optimal driving route, and sends dispatch instructions to relevant vehicles; Step 4: The task matching module receives and analyzes the transportation demand, sets task matching constraints, and selects available vehicles that meet the load capacity and are in normal operation status based on the vehicle operation status data, calculates the optimal matching solution, and sends task execution instructions to the successfully matched vehicles; Step 5: The exception handling module monitors the vehicle operation status in real time, identifies abnormal situations, executes emergency dispatch strategies according to the type of abnormality, and sends abnormal notifications to the user interaction module. After completing the exception handling, it updates the task execution status.

[0017] Compared with the prior art, the present invention has the following beneficial effects: 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.

[0018] 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.

[0019] 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

[0020] Figure 1 It is a schematic diagram of the transport vehicle reservation management and scheduling system module of the present invention; Figure 2 It is a flow chart of the method for transport vehicle reservation management and scheduling of the present invention. DETAILED DESCRIPTION

[0021] 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.

[0022] See also Figure 1 , the present invention provides the following technical solutions: 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; The vehicle information collection module is configured to collect the operating status information of the transport vehicle in real time; the reservation management module is configured to process the user's vehicle reservation request and allocate resources in combination with the vehicle status and reservation request; the scheduling optimization module is configured to generate the optimal vehicle scheduling plan based on the real-time collected data and vehicle reservation status; the task matching module is configured to perform intelligent matching based on the vehicle status and task requirements; the exception handling module is configured to handle exceptions in emergencies; the user interaction module is configured to provide reservation submission, modification and cancellation functions, display vehicle status, task progress and estimated arrival time, and receive and store user feedback information.

[0023] The vehicle information collection module obtains the real-time location data of the vehicle through GPS and IoT sensor devices, and obtains the vehicle's operating status parameters, including load status, fuel level, driving speed and historical travel data, to determine whether the vehicle is currently in a task execution state, and record the start time, expected end time and task completion status of the task.

[0024] In the above embodiment, the introduction of the vehicle information collection module greatly improves the real-time monitoring capability of the transport vehicle. Through the combination of GPS and IoT sensors, the system can accurately obtain the real-time location, load status, fuel level, driving speed and historical travel data of the vehicle. The collection of this 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 the task execution state, and record the start time, expected end time and task completion status of the task. The full automatic monitoring is realized through the IoT technology, making the vehicle status information more reliable and efficient.

[0025] Reservation management module, including: A reservation processing unit is configured to receive a vehicle reservation request submitted by a user, the reservation request including a reservation time, transportation requirements, a departure point and a destination point, parse the reservation request information and pre-examine the reservation request; The resource allocation unit is configured to combine the vehicle reservation request, interact with the scheduling optimization module and the task matching module according to the real-time operating status parameters provided by the vehicle information collection module and the task execution status of the vehicle, obtain the optimal matching solution, lock the vehicle resources based on the optimal matching solution, and generate reservation confirmation information, which 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.

[0026] In the above embodiment, the introduction of the reservation management module optimizes the reservation process of transport vehicles, allowing users to book transport resources more efficiently. By automatically parsing the user's reservation request, including key information such as reservation time, transportation requirements, departure point and destination point, the rationality of the reservation request can be automatically reviewed to ensure the integrity and accuracy of the reservation information. In addition, the system can also perform preliminary screening for specific transportation needs, such as recommending suitable vehicle types based on the type of goods to avoid waste of resources or mismatch of vehicle specifications. Based on the real-time collected vehicle operation status data, the optimal match is performed, and the scheduling optimization module and the task matching module are interacted to ensure the rationality of resource allocation, which greatly shortens the reservation processing time, improves the reservation response speed, and reduces the need for manual intervention. At the same time, the generation and automatic sending of reservation confirmation information allows users to obtain reservation results in real time, improving the user experience.

[0027] The appointment management module is also used for: Obtain a set of historical operating status parameters of each vehicle in the effective operating cycle in the vehicle information collection module; The real-time operating status parameters are combined with the historical operating status parameter set to obtain a comprehensive operating status set for 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 state subsets corresponding to each type of parameter in the classified operating state set according to the time series characteristics, inputting the sorting results into the same coordinate system, and performing curve fitting to obtain a vehicle transportation state curve set of the current vehicle; Analyze the curve fluctuation of each state curve in the vehicle transportation state curve set, so as to obtain the comprehensive transportation performance of the current vehicle; Combine the comprehensive transportation performance of each vehicle with the vehicle's task execution status, thereby interacting with the scheduling optimization module and the task matching module to obtain an initial matching solution; Obtain the user satisfaction of each vehicle in the historical transportation process, and then adjust the vehicle matching results in the initial matching plan based on the user satisfaction to obtain the optimized matching plan; Obtain the request response time and request response agreement of each vehicle to the vehicle reservation request within the effective operation cycle, and obtain the request impact factor of each vehicle; Determine the request influence weight of the request influence factor based on the historical influence degree of the request influence factor on the vehicle matching solution of each vehicle during the effective operation cycle; The optimized matching scheme is optimized again by combining the vehicle's request impact factor and the corresponding request impact weight to obtain the optimal matching scheme.

[0028] In the above embodiment, the effective operation cycle refers to the effective time period during which the vehicle is performing a task or operating.

[0029] In the above embodiment, the historical running state parameter set refers to a set of all historical vehicle state data or parameters collected by the vehicle information collection module within the effective running cycle, which may include speed, acceleration, position, fuel consumption rate, etc.

[0030] In the above embodiment, the real-time operating status parameters refer to the status 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.

[0031] In the above embodiment, the comprehensive operating status set is a vehicle status data set obtained by combining historical operating status parameters with real-time operating status parameters.

[0032] In the above embodiment, the classified operating state set is a set obtained by classifying the parameters in the comprehensive operating state set according to parameter types, wherein the parameter types include speed parameters, fuel consumption parameters, and the like.

[0033] In the above embodiment, the vehicle transportation state curve set is a curve set of state curves obtained by sorting the parameters in the classified operation state set in time series, inputting the sorting results into the same coordinate system, and performing curve fitting.

[0034] In the above embodiment, the comprehensive transportation performance is an evaluation of the overall transportation performance of the vehicle obtained by analyzing the curve volatility in the vehicle transportation status curve set, wherein the volatility of each curve is different and the corresponding performance is also different. The comprehensive transportation performance is obtained by integrating the curve volatility of each curve in the vehicle transportation status curve set. The range of curve volatility is (0, 1). The larger the curve volatility value, the worse the corresponding performance. For example, the curve volatility of the three curves A, B, and C is 0.12, 0.09, and 0.15, and the comprehensive transportation performance is 1-(0.12+0.09+0.15) / 3=0.88.

[0035] In the above embodiment, the task execution status includes information such as whether the vehicle is currently executing a task, the type of task being executed, and the progress.

[0036] In the above embodiment, the initial matching scheme combines the comprehensive transportation performance of each vehicle with the task execution status of the vehicle, and interacts with the scheduling optimization module and the task matching module to preliminarily generate a matching scheme based on the vehicle and task information.

[0037] In the above embodiment, user satisfaction refers to the historical user's satisfaction evaluation on vehicle performance, transportation service, etc. after using the vehicle transportation service, wherein the user satisfaction can be a five-point system, a ten-point system, etc., for example, the user satisfaction is 4 points.

[0038] In the above embodiment, the optimized matching scheme refers to an improved matching scheme obtained by adjusting the initial matching scheme in combination with user satisfaction. For example, if the user satisfaction of vehicle A is 9 points and the user satisfaction of vehicle B is 8 points, and the matching priorities of vehicle A and vehicle B are the same in the initial matching scheme, the matching priority of vehicle A is increased, thereby adjusting the initial matching scheme. After the adjustment, the matching priority of vehicle A is higher than that of vehicle B.

[0039] In the above embodiment, the request response time refers to the time required for the vehicle to respond to the reservation request after receiving the reservation request. For example, the request response time is 1 minute.

[0040] In the above embodiment, the request response agreement degree is the vehicle's acceptance degree or agreement rate to the reservation request. For example, during the effective operation cycle of the vehicle, the number of times reservation confirmation information is issued 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 agreement degree of vehicle A is 0.8.

[0041] In the above embodiment, the request impact factor is calculated based on the request response time and the request response agreement, and is an indicator used to reflect the ability or efficiency of the vehicle in processing reservation requests.

[0042] In the above embodiment, the request influence weight is a weight determined according to the historical influence of the request influence factor on the vehicle matching scheme during the effective operation cycle of the vehicle, and is used to consider the importance of the request influence factor when optimizing the matching scheme. The value range of the request influence weight is (0,1). For example, the request influence weight can be 0.25.

[0043] In the above embodiment, the optimal matching solution is the final matching solution obtained after further adjusting the optimized matching solution in combination with the request impact factor of the vehicle and the corresponding request impact weight.

[0044] The working principle of the above technical solution is: first, obtain historical operating status parameters, obtain a set of historical operating status parameters, and perform parameter synthesis in combination with real-time operating status parameters, and classify the parameter synthesis results based on different parameter types. Next, sort the classified status parameters according to time series characteristics, and input the sorting 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, and then optimize the plan based on the user satisfaction of the vehicle in the historical transportation process, and optimize the plan again based on the impact of the reservation request of each vehicle to obtain the optimal matching plan.

[0045] The beneficial effects of the above technical solution are: by combining historical operating status parameters and real-time operating status parameters to determine the comprehensive transportation performance of each vehicle in the process of executing the task, determine the initial matching plan, and optimize the plan in combination with user satisfaction and the impact of vehicle reservation requests to obtain the optimal matching plan, which can make the matching plan for vehicles and tasks more accurate, minimize transportation time and cost, and improve overall transportation efficiency.

[0046] Scheduling optimization module, including: A data analysis unit is configured to obtain and store historical transport task data, the historical transport task data including task execution time, vehicle driving path, transport efficiency and abnormal situation records, analyze the vehicle driving path and combine it with road traffic information, evaluate the traffic conditions of different time periods and different routes, and identify efficient transport paths; Monitor the current task load, including the number of tasks to be performed, the progress of the assigned tasks, and the use of transportation resources; A route optimization unit is configured to collect current road traffic information, including road congestion, construction sections, and accident information, and evaluate the road traffic status of each route in combination with historical traffic information; The optimal driving route is calculated based on the vehicle's current location, destination, task urgency and road conditions. The optimal driving route is calculated based on the principles of shortest path, optimal task matching and minimum empty driving rate. The optimal vehicle dispatch plan is generated based on the optimal driving route and sent to relevant vehicles.

[0047] In the above embodiment, the intelligent scheduling of transportation tasks is realized through the collaborative work of the data analysis unit and the path optimization unit. By storing and analyzing historical transportation task data, including information such as task execution time, vehicle driving path, transportation efficiency and abnormal conditions, the system can identify efficient transportation paths and provide optimization references for the scheduling of future tasks. For example, the system can identify peak hours or congested sections within certain time periods based on historical task data, so as to avoid these unfavorable factors in the future scheduling process and improve transportation efficiency. By collecting current road traffic information in real time, including road congestion, construction sections and accident information, and combining historical traffic data to evaluate the traffic status of different routes. Based on this information, the system can calculate the optimal driving path, and adopt optimization principles such as the shortest path, optimal task matching, and minimum empty driving rate to generate the optimal vehicle scheduling plan, which can minimize transportation time and cost and improve overall transportation efficiency.

[0048] Task matching module, including: A task parsing unit is configured to receive and parse transportation requirements, the transportation requirements including task type, departure point, destination point, cargo type, cargo weight, time requirement and priority information, and set constraint conditions for matching tasks according to the time requirement and priority information of the transportation task; The vehicle screening unit is configured to receive the transportation demand data provided by the task analysis unit, and interact with the vehicle information collection module to obtain the real-time operation status information of the vehicle, and screen available vehicles that meet the load capacity, are in normal operation, and are not in the task execution, fault or maintenance state according to the transportation demand, and preliminarily screen candidate vehicles that are close and suitable for executing the task in combination with the current location and historical driving trajectory of the vehicle, and generate a candidate vehicle list; A matching calculation unit is configured to calculate optional matching schemes between tasks and vehicles based on the state information of the vehicles in the candidate vehicle list, based on the shortest path and the optimal task matching principle, and optimize the optional matching scheme results in combination with the optimal vehicle scheduling scheme provided by the scheduling optimization module; Among the optional matching solutions, a vehicle with a short driving distance, high mission efficiency and low fuel consumption is selected to generate the optimal matching solution; The matching confirmation unit is configured to receive the optimal matching solution provided by the matching calculation unit, confirm the final task allocation solution 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 solution, the real-time operation status information of the vehicle is continuously monitored, and when a vehicle failure, route deviation or sudden traffic situation occurs, the task matching solution is adjusted interactively with the exception handling module; Based on the final task allocation plan, task execution instructions are sent to the successfully matched vehicles. The task execution instructions include task details, departure location, destination point, optimal driving route and estimated completion time to generate task matching results.

[0049] In the above embodiment, by analyzing the transportation demand, considering the task type, departure point, destination point, cargo type, cargo weight, time requirements and priority information, the rationality of task matching is ensured, and by real-time monitoring of vehicle status, available vehicles that meet the task requirements are screened out, and the candidate vehicle list is optimized in combination with the vehicle's historical driving trajectory and current location, 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 principles, the candidate vehicles are calculated and screened to select the vehicle with the highest task efficiency and the lowest fuel consumption. In addition, the running status of the vehicle is continuously monitored during the task execution process, 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 the transportation task smoother and reducing task delays and resource waste.

[0050] The matching confirmation unit is also used to: Determine the importance of vehicle driving distance, mission execution efficiency, and fuel consumption to the current task in combination with task requirements, and thus determine the vehicle impact weights of vehicle driving distance, mission execution efficiency, and fuel consumption; The vehicle influence weight based on the driving distance, task execution efficiency, and fuel consumption is combined with the vehicle's driving distance, task execution efficiency, and fuel consumption to determine a first optimization factor for optimizing the optimal matching solution; Obtain the road condition of the vehicle arriving at the mission departure point in the optimal matching solution, and determine the driving condition of the first vehicle corresponding to the vehicle arriving at the mission departure point in combination with the real-time road traffic condition of the vehicle arriving at the mission departure point; Based on the preset departure time and estimated completion time of the vehicle from the mission departure point to the destination point in the optimal matching solution, the historical road traffic states of the same time interval are selected from the historical road traffic state set to obtain the optimal historical road traffic state set; The real-time road traffic status from the mission departure point to the destination point and each historical road traffic status in the optimal historical road traffic status set are input into a preset road traffic prediction model, so as to predict the road traffic status of the vehicle from the mission departure point to the destination point and obtain the predicted road traffic status; Obtain the road condition of the vehicle's optimal driving path from the mission starting point to the destination point, and determine the second vehicle driving condition of the vehicle during the mission process in combination with the predicted road traffic condition; Determine a second optimization factor for optimizing the optimal matching solution based on the first vehicle driving condition and the second vehicle driving condition combined with the vehicle weather influence coefficient; Obtaining the task urgency of the current task, and optimizing the optimal matching solution in combination with the first optimization factor and the second optimization factor, to determine the initial task allocation solution for the current vehicle to perform the task; The initial task allocation plan is simulated to obtain the plan decision score of the simulated plan, and the initial task allocation plan with a plan decision score higher than the preset minimum decision score is taken as the final task allocation plan.

[0051] In the above embodiment, the mission requirement refers to the specific requirements of the mission that needs to be performed, such as the mission type, mission location, mission time, etc. The mission requirement will affect the importance of factors such as vehicle driving distance, mission execution efficiency and fuel consumption.

[0052] In the above embodiment, the vehicle travel distance refers to the total distance that the vehicle needs to travel from the starting point to the destination point.

[0053] In the above embodiment, the task execution efficiency is determined by the speed and quality of the vehicle completing the task, which 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].

[0054] In the above embodiment, fuel consumption refers to the amount of fuel consumed by the vehicle during driving.

[0055] In the above embodiment, the vehicle influence weight is determined based on the importance of vehicle travel distance, task execution efficiency and fuel consumption to the current task. The vehicle influence weight is used to consider the importance of different factors when selecting the optimal matching solution, where the value range of the vehicle influence weight is (0,1).

[0056] In the above embodiment, the first optimization factor is an optimization reference for the best matching solution between the vehicle and the task determined by combining factors such as the vehicle's driving distance, task execution efficiency, and fuel consumption.

[0057] In the above embodiment, the road surface state refers to the road surface condition of the road on which the vehicle is traveling, such as whether the road surface is flat, whether there are potholes, and the road surface friction, etc. The road surface state will affect the driving condition and fuel consumption of the vehicle.

[0058] In the above embodiment, the real-time road traffic status refers to the real-time information such as the real-time driving speed of vehicles on the current road, traffic flow, etc. The real-time road traffic status reflects the congestion level and traffic capacity of the road.

[0059] In the above embodiment, the first vehicle driving condition is a vehicle driving condition determined based on the road surface condition and real-time road traffic condition from the current position of the vehicle to the mission starting point, reflecting the driving condition of the vehicle before starting to perform the mission.

[0060] In the above embodiment, the optimal historical road traffic state set is a state set obtained by screening out historical road traffic state with the same time interval as the preset departure time and the expected completion time of the vehicle from the historical road traffic state set. For example, if the preset departure time is 10 o'clock, the expected completion time is 6 hours, and the expected arrival time is 16 o'clock, then the historical road traffic state corresponding to the time interval from 10 o'clock to 16 o'clock in the historical road traffic state set is extracted to obtain the optimal historical road traffic state set.

[0061] In the above embodiment, the preset road traffic prediction model is a mathematical model for predicting future road traffic conditions, and the prediction can be performed based on information such as historical road traffic conditions and real-time road traffic conditions in the optimal historical road traffic condition set.

[0062] In the above embodiment, the predicted road traffic status is the result obtained by using a preset road traffic prediction model to predict the road traffic status of the vehicle from the mission starting point to the destination point.

[0063] In the above embodiment, the second vehicle driving condition is the driving condition of the vehicle during the execution of the task determined based on the predicted road traffic condition and road surface condition.

[0064] In the above embodiment, the vehicle weather impact coefficient is a coefficient used to evaluate the impact of weather on vehicle driving conditions. 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 vehicle driving speed and fuel consumption.

[0065] In the above embodiment, the second optimization factor is an optimization reference for the best matching solution between the vehicle and the task determined by combining the vehicle weather influence coefficient, the first vehicle driving condition and the second vehicle driving condition.

[0066] In the above embodiment, the task urgency is the importance and urgency of the current task. The task urgency will affect the formulation and optimization of the task allocation plan.

[0067] In the above embodiment, 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.

[0068] In the above embodiment, the solution simulation is to input the initial task allocation solution into the virtual machine for simulation operation to evaluate the effect of the initial task allocation solution in the actual execution process, which is helpful to discover potential problems and optimize them.

[0069] In the above embodiment, the solution decision score is the score of the solution simulation result obtained after evaluating the simulation solution, which is used to reflect the execution effect of the simulation solution in the actual execution process. The evaluation can be performed based on multiple task completion indicators (such as task completion time, fuel consumption, etc.), where the value of the solution decision score is .

[0070] In the above embodiment, the preset minimum decision score is the minimum score threshold for evaluating whether the simulation scheme is qualified. The value range of the preset minimum decision score is (5, 8), and only the initial task allocation scheme whose scheme decision score is higher than the preset minimum decision score can be used as the final task allocation scheme.

[0071] The working principle of the above technical solution is: first, by combining the current task requirements to determine the vehicle impact weights of vehicle driving distance, task execution efficiency, and fuel consumption, the first optimization factor of the best matching solution is determined; then, the vehicle weather impact coefficient is combined with the first vehicle driving condition determined by the road surface condition from the current location to the departure location and the real-time road traffic status, and the second vehicle driving condition determined by the road surface condition from the departure location to the destination location and the predicted road traffic status, to obtain the second optimization factor; then, based on the urgency of the task, the solution is optimized in combination with the first optimization factor and the second optimization factor, and simulation is performed based on the solution optimization results; finally, the solution decision score is determined based on the simulation results; when the solution decision score is higher than the preset minimum decision score, the solution optimization result is used as the final task allocation solution to execute the task.

[0072] The beneficial effects of the above technical solution are: by determining the vehicle impact weight in combination with task requirements, adjusting the optimal matching solution, optimizing the solution in combination with the weather impact coefficient and vehicle driving conditions, and optimizing the solution again in combination with the task urgency, the obtained optimization solution can be made more consistent with the real-time vehicle conditions, and the final task allocation solution can be determined, thereby improving the accuracy of task matching, making the execution of transportation tasks smoother, and reducing task delays and resource waste.

[0073] Exception handling module, including: An abnormality detection unit is configured to receive and analyze the real-time operation status data provided by the vehicle information collection module, and set abnormal situation judgment conditions, the abnormal situation including vehicle failure, route deviation, sudden traffic conditions and abnormal task execution; When an abnormal situation is detected, the abnormal event data is recorded, which includes the time of occurrence, type of abnormality and scope of impact; An 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 failure occurs, determine whether the faulty vehicle can continue to perform the task. If not, obtain alternative vehicle resources from the task matching module, select the best alternative vehicle and send an emergency dispatch instruction; When a route deviation occurs, the deviation degree of the current vehicle is calculated and combined with the road traffic status provided by the scheduling optimization module to generate an adjusted driving path and send a path adjustment instruction to the abnormal vehicle; When an unexpected traffic situation occurs, the task execution path is recalculated based on the real-time road traffic status provided by the scheduling optimization module, and a new optimal driving plan is generated. Based on the optimal driving plan, new driving instructions are sent to the affected vehicles. When an abnormal task execution occurs, such as if the vehicle is stranded for a long time or fails to complete the task as planned, a task status confirmation instruction is sent to the vehicle to confirm the reason for the delay, and the task execution plan is adjusted in combination with the task matching module and the scheduling optimization module; Record task execution exceptions and update the task execution status after exception handling is completed.

[0074] When a route deviation occurs, the deviation degree of the current vehicle is calculated and combined with the road traffic status provided by the scheduling optimization module to generate an adjusted driving path, and a path adjustment instruction is sent to the abnormal vehicle, including: When a route deviation occurs, real-time vehicle status parameters of the vehicle are obtained based on 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; Determine the vehicle deviation index based on the lateral position and current heading angle in the real-time vehicle position combined with the lateral coordinates and tangent direction angle of the optimal driving path at the vehicle position; Determine the time warning index based on the current heading angle, real-time vehicle speed and the lateral distance from the current vehicle position to the standard lane line; The deviation coefficient of the current vehicle is calculated by combining the vehicle deviation index and the time warning index, so as to determine the deviation degree of the current vehicle; According to the current vehicle's deviation degree and the road traffic status provided by the scheduling optimization module, an adjusted driving path is generated and a path adjustment instruction is sent to the abnormal vehicle.

[0075] In the above embodiment, the deviation coefficient of the current vehicle is T; ; Where T is the deviation coefficient of the current vehicle, is the real-time lateral position of the current vehicle, The lateral coordinates of the vehicle position corresponding to the preset driving path for the final task allocation plan, is the current heading angle, The final task allocation solution corresponds to the tangent direction angle of the preset driving path at the vehicle position. is the lateral distance from the current vehicle position to the standard lane line, is the current real-time speed of the vehicle, is the weight coefficient of distance deviation, is the weight coefficient of heading deviation, is the deviation indicator, is the influence weight of the deviation index, It is a time warning indicator. is the impact weight of the time warning indicator, e is the base of the natural logarithm, is the road anomaly factor corresponding to the current vehicle position, where the road anomaly factor is related to the impact of road congestion and road construction, and 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). The sum of the weight coefficients of distance deviation and heading deviation is 1, and the sum of the weights of the deviation index influence weight and the time warning index influence weight is 1. The value range of the weight coefficients of distance deviation and heading deviation is (0,1), and the value range of the weights of the deviation index influence weight and the time warning index influence weight is (0,1).

[0076] In the above embodiment, The task execution path simulation is performed based on the final task allocation plan combined with vehicle characteristics, so as to determine the tangent direction angle and lateral coordinate of each vehicle position in the preset driving path corresponding to the final task allocation plan based on the simulation results.

[0077] In the above embodiment, based on the monotonicity, smooth data and dimensionless processing characteristics of the square root function, the square root function is selected as the calculation function of 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, the distance deviation and the heading deviation are both small values, and the square root function has good stability when processing small values, which can avoid the situation where the denominator is zero or the result tends to infinity, thereby ensuring the stability and reliability of the driving assistance system.

[0078] In the above embodiment, the weight coefficients of the distance deviation and the heading deviation, as well as the deviation index influence weight and the time warning index influence weight are all obtained by using the hierarchical analysis method to compare the importance of each other and solve the matrix constructed after scoring. For example, for the weight coefficients corresponding to the distance deviation of D and the heading deviation of H, the judgment is given by the 1-9 scaling method according to the degree of importance, forming 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 opposite degrees of importance). 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 each row of the normalized matrix to get the weight vector, and normalize the weight vector to get 0.2, 0.8.

[0079] In the above embodiment, the minimum threshold of sinθ is 0.001. If sinθ is less than the minimum threshold, sinθ is replaced by the minimum threshold to calculate the deviation coefficient.

[0080] The beneficial effect of the above technical solution is: by calculating the deviation between the vehicle driving condition of the current vehicle and the predicted vehicle driving condition of the corresponding vehicle at the current moment in the final task allocation plan, the degree of deviation of the current vehicle is judged, and the path is adjusted in combination with the road traffic status provided by the scheduling optimization module. This can make the abnormal judgment of the current vehicle more timely and accurate, and at the same time, the driving instruction can be adjusted in time, thereby improving the accuracy of the transportation task.

[0081] In the above embodiments, through the collaborative work of the anomaly detection unit and the emergency dispatch unit, efficient anomaly monitoring and processing are achieved, vehicle operation status data is analyzed in real time, abnormal situation judgment conditions are set, and relevant data is automatically recorded when an anomaly occurs, so that problems can be discovered in the first time, and the corresponding emergency handling mechanism is triggered, and different emergency strategies are executed according to the type of anomaly. For example, in the event 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 the route deviates or an emergency traffic condition occurs, the system can combine the road traffic status analysis of the dispatch optimization module, recalculate the optimal driving path, and send new driving instructions to the affected vehicles, which greatly improves the reliability of the system and ensures the timely completion of the transportation task.

[0082] See also Figure 2The method for transport vehicle reservation management and scheduling based on the Internet of Things is applied to the above-mentioned transport vehicle reservation management and scheduling system based on the Internet of Things, and includes the following steps: Step 1: Through the vehicle information collection module, use GPS and IoT sensors to obtain the real-time location data, load status, fuel level, driving speed and historical travel data of the transport vehicle, determine whether the vehicle is in the task execution state, and record the start time, expected 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 and pre-examines the reservation request in combination with the vehicle operation status parameters provided by the vehicle information collection module. In combination with the scheduling optimization module and the task matching module, the optimal matching solution is obtained, the vehicle resources are locked, and the reservation confirmation information is generated and sent to the user interaction module. Step 3: The dispatch optimization module obtains and stores historical transportation task data, evaluates efficient transportation routes based on road traffic information, calculates the optimal driving route based on the vehicle's current location, destination, task urgency, and road traffic conditions, generates the optimal vehicle dispatch plan based on the optimal driving route, and sends dispatch instructions to relevant vehicles; Step 4: The task matching module receives and analyzes the transportation demand, sets task matching constraints, and selects available vehicles that meet the load capacity and are in normal operation status based on the vehicle operation status data, calculates the optimal matching solution, and sends task execution instructions to the successfully matched vehicles; Step 5: The exception handling module monitors the vehicle operation status in real time, identifies abnormal situations, executes emergency dispatch strategies according to the type of abnormality, and sends abnormal notifications to the user interaction module. After completing the exception handling, it updates the task execution status.

[0083] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. The transportation vehicle reservation management and scheduling system based on the Internet of Things is characterized by: include: A vehicle information collection module configured to collect operating status information of transport vehicles in real time; A reservation management module is configured to process a user's vehicle reservation request, and to allocate resources in combination with the vehicle status and the reservation request, to determine the comprehensive transportation performance of the vehicle in the process of executing the task in combination with the historical operation status parameters and the real-time operation status parameters, and to influence the optimization plan in combination with the user satisfaction and the request for the vehicle to make a reservation; The scheduling optimization module is configured to determine the vehicle impact weight based on the real-time collected data and vehicle reservation status, combined with the task requirements, and optimize the plan based on the weather impact coefficient, vehicle driving conditions and task urgency, to generate the optimal vehicle scheduling plan; A task matching module configured to intelligently match vehicle status with task requirements; an exception handling module, configured to handle the exception in an emergency situation, calculate the deviation between the vehicle driving condition of the current vehicle and the vehicle predicted driving condition of the corresponding vehicle at the current moment in the final task allocation plan, determine the degree of vehicle deviation and adjust the path; The user interaction module is configured to provide appointment submission, modification and cancellation functions, and display vehicle status and task progress.

2. The transportation vehicle reservation management and scheduling system based on the Internet of Things as claimed in claim 1, characterized in that: The vehicle information collection module obtains the real-time location data of the vehicle through GPS and IoT sensor devices, and obtains the vehicle's operating status parameters, which include load status, fuel level, driving speed and historical travel data, to determine whether the vehicle is currently in a task execution state, and records the start time, expected end time and task completion status of the task.

3. The transportation vehicle reservation management and scheduling system based on the Internet of Things as claimed in claim 1, characterized in that: The reservation management module comprises: A reservation processing unit is configured to receive a vehicle reservation request submitted by a user, the reservation request including a reservation time, transportation requirements, a departure point and a destination point, parse the reservation request information and pre-examine the reservation request; The resource allocation unit is configured to combine the vehicle reservation request, interact with the scheduling optimization module and the task matching module according to the real-time operating status parameters provided by the vehicle information collection module and the task execution status of the vehicle, obtain the optimal matching solution, lock the vehicle resources based on the optimal matching solution, and generate reservation confirmation information, which 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.

4. The transportation vehicle reservation management and dispatching system based on the Internet of Things as claimed in claim 3 is characterized by: The reservation management module is also used for: Obtain a set of historical operating status parameters of each vehicle in the effective operating cycle in the vehicle information collection module; The real-time operating status parameters are combined with the historical operating status parameter set to obtain a comprehensive operating status set for 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 state subsets corresponding to each type of parameter in the classified operating state set according to the time series characteristics, inputting the sorting results into the same coordinate system, and performing curve fitting to obtain a vehicle transportation state curve set of the current vehicle; Analyze the curve fluctuation of each state curve in the vehicle transportation state curve set, so as to obtain the comprehensive transportation performance of the current vehicle; Combine the comprehensive transportation performance of each vehicle with the vehicle's task execution status, thereby interacting with the scheduling optimization module and the task matching module to obtain an initial matching solution; Obtain the user satisfaction of each vehicle in the historical transportation process, and then adjust the vehicle matching results in the initial matching plan based on the user satisfaction to obtain the optimized matching plan; Obtain the request response time and request response agreement of each vehicle to the vehicle reservation request within the effective operation cycle, and obtain the request impact factor of each vehicle; Determine the request influence weight of the request influence factor based on the historical influence degree of the request influence factor on the vehicle matching solution of each vehicle during the effective operation cycle; The optimized matching scheme is optimized again by combining the vehicle's request impact factor and the corresponding request impact weight to obtain the optimal matching scheme.

5. The transportation vehicle reservation management and scheduling system based on the Internet of Things as claimed in claim 1, characterized in that: The scheduling optimization module includes: A data analysis unit is configured to obtain and store historical transport task data, wherein the historical transport task data includes task execution time, vehicle driving path, transport efficiency and abnormal situation records, analyze the vehicle driving path and combine it with road traffic information, evaluate the traffic conditions of different time periods and different routes, and identify efficient transport paths; Monitor the current task load, including the number of tasks to be performed, the progress of the assigned tasks, and the use of transportation resources; A route optimization unit is configured to collect current road traffic information, including road congestion, construction sections, and accident information, and evaluate the road traffic status of each route in combination with historical traffic information; The optimal driving path is calculated based on the vehicle's current location, destination, task urgency and road traffic conditions. The optimal driving path is calculated based on the principles of shortest path, optimal task matching and minimum empty driving rate. The optimal vehicle dispatch plan is generated based on the optimal driving path and sent to the relevant vehicles.

6. The transportation vehicle reservation management and dispatching system based on the Internet of Things as claimed in claim 1, characterized in that: The task matching module includes: A task parsing unit is configured to receive and parse transportation requirements, wherein the transportation requirements include task type, departure location, destination point, cargo type, cargo weight, time requirement and priority information, and set constraint conditions for matching tasks according to the time requirement and priority information of the transportation task; The vehicle screening unit is configured to receive the transportation demand data provided by the task analysis unit, and interact with the vehicle information collection module to obtain the real-time operation status information of the vehicle, and screen available vehicles that meet the load capacity, are in normal operation, and are not in the task execution, failure or maintenance state according to the transportation demand, and preliminarily screen candidate vehicles that are close and suitable for executing the task in combination with the current location and historical driving trajectory of the vehicle, and generate a candidate vehicle list; A matching calculation unit is configured to calculate optional matching schemes between tasks and vehicles based on the state information of the vehicles in the candidate vehicle list, based on the shortest path and the optimal task matching principle, and optimize the optional matching scheme results in combination with the optimal vehicle scheduling scheme provided by the scheduling optimization module; Among the optional matching solutions, a vehicle with a short driving distance, high mission efficiency and low fuel consumption is selected to generate the optimal matching solution; The matching confirmation unit is configured to receive the optimal matching solution provided by the matching calculation unit, confirm the final task allocation solution 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 solution, the real-time operation status information of the vehicle is continuously monitored, and when a vehicle failure, route deviation or sudden traffic situation occurs, the task matching solution is adjusted interactively with the exception handling module; Based on the final task allocation plan, a task execution instruction is sent to the successfully matched vehicle, and the task execution instruction includes task details, departure location, destination point, optimal driving path and estimated completion time to generate a task matching result.

7. The transportation vehicle reservation management and dispatching system based on the Internet of Things as claimed in claim 6 is characterized by: The matching confirmation unit is further used for: Determine the importance of vehicle driving distance, mission execution efficiency, and fuel consumption to the current task in combination with task requirements, and thus determine the vehicle impact weights of vehicle driving distance, mission execution efficiency, and fuel consumption; The vehicle influence weight based on the driving distance, task execution efficiency, and fuel consumption is combined with the vehicle's driving distance, task execution efficiency, and fuel consumption to determine a first optimization factor for optimizing the optimal matching solution; Obtain the road condition of the vehicle arriving at the mission departure point in the optimal matching solution, and determine the driving condition of the first vehicle corresponding to the vehicle arriving at the mission departure point in combination with the real-time road traffic condition of the vehicle arriving at the mission departure point; Based on the preset departure time and estimated completion time of the vehicle from the mission departure point to the destination point in the optimal matching solution, the historical road traffic states of the same time interval are selected from the historical road traffic state set to obtain the optimal historical road traffic state set; The real-time road traffic status from the mission departure point to the destination point and each historical road traffic status in the optimal historical road traffic status set are input into a preset road traffic prediction model, so as to predict the road traffic status of the vehicle from the mission departure point to the destination point and obtain the predicted road traffic status; Obtain the road condition of the vehicle's optimal driving path from the mission starting point to the destination point, and determine the second vehicle driving condition of the vehicle during the mission process in combination with the predicted road traffic condition; Determine a second optimization factor for optimizing the optimal matching solution based on the first vehicle driving condition and the second vehicle driving condition combined with the vehicle weather influence coefficient; Obtaining the task urgency of the current task, and optimizing the optimal matching solution in combination with the first optimization factor and the second optimization factor, to determine the initial task allocation solution for the current vehicle to perform the task; The initial task allocation plan is simulated to obtain the plan decision score of the simulated plan, and the initial task allocation plan with a plan decision score higher than the preset minimum decision score is taken as the final task allocation plan.

8. The transportation vehicle reservation management and dispatching system based on the Internet of Things as claimed in claim 1, characterized in that: The exception handling module comprises: An abnormality detection unit is configured to receive and analyze the real-time operating status data provided by the vehicle information collection module, and set abnormal situation judgment conditions, wherein the abnormal situation includes vehicle failure, route deviation, sudden traffic conditions, and abnormal task execution; When an abnormal situation is detected, the abnormal event data is recorded, and the abnormal event data includes the abnormal occurrence time, abnormal type and impact range; An 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 failure occurs, determine whether the faulty vehicle can continue to perform the task. If not, obtain alternative vehicle resources from the task matching module, select the best alternative vehicle and send an emergency dispatch instruction; When a route deviation occurs, the deviation degree of the current vehicle is calculated and combined with the road traffic status provided by the scheduling optimization module to generate an adjusted driving path and send a path adjustment instruction to the abnormal vehicle; When an unexpected traffic situation occurs, the task execution path is recalculated based on the real-time road traffic status provided by the scheduling optimization module, and a new optimal driving plan is generated. Based on the optimal driving plan, new driving instructions are sent to the affected vehicles. When an abnormal task execution occurs, such as if the vehicle is stranded for a long time or fails to complete the task as planned, a task status confirmation instruction is sent to the vehicle to confirm the reason for the delay, and the task execution plan is adjusted in combination with the task matching module and the scheduling optimization module; Record task execution exceptions and update the task execution status after exception handling is completed.

9. The transportation vehicle reservation management and dispatching system based on the Internet of Things as claimed in claim 8, characterized in that: When a route deviation occurs, the deviation degree of the current vehicle is calculated and combined with the road traffic status provided by the scheduling optimization module to generate an adjusted driving path, and a path adjustment instruction is sent to the abnormal vehicle, including: When a route deviation occurs, real-time vehicle status parameters of the vehicle are obtained based on 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; Determine the vehicle deviation index based on the lateral position and current heading angle in the real-time vehicle position combined with the lateral coordinates and tangent direction angle of the optimal driving path at the vehicle position; Determine the time warning index based on the current heading angle, real-time vehicle speed and the lateral distance from the current vehicle position to the standard lane line; The deviation coefficient of the current vehicle is calculated by combining the vehicle deviation index and the time warning index, so as to determine the deviation degree of the current vehicle; According to the current vehicle's deviation degree and the road traffic status provided by the scheduling optimization module, an adjusted driving path is generated and a path adjustment instruction is sent to the abnormal vehicle.

10. A transportation vehicle reservation management and scheduling method based on the Internet of Things, applied to a transportation vehicle reservation management and scheduling system based on the Internet of Things as claimed in any one of claims 1 to 9, characterized in that: The steps include: Step 1: Through the vehicle information collection module, use GPS and IoT sensors to obtain the real-time location data, load status, fuel level, driving speed and historical travel data of the transport vehicle, determine whether the vehicle is in the task execution state, and record the start time, expected 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 and pre-examines the reservation request in combination with the vehicle operation status parameters provided by the vehicle information collection module. In combination with the scheduling optimization module and the task matching module, the optimal matching solution is obtained, the vehicle resources are locked, and the reservation confirmation information is generated and sent to the user interaction module. Step 3: The dispatch optimization module obtains and stores historical transportation task data, evaluates efficient transportation routes based on road traffic information, calculates the optimal driving route based on the vehicle's current location, destination, task urgency, and road traffic conditions, generates the optimal vehicle dispatch plan based on the optimal driving route, and sends dispatch instructions to relevant vehicles; Step 4: The task matching module receives and analyzes the transportation demand, sets the task matching constraints, and selects available vehicles that meet the load capacity and are in normal operation status based on the vehicle operation status data, calculates the optimal matching solution, and sends the task execution instruction to the successfully matched vehicle; Step 5: The exception handling module monitors the vehicle operation status in real time, identifies abnormal situations, executes emergency dispatch strategies according to the type of abnormality, and sends abnormal notifications to the user interaction module. After completing the exception handling, it updates the task execution status.

Images