A material transportation and unloading management system based on multi-source positioning

Abstract

The application relates to a material transportation and unloading management system based on multi-source positioning, and relates to the technical field of material transportation management. The system comprises the following modules: a task management module for generating transportation task information, a vehicle scheduling module for determining a transportation scheduling scheme and operation guidance information, a multi-source positioning module for determining the vehicle position of a target vehicle, and a monitoring module for determining monitoring information of the target vehicle, internal regulation strategies and hierarchical unloading decision information. The application can realize efficient material transportation and unloading management, accurately schedule vehicles, accurately obtain the vehicle position, effectively monitor the material state, and improve the overall transportation efficiency and management level.

Description

Technical Field

[0001] This invention relates to the field of material transportation management technology, and in particular to a material transportation and unloading management system based on multi-source positioning. Background Technology

[0002] In the field of water conservancy engineering construction, the transportation and unloading management of dam materials is a key link in dam filling construction, and its transportation efficiency and management level directly affect the progress and quality of the project.

[0003] Traditional material transportation and unloading management often employs manual scheduling and simple location methods. Manual scheduling relies primarily on experienced managers to arrange vehicles and assign tasks based on site conditions, a method that can handle small-scale material transportation tasks to a certain extent. For vehicle location, a single satellite positioning system is typically used to obtain the approximate location information of the vehicles. As for material monitoring, it often involves only simple visual inspections or sampling tests.

[0004] However, traditional management methods have significant drawbacks. Manual dispatching is easily influenced by subjective factors, making comprehensive and accurate dispatching difficult and potentially leading to unreasonable vehicle allocation and low transportation efficiency. Single-method positioning is susceptible to signal interference in complex environments, such as tunnels and mountainous areas, making it impossible to accurately determine vehicle location and thus affecting the execution of transportation tasks. Simple material monitoring methods cannot grasp the real-time and comprehensive changes in material status, failing to promptly detect quality problems during transportation, thereby affecting the accuracy of unloading decisions. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a material transportation and unloading management system based on multi-source positioning, which aims to solve at least one of the above-mentioned technical problems.

[0006] The technical solution of the present invention to solve the above-mentioned technical problems is as follows: Firstly, this application provides a material transportation and unloading management system based on multi-source positioning, which adopts the following technical solution: A material transport and unloading management system based on multi-source positioning, comprising: The task management module is used to obtain project progress information and work environment information of the construction area, and generate transportation task information based on the project progress information, work environment information and preset construction plan; The vehicle dispatch module is used to obtain the qualification information of the associated drivers and the vehicle information of the corresponding transport vehicles based on the transport task information; to evaluate each associated transport vehicle based on the transport task information, vehicle information, operating environment information, qualification information and a preset vehicle evaluation function to obtain the suitability index of each transport vehicle; and to determine the transport dispatch plan and operation guidance information based on the suitability index of each transport vehicle and the transport task information. A multi-source positioning module is used to acquire the location signal information and transportation environment information of each target vehicle, and determine the vehicle position of the target vehicle based on the location signal information and transportation environment information. The monitoring module is used to acquire monitoring information of the materials inside each target vehicle; predict the material status change trend in future time periods based on the material monitoring information, the vehicle location of the target vehicle, and the transportation environment information; determine the monitoring information of the target vehicle based on the vehicle location, path, and the material status change trend in future time periods; and determine the internal control strategy and graded unloading decision information of the target vehicle based on the monitoring information.

[0007] The beneficial effects of this invention are as follows: The task management module can generate transportation task information based on the construction progress information, working environment, and construction plan of the construction area, ensuring that the transportation tasks meet the actual needs of construction; the vehicle scheduling module performs multi-dimensional matching and optimization of driver qualifications, vehicle status, environmental risks, and transportation tasks, realizing intelligent assessment and optimal allocation of transportation resources. Simultaneously, combined with the high-precision, continuous, and reliable vehicle location information provided by the multi-source positioning module in complex construction environments, a refined scheduling scheme that balances efficiency and safety can be formulated, and real-time collaborative collision avoidance between vehicles can be achieved, significantly reducing congestion and safety accident risks and improving transportation efficiency; the monitoring module predicts the trend of material moisture content changes based on material monitoring information and vehicle location, determines monitoring information, internal control strategies, and graded unloading decision information, ensuring the quality of material transportation and the rationality of unloading.

[0008] Based on the above technical solution, the present invention can be further improved as follows.

[0009] Furthermore, the task management module is specifically used for: The task management module is specifically used for: Acquire elevation point cloud data of the construction area, historical construction rates, real-time on-site status information of construction machinery, and work environment information; Outlier removal and smoothing filtering are performed on the elevation point cloud data to obtain preprocessed point cloud data. Based on the preprocessed point cloud data and the preset grid method, the current digital elevation model is constructed. Based on the current digital elevation model and historical digital elevation models, determine the volume change of the construction area; The current construction rate is calculated by reverse calculation based on the volume change. Based on the current working area of ​​the current digital elevation model and the preset construction plan, determine the design difference; The current construction rate, historical construction rate, design difference, real-time construction machinery status and working environment information are input into a preset demand prediction model to determine transportation task information. The transportation task information includes task priority, material type and specifications, transportation volume, source material yard location and target unloading area coordinates. The priority is determined based on the degree of predicted demand change trend, and the target unloading area coordinates and material type and specifications are determined based on the predicted working surface and the design parameters of the working surface.

[0010] The beneficial effects of adopting the above-mentioned further solution are as follows: The task management module processes elevation point cloud data to construct a high-precision current digital elevation model, and compares it with historical models to calculate volume changes, thereby accurately deriving the real-time construction rate. The real-time derived current construction rate, historical construction rate, design difference, construction machinery status, and work environment information are input into a preset demand prediction model for comprehensive analysis. This analysis considers instantaneous work progress and integrates historical trends, design objectives, construction capabilities, and environmental impacts, making the prediction of future material demand more comprehensive and accurate. Based on the prediction results, dynamically generated transportation task information ensures that material supply matches on-site filling needs.

[0011] Furthermore, the vehicle dispatching module, when determining the transportation dispatching plan and operational guidance information based on the suitability index and transportation task information of each transport vehicle, is specifically used for: The required number of vehicles is determined based on the volume of transportation. Based on the required number of vehicles and the suitability index of each transport vehicle, multiple target vehicles are identified. Based on the transportation task information, the operating environment information, and the information files of each target vehicle, the comprehensive risk level of the transportation task information is determined. The information files include historical transportation records, maintenance records, and performance degradation data. Based on the comprehensive risk level, task priority, material type and specifications, transportation volume, source material yard location and target unloading area coordinates of multiple target vehicles and transportation task information, an initial scheduling plan including the reserved passage time period and route is generated for each target vehicle through a spatiotemporal resource reservation model, resulting in an initial transportation scheduling scheme. The spatiotemporal resource reservation model aims to minimize the probability of total transportation time and total route conflict. Based on the initial transportation scheduling scheme, the wireless communication information of vehicles in the transportation state, and the preset cooperative collision avoidance decision model, the real-time collision avoidance decision information of each target vehicle is determined; Based on the comprehensive risk level, the initial transportation scheduling plan, and the real-time collision avoidance decision information, the initial transportation scheduling plan is optimized using an optimization algorithm to determine the final transportation scheduling plan. Based on transportation scheduling plans and real-time collision avoidance decision information, operational guidance information is determined.

[0012] The beneficial effects of adopting the above-mentioned further solutions are as follows: the vehicle scheduling module determines the required number of vehicles based on the transportation volume and selects target vehicles in conjunction with the applicability index, ensuring that an appropriate number and quality of vehicles are deployed to participate in the transportation task; the comprehensive risk level is determined based on transportation task information, operating environment information, and vehicle information files, allowing for a comprehensive consideration of the potential risks of the transportation task; the initial transportation scheduling plan is generated using a spatiotemporal resource reservation model, which minimizes the probability of total transportation time and total route conflicts, improving transportation efficiency; real-time collision avoidance decision information is determined based on the initial transportation scheduling plan, vehicle wireless communication information, and collaborative collision avoidance decision model, effectively avoiding vehicle collisions; the initial transportation scheduling plan is optimized through optimization algorithms, with the goal of minimizing overall cost, reducing transportation costs; and finally, operational guidance information is determined based on the transportation scheduling plan and real-time collision avoidance decision information, providing clear guidance to drivers and ensuring the efficient and safe operation of the material transportation and unloading management system.

[0013] Furthermore, the vehicle dispatching module, when determining the comprehensive risk level of the transportation task based on the transportation task information, the operating environment information, and the information files of each target vehicle, specifically uses the following: Based on the material type and specifications in the transportation task information, the material type hazard level is determined, and based on the material type hazard level, a preset task risk database is queried to determine the task risk coefficient; Based on the road slope value and weather condition value in the work environment information, calculate the percentage deviation of the road slope from the safe driving design slope and the degree of deviation of the weather condition from the safe operation standard, and determine the environmental risk coefficient based on the percentage deviation and degree of deviation. Based on the failure rate, maintenance records, and performance degradation data in the historical transportation records of each target vehicle, the inherent risk coefficient of each target vehicle is determined. The task risk coefficient, environmental risk coefficient, and each inherent risk coefficient are input into the risk level assessment model to obtain the comprehensive risk value; The overall risk level of the transportation task is determined based on the numerical range of the comprehensive risk value.

[0014] The beneficial effects of adopting the above-mentioned further solutions are as follows: Determining the material type hazard level based on material type specifications and obtaining the task risk coefficient from the preset task risk database allows for the prediction of transportation task risks due to material type based on historical accident statistics; calculating the percentage deviation of road slope and the deviation of weather conditions to determine the environmental risk coefficient allows for consideration of the impact of the operating environment on transportation tasks; determining the inherent risk coefficient based on vehicle historical transportation records, maintenance records, and performance degradation data allows for understanding the vehicle's own potential risks; inputting each risk coefficient into the evaluation model to obtain a comprehensive risk value, and then determining the risk level, helps to more accurately and comprehensively assess the comprehensive risk of transportation tasks, providing a reliable basis for subsequent transportation scheduling optimization, and ensuring transportation safety and efficiency.

[0015] Furthermore, the vehicle scheduling module is used to optimize the initial transportation scheduling plan based on the comprehensive risk level, the initial transportation scheduling plan, and the real-time collision avoidance decision information using an optimization algorithm. Specifically, when determining the transportation scheduling plan, it is used for: Based on the comprehensive risk level, the exploration factor and development factor of the optimization algorithm are adjusted, and the optimization algorithm is an improved particle swarm optimization algorithm. Based on the adjusted optimization algorithm, the initial scheduling plan, and the real-time collision avoidance decision, the initial transportation scheduling scheme is iteratively solved and optimized with preset optimization objectives and constraints to obtain a transportation scheduling scheme that satisfies the constraints and optimizes the objectives. The preset optimization objective is as follows: MinimizeZ = Σ(α × waiting time + β × delay loss + γ × energy cost); Where α, β, and γ are weight coefficients that are dynamically configured according to the task stage; The constraints include the number of vehicles required for the transportation task, the upper limit of the real-time traffic capacity of a single road, and the safety threshold of the vehicle's battery state of charge.

[0016] The beneficial effects of adopting the above-mentioned further scheme are as follows: By adjusting and improving the exploration and development factors of the particle swarm algorithm based on the comprehensive risk level, the vehicle scheduling module enables the algorithm to flexibly adjust the search strategy according to the risk situation, thereby improving the adaptability and search efficiency of the algorithm; by iteratively solving and optimizing the initial transportation scheduling scheme with preset optimization objectives and constraints, the optimal transportation scheduling scheme can be obtained, which satisfies the constraints such as the number of vehicles required for the transportation task, the upper limit of road capacity, and the safety threshold of vehicle battery charge status, and minimizes the comprehensive cost of waiting time, delay loss, and energy consumption.

[0017] Furthermore, the multi-source positioning module, when determining the vehicle position of the target vehicle based on the target vehicle's location signal information and transportation environment information, specifically uses the following methods: Based on the target vehicle's historical trajectory data, terrain digital elevation model, transportation environment information, and a preset signal masking prediction model, the signal blind spot of the road section ahead of the target vehicle and the corresponding confidence score are predicted. The transportation environment information includes satellite ephemeris and meteorological visibility data. Based on the signal blind spots of the road section in front of the target vehicle, the corresponding confidence score, and the preset tunnel map, the position and path of the target vehicle are matched with the blind spot heat map and electronic map in real time to determine the target positioning mode. Based on the signal blind spots of the road section in front of the target vehicle and the corresponding confidence score, target positioning mode, position signal information and real-time vehicle motion status, a fusion weight matrix of position signal information is determined. The fusion weight matrix represents the contribution weight of each position sensor data source in the filtering and fusion process. Based on the fusion weight matrix, position signal information, and a preset adaptive Kalman filter algorithm, the initial fusion positioning of the target vehicle is determined. Based on vehicle dynamics parameters, terrain digital elevation model, pre-set tunnel map and vehicle environment perception data, a vehicle environment digital twin model is constructed and updated. The vehicle environment digital twin model is used to perform error compensation and trajectory optimization on the initial fusion positioning to obtain the target positioning coordinates. Based on a preset electronic fence and historical trajectory, collision detection and continuity analysis are performed on the target location coordinates to obtain the vehicle position of the target vehicle.

[0018] The beneficial effects of adopting the above-mentioned further scheme are as follows: The multi-source positioning module combines historical trajectory data and terrain digital elevation models to predict the signal blind spots and confidence scores of the road sections ahead of the target vehicle. It uses the signal blind spots to determine the target positioning mode, and then determines the fusion weight matrix of the position signal information. The initial fusion positioning is determined by the adaptive Kalman filter algorithm. Then, the initial fusion positioning is obtained by error compensation and trajectory optimization using the vehicle environment digital twin model. Finally, the vehicle position of the target vehicle is determined by collision detection and continuity analysis. This can improve the accuracy and reliability of the target vehicle positioning and provide more accurate location information for material transportation and unloading management.

[0019] Furthermore, the multi-source positioning module, when determining the fusion weight matrix of position signal information based on the signal blind spot of the road segment ahead of the target vehicle and the corresponding confidence score, target positioning mode, position signal information, and the real-time motion state of the vehicle, is specifically used for: Based on preset quality assessment rules, the signal quality of satellite positioning signals, UWB positioning signals and inertial navigation data in the location signal information is assessed to obtain the quality assessment index of each signal; Based on the target positioning pattern, determine the initial set of fusion weight coefficients; Based on the signal blind spots of the road section in front of the target vehicle and the corresponding confidence scores, the real-time motion status of the vehicle, the quality assessment indicators of each signal, and the preset weight dynamic adjustment function, the initial set of fusion weight coefficients is adjusted to obtain the target fusion weight. Based on the target fusion weights, a fusion weight matrix for the adaptive Kalman filter algorithm is constructed, wherein the fusion weight matrix is ​​a diagonal matrix and the values ​​of the elements on its diagonal are determined by the target fusion weights.

[0020] The beneficial effects of adopting the above-mentioned further scheme are as follows: the multi-source positioning module performs signal quality assessment on satellite positioning signals, UWB positioning signals, and inertial navigation data to obtain corresponding quality assessment indicators, which can accurately understand the quality of each signal; the initial fusion weight coefficient set is determined according to the target positioning mode, providing a basis for subsequent adjustments; by combining the signal blind zone, the real-time motion state of the vehicle, the quality assessment indicators, and the dynamic adjustment function of the weights, the initial fusion weight coefficient set can be adjusted to obtain a target fusion weight that is more in line with the actual situation; and the fusion weight matrix used for the adaptive Kalman filter algorithm can accurately characterize the relative contribution of each sensor data source in the filtering and fusion process, thereby more accurately determining the position of the target vehicle.

[0021] Furthermore, when the monitoring module determines the monitoring information of the target vehicle based on the vehicle's location, path, and the trend of material moisture content changes at different depths in the future time period, it is specifically used for: The real-time vehicle location of the target vehicle is compared with the predetermined transportation route to calculate the path tracking deviation. Calculate the material state stability index based on the future trend of material moisture content change. Based on the path tracking deviation and the material state stability index, a comprehensive monitoring state vector is generated; Based on the comprehensive monitoring state vector and the preset state action mapping rule library, the monitoring information of the target vehicle is matched and determined. The monitoring information includes the vehicle deviation alarm level, the material status warning level, or the suggested timing of control intervention.

[0022] The beneficial effects of adopting the above-mentioned further solutions are as follows: the monitoring module compares the real-time location of the target vehicle with the predetermined path to calculate the path tracking deviation, which can promptly detect whether the vehicle deviates from the predetermined route; it calculates the material state stability index based on the material moisture content change trend, which can grasp the material state; it generates a comprehensive monitoring state vector, which can comprehensively reflect the vehicle transportation and material state; and it determines the monitoring information based on the comprehensive monitoring state vector and the preset rule base, which can provide vehicle deviation alarm level, material state warning level, or suggested control intervention timing, thereby achieving effective monitoring of the target vehicle.

[0023] Secondly, this application provides a material transportation and unloading management method based on multi-source positioning, which adopts the following technical solution: A material transportation and unloading management method based on multi-source positioning, comprising: Obtain project progress information and work environment information of the construction area, and generate transportation task information based on the project progress information, work environment information and preset construction plan; Based on the transportation task information, obtain the qualification information of the associated drivers and the vehicle information of the corresponding transportation vehicles; based on the transportation task information, vehicle information, operating environment information, qualification information and a preset vehicle evaluation function, evaluate each associated transportation vehicle to obtain the applicability index of each transportation vehicle; based on the applicability index of each transportation vehicle and the transportation task information, determine the transportation scheduling plan and operation guidance information. For each target vehicle, the location signal information and transportation environment information of the target vehicle are acquired, and the vehicle position of the target vehicle is determined based on the location signal information and transportation environment information of the target vehicle. For each target vehicle, acquire monitoring information of the materials inside the vehicle; based on the monitoring information, the vehicle's location, and transportation environment information, predict the material state change trend for future periods; based on the vehicle's location, path, and the predicted material state change trend for future periods, determine the target vehicle's monitoring information, and based on this monitoring information, determine the target vehicle's internal control strategy and graded unloading decision information.

[0024] Thirdly, this application provides a material transportation and unloading management method based on multi-source positioning, which adopts the following technical solution: A computer-readable storage medium, characterized in that it includes a computer program or instructions that, when executed on a computer, cause the computer to perform the method as described in the second aspect.

[0025] Additional aspects and advantages of this application will be set forth in part in the description which follows, and will become apparent from the description or may be learned by practice of this application. Attached Figure Description

[0026] Figure 1 This is a schematic diagram of a material transportation and unloading management system based on multi-source positioning, provided as an embodiment of the present invention. Figure 2 This is a flowchart illustrating a material transportation and unloading management method based on multi-source positioning, as provided in one embodiment of the present invention. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0028] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article, unless otherwise specified, generally indicates that the preceding and following related objects have an "or" relationship.

[0029] like Figure 1 As shown, a material transport and unloading management system 100 based on multi-source positioning includes: The task management module 101 is used to obtain the project progress information and work environment information of the construction area, and generate transportation task information based on the project progress information, work environment information and preset construction plan. The transportation task information includes task priority, material type and specifications, transportation volume, source material yard location and target unloading area coordinates. The vehicle dispatch module 102 is used to obtain the qualification information of associated drivers and the vehicle information of corresponding transport vehicles based on the transport task information. The qualification information includes driving qualification level, specific material type operation experience and working hours information. The vehicle information includes status, type and location. Based on the transport task information, vehicle information, working environment information, qualification information and a preset vehicle evaluation function, each associated transport vehicle is evaluated to obtain the suitability index of each transport vehicle. Based on the suitability index of each transport vehicle and the transport task information, a transport dispatch plan and operation guidance information are determined. The transport dispatch plan includes multiple target vehicles, the communication time period and path of each target vehicle. The multi-source positioning module 103 is used to acquire the location signal information and transportation environment information of each target vehicle, and determine the vehicle position of the target vehicle based on the location signal information and transportation environment information; the location signal information includes satellite positioning signals, UWB positioning signals and inertial navigation data, and the transportation environment information includes a terrain digital elevation model and a preset tunnel map; The monitoring module 104 is used to acquire monitoring information of the material inside each target vehicle, including multi-layer moisture content data; based on the monitoring information of the material, the vehicle location of the target vehicle, and transportation environment information, predict the trend of material moisture content change at different depths in the future; based on the vehicle location and path of the target vehicle and the trend of material moisture content change at different depths in the future, determine the monitoring information of the target vehicle, and based on the monitoring information, determine the internal control strategy and graded unloading decision information of the target vehicle.

[0030] In this system, the task management module 101 generates transportation task information based on the project progress information, working environment, and construction plan of the construction area, ensuring that the transportation tasks meet the actual needs of dam construction. The vehicle dispatching module 102 performs multi-dimensional matching and optimization of driver qualifications, vehicle status, environmental risks, and transportation tasks, realizing intelligent assessment and optimal allocation of transportation resources. At the same time, combined with the high-precision, continuous, and reliable vehicle location information provided by the multi-source positioning module 103 in complex construction environments, a refined dispatching plan that balances efficiency and safety can be formulated, and real-time collaborative collision avoidance between vehicles can be achieved, significantly reducing congestion and safety accident risks and improving transportation efficiency. The monitoring module 104 predicts the trend of material moisture content changes based on material monitoring information and vehicle location, determines monitoring information, internal control strategies, and graded unloading decision information, which can ensure the quality of material transportation and the rationality of unloading.

[0031] In this embodiment of the application, the task management module 101 is specifically used for: Acquire elevation point cloud data, historical filling rate, real-time on-site status information of construction machinery, and work environment information of the construction area; Outlier removal and smoothing filtering are performed on the elevation point cloud data of the construction area to obtain preprocessed point cloud data. Based on the preprocessed point cloud data and the preset grid method, the current digital elevation model is constructed. Based on the current digital elevation model and historical digital elevation models, determine the volume change of the construction area; The current construction rate is calculated by reverse calculation based on the volume change. Based on the current working area of ​​the current digital elevation model and the preset construction plan, determine the design difference; The current construction rate, historical construction rate, design difference, real-time construction machinery status and working environment information are input into a preset demand prediction model to determine transportation task information. The transportation task information includes task priority, material type and specifications, transportation volume, source material yard location and target unloading area coordinates. The priority is determined based on the degree of predicted demand change trend, and the target unloading area coordinates and material type and specifications are determined based on the predicted working surface and the design parameters of the working surface.

[0032] In the above embodiments, elevation point cloud data can be obtained through devices such as lidar, historical filling rates can be obtained from the project's historical records, real-time on-site status information of construction machinery can be obtained through sensors installed on the construction machinery, and work environment information can include weather, road conditions, etc., which can be obtained through weather stations and traffic monitoring equipment.

[0033] In this embodiment of the application, when the vehicle dispatching module 102 obtains the qualification information of the associated driver and the vehicle information of the corresponding transport vehicle based on the transport task information, it is specifically used for: Obtain the material type and specifications, the road complexity and safety requirements corresponding to the coordinates from the source material yard to the target unloading area from the transportation task information; Match the material type and specifications, the location of the source material yard to the coordinates of the target unloading area, the road complexity and safety requirements, with the pre-established mapping relationship database to determine the required target driving qualification level and operating experience requirements; Based on the target driving qualification level and operating experience requirements, a preset driver qualification database is queried to obtain the associated drivers and qualification information that meet the requirements; Based on the qualified associated drivers, the vehicle information of the transport vehicles is determined, and the status of the vehicle information is either transport status or pending dispatch status.

[0034] In this embodiment, the vehicle dispatching module 102 evaluates each associated transport vehicle based on transport task information, vehicle information, operating environment information, qualification information, and a preset vehicle evaluation function. The vehicle evaluation function can comprehensively consider factors such as vehicle performance and driver qualifications.

[0035] In this embodiment of the application, the vehicle scheduling module 102, when determining the transportation scheduling plan and operation guidance information based on the applicability index and transportation task information of each transportation vehicle, is specifically used for: The required number of vehicles is determined based on the volume of transportation. Based on the required number of vehicles and the suitability index of each transport vehicle, multiple target vehicles are identified. Based on the transportation task information, the operating environment information, and the information files of each target vehicle, the comprehensive risk level of the transportation task information is determined. The information files include historical transportation records, maintenance records, and performance degradation data. Based on the comprehensive risk level, task priority, material type and specifications, transportation volume, source material yard location and target unloading area coordinates of multiple target vehicles and transportation task information, an initial scheduling plan including the reserved passage time period and route is generated for each target vehicle through a spatiotemporal resource reservation model, resulting in an initial transportation scheduling scheme. The spatiotemporal resource reservation model aims to minimize the probability of total transportation time and total route conflict. Based on the initial transportation scheduling scheme, the wireless communication information of vehicles in the transportation state, and the preset cooperative collision avoidance decision model, the real-time collision avoidance decision information of each target vehicle is determined. The wireless communication information includes the vehicle position, speed, and direction of movement. The preset cooperative collision avoidance decision model is used to calculate the collision risk in real time based on the relative motion relationship between vehicles. Based on the comprehensive risk level, the initial transportation scheduling plan, and the real-time collision avoidance decision information, the initial transportation scheduling plan is optimized by an optimization algorithm to determine the transportation scheduling plan. The optimization algorithm aims to minimize the overall cost. Based on transportation scheduling plans and real-time collision avoidance decision information, operational guidance information is determined.

[0036] In the above embodiments, specifically, when the vehicle dispatching module 102 determines the comprehensive risk level of the transportation task based on the transportation task information, the operating environment information, and the information files of each target vehicle, it is specifically used for: Based on the material type and specifications in the transportation task information, the material type hazard level is determined, and based on the material type hazard level, a preset task risk database is queried to determine the task risk coefficient. The preset task risk database is based on historical accident statistics. Based on the road slope value and weather condition value in the work environment information, calculate the percentage deviation of the road slope from the safe driving design slope and the degree of deviation of the weather condition from the safe operation standard, and determine the environmental risk coefficient based on the percentage deviation and degree of deviation. Based on the failure rate, maintenance records, and performance degradation data in the historical transportation records of each target vehicle, the inherent risk coefficient of each target vehicle is determined. The task risk coefficient, environmental risk coefficient, and each inherent risk coefficient are input into the risk level assessment model to obtain the comprehensive risk value; The overall risk level of the transportation task is determined based on the numerical range of the comprehensive risk value.

[0037] By conducting a more detailed assessment of the overall risk level of transportation tasks, the vehicle dispatching module can more accurately formulate transportation dispatching plans, improving the safety and reliability of transportation. Considering multiple factors such as material type hazard level, environmental factors, and inherent vehicle risks, the risk assessment is more comprehensive and accurate, providing a more reliable basis for vehicle dispatching. Compared to traditional dispatching methods, it can better cope with various risk situations and reduce the probability of accidents.

[0038] In the above embodiments, when the vehicle scheduling module 102 determines the real-time collision avoidance decision information for each target vehicle based on the initial transportation scheduling scheme, the wireless communication information of vehicles in the transportation state, and the preset cooperative collision avoidance decision model, it is specifically used for: The vehicle's passage request is mapped to a specific spatiotemporal grid. When two or more vehicles request the same spatiotemporal grid, a conflict resolution algorithm is triggered. This algorithm calculates a comprehensive priority based on vehicle type, task urgency, and historical on-time rate, and automatically calculates and recommends alternative routes for low-priority vehicles. The real-time collision avoidance decision is generated using the collaborative collision avoidance decision model: For each vehicle, the motion status of other vehicles within a 50-meter radius and road geometry parameters are perceived in real time; the collision risk value is calculated using a preset formula: Risk = (relative distance / approach speed) × road complexity coefficient. Decisions are made based on the range of collision risk values ​​to obtain real-time collision avoidance decision information for each target vehicle.

[0039] In the above embodiments, the vehicle scheduling module 102 is used to optimize the initial transportation scheduling plan based on the comprehensive risk level, the initial transportation scheduling plan, and the real-time collision avoidance decision information using an optimization algorithm. Specifically, when determining the transportation scheduling plan, it is used for: Based on the comprehensive risk level, the exploration factor and development factor of the optimization algorithm are adjusted, and the optimization algorithm is an improved particle swarm optimization algorithm. Based on the adjusted optimization algorithm, the initial scheduling plan, and the real-time collision avoidance decision, the initial transportation scheduling scheme is iteratively solved and optimized with preset optimization objectives and constraints to obtain a transportation scheduling scheme that satisfies the constraints and optimizes the objectives. The preset optimization objective is as follows: MinimizeZ = Σ(α × waiting time + β × delay loss + γ × energy cost); Where α, β, and γ are weight coefficients that are dynamically configured according to the task stage; The constraints include the number of vehicles required for the transportation task, the upper limit of the real-time traffic capacity of a single road, and the safety threshold of the vehicle's battery state of charge. The adjustment of the exploration and development factors of the optimization algorithm based on the comprehensive risk level includes: If the overall risk level is greater than the preset risk threshold, the value of the exploration factor is increased based on the first step, and the value of the development factor is decreased based on the second step. If the overall risk level is less than the preset risk threshold, the value of the exploration factor is reduced based on the third step, and the value of the development factor is increased based on the fourth step.

[0040] The four steps are as follows: Step 1 refers to the increment used to increase the exploration factor value when the overall risk level is greater than the preset risk threshold; this is a preset positive value. Step 2 refers to the decrement used to decrease the development factor value when the overall risk level is greater than the preset risk threshold; this is a preset positive value. Step 3 refers to the decrement used to decrease the exploration factor value when the overall risk level is less than the preset risk threshold; this is a preset positive value. Step 4 refers to the increment used to increase the development factor value when the overall risk level is less than the preset risk threshold; this is a preset positive value.

[0041] The first and second step lengths are determined based on the degree to which the overall risk level exceeds a preset risk threshold. When the overall risk level is greater than the preset risk threshold, the difference between the overall risk level and the preset risk threshold is calculated. This difference is then used to calculate the first and second step lengths using a preset risk-step length mapping function. The third and fourth step lengths are determined based on the degree to which the preset risk threshold exceeds the overall risk level. When the overall risk level is less than the preset risk threshold, the difference between the preset risk threshold and the overall risk level is calculated. This difference is then used to calculate the third and fourth step lengths using a preset safety-step length mapping function.

[0042] Through the aforementioned step size adjustment mechanism, when the overall risk level is high, the first step is larger to significantly increase the value of the exploration factor, enabling the algorithm to increase its global search effort and find more possible safe scheduling schemes. Simultaneously, the second step is larger to significantly decrease the value of the development factor, reducing the algorithm's dependence on the current local optimum and avoiding the use of potentially unsafe scheduling schemes under high-risk conditions. When the overall risk level is low, the third step is larger to decrease the value of the exploration factor, reducing unnecessary global searches and saving computational resources. Simultaneously, the fourth step is larger to increase the value of the development factor, enabling the algorithm to quickly converge to the current optimal scheduling scheme and improve scheduling efficiency. Furthermore, upper and lower bound constraints are set for the adjustment of the exploration and development factors to ensure the stability and convergence of the algorithm.

[0043] The vehicle scheduling module 102 adjusts the exploration and development factors of the optimization algorithm based on the comprehensive risk level, enabling a better balance between the breadth and depth of the search under different risk conditions, making the optimization algorithm more flexible and efficient. With the goal of minimizing overall cost, it considers factors such as waiting time, delay losses, and energy costs, while also incorporating constraints to obtain a more reasonable transportation scheduling scheme, improving transportation efficiency and economy. Compared to traditional optimization methods, it exhibits better adaptability and optimization effectiveness.

[0044] As a further optional implementation of this application, a lifecycle management module is also included, used for: A unique digital identity is established and bound to each transport vehicle, and the digital identity is used to associate and store the entire lifecycle information of the transport vehicle; Based on the digital identity, an information file for the transport vehicle is established and associated with its storage. The information file includes historical transport records, maintenance records, and performance degradation data. In response to a vehicle completing a transportation task or performing maintenance operations, an update to the information file is triggered by scanning the digital identity. Based on the updated information archive, the frequency of similar failures under specific failure modes is statistically analyzed. If the frequency of the same type of failure exceeds a preset frequency threshold, a preset vehicle performance evaluation model is invoked to calculate the vehicle's transportation efficiency, maintenance cost, and reliability indicators based on historical operating data. If the reliability index is less than a preset reliability threshold, a suggestion to update or replace the transport vehicle is generated, and a vehicle scheduling optimization process is triggered. The vehicle scheduling optimization process includes reducing the applicability weight of the transport vehicle in subsequent task assignments.

[0045] The lifecycle management module manages vehicles throughout their entire lifecycle by establishing digital identities and information profiles. Timely updates to these profiles allow for accurate monitoring of vehicle status and performance changes. By analyzing failure frequency and evaluating vehicle efficiency, it can promptly identify vehicle problems and make recommendations for replacement and scheduling optimizations. This ensures the overall performance and reliability of transport vehicles, improving the efficiency and quality of transportation management. Compared to traditional vehicle management methods, this approach is more scientific and intelligent.

[0046] In this embodiment of the application, the multi-source positioning module 103, when determining the vehicle position of the target vehicle based on the target vehicle's position signal information and transportation environment information, is specifically used for: Based on the target vehicle's historical trajectory data, terrain digital elevation model, transportation environment information, and a preset signal masking prediction model, the signal blind spot of the road section ahead of the target vehicle and the corresponding confidence score are predicted. The transportation environment information includes satellite ephemeris and meteorological visibility data. Based on the signal blind spots of the road section in front of the target vehicle, the corresponding confidence score, and the preset tunnel map, the position and path of the target vehicle are matched with the blind spot heat map and electronic map in real time to determine the target positioning mode. Based on the signal blind spots of the road section in front of the target vehicle and the corresponding confidence score, target positioning mode, position signal information and real-time vehicle motion status, a fusion weight matrix of position signal information is determined. The fusion weight matrix represents the contribution weight of each position sensor data source in the filtering and fusion process. Satellite positioning signals can be obtained through the vehicle's satellite positioning receiver, UWB positioning signals can be obtained through UWB base stations and vehicle-mounted UWB tags, and inertial navigation data can be obtained through the vehicle's inertial measurement unit. Based on the fusion weight matrix, position signal information, and a preset adaptive Kalman filter algorithm, the initial fusion positioning of the target vehicle is determined. Based on vehicle dynamics parameters, terrain digital elevation model, pre-set tunnel map and vehicle environment perception data, a vehicle environment digital twin model is constructed and updated. The vehicle environment digital twin model is used to perform error compensation and trajectory optimization on the initial fusion positioning to obtain the target positioning coordinates. Based on a preset electronic fence and historical trajectory, collision detection and continuity analysis are performed on the target location coordinates to obtain the vehicle position of the target vehicle.

[0047] In the above implementation, the preset signal obstruction prediction model takes the vehicle's historical trajectory, high-precision terrain digital elevation model, current satellite ephemeris and meteorological data as input, and predicts the probabilistic blind spots of the road section in front of the vehicle due to terrain or building obstruction through geometric analysis and historical pattern learning, and outputs a blind spot heat map with confidence score.

[0048] In the above embodiments, the multi-source positioning module 103, when determining the fusion weight matrix of position signal information based on the signal blind spot of the road segment ahead of the target vehicle and the corresponding confidence score, target positioning mode, position signal information and real-time vehicle motion state, is specifically used for: Based on preset quality assessment rules, the signal quality of satellite positioning signals, UWB positioning signals and inertial navigation data in the location signal information is assessed to obtain quality assessment indicators for each signal. The quality assessment indicators include: the number of visible satellites, position accuracy attenuation factor and carrier-to-noise ratio of satellite positioning signals; the number of effective base stations, signal strength and multipath interference level of UWB positioning signals; and the gyroscope zero bias stability and accelerometer measurement noise level of inertial navigation data. Based on the target positioning pattern, determine the initial set of fusion weight coefficients; Based on the signal blind spots of the road section in front of the target vehicle and the corresponding confidence scores, the real-time motion status of the vehicle, the quality assessment indicators of each signal, and the preset weight dynamic adjustment function, the initial set of fusion weight coefficients is adjusted to obtain the target fusion weight. Based on the target fusion weights, a fusion weight matrix for the adaptive Kalman filter algorithm is constructed, wherein the fusion weight matrix is ​​a diagonal matrix, and the values ​​of the elements on its diagonal are determined by the target fusion weights. The fusion weight matrix is ​​used to characterize the relative contribution of each sensor data source in the filtering and fusion process.

[0049] In the above implementation, when the vehicle is in an open area and there is no blind spot predicted threat, a dominant reference weight is assigned to GNSS data, an auxiliary reference weight is assigned to inertial navigation data, and the reference weight of UWB data is zero. When a vehicle approaches a predicted blind spot at the entrance of a canyon or tunnel, a dominant baseline weight is assigned to the UWB data, the baseline weight of the inertial navigation data is increased simultaneously, and the baseline weight of the GNSS data is reduced based on the confidence score. When a vehicle confirms that it has entered the tunnel, the baseline weight of the GNSS data is reset to zero, a baseline weight combination with UWB data and inertial navigation data as the core is constructed, and differentiated baseline weights are assigned to the UWB data according to the coverage density of UWB base stations. When the vehicle leaves the blind spot and the GNSS signal is reacquired, a time-incrementing function is used to restore the baseline weight of the GNSS data, and the baseline weights of other data sources are adjusted accordingly.

[0050] The multi-source positioning module 103 assesses the quality of location signals and dynamically adjusts the fusion weight matrix based on signal quality and the transportation environment. This allows for more efficient fusion of multiple positioning signals under different transportation conditions, improving positioning accuracy and reliability. By considering factors such as signal quality and vehicle motion, the module offers greater flexibility and adaptability to complex environments. Compared to traditional positioning methods, it better addresses issues like signal obstruction and interference, providing more accurate vehicle location information for material transportation management.

[0051] In this embodiment of the application, the monitoring information of the materials inside the target vehicle includes real-time multi-layer moisture content data collected by multiple moisture sensors deployed on the upper, middle and lower layers of the materials.

[0052] The monitoring module 104 is used to predict the future trend of material moisture content changes at different depths based on the monitoring information of the material, the vehicle location of the target vehicle, and the transportation environment information. Specifically, it is used for: The multilayer moisture content data is used as the initial condition for the model input field; The transportation environment information and the future weather forecast data obtained by back-calculating vehicle location information and time information are used as the model boundary conditions; Run the moisture migration prediction model, which is coupled with the unsaturated soil hydrothermal transport equation, to calculate the spatiotemporal distribution of temperature and moisture content of materials at different depths over a future set time period. Output the predicted moisture content change curves of the surface, middle and core layers of the material within a future set time period.

[0053] In this embodiment of the application, when the monitoring module 104 determines the monitoring information of the target vehicle based on the vehicle location, path, and the trend of material moisture content changes at different depths in the future time period, it is specifically used for: The real-time vehicle location of the target vehicle is compared with the predetermined transportation route to calculate the path tracking deviation. Calculate the material state stability index based on the future trend of material moisture content change. Based on the path tracking deviation and the material state stability index, a comprehensive monitoring state vector is generated; Based on the comprehensive monitoring state vector and the preset state action mapping rule library, the monitoring information of the target vehicle is matched and determined. The monitoring information includes the vehicle deviation alarm level, the material status warning level, or the suggested timing of control intervention.

[0054] In this embodiment of the application, when the monitoring module 104 determines the internal control strategy of the target vehicle based on the monitoring information, it is specifically used for: When the material status change warning level in the monitoring information indicates a risk of surface water loss, the internal control strategy is to generate and send instructions to close the vents or activate the humidification system. When monitoring information indicates a risk of rain or surface water absorption, the internal control strategy generates and sends an instruction to initiate automatic tarpaulin covering. When the monitoring information indicates that the vehicle's path deviation warning level exceeds the threshold, the internal control strategy is to generate and send a navigation prompt instruction containing the corrective path to the vehicle terminal.

[0055] In this embodiment of the application, when the monitoring module 104 determines the graded unloading decision information of the target vehicle based on the monitoring information, it is specifically used for: Based on the material status warning level, the unloading decision is divided into multiple levels; When the material status warning level is the first level, the graded unloading decision information is to guide the material to the standard unloading area to perform the normal unloading process. When the material status warning level is Level 2, the graded unloading decision information is to guide the material to the pretreatment area, and unload it after ventilation or static homogenization for a specified period of time. When the material status warning level is level three, the graded unloading decision information is to guide the material to the inspection area, and wait for on-site confirmation by quality personnel to decide whether to unload or return the material to the site.

[0056] The tiered unloading decision information, along with the estimated arrival time of the vehicles, is sent to the dispatch terminal and vehicle terminal at the target unloading area.

[0057] The material condition early warning level is determined based on the material condition stability index in the comprehensive monitoring condition vector. The material condition stability index is a comprehensive value calculated based on the trend of material moisture content changes at different depths over future periods. It is used to characterize the degree and trend of the material's moisture content deviating from the target construction requirements during transportation.

[0058] The conditions for setting the first level are: the predicted moisture content change curves of the material at each depth layer (surface, middle, and core layers) are all within the allowable range of the target construction moisture content, and the material stability index is greater than or equal to the first stability threshold. The first stability threshold indicates that the overall deviation of the material's moisture content from the target value is within a safe and controllable range. Under the first level conditions, the material's moisture content meets the construction quality requirements; therefore, the graded unloading decision information guides the material to the standard unloading area to execute the normal unloading process, meaning the material can be directly used for dam filling operations.

[0059] The conditions for setting the second level are as follows: the predicted moisture content change curve of a certain depth layer of the material shows a trend of deviating from the allowable range of the target construction moisture content, but the degree of deviation does not exceed the preset severe deviation threshold, and the material state stability index is between the second stability threshold and the first stability threshold. The second stability threshold indicates that the overall deviation of the material moisture content from the target value is within the range requiring pretreatment intervention. Under the conditions of the second level, the material moisture content has a certain deviation but can be restored to the qualified range through pretreatment measures. Therefore, the graded unloading decision information is to guide the material to the pretreatment area for ventilation or static homogenization for a specified period before unloading.

[0060] The criteria for Level 3 are: the predicted moisture content change curves of the material at multiple depth layers deviate significantly from the allowable range of the target construction moisture content, and the material stability index is less than the second stability threshold. Under Level 3 conditions, the material moisture content deviates significantly from the construction quality standards, and simple pretreatment measures are insufficient to ensure that the material is restored to a qualified state, posing a risk to the dam filling quality. Therefore, the graded unloading decision information guides the material to the inspection area, awaiting on-site confirmation by quality personnel to determine whether to unload or return it to the site.

[0061] As a further optional implementation in this application embodiment, the vehicle dispatching module 102 is also used to: After the target vehicle completes transportation and unloading, obtain the execution effect information corresponding to the transportation scheduling plan; Based on the execution effect information of the transportation scheduling scheme, the exploration factor and development factor in the preset optimization algorithm are adaptively adjusted. Based on the efficiency of transportation task completion, the accuracy of unloading, and safety compliance, the actual execution effect score of the transportation task information is calculated; The actual performance score is compared with a preset performance expectation threshold. If the actual performance score is less than the expected performance threshold, the value of the exploration factor is increased based on the fifth step, and the value of the development factor is decreased based on the sixth step.

[0062] The vehicle dispatching module 102 is used to calculate the actual execution effect score of the transportation task information based on the efficiency of transportation task completion, the accuracy of unloading, and safety compliance. Specifically, it is used for: Obtain data on the actual and planned transportation time of the transportation task, the deviation between the actual unloading location and the target location, and the difference between the actual operation process and the standard process; The task completion efficiency score is obtained based on the ratio function of actual transportation time to planned transportation time. The unloading accuracy score is obtained based on the deviation function between the actual unloading position and the target position; A safety compliance score is obtained based on the difference function between the actual operation process and the standard process; Based on a preset weighting strategy, the task completion efficiency score, unloading accuracy score, and safety compliance score are weighted and fused to obtain the actual execution effect score.

[0063] By collecting multi-dimensional performance information such as actual transportation time, unloading location deviation, and operational compliance, and quantifying it into a comprehensive performance score, the system not only objectively reflects the true performance of the scheduling scheme, but more importantly, it serves as a feedback signal used to adjust the core parameters of the optimization algorithm (such as improving the particle swarm optimization algorithm). When the score is below the target, the "exploration" weight is automatically increased to seek a better scheduling mode, while the "development" weight is decreased to avoid getting trapped in local optima. This allows the entire scheduling system to continuously learn from historical experience and adapt to the dynamic changes at the construction site, thereby gradually approaching the globally optimal scheduling efficiency.

[0064] Figure 2 A flowchart illustrating a material transportation and unloading management method based on multi-source positioning is shown.

[0065] like Figure 2 As shown, a material transportation and unloading management method based on multi-source positioning includes: S1, Obtain project progress information and work environment information of the construction area, and generate transportation task information based on the project progress information, work environment information and preset construction plan; S2, based on the transportation task information, obtain the qualification information of the associated driver and the vehicle information of the corresponding transportation vehicle; based on the transportation task information, vehicle information, operating environment information, qualification information and a preset vehicle evaluation function, evaluate each associated transportation vehicle to obtain the applicability index of each transportation vehicle; based on the applicability index of each transportation vehicle and the transportation task information, determine the transportation scheduling plan and operation guidance information. S3, for each target vehicle, acquire the target vehicle's location signal information and transportation environment information, and determine the target vehicle's location based on the target vehicle's location signal information and transportation environment information; S4. For each target vehicle, acquire monitoring information of the materials inside the target vehicle; based on the monitoring information of the materials, the vehicle location of the target vehicle, and the transportation environment information, predict the material state change trend in the future period; based on the vehicle location, path, and the material state change trend in the future period, determine the monitoring information of the target vehicle, and based on the monitoring information, determine the internal control strategy and graded unloading decision information of the target vehicle.

[0066] The material transportation and unloading management method based on multi-source positioning described above can be referred to in correspondence with the material transportation and unloading management system 100 based on multi-source positioning described above.

[0067] The terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0068] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the foregoing application concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions claimed in this application.

Claims

1. A material transport and unloading management system based on multi-source positioning, characterized in that, include: The task management module is used to obtain project progress information and work environment information of the construction area, and generate transportation task information based on the project progress information, work environment information and preset construction plan; The vehicle dispatch module is used to obtain the qualification information of the associated drivers and the vehicle information of the corresponding transport vehicles based on the transport task information; to evaluate each associated transport vehicle based on the transport task information, vehicle information, operating environment information, qualification information and a preset vehicle evaluation function to obtain the suitability index of each transport vehicle; and to determine the transport dispatch plan and operation guidance information based on the suitability index of each transport vehicle and the transport task information. A multi-source positioning module is used to acquire the location signal information and transportation environment information of each target vehicle, and determine the vehicle position of the target vehicle based on the location signal information and transportation environment information. The monitoring module is used to acquire monitoring information of the materials inside each target vehicle. Based on the monitoring information of the material, the vehicle location of the target vehicle, and the transportation environment information, the trend of material status change in future time periods is predicted. Based on the vehicle location, path, and material state change trend of the target vehicle in the future time period, the monitoring information of the target vehicle is determined, and based on the monitoring information, the internal control strategy and graded unloading decision information of the target vehicle are determined.

2. The material transportation and unloading management system based on multi-source positioning according to claim 1, characterized in that, The task management module is specifically used for: Acquire elevation point cloud data of the construction area, historical construction rates, real-time on-site status information of construction machinery, and work environment information; Outlier removal and smoothing filtering are performed on the elevation point cloud data to obtain preprocessed point cloud data. Based on the preprocessed point cloud data and the preset grid method, the current digital elevation model is constructed. Based on the current digital elevation model and historical digital elevation models, determine the volume change of the construction area; The current construction rate is calculated by reverse calculation based on the volume change. Based on the current working area of ​​the current digital elevation model and the preset construction plan, determine the design difference; The current construction rate, historical construction rate, design difference, real-time construction machinery status and working environment information are input into a preset demand prediction model to determine transportation task information. The transportation task information includes task priority, material type and specifications, transportation volume, source material yard location and target unloading area coordinates. The priority is determined based on the degree of predicted demand change trend, and the target unloading area coordinates and material type and specifications are determined based on the predicted working surface and the design parameters of the working surface.

3. The material transportation and unloading management system based on multi-source positioning according to claim 1, characterized in that, The vehicle dispatching module, when determining the transportation dispatching plan and operational guidance information based on the suitability index and transportation task information of each transport vehicle, is specifically used for: Determine the required number of vehicles based on the volume of transportation. Based on the required number of vehicles and the suitability index of each transport vehicle, multiple target vehicles are identified. Based on the transportation task information, the operating environment information, and the information files of each target vehicle, the comprehensive risk level of the transportation task information is determined. The information files include historical transportation records, maintenance records, and performance degradation data. Based on the comprehensive risk level, task priority, material type and specifications, transportation volume, source material yard location and target unloading area coordinates of multiple target vehicles and transportation task information, an initial scheduling plan including the reserved passage time period and route is generated for each target vehicle through a spatiotemporal resource reservation model, resulting in an initial transportation scheduling scheme. The spatiotemporal resource reservation model aims to minimize the probability of total transportation time and total route conflict. Based on the initial transportation scheduling scheme, the wireless communication information of vehicles in the transportation state, and the preset cooperative collision avoidance decision model, the real-time collision avoidance decision information of each target vehicle is determined; Based on the comprehensive risk level, the initial transportation scheduling plan, and the real-time collision avoidance decision information, the initial transportation scheduling plan is optimized using an optimization algorithm to determine the final transportation scheduling plan. Based on transportation scheduling plans and real-time collision avoidance decision information, operational guidance information is determined.

4. The material transportation and unloading management system based on multi-source positioning according to claim 3, characterized in that, The vehicle dispatching module, when determining the comprehensive risk level of the transportation task based on the transportation task information, the operating environment information, and the information files of each target vehicle, specifically uses the following: Based on the material type and specifications in the transportation task information, the material type hazard level is determined, and based on the material type hazard level, a preset task risk database is queried to determine the task risk coefficient; Based on the road slope value and weather condition value in the work environment information, calculate the percentage deviation of the road slope from the safe driving design slope and the degree of deviation of the weather condition from the safe operation standard, and determine the environmental risk coefficient based on the percentage deviation and degree of deviation. Based on the failure rate, maintenance records, and performance degradation data in the historical transportation records of each target vehicle, the inherent risk coefficient of each target vehicle is determined. The task risk coefficient, environmental risk coefficient, and each inherent risk coefficient are input into the risk level assessment model to obtain the comprehensive risk value; The overall risk level of the transportation task is determined based on the numerical range of the comprehensive risk value.

5. A material transportation and unloading management system based on multi-source positioning according to claim 3, characterized in that, The vehicle dispatching module is used to optimize the initial transportation dispatching plan based on the comprehensive risk level, the initial transportation dispatching plan, and the real-time collision avoidance decision information using an optimization algorithm. Specifically, when determining the transportation dispatching plan, it is used for: Based on the comprehensive risk level, the exploration factor and development factor of the optimization algorithm are adjusted, and the optimization algorithm is an improved particle swarm optimization algorithm. Based on the adjusted optimization algorithm, the initial scheduling plan, and the real-time collision avoidance decision, the initial transportation scheduling scheme is iteratively solved and optimized with preset optimization objectives and constraints to obtain a transportation scheduling scheme that satisfies the constraints and optimizes the objectives. The preset optimization objective is as follows: MinimizeZ = Σ(α × waiting time + β × delay loss + γ × energy cost); Where α, β, and γ are weight coefficients that are dynamically configured according to the task stage; The constraints include the number of vehicles required for the transportation task, the upper limit of the real-time traffic capacity of a single road, and the safety threshold of the vehicle's battery state of charge.

6. A material transportation and unloading management system based on multi-source positioning according to claim 1, characterized in that, The multi-source positioning module, when determining the vehicle position of the target vehicle based on the target vehicle's position signal information and transportation environment information, specifically uses the following methods: Based on the target vehicle's historical trajectory data, terrain digital elevation model, transportation environment information, and a preset signal masking prediction model, the signal blind spot of the road section ahead of the target vehicle and the corresponding confidence score are predicted. The transportation environment information includes satellite ephemeris and meteorological visibility data. Based on the signal blind spots of the road section in front of the target vehicle, the corresponding confidence score, and the preset tunnel map, the position and path of the target vehicle are matched with the blind spot heat map and electronic map in real time to determine the target positioning mode. Based on the signal blind spots of the road section in front of the target vehicle and the corresponding confidence score, target positioning mode, position signal information and real-time vehicle motion status, a fusion weight matrix of position signal information is determined. The fusion weight matrix represents the contribution weight of each position sensor data source in the filtering and fusion process. Based on the fusion weight matrix, position signal information, and a preset adaptive Kalman filter algorithm, the initial fusion positioning of the target vehicle is determined. Based on vehicle dynamics parameters, terrain digital elevation model, pre-set tunnel map and vehicle environment perception data, a vehicle environment digital twin model is constructed and updated. The vehicle environment digital twin model is used to perform error compensation and trajectory optimization on the initial fusion positioning to obtain the target positioning coordinates. Based on a preset electronic fence and historical trajectory, collision detection and continuity analysis are performed on the target location coordinates to obtain the vehicle position of the target vehicle.

7. A material transportation and unloading management system based on multi-source positioning according to claim 6, characterized in that, The multi-source positioning module, when determining the fusion weight matrix of position signal information based on the signal blind spots of the road segment ahead of the target vehicle and the corresponding confidence score, target positioning mode, position signal information, and the real-time motion state of the vehicle, is specifically used for: Based on preset quality assessment rules, the signal quality of satellite positioning signals, UWB positioning signals and inertial navigation data in the location signal information is assessed to obtain the quality assessment index of each signal; Based on the target positioning pattern, determine the initial set of fusion weight coefficients; Based on the signal blind spots of the road section in front of the target vehicle and the corresponding confidence scores, the real-time motion status of the vehicle, the quality assessment indicators of each signal, and the preset weight dynamic adjustment function, the initial set of fusion weight coefficients is adjusted to obtain the target fusion weight. Based on the target fusion weights, a fusion weight matrix for the adaptive Kalman filter algorithm is constructed, wherein the fusion weight matrix is ​​a diagonal matrix and the values ​​of the elements on its diagonal are determined by the target fusion weights.

8. A material transportation and unloading management system based on multi-source positioning according to claim 1, characterized in that, The monitoring module, when determining the monitoring information of the target vehicle based on the vehicle's location, path, and the future trend of material moisture content changes at different depths, specifically uses the following: The real-time vehicle location of the target vehicle is compared with the predetermined transportation route to calculate the path tracking deviation. Calculate the material state stability index based on the future trend of material moisture content change. Based on the path tracking deviation and the material state stability index, a comprehensive monitoring state vector is generated; Based on the comprehensive monitoring state vector and the preset state action mapping rule library, the monitoring information of the target vehicle is matched and determined. The monitoring information includes the vehicle deviation alarm level, the material status warning level, or the suggested timing of control intervention.

9. A material transportation and unloading management method based on multi-source positioning, characterized in that, include: Obtain project progress information and work environment information of the construction area, and generate transportation task information based on the project progress information, work environment information and preset construction plan; Based on the transportation task information, obtain the qualification information of the associated drivers and the vehicle information of the corresponding transportation vehicles; based on the transportation task information, vehicle information, operating environment information, qualification information and a preset vehicle evaluation function, evaluate each associated transportation vehicle to obtain the applicability index of each transportation vehicle; based on the applicability index of each transportation vehicle and the transportation task information, determine the transportation scheduling plan and operation guidance information. For each target vehicle, the location signal information and transportation environment information of the target vehicle are acquired, and the vehicle position of the target vehicle is determined based on the location signal information and transportation environment information of the target vehicle. For each target vehicle, acquire monitoring information on the materials inside the target vehicle; Based on the monitoring information of the material, the vehicle location of the target vehicle, and the transportation environment information, the trend of material status change in future time periods is predicted. Based on the vehicle location, path, and material state change trend of the target vehicle in the future time period, the monitoring information of the target vehicle is determined, and based on the monitoring information, the internal control strategy and graded unloading decision information of the target vehicle are determined.

10. A computer-readable storage medium, characterized in that, It includes a computer program or instructions that, when run on a computer, cause the computer to perform the method as described in claim 9.

Images