A method and system for scheduling the arrival of vehicles at a construction waste recycling plant.

Abstract

This invention relates to the field of vehicle scheduling optimization technology, and more particularly to a method and system for scheduling the number of vehicles arriving at a construction waste recycling plant. It includes the following steps: S1: Acquire transportation-related data of plant buildings and freight vehicles, and obtain the appropriate scheduling value for plant buildings using an adaptive scheduling calculation formula based on the transportation-related data; S2: Acquire real-time operating data of water trucks and cargo-related data, and obtain the final adjustment value using a final adjustment formula based on the real-time operating data, cargo-related data, and the appropriate scheduling value of plant buildings; S3: Designate plant buildings with a final adjustment value greater than or equal to a first preset threshold as backup routes for vehicle scheduling. This invention improves production efficiency and reduces operating costs by combining parameters such as the real-time transit time and location of water trucks, cargo temperature and humidity requirements, and hazard levels, based on the synergistic effect of freight vehicles and water spraying operations and material characteristics.

Description

Technical Field

[0001] This invention relates to the field of vehicle scheduling optimization technology, and in particular to a scheduling method and system for the number of vehicles arriving at a construction waste recycling plant. Background Technology

[0002] Construction waste recycling plants are crucial for solving the problems of construction waste collection, sorting, and reuse. The efficiency of their internal transportation scheduling directly affects the resource recovery rate and the overall operating cost of the plant. Traditional plant traffic scheduling methods often rely on manual experience or static rules, such as vehicle dispatch schemes based on fixed routes and priorities. These schemes cannot fully consider the actual needs of the plant and are prone to lacking dynamic adjustment mechanisms. They cannot be optimized in a timely manner according to the road congestion level, fleet composition, and material properties, resulting in redundant fleet operation trajectories and increased fuel consumption and labor costs. Different materials have special requirements for temperature and humidity, and static scheduling schemes cannot take into account the characteristics of the materials, making it difficult to meet material quality or environmental protection standards. Summary of the Invention

[0003] To overcome the shortcomings of not being able to effectively utilize the route optimization of factory buildings and lacking effective utilization of the dynamic temperature and humidity effects, this invention provides a scheduling method and system for the number of vehicles arriving at a construction waste recycling plant.

[0004] The technical solution is as follows: A method for scheduling the number of vehicles arriving at a construction waste recycling plant, comprising the following steps:

[0005] S1: Obtain transportation-related data of factory buildings and freight vehicles, and use the adaptive scheduling calculation formula based on the transportation-related data to obtain the adaptive scheduling value of factory buildings;

[0006] S2: Obtain the real-time working data and cargo-related data of the sprinkler truck, and use the final adjustment formula to obtain the final adjustment value based on the real-time working data, cargo-related data and the adaptive scheduling value of the factory buildings;

[0007] S3: Designate factory buildings with a final adjustment value greater than or equal to the first preset threshold as backup routes for vehicle dispatching;

[0008] S4: Obtain the final adjustment value for each freight vehicle, and adjust the number of freight vehicles arriving based on the final adjustment value for each freight vehicle and the alternative routes for vehicle scheduling.

[0009] Preferably, the step of acquiring transportation-related data of factory buildings and freight vehicles, and obtaining the adaptive scheduling value of factory buildings using the adaptive scheduling calculation formula based on the transportation-related data, includes: acquiring transportation-related data of factory buildings and freight vehicles, wherein the transportation-related data includes the standard height of factory building doors, the actual height of freight vehicles when carrying goods, the standard width of factory building doors, the actual width of freight vehicles when carrying goods, the available path space of factory buildings in each time period, the required path space of freight vehicles when carrying goods, the number of factory building doors that meet the requirements, and the number of personnel in factory buildings in each time period, wherein the number of factory building doors is the number of freight vehicles and the goods they carry that have the ability to pass through each factory building door when they are carrying goods; wherein the factory building door is the door that directly connects the factory building to the external road, and the adaptive scheduling value of the factory buildings is obtained using the adaptive scheduling calculation formula based on the transportation-related data.

[0010] Preferably, obtaining the adaptive scheduling value of the factory buildings based on the transportation-related data using the adaptive scheduling calculation formula includes: wherein the adaptive scheduling calculation formula is:

[0011]

[0012] In the formula, S is the adaptive scheduling value of the factory buildings; h0 is the standard height of the factory building door; h is the actual height of the delivery vehicle when carrying goods; w0 is the standard width of the factory building door; w is the actual width of the delivery vehicle when carrying goods; s0 is the available path space of the factory buildings in the current time period; s is the required path space of the delivery vehicle when carrying goods; n h n represents the number of factory buildings that meet the requirements; c represents the number of personnel in the factory buildings during the current time period; and c represents the complexity of vehicle movement within the factory buildings.

[0013] α1, α2, α3, α4, and α5 are the weight adjustment coefficients for the adaptive scheduling values; ε is the adjustment parameter for the adaptive scheduling values.

[0014] Preferably, c represents the driving complexity of the vehicle within the factory buildings, including: constructing an undirected graph based on the factory building structure and internal road topology, wherein the node set is the intersection of the factory building gate and the internal roads of the factory buildings, and the edge set is the passable road segments within the factory buildings; mapping the historical driving trajectory of the freight vehicle and the corresponding state variables onto the node set and edge set to form a spatiotemporal feature sequence; using GCN to extract spatial features from the graph structure; inputting the spatial features and temporal features into an LSTM; and outputting the driving complexity through a fully connected regression layer, wherein the corresponding state variables include the freight vehicle speed, acceleration, and steering angle sequence.

[0015] Preferably, the step of acquiring real-time operating data and cargo-related data of the sprinkler truck, and obtaining a final adjustment value using a final adjustment formula based on the real-time operating data, cargo-related data, and the adaptive scheduling value of the factory buildings, includes: real-time operating data including the time and corresponding location data of the sprinkler truck passing through the factory buildings during operation; cargo-related data including the hazard level value of the cargo carried by the transport vehicle and the distance between the transport vehicle and the target area; obtaining a final adjustment value using a final adjustment formula based on the real-time operating data, cargo-related data, and the adaptive scheduling value of the factory buildings; and obtaining the hazard level value based on the hazard class of the cargo, the cargo stacking stability coefficient, and the cargo temperature and humidity sensitivity coefficient.

[0016] Preferably, obtaining the final adjustment value using the final adjustment formula includes: wherein the final adjustment formula is:

[0017]

[0018] In the formula, P is the final adjustment value; S is the adaptive scheduling value of the factory buildings; t is the interval between the current time and the time when the sprinkler truck passed through the factory buildings during the most recent operation; t0 is the optimal standard time for the transport vehicle to coordinate with the sprinkler truck when carrying goods; need is the temperature and humidity requirement data of the goods; f() is the coordination function of the goods and the sprinkler truck based on the attributes of the goods; dan is the danger level value of the goods carried by the transport vehicle; dis is the distance between the transport vehicle and the target area; β1, β2, and β3 are the weight adjustment coefficients of the final adjustment value.

[0019] Preferably, f() is a collaborative function that coordinates the cargo attributes with the water truck, including: the collaborative function is used to obtain the collaborative value of the delivery vehicle departing from the gate of each factory building based on real-time work data and cargo attributes, and to obtain the final adjustment value based on the collaborative value, wherein the cargo attributes include the temperature and humidity requirements data of the cargo.

[0020] Preferably, the step of obtaining the final adjustment value of each freight vehicle and adjusting the number of arriving freight vehicles based on the final adjustment value of each freight vehicle and the vehicle scheduling backup route includes: taking freight vehicles with a final adjustment value greater than a second preset threshold as arriving vehicles, obtaining the number of arriving vehicles; when the number of vehicles required for the target area is less than the number of arriving vehicles, scheduling is performed based on the final adjustment value of each freight vehicle, and the route of each freight vehicle is optimized based on the final adjustment value to obtain the optimal route of each freight vehicle; when the number of vehicles required for the target area is greater than or equal to the number of arriving vehicles, freight vehicles with a final adjustment value less than or equal to the second preset threshold are scheduled according to the normal planned route.

[0021] Preferably, the step of optimizing the routes of each freight vehicle based on the final adjustment value to obtain the optimal path for each freight vehicle includes: using the reciprocal of the final adjustment value of each freight vehicle as the edge weight, and using the Dijkstra algorithm based on the edge weight to obtain the optimal path for each freight vehicle.

[0022] Preferably, a scheduling system for the number of vehicles arriving at a construction waste recycling plant further includes:

[0023] The data acquisition module is used to acquire transportation-related data, real-time operating data of water trucks, and cargo-related data.

[0024] The adaptability calculation module is used to obtain the adaptability scheduling value of the factory buildings based on the transportation-related data and the adaptability scheduling calculation formula.

[0025] The driving complexity acquisition module is used to construct an undirected graph based on the factory building structure and internal road topology, and output the driving complexity through a fully connected layer and a regression layer using GCN and LSTM.

[0026] The adjustment value acquisition module is used to obtain the final adjustment value using the final adjustment formula.

[0027] The arrival number adjustment module is used to obtain the final adjustment value of each freight vehicle, and adjust the arrival number of freight vehicles based on the final adjustment value of each freight vehicle and the vehicle scheduling backup route.

[0028] Beneficial effects:

[0029] 1. This invention uses transportation-related data and an adaptive scheduling calculation formula to calculate the adaptive scheduling value of each factory building, thereby achieving precise matching of the size of the delivery vehicle and cargo with the factory buildings. It also incorporates the interior of the factory buildings into the road optimization for delivery vehicles, ensuring that delivery vehicles have accurate and rapid route planning in different time periods.

[0030] 2. This invention obtains the final adjustment value by using a final adjustment formula based on the real-time working data, cargo-related data, and the adaptive scheduling value of the factory buildings. According to the actual synergistic effect of vehicles and auxiliary water spraying operations and the characteristics of materials, the invention plans the routes of the transport vehicles at each entrance of the factory buildings to ensure the positive synergy between the transport vehicles and the water trucks. This allows the transport vehicles to transport under appropriate temperature and humidity conditions, effectively control dust, optimize the temperature and humidity environment of materials, and meet environmental protection and material quality requirements.

[0031] 3. Based on the internal road topology and building structure of the factory area, an undirected graph is constructed, and the historical trajectory of the delivery vehicle is mapped to the nodes and edges. Spatial features are extracted using a graph convolutional network, and temporal features are learned by combining LSTM. The driving complexity is output, which can reflect the actual difficulty of passing through complex road sections and intersection nodes in the factory area in real time, and effectively avoid congestion points and high-risk areas. Attached Figure Description

[0032] Figure 1 This is a flowchart of the scheduling method for the number of vehicles arriving at the construction waste recycling plant according to the present invention;

[0033] Figure 2 This is a schematic diagram of the scheduling system for the number of vehicles arriving at the construction waste recycling plant according to the present invention. Detailed Implementation

[0034] References to embodiments herein mean that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0035] Example 1: A method for scheduling the number of vehicles arriving at a construction waste recycling plant, such as... Figure 1 As shown, it includes the following steps:

[0036] S1: Obtain transportation-related data of factory buildings and freight vehicles, and use the adaptive scheduling calculation formula based on the transportation-related data to obtain the adaptive scheduling value of factory buildings;

[0037] Acquire transportation-related data for factory buildings and delivery vehicles. This data includes the standard height of factory building doors, the actual height of delivery vehicles carrying goods, the standard width of factory building doors, the actual width of delivery vehicles carrying goods, the available path space for factory buildings in each time period, the required path space for delivery vehicles carrying goods, the number of factory building doors that meet the requirements, and the number of personnel in factory buildings in each time period. The number of factory building doors refers to the number of delivery vehicles and their cargo that have the capacity to pass through all factory building doors. Factory building doors are the doors that directly connect factory buildings to external roads. Based on the transportation-related data, an adaptive scheduling calculation formula is used to obtain the adaptive scheduling value for factory buildings.

[0038] It should be noted that the standard height of factory building doors is obtained from the factory building floor plan or BIM, which shows the design clear height of each doorway; the actual height of cargo vehicles is collected in real time by scanning the top of the cargo using onboard laser rangefinders or lidar; the standard width of factory building doors is also obtained from BIM or CAD drawings, showing the design clearance width for passage; the actual width of cargo vehicles is measured by using onboard lateral ultrasonic sensors to measure the distance between the outer edges of the cargo on both sides; and the available path space of factory buildings within each time period is calculated by the factory management platform based on the real-time open area point cloud transmitted from online road monitoring cameras, combined with the road topology model. The net available area for vehicle passage during that time period; the path space required for freight vehicles to transport goods, estimated by using the vehicle positioning system and road geometry information based on the vehicle chassis width, turning radius, and lateral space occupied by the load; the number of factory building doors that meet the requirements, calculated by counting the total number of all building doors that can be passed under the current dimensions of the freight vehicle, i.e., simultaneously satisfying h ≤ h0 and w ≤ w0; the number of personnel in factory buildings during each time period, counted in real time by using the access control system and the personnel positioning system wearing RFID tags within the factory area.

[0039] The formula for calculating adaptive scheduling is:

[0040]

[0041] In the formula, S is the adaptive scheduling value of the factory buildings; h0 is the standard height of the factory building door; h is the actual height of the delivery vehicle when carrying goods; w0 is the standard width of the factory building door; w is the actual width of the delivery vehicle when carrying goods; s0 is the available path space of the factory buildings in the current time period; s is the required path space of the delivery vehicle when carrying goods; n h n represents the number of factory buildings that meet the requirements; c represents the number of personnel in the factory buildings during the current time period; and c represents the complexity of vehicle movement within the factory buildings.

[0042] α1, α2, α3, α4, and α5 are the weight adjustment coefficients for the adaptive scheduling values; ε is the adjustment parameter for the adaptive scheduling values.

[0043] It should be noted that by adapting the scheduling calculation formula, precise route planning for delivery vehicles can be achieved, accurately utilizing the space inside factory buildings to ensure efficient operation of delivery vehicles and fully leveraging the idle path space within the factory buildings, through max(n h -1,0), excludes the case where only one door of the factory building meets the requirements, prevents the situation where the delivery vehicle can only enter and not exit or only exit and not enter, and enhances the scheduling advantages brought by the multi-door passage capacity.

[0044] An undirected graph is constructed based on the factory building structure and internal road topology. The node set represents the intersection of factory building gates and internal roads, and the edge set represents the passable road segments within the factory buildings. The historical driving trajectory of the delivery vehicles and the corresponding state variables are mapped onto the node set and edge set to form a spatiotemporal feature sequence. The spatial features are extracted using GCN on the graph structure. The spatial features and temporal features are input into LSTM, and the driving complexity is output through a fully connected regression layer. The corresponding state variables include the sequence of delivery vehicle speed, acceleration, and steering angle.

[0045] It should be noted that the undirected graph construction is based on the factory building floor plan or digital model. All factory building entrances and internal road intersections are extracted as the node set of the undirected graph. The passable road segments within the factory buildings are used as the edge set. Each edge records the length, width, and curvature information of the corresponding road segment. Using historical driving trajectory data of freight vehicles (GPS trajectory or vehicle positioning system data), sampling points along the vehicle's path are mapped to corresponding graph nodes or edges. For each sampling point, the vehicle's speed, acceleration, and steering angle sequences are collected and recorded as the spatiotemporal features of that node or edge. These node and edge features with state variables are then used to construct the undirected graph. The adjacency matrix of the vector and graph is input into the graph convolutional network. Through multi-layer graph convolution operations, the spatial relationship features between nodes and road segments are learned. The GCN outputs a high-dimensional spatial feature vector for each node and edge, which can capture the road topology and structural complexity. The spatial features extracted by the GCN are input into the Long Short-Term Memory network in time sequence, together with the temporal features of the original speed, acceleration and steering angle. The LSTM can learn the dynamic state changes of the vehicle during driving. After the last time step of the LSTM, a fully connected regression layer is connected to map the fused spatiotemporal feature vector back to a scalar, which is used as the driving complexity of the vehicle inside the factory building.

[0046] S2: Obtain the real-time working data and cargo-related data of the sprinkler truck, and use the final adjustment formula to obtain the final adjustment value based on the real-time working data, cargo-related data and the adaptive scheduling value of the factory buildings;

[0047] Real-time operational data includes the time and location data of the water truck passing through the factory buildings during its operation; cargo-related data includes the hazard level value of the cargo carried by the transport vehicle and the distance between the transport vehicle and the target area. Based on the real-time operational data, cargo-related data, and the adaptive scheduling value of the factory buildings, the final adjustment value is obtained using the final adjustment formula. The hazard level value is obtained based on the hazard class of the cargo, the cargo stacking stability coefficient, and the cargo temperature and humidity sensitivity coefficient.

[0048] It should be noted that GPS is used to obtain data on the time and location of water trucks passing through factory buildings; the hazard level value is calculated by fusion of on-site sensors based on the hazard level of the goods, the stability coefficient of the goods stacking, and the temperature and humidity sensitivity coefficient; the distance between the delivery vehicle and the target area is calculated by measuring the path distance between the current position of the delivery vehicle and the entrance of the target area through vehicle positioning, and the final adjustment value corresponding to each factory building path is calculated and output as the basis for subsequent vehicle scheduling and path priority judgment. If each factory building has multiple entrances, there will be multiple final adjustment values. For example, if one factory building has three entrances A, B, and C, and the exits are B and C after entering from A, there will be final adjustment values ​​for the B exit path planning and the C exit path planning. That is, there are time intervals when the water truck passes through the three entrances A, B, and C respectively. Based on the different temperature and humidity conditions at the entrances of each factory building, delivery vehicle scheduling and path planning can be better carried out.

[0049] The final adjustment formula is:

[0050]

[0051] In the formula, P is the final adjustment value; S is the adaptive scheduling value of the factory buildings; t is the interval between the current time and the time when the sprinkler truck passed through the factory buildings during the most recent operation; t0 is the optimal standard time for the transport vehicle to coordinate with the sprinkler truck when carrying goods; need is the temperature and humidity requirement data of the goods; f() is the coordination function of the goods and the sprinkler truck based on the attributes of the goods; dan is the danger level value of the goods carried by the transport vehicle; dis is the distance between the transport vehicle and the target area; β1, β2, and β3 are the weight adjustment coefficients of the final adjustment value.

[0052] It should be noted that t0 is the optimal standard time for coordination between the transport vehicle and the water truck when transporting goods. It is set based on historical data or expert experience. For example, it is found that spraying the water truck 5 minutes before the arrival of the transport vehicle can best meet the temperature and humidity requirements. need is the temperature and humidity requirement data of the goods. It is read from the cargo management system to obtain the temperature and humidity sensitivity coefficient and the optimal dust prevention requirements of each batch of goods. dan is the hazard level value of the goods transported by the transport vehicle. It is calculated by weighting the hazard level, stacking stability coefficient and temperature and humidity sensitivity coefficient. After normalizing the real-time working data, cargo-related data and the adaptation scheduling value of the factory buildings, the final adjustment value is obtained by using the final adjustment formula.

[0053] The coordination function is used to obtain the coordination value of the delivery vehicle departing from the gate of each factory building based on real-time work data and cargo attributes, and to obtain the final adjustment value based on the coordination value, wherein the cargo attributes include the temperature and humidity requirements of the cargo.

[0054] It should be noted that, based on field tests or historical data, a function is established to increase the ambient humidity and slightly decrease the temperature of the sprinkler truck at different time intervals. This comprehensively reflects the matching degree between the sprinkler truck's operating efficiency and the temperature and humidity requirements of the goods, providing a reliable quantitative indicator for the synergistic effect term in the final adjustment value.

[0055] S3: Designate factory buildings with a final adjustment value greater than or equal to the first preset threshold as backup routes for vehicle dispatching;

[0056] S4: Obtain the final adjustment value for each freight vehicle, and adjust the number of freight vehicles arriving based on the final adjustment value for each freight vehicle and the alternative routes for vehicle scheduling.

[0057] Delivery vehicles whose final adjustment value is greater than the second preset threshold are considered as reachable vehicles, and the number of reachable vehicles is obtained. When the number of vehicles required for the target area is less than the number of reachable vehicles, they are scheduled according to the final adjustment value of each delivery vehicle, and the routes of each delivery vehicle are optimized according to the final adjustment value to obtain the best path for each delivery vehicle. When the number of vehicles required for the target area is greater than or equal to the number of reachable vehicles, delivery vehicles whose final adjustment value is less than or equal to the second preset threshold are scheduled according to the normal planned route.

[0058] It should be noted that the process involves obtaining the final adjustment values ​​for all delivery vehicles, a second preset threshold set based on historical experience, and the number of vehicles required for the target area as issued by the work plan or dispatching instructions. Then, iterates through all delivery vehicles. If multiple final adjustment values ​​for factory buildings are greater than or equal to the second preset threshold, the delivery vehicle is marked as an reachable vehicle, and the number of reachable vehicles is counted. If the number of vehicles required for the target area is less than the number of reachable vehicles, all reachable vehicles are sorted and dispatched according to their final adjustment values ​​from largest to smallest. If the number of vehicles required for the target area is greater than or equal to the number of reachable vehicles, delivery vehicles with final adjustment values ​​less than or equal to the second preset threshold are dispatched according to the normal planned routes.

[0059] The reciprocal of the final adjustment value of each freight vehicle is used as the edge weight. Based on the edge weight, Dijkstra's algorithm is used to obtain the optimal path for each freight vehicle.

[0060] Example 2: Based on Example 1, a scheduling system for the number of vehicles arriving at a construction waste recycling plant, such as... Figure 2 As shown, it also includes:

[0061] The data acquisition module is used to acquire transportation-related data, real-time operating data of water trucks, and cargo-related data.

[0062] The adaptability calculation module is used to obtain the adaptability scheduling value of the factory buildings based on the transportation-related data and the adaptability scheduling calculation formula.

[0063] The driving complexity acquisition module is used to construct an undirected graph based on the factory building structure and internal road topology, and output the driving complexity through a fully connected layer and a regression layer using GCN and LSTM.

[0064] The adjustment value acquisition module is used to obtain the final adjustment value using the final adjustment formula.

[0065] The arrival number adjustment module is used to obtain the final adjustment value of each freight vehicle, and adjust the arrival number of freight vehicles based on the final adjustment value of each freight vehicle and the vehicle scheduling backup route.

[0066] Although the present invention has been described in detail with reference to the above embodiments, it will be apparent to those skilled in the art that various changes or modifications can be made to the invention without departing from the principles and spirit of the invention as defined by the claims. Therefore, the detailed description of the embodiments in this disclosure is for illustrative purposes only and is not intended to limit the invention; rather, the scope of protection is defined by the content of the claims.

Claims

1. A method for scheduling the arrival of vehicles at a construction waste recycling plant, characterized in that, The method comprises the following steps: S1: obtaining transportation related data of factory buildings and delivery vehicles, and using an adaptive scheduling calculation formula according to the transportation related data to obtain an adaptive scheduling value of the factory buildings; S2: obtaining real-time working data and cargo related data of the water spraying vehicle, and using a final adjustment formula to obtain a final adjustment value according to the real-time working data, the cargo related data and the adaptive scheduling value of the factory buildings; S3: regarding the factory buildings with the final adjustment value greater than or equal to a first preset threshold as vehicle scheduling standby paths; S4: obtaining final adjustment values of each delivery vehicle, and adjusting the number of delivery vehicles to arrive according to the final adjustment values of each delivery vehicle and the vehicle scheduling standby paths; The adaptive scheduling calculation formula is as follows: ； In the formula, is the adaptive scheduling value of the factory building; is the standard height of the factory building door; is the actual height of the goods carried by the delivery vehicle; is the standard width of the factory building door; is the actual width of the goods carried by the delivery vehicle; is the available path space of the factory building in the current time period; is the required path space of the goods carried by the delivery vehicle in the factory building; is the number of factory building doors that meet the requirements of the factory building; is the number of personnel in the factory building in the current time period; is the driving complexity of the vehicle in the factory building; is the weight adjustment coefficient of the adaptive scheduling value; is the adjustment parameter of the adaptive scheduling value; The For the driving complexity of the vehicle in the factory building, including: constructing an undirected graph based on the factory building structure and the internal road topology, wherein the node set is the factory building door and the intersection of the internal road of the factory building, the edge set is the passable road section in the factory building, mapping the historical driving trajectory of the delivery vehicle and the corresponding state quantity to the node set and the edge set to form a space-time feature sequence, using GCN to extract the spatial features of the graph structure, inputting the spatial features and the time sequence features into the LSTM, and outputting the driving complexity through the full connection layer regression layer, wherein the corresponding state quantity includes the speed, acceleration and steering angle sequence of the delivery vehicle. The final adjustment formula is as follows: ； In the formula, is the final adjustment value; is the adaptive scheduling value of the factory building; is the interval time between the current time and the time when the last time the watering truck passed through the factory building; is the optimal standard time for the delivery vehicle to carry goods in cooperation with the watering truck; is the temperature and humidity requirement data of the goods; is the cooperation function according to the cooperation of the goods attribute and the watering truck; is the risk value of the delivery vehicle carrying the goods; is the distance between the delivery vehicle and the target area; is the weight adjustment coefficient of the final adjustment value; The final adjustment values greater than a second preset threshold are regarded as reachable vehicles, and the number of reachable vehicles is obtained; when the number of vehicles required by the target area is less than the number of reachable vehicles, the delivery vehicles are scheduled according to the final adjustment values, and the routes of the delivery vehicles are optimized according to the final adjustment values to obtain the best paths of the delivery vehicles; when the number of vehicles required by the target area is greater than or equal to the number of reachable vehicles, the delivery vehicles with the final adjustment values less than or equal to the second preset threshold are scheduled according to normal planned routes.

2. The scheduling method of the number of vehicle arrivals at a construction waste resource factory according to claim 1, characterized in that, The number of factory building doors refers to the number of doors through which the delivery vehicles and the carried goods can pass when the delivery vehicles carry goods; the factory building door refers to a door directly connected to a road outside the factory.

3. The method of claim 1, wherein, The real-time working data of the water spraying vehicle includes the time and corresponding position data of the water spraying vehicle when the water spraying vehicle works through the factory buildings; the dangerous degree value is obtained according to the dangerous level of the goods, the stable stacking coefficient of the goods and the temperature and humidity sensitivity coefficient of the goods.

4. The method of claim 1, wherein, The The cooperative function is used to obtain a cooperative value of the delivery vehicle from the door of each factory building according to real-time working data and the cargo attribute, and obtain a final adjustment value according to the cooperative value, wherein the cargo attribute contains temperature and humidity requirement data of the cargo.

5. The method of claim 1, wherein, The inverse of the final adjustment value of each delivery vehicle is regarded as an edge weight, and the best paths of the delivery vehicles are obtained by using the Dijkstra algorithm according to the edge weight.

6. A scheduling system for the number of vehicles arriving at a construction waste resource factory according to any one of claims 1-5, characterized in that, The method further comprises the following steps: The data acquisition module is configured to obtain the transportation related data, the real-time working data of the water spraying vehicle and the cargo related data; The adaptive degree calculation module is configured to use the adaptive scheduling calculation formula to obtain the adaptive scheduling value of the factory buildings according to the transportation related data; The driving complexity acquisition module is configured to construct an undirected graph based on a factory building structure and an internal road topology, use a GCN and an LSTM, and output driving complexity through a fully connected layer regression layer. The adjustment value acquisition module is configured to obtain a final adjustment value using a final adjustment formula. The arrival number adjustment module is configured to obtain the final adjustment value of each delivery vehicle, and adjust the arrival number of the delivery vehicles according to the final adjustment value of each delivery vehicle and a vehicle scheduling backup path.

Images