Shipping site scheduling management method and device, electronic equipment and storage medium

Abstract

This application provides a method, apparatus, electronic device, and storage medium for dispatching and management at a shipping site. The method includes: acquiring vehicle and cargo information corresponding to the current time and the target shipping task; the vehicle information includes the vehicle's remaining journey information and driving environment information; and the cargo information includes the time required for cargo dispatching. The method further includes: determining the ideal travel time and delay time of the vehicle based on the remaining journey information and driving environment information; determining the estimated arrival time of the vehicle based on the current time, ideal travel time, and delay time; and determining a pre-dispatch time for cargo based on the estimated arrival time and the time required for cargo dispatching, thereby optimizing cargo dispatching efficiency and the utilization rate of shipping site resources. By using a layered time accumulation method to predict the arrival time of the vehicle, the accuracy of vehicle arrival time prediction is improved, and the pre-dispatch time for cargo is determined accordingly, thus optimizing cargo dispatching efficiency and the utilization rate of shipping site resources.

Description

Technical Field

[0001] This application relates to the field of logistics scheduling technology, and in particular to a method, apparatus, electronic device and storage medium for dispatch site scheduling management. Background Technology

[0002] The rapid development of the manufacturing industry has boosted the production capacity of manufacturing enterprises, leading to a surge in demand for shipping and logistics, particularly for automobiles, high-end home appliances, and precision equipment. Efficient and precise shipment scheduling has become crucial for ensuring efficient whole-truckload logistics, reducing operating costs, and improving customer satisfaction. Especially during operations at shipping depots, achieving seamless connections between transport vehicles and cargo awaiting loading, reducing waiting times, and maximizing the utilization of loading point resources have become key areas for optimizing the logistics system.

[0003] In related technologies, the operational modes of dispatch management at shipping sites mainly adopt the following methods: first, a manual dispatch mode relying on human experience for telephone communication and on-site judgment; second, a dispatch method using a fixed time slot reservation mechanism; and third, a semi-automated dispatch system using radio frequency identification or basic satellite positioning technology. However, the dispatch management of shipping sites still suffers from low efficiency, making it difficult to meet the demand for high-efficiency and high-resource-utilization cargo dispatch. Summary of the Invention

[0004] This application discloses a dispatching site scheduling and management method, device, electronic equipment, and storage medium, which are used to solve the technical problem that the dispatching site scheduling and management efficiency is low and cannot meet the demand for high-efficiency and high-resource-utilization cargo dispatching.

[0005] In a first aspect, this application provides a dispatching site management method, the method comprising: acquiring carrier vehicle information and cargo information corresponding to the current time and the target dispatching task, wherein the carrier vehicle information includes the remaining journey information and driving environment information of the carrier vehicle to the dispatching site, and the cargo information includes the cargo dispatching time required; determining the ideal travel time and delay time of the carrier vehicle to the dispatching site based on the remaining journey information and the driving environment information; determining the estimated arrival time of the carrier vehicle based on the current time, the ideal travel time and the delay time; determining the cargo pre-dispatch time based on the estimated arrival time and the cargo dispatching time required, and pre-dispatching all cargo corresponding to the target dispatching task based on the cargo pre-dispatch time.

[0006] In a first possible implementation of the first aspect, determining the ideal travel time and delay time of the transport vehicle to the dispatch site based on the remaining travel information and the driving environment information includes: determining the ideal travel time based on the remaining travel information; determining a first delay time based on the ideal travel time, a traffic congestion index, and a preset congestion delay weight; and determining a second delay time based on the ideal travel time, a weather condition index, and a preset weather delay weight, wherein the driving environment information includes the traffic congestion index and the weather condition index; and superimposing the first delay time and the second delay time to obtain the delay time.

[0007] In the first possible implementation of the first aspect, by refining the delay time into a first delay time and a second delay time caused by traffic congestion and weather conditions respectively, and introducing traffic congestion index, weather condition index and corresponding delay weights for quantitative calculation, the various external environmental factors affecting vehicle travel time are comprehensively and meticulously considered. This significantly improves the prediction accuracy of the estimated arrival time of the transport vehicle. The improved accuracy of the estimated arrival time enables more precise cargo pre-scheduling, realizing cargo preparation before arrival, which greatly shortens the waiting time of the transport vehicle, significantly improves the utilization rate of loading points, and makes the overall operation rhythm of the dispatching site more compact and efficient.

[0008] In a second possible implementation of the first aspect, the correction method for the congestion delay weight and the weather delay weight includes: obtaining the prediction error of the arrival time of the transport vehicles in multiple dispatch tasks; calculating the proportion of the prediction error in multiple dispatch tasks that is greater than a preset error threshold; if the proportion is greater than a preset proportion threshold, then correcting the congestion delay weight and the weather delay weight according to a preset learning rate and the proportion, respectively, to obtain the corrected congestion delay weight and the corrected weather delay weight.

[0009] In the second possible implementation of the first aspect, the system performance is continuously improved through an adaptive weight correction mechanism. This allows the delay weights to be adjusted according to changes in actual traffic conditions and weather patterns, thus becoming closer to the actual situation. To a certain extent, this avoids the prediction bias that may be caused by using fixed weights, making the prediction of the expected arrival time of the transport vehicles more accurate.

[0010] In a third possible implementation of the first aspect, the method for determining the pre-scheduling time of the goods further includes: determining a pre-scheduling time based on the required time for goods scheduling and a preset buffer time, wherein the buffer time is used to compensate for deviations in the required time for goods scheduling caused by uncertainties in the goods scheduling process; and determining the pre-scheduling time of the goods based on the expected arrival time and the pre-scheduling time.

[0011] In the third possible implementation of the first aspect, a buffer period is introduced to effectively compensate for the difference between the time required for cargo scheduling and the initial estimate caused by factors such as the complexity of the actual operating environment and unexpected events. This can effectively reduce the waiting time of carrier vehicles and improve the overall operating efficiency and resource utilization of the dispatching site.

[0012] In the fourth possible implementation of the first aspect, the remaining travel information includes the remaining mileage and historical average speed, wherein the mileage is determined based on the target positioning data of the transport vehicle and the location data of the dispatch site; the method for determining the target positioning data includes: acquiring multi-source positioning data of the transport vehicle and quality indicators of various positioning data; if the various positioning data are the same, then any one of the various positioning data is determined as the target positioning data; if the various positioning data have deviations, then the confidence weights of the various positioning data are determined based on the quality indicators of the various positioning data, and the various positioning data are weighted according to each confidence weight to obtain the target positioning data.

[0013] In the fourth possible implementation of the first aspect, by acquiring multi-source positioning data and combining it with its quality indicators, the data is directly selected when they are consistent, and weighted fusion is used when there are deviations in the data. This ensures the reliability and stability of the target positioning data. This accurate target positioning data makes the calculation of the remaining road segment's mileage more precise, thereby improving the estimation accuracy of the ideal travel time. Ultimately, it helps to more accurately predict the expected arrival time of the carrier vehicle, and on this basis, determine a more reasonable cargo pre-schedule time.

[0014] In the fifth possible implementation of the first aspect, after obtaining the carrier vehicle information, the method further includes: if all goods corresponding to the target shipment task need to be loaded onto multiple carrier vehicles, then the scheduling strategy corresponding to the target shipment task is used as a decision variable, with the goal of minimizing the value of a pre-constructed scheduling objective function, and the target scheduling strategy corresponding to the target shipment task is determined according to preset loading operation constraints; wherein, the target scheduling strategy includes multiple target loading points allocated to the target shipment task, the amount of goods loaded at each target loading point, and the target loading point corresponding to each carrier vehicle.

[0015] In the fifth possible implementation of the first aspect, when the target shipment task requires multiple carrier vehicles to complete together, a complex scheduling strategy is used as a decision variable, and the goal is to minimize the value of the pre-built scheduling objective function. At the same time, the preset loading operation constraints are strictly followed, so as to intelligently determine the optimal target scheduling strategy. Through refined scheduling, the collaborative operation between multiple carrier vehicles and multiple loading points becomes efficient and orderly, which significantly improves the overall operation efficiency and resource utilization of the shipment site.

[0016] In the sixth possible implementation of the first aspect, the scheduling objective function includes the waiting time function of the multiple carrier vehicles, the utilization rate penalty function of all loading points at the dispatch site, and the movement distance penalty function of all goods corresponding to the target dispatch task; the loading operation constraints include that any goods can only be assigned to one loading point, any loading point can be assigned to at most one carrier vehicle at the same time, the arrival time of any goods at the corresponding loading point must be earlier than the arrival time of the corresponding carrier vehicle, and the weight of goods loaded onto any carrier vehicle must be less than or equal to the load capacity of the any carrier vehicle.

[0017] In the sixth possible implementation of the first aspect, the scheduling objective function comprehensively considers the waiting time of multiple transport vehicles, the utilization rate of all loading points in the dispatching site, and the movement distance of all goods corresponding to the target dispatching task. It can comprehensively evaluate the advantages and disadvantages of the scheduling strategy. At the same time, combined with the loading operation constraints, it ensures the feasibility and rationality of the scheduling scheme in actual operation, thereby generating an efficient, balanced target scheduling strategy that meets the requirements of actual operation. It can significantly reduce vehicle waiting time, improve the utilization rate of loading points, reduce cargo handling costs, and comprehensively improve the overall operational efficiency and resource utilization of the dispatching site.

[0018] Secondly, this application also provides a dispatching site management device, the device comprising: a data acquisition module, used to acquire the carrier vehicle information and cargo information corresponding to the current time and the target dispatching task, wherein the carrier vehicle information includes the remaining journey information and driving environment information of the carrier vehicle to the dispatching site, and the cargo information includes the cargo dispatching time required; a time calculation module, used to determine the ideal travel time and delay time of the carrier vehicle to the dispatching site based on the remaining journey information and the driving environment information; a time prediction module, used to determine the expected arrival time of the carrier vehicle based on the current time, the ideal travel time and the delay time; and a cargo dispatching module, used to determine the cargo pre-dispatch time based on the expected arrival time and the cargo dispatching time required, and to pre-dispatch all cargo corresponding to the target dispatching task based on the cargo pre-dispatch time.

[0019] Thirdly, this application also provides an electronic device, including: a processor; and a storage device for storing a program, which, when executed by the processor, causes the electronic device to implement the shipping site scheduling and management method described above.

[0020] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a computer's processor, causes the computer to perform the shipping site scheduling and management method as described above.

[0021] The beneficial effects of this application are as follows: This application provides a dispatching site scheduling management method, device, electronic equipment, and storage medium. First, it acquires the carrier vehicle information and cargo information corresponding to the current time and the target dispatching task. The carrier vehicle information includes the remaining journey information and driving environment information of the carrier vehicle to the dispatching site. The cargo information includes the time required for cargo dispatching. Then, based on the remaining journey information and driving environment information, it determines the ideal travel time and delay time for the carrier vehicle to reach the dispatching site. Next, based on the current time, ideal travel time, and delay time, it determines the estimated arrival time of the carrier vehicle. Finally, based on the estimated arrival time and the time required for cargo dispatching, it determines the pre-dispatch time for the cargo and adjusts the target dispatching task accordingly. All goods are pre-scheduled. By tracking the remaining travel time and driving environment information of the carrier vehicles in real time, the ideal travel time and delay time of the carrier vehicles to the dispatching site are determined. The arrival time of the carrier vehicles is predicted by using a layered time accumulation method, which improves the accuracy of the prediction of the arrival time of the carrier vehicles. Based on this, the pre-scheduling time of the goods is determined and the goods are pre-scheduled. It can arrange the goods to be loaded to be moved to the loading point in advance before the arrival of the carrier vehicles, reduce the unnecessary waiting time of the carrier vehicles, and avoid the problem of the loading point being idle during the waiting period after the carrier vehicles arrive. This improves the dispatching management efficiency of the dispatching site and optimizes the efficiency of goods dispatching and the utilization rate of dispatching site resources. Attached Figure Description

[0022] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0023] In the attached diagram:

[0024] Figure 1 This is a schematic diagram illustrating an implementation environment of an optional shipping site scheduling and management device, as shown in an exemplary embodiment of this application. Figure 2This is a schematic flowchart illustrating an optional shipping site scheduling and management method, as shown in an exemplary embodiment of this application. Figure 3 This is a block diagram illustrating an optional shipping site scheduling and management device, as shown in an exemplary embodiment of this application. Figure 4 This is a block diagram illustrating an optional shipping site scheduling and management system, as shown in an exemplary embodiment of this application. Figure 5 This is a schematic diagram illustrating the structure of an optional electronic device, as shown in an exemplary embodiment of this application. Detailed Implementation

[0025] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0026] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this application. The drawings only show the components related to this application and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the shape, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0027] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the present application. However, it will be apparent to those skilled in the art that embodiments of the present application may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the present application.

[0028] A shipping site refers to a physical location used for the collection, loading, and dispatch of goods, such as a logistics center, factory warehouse, or port terminal. The operational modes of shipping site scheduling and management mainly employ the following methods: first, a manual scheduling mode relying on human experience through telephone communication and on-site judgment; second, a scheduling method using a fixed-time reservation mechanism; and third, a semi-automated scheduling system using radio frequency identification (RFID) or basic satellite positioning technology. However, the inventors of this application have found that the above methods still have the following drawbacks: First, time prediction is inaccurate, failing to obtain real-time and accurate location information and driving status of transport vehicles, resulting in significant deviations in arrival time predictions; second, scheduling response is delayed, lacking a lead-time scheduling mechanism, with dispatching of goods to be loaded only beginning after the transport vehicles arrive, causing unnecessary waiting time for the transport vehicles; third, resource utilization is low, with loading point resources not being fully utilized, remaining idle while the transport vehicles are waiting. Therefore, the cargo scheduling of shipping sites still suffers from low efficiency, making it difficult to meet the demand for high-efficiency, high-resource-utilization cargo dispatch.

[0029] Therefore, please see Figure 1 , Figure 1 This is a schematic diagram illustrating an implementation environment of an optional shipping site scheduling and management device, as shown in an exemplary embodiment of this application. Figure 1 As shown, the implementation environment may include a dispatch site scheduling and management device 110 and a computer device 120. The dispatch site scheduling and management device 110 can be installed within the computer device 120 to realize the scheduling and management of the dispatch site. The computer device 120 can be at least one of a desktop graphics processing unit (GPU) computer, a GPU computing cluster, or a neural network computer. This dispatch site scheduling and management device 110, by tracking the remaining travel information and driving environment information of the transport vehicles in real time, determines the ideal travel time and delay time of the transport vehicles to the dispatch site. It predicts the arrival time of the transport vehicles using a layered time accumulation method, improving the accuracy of the arrival time prediction. Based on this, it determines the pre-scheduling time for goods and performs pre-scheduling, enabling the goods to be loaded to be moved to the loading point in advance before the arrival of the transport vehicles, reducing unnecessary waiting time for the transport vehicles, and to a certain extent avoiding the problem of the loading point being idle during the waiting period after the transport vehicles arrive. This improves the efficiency of dispatch site scheduling and management, optimizes the efficiency of goods dispatch, and increases the utilization rate of dispatch site resources.

[0030] Please see Figure 2 , Figure 2 This is a schematic flowchart illustrating an optional shipping yard scheduling and management method, as shown in an exemplary embodiment of this application. This method can be applied to... Figure 1The implementation environment shown is specifically executed by the shipping site scheduling and management device 110 within that implementation environment. It should be understood that this method can also be applied to other exemplary implementation environments and executed by devices in other implementation environments; this embodiment does not limit the implementation environment to which the method is applicable.

[0031] like Figure 2 As shown, in an exemplary embodiment, the shipping site scheduling and management method includes at least steps S210 to S240, which are described in detail below: Step S210: Obtain the carrier vehicle information and cargo information corresponding to the current time and the target shipping task. The carrier vehicle information includes the remaining journey information and driving environment information of the carrier vehicle to the shipping site, and the cargo information includes the time required for cargo scheduling.

[0032] The current time can be provided by the system clock; the target shipment task refers to the logistics operation task that needs to be scheduled and executed, which involves loading specified goods from the shipment site and transporting them to the destination by carrier vehicles; carrier vehicle information refers to various data related to the carrier vehicles executing the target shipment task; cargo information refers to various data related to the cargo to be shipped corresponding to the target shipment task; remaining journey information refers to data related to the uncompleted travel segments between the carrier vehicle's current location and the shipment site; driving environment information refers to various data of the environment in which the carrier vehicle is located; and cargo scheduling time refers to the total time required to move all cargo in the target shipment task to the corresponding loading point.

[0033] Step S220: Based on the remaining trip information and driving environment information, determine the ideal travel time and delay time for the carrier vehicle to reach the dispatch site.

[0034] Ideal travel time refers to the time required for a carrier vehicle to travel from its current location to the dispatch site at normal speed without any external interference or delays; delay time refers to the actual additional travel time of the carrier vehicle that exceeds the ideal travel time due to driving environment factors.

[0035] Step S230: Determine the estimated arrival time of the transport vehicle based on the current time, ideal travel time, and delay time.

[0036] The estimated arrival time refers to the specific time that the transport vehicle is expected to arrive at the dispatch site after taking into account the ideal travel time and the delay time.

[0037] Step S240: Determine the cargo pre-scheduling time based on the estimated arrival time and the time required for cargo scheduling, and pre-schedule all cargo corresponding to the target shipment task according to the cargo pre-scheduling time.

[0038] Among them, cargo pre-schedule time refers to the start time of cargo scheduling operations arranged in advance before the expected arrival of the carrier vehicle to ensure that the cargo can arrive at the loading point in a timely manner; pre-schedule refers to planning and arranging a series of operations such as the movement and assembly of cargo at the dispatch site in advance based on the expected arrival time of the carrier vehicle, so as to achieve seamless connection between the carrier vehicle and the cargo.

[0039] In this embodiment, by tracking the remaining travel time and driving environment information of the transport vehicle in real time, the ideal travel time and delay time of the transport vehicle to the dispatch site are determined. By adopting a layered time accumulation method, the arrival time of the transport vehicle is predicted by comprehensively considering the ideal travel time and the delay time. This can more accurately predict the estimated arrival time of the transport vehicle. Therefore, based on the estimated arrival time and the time required for cargo scheduling, the pre-scheduling time of the cargo can be determined in advance, and the cargo can be pre-scheduled. This allows for the arrangement of cargo to be loaded to be moved to the loading point in advance before the arrival of the transport vehicle, reducing unnecessary waiting time for the transport vehicle and avoiding the problem of the loading point being idle during the waiting period after the transport vehicle arrives. In other words, it solves the problem of vehicle waiting and loading point idleness caused by inaccurate time prediction to a certain extent, realizing seamless connection between the transport vehicle and the cargo to be loaded, thereby improving the dispatch management efficiency of the dispatch site and optimizing the efficiency of cargo dispatch and the utilization rate of dispatch site resources.

[0040] In one embodiment, determining the ideal travel time and delay time of the transport vehicle to the dispatch site based on the remaining travel information and driving environment information includes: determining the ideal travel time based on the remaining travel information; determining a first delay time based on the ideal travel time, a traffic congestion index, and a preset congestion delay weight; and determining a second delay time based on the ideal travel time, a weather condition index, and a preset weather delay weight, wherein the driving environment information includes a traffic congestion index and a weather condition index; and superimposing the first delay time and the second delay time to obtain the total delay time.

[0041] The ideal travel time refers to the time required for a vehicle to travel from its current location to the dispatch site under ideal conditions, unaffected by external driving environmental factors (such as traffic congestion or severe weather). The first delay time refers to the additional travel time caused by traffic congestion; the traffic congestion index is an indicator that measures the degree of current road congestion, expressed numerically, with higher values ​​indicating more severe congestion; the preset congestion delay weight is an empirical parameter used to quantify the impact of the traffic congestion index on travel time. The second delay time refers to the additional travel time caused by weather conditions; the weather condition index is an indicator that measures the impact of current weather on driving, expressed numerically, with higher values ​​indicating more severe weather; the weather delay weight is an empirical parameter used to quantify the impact of the weather condition index on travel time; the total delay time is the additional increase in actual vehicle travel time relative to the ideal travel time after comprehensively considering various external environmental factors.

[0042] In this embodiment, considering that the complexity of the driving environment during actual vehicle operation may lead to inaccurate calculations of travel time, such as failing to fully account for the impact of traffic congestion and weather conditions on travel time, thus affecting the accuracy of the estimated arrival time and consequently the efficiency and reliability of cargo pre-scheduling, this embodiment addresses this issue. By refining the delay time into a first delay time and a second delay time caused by traffic congestion and weather conditions respectively, and introducing traffic congestion indices, weather condition indices, and corresponding delay weights for quantitative calculation, this method comprehensively and meticulously considers various external environmental factors affecting vehicle travel time. This detailed delay time calculation method significantly improves the prediction accuracy of the estimated arrival time of the transport vehicle. The improved accuracy of the estimated arrival time allows for more precise cargo pre-scheduling, enabling pre-arrangement of goods before arrival, significantly reducing the waiting time of transport vehicles, significantly improving the utilization rate of loading points, and making the overall operation rhythm of the dispatching site more compact and efficient.

[0043] For example, determining the ideal travel time based on the remaining travel information includes: determining the ideal travel time based on the remaining mileage of the road segment and the historical average travel speed, wherein the remaining travel information includes the remaining mileage of the road segment and the historical average travel speed.

[0044] Among them, the historical average driving speed can reflect the ability of the transport vehicle to pass under unobstructed conditions. Introducing the historical average driving speed to calculate the ideal driving time can objectively depict the traffic efficiency of the road segment under normal conditions, avoid misjudging short-term abnormal fluctuations as inherent road conditions, and thus improve the accuracy of the calculation of the ideal driving time.

[0045] In this exemplary embodiment, the formula for calculating the ideal driving time is: Equation (1) in, Indicates the ideal driving time; Indicates the remaining mileage of the route; This indicates the historical average speed of the remaining road segment.

[0046] For example, the first delay duration is determined based on the ideal travel time, the traffic congestion index and the preset congestion delay weight, including: calculating the first product of the traffic congestion index and the congestion delay weight; calculating the second product of the ideal travel time and the first product to obtain the first delay duration.

[0047] In this exemplary embodiment, the formula for calculating the first delay duration is: Equation (2) in, Indicates the first delay duration; This represents the traffic congestion index, with a value ranging from 0 to 1, where 0 indicates smooth traffic and 1 indicates severe congestion. This represents the congestion delay weight, with an initial value typically set to 0.5, which is an empirical value. Indicates the ideal driving time.

[0048] For example, the second delay duration is determined based on the ideal driving time, the weather condition index, and the preset weather delay weight, including: calculating the third product of the weather condition index and the weather delay weight; and calculating the fourth product of the ideal driving time and the third product to obtain the second delay duration.

[0049] In this exemplary embodiment, the formula for calculating the second delay duration is: Equation (3) in, Indicates the second delay duration; This represents the weather condition index, with a value ranging from 0 to 1, where 0 indicates sunny weather and 1 indicates severe weather. This indicates the weight of weather delays; the initial value is usually 0.3, which is an empirical value. Indicates the ideal driving time.

[0050] For example, the methods for superimposing the first delay duration and the second delay duration include: directly superimposing the first delay duration and the second delay duration, weighted superposition of the first delay duration and the second delay duration, and direct nonlinear superposition of the first delay duration and the second delay duration.

[0051] For example, determining the estimated arrival time of the transport vehicle based on the current time, the ideal travel time, and the delay time includes: adding the sum of the ideal travel time and the delay time to the current time to obtain the estimated arrival time of the transport vehicle.

[0052] In this exemplary embodiment, the formula for calculating the estimated arrival time is: Equation (4) in, Indicates the estimated arrival time; Indicates the current time; Indicates the ideal driving time; Indicates the first delay duration; Indicates the second delay duration; Indicates the duration of the delay.

[0053] In one embodiment, the method for correcting the congestion delay weight and the weather delay weight includes: obtaining the prediction error of the arrival time of the transport vehicles in multiple dispatch tasks; calculating the proportion of the prediction error in multiple dispatch tasks that is greater than a preset error threshold; if the proportion is greater than a preset proportion threshold, then correcting the congestion delay weight and the weather delay weight according to the preset learning rate and proportion, respectively, to obtain the corrected congestion delay weight and the corrected weather delay weight.

[0054] The prediction error refers to the difference between the predicted arrival time of the transport vehicle and its actual arrival time. To obtain this error, the system records the actual arrival time of the transport vehicle after it arrives at the dispatch site. This error data is continuously collected and stored to form a historical error dataset, which is used to correct the congestion delay weight and weather delay weight in subsequent applications. The preset error threshold represents the acceptable range of prediction error, and the preset ratio threshold is used to reflect the overall accuracy level of the current prediction model in practical applications. The preset error threshold, preset ratio threshold, and learning rate can be set according to specific circumstances or needs. For example, the learning rate can be set to a range of 0.001-0.05.

[0055] In this embodiment, considering that in actual operation, fixed congestion delay weights and weather delay weights may not accurately reflect real-time changes in traffic and weather conditions, leading to deviations in the prediction of vehicle arrival times and thus affecting the accuracy of cargo pre-scheduling, an adaptive optimization mechanism for congestion delay weights and weather delay weights is established to improve the accuracy of arrival time prediction.

[0056] Specifically, the system counts the number of shipment tasks whose prediction errors exceed a preset error threshold, thereby determining the proportion of prediction errors exceeding the preset error threshold. When the proportion of prediction errors exceeding the preset error threshold is greater than a preset proportion threshold, it indicates that the current congestion delay weights and weather delay weights may no longer be applicable to the current situation and need to be adjusted. At this time, the system will adaptively correct the congestion delay weights and weather delay weights based on the preset learning rate and the current statistical proportion. The learning rate and proportion determine the correction step size of the congestion delay weights and weather delay weights.

[0057] In addition, after the congestion delay weight and weather delay weight are corrected, the system will use historical data for verification. Only the correction results that pass the verification will be saved to ensure the effectiveness of the weight correction.

[0058] In this way, by collecting data, performing error analysis, and verifying weights, the algorithm weights are automatically adjusted, achieving continuous improvement in system performance. This adaptive correction mechanism allows the delay weights to be adjusted according to changes in actual traffic conditions and weather patterns, thus becoming closer to the actual situation. To a certain extent, this avoids the prediction bias that may be caused by using fixed weights, making the prediction of the estimated arrival time of the transport vehicles more accurate.

[0059] For example, if the estimated arrival time is earlier than the actual arrival time, the formula for calculating the corrected congestion delay weight is as follows: Equation (5) in, This indicates the corrected congestion delay weight; This indicates the congestion delay weights before the correction; Indicates the learning rate; Indicates proportion; The revised formula for calculating the weather delay weight is as follows: Equation (6) in, This indicates the corrected weather delay weighting; Indicates the weather delay weights before correction; Indicates the learning rate; Indicates proportion.

[0060] For example, if the estimated arrival time is later than the actual arrival time, the formula for calculating the corrected congestion delay weight is as follows: Equation (7) in, This indicates the corrected congestion delay weights; This indicates the congestion delay weights before the correction; Indicates the learning rate; Indicates proportion; The revised formula for calculating the weather delay weight is as follows: Equation (8) in, This indicates the corrected weather delay weighting; Indicates the weather delay weights before correction; Indicates the learning rate; Indicates proportion.

[0061] In one possible embodiment, determining the pre-scheduling time for goods based on the estimated arrival time and the time required for goods dispatch includes: subtracting the time required for goods dispatch from the estimated arrival time to obtain the pre-scheduling time for goods.

[0062] As one possible implementation, the formula for calculating the cargo pre-scheduling time is: Equation (9) in, Indicates the pre-scheduling time for goods; Indicates the estimated arrival time; This indicates the time required for cargo dispatch.

[0063] In one embodiment, the method for determining the pre-scheduling time of goods further includes: determining the pre-scheduling time of goods based on the time required for goods scheduling and a preset buffer time, wherein the buffer time is used to compensate for the deviation of the time required for goods scheduling caused by uncertainties in the goods scheduling process; and determining the pre-scheduling time of goods based on the expected arrival time and the pre-scheduling time of goods.

[0064] Among them, buffer time refers to a flexible period of time preset to cope with various uncertainties that may occur during the cargo scheduling process, in order to compensate for these unpredictable deviations; cargo pre-scheduling advance time refers to the total length of time reserved for cargo scheduling in advance before the carrier vehicle is expected to arrive at the dispatch site, in order to ensure that the cargo can be scheduled and loaded on the vehicle in a timely manner, taking into account the actual operation time required for cargo scheduling as well as the extra time to cope with uncertainties.

[0065] In addition, the buffer duration can be set based on historical data statistics, expert experience, risk assessment models, etc. For example, statistical analysis can be conducted based on the difference distribution between the actual scheduling duration and the estimated scheduling duration in historical scheduling tasks, and the mean, median or a certain percentile can be taken as the buffer duration. Uncertain factors include, but are not limited to, differences in the proficiency of on-site operators, temporary failures of loading and unloading equipment, increased time for cargo information verification, impact of sudden weather conditions and delays in previous tasks.

[0066] In this embodiment, considering that actual cargo scheduling may be affected by various uncertainties, leading to a deviation between the actual scheduling time and the preset cargo scheduling time, resulting in an overly tight pre-scheduling time and thus affecting the overall scheduling efficiency and resource utilization of the shipping site, a buffer period is introduced to effectively compensate for the difference between the cargo scheduling time required and the initial estimate caused by the complexity of the actual operating environment and unforeseen events. This effectively reduces the waiting time of transport vehicles and improves the overall operating efficiency and resource utilization of the shipping site.

[0067] For example, determining the pre-scheduling time of goods based on the required time for goods scheduling and the preset buffer time includes: calculating the sum of the required time for goods scheduling and the buffer time to obtain the pre-scheduling time of goods.

[0068] In this exemplary embodiment, the formula for calculating the pre-scheduling lead time for goods is: Equation (10) in, Indicates the lead time for cargo pre-scheduling; Indicates the time required for cargo dispatch; Indicates the buffer duration.

[0069] For example, determining the pre-scheduling time of goods based on the estimated arrival time and the pre-scheduling lead time includes: subtracting the pre-scheduling lead time of goods from the estimated arrival time to obtain the pre-scheduling time of goods.

[0070] In this exemplary embodiment, the formula for calculating the cargo pre-scheduling time is: Equation (11) in, Indicates the pre-scheduling time for goods; Indicates the estimated arrival time; This indicates the lead time for cargo pre-scheduling.

[0071] In one embodiment, the remaining travel information includes the remaining mileage and historical average speed. The mileage is determined based on the target positioning data of the transport vehicle and the location data of the dispatch site. The method for determining the target positioning data includes: acquiring multi-source positioning data of the transport vehicle and quality indicators of various positioning data; if the various positioning data are the same, then any one of the various positioning data is determined as the target positioning data; if the various positioning data have deviations, then the confidence weight of each positioning data is determined based on the quality indicators of each positioning data, and the various positioning data are weighted according to each confidence weight to obtain the target positioning data.

[0072] Among them, target location data refers to data that, after processing and filtering, is considered to most accurately represent the current geographical location of the transport vehicle; the target location data and the location data of the dispatch site can be geographical coordinates.

[0073] In this embodiment, considering that the location data of the transport vehicles may be from multiple sources and of varying quality, which could lead to inaccurate acquisition of remaining journey information, thus affecting the accuracy of subsequent estimated arrival time and cargo pre-scheduling time, and consequently reducing the reliability of the entire scheduling scheme, the target location data is determined by first acquiring multi-source location data of the transport vehicles and simultaneously acquiring the quality indicators of these location data. When these multi-source location data are consistent or consistent within a preset error range, it indicates that they all have high reliability, and any one of them can be determined as the target location data. However, if there are deviations among these location data or deviations exceeding the preset error range, a confidence weight is determined for each data source based on the quality indicators of each location data. Subsequently, the location data from different sources are weighted and fused with their corresponding confidence weights to obtain a comprehensive and reliable target location data.

[0074] In this way, by acquiring multi-source positioning data and combining it with its quality indicators, the data can be directly selected when they are consistent, and weighted fusion can be used when there are deviations in the data. This ensures the reliability and stability of the target positioning data. This accurate target positioning data makes the calculation of the remaining road mileage more precise, thereby improving the estimation accuracy of the ideal travel time. Ultimately, it helps to more accurately predict the expected arrival time of the transport vehicle, and on this basis, determine a more reasonable pre-scheduling time for the cargo.

[0075] For example, the multi-source positioning data includes first positioning data from the vehicle positioning system, second positioning data from the mobile terminal, and third positioning data from the communication base station; the quality indicators include signal strength, positioning accuracy, data continuity, speed reasonableness, and trajectory continuity.

[0076] The vehicle positioning system is installed in the driver's cab of the transport vehicle and provides high-precision, high-stability positioning data through dual-mode positioning using both the Global Positioning System (GPS) and the BeiDou Navigation Satellite System (BDS). Mobile terminals, such as the driver's mobile phone, use applications running on the device to perform positioning through a built-in positioning module, ensuring continuous positioning service. Base station positioning estimates the vehicle's location through signal interaction between the transport vehicle and multiple base stations. Typically, the accuracy of these three positioning data types, from highest to lowest, is ranked as follows: first positioning data, second positioning data, and third positioning data.

[0077] In this exemplary embodiment, during the process of determining the confidence weights of various positioning data based on the quality indicators of various positioning data, positioning data with higher accuracy, stronger signal, better data continuity, better speed rationality, and better trajectory continuity will be assigned higher confidence weights.

[0078] The formula for calculating the confidence weight of the first location data is: Equation (12) The formula for calculating the confidence weight of the second location data is: Equation (13) The formula for calculating the confidence weight of the third location data is: Equation (14) in, , and The confidence weights for the first, second, and third location data are, in order. , and The quality indicators for the first, second, and third positioning data are, in order; The formula for calculating target location data is: Equation (15) in, This represents the target location data; , and The confidence weights for the first, second, and third location data are, in order. , and The data are, in order, the first location data, the second location data, and the third location data.

[0079] In one embodiment, after obtaining the carrier vehicle information, the method further includes: if all the goods corresponding to the target shipment task need to be loaded onto multiple carrier vehicles, then the scheduling strategy corresponding to the target shipment task is used as a decision variable, with the goal of minimizing the value of the pre-constructed scheduling objective function, and the target scheduling strategy corresponding to the target shipment task is determined according to the preset loading operation constraints; wherein, the target scheduling strategy includes multiple target loading points allocated to the target shipment task, the amount of goods loaded at each target loading point, and the target loading point corresponding to each carrier vehicle.

[0080] The target scheduling strategy is refined and operable, including multiple target loading points allocated to the target shipping task, i.e., which specific loading points will be used for the task; the loading volume corresponding to each target loading point, i.e., the amount of cargo that needs to be scheduled for each loading point; and the target loading point corresponding to each carrier vehicle, i.e., which specific loading point each carrier vehicle should go to for loading cargo.

[0081] In this embodiment, considering the large volume of goods in the target shipment task, requiring multiple transport vehicles to complete loading, relying solely on a single pre-scheduled cargo time is insufficient to effectively coordinate the operations between multiple vehicles and loading points. This could lead to problems such as excessively long vehicle waiting times, low loading point utilization, or unreasonable cargo movement paths, thus failing to meet the demand for efficient and resource-efficient cargo shipments. Therefore, the scheduling strategy for the target shipment task is used as a decision variable. With the goal of minimizing the value of the scheduling objective function, various possible scheduling schemes are explored based on the loading operation constraints, and the final target scheduling strategy is determined to optimize the entire shipment process of the target shipment task.

[0082] Specifically, in order to evaluate the merits of various feasible scheduling strategies, a scheduling objective function is pre-constructed to quantify the performance of scheduling strategies in terms of efficiency, cost, or resource utilization. The goal is to minimize the value of this scheduling objective function, which means seeking a scheduling strategy that is optimal in terms of comprehensive indicators. At the same time, in the process of determining the target scheduling strategy, the loading operation constraints are strictly followed. These constraints are physical, logical, or operational rules that must be met in the actual working environment, ensuring that the generated target scheduling strategy is feasible and meets actual needs.

[0083] In this way, when a target shipment task requires multiple carrier vehicles to complete together, the complex scheduling strategy is used as a decision variable, and guided by minimizing the value of the pre-built scheduling objective function, while strictly following the preset loading operation constraints, so as to intelligently determine the optimal target scheduling strategy. Through refined scheduling, the collaborative operation between multiple carrier vehicles and multiple loading points becomes efficient and orderly, significantly improving the overall operational efficiency and resource utilization of the shipment site.

[0084] In one embodiment, the scheduling objective function includes a waiting time function for multiple transport vehicles, a utilization penalty function for all loading points at the dispatch site, and a travel distance penalty function for all goods corresponding to the target dispatch task; the loading operation constraints include that any goods can only be assigned to one loading point, any loading point can be assigned to at most one transport vehicle at any given time, the arrival time of any goods at the corresponding loading point must be earlier than the arrival time of the corresponding transport vehicle, and the weight of goods loaded onto any transport vehicle must be less than or equal to the load capacity of any transport vehicle.

[0085] Among them, the waiting time function for multiple carrier vehicles is used to quantify the total or average time spent by all carrier vehicles participating in the target shipment task waiting for loading at the shipment site. If the goods to be loaded are not in place when the carrier vehicle arrives, a waiting time is generated. The goal of this function is to minimize the waiting time of each carrier vehicle. The utilization rate penalty function for all loading points in the shipment site is used to measure the utilization efficiency and balance of each loading point in the shipment site. The goal of this function is to reduce the idle time of loading points and improve utilization. The movement distance penalty function for all goods corresponding to the target shipment task is used to quantify the total distance required for all goods in the target shipment task to move from their initial storage location to the designated loading point. The goal of this function is to reduce the handling cost and time of goods in the site and improve operational efficiency.

[0086] In loading operation constraints, any cargo can only be assigned to one loading point to ensure that each piece of cargo has a clear loading destination during scheduling, minimizing duplicate allocation or omissions. Any loading point can be assigned at most one transport vehicle at any given time, including both cases where any loading point is assigned one transport vehicle at the same time and cases where no transport vehicle is assigned at the same time. That is, the number of transport vehicles assigned to any loading point at any given time ∈ {0,1}, simulating the exclusivity of loading point resources in actual loading operations, and minimizing conflicts caused by multiple transport vehicles occupying the same loading point simultaneously. The arrival time of any cargo at its corresponding loading point must be earlier than the arrival time of the corresponding transport vehicle, ensuring that the cargo is ready before the vehicle arrives at the loading point, minimizing long waiting times for transport vehicles. The weight of cargo loaded onto any transport vehicle must be less than or equal to the load capacity of any transport vehicle, ensuring that the loading amount of each transport vehicle does not exceed its maximum carrying capacity. These constraints are usually expressed in the optimization model in the form of equations or inequalities to limit the range of values ​​for decision variables.

[0087] In this embodiment, the scheduling objective function comprehensively considers the waiting time of multiple transport vehicles, the utilization rate of all loading points in the dispatching site, and the movement distance of all goods corresponding to the target dispatching task. It can comprehensively evaluate the advantages and disadvantages of the scheduling strategy. At the same time, combined with the loading operation constraints, it ensures the feasibility and rationality of the scheduling scheme in actual operation, thereby generating an efficient, balanced target scheduling strategy that meets the requirements of actual operation. This strategy can significantly reduce vehicle waiting time, improve loading point utilization, reduce cargo handling costs, and comprehensively improve the overall operational efficiency and resource utilization of the dispatching site.

[0088] For example, the expression for the scheduling objective function is: Equation (16) in, This represents the value of the scheduling objective function; the smaller the value, the better the scheduling strategy. A function representing the waiting time of multiple transport vehicles; This function represents the utilization penalty for all loading points at the shipping site. This represents the penalty function for the movement distance of all goods corresponding to the target shipping task; This represents the penalty coefficient for the utilization penalty of all loading points at the shipping site; This represents the penalty coefficient for the distance traveled by all goods corresponding to the target shipping task.

[0089] In this exemplary embodiment, waiting time is the primary objective, while the utilization rate of loading points and the distance the cargo travels are the penalty factors. , It can be set according to specific needs or situations, for example Set it to 0.3~0.5. Set to 0.2~0.4 to indicate the importance relative to the waiting time.

[0090] For example, the expression for the utilization penalty function of all loading points at the shipping site is: Equation (17) in, Indicates the vehicle being transported Waiting time; Indicates the number of vehicles being transported; transport vehicles The formula for calculating the waiting time is: Equation (18) in, Indicates the vehicle being transported Waiting time; Indicates the vehicle being transported Cargo readiness time at the corresponding loading point; Indicates the vehicle being transported Arrival time.

[0091] For example, the expression for the utilization penalty function of all loading points at the shipping site is: Equation (19) in, Indicates the loading point The total available time is the loading point during the work shift. The theoretical usable duration; Indicates the loading point The idle time is the loading point. The difference between the total available time and the actual loading time.

[0092] For example, the expression for the penalty function for the movement distance of all goods corresponding to the target shipping task is: Equation (20) in, Indicates the goods corresponding to the target shipment task. The distance traveled; This indicates the quantity of goods corresponding to the target shipment task.

[0093] The aforementioned dispatching and management method for shipping sites first obtains the vehicle and cargo information corresponding to the current time and the target shipping task. The vehicle information includes the remaining journey time and driving environment information of the vehicle to the dispatching site. The cargo information includes the time required for cargo dispatching. Next, based on the remaining journey and driving environment information, the ideal travel time and delay time for the vehicle to reach the dispatching site are determined. Then, based on the current time, ideal travel time, and delay time, the estimated arrival time of the vehicle is determined. Finally, based on the estimated arrival time and the time required for cargo dispatching, the pre-dispatch time for cargo is determined, and all cargo corresponding to the target shipping task is pre-dispatched according to the pre-dispatch time. By tracking the remaining travel time and driving environment information of carrier vehicles in real time, the ideal travel time and delay time of carrier vehicles to the dispatching site are determined. The arrival time of carrier vehicles is predicted by using a layered time accumulation method, which improves the accuracy of the prediction of the arrival time of carrier vehicles. Based on this, the pre-scheduling time of goods is determined and goods pre-scheduled. It is possible to arrange the goods to be loaded to be moved to the loading point in advance before the arrival of carrier vehicles, reduce the unnecessary waiting time of carrier vehicles, and avoid the problem of the loading point being idle during the waiting period after the carrier vehicles arrive. This improves the dispatching management efficiency of the dispatching site and optimizes the efficiency of goods dispatching and the utilization rate of dispatching site resources.

[0094] Please see Figure 3 , Figure 3 This is a block diagram illustrating an optional shipping yard scheduling and management device, as shown in an exemplary embodiment of this application. The device can be applied to... Figure 1 The implementation environment shown is intended to illustrate an application of this device in other exemplary environments. This embodiment does not limit the application environment to which the device is applicable.

[0095] like Figure 3 As shown, in an exemplary embodiment, the shipping site scheduling and management device 300 includes at least a data acquisition module 310, a duration calculation module 320, a time prediction module 330, and a cargo scheduling module 340, which are described in detail below: The data acquisition module 310 is used to acquire the carrier vehicle information and cargo information corresponding to the current time and the target shipping task. The carrier vehicle information includes the remaining journey information and driving environment information of the carrier vehicle to the shipping site, and the cargo information includes the time required for cargo scheduling. The time calculation module 320 is used to determine the ideal travel time and delay time of the carrier vehicle to the dispatch site based on the remaining travel information and driving environment information; The time prediction module 330 is used to determine the estimated arrival time of the transport vehicle based on the current time, ideal travel time and delay time; The cargo scheduling module 340 is used to determine the cargo pre-scheduling time based on the estimated arrival time and the cargo scheduling time required, and to pre-schedule all cargo corresponding to the target shipment task according to the cargo pre-scheduling time.

[0096] It should be noted that the shipping site scheduling and management device provided in the above embodiments and the shipping site scheduling and management method provided in the above embodiments belong to the same concept. The content of the operation performed by each module has been described in detail in the method embodiments, and will not be repeated here.

[0097] Please see Figure 4 , Figure 4 This is a block diagram illustrating an optional shipping site scheduling and management system, as shown in an exemplary embodiment of this application. Figure 4 As shown, the shipping site scheduling and management system 400 adopts a layered architecture, divided into a data perception layer, a network transmission layer, a data processing layer, a decision control layer, and an execution layer. It includes at least a positioning subsystem 410, an arrival time prediction subsystem 420, an intelligent scheduling algorithm subsystem 430, a cargo management subsystem 440, a loading point management subsystem 450, and a central scheduling control subsystem 460. Among them, the positioning subsystem 410 is located in the data perception layer, the arrival time prediction subsystem 420 is located in the data processing layer, the intelligent scheduling algorithm subsystem 430 and the central scheduling control subsystem 460 are located in the decision control layer, and the cargo management subsystem 440 and the loading point management subsystem 450 are located in the execution layer. The subsystem includes a positioning subsystem 410 for obtaining target positioning data of the transport vehicle; an arrival time prediction subsystem 420, comprising the aforementioned data acquisition module 310, duration calculation module 320, and time prediction module 330, for predicting the estimated arrival time of the transport vehicle; an intelligent scheduling algorithm subsystem 430, comprising the aforementioned cargo scheduling module 340, for determining scheduling strategies and performing dispatch control; a cargo management subsystem 440 for controlling the movement of cargo to be loaded to designated loading points; a loading point management subsystem 450 for monitoring the status of loading points; and a central dispatch control subsystem 460 for coordinating the work of each subsystem.

[0098] Please see Figure 5 , Figure 5 This is a schematic diagram illustrating the structure of an optional electronic device, as shown in an exemplary embodiment of this application. Figure 5 A schematic diagram of a computer system suitable for implementing the embodiments of this application is shown. It should be noted that... Figure 5 The computer system 500 of the electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0099] like Figure 5 As shown, the computer system 500 includes a Central Processing Unit (CPU) 501, which can perform various appropriate actions and processes, such as executing the methods described in the above embodiments, based on programs stored in Read-Only Memory (ROM) 502 or programs loaded from storage portion 508 into Random Access Memory (RAM) 503. The RAM 503 also stores various programs and data required for system operation. The CPU 501, ROM 502, and RAM 503 are interconnected via a bus 504. An Input / Output (I / O) interface 505 is also connected to the bus 504.

[0100] The following components are connected to I / O interface 505: an input section 506 including a keyboard, mouse, etc.; an output section 507 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to I / O interface 505 as needed. Removable media 511, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 510 as needed so that computer programs read from them can be installed into storage section 508 as needed.

[0101] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 509, and / or installed from removable medium 511. When the computer program is executed by central processing unit (CPU) 501, it performs various functions defined in the system of this application.

[0102] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a computer's processor, causes the computer to perform the shipping site scheduling and management method as described above. This computer-readable storage medium may be included in the electronic device described in the above embodiments, or it may exist independently and not deployed within that electronic device.

[0103] It should be noted that the computer-readable medium shown in the embodiments of this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying a computer-readable computer program. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0104] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in this application should still be covered by the claims of this application.

Claims

1. A method for scheduling and managing shipping yards, characterized in that, The method includes: Obtain the carrier vehicle information and cargo information corresponding to the current time and the target shipping task. The carrier vehicle information includes the remaining journey information and driving environment information of the carrier vehicle to the shipping site. The cargo information includes the time required for cargo scheduling. Based on the remaining trip information and the driving environment information, determine the ideal travel time and delay time for the transport vehicle to reach the dispatch site; The estimated arrival time of the transport vehicle is determined based on the current time, the ideal travel time, and the delay time. Based on the estimated arrival time and the time required for cargo scheduling, a pre-scheduling time for cargo is determined, and all cargo corresponding to the target shipment task is pre-scheduled according to the pre-scheduling time.

2. The shipping site scheduling and management method according to claim 1, characterized in that, The step of determining the ideal travel time and delay time of the transport vehicle to the dispatch site based on the remaining travel information and the driving environment information includes: The ideal travel time is determined based on the remaining trip information; A first delay duration is determined based on the ideal driving time, the traffic congestion index, and a preset congestion delay weight; and a second delay duration is determined based on the ideal driving time, the weather condition index, and a preset weather delay weight, wherein the driving environment information includes the traffic congestion index and the weather condition index. The first delay duration and the second delay duration are added together to obtain the delay duration.

3. The shipping site scheduling and management method according to claim 2, characterized in that, The correction methods for the congestion delay weight and the weather delay weight include: Obtain the prediction error of the arrival time of the carrier vehicles in multiple shipping tasks; The proportion of prediction errors exceeding a preset error threshold in multiple shipping missions is statistically analyzed. If the ratio is greater than a preset ratio threshold, then the congestion delay weight and the weather delay weight are corrected according to the preset learning rate and the ratio, respectively, to obtain the corrected congestion delay weight and the corrected weather delay weight.

4. The shipping site scheduling and management method according to claim 1, characterized in that, The method for determining the pre-scheduled cargo time also includes: Based on the required time for cargo scheduling and the preset buffer time, the pre-scheduling time for cargo is determined, wherein the buffer time is used to compensate for the deviation of the required time for cargo scheduling caused by uncertainties in the cargo scheduling process; The cargo pre-scheduling time is determined based on the estimated arrival time and the cargo pre-scheduling lead time.

5. The shipping site scheduling and management method according to claim 1 or 2, characterized in that, The remaining trip information includes the remaining mileage and historical average speed, and the mileage is determined based on the target positioning data of the transport vehicle and the location data of the dispatch site. The method for determining the target location data includes: Acquire the multi-source positioning data of the transport vehicle and the quality indicators of various positioning data; If the various positioning data are the same, then any one of the various positioning data will be determined as the target positioning data; If there are deviations in the various positioning data, then the confidence weights of the various positioning data are determined according to the quality indicators of the various positioning data, and the various positioning data are weighted according to each confidence weight to obtain the target positioning data.

6. The shipping site scheduling and management method according to any one of claims 1 to 4, characterized in that, After obtaining the carrier vehicle information, the process also includes: If all the goods corresponding to the target shipment task need to be loaded onto multiple carrier vehicles, then the scheduling strategy corresponding to the target shipment task is used as a decision variable. The target scheduling strategy corresponding to the target shipment task is determined based on the preset loading operation constraints, with the goal of minimizing the value of the pre-constructed scheduling objective function. The target scheduling strategy includes multiple target loading points allocated to the target shipping task, the amount of cargo loaded at each target loading point, and the target loading point corresponding to each carrier vehicle.

7. The shipping site scheduling and management method according to claim 6, characterized in that, The scheduling objective function includes the waiting time function of the multiple transport vehicles, the utilization rate penalty function of all loading points at the dispatch site, and the movement distance penalty function of all goods corresponding to the target dispatch task. The loading operation constraints include that any cargo can only be assigned to one loading point, any loading point can be assigned to at most one carrier vehicle at any given time, the arrival time of any cargo at its corresponding loading point must be earlier than the arrival time of the corresponding carrier vehicle, and the weight of the cargo loaded onto any carrier vehicle must be less than or equal to the carrying capacity of the carrier vehicle.

8. A dispatching and management device for a shipping site, characterized in that, The device includes: The data acquisition module is used to obtain the carrier vehicle information and cargo information corresponding to the target shipping task at the current time. The carrier vehicle information includes the remaining journey information and driving environment information of the carrier vehicle to the shipping site, and the cargo information includes the time required for cargo scheduling. The time calculation module is used to determine the ideal travel time and delay time of the transport vehicle to the dispatch site based on the remaining travel information and the driving environment information; The time prediction module is used to determine the estimated arrival time of the transport vehicle based on the current time, the ideal travel time, and the delay time. The cargo scheduling module is used to determine the cargo pre-scheduling time based on the estimated arrival time and the cargo scheduling time required, and to pre-schedule all cargo corresponding to the target shipment task according to the cargo pre-scheduling time.

9. An electronic device, characterized in that: include: processor; A storage device for storing a program that, when executed by the processor, causes the electronic device to implement the shipping site scheduling and management method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: It stores a computer program that, when executed by the computer's processor, causes the computer to perform the shipping site scheduling and management method as described in any one of claims 1 to 7.

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