Vehicle scheduling method, computer equipment, storage medium and program product

Abstract

The invention relates to a vehicle scheduling method, computer equipment, a storage medium and a program product. The method comprises the following steps: inputting the number of vehicles to be unloaded, information of goods to be unloaded in the vehicles to be unloaded and information of goods to be processed corresponding to unloading ports in a transfer yard into a digital twin model corresponding to the transfer yard; and the digital twin model outputs target unloading port identification information corresponding to each to-be-unloaded vehicle based on the vehicle number, the to-be-processed cargo information and the to-be-unloaded cargo information, and the target unloading port identification information is sent to the corresponding to-be-unloaded vehicle to indicate the to-be-unloaded vehicle to unload cargos at the target unloading port. Compared with a traditional mode that only vehicles are dispatched to idle unloading ports nearby, according to the scheme, the number of the to-be-unloaded vehicles, the cargo information in the to-be-unloaded vehicles and the cargo information of the unloading ports are combined, after the cargo processing loads of all the unloading ports are predicted in combination with the digital twin model, the to-be-unloaded vehicles are dispatched to the appropriate unloading ports for unloading, and the unloading efficiency of the to-be-unloaded vehicles is improved. And the cargo processing efficiency is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of logistics sorting, and in particular to a vehicle scheduling method and device, a computer device, a storage medium and a computer program product. BACKGROUND

[0002] In the process of transporting express items, the express items need to be unloaded and sorted in a transfer station to achieve accurate transportation of the express items. In the logistics transfer station, the express items in the vehicles need to be unloaded. At present, the vehicle scheduling method for unloading in the logistics transfer station is usually to schedule the vehicles to idle unloading docks for unloading. However, the scheduling method of directly scheduling the vehicles to the idle unloading docks for unloading is likely to cause the main line sorting pressure of the unloading docks to be too high, thereby reducing the sorting efficiency.

[0003] Therefore, the current vehicle scheduling method has the defect of low sorting efficiency. SUMMARY

[0004] Therefore, it is necessary to provide a vehicle scheduling method, device, computer equipment, computer readable storage medium and computer program product capable of improving the sorting efficiency of express items in view of the above technical problems.

[0005] In a first aspect, the present application provides a vehicle scheduling method, which comprises:

[0006] obtaining the number of vehicles to be unloaded and the information of goods to be unloaded in each vehicle to be unloaded, and obtaining the information of goods to be processed corresponding to each unloading dock in the transfer station;

[0007] inputting the number of vehicles, the information of goods to be unloaded and the information of goods to be processed into a digital twin model corresponding to the transfer station, and outputting the target unloading dock identification information corresponding to each vehicle to be unloaded by the digital twin model according to the number of vehicles, the information of goods to be processed and the information of goods to be unloaded;

[0008] based on the target unloading dock identification information, instructing the corresponding vehicle to be unloaded to unload goods at the target unloading dock corresponding to the target unloading dock identification information.

[0009] In one of the embodiments, the number of vehicles to be unloaded is obtained, which comprises:

[0010] obtaining the first number of vehicles to be unloaded in the first vehicle to be unloaded in the transfer station;

[0011] obtaining the road network information corresponding to the second vehicle to be unloaded to be arrived at the transfer station, and determining the second number of vehicles to be unloaded to be arrived at the transfer station in a future preset time period according to the road network information;

[0012] According to the first vehicle number of the first vehicle to be unloaded and the second vehicle number of the second vehicle to be unloaded, the vehicle number of the vehicle to be unloaded is obtained.

[0013] In one of the embodiments, the inputting of the vehicle number, the to-be-unloaded cargo information and the to-be-processed cargo information into the digital twin model corresponding to the transfer station comprises:

[0014] The first vehicle number, the second vehicle number, the to-be-unloaded cargo information and the to-be-processed cargo information are inputted into the digital twin model corresponding to the transfer station, and the digital twin model determines the vehicle number of the transfer station in a future preset time period according to the first vehicle number and the second vehicle number.

[0015] When it is detected that there is a time point in the future preset time period in which the vehicle number is greater than a preset vehicle number threshold, target unloading port identifier information of each first vehicle to be unloaded is outputted according to the to-be-processed cargo information corresponding to each unloading port and the to-be-unloaded cargo information of each first vehicle to be unloaded.

[0016] In one of the embodiments, the to-be-processed cargo information comprises a to-be-processed cargo quantity, and the to-be-unloaded cargo information comprises a to-be-unloaded cargo quantity; the outputting of the target unloading port identifier information of each first vehicle to be unloaded according to the to-be-processed cargo information corresponding to each unloading port and the to-be-unloaded cargo information of each first vehicle to be unloaded comprises:

[0017] Position information of each first vehicle to be unloaded in the transfer station is acquired, and distances between each first vehicle to be unloaded and each unloading port are determined according to the position information.

[0018] For each unloading port, a historical to-be-processed cargo quantity of the unloading port is acquired, and a to-be-processed cargo quantity of the unloading port in a future preset time period is acquired according to the to-be-processed cargo quantity and the historical to-be-processed cargo quantity.

[0019] If there is a time point in the future preset time period in which the to-be-processed cargo quantity is greater than a preset to-be-processed cargo quantity threshold, a target first vehicle to be unloaded is determined from the first vehicles to be unloaded, and an identifier of the unloading port is determined as target unloading port identifier information of the target first vehicle to be unloaded; the target first vehicle to be unloaded is a first vehicle to be unloaded whose to-be-unloaded cargo quantity is less than a second preset to-be-unloaded cargo quantity threshold and whose distance to the unloading port is less than a preset distance threshold.

[0020] In one of the embodiments, the acquiring of the to-be-processed cargo quantity of the unloading port in the future preset time period according to the to-be-processed cargo quantity and the historical to-be-processed cargo quantity comprises:

[0021] According to the historical quantity of goods to be processed, a historical quantity sequence of goods corresponding to the unloading port is obtained; the historical quantity sequence of goods includes historical quantities of goods to be processed at multiple time points in a historical time period;

[0022] The quantity of goods to be processed and the historical quantity sequence of goods are input into a prediction model in the digital twin model, and the prediction model determines the quantity of goods to be processed of the unloading port in a future preset time period based on the quantity of goods to be processed and the historical quantities of goods to be processed at multiple time points in the historical time period.

[0023] In one of the embodiments, based on the target unloading port identification information, the corresponding vehicle to be unloaded is instructed to unload goods at the target unloading port corresponding to the target unloading port identification information.

[0024] The target unloading port identification information corresponding to each vehicle to be unloaded is sent to a public display device in the transfer field;

[0025] Each target unloading port identification information is displayed through the public display device to instruct the corresponding vehicle to be unloaded to unload goods at the target unloading port corresponding to the target unloading port identification information.

[0026] In one of the embodiments, the method further comprises:

[0027] According to the number of vehicles to be unloaded corresponding to each target unloading port, the information of goods to be unloaded in each vehicle to be unloaded, and the information of goods to be processed corresponding to each target unloading port, the rate of processing goods of each target unloading port is determined, and the rate of processing goods is displayed.

[0028] In a second aspect, the application provides a vehicle scheduling device, the device comprising:

[0029] The acquisition module is configured to acquire the number of vehicles to be unloaded and the information of goods to be unloaded in each vehicle to be unloaded, and acquire the information of goods to be processed corresponding to each unloading port in the transfer field;

[0030] The input module is configured to input the number of vehicles, the information of goods to be unloaded, and the information of goods to be processed into a digital twin model corresponding to the transfer field, and output target unloading port identification information corresponding to each vehicle to be unloaded from the digital twin model according to the number of vehicles, the information of goods to be processed, and the information of goods to be unloaded.

[0031] The scheduling module is configured to instruct the corresponding vehicle to be unloaded to unload goods at the target unloading port corresponding to the target unloading port identification information based on the target unloading port identification information.

[0032] Thirdly, this application provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described method.

[0033] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0034] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method.

[0035] The aforementioned vehicle dispatching method, apparatus, computer equipment, storage medium, and computer program products input the number of vehicles to be unloaded, the information on the goods to be unloaded in the vehicles, and the information on the goods to be processed at each unloading port in the transfer station into a digital twin model corresponding to the transfer station. The digital twin model then outputs target unloading port identification information for each vehicle based on the number of vehicles, the information on the goods to be processed, and the information on the goods to be unloaded. This target unloading port identification information is sent to the corresponding vehicle to instruct it to unload its goods at the target unloading port. Compared to the traditional method of simply dispatching vehicles to the nearest available unloading port, this solution improves the efficiency of goods processing by combining the number of vehicles to be unloaded, the information on the goods in the vehicles, and the information on the goods at the unloading ports, and using the digital twin model to predict the cargo processing load at each unloading port. Attached Figure Description

[0036] Figure 1 This is a flowchart illustrating a vehicle dispatching method in one embodiment;

[0037] Figure 2 This is a flowchart illustrating the model scheduling steps in one embodiment;

[0038] Figure 3 This is a flowchart illustrating the model scheduling steps in another embodiment;

[0039] Figure 4 This is a structural block diagram of a vehicle dispatching device in one embodiment;

[0040] Figure 5 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0041] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0042] In one embodiment, such as Figure 1 As shown, a vehicle dispatching method is provided. This embodiment illustrates the method by applying it to a terminal. It is understood that this method can also be applied to a server, or to a system including both a terminal and a server, and is implemented through the interaction between the terminal and the server. The method includes the following steps:

[0043] Step S202: Obtain the number of vehicles to be unloaded and the information on the goods to be unloaded in each vehicle, and obtain the information on the goods to be processed corresponding to each unloading port in the transfer yard.

[0044] This solution can be applied to vehicle dispatching in logistics transit hubs. A transit hub can accommodate multiple vehicles awaiting unloading and can include multiple unloading ports. The vehicles awaiting unloading can be loaded with goods such as parcels and express packages. The unloading ports are entrances for unloading goods from these vehicles and can be connected to corresponding conveyor systems. These conveyor systems transport goods to a main line connected to them, where they are scanned and sorted before being moved to the next vehicle for transport. The conveyor systems can be belt conveyors, with one conveyor system corresponding to each unloading port, and each conveyor system can be connected to multiple main lines. The terminal needs to dispatch the vehicles awaiting unloading to the corresponding unloading ports for unloading, so that goods can be sorted on the main line.

[0045] The terminal can acquire the number of vehicles awaiting unloading and information about the goods to be unloaded in each vehicle. The terminal can communicate with image acquisition devices installed in the transfer area. These image acquisition devices can be located at the entrance of the transfer area, allowing them to capture vehicle identification information, such as license plate numbers, when vehicles enter. The terminal can acquire the license plate numbers of each vehicle awaiting unloading captured by the image acquisition devices and retrieve relevant information about the corresponding vehicle from a pre-set database, such as the quantity and type of goods to be unloaded. Furthermore, the terminal can also retrieve vehicle-related information using the license plate numbers, including but not limited to vehicle size, driver's name, and cargo information. The terminal can also calculate the total number of vehicles awaiting unloading in the transfer area by counting the number of license plate numbers captured by the image acquisition devices.

[0046] The terminal can also acquire information on goods awaiting processing at each unloading point in the aforementioned transfer area. This information can include details about the goods on the conveyor equipment at each unloading point and on the main line. This information can include various details, such as the type and quantity of goods. Image acquisition devices can also be installed at each unloading point, i.e., on each conveyor equipment in the transfer area. The terminal can acquire images of each unloading point and each conveyor equipment captured by these image acquisition devices, and then use deep learning algorithms to detect the quantity and type of goods on the conveyor equipment in real time, using this information as the goods awaiting processing information.

[0047] Step S204: Input the number of vehicles, information on goods to be unloaded, and information on goods to be processed into the digital twin model corresponding to the transfer station. The digital twin model then outputs the target unloading port identification information for each vehicle to be unloaded based on the number of vehicles, information on goods to be processed, and information on goods to be unloaded.

[0048] The terminal can pre-construct digital twin models corresponding to the aforementioned transfer area. These digital twin models can include unloading port models and matrix area models. The unloading port model includes models of elements in the transfer area such as vehicles, unloading personnel, conveyor belts (transfer equipment), and goods. Each digital twin can include a 3D model, a mechanistic model, and a data model. The 3D model includes a spatial three-dimensional model of each element in the transfer area, presented in a visual format. The mechanistic model includes models corresponding to the attribute information of each element in the transfer area; for example, for a conveyor belt, its mechanistic model is built based on the conveyor belt's friction, rotational speed, and other attribute parameters. The data model includes models corresponding to the operational data of each element in the transfer area; for example, for vehicles, its data model is built based on information such as the vehicle's loading rate. The matrix area model can be a model of the main line corresponding to the conveyor equipment at the unloading port, including models of swing wheels and scanners. The swing wheels and scanners can be devices used for sorting goods on the main line. Similarly, the digital twins corresponding to the swing wheels and scanners can each include corresponding 3D models, mechanistic models, and data models.

[0049] Thus, the terminal can collect relevant information about each element in the transfer center and use the digital twin model of the transfer center combined with the collected relevant information to simulate the operation of the transfer center. For example, the terminal can input the collected number of vehicles, information on goods to be unloaded, and information on goods to be processed into the digital twin model corresponding to the transfer center. The digital twin model will then output the target unloading port identification information corresponding to each vehicle to be unloaded based on the aforementioned number of vehicles, information on goods to be processed, and information on goods to be unloaded. The target unloading port identification information can be the identification information of the unloading port that each vehicle to be unloaded needs to go to, such as the unloading port number or the location of the unloading port. The aforementioned digital twin model can predict the operating status of the transfer station in a future preset time period based on the number of vehicles, the information of goods to be unloaded, and the information of goods to be processed, through machine learning algorithms, combined with the 3D model, mechanism model, and data model corresponding to each element, and thus determine the processing load of each unloading port. Based on the processing load of each unloading port, the digital twin model determines the vehicles to be unloaded corresponding to each unloading port. For example, for unloading ports with excessive processing load, the target unloading port identification information of vehicles with fewer goods to be unloaded is determined as the identification of that unloading port, thereby achieving load balancing of each unloading port.

[0050] Step S206: Based on the target unloading port identification information, instruct the corresponding vehicle to unload goods at the target unloading port corresponding to the target unloading port identification information.

[0051] The terminal can obtain the target unloading port identification information corresponding to each vehicle to be unloaded from the aforementioned digital twin model. Based on this information, the terminal can instruct the corresponding vehicle to unload goods at the target unloading port corresponding to that identification information. For example, the terminal can provide targeted guidance to the vehicle to unload goods. For vehicles with a central control system, the terminal can send the target unloading port identification information to the vehicle's central control system or other terminal devices. After receiving the target unloading port identification information, the vehicle terminal can determine the number or location of the target unloading port based on this information. The vehicle can then drive to the corresponding target unloading port at that number or location to unload goods. The unloaded goods are then transported to the main line for scanning, sorting, and loading via a conveyor system.

[0052] In addition, in some embodiments, the terminal can also instruct the vehicles to be unloaded to the target unloading port for unloading through public guidance. For example, the terminal can also send the identification information of each vehicle to be unloaded and the corresponding target unloading port to the public display device in the transfer station. The public display device can then display the target unloading port identification information of each vehicle to be unloaded, such as the number and location of the target unloading port for each vehicle. Based on the information displayed on the public display device, each vehicle can unload its goods at the corresponding target unloading port.

[0053] In another embodiment, the terminal can also determine the cargo processing rate of each unloading port after scheduling each unloading vehicle. For example, the terminal can determine the cargo processing rate of each target unloading port based on the number of unloading vehicles corresponding to each target unloading port, the cargo information to be unloaded in each unloading vehicle, and the cargo information to be processed corresponding to each target unloading port. In this embodiment, the terminal can determine the cargo processing rate of each target unloading port after scheduling based on the number of unloading vehicles corresponding to each target unloading port, the quantity and type of cargo to be unloaded in each unloading vehicle, and the quantity and type of cargo to be processed corresponding to each target unloading port. Thus, the terminal can obtain information such as the scheduling results of each unloading vehicle and the expected efficiency of each target unloading port. Furthermore, the terminal can also display the cargo processing rate. Specifically, the terminal can visualize the scheduling results of the target unloading ports corresponding to each unloading vehicle and the determined cargo processing rate on a public display device in the transfer station or on a terminal. Furthermore, the terminal can also use the aforementioned cargo processing rate to determine the scheduling effect of the aforementioned digital twin model, and then determine whether the digital twin model needs to be optimized. For example, when the cargo processing rate of a certain unloading port decreases after scheduling, the terminal determines that the digital twin model needs to be optimized.

[0054] In the aforementioned vehicle dispatching method, the number of vehicles to be unloaded, the information on the goods to be unloaded in the vehicles, and the information on the goods to be processed corresponding to each unloading port in the transfer station are input into the digital twin model corresponding to the transfer station. The digital twin model outputs the target unloading port identification information for each vehicle based on the number of vehicles, the information on the goods to be processed, and the information on the goods to be unloaded. The target unloading port identification information is then sent to the corresponding vehicle to instruct it to unload its goods at the target unloading port corresponding to the target unloading port identification information. Compared to the traditional method of simply dispatching vehicles to the nearest available unloading port, this solution improves the efficiency of goods processing by combining the number of vehicles to be unloaded, the information on the goods in the vehicles, and the information on the goods at the unloading ports, and by using the digital twin model to predict the cargo processing load of each unloading port.

[0055] In one embodiment, obtaining the number of vehicles to be unloaded includes: obtaining the first number of vehicles to be unloaded in the transfer station; obtaining road network information corresponding to the second vehicles to be unloaded that are to arrive at the transfer station; determining the second number of vehicles to be unloaded that will arrive at the transfer station within a preset time period based on the road network information; and obtaining the total number of vehicles to be unloaded based on the first number of vehicles to be unloaded and the second number of vehicles to be unloaded.

[0056] In this embodiment, the vehicles to be unloaded can include vehicles at different locations. For example, they can include vehicles currently parked in the transfer station, and vehicles that are en route to the transfer station but have not yet arrived. The terminal can obtain the first number of vehicles in the transfer station. Specifically, the terminal can determine the first number of vehicles in the transfer station in real time by acquiring images of the transfer station from image acquisition devices and using a deep learning algorithm.

[0057] Considering the randomness of vehicle arrivals, the terminal can predict the vehicles about to arrive at the transfer station. The terminal can obtain road network information corresponding to the second unloading vehicle waiting to arrive at the transfer station. This road network information includes traffic conditions, location, and driving data of the second unloading vehicle. Based on this road network information, the terminal can determine the number of the second unloading vehicle to arrive at the transfer station within a preset future time period. For example, the terminal can input the aforementioned road network information into a trained probability model. The probability model, through simulation and prediction, combined with the aforementioned road network information, predicts the arrival time of the second unloading vehicle and outputs the number of the second unloading vehicle to arrive at the transfer station within the preset future time period (e.g., within the next ten minutes). Therefore, the terminal can obtain the total number of vehicles to be unloaded based on the first number of vehicles and the second number of vehicles. The terminal can then use this number of vehicles, which includes various states, to schedule the unloading points for the vehicles to be unloaded.

[0058] Through this embodiment, the terminal can combine the unloading vehicles waiting to be unloaded at the transfer station and the vehicles that are about to arrive to be unloaded, and schedule the unloading ports of each unloading vehicle. By combining the information of vehicles that may arrive in the future, the terminal can accurately predict the cargo handling load of each unloading port, thereby improving the accuracy of scheduling vehicles to the corresponding target unloading port.

[0059] In one embodiment, such as Figure 2 As shown, Figure 2This is a flowchart illustrating the model scheduling steps in one embodiment. The process involves inputting the number of vehicles, information on goods to be unloaded, and information on goods to be processed into the digital twin model corresponding to the transfer station. The steps include: Step S302, inputting the first number of vehicles, the second number of vehicles, information on goods to be unloaded, and information on goods to be processed into the digital twin model corresponding to the transfer station, whereby the digital twin model determines the number of vehicles in the transfer station within a future preset time period based on the first and second vehicle numbers; Step S304, when a time point is detected where the number of vehicles exceeds a preset vehicle number threshold within the future preset time period, outputting the target unloading port identification information for each first unloading vehicle based on the information on goods to be processed corresponding to each unloading port and the information on goods to be unloaded for each first unloading vehicle.

[0060] In this embodiment, the terminal can schedule the unloading points of each vehicle to be unloaded using the digital twin model corresponding to the aforementioned transfer station. The terminal can input the number of vehicles, the information on goods to be unloaded, and the information on goods to be processed into the digital twin model corresponding to the transfer station. The digital twin model then determines the number of vehicles in the transfer station within a future preset time period based on the first number of vehicles and the second number of vehicles. Specifically, the terminal can use a trained probability model, combined with the first number of vehicles, the second number of vehicles, and the road network information of each second number of vehicles, to predict the number of the first number of vehicles in the transfer station at various time points within a future time period (e.g., within the next ten minutes).

[0061] After obtaining the number of vehicles at each time point within the aforementioned future preset time period, the terminal can detect whether there is a time point within the aforementioned future preset time period where the number of vehicles exceeds the preset vehicle number threshold. If so, the terminal determines that the unloading port is at risk of being overloaded within the aforementioned future preset time period. Therefore, the terminal determines that it is necessary to schedule each of the first unloading vehicles in the transfer yard at the current moment. Then, the terminal can output the target unloading port identification information of each of the first unloading vehicles based on the pending cargo information corresponding to each unloading port and the pending unloading cargo information of each first unloading vehicle.

[0062] Specifically, after the first unloading vehicle enters the transfer yard, it will park near the entrance of the transfer yard to wait for unloading. When the terminal recognizes that the first unloading vehicle needs to be rescheduled, it can reschedule the first unloading vehicle that is closer to the entrance to a more distant unloading port, thereby freeing up parking space for the upcoming unloading vehicle.

[0063] Through this embodiment, the terminal can use a digital twin model to predict the cargo handling load (represented by the number of vehicles) at the unloading port within a preset time period based on the first and second vehicle counts mentioned above. Then, when a high cargo handling load is detected, the terminal can schedule the first unloading vehicle in the transfer station, thereby improving the efficiency of cargo handling.

[0064] In one embodiment, such as Figure 3 As shown, Figure 3 This is a flowchart illustrating the model scheduling steps in another embodiment. Based on the pending cargo information corresponding to each unloading port and the pending cargo information of each first unloading vehicle, the target unloading port identification information for each first unloading vehicle is output, including: Step S402, obtaining the location information of each first unloading vehicle in the transfer station, and determining the distance between each first unloading vehicle and each unloading port based on the location information; Step S404, obtaining the historical pending cargo quantity of the unloading port, and obtaining the pending cargo quantity of the unloading port within a future preset time period based on the pending cargo quantity and the historical pending cargo quantity; Step S406, if there is a time point within the future preset time period where the pending cargo quantity is greater than a preset pending cargo quantity threshold, a target first unloading vehicle is determined from among the first unloading vehicles, and the identifier of the unloading port is determined as the target unloading port identification information of the target first unloading vehicle; the target first unloading vehicle is the first unloading vehicle whose pending cargo quantity is less than a second preset pending cargo quantity threshold and whose distance from the unloading port is less than a preset distance threshold.

[0065] In this embodiment, the information on goods to be processed includes the quantity of goods to be processed, and the information on goods to be unloaded includes the quantity of goods to be unloaded. Furthermore, the first unloading vehicles in the transfer yard can be parked at different locations, so the terminal can obtain the location information of each of the first unloading vehicles in the transfer yard. For example, the terminal can obtain on-site images of the transfer yard captured by image acquisition equipment, and then the terminal can obtain the location information of each first unloading vehicle parked in the transfer yard through image analysis algorithms. The unloading ports can be set at different locations in the transfer yard, so the terminal can also obtain the location information of each unloading port in the transfer yard. Therefore, the terminal can determine the distance between each first unloading vehicle and each unloading port based on the location information of the first unloading vehicles and the location information of the unloading ports.

[0066] For each unloading port, the terminal can obtain the quantity of goods awaiting processing at that port, such as the quantity of goods on the corresponding conveyor equipment and the quantity of goods on the main line connected to the conveyor equipment. This quantity can be used as the quantity of goods awaiting processing at the unloading port. Furthermore, the terminal can also obtain the historical quantity of goods awaiting processing at the unloading port, such as the quantity of goods that needed to be processed at a historical point in time. Therefore, based on the aforementioned quantity of goods awaiting processing and the historical quantity of goods awaiting processing, the terminal can determine the quantity of goods awaiting processing at the unloading port within a predetermined future time period.

[0067] Specifically, regarding the quantity of goods to be processed at the unloading port, the terminal can use image acquisition devices installed at the unloading port to collect unloading videos of each unloading port in real time and transmit them to the terminal in real time. The terminal uses the images and videos collected by the image acquisition devices to detect the quantity of goods on the conveyor equipment in each unloading port in real time through deep learning algorithms, which is taken as the quantity of goods to be processed. Furthermore, the terminal can also use machine learning to predict the quantity of goods to be processed in the aforementioned future preset time period.

[0068] The terminal can detect whether there exists a point in time within the aforementioned preset future time period where the number of goods to be processed at that point exceeds a preset threshold for the number of goods to be processed. If so, the terminal determines that the cargo processing load at the unloading port is too high and the unloading port is too busy. The terminal can then identify a target first unloading vehicle from among the aforementioned first unloading vehicles that has a smaller number of goods to be unloaded and is closer to the unloading port, and assign the target unloading port identifier information of the target first unloading vehicle as the identifier of that unloading port. Specifically, the number of goods to be unloaded from the target first unloading vehicle is less than a second preset threshold for the number of goods to be unloaded, and the distance from the unloading port is less than a preset distance threshold.

[0069] Specifically, when using the digital twin model for scheduling, the terminal can utilize the number of vehicles waiting in the yard (first unloading vehicle), the number of vehicles about to arrive (second unloading vehicle), the quantity of goods to be unloaded in the vehicles, the conveyor equipment at the unloading port, and the quantity of goods to be sorted on the main line. This information is transmitted in real-time to the digital twin model. The digital twin model then determines the main line busyness level of each unloading port (reflected by the quantity of goods to be processed within a preset future time period) and schedules appropriate vehicles to the corresponding unloading port according to the busyness level. For example, for a busy main line (reflected by the quantity of goods to be processed exceeding a preset threshold), the terminal uses the digital twin model to allocate vehicles with lower loading rates (based on a preset threshold for the quantity of goods to be unloaded) and closer to the unloading port (based on a preset distance threshold) to unload goods at that port. For a less busy main line, the terminal uses the digital twin model to allocate vehicles with higher loading rates or farther from the unloading port to unload goods at that port.

[0070] Furthermore, after scheduling each of the first vehicles to be unloaded, the terminal can output the estimated sorting time for each unloading port under that scheduling condition, thereby determining the sorting efficiency. Moreover, as the main line and vehicles change, the terminal can detect in real time the number of vehicles and the quantity of goods to be processed at each unloading port within the aforementioned preset time period. This allows it to utilize a digital twin model to update the scheduling strategy for each vehicle to be unloaded in real time, thereby improving the accuracy of vehicle scheduling and updating sorting results such as the estimated sorting time in real time.

[0071] In this embodiment, the terminal can use a digital twin model to determine the target unloading port identification information for each vehicle based on its location and the predicted quantity of goods to be processed at each unloading port. This allows the vehicles to unload goods at the corresponding target unloading port, improving cargo processing efficiency. Furthermore, by analyzing vehicle waiting times and mainline sorting conditions in the transfer area in real time, the probability of package congestion can be predicted, and optimal vehicle scheduling decisions can be made promptly, directing vehicles to suitable unloading ports and alleviating pressure on the mainline sorting system.

[0072] In one embodiment, obtaining the quantity of goods to be processed at the unloading port within a preset future time period based on the quantity of goods to be processed and the historical quantity of goods to be processed includes:

[0073] Based on the historical quantity of goods to be processed, a sequence of historical quantities of goods corresponding to the unloading port is obtained. The historical quantity sequence of goods includes the quantity of goods to be processed at multiple points in time within a historical period. The quantity of goods to be processed and the historical quantity sequence of goods are input into the prediction model in the digital twin model. The prediction model determines the quantity of goods to be processed at the unloading port in a future preset time period based on the quantity of goods to be processed and the quantity of goods to be processed at multiple points in time within a historical period.

[0074] In this embodiment, the terminal can use a digital twin model to combine historical and current pending cargo information at each unloading port to predict changes in pending cargo information. After obtaining the historical pending cargo quantity at each unloading port, the terminal can combine the historical pending cargo quantities at multiple time points within a historical period, thereby obtaining a sequence of historical cargo quantities corresponding to each unloading port based on the historical pending cargo quantities.

[0075] The aforementioned digital twin model can also be equipped with a trained prediction model. The terminal can input the quantity of goods to be processed and the historical quantity sequence of goods into the prediction model within the digital twin model. The prediction model can be trained based on historical quantity sequence samples, historical quantity samples of goods to be processed, and predicted quantity samples of goods to be processed. For example, for a transshipment hub, the change in the quantity of goods to be processed at each unloading point within a preset period can be relatively fixed. For instance, the quantity of goods to be processed at each unloading point may differ at different times of the day, but the daily changes in the quantity of goods to be processed at each unloading point are quite similar. Therefore, the terminal can combine the historical quantity of goods to be processed at each unloading point to predict the quantity of goods to be processed at each unloading point in a future preset time period.

[0076] The terminal can use the aforementioned prediction model to predict the number of goods to be processed at the unloading point within a future preset time period, based on the quantity of goods to be processed and the historical quantity of goods to be processed at multiple time points within a historical time period. For example, the prediction model can predict the quantity of goods to be processed within the next ten minutes. Specifically, the terminal can use machine learning to predict the sorting situation in the yard (represented by the quantity of goods to be processed within the future preset time period). Each unloading point corresponds to a conveyor belt (conveyor equipment), and a conveyor belt can be connected to multiple main lines. Scanners can be installed on the main lines to scan the information of the goods, forming corresponding scan data. Thus, the terminal can obtain the quantity of goods to be processed based on the quantity of goods on the conveyor belt at the unloading point and the scan data on the main line scanners. Combining this with the historical quantity sequence of goods within a historical time period, the terminal can use the aforementioned time-series-based prediction model to predict the incoming volume (quantity of goods to be processed) and the busyness of the main line sorting within the next ten minutes.

[0077] Through this embodiment, the terminal can combine the historical and current quantities of goods to be processed at the unloading port, and use a prediction model to predict the cargo processing load information (quantity of goods to be processed in the future preset time period) of each unloading port. Thus, the terminal can schedule vehicles based on the cargo processing load information of each unloading port, thereby improving the efficiency of cargo processing.

[0078] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0079] Based on the same inventive concept, this application also provides a vehicle scheduling device for implementing the vehicle scheduling method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more vehicle scheduling device embodiments provided below can be found in the limitations of the vehicle scheduling method described above, and will not be repeated here.

[0080] In one embodiment, such as Figure 4As shown, a vehicle dispatching device is provided, including: an acquisition module 500, an input module 502, and a dispatching module 504, wherein:

[0081] The acquisition module 500 is used to acquire the number of vehicles to be unloaded and the information on the goods to be unloaded in each vehicle, as well as the information on the goods to be processed corresponding to each unloading port in the transfer yard.

[0082] The input module 502 is used to input the number of vehicles, the information on goods to be unloaded, and the information on goods to be processed into the digital twin model corresponding to the transfer station. The digital twin model outputs the target unloading port identification information corresponding to each vehicle to be unloaded based on the number of vehicles, the information on goods to be processed, and the information on goods to be unloaded.

[0083] The scheduling module 504 is used to instruct the corresponding unloading vehicle to unload goods at the target unloading port corresponding to the target unloading port identification information based on the target unloading port identification information.

[0084] In one embodiment, the acquisition module 500 is used to acquire the first number of vehicles of the first unloading vehicle in the transfer station; acquire the road network information corresponding to the second unloading vehicle to arrive at the transfer station; determine the second number of vehicles of the second unloading vehicle to arrive at the transfer station within a future preset time period based on the road network information; and obtain the number of vehicles to be unloaded based on the first number of vehicles of the first unloading vehicle and the second number of vehicles of the second unloading vehicle.

[0085] In one embodiment, the input module 502 is used to input the first number of vehicles, the second number of vehicles, the information on goods to be unloaded, and the information on goods to be processed into the digital twin model corresponding to the transfer station. The digital twin model determines the number of vehicles in the transfer station within a future preset time period based on the first number of vehicles and the second number of vehicles. When it is detected that there is a time point within the future preset time period where the number of vehicles is greater than a preset vehicle number threshold, the target unloading port identification information of each first unloading vehicle is output based on the information on goods to be processed corresponding to each unloading port and the information on goods to be unloaded of each first unloading vehicle.

[0086] In one embodiment, the input module 502 is used to obtain the location information of each first unloading vehicle in the transfer yard, and determine the distance between each first unloading vehicle and each unloading port based on the location information; for each unloading port, obtain the historical quantity of goods to be processed at the unloading port, and obtain the quantity of goods to be processed at the unloading port in a future preset time period based on the quantity of goods to be processed and the historical quantity of goods to be processed; if there is a time point in the future preset time period where the quantity of goods to be processed is greater than a preset threshold for the number of goods to be processed, determine the target first unloading vehicle from among the first unloading vehicles, and determine the identifier of the unloading port as the target unloading port identifier information of the target first unloading vehicle; the target first unloading vehicle is the first unloading vehicle whose quantity of goods to be unloaded is less than a second preset threshold for the quantity of goods to be unloaded and whose distance from the unloading port is less than a preset distance threshold.

[0087] In one embodiment, the input module 502 is used to obtain a historical cargo quantity sequence corresponding to the unloading port based on the historical cargo quantity to be processed; the historical cargo quantity sequence includes the historical cargo quantity to be processed at multiple time points within a historical time period; the cargo quantity to be processed and the historical cargo quantity sequence are input into the prediction model in the digital twin model, and the prediction model determines the cargo quantity to be processed at the unloading port in a future preset time period based on the cargo quantity to be processed and the historical cargo quantity to be processed at multiple time points within a historical time period.

[0088] In one embodiment, the scheduling module 504 is used to send the identification information of each target unloading port corresponding to each vehicle to be unloaded to the public display device in the transfer yard; and to display the identification information of each target unloading port through the public display device to instruct the corresponding vehicle to unload goods at the target unloading port corresponding to the identification information of the target unloading port.

[0089] In one embodiment, the above-mentioned device further includes: a display module, configured to determine the cargo processing rate of each target unloading port based on the number of vehicles to be unloaded corresponding to each target unloading port, the cargo information to be unloaded in each vehicle to be unloaded, and the cargo information to be processed corresponding to each target unloading port, and display the cargo processing rate.

[0090] Each module in the aforementioned vehicle dispatching device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0091] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 5As shown, the computer device includes a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a vehicle dispatching method. The display unit of the computer device forms a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device casing, or an external keyboard, touchpad, or mouse.

[0092] Those skilled in the art will understand that Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0093] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the vehicle scheduling method described above.

[0094] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the vehicle scheduling method described above.

[0095] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the vehicle scheduling method described above.

[0096] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0097] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0098] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0099] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A vehicle dispatching method, characterized in that, The method includes: Obtain the number of vehicles to be unloaded and the information on the goods to be unloaded in each vehicle, and obtain the information on the goods to be processed corresponding to each unloading port in the transfer station. The number of vehicles, the information on goods to be unloaded, and the information on goods to be processed are input into the digital twin model corresponding to the transfer station. The digital twin model then outputs the target unloading port identification information corresponding to each vehicle to be unloaded based on the number of vehicles, the information on goods to be processed, and the information on goods to be unloaded. Based on the target unloading port identification information, the corresponding vehicle to be unloaded is instructed to unload its goods at the target unloading port corresponding to the target unloading port identification information.

2. The method according to claim 1, characterized in that, The process of obtaining the number of vehicles to be unloaded includes: Obtain the first number of vehicles in the first unloading vehicle in the transfer station; Obtain the road network information corresponding to the second unloading vehicle that is to arrive at the transfer station, and determine the number of the second unloading vehicle that will arrive at the transfer station within a future preset time period based on the road network information; The number of vehicles to be unloaded is obtained based on the first number of vehicles in the first unloaded vehicle group and the second number of vehicles in the second unloaded vehicle group.

3. The method according to claim 2, characterized in that, The step of inputting the number of vehicles, the information on goods to be unloaded, and the information on goods to be processed into the digital twin model corresponding to the transfer station includes: The first vehicle count, the second vehicle count, the cargo information to be unloaded, and the cargo information to be processed are input into the digital twin model corresponding to the transfer station. The digital twin model then determines the number of vehicles in the transfer station within a future preset time period based on the first vehicle count and the second vehicle count. When a time point is detected in which the number of vehicles exceeds a preset vehicle number threshold within the preset future time period, the target unloading port identification information of each first unloading vehicle is output based on the pending cargo information corresponding to each unloading port and the pending cargo information of each first unloading vehicle.

4. The method according to claim 3, characterized in that, The pending cargo information includes the quantity of pending cargo, and the pending unloading cargo information includes the quantity of pending unloading cargo; the step of outputting the target unloading port identification information for each first unloading vehicle based on the pending cargo information corresponding to each unloading port and the pending unloading cargo information of each first unloading vehicle includes: Obtain the location information of each of the first vehicles to be unloaded in the transfer yard, and determine the distance between each of the first vehicles to be unloaded and each unloading port based on the location information; For each unloading port, obtain the historical quantity of goods to be processed at the unloading port, and based on the quantity of goods to be processed and the historical quantity of goods to be processed, obtain the quantity of goods to be processed at the unloading port in a future preset time period. If there is a time point in the future preset time period where the number of goods to be processed is greater than a preset threshold for the number of goods to be processed, a target first unloading vehicle is determined from the first unloading vehicles, and the identifier of the unloading port is determined as the target unloading port identifier information of the target first unloading vehicle; the target first unloading vehicle is the first unloading vehicle where the number of goods to be unloaded is less than a second preset threshold for the number of goods to be unloaded and the distance from the unloading port is less than a preset threshold.

5. The method according to claim 4, characterized in that, The step of obtaining the number of goods to be processed at the unloading port within a future preset time period based on the number of goods to be processed and the historical number of goods to be processed includes: Based on the historical pending cargo quantity, a historical cargo quantity sequence corresponding to the unloading port is obtained; the historical cargo quantity sequence includes the historical pending cargo quantity at multiple time points within a historical time period. The quantity of goods to be processed and the sequence of historical goods quantities are input into the prediction model in the digital twin model. The prediction model determines the quantity of goods to be processed at the unloading port in a future preset time period based on the quantity of goods to be processed and the historical quantity of goods to be processed at multiple time points within the historical time period.

6. The method according to claim 1, characterized in that, The step of instructing the corresponding vehicle to unload goods at the target unloading port corresponding to the target unloading port identification information based on the target unloading port identification information includes: Send the identification information of each target unloading port corresponding to each vehicle to be unloaded to the public display equipment in the transfer station; The public display device displays the identification information of each target unloading port to instruct the corresponding vehicle to unload goods at the target unloading port corresponding to the identification information.

7. The method according to any one of claims 1 to 6, characterized in that, The method further includes: Based on the number of vehicles to be unloaded corresponding to each target unloading port, the information on the goods to be unloaded in each vehicle, and the information on the goods to be processed corresponding to each target unloading port, the cargo processing rate of each target unloading port is determined and displayed.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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