Vehicle dispatching method, device and equipment and storage medium
By calculating the priority score of transportation tasks when dispatching a vehicle in the vehicle, the problem of lack of scientificity in the vehicle dispatch method in the prior art is solved, and more efficient logistics transportation and reduced transportation costs are achieved.
Patent Information
- Application Number
- CN202411536965.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-30
- Publication Date
- 2025-02-28
AI Technical Summary
The vehicle dispatch method in the prior art lacks scientific nature, resulting in high logistics and transportation costs.
By receiving the dispatch request of the target vehicle, the running distance and waiting time of the transportation task are determined based on its location information, the priority score of each transportation task is calculated, and the vehicle is dispatched to the transportation task with the highest priority.
It has achieved more scientific and intelligent precise dispatch of vehicles, improved logistics and transportation efficiency, and reduced transportation costs.
Smart Images
Figure CN119539665A_ABST
Abstract
Description
Technical Field
[0001] Embodiments of the present invention relate to the field of computer technology, and in particular to a vehicle dispatching method, apparatus, device and storage medium. Background Art
[0002] The dispatching of transport vehicles in the logistics and transportation system involves multiple processes such as vehicle dispatch, vehicle arrival at the starting node, loading at the starting node, vehicle arrival at the ending node, and unloading at the ending node. The coordination and management of these processes are important tasks to ensure the smooth progress of logistics and transportation, and are of great significance to improving logistics work efficiency and reducing logistics costs.
[0003] In the prior art, vehicles are usually dispatched for each transport task according to the generation time of the transport task.
[0004] In the process of implementing the present invention, the inventors found that there are at least the following technical problems in the prior art:
[0005] The lack of scientific vehicle dispatch methods leads to high logistics and transportation costs. Summary of the invention
[0006] The present invention provides a vehicle dispatching method, device, equipment and storage medium, which can dispatch vehicles according to the priority of transportation tasks.
[0007] In a first aspect, an embodiment of the present invention provides a vehicle dispatching method, comprising:
[0008] Receiving a dispatch request for a target vehicle, and determining a running distance of each transport task according to the location information of the target vehicle;
[0009] Determine the waiting time of each of the transport tasks, and determine the priority score of each of the transport tasks according to the waiting time and the running distance of each of the transport tasks;
[0010] A target transportation task is determined according to the priority score of each transportation task, and the target vehicle is dispatched to the target transportation task.
[0011] In a second aspect, an embodiment of the present invention further provides a vehicle dispatching device, including:
[0012] A determination module, used to receive a dispatch request for a target vehicle and determine the running distance of each transport task according to the location information of the target vehicle;
[0013] An execution module, used to determine the waiting time of each of the transport tasks, and determine the priority score of each of the transport tasks according to the waiting time and the running distance of each of the transport tasks;
[0014] The dispatching module is used to determine a target transportation task according to the priority score of each transportation task, and dispatch the target vehicle to the target transportation task.
[0015] In a third aspect, an embodiment of the present invention further provides a computer device, the computer device comprising:
[0016] one or more processors;
[0017] a storage device for storing one or more programs,
[0018] When the one or more programs are executed by the one or more processors, the one or more processors implement the vehicle dispatch method as described in any one of the first aspects.
[0019] In a fourth aspect, an embodiment of the present invention further provides a storage medium comprising computer executable instructions, wherein the computer executable instructions, when executed by a computer processor, are used to execute a vehicle dispatch method as described in any one of the first aspects.
[0020] In a fifth aspect, an embodiment of the present invention further provides a computer program product, characterized in that the computer program product comprises a computer program, and when the computer program is executed by a processor, the vehicle dispatch method according to any one of the first aspects is implemented.
[0021] The embodiments of the above invention have the following advantages or beneficial effects:
[0022] An embodiment of the present invention provides a vehicle dispatch method, including: receiving a dispatch request for a target vehicle, determining the running distance of each transport task according to the location information of the target vehicle; determining the waiting time of each transport task, determining the priority score of each transport task according to the waiting time and the running distance of each transport task; determining the target transport task according to the priority score of each transport task, and dispatching the target vehicle to the target transport task. The above technical scheme, after receiving the dispatch request for the target vehicle, can first determine the running distance and waiting time required for the target vehicle to perform each transport task, then determine the priority score of each transport task according to the running distance and waiting time required for the target vehicle to perform each transport task, and then determine the target transport task with the highest degree of transportation urgency according to the priority score of each transport task, and dispatch the target vehicle to the target transport task, so as to dispatch the target vehicle to the target transport task with the highest degree of transportation urgency, thereby realizing a more scientific, intelligent and precise dispatch of the target vehicle. BRIEF DESCRIPTION OF THE DRAWINGS
[0023] Figure 1 A flow chart of a vehicle dispatching method provided by an embodiment of the present invention;
[0024] Figure 2 A flow chart of another vehicle dispatching method provided by an embodiment of the present invention;
[0025] Figure 3 A schematic diagram of the structure of a vehicle dispatching device provided by an embodiment of the present invention;
[0026] Figure 4 A schematic diagram of the structure of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION
[0027] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for ease of description, only parts related to the present invention, rather than all structures, are shown in the accompanying drawings.
[0028] It should be mentioned before discussing exemplary embodiments in more detail that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow charts describe various operations (or steps) as sequential processes, many operations therein can be implemented in parallel, concurrently or simultaneously. In addition, the order of various operations can be rearranged. The process can be terminated when its operation is completed, but can also have additional steps not included in the accompanying drawings. The process can correspond to methods, functions, procedures, subroutines, subprograms, etc. In addition, the embodiments in the present invention and the features in the embodiments can be combined with each other without conflict.
[0029] Figure 1 The flowchart of a vehicle dispatching method provided by an embodiment of the present invention is applicable to situations where idle vehicles in a logistics transportation system need to be reasonably dispatched. The method can be executed by a vehicle dispatching device, which can be implemented by software and / or hardware. Figure 1 The method specifically comprises the following steps:
[0030] Step 110: Receive a dispatch request for a target vehicle, and determine the running distance of each transportation task according to the location information of the target vehicle.
[0031] In the logistics transportation system, vehicles are used to transport items from one transportation node to another transportation node. The departure transportation node can be the starting node, and the arrival transportation node can be the ending node. The transportation node can be a site, a station, a transfer center, etc. in the logistics transportation system. Specifically, the target vehicle can be understood as any idle vehicle in the logistics transportation system. After receiving the dispatch request for the target vehicle, the running distance of each transportation task can be determined. The running distance of each transportation task can be understood as the distance that the target vehicle needs to run to perform each transportation task. First, the distance between the target vehicle and the starting node of each transportation task can be determined, and then the distance between the starting node and the ending node of each transportation task can be determined. Then, the running distance of each transportation task can be determined based on the distance between the target vehicle and the starting node of each transportation task and the distance between the starting node and the ending node of each transportation task.
[0032] In the embodiment of the present invention, the running distance of each transport task is determined according to the position information of the target vehicle, so as to determine the running distance required for the target vehicle to perform each transport task.
[0033] Step 120: Determine the waiting time of each of the transport tasks, and determine the priority score of each of the transport tasks according to the waiting time and the running distance of each of the transport tasks.
[0034] The waiting time of a transport task can be understood as the waiting time required for the target vehicle to perform the transport task, which can be determined based on the starting waiting time of the starting node of the transport task and the ending waiting time of the ending node. For a transport node, the waiting time can be understood as the time interval between the time when the transport vehicle arrives at the transport node and the time when loading and unloading begins. Therefore, the waiting time of a transport node can be determined based on the number of queued vehicles and the number of vehicles in transit corresponding to the transport node and the operation time corresponding to the transport node. The operation time corresponding to the transport node can be understood as the time required for the transport node to load and unload the items loaded by the transport vehicle.
[0035] Since the time required for a transport node to load and unload items loaded by a transport vehicle is related to the configuration of the transport node, specifically the number of staff configured at the transport node, staff working hours, number of loading and unloading points, device equipment data, etc., the operating time of each transport node at each time point can be determined based on the time prediction model.
[0036] Specifically, for each transport task, the starting waiting time of the starting node and the ending waiting time of the ending node of the transport task can be determined first. Specifically, the node configuration of the starting node and the node configuration of the terminal node can be input into the time prediction model as input information, so that the time prediction model determines the operation time of the starting node at each time point according to the node configuration of the starting node, and determines the operation time of the ending node at each time point according to the node configuration of the ending node. Secondly, the number of queued vehicles and the number of vehicles in transit corresponding to the starting node and the number of queued vehicles and the number of vehicles in transit corresponding to the ending node can be determined. According to the number of queued vehicles and the number of vehicles in transit corresponding to the starting node and the operation time of the starting node, the starting waiting time of the starting node can be determined, and according to the number of queued vehicles and the number of vehicles in transit corresponding to the ending node and the operation time of the ending node, the ending waiting time of the ending node can be determined. Then, the waiting time of the transport task can be determined according to the starting waiting time of the starting node and the ending waiting time of the ending node.
[0037] The waiting time and running distance of each transport task affect the dispatch of the vehicle. Therefore, the priority score of each transport task can be determined according to the waiting time and running distance of each transport task, so as to determine the target transport task that most needs vehicle dispatch according to the priority score.
[0038] Specifically, the priority score of the transportation task can be calculated based on the waiting time and the time weight corresponding to the waiting time, as well as the running distance and the distance weight corresponding to the running distance.
[0039] In an embodiment of the present invention, the waiting time of each transport task is determined to determine the waiting time required for the target vehicle to perform each transport task. The priority score of each transport task is determined based on the waiting time and running distance of each transport task, providing reference data for determining the target transport task to be dispatched by the target vehicle.
[0040] Step 130: determine a target transport task according to the priority score of each transport task, and dispatch the target vehicle to the target transport task.
[0041] Specifically, the priority score of the transport task can reflect the transport urgency of the transport task. The transport task with a higher priority score has a higher transport urgency. Therefore, after determining the priority scores of each transport task, the transport task corresponding to the highest priority score can be determined as the target transport task to achieve the determination of the target transport task. Furthermore, the target vehicle can be dispatched to the target transport task to achieve intelligent dispatch of the target vehicle.
[0042] In an embodiment of the present invention, a target transport task with the highest transport urgency is determined according to the priority scores of each transport task, and a target vehicle is dispatched to the target transport task, thereby achieving accurate dispatch of the target vehicle.
[0043] The vehicle dispatch method provided by the embodiment of the present invention includes: receiving a dispatch request for a target vehicle, determining the running distance of each transport task according to the location information of the target vehicle; determining the waiting time of each transport task, determining the priority score of each transport task according to the waiting time and the running distance of each transport task; determining the target transport task according to the priority score of each transport task, and dispatching the target vehicle to the target transport task. The above technical scheme, after receiving the dispatch request for the target vehicle, can first determine the running distance and waiting time required for the target vehicle to perform each transport task, and then determine the priority score of each transport task according to the running distance and waiting time required for the target vehicle to perform each transport task, and then determine the target transport task with the highest transportation urgency according to the priority score of each transport task, and dispatch the target vehicle to the target transport task, so as to dispatch the target vehicle to the target transport task with the highest transportation urgency, thereby realizing a more scientific, intelligent and precise dispatch of the target vehicle.
[0044] Figure 2 This is a flow chart of another vehicle dispatching method provided by an embodiment of the present invention. The embodiment of the present invention can be applied to situations where idle vehicles in a logistics transportation system need to be reasonably dispatched. The embodiment of the present invention is specific based on the above embodiment, and the explanations of the terms that are the same or corresponding to the above embodiments are not repeated here. Figure 2 , the vehicle dispatching method provided by the embodiment of the present invention includes:
[0045] Step 210: Receive a dispatch request for a target vehicle, and determine the running distance of each transportation task according to the location information of the target vehicle.
[0046] The target vehicle can be understood as any idle vehicle in the logistics transportation system. The position information of each vehicle in the logistics transportation system is determined, and if the position information of the vehicle remains unchanged within a preset time, the vehicle is determined to be an idle vehicle.
[0047] Specifically, after the target vehicle receives the dispatch request, for each transport task, first, the start distance of the transport task can be determined. The start distance of the transport task can be understood as the distance between the target vehicle and the starting node of the transport task. Specifically, the start distance of the transport task can be determined based on the location information of the target vehicle and the starting position of the starting node of each transport task. Secondly, the transportation distance of the transport task can be determined. The transportation distance of the transport task can be understood as the distance between the starting node and the ending node of the transport task. Specifically, the transportation distance of the transport task can be determined based on the starting position of the starting node of the transport task and the ending position of the ending node. Then, the running distance of the transport task can be determined based on the start distance and transportation distance of the transport task.
[0048] In the embodiment of the present invention, the start distance of each transport task is determined according to the position information of the target vehicle and the starting position of the starting node of each transport task, the transportation distance of each transport task is determined according to the starting position of the starting node of each transport task and the ending position of the ending node, and the running distance of each transport task is determined according to the start distance and the transportation distance of each transport task, so as to determine the distance required for the target vehicle to perform each transport task. Moreover, when determining the target transport task to be dispatched by the target vehicle in each transport task, it is necessary to refer to the distance required for the target vehicle to perform each transport task. Therefore, determining the running distance of each transport task facilitates determining the target transport task in each transport task, and provides a data basis for the intelligent dispatch of vehicles.
[0049] Step 220: Determine the waiting time for each of the transport tasks.
[0050] In one implementation, step 220 may specifically include:
[0051] For each of the transport tasks, the starting operation time corresponding to the starting node and the ending operation time corresponding to the ending node of the transport task are determined based on the time prediction model; the starting waiting time is determined according to the number of queued vehicles and the number of vehicles in transit corresponding to the starting node and the starting operation time, and the ending waiting time is determined according to the number of queued vehicles and the number of vehicles in transit corresponding to the ending node and the ending operation time; the waiting time is determined according to the starting waiting time and the ending waiting time.
[0052] Specifically, for each transportation task, the node configurations of the starting node and the ending node of the transportation task can be respectively input into the time prediction model as input information. The time prediction model can analyze the node characteristics of the starting node according to the node configuration of the starting node, and determine the starting operation time of the starting node according to the node characteristics of the starting node. Similarly, the time prediction model can analyze the node characteristics of the starting and ending nodes according to the node configuration of the ending node, and determine the termination operation time of the termination node according to the node characteristics of the starting and ending nodes.
[0053] The waiting time of a transport node is determined by the number of queued vehicles and vehicles in transit corresponding to the transport node and the operation time of the transport node. Therefore, the starting waiting time can be determined according to the number of queued vehicles and vehicles in transit corresponding to the starting node and the starting operation time, and the ending waiting time can be determined according to the number of queued vehicles and vehicles in transit corresponding to the ending node and the ending operation time. Specifically, the starting waiting time can be determined based on the formula Q1=(N1+C1)*T1, where Q1 represents the starting waiting time, N1 represents the number of queued vehicles corresponding to the starting node, C1 represents the number of vehicles in transit corresponding to the starting node, and T1 represents the starting operation time of the starting node. The ending waiting time can be determined based on the formula Q2=(N2+C2)*T2, where Q2 represents the ending waiting time, N2 represents the number of queued vehicles corresponding to the ending node, C2 represents the number of vehicles in transit corresponding to the ending node, and T2 represents the ending operation time of the ending node.
[0054] The waiting time of the transport task is determined by the starting waiting time of the starting node of the transport task and the ending waiting time of the ending node. Therefore, the waiting time can be determined based on the starting waiting time and the ending waiting time. Specifically, the waiting time can be determined based on the formula Q=Q1+Q2, where Q represents the waiting time of the transport task.
[0055] Furthermore, the training process of the time prediction model includes the following steps:
[0056] The sample operation time of each transport node is obtained at each historical moment within a historical time period; the node configuration of each transport node and the sample operation time of each transport node at each historical moment are input into the model to be trained as training data, the model to be trained is trained, and the loss function is calculated; the model is optimized based on the back propagation algorithm until the loss function converges to obtain the time prediction model.
[0057] The historical time period can be one week before the current moment, the historical moment can be understood as any moment within each hour, and the model to be trained can be a tree model. The tree model has the advantages of small data requirements, easy learning, and good interpretability.
[0058] The sample operation time of each transport node is obtained at each hour of each day in the week before the current moment, that is, the sample operation time of each transport node can be obtained at any time in each hour of each day in the week before the current moment. The node configuration of each transport node and the sample operation time of each transport node at each hour of each day in the week before the current moment are input into the to-be-trained model as training data, and the to-be-trained model is trained. The to-be-trained model can determine the predicted operation time of each transport node at each hour of each day in the week before the current moment according to the node configuration of each transport node, and then calculate the loss function according to the sample operation time and predicted operation time of each transport node at each hour of each day in the week before the current moment, which can be specifically based on the formula L=(y i -r i ) 2 Calculate the loss function, where L represents the square loss and y i represents the predicted operation time, r i Represents the sample operation time. The model is optimized based on the back propagation algorithm until the loss function converges, and the time prediction model can be obtained.
[0059] It should be noted that after obtaining the sample operation time of each transportation node at each historical moment in the historical time period, it is necessary to perform data preprocessing on the obtained data. Specifically, the raw data can be processed into data that can be directly used for model training based on operations such as data cleaning, data completion, and data transformation. For data cleaning, feature dimensions with the same attributes and values can be removed, feature dimensions with no actual meaning (for example, information such as transportation node identification) can be removed, and data with too many missing features can be removed; for data completion, the sample operation time of the missing hour can be supplemented based on the sample operation time of the two hours adjacent to the missing hour, or the sample operation time of the missing hour can be supplemented based on the average of the sample operation time of each hour of the day; for data transformation, continuous data can be converted into float type, and categorical data can be one-hot encoded. One-hot encoding, also known as unique hot encoding or one-bit effective encoding, uses an N-bit state register to encode N states, each state has its own independent register bit, and at any time, only one state register is valid.
[0060] In an embodiment of the present invention, the starting operation time corresponding to the starting node of each transport task and the terminating operation time corresponding to the terminating node are determined based on a time prediction model; the starting waiting time of each transport task is determined according to the number of queued vehicles and the number of vehicles in transit corresponding to the starting node of each transport task and the starting operation time; the terminating waiting time of each transport task is determined according to the number of queued vehicles and the number of vehicles in transit corresponding to the terminating node of each transport task and the terminating operation time; the waiting time of each transport task is determined according to the starting waiting time of each transport task and the terminating waiting time of each transport task, thereby improving the accuracy of determining the waiting time of each transport task, and realizing the determination of the waiting time required for the target vehicle to perform each transport task; and, when determining the target transport task to be dispatched by the target vehicle in each transport task, it is also necessary to refer to the waiting time required for the target vehicle to perform each transport task. Therefore, determining the waiting time of each transport task facilitates determining the target transport task in each transport task, and provides a data basis for the intelligent dispatch of vehicles.
[0061] Step 230: Determine the priority score of each of the transport tasks according to the waiting time and the running distance of each of the transport tasks.
[0062] In one implementation, step 230 may specifically include:
[0063] The time weight is determined according to the waiting time of each historical transport task in a first historical time period and the waiting time of each historical transport task in a second historical time period, wherein the first historical time period is included in the second historical time period; the distance weight is determined according to the running distance of each historical transport task in the first historical time period and the running distance of each historical transport task in the second historical time period; the priority score of each transport task is determined according to the waiting time of each transport task and the time weight as well as the running distance and the distance weight.
[0064] The time weight corresponding to the waiting time and the distance weight corresponding to the running distance can be set according to the attention paid by the vehicle dispatch to the waiting time and the running distance. Of course, an adaptive weight update algorithm can also be used to update the time weight corresponding to the waiting time and the distance weight corresponding to the running distance with the iteration of the shift, so as to determine a more reasonable priority score for each transportation task. A shift can be understood as a day. Therefore, the time weight and distance weight can be updated every day, that is, the time weight and distance weight can be determined every day.
[0065] Among them, the first historical time period can be understood as the previous day, the second historical time period can be understood as the previous week, the waiting time of each historical transport task in the first historical time period can be understood as the average of the waiting time of each historical transport task in the previous day, and the waiting time of each historical transport task in the second historical time period can be understood as the average of the waiting time of each historical transport task in each day of the previous week.
[0066] Therefore, the distance weight adjustment factor can be determined based on the average of the running distances of each historical transport task in the previous day and the average of the running distances of each historical transport task in each day in the previous week. Specifically, it can be based on the formula Determine the distance weight adjustment factor, where η1 represents the distance weight adjustment factor, D represents the average running distance of each historical transportation task in the previous day, and D i It represents the average of the running distances of each historical transport task on each day in the previous week. Then, the distance weight of the current day can be determined according to the distance weight of the previous day and the distance weight adjustment factor, specifically based on the formula W1=w1+w1*η1, where W1 represents the distance weight of the current day and w1 represents the distance weight of the previous day.
[0067] Similarly, the time weight adjustment factor can be determined based on the average of the waiting time of each historical transport task in the previous day and the average of the waiting time of each historical transport task in each day in the previous week. Specifically, it can be based on the formula Among them, η2 represents the time weight adjustment factor, Q represents the average waiting time of each historical transportation task in the previous day, and Q i It represents the average of the waiting time of each historical transport task on each day in the previous week. Then, the time weight of the current day can be determined according to the time weight of the previous day and the time weight adjustment factor, which can be based on the formula W2=w2+w2*η2, where W2 represents the time weight of the current day and w2 represents the time weight of the previous day.
[0068] For each transport task, the priority score of the transport task can be determined based on the waiting time and time weight of the transport task, as well as the running distance and distance weight. Specifically, the priority score of the transport task can be determined based on the formula S=W1*D′+W2*Q, where S represents the priority score of the transport task, D′ represents the running distance of the transport task, and Q′ represents the waiting time of the transport task, and D′ and Q′ are normalized.
[0069] In the embodiment of the present invention, the priority score of each transport task is determined according to the waiting time and the running distance of each transport task, so as to provide reference data for determining the target transport task to be dispatched by the target vehicle.
[0070] Step 240: Determine a target transport task according to the priority score of each transport task.
[0071] In one implementation, step 240 may specifically include:
[0072] The transport tasks are sorted from high to low according to the priority scores, and the transport task corresponding to the highest priority score is determined as the target transport task.
[0073] Specifically, after determining the priority score of each transport task, the transport tasks may be sorted from high to low according to the priority score, and the transport task corresponding to the highest priority score may be determined as the target transport task, thereby achieving determination of the target transport task.
[0074] In the embodiment of the present invention, it is achieved that the target transport task with the highest transport urgency is determined according to the priority score of each transport task.
[0075] Step 250: dispatch the target vehicle to the target transportation task.
[0076] Specifically, the target vehicle can be dispatched to the target transportation task to realize the intelligent dispatch of the target vehicle.
[0077] Of course, after the target vehicle is dispatched to the target transportation task, the target vehicle and the target transportation task can continue to be determined to continuously dispatch the idle vehicles to the transportation tasks until the logistics transportation system stops running, that is, the logistics transportation system no longer generates transportation tasks.
[0078] In the embodiment of the present invention, the target vehicle is dispatched to the target transportation task with the highest transportation urgency, thereby achieving accurate dispatch of the target vehicle.
[0079] The vehicle dispatch method provided by an embodiment of the present invention includes: receiving a dispatch request for a target vehicle, determining the running distance of each transport task according to the location information of the target vehicle; determining the waiting time of each transport task; determining the priority score of each transport task according to the waiting time and the running distance of each transport task; determining a target transport task according to the priority score of each transport task; and dispatching the target vehicle to the target transport task. The above technical scheme, after receiving the dispatch request for the target vehicle, can first determine the start distance of each transport task according to the location information of the target vehicle and the starting position of the starting node of each transport task, determine the transportation distance of each transport task according to the starting position of the starting node of each transport task and the ending position of the ending node, and determine the running distance of each transport task according to the start distance and transportation distance of each transport task, so as to determine the running distance required for the target vehicle to perform each transport task. Secondly, the starting operation time corresponding to the starting node of each transport task and the ending operation time corresponding to the ending node can be determined based on the time prediction model, the starting waiting time of each transport task can be determined according to the number of queued vehicles and the number of vehicles in transit corresponding to the starting node of each transport task and the starting operation time, the ending waiting time of each transport task can be determined according to the number of queued vehicles and the number of vehicles in transit corresponding to the ending node of each transport task and the ending operation time, and the waiting time of each transport task can be determined according to the starting waiting time of each transport task and the ending waiting time of each transport task, so as to improve Determine the accuracy of the waiting time for each transport task, and determine the waiting time required for the target vehicle to perform each transport task. In addition, when determining the target transport task to which the target vehicle will be dispatched in each transport task, it is also necessary to refer to the distance required for the target vehicle to run and the waiting time required to perform each transport task. Therefore, determining the running distance and waiting time of each transport task is convenient for determining the target transport task in each transport task, providing a data basis for the intelligent dispatch of vehicles. Then, the priority score of each transport task can be determined according to the running distance and waiting time required for the target vehicle to perform each transport task, so as to comprehensively consider the waiting time and running distance of each transport task to determine the priority score of each transport task. Then, the target transport task with the highest degree of transportation urgency can be determined according to the priority score of each transport task, and the target vehicle can be dispatched to the target transport task, so as to dispatch the target vehicle to the target transport task with the highest degree of transportation urgency, thereby realizing a more scientific, intelligent and precise dispatch of the target vehicles, improving logistics transportation efficiency and reducing supply chain costs.
[0080] In addition, an adaptive weight update algorithm is used to update the time weight and distance weight required to calculate the priority score, which further optimizes vehicle dispatch and reduces the waiting time and running distance required for vehicles to perform transportation tasks.
[0081] Figure 3The schematic diagram of the structure of a vehicle dispatching device provided by an embodiment of the present invention. The device and the vehicle dispatching methods of the above embodiments belong to the same inventive concept, and the details not described in detail in the embodiments of the vehicle dispatching device can refer to the embodiments of the above vehicle dispatching methods.
[0082] The specific structure of the vehicle dispatching device is as follows: Figure 3 As shown, including:
[0083] The determination module 310 is used to receive a dispatch request for a target vehicle and determine the running distance of each transport task according to the location information of the target vehicle;
[0084] An execution module 320 is used to determine the waiting time of each of the transport tasks, and determine the priority score of each of the transport tasks according to the waiting time and the running distance of each of the transport tasks;
[0085] The dispatching module 330 is used to determine a target transportation task according to the priority score of each transportation task, and dispatch the target vehicle to the target transportation task.
[0086] Based on the above embodiment, the execution module 320 is specifically used for:
[0087] For each of the transport tasks, the starting operation time corresponding to the starting node of the transport task and the ending operation time corresponding to the ending node are determined based on the time prediction model; the starting waiting time is determined according to the number of queued vehicles and the number of vehicles in transit corresponding to the starting node and the starting operation time, and the ending waiting time is determined according to the number of queued vehicles and the number of vehicles in transit corresponding to the ending node and the ending operation time; the waiting time is determined according to the starting waiting time and the ending waiting time; the priority score of each of the transport tasks is determined according to the waiting time and time weight of each of the transport tasks and the running distance and distance weight.
[0088] Based on the above embodiment, the execution module 320 is further used for:
[0089] Before determining the priority score of each transport task based on the waiting time and time weight as well as the running distance and distance weight of each transport task, the time weight is determined based on the waiting time of each historical transport task in a first historical time period and the waiting time of each historical transport task in a second historical time period, wherein the first historical time period is included in the second historical time period; the distance weight is determined based on the running distance of each historical transport task in the first historical time period and the running distance of each historical transport task in the second historical time period.
[0090] Based on the above embodiment, the device further includes:
[0091] The training module is used to obtain the sample operation time of each transport node at each historical moment within a historical time period; input the node configuration of each transport node and the sample operation time of each transport node at each historical moment as training data into the model to be trained, perform model training on the model to be trained, and calculate the loss function; perform model optimization based on the back propagation algorithm until the loss function converges to obtain the time prediction model.
[0092] Based on the above embodiment, the dispatch module 330 is specifically used for:
[0093] The transport tasks are sorted from high to low according to the priority scores, and the transport task corresponding to the highest priority score is determined as the target transport task; and the target vehicle is dispatched to the target transport task.
[0094] The vehicle dispatching device provided in the embodiment of the present invention can execute the vehicle dispatching method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the vehicle dispatching method.
[0095] It is worth noting that in the embodiment of the above-mentioned vehicle dispatch device, the various units and modules included are only divided according to functional logic, but are not limited to the above-mentioned division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.
[0096] Figure 4 A schematic diagram of the structure of a computer device provided in an embodiment of the present invention. Figure 4 A block diagram of an exemplary computer device 4 suitable for use in implementing embodiments of the present invention is shown. Figure 4 The computer device 4 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.
[0097] like Figure 4 As shown, the computer device 4 is in the form of a general-purpose computing electronic device. The components of the computer device 4 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 connecting different system components (including the system memory 28 and the processing unit 16).
[0098] Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor or a local bus using any of a variety of bus architectures. By way of example, these architectures include, but are not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MAC) bus, an Enhanced ISA bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus.
[0099] The computer device 4 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the computer device 4, including volatile and non-volatile media, removable and non-removable media.
[0100] The system memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. The computer device 4 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 34 may be used to read and write non-removable, non-volatile magnetic media ( Figure 4 not shown, usually called a "hard drive"). Although Figure 4 Not shown in the figure, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, a DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to the bus 18 via one or more data medium interfaces. The system memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present invention.
[0101] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in system memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment. Program modules 42 generally perform the functions and / or methods of the embodiments described herein.
[0102] The computer device 4 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), one or more devices that enable a user to interact with the computer device 4, and / or any device that enables the computer device 4 to communicate with one or more other computing devices (e.g., network card, modem, etc.). Such communication may be performed through an input / output (I / O) interface 22. Furthermore, the computer device 4 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter 20. Figure 4 As shown, the network adapter 20 communicates with other modules of the computer device 4 via the bus 18. It should be understood that although Figure 4 Not shown, other hardware and / or software modules may be used in conjunction with computer device 4, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.
[0103] The processing unit 16 executes various functional applications and page displays by running the program stored in the system memory 28, for example, implementing the vehicle dispatch method provided in the embodiment of the present invention, the method comprising:
[0104] Receiving a dispatch request for a target vehicle, and determining a running distance of each transport task according to the location information of the target vehicle;
[0105] Determine the waiting time of each of the transport tasks, and determine the priority score of each of the transport tasks according to the waiting time and the running distance of each of the transport tasks;
[0106] A target transportation task is determined according to the priority score of each transportation task, and the target vehicle is dispatched to the target transportation task.
[0107] Of course, those skilled in the art can understand that the processor can also implement the technical solution of the vehicle dispatch method provided by any embodiment of the present invention.
[0108] An embodiment of the present invention provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, a vehicle dispatch method provided by an embodiment of the present invention is implemented, for example, and the method includes:
[0109] Receiving a dispatch request for a target vehicle, and determining a running distance of each transport task according to the location information of the target vehicle;
[0110] Determine the waiting time of each of the transport tasks, and determine the priority score of each of the transport tasks according to the waiting time and the running distance of each of the transport tasks;
[0111] A target transportation task is determined according to the priority score of each transportation task, and the target vehicle is dispatched to the target transportation task.
[0112] The computer storage medium of the embodiment of the present invention can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to: an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it.
[0113] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.
[0114] The program code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
[0115] Computer program code for performing the operations of the present invention may be written in one or more programming languages or a combination thereof, including object-oriented programming languages such as Java, Smalltalk, C++, and conventional procedural programming languages such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).
[0116] It should be understood by those skilled in the art that the modules or steps of the present invention described above can be implemented by a general-purpose computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, optionally, they can be implemented by a program code executable by a computer device, so that they can be stored in a storage device and executed by the computing device, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.
[0117] Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims
1. A vehicle dispatching method, characterized in that: include: Receiving a dispatch request for a target vehicle, and determining a running distance of each transport task according to the location information of the target vehicle; Determine the waiting time of each of the transport tasks, and determine the priority score of each of the transport tasks according to the waiting time and the running distance of each of the transport tasks; A target transportation task is determined according to the priority score of each transportation task, and the target vehicle is dispatched to the target transportation task.
2. The vehicle dispatching method according to claim 1, characterized in that: Determine the waiting time for each of the transport tasks, including: For each of the transport tasks, determining the starting operation time corresponding to the starting node of the transport task and the ending operation time corresponding to the ending node based on the time prediction model; Determine the starting waiting time according to the number of queued vehicles and the number of vehicles on the way corresponding to the starting node and the starting operation time, and determine the ending waiting time according to the number of queued vehicles and the number of vehicles on the way corresponding to the ending node and the ending operation time; The waiting time is determined according to the starting waiting time and the ending waiting time.
3. The vehicle dispatching method according to claim 2, characterized in that: The training process of the time prediction model includes the following steps: Obtain the sample operation time of each transportation node at each historical moment in the historical time period; Input the node configuration of each of the transport nodes and the sample operation time of each of the transport nodes at each of the historical moments as training data into the model to be trained, perform model training on the model to be trained, and calculate the loss function; The model is optimized based on the back propagation algorithm until the loss function converges to obtain the time prediction model.
4. The vehicle dispatching method according to claim 1, characterized in that: Determining the priority score of each of the transport tasks according to the waiting time and the running distance of each of the transport tasks includes: The priority score of each transport task is determined according to the waiting time and time weight of each transport task and the running distance and distance weight.
5. The vehicle dispatching method according to claim 4, characterized in that: Before determining the priority score of each of the transport tasks according to the waiting time and the time weight and the running distance and the distance weight of each of the transport tasks, the method further includes: Determine a time weight according to the waiting time of each historical transport task in a first historical time period and the waiting time of each historical transport task in a second historical time period, wherein the first historical time period is included in the second historical time period; A distance weight is determined according to the running distance of each of the historical transportation tasks in the first historical time period and the running distance of each of the historical transportation tasks in the second historical time period.
6. The vehicle dispatching method according to claim 1, characterized in that: Determining a target transportation task according to the priority score of each transportation task includes: The transport tasks are sorted from high to low according to the priority scores, and the transport task corresponding to the highest priority score is determined as the target transport task.
7. A vehicle dispatching device, characterized in that: include: A determination module, used to receive a dispatch request for a target vehicle and determine the running distance of each transport task according to the location information of the target vehicle; An execution module, used to determine the waiting time of each of the transport tasks, and determine the priority score of each of the transport tasks according to the waiting time and the running distance of each of the transport tasks; The dispatching module is used to determine a target transportation task according to the priority score of each transportation task, and dispatch the target vehicle to the target transportation task.
8. A computer device, characterized in that: The computer device comprises: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the vehicle dispatch method as described in any one of claims 1-6.
9. A storage medium containing computer executable instructions, characterized in that: The computer executable instructions are used to perform the vehicle dispatch method as described in any one of claims 1 to 6 when executed by a computer processor.
10. A computer program product, characterized in that The computer program product comprises a computer program, which, when executed by a processor, implements the vehicle dispatch method according to any one of claims 1 to 6.