Methods, apparatus, equipment and storage media for generating scheduling task information

Abstract

This disclosure provides a method, apparatus, device, and storage medium for generating scheduling task information, relating to the field of logistics and transportation. The method includes: acquiring historical fusion scheduling information for multiple target routes, wherein the historical fusion scheduling information comprises information on routes that were fused and scheduled before the current time; obtaining the current fusion probability of the multiple target routes based on the historical fusion scheduling information; acquiring the cargo volume to be transported corresponding to each target route; and generating target vehicle scheduling task information based on the current fusion probability of the multiple target routes and the cargo volume to be transported on each target route, so as to schedule the target vehicles to transport the cargo to be transported corresponding to the multiple target routes. This method improves the automation level of the logistics and transportation scheduling process.

Description

Technical Field

[0001] This disclosure relates to the field of logistics and transportation, and more specifically, to a method, apparatus, device, and readable storage medium for generating scheduling task information. Background Technology

[0002] Logistics transportation scheduling involves collecting transportation demands, developing transportation plans, generating transportation tasks, booking vehicles, and dispatching vehicles. Scheduling is mostly done manually, requiring dispatchers to manually enter scheduling information into the transportation system before commencing operations. Therefore, providing an automated scheduling process is a pressing issue that needs to be addressed.

[0003] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0004] The purpose of this disclosure is to provide a method, apparatus, device, and readable storage medium for generating scheduling task information, which at least to some extent improves the automation level of the logistics transportation scheduling process.

[0005] Other features and advantages of this disclosure will become apparent from the following detailed description, or may be learned in part by practice of this disclosure.

[0006] According to one aspect of this disclosure, a method for generating scheduling task information is provided, comprising: acquiring historical fusion scheduling information of multiple target routes, wherein the historical fusion scheduling information is information of routes among the multiple target routes that were fused and scheduled before the current time; obtaining the current fusion probability of the multiple target routes based on the historical fusion scheduling information; acquiring the cargo volume to be transported corresponding to each target route; and generating target vehicle scheduling task information based on the current fusion probability of the multiple target routes and the cargo volume to be transported on each target route, so as to schedule the target vehicles to transport the cargo to be transported corresponding to the multiple target routes.

[0007] According to one embodiment of this disclosure, the historical fusion scheduling information includes the number of fusion scheduling operations for every two target lines among the plurality of target lines within a preset historical time period; obtaining the historical fusion scheduling information of the plurality of target lines includes: obtaining the preset historical time period; obtaining the number of fusion scheduling operations for every two target lines among the plurality of target lines within the preset historical time period; the current fusion probability of the plurality of target lines includes the current fusion probability of every two target lines among the plurality of target lines; obtaining the current fusion probability of the plurality of target lines based on the historical fusion scheduling information includes: obtaining the current fusion probability of every two target lines among the plurality of target lines based on the number of fusion scheduling operations for every two target lines among the plurality of target lines within the preset historical time period.

[0008] According to one embodiment of this disclosure, generating target vehicle scheduling task information based on the current fusion probability of the plurality of target routes and the cargo volume to be transported on each target route includes: when the current fusion probability of two target routes among the plurality of target routes is greater than a preset fusion probability threshold, adding the cargo volumes to be transported on the two target routes to obtain the total cargo volume to be transported on the two target routes; obtaining transportation information of candidate vehicles to transport the two target routes, the transportation information including rated cargo volume and unit transportation cost; obtaining the estimated total cost of each candidate vehicle transporting the total cargo volume to be transported on the two target routes based on the rated cargo volume and unit transportation cost of each candidate vehicle; determining the target vehicle from the plurality of candidate vehicles based on the estimated total cost of each candidate vehicle transporting the total cargo volume to be transported on the two target routes, and generating the target vehicle scheduling task information.

[0009] According to one embodiment of this disclosure, generating target vehicle scheduling task information based on the current fusion probability of the plurality of target routes and the cargo volume to be transported on each target route includes: when the current fusion probability of two target routes among the plurality of target routes is not greater than a preset fusion probability threshold, obtaining transportation information of candidate vehicles undertaking the two target routes respectively, the transportation information including rated cargo volume and unit transportation cost; obtaining the estimated cost of each candidate vehicle transporting the cargo volume to be transported on the two target routes respectively based on the rated cargo volume and unit transportation cost of each candidate vehicle; combining the candidate vehicles undertaking the two target routes, obtaining the estimated total cost corresponding to each group of candidate vehicles based on the estimated cost of each candidate vehicle; determining a target vehicle from the plurality of candidate vehicles as the target vehicle based on the estimated total cost of each group of candidate vehicles, and generating the target vehicle scheduling task information.

[0010] According to one embodiment of this disclosure, the method further includes: obtaining the cargo volume to be transported on the currently schedulable route; and if the cargo volume to be transported on the currently schedulable route is greater than a preset cargo volume threshold, obtaining the currently schedulable route as the target route.

[0011] According to one embodiment of this disclosure, the method further includes: obtaining a schedulable time range; and obtaining a route with a preset departure time within the schedulable time range as the currently schedulable route.

[0012] According to one embodiment of this disclosure, obtaining the schedulable time range includes: obtaining the current time; obtaining a schedulable time threshold and a non-schedulable time threshold before departure; adding the current time to the schedulable time threshold and the non-schedulable time threshold before departure respectively to obtain two time endpoints of the schedulable time range.

[0013] According to another aspect of this disclosure, a scheduling task information generation device is provided, comprising: a historical fusion scheduling information acquisition module, used to acquire historical fusion scheduling information of multiple target routes, wherein the historical fusion scheduling information is information of routes among the multiple target routes that were fused and scheduled before the current time; a current fusion probability acquisition module, used to acquire the current fusion probability of the multiple target routes based on the historical fusion scheduling information; a cargo volume to be transported acquisition module, used to acquire the cargo volume to be transported corresponding to each target route; and a scheduling task information generation module, used to generate target vehicle scheduling task information based on the current fusion probability of the multiple target routes and the cargo volume to be transported on each target route, so as to schedule the target vehicles to transport the cargo to be transported corresponding to the multiple target routes.

[0014] According to one embodiment of this disclosure, the historical fusion scheduling information includes the number of fusion scheduling operations for every two target lines among the plurality of target lines within a preset historical time period; the historical fusion scheduling information acquisition module includes: a preset historical time period acquisition module, used to acquire the preset historical time period; a historical fusion scheduling count acquisition module, used to acquire the number of fusion scheduling operations for every two target lines among the plurality of target lines within the preset historical time period; the current fusion probability of the plurality of target lines includes the current fusion probability of every two target lines among the plurality of target lines; the current fusion probability acquisition module is further used to: obtain the current fusion probability of every two target lines among the plurality of target lines based on the number of fusion scheduling operations for every two target lines among the plurality of target lines within the preset historical time period.

[0015] According to an embodiment of this disclosure, the scheduling task information generation module includes: a total cargo volume to be transported module, used to add the cargo volumes to be transported on the two target routes to obtain the total cargo volume to be transported on the two target routes when the current fusion probability of two target routes among the plurality of target routes is greater than a preset fusion probability threshold; a first transportation information acquisition module, used to acquire transportation information of candidate vehicles undertaking the two target routes, the transportation information including rated cargo volume and unit transportation cost; a first total cost calculation module, used to obtain the estimated total cost of each candidate vehicle transporting the total cargo volume to be transported on the two target routes based on the rated cargo volume and unit transportation cost of each candidate vehicle; and a first target vehicle determination module, used to determine the target vehicle from the plurality of candidate vehicles based on the estimated total cost of each candidate vehicle transporting the total cargo volume to be transported on the two target routes, and generate the target vehicle scheduling task information.

[0016] According to an embodiment of this disclosure, the scheduling task information generation module includes: a second transportation information acquisition module, used to acquire transportation information of candidate vehicles undertaking the two target routes respectively, when the current fusion probability of two target routes among the multiple target routes is not greater than a preset fusion probability threshold, the transportation information including rated cargo volume and unit transportation cost; a transportation cost calculation module, used to obtain the estimated cost of each candidate vehicle transporting the cargo volume to be transported on the two target routes respectively, based on the rated cargo volume and unit transportation cost of each candidate vehicle; a second total cost calculation module, used to combine the candidate vehicles undertaking the two target routes and obtain the estimated total cost corresponding to each group of candidate vehicles based on the estimated cost of each candidate vehicle; and a second target vehicle determination module, used to determine a group of target vehicles as the target vehicles from multiple groups of candidate vehicles based on the estimated total cost of each group of candidate vehicles, and generate the target vehicle scheduling task information.

[0017] According to one embodiment of this disclosure, the cargo volume acquisition module is further configured to acquire the cargo volume to be transported on the currently schedulable route; the device further includes: a target route acquisition module, configured to acquire the currently schedulable route as the target route when the cargo volume to be transported on the currently schedulable route is greater than a preset cargo volume threshold.

[0018] According to one embodiment of this disclosure, the apparatus further includes: a schedulable time range acquisition module, used to acquire a schedulable time range; and a current schedulable route acquisition module, used to acquire routes with preset departure times within the schedulable time range as the current schedulable routes.

[0019] According to an embodiment of this disclosure, the schedulable time range acquisition module includes: a current time acquisition module for acquiring the current time; a duration threshold acquisition module for acquiring a schedulable duration threshold before departure and a non-schedulable duration threshold before departure; and a schedulable time range calculation module for adding the current time to the schedulable duration threshold before departure and the non-schedulable duration threshold before departure, respectively, to obtain two time endpoints of the schedulable time range.

[0020] According to another aspect of this disclosure, an apparatus is provided, comprising: a memory, a processor, and executable instructions stored in the memory and executable in the processor, wherein the processor, when executing the executable instructions, implements any of the methods described above.

[0021] According to another aspect of this disclosure, a computer-readable storage medium is provided that stores computer-executable instructions thereon, which, when executed by a processor, implement any of the methods described above.

[0022] The scheduling task information generation method provided in the embodiments of this disclosure obtains historical fusion scheduling information of multiple target routes and obtains the current fusion probability of multiple target routes based on the historical fusion scheduling information. Then, it generates target vehicle scheduling task information based on the current fusion probability of multiple target routes and the amount of goods to be transported on each target route, so as to schedule target vehicles to transport goods to be transported corresponding to multiple target routes, thereby improving the automation level of the logistics transportation scheduling process to a certain extent.

[0023] It should be understood that the above general description and the following detailed description are merely exemplary and do not limit this disclosure. Attached Figure Description

[0024] The above and other objects, features and advantages of this disclosure will become more apparent from a detailed description of exemplary embodiments thereof with reference to the accompanying drawings.

[0025] Figure 1 A schematic diagram of a system structure according to an embodiment of this disclosure is shown.

[0026] Figure 2 A flowchart of a method for generating scheduling task information is shown in an embodiment of this disclosure.

[0027] Figure 3 It shows Figure 2 The step S202 shown is a schematic diagram of the processing procedure in one embodiment.

[0028] Figure 4 It shows Figure 2 The step S204 shown is a schematic diagram of the processing procedure in one embodiment.

[0029] Figure 5 It shows Figure 2 The step S208 shown is a schematic diagram of the processing procedure in one embodiment.

[0030] Figure 6 It shows Figure 2 The step S208 shown is a schematic diagram of the processing procedure in another embodiment.

[0031] Figure 7 It is based on Figures 2 to 6 The flowchart illustrates a scheduling task generation method based on remaining cargo volume analysis.

[0032] Figure 8 It is based on Figures 2 to 7 The diagram shows an implementation of an intelligent scheduling method.

[0033] Figure 9 A block diagram of a scheduling task information generation apparatus according to an embodiment of the present disclosure is shown.

[0034] Figure 10 A block diagram of another scheduling task information generation apparatus is shown in an embodiment of this disclosure.

[0035] Figure 11 A schematic diagram of the structure of an electronic device according to an embodiment of the present disclosure is shown. Detailed Implementation

[0036] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that this disclosure will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The drawings are merely illustrative of this disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted.

[0037] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced with one or more of the specific details omitted, or other methods, apparatuses, steps, etc., can be employed. In other instances, well-known structures, methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this disclosure.

[0038] Furthermore, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this disclosure, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified. The symbol " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0039] In this disclosure, unless otherwise expressly specified and limited, the term "connection" and similar terms should be interpreted broadly, for example, it can refer to an electrical connection or the ability to communicate with each other; it can refer to a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms in this disclosure according to the specific circumstances.

[0040] The following explains some of the terms used in this application.

[0041] L: The full name of the English word is Line, which represents a transportation route. For example, the route from Beijing to Shanghai can be called the L1 route, and the route from Inner Mongolia to Xi'an can be called the L2 route.

[0042] V: The full English name is Vehicle, which represents the vehicle type. For example, V1 can represent a 17.5M semi-trailer.

[0043] SVW: Surplus Volume Weight, representing the remaining cargo volume. It can be expressed as the volume of cargo remaining on a corresponding route. SVW = Total volume to be transported on route L - Total volume already transported.

[0044] TVM stands for Total Volume Weight, representing the total volume of cargo on a route.

[0045] Fusion scheduling: This refers to the vehicle scheduling of goods from two routes, for example, transporting goods from route L1 and route L2 on the same train.

[0046] Dispatch tasks: a general term for transportation demand, transportation plans, transportation tasks, vehicle booking tasks, and vehicle dispatch tasks.

[0047] Currently, dispatchers need to input a large amount of transportation information into the system every day. However, there are many fixed scheduling methods in transportation scheduling, such as from point A to point B. Almost every day, they need to perform repetitive transportation scheduling work based on the cargo volume on the route, manually generating transportation tasks and specifying transportation plans. This results in low efficiency and high labor costs in the logistics transportation scheduling process.

[0048] Therefore, this disclosure provides a method for generating scheduling task information. By acquiring historical fusion scheduling information of multiple target routes and obtaining the current fusion probability of multiple target routes based on the historical fusion scheduling information, and then generating target vehicle scheduling task information based on the current fusion probability of multiple target routes and the amount of goods to be transported on each target route, the method can be used to schedule target vehicles to transport goods to be transported on multiple target routes, thereby improving the automation level of the logistics transportation scheduling process to a certain extent.

[0049] Figure 1 An exemplary system architecture 10 is shown that can be applied to the scheduling task information generation method or scheduling task information generation apparatus of this disclosure.

[0050] like Figure 1 As shown, system architecture 10 may include terminal device 102, network 104, server 106, and database 108. Terminal device 102 may be various electronic devices with a display screen and supporting input and output, including but not limited to smartphones, tablets, laptops, desktop computers, wearable devices, virtual reality devices, smart home devices, etc. Network 104 is used as a medium to provide a communication link between terminal device 102 and server 106. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables. Server 106 may be a server or server cluster providing various services. Database 108 may be large database software located on a server or small database software installed on a computer, used for storing data.

[0051] Users can use terminal device 102 to interact with server 106 and database 108 via network 104 to receive or send data. For example, a scheduler can receive and view scheduling tasks generated on server 106 via network 104 on terminal device 102. Alternatively, a scheduler can perform operations on terminal device 102, modifying the configuration of scheduling tasks generated on server 106 via network 104. Furthermore, a user can receive and view a list of historical scheduling task data stored in database 108 via network 104 on terminal device 102.

[0052] Server 106 can also receive data from or send data to database 108 via network 104. For example, server 106 can be a background processing server, used to obtain scheduling line information from database 108 via network 104, perform historical scheduling analysis, and generate a scheduling plan. Alternatively, server 106 can send the generated scheduling task information to database 108 for storage while simultaneously feeding it back to terminal device 102.

[0053] It should be understood that Figure 1The number of terminal devices, networks, servers, and databases shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, servers, and databases can be included.

[0054] Figure 2 This is a flowchart illustrating a method for generating scheduling task information according to an exemplary embodiment. For example... Figure 2 The method shown can be applied, for example, to the server side of the above system, or to the terminal devices of the above system.

[0055] refer to Figure 2 The method 20 provided in this embodiment may include the following steps.

[0056] In step S202, historical fusion scheduling information of multiple target lines is obtained.

[0057] In some embodiments, the target route is a route with transportation demand at the current time, and the route is a transportation route between two locations, such as trunk lines and branch lines in a logistics system. For example, currently schedulable routes can be obtained first, and then a cargo volume threshold can be set. Routes with cargo volumes exceeding the threshold become the target routes. Specific implementation methods can be found in [reference needed]. Figure 3 .

[0058] In some embodiments, historical fusion scheduling information may be information about lines among multiple target lines that were fused and scheduled before the current time. For example, it may include lines that were fused and scheduled every two or three of the multiple target lines within a preset historical time period, and may also include the number of fusion and scheduling operations for every two target lines among the multiple target lines within the preset historical time period, etc.

[0059] In step S204, the current fusion probability of multiple target lines is obtained based on historical fusion scheduling information.

[0060] In some embodiments, for example, the current fusion probability of two target lines can be calculated based on the historical fusion count of each pair of target lines among multiple target lines. Specific implementation details can be found in [reference needed]. Figure 4 .

[0061] In other embodiments, multiple lines can be merged based on their starting and ending points. For example, line L3 starts at point A and ends at point B, line L4 starts at point C and ends at point D, and line L5 starts at point E and ends at point F. Points C, D, E, and F are all along the route from point A to point B. Traveling from point A to point B does not require taking many detours to pass through these locations. The historical merging counts of lines L3, L4, and L5 can be obtained. When the number of merging counts exceeds a preset threshold, the current merging probability of these three lines can be determined as 100%.

[0062] In step S206, the volume of goods to be transported corresponding to each target route is obtained.

[0063] In some embodiments, for example, the target route can be selected based on the amount of goods to be transported (i.e., the remaining amount of goods). When determining the target route, the amount of goods to be transported for the target route can be obtained according to its code. For specific implementation details, please refer to [reference needed]. Figure 3 .

[0064] In step S208, target vehicle scheduling task information is generated based on the current fusion probability of multiple target routes and the amount of cargo to be transported on each target route, so as to schedule target vehicles to transport the cargo to be transported on multiple target routes.

[0065] In some embodiments, the current fusion probability of the integrated route can be calculated first, then the vehicle fusion transportation cost and independent transportation cost can be calculated, and finally the scheduling scheme can be determined. Figure 4 Taking the current fusion probability of lines L1 and L2, which are greater than the threshold, as an example in the embodiment, the relevant information of the selectable vehicle models is obtained as shown in Table 1 below.

[0066] Table 1

[0067] Model Rated volume Rated load cost <![CDATA[V1]]> <![CDATA[40m 3 ]]> 40kg 5000 yuan <![CDATA[V2]]> <![CDATA[70m 3 ]]> 70kg 8000 yuan

[0068] The information on the goods to be transported on routes L1 and L2 is shown in Table 2 below.

[0069] Table 2

[0070] line volume load capacity Fusionable <![CDATA[L1]]> <![CDATA[60m 3 ]]> 60kg <![CDATA[Can be integrated with L2]]> <![CDATA[L2]]> <![CDATA[60m 3 ]]> 60kg <![CDATA[Can be fused with L1]]>

[0071] If each route is independent, the required vehicle type and cost are shown in Table 3 below.

[0072] Table 3

[0073]

[0074]

[0075] If integrated transportation is used, the required vehicle types and costs are shown in Table 4 below.

[0076] Table 4

[0077]

[0078] Based on Tables 1 to 4 above, the transportation method with the lowest cost is L1+L2 fusion scheduling, which requires 3 V1 vehicles. Then, the route and selected target vehicles can be used as parameters to construct a message for creating a scheduling task. This task is then sent to a message queue. The consumer side pulls the message from the queue and creates the corresponding scheduling task based on the retrieved parameter information.

[0079] In other embodiments, for example, when route fusion is determined based on the current fusion probability, the cost of candidate vehicles can be calculated based on the total cargo volume to be transported on the fused routes. The target vehicle with the lowest cost is then selected from the candidate vehicles for transportation, and a target vehicle scheduling task is generated accordingly. Specific implementation details can be found in [reference needed]. Figure 5 .

[0080] In other embodiments, for example, if it is determined that route fusion will not be performed based on the current fusion probability, the target vehicle with the lowest cost can be selected for transportation based on the total amount of goods to be transported on each route, and a target vehicle scheduling task can be generated accordingly. Specific implementation details can be found in [reference needed]. Figure 6 .

[0081] According to the scheduling task information generation method provided in this disclosure, historical fusion scheduling information of multiple target routes is obtained, and the current fusion probability of multiple target routes is obtained based on the historical fusion scheduling information. Then, target vehicle scheduling task information is generated based on the current fusion probability of multiple target routes and the amount of goods to be transported on each target route. This is used to schedule target vehicles to transport the goods to be transported corresponding to multiple target routes, which improves the automation level of the logistics transportation scheduling process to a certain extent. The system further intelligently analyzes the amount of goods to be transported on the route and automatically generates scheduling tasks such as transportation tasks, transportation plans, and transportation assignments in the system. After opening the system, the dispatcher only needs to confirm and filter, which undoubtedly greatly reduces the workload of personnel scheduling and improves work efficiency.

[0082] Figure 3 It shows Figure 2 The step S202 shown is a schematic diagram of the processing procedure in one embodiment. (See attached diagram.) Figure 3 As shown in the embodiments of this disclosure, step S202 may further include the following steps.

[0083] Step S302: Obtain the current time.

[0084] Step S304: Obtain the schedulable time threshold and the non-schedulable time threshold before departure.

[0085] Step S306: Add the current time to the pre-departure schedulable time threshold and the pre-departure non-schedulable time threshold respectively to obtain the two time endpoints of the schedulable time range.

[0086] In some embodiments, the schedulable time range is calculated based on the following: the time range is unschedulable within the current time T1 and the time range is schedulable within the time range of H1 hours before the estimated departure time (i.e., the unschedulable time threshold before departure) and schedulable within the time range of H2 hours before the estimated departure time (i.e., the schedulable time threshold before departure). The formula for the time range can be:

[0087] Estimated departure start time (start of the available time range) = T1 + H1

[0088] Estimated departure end time (end of the schedulable time range) = T1 + H2. For example, if the current time T1 is 6:00 AM, and there is no schedulable time within 0.5 hours before the estimated departure time (H1), and a schedulable time within 2 hours before the estimated departure time (H2), then the calculated schedulable time range is from 6:30 AM to 8:30 AM.

[0089] Step S308: Obtain routes with preset departure times within the schedulable time range as currently schedulable routes.

[0090] In some embodiments, the departure times of logistics vehicles on a transportation route are typically preset and fixed, such as multiple departure times within a day, like once every 3 hours between 0:00 and 24:00, or two trips a day, such as 6:00 AM and 6:00 PM. After obtaining the available time range, routes with preset departure times within this available time range can be acquired, and route information for these routes can be obtained, such as the route code L1, total cargo volume TVM1, and the amount of cargo already transported on the route.

[0091] Step S310: Obtain the amount of cargo to be transported on the currently schedulable routes.

[0092] In some embodiments, transportation demand can be obtained based on the route code of the currently schedulable route, a transportation plan list can be obtained using the transportation demand code, and transportation task information can be queried based on the transportation task code on the transportation plan to obtain the amount of freight already transported on the current route, thereby obtaining the remaining freight volume SVW1, where SVW1 = TVM1 - the amount of freight already transported on the route. For example, the total freight volume TVM1 of route L1 is 100M. 3 (cubic meters), the total cargo volume of L2, TVM2, is 120M. 3 The freight volume transported on routes L1 and L2 is 40M each. 3 Then SVW1 = 60M 3 SVW2 = 80M 3 .

[0093] Step S312: If the amount of goods to be transported on the currently schedulable route is greater than the preset transport volume threshold, the currently schedulable route is selected as the target route.

[0094] In some embodiments, for example, if the preset transport volume threshold configured by the system at this time is G 阈值 =40M 3 Then we can obtain that both SVW1 and SVW2 are greater than G. 阈值 At this point, the current transportation route and cargo volume L1_SVW1 and L2_SVW2 can be determined.

[0095] According to the target route screening method provided in this disclosure, after selecting the currently schedulable route based on the schedulable time range, the remaining cargo volume of the currently schedulable route is obtained, and then the target routes with a remaining cargo volume greater than a preset threshold are screened out. The target routes can be quickly obtained from multiple candidate routes for fusion analysis, thereby improving the efficiency of logistics transportation scheduling.

[0096] Figure 4 It shows Figure 2 The diagram illustrates steps S202 and S204 in one embodiment. Figure 4 As shown in the embodiments of this disclosure, steps S202 and S204 may further include the following steps.

[0097] Step S402: Obtain the preset historical time period.

[0098] In some embodiments, for example, the historical time period to be referenced can be preset to 3 days, 5 days, or 10 days from now, etc.

[0099] Step S404: Obtain the number of times each pair of target lines is merged and scheduled within a preset historical time period.

[0100] In some embodiments, for example, if the preset historical time period M = 3 days, the fusion scheduling times data of line L1, line L2 and line L3 can be obtained as shown in Table 5 below.

[0101] Table 5

[0102] Day 3 Day 2 Day 1 <![CDATA[2*L1_L2]]> <![CDATA[1*L1_L2]]> <![CDATA[1*L1_L2]]> <![CDATA[5*L1_L3]]> <![CDATA[1*L1_L3]]>

[0103] Wherein, n*L1_L2 represents the n times that line L1 and line L2 are merged. If it is empty, it means that there is no merge data for this line on that day. For example, there is no merge data for line L1 and line L3 on the 3rd day (there is no merge data for line L2 and line L3 on these 3 days).

[0104] Step S406: Obtain the current fusion probability of each pair of target lines in the multiple target lines based on the number of fusion scheduling times of each pair of target lines within a preset historical time period.

[0105] In some embodiments, the probability of line fusion scheduling can be calculated based on the number of line fusions in historical M days. Taking Table 1 as an example, since L1_L2 appears once on day 3, day 2, and day 1, the current fusion probability P of line L1 and line L2 is: (L1_L2) =100%; Since L1_L3 appears once on day 1 and day 2, then: the current fusion probability P of line L1 and line L3 is... (L1_L3) =66.66%; L2 and L3 did not appear in these three days, therefore P(L2_L3) =0. Then, the current fusion probability table for each pair of target lines among multiple target lines can be obtained, as shown in Table 6 below.

[0106] Table 6

[0107] <![CDATA[P (L1_L2) =100%]]> <![CDATA[P (L1_L3) =66.66%]]> <![CDATA[P (L2_L3) =0]]>

[0108] If the system configuration probability threshold is P at this time 阈值 =75%, then only P can be obtained. (L1_L2) >P 阈值 At this point, the line combination L1_L2 for integrated scheduling can be obtained, while line L3 is scheduled separately.

[0109] According to the route fusion method provided in this disclosure, the current fusion probability of each pair of routes can be obtained by analyzing the required freight volume of the route and based on the historical fusion status of each pair of routes in the target route, thereby determining whether to merge and schedule the routes. This can result in a route fusion plan that is more in line with the actual situation and improve the availability of the automated transportation scheduling system.

[0110] Figure 5 It shows Figure 2 The step S208 shown is a schematic diagram of the processing procedure in one embodiment. (See attached diagram.) Figure 5 As shown in the present embodiment, step S208 may further include the following steps.

[0111] Step S502: If the current fusion probability of two target routes among multiple target routes is greater than the preset fusion probability threshold, the cargo volumes to be transported on the two target routes are added together to obtain the total cargo volume to be transported on the two target routes.

[0112] Step S504: Obtain transportation information for candidate vehicles carrying the two target routes. The transportation information includes the rated cargo volume and unit transportation cost. The unit transportation cost can be the cost of a single trip (including round trip) or the cost of a one-way trip.

[0113] Step S506: Based on the rated cargo volume and unit transportation cost of each candidate vehicle, obtain the estimated total cost for each candidate vehicle to transport the total cargo volume to be transported on the two target routes.

[0114] Step S508: Based on the estimated total cost of transporting the total amount of goods to be transported on the two target routes by each candidate vehicle, determine the target vehicle from multiple candidate vehicles and generate target vehicle scheduling task information.

[0115] Figure 6 It shows Figure 2 The step S208 shown is a schematic diagram of the processing procedure in another embodiment. (See diagram below.) Figure 6As shown in the present embodiment, step S208 may further include the following steps.

[0116] Step S602: If the current fusion probability of two target routes among multiple target routes is not greater than the preset fusion probability threshold, obtain the transportation information of candidate vehicles carrying the two target routes respectively. The transportation information includes the rated cargo volume and unit transportation cost.

[0117] Step S604: Based on the rated cargo volume and unit transportation cost of each candidate vehicle, obtain the estimated cost for each candidate vehicle to transport the cargo volume to be transported on the two target routes.

[0118] Step S606: Combine the candidate vehicles for the two target routes and obtain the estimated total cost for each group of candidate vehicles based on the estimated cost of each candidate vehicle.

[0119] Step S608: Based on the estimated total cost of each group of candidate vehicles, determine a target vehicle from multiple groups of candidate vehicles and generate target vehicle scheduling task information.

[0120] Figure 7 It is based on Figures 2 to 6 The flowchart illustrates a scheduling task generation method based on remaining cargo volume analysis. Figure 7 As shown, the method may include the following steps.

[0121] Step S702: Traverse the routes that can be transported at the current time in reverse order. Obtain a list of routes that can be transported at the current time (i.e., currently schedulable routes). For specific implementation details, refer to steps S302 to S308. Analyze one route at a time, for example, route L1.

[0122] Step S704: Calculate the remaining cargo volume of the route. The remaining cargo volume SVW1 of the route is obtained. For a detailed implementation, please refer to step S310.

[0123] Step S706: Calculate the historical M-day line fusion scheduling information. Obtain the fusion scheduling information for line L1. For specific implementation details, refer to steps S402 to S404.

[0124] Step S708: Calculate the probability of fusion scheduling occurring. For a specific implementation, refer to step S406. If the probability is greater than the configured threshold, the conditions for fusion scheduling are met.

[0125] Step S710: Select vehicle type and create scheduling task. Based on information such as vehicle load capacity and transportation costs, select a suitable vehicle type, construct the relevant parameters for the scheduling task, and send the message to the message queue. For specific implementation details, please refer to step S208.

[0126] According to the scheduling task information generation method provided in this disclosure, an automatic scheduling algorithm based on the analysis of remaining cargo volume on the route is implemented. The calculated cargo volume and vehicle type are used to automatically create scheduling tasks, which greatly reduces the workload of manual operation for scheduling staff and also greatly reduces the error rate of manual operation.

[0127] Figure 8 It is based on Figures 2 to 7 The diagram illustrates the implementation of an intelligent scheduling method. For example... Figure 8 As shown, firstly, the Logistics Bus (LSB) delayed task system 8002 issues analysis tasks (S802). The issuance of analysis tasks can be managed by the LSB delayed task system. Scheduled tasks can be issued using Cron expressions, for example, 0 30***? means that the analysis task is issued every 30 minutes of every hour every day.

[0128] Then, the scheduling analysis module 8004 uses Figure 7 The automatic scheduling algorithm shown begins analyzing the task (S804): The automatic scheduling algorithm analyzes the routes with transportation demand at the current time, calculates the remaining freight volume SVW1 and the fusion scheduling probability G of each route, and compares it with the remaining freight volume threshold SVW configured by the system. 阈值 and the fusion scheduling probability G 阈值 Compare them; if SVW1 > SVW 阈值 And G > G 阈值 Then create a message for a merged scheduling task, if SVW1>SVW 阈值 And G <G 阈值 Then create a message for a single-line scheduling task (S806).

[0129] Then, a message is sent to create a scheduling task, which tells the message queue system 8006 that the scheduling task can be created immediately. The message queue system 8006 receives the new message (S808), and then the scheduling task consumer 8008 creates the scheduling task (S810), simulating manual operation by the scheduling staff.

[0130] According to the automatic dispatching method provided in the embodiments of this disclosure, for trunk and branch lines, based on the analysis of the remaining cargo volume of the line, transportation demand and transportation tasks are automatically generated, and vehicles are automatically dispatched in scenarios with drivers, thereby realizing automatic dispatching. This enables dispatchers to book and dispatch vehicles in an intelligent and automated manner, reducing the workload of dispatchers and improving work efficiency.

[0131] Figure 9 This is a block diagram illustrating a scheduling task information generation apparatus according to an exemplary embodiment. Figure 9 The device shown can be applied, for example, to the server side of the above system, or to the terminal device of the above system.

[0132] refer to Figure 9 The apparatus 90 provided in this embodiment may include a historical fusion scheduling information acquisition module 902, a current fusion probability acquisition module 904, a cargo volume to be transported acquisition module 906, and a scheduling task information generation module 908.

[0133] The historical fusion scheduling information acquisition module 902 can be used to acquire historical fusion scheduling information of multiple target lines. The historical fusion scheduling information is the information of the lines that were fused and scheduled before the current time among the multiple target lines.

[0134] The current fusion probability acquisition module 904 can be used to obtain the current fusion probability of multiple target lines based on historical fusion scheduling information.

[0135] The cargo volume acquisition module 906 can be used to acquire the cargo volume to be transported for each target route.

[0136] The scheduling task information generation module 908 can be used to generate target vehicle scheduling task information based on the current fusion probability of multiple target routes and the amount of goods to be transported on each target route, so as to schedule target vehicles to transport the goods to be transported on multiple target routes.

[0137] Figure 10 This is a block diagram illustrating another scheduling task information generation apparatus according to an exemplary embodiment. Figure 10 The device shown can be applied, for example, to the server side of the above system, or to the terminal device of the above system.

[0138] refer to Figure 10 The apparatus 100 provided in this embodiment may include a historical fusion scheduling information acquisition module 1002, a current fusion probability acquisition module 1004, a cargo volume to be transported acquisition module 1006, a scheduling task information generation module 1008, a target route acquisition module 10010, a schedulable time range acquisition module 10012, and a current schedulable route acquisition module 10014. The historical fusion scheduling information acquisition module 1002 may include a preset historical time period acquisition module 10022 and a historical fusion scheduling count acquisition module 10024. The scheduling task information generation module 1008... It may include: a total cargo volume acquisition module 10082, a first transportation information acquisition module 10084, a first total cost calculation module 10086, a first target vehicle determination module 10088, a second transportation information acquisition module 100810, a transportation cost calculation module 100812, a second total cost calculation module 100814, and a second target vehicle determination module 100816. The schedulable time range acquisition module 10012 may include: a current time acquisition module 100122, a duration threshold acquisition module 100124, and a schedulable time range calculation module 100126.

[0139] The schedulable time range acquisition module 10012 can be used to obtain the schedulable time range.

[0140] The current time acquisition module 100122 can be used to obtain the current time.

[0141] The duration threshold acquisition module 100124 can be used to acquire the schedulable duration threshold and the non-schedulable duration threshold before departure.

[0142] The schedulable time range calculation module 100126 can be used to add the current time to the schedulable time threshold before departure and the non-schedulable time threshold before departure to obtain the two time endpoints of the schedulable time range.

[0143] The module 10014 for obtaining currently schedulable routes can be used to obtain routes whose preset departure times are within the schedulable time range as currently schedulable routes.

[0144] The target route acquisition module 10010 can be used to acquire the currently schedulable route as the target route when the amount of goods to be transported on the currently schedulable route is greater than the preset transport volume threshold.

[0145] The historical fusion scheduling information acquisition module 1002 can be used to acquire historical fusion scheduling information for multiple target lines. This historical fusion scheduling information comprises information about the lines that underwent fusion scheduling before the current time. The historical fusion scheduling information may include the number of fusion scheduling operations between every two target lines within a preset historical time period.

[0146] The preset historical time period acquisition module 10022 can be used to acquire preset historical time periods.

[0147] The historical fusion scheduling count acquisition module 10024 can be used to acquire the number of fusion schedulings of every two target lines among multiple target lines within a preset historical time period.

[0148] The current fusion probability acquisition module 1004 can be used to obtain the current fusion probability of multiple target lines based on historical fusion scheduling information. The current fusion probability of multiple target lines includes the current fusion probability of every two target lines among the multiple target lines.

[0149] The current fusion probability acquisition module 1004 can also be used to obtain the current fusion probability of each pair of target lines in the multiple target lines based on the number of fusion scheduling times of each pair of target lines in the preset historical time period.

[0150] The cargo volume acquisition module 1006 can be used to acquire the cargo volume to be transported for each target route.

[0151] The cargo volume acquisition module 1006 can also be used to acquire the cargo volume to be transported on currently schedulable routes.

[0152] The scheduling task information generation module 1008 can be used to generate target vehicle scheduling task information based on the current fusion probability of multiple target routes and the amount of goods to be transported on each target route, so as to schedule target vehicles to transport the goods to be transported on multiple target routes.

[0153] The total cargo volume to be transported module 10082 can be used to add the cargo volumes to be transported on two target routes when the current fusion probability of two target routes is greater than a preset fusion probability threshold, so as to obtain the total cargo volume to be transported on the two target routes.

[0154] The first transportation information acquisition module 10084 can be used to acquire transportation information of candidate vehicles carrying two target routes. The transportation information includes the rated cargo volume and unit transportation cost.

[0155] The first total cost calculation module 10086 can be used to obtain the estimated total cost for each candidate vehicle to transport the total amount of goods to be transported on two target routes based on the rated cargo volume and unit transportation cost of each candidate vehicle.

[0156] The first target vehicle determination module 10088 can be used to determine the target vehicle from multiple candidate vehicles based on the estimated total cost of transporting the total amount of goods to be transported on two target routes by each candidate vehicle, and generate target vehicle scheduling task information.

[0157] The second transportation information acquisition module 100810 can be used to acquire the transportation information of candidate vehicles carrying two target routes respectively, when the current fusion probability of two target routes in multiple target routes is not greater than a preset fusion probability threshold. The transportation information includes the rated cargo volume and unit transportation cost.

[0158] The transportation cost calculation module 100812 can be used to obtain the estimated cost of transporting the cargo volume of each candidate vehicle on two target routes based on the rated cargo volume and unit transportation cost of each candidate vehicle.

[0159] The second total cost calculation module 100814 can be used to combine candidate vehicles that carry two target routes and obtain the estimated total cost corresponding to each group of candidate vehicles based on the estimated cost of each candidate vehicle.

[0160] The second target vehicle determination module 100816 can be used to determine a target vehicle from multiple groups of candidate vehicles based on the estimated total cost of each group of candidate vehicles, and generate target vehicle scheduling task information.

[0161] The specific implementation of each module in the device provided in this embodiment can be referred to the content of the above method, and will not be repeated here.

[0162] Figure 11 A schematic diagram of the structure of an electronic device according to an embodiment of this disclosure is shown. It should be noted that... Figure 11 The devices shown are merely examples of computer systems and should not be construed as limiting the functionality and scope of use of the embodiments disclosed herein.

[0163] like Figure 11 As shown, device 1100 includes a central processing unit (CPU) 1101, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 1102 or a program loaded from storage section 1108 into random access memory (RAM) 1103. The RAM 1103 also stores various programs and data required for the operation of device 1100. CPU 1101, ROM 1102, and RAM 1103 are interconnected via bus 1104. Input / output (I / O) interface 1105 is also connected to bus 1104.

[0164] The following components are connected to I / O interface 1105: an input section 1106 including a keyboard, mouse, etc.; an output section 1107 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 1108 including a hard disk, etc.; and a communication section 1109 including a network interface card such as a LAN card, modem, etc. The communication section 1109 performs communication processing via a network such as the Internet. A drive 1110 is also connected to I / O interface 1105 as needed. Removable media 1111, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 1110 as needed so that computer programs read from them can be installed into storage section 1108 as needed.

[0165] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 1109, and / or installed from removable medium 1111. When the computer program is executed by central processing unit (CPU) 1101, it performs the functions defined above in the system of this disclosure.

[0166] It should be noted that the computer-readable medium disclosed herein may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0167] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0168] The modules described in the embodiments of this disclosure can be implemented in software or hardware. The described modules can also be housed in a processor; for example, a processor may be described as including a historical fusion scheduling information acquisition module, a current fusion probability acquisition module, a cargo volume to be transported acquisition module, and a scheduling task information generation module. The names of these modules do not necessarily limit the module itself; for example, the scheduling task information generation module may also be described as "a module that generates scheduling task information based on messages pulled from a message queue."

[0169] In another aspect, this disclosure also provides a computer-readable medium, which may be included in the device described in the above embodiments; or it may exist independently and not assembled into the device. The computer-readable medium carries one or more programs, which, when executed by the device, cause the device to include:

[0170] Obtain historical fusion scheduling information for multiple target routes; obtain the current fusion probability of multiple target routes based on the historical fusion scheduling information; obtain the cargo volume to be transported for each target route; generate target vehicle scheduling task information based on the current fusion probability of multiple target routes and the cargo volume to be transported for each target route, so as to schedule target vehicles to transport cargo corresponding to multiple target routes.

[0171] Exemplary embodiments of this disclosure have been specifically shown and described above. It should be understood that this disclosure is not limited to the detailed structures, arrangements, or implementations described herein; rather, this disclosure is intended to cover various modifications and equivalent arrangements contained within the spirit and scope of the appended claims.

Claims

1. A method for generating scheduling task information, characterized in that, include: Obtain historical fusion scheduling information for multiple target lines, wherein the historical fusion scheduling information is the information of the multiple target lines that were fused and scheduled before the current time; The current fusion probability of the multiple target lines is obtained based on the historical fusion scheduling information; Obtain the volume of goods to be transported for each target route; Target vehicle scheduling task information is generated based on the current fusion probability of the multiple target routes and the amount of cargo to be transported on each target route, so as to schedule the target vehicles to transport the cargo to be transported corresponding to the multiple target routes. The historical fusion scheduling information includes the number of fusion scheduling operations for every two target lines among the multiple target lines within a preset historical time period. The acquisition of historical fusion scheduling information for multiple target lines includes: Obtain the preset historical time period; Obtain the number of times each pair of target lines is merged and scheduled within the preset historical time period; The current fusion probability of the multiple target lines includes the current fusion probability of every two target lines among the multiple target lines; The step of obtaining the current fusion probability of the multiple target lines based on the historical fusion scheduling information includes: The current fusion probability of each pair of target lines is obtained based on the number of fusion scheduling operations of each pair of target lines within the preset historical time period. The step of generating target vehicle scheduling task information based on the current fusion probability of the multiple target routes and the amount of cargo to be transported on each target route includes: If the current fusion probability of two target routes among the multiple target routes is greater than a preset fusion probability threshold, the cargo volumes to be transported on the two target routes are added together to obtain the total cargo volume to be transported on the two target routes. Obtain transportation information for candidate vehicles carrying the two target routes, the transportation information including rated cargo volume and unit transportation cost; wherein the unit transportation cost is the vehicle's single-trip transportation cost or one-way transportation cost. The estimated total cost for transporting the total cargo volume to be transported on the two target routes by each candidate vehicle is obtained based on the rated cargo volume and unit transportation cost of each candidate vehicle. The target vehicle is determined from multiple candidate vehicles based on the estimated total cost of transporting the total cargo to be transported on the two target routes using each candidate vehicle, and the target vehicle scheduling task information is generated.

2. The method according to claim 1, characterized in that, The process of generating target vehicle scheduling task information based on the current fusion probability of the multiple target routes and the amount of cargo to be transported on each target route includes: If the current fusion probability of two target routes among the multiple target routes is not greater than a preset fusion probability threshold, the transportation information of candidate vehicles carrying the two target routes is obtained respectively. The transportation information includes the rated cargo volume and the unit transportation cost. The estimated cost for each candidate vehicle to transport the cargo volume to be transported on each of the two target routes is obtained based on the rated cargo volume and unit transportation cost of each candidate vehicle. The candidate vehicles for the two target routes are combined, and the estimated total cost for each group of candidate vehicles is obtained based on the estimated cost of each candidate vehicle. Based on the estimated total cost of each group of candidate vehicles, a target vehicle is determined from multiple groups of candidate vehicles, and the target vehicle scheduling task information is generated.

3. The method according to claim 1, characterized in that, Also includes: Get the amount of cargo to be transported on currently schedulable routes; If the amount of cargo to be transported on the currently schedulable route is greater than a preset cargo volume threshold, the currently schedulable route is identified as the target route.

4. The method according to claim 3, characterized in that, Also includes: Obtain the schedulable time range; The routes with preset departure times within the schedulable time range are the currently schedulable routes.

5. The method according to claim 4, characterized in that, The obtained schedulable time range includes: Get the current time; Obtain the threshold for the schedulable time before departure and the threshold for the unschedulable time before departure; The current time is added to the pre-departure schedulable time threshold and the pre-departure non-schedulable time threshold respectively to obtain the two time endpoints of the schedulable time range.

6. A scheduling task information generation device, characterized in that, include: The historical fusion scheduling information acquisition module is used to acquire historical fusion scheduling information of multiple target lines, wherein the historical fusion scheduling information is the information of the multiple target lines that were fused and scheduled before the current time; The current fusion probability acquisition module is used to obtain the current fusion probability of the multiple target lines based on the historical fusion scheduling information; The cargo volume acquisition module is used to acquire the cargo volume to be transported for each target route. The scheduling task information generation module is used to generate target vehicle scheduling task information based on the current fusion probability of the multiple target routes and the amount of goods to be transported on each target route, so as to schedule the target vehicles to transport the goods to be transported corresponding to the multiple target routes. The historical fusion scheduling information includes the number of fusion scheduling operations for every two target lines among the multiple target lines within a preset historical time period. The acquisition of historical fusion scheduling information for multiple target lines includes: Obtain the preset historical time period; Obtain the number of times each pair of target lines is merged and scheduled within the preset historical time period; The current fusion probability of the multiple target lines includes the current fusion probability of every two target lines among the multiple target lines; The step of obtaining the current fusion probability of the multiple target lines based on the historical fusion scheduling information includes: The current fusion probability of each pair of target lines is obtained based on the number of fusion scheduling operations of each pair of target lines within the preset historical time period. The step of generating target vehicle scheduling task information based on the current fusion probability of the multiple target routes and the amount of cargo to be transported on each target route includes: If the current fusion probability of two target routes among the multiple target routes is greater than a preset fusion probability threshold, the cargo volumes to be transported on the two target routes are added together to obtain the total cargo volume to be transported on the two target routes. Obtain transportation information for candidate vehicles carrying the two target routes, the transportation information including rated cargo volume and unit transportation cost; wherein the unit transportation cost is the vehicle's single-trip transportation cost or one-way transportation cost. The estimated total cost for transporting the total cargo volume to be transported on the two target routes by each candidate vehicle is obtained based on the rated cargo volume and unit transportation cost of each candidate vehicle. The target vehicle is determined from multiple candidate vehicles based on the estimated total cost of transporting the total cargo to be transported on the two target routes using each candidate vehicle, and the target vehicle scheduling task information is generated.

7. An apparatus comprising: A memory, a processor, and executable instructions stored in the memory and executable in the processor, characterized in that the processor, when executing the executable instructions, implements the method as described in any one of claims 1-5.

8. A computer-readable storage medium having computer-executable instructions stored thereon, characterized in that, When the executable instructions are executed by the processor, they implement the method as described in any one of claims 1-5.

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