Transportation scheduling method and device, electronic equipment and storage medium

Abstract

The present disclosure provides a transportation scheduling method and device, electronic equipment and storage medium, relating to the technical field of transportation scheduling. The transportation scheduling method comprises: obtaining a plurality of historical scheduling tasks; generating a scheduling model based on the plurality of historical scheduling tasks, to configure a to-be-executed task based on the scheduling model; when it is detected that a delivery condition is met, delivering the to-be-executed task to a scheduling end, to generate a scheduling task based on the to-be-executed task by the scheduling end. Through the technical solution of the present disclosure, the to-be-executed scheduling task, i.e. the to-be-executed task, can be configured according to the scheduling model, the automatic generation of transportation demand and transportation task is realized on the premise of meeting the scheduling demand, the automatic operation of the car booking and dispatching operation of the scheduling post is realized, the workload of the scheduling personnel is reduced, and the scheduling work efficiency is improved.

Description

Technical Field

[0001] This disclosure relates to the field of transportation capacity scheduling technology, and in particular to a transportation scheduling method, apparatus, electronic device and computer-readable storage medium. Background Technology

[0002] Transportation scheduling tasks require dispatchers to manually enter the corresponding scheduling information into the transportation system before the transportation work can be started, resulting in a large workload and low work efficiency for dispatchers, which in turn affects the efficiency of transportation scheduling.

[0003] It should be noted that the information disclosed in the background section above is only used 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 transportation scheduling method, apparatus, electronic device, and computer-readable storage medium that can at least partially improve the problem of low target edge recognition accuracy in related technologies, which affects the accurate segmentation of targets.

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

[0006] According to one aspect of this disclosure, a transportation scheduling method is provided, comprising: acquiring a plurality of historical scheduling tasks; generating a scheduling model based on the plurality of historical scheduling tasks, and configuring tasks to be executed based on the scheduling model; and, upon detecting that a distribution condition is met, distributing the tasks to be executed to a scheduling terminal, so that the scheduling terminal generates scheduling tasks based on the tasks to be executed.

[0007] In one embodiment, generating a scheduling model based on the plurality of historical scheduling tasks, and configuring tasks to be executed based on the scheduling model, specifically includes: traversing the plurality of historical scheduling tasks, identifying multiple scheduling routes, the scheduling routes including operating routes and transport vehicle types; configuring the scheduling model based on the multiple scheduling routes and probability thresholds; calculating the scheduling probability of each scheduling route based on the scheduling model; and configuring the tasks to be executed according to the scheduling routes whose scheduling probabilities are greater than the probability thresholds.

[0008] In one embodiment, traversing the plurality of historical scheduling tasks and identifying multiple scheduling routes specifically includes: traversing the plurality of historical scheduling tasks to extract the route start point, route end point, and vehicle type of the historical routes in the historical scheduling tasks; identifying historical routes whose route start point is within a first preset range and whose route end point is within a second preset range as the same fused route; and identifying historical routes belonging to the same fused route and having the same vehicle type as one scheduling route.

[0009] In one embodiment, acquiring multiple historical scheduling tasks specifically includes: triggering a timed analysis task based on timed task rules; and acquiring the historical scheduling tasks within a preset number of time units based on the timed analysis task.

[0010] In one embodiment, the scheduling line further includes the number of runs per unit time. The step of calculating the scheduling probability of each scheduling line based on the scheduling model specifically includes: detecting whether the scheduling line appears in each unit time based on the scheduling model; when the scheduling line appears in a unit time, counting is performed to obtain the statistical scheduling number after the detection is completed for the preset number of unit time periods; calculating the ratio between the scheduling number and the preset number, and determining the ratio as the scheduling probability.

[0011] In one embodiment, generating a scheduling model based on the plurality of historical scheduling tasks, and configuring tasks to be executed based on the scheduling model, specifically includes: training a model based on the plurality of historical scheduling tasks to generate the scheduling model; inputting scheduling requirement parameters into the scheduling model, so that the scheduling model configures the tasks to be executed based on the scheduling requirement parameters, wherein the historical scheduling tasks include demand information, transportation plan information, transportation task information, ride-hailing task information, and vehicle dispatch task information.

[0012] In one embodiment, the method further includes: marking the historical scheduling tasks that identify the scheduling line so as to skip the marked historical scheduling tasks when configuring the next task to be executed.

[0013] In one embodiment, the step of sending the task to be executed to the scheduling terminal when the sending condition is detected to be met, so that the scheduling terminal can generate a scheduling task based on the task to be executed, specifically includes: when the current time reaches a preset time, determining that the sending condition is met, and sending the task to be executed to the scheduling terminal.

[0014] According to another aspect of this disclosure, a transportation scheduling device is provided, comprising: an acquisition module for acquiring a plurality of historical scheduling tasks; a configuration module for generating a scheduling model based on the plurality of historical scheduling tasks, and configuring tasks to be executed based on the scheduling model; and a delivery module for delivering the tasks to be executed to a scheduling terminal when a delivery condition is detected, so that the scheduling terminal generates scheduling tasks based on the tasks to be executed.

[0015] According to another aspect of this disclosure, an electronic device is provided, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform the transportation scheduling method described in the other aspect above by executing the executable instructions.

[0016] According to another aspect of this disclosure, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the transportation scheduling method described above.

[0017] The transportation scheduling scheme provided by the embodiments of this disclosure acquires multiple historical scheduling tasks, obtains historical scheduling experience based on the analysis of historical scheduling tasks, and further generates a scheduling model based on the historical scheduling experience. Thus, it can configure the scheduling tasks to be executed according to the scheduling model. Under the premise of ensuring that scheduling needs are met, it realizes the automatic generation of transportation needs and transportation tasks, and the automated operation of scheduling personnel in booking and dispatching vehicles, reducing the workload of scheduling personnel and improving the efficiency of scheduling work.

[0018] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0019] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure. It is obvious that the drawings described below are merely some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0020] Figure 1 This diagram illustrates a transportation dispatching system structure according to an embodiment of the present disclosure;

[0021] Figure 2 This diagram illustrates a flowchart of a transportation scheduling method according to an embodiment of the present disclosure;

[0022] Figure 3 A flowchart of another transportation scheduling method in an embodiment of this disclosure is shown;

[0023] Figure 4 A flowchart of yet another transportation scheduling method according to an embodiment of this disclosure is shown;

[0024] Figure 5 A flowchart of yet another transportation scheduling method according to an embodiment of this disclosure is shown;

[0025] Figure 6 This diagram illustrates an intelligent scheduling system according to an embodiment of the present disclosure;

[0026] Figure 7 A flowchart of yet another transportation scheduling method according to an embodiment of this disclosure is shown;

[0027] Figure 8 This diagram illustrates a transportation scheduling device according to an embodiment of the present disclosure;

[0028] Figure 9 A schematic diagram of an electronic device according to an embodiment of the present disclosure is shown. Detailed Implementation

[0029] 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 described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0030] Furthermore, the accompanying 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. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0031] The solution provided in this application acquires multiple historical scheduling tasks, analyzes these tasks to gain historical scheduling experience, and then generates a scheduling model based on this experience. This model allows for the configuration of scheduling tasks to be executed, thus enabling the automatic generation of transportation needs and tasks while ensuring that scheduling requirements are met. It also automates the booking and dispatching operations of the dispatching staff, reducing the workload of dispatchers and improving scheduling efficiency.

[0032] To facilitate understanding, the following is an explanation of several terms (abbreviations) used in this application.

[0033] L: Short for Line, such as L1 from Beijing to Shanghai, L2 from Inner Mongolia to Xi'an.

[0034] V: Vehicle Type, such as V1 indicating a 17.5M semi-trailer.

[0035] Dispatch tasks: This is a collective term that includes transportation demand, transportation plans, transportation tasks, vehicle booking tasks, and vehicle dispatch tasks.

[0036] Cron is a string that is separated by 5 or 6 spaces and divided into 6 or 7 fields, each field representing a meaning.

[0037] Figure 1 The diagram shows a structural schematic of a transportation scheduling system according to an embodiment of the present disclosure, including multiple terminals 120 and a server cluster 140.

[0038] Terminal 120 can be a mobile terminal such as a mobile phone, game console, tablet computer, e-book reader, smart glasses, MP4 (Moving Picture Experts Group Audio Layer IV) player, smart home device, AR (Augmented Reality) device, VR (Virtual Reality) device, etc. Alternatively, terminal 120 can also be a personal computer (PC), such as a laptop computer and a desktop computer, etc.

[0039] The terminal 120 may contain an application for providing transportation scheduling.

[0040] Terminal 120 is connected to server cluster 140 via a communication network. Optionally, the communication network can be a wired network or a wireless network.

[0041] Server cluster 140 may be a single server, a combination of several servers, a virtualization platform, or a cloud computing service center. Server cluster 140 provides backend services for transportation scheduling applications. Optionally, server cluster 140 undertakes the primary computing tasks, while terminal 120 undertakes secondary computing tasks; alternatively, server cluster 140 undertakes secondary computing tasks, while terminal 120 undertakes the primary computing tasks; or, terminal 120 and server cluster 140 collaborate on computing using a distributed computing architecture.

[0042] In some alternative embodiments, server cluster 140 is used to store transportation scheduling models, etc.

[0043] Optionally, the application clients installed on different terminals 120 may be the same, or the application clients installed on two terminals 120 may be clients of the same type of application on different control system platforms. Depending on the terminal platform, the specific form of the application client may also differ; for example, the application client may be a mobile client, a PC client, or a World Wide Web (WWW) client.

[0044] Those skilled in the art will understand that the number of terminals 120 described above can be more or less. For example, there may be only one terminal, or there may be dozens or hundreds of terminals, or even more. This application does not limit the number of terminals or the type of device.

[0045] Optionally, the system may also include a management device ( Figure 1 (Not shown), the management device is connected to the server cluster 140 via a communication network. Optionally, the communication network can be a wired network or a wireless network.

[0046] Optionally, the aforementioned wireless or wired networks use standard communication technologies and / or protocols. The network is typically the Internet, but can also be any network, including but not limited to Local Area Networks (LANs), Metropolitan Area Networks (MANs), Wide Area Networks (WANs), mobile, wired or wireless networks, private networks, or any combination of virtual private networks. In some embodiments, technologies and / or formats including Hyper Text Markup Language (HTML), Extensible Markup Language (XML), etc., are used to represent data exchanged over the network. Furthermore, conventional encryption technologies such as Secure Socket Layer (SSL), Transport Layer Security (TLS), Virtual Private Networks (VPNs), and Internet Protocol Security (IPsec) can be used to encrypt all or some links. In other embodiments, custom and / or dedicated data communication technologies can be used to replace or supplement the aforementioned data communication technologies.

[0047] The following will describe in more detail the various steps of the transportation scheduling method in this exemplary embodiment, with reference to the accompanying drawings and embodiments.

[0048] Figure 2 This diagram illustrates a transportation scheduling method according to an embodiment of the present disclosure. The method provided in this embodiment can be executed by any electronic device with computing power, such as... Figure 1 Terminal 120 and / or server cluster 140 in the example. In the following illustration, terminal 120 is used as the execution subject.

[0049] like Figure 2 As shown, server cluster 140 executes a transportation scheduling method, including the following steps:

[0050] Step S202: Obtain multiple historical scheduled tasks.

[0051] Among them, historical scheduling tasks can be scheduling tasks that were executed within a certain period of time, starting from the current operation time.

[0052] In addition, historical dispatch tasks can be obtained through handover information sent by carrier drivers after departure, and the handover information can be generated in the form of handover vouchers.

[0053] Step S204: Generate a scheduling model based on multiple historical scheduling tasks, and configure the tasks to be executed based on the scheduling model.

[0054] The scheduling model can be a model generated based on filtering rules, which can be used to select re-executed tasks from multiple historical scheduling tasks to achieve automatic generation of tasks to be executed.

[0055] In addition, the scheduling model can also be a training model generated by training on historical scheduling tasks. By training the machine learning model, the pattern of scheduling task issuance can be obtained, thereby realizing the automatic generation of tasks to be executed.

[0056] Specifically, since there is a large amount of fixed scheduling information in transportation scheduling, such as transportation from point A to point B, there are almost the same number of vehicle types and cargo volumes every day. By analyzing historical experience patterns, a scheduling model is generated, and scheduling tasks to be executed are configured based on the scheduling model. Then, scheduling tasks to be executed, including demand information, transportation plan information, transportation task information, ride-hailing task information, and vehicle dispatch task information, are automatically generated.

[0057] Step S206: When the distribution conditions are detected, the task to be executed is distributed to the scheduling terminal so that the scheduling terminal can generate a scheduling task based on the task to be executed.

[0058] After confirmation and filtering by the scheduler, a formal scheduling task is generated, which can reduce the workload of the scheduler and improve work efficiency.

[0059] In addition, the conditions for issuance can be automatically triggered by time or manually triggered. Automatic triggering by time can be delayed or real-time.

[0060] Furthermore, operations such as loading goods, booking vehicles, and dispatching vehicles are performed based on scheduling tasks to generate handover vouchers after the driver departs.

[0061] In this embodiment, by acquiring multiple historical scheduling tasks, historical scheduling experience is obtained based on the analysis of historical scheduling tasks. Furthermore, a scheduling model is generated based on the historical scheduling experience, thereby enabling the configuration of scheduling tasks to be executed according to the scheduling model. Under the premise of ensuring that scheduling needs are met, the automatic generation of transportation needs and transportation tasks is realized, as well as the automated operation of scheduling personnel in booking and dispatching vehicles, reducing the workload of scheduling personnel and improving scheduling efficiency.

[0062] like Figure 3 As shown, in one embodiment, step S204, generating a scheduling model based on multiple historical scheduled tasks, and configuring tasks to be executed based on the scheduling model, specifically includes:

[0063] Step S302: Traverse multiple historical scheduling tasks and identify multiple scheduling routes, which include operating routes and vehicle types.

[0064] Among them, the dispatch route refers to the driving route that appeared in the historical dispatch tasks, and is represented by the operating route and the vehicle type.

[0065] Step S304: Configure the scheduling model based on multiple scheduling lines and probability thresholds.

[0066] Specifically, the scheduling model parameters include scheduling route parameters and probability threshold parameters, so as to select at least one route that meets the scheduling requirements from multiple scheduling routes based on the probability threshold parameters.

[0067] Step S306: Calculate the scheduling probability of each scheduling line based on the scheduling model.

[0068] The scheduling probability can be based on the frequency of the scheduling route in historical scheduling tasks. The higher the frequency, the higher the demand rate of the scheduling route.

[0069] Step S308: Configure the tasks to be executed based on the scheduling lines whose scheduling probability is greater than the probability threshold.

[0070] In this way, the reliability of the task to be executed can be ensured by setting a reasonable probability threshold. The probability threshold can be set based on parameters such as scheduling workload and expected reliability of automatically configured scheduling tasks.

[0071] In this embodiment, as an implementation method for configuring tasks to be executed based on a scheduling model, a traversal approach is adopted to extract historical running routes and transport vehicle types from multiple historical scheduling tasks as scheduling routes. Furthermore, based on a preset probability threshold, tasks to be executed are determined from multiple scheduling routes to achieve automatic creation of tasks to be executed. The aforementioned scheduling model has a high fault tolerance rate, and the tasks to be executed determined based on the aforementioned method have high reliability.

[0072] like Figure 4 As shown, in one embodiment, step S302, which involves traversing multiple historical scheduling tasks and identifying multiple scheduling lines, includes the following specific implementation:

[0073] Step S402: Traverse multiple historical scheduling tasks to extract the starting point, ending point, and vehicle type of the historical routes from the historical scheduling tasks.

[0074] Step S404: Historical routes whose starting point is within a first preset range and whose ending point is within a second preset range are identified as the same fusion route.

[0075] Step S406: Identify historical routes that belong to the same fusion route and have the same type of transport vehicle as a single dispatch route.

[0076] In this embodiment, if two routes have the same starting point and similar ending points, or the same ending points and similar starting points, the two routes can be merged into the same scheduling route. The setting of merged routes is conducive to improving scheduling and transportation efficiency. By combining the matching transportation vehicle type, a scheduling route is obtained, thereby enabling the selection of tasks to be executed that meet the automatic generation conditions from multiple scheduling routes, so as to further realize the issuance and execution of scheduling tasks.

[0077] like Figure 5 As shown, in one embodiment, step S202, obtaining multiple historical scheduled tasks, is implemented in a specific way, including:

[0078] Step S502: Trigger a scheduled analysis task based on the scheduled task rules.

[0079] The scheduled task rules can be represented by Cron expressions, which are used to execute scheduled tasks in a program.

[0080] Step S504: Based on the timed analysis task, obtain the historical scheduled tasks that have passed through a preset number of unit time periods.

[0081] Preferably, the unit of time is one day.

[0082] Specifically, in the application scenario of delayed dispatching tasks, a delayed task management system is set up, and Cron expressions are used to represent scheduled tasks. For example, "0 30 01**?" can indicate that task analysis is triggered starting at 1:30 AM every day. After obtaining multiple historical dispatching tasks, the dispatching model analyzes the historical dispatching tasks for M days and calculates each dispatching route, including the running route and the type of transport vehicle. For example, (L1_L2_V1) can be used to represent a dispatching route. The dispatching probability G of (L1_L2_V1) is calculated and compared with the probability threshold G0 configured by the system. If G > G0, a delayed task of the dispatching task is created, that is, a task to be executed. In other words, the delayed task management system is notified to dispatch the task to be executed at 8:00 AM the next day, realizing the simulation of manual operation for the dispatching staff.

[0083] In one embodiment, the scheduling line also includes the number of runs per unit time. Step S304, a specific implementation of calculating the scheduling probability of each scheduling line based on the scheduling model, includes:

[0084] Step S506: Detect whether the scheduled line appears in each unit of time based on the scheduling model.

[0085] Step S508: When a dispatch line is detected to appear within a unit of time, a count is performed so that the number of dispatch lines can be obtained after a preset number of unit time detections are completed.

[0086] The detection and scheduling function checks whether a line appears within each unit of time. If it appears, it is counted once regardless of how many times it appears. If it does not appear, it is counted as 0 times.

[0087] Step S510: Calculate the ratio between the number of scheduled tasks and the preset number, and determine the ratio as the scheduling probability.

[0088] Specifically, the scheduling route can be represented as N*L1_L2_V, where N represents the number of times the route runs in a day, and V represents the type of transport vehicle.

[0089] Assuming M=3, the historical scheduling tasks are shown in Table 1.

[0090] Table 1

[0091] Day 3 Day 2 Day 1 2*L1_L2_V1 1*L1_L2_V1 1*L1_L2_V1 <![CDATA[5*L1_L2_V2]]>

[0092] Specifically, after obtaining the scheduling routes over the past M days, the probability of each route appearing can be further calculated.

[0093] As shown in Table 1, since L1_L2_V1 appears once each on days 1, 2, and 3, the scheduling probability of L1_L2_V1 is G(1*L1_L2_V1) = 100%. Since L1_L2_V1 appears twice on day 3, its scheduling probability is G(2*L1_L2_V1) = 33.33%. Similarly, L1_L2_V2 appears five times, so its probability is G(5*L1_L2_V2) = 33.33%. Table 2 is derived from these calculations.

[0094] Table 2

[0095] G(1*L1_L2_V1)=100% G(2*L1_L2_V1)=33.33% G(5*L1_L2_V2)=33.33%

[0096] Furthermore, by configuring the probability threshold to G0 = 75%, it can be determined that G(L1_L2_V1) > G0. That is, the task to be traveled is configured based on the scheduling route L1_L2_V1.

[0097] In this embodiment, the trigger time of the analysis task is limited by a timed task rule, so that the analysis task is triggered when the time is reached. Then, the historical scheduling tasks within the specified time period are obtained by tracing, and the scheduling route, including the number of times the unit runs, the route and the vehicle type information, is determined based on the historical scheduling tasks. Then, the scheduling probability of the scheduling route is calculated based on the statistical method of whether the scheduling route appears in each unit of time. Finally, based on the comparison result with the probability threshold, it is determined whether to configure the task to be executed based on the scheduling route. Thus, the configured task to be executed includes not only information such as route and vehicle type, but also the number of trips in a day, which helps to further simplify the manual scheduling operation steps.

[0098] In one embodiment, a scheduling model is generated based on multiple historical scheduling tasks, and the tasks to be executed are configured based on the scheduling model. Specifically, this includes: training the execution model based on multiple historical scheduling tasks to generate the scheduling model; inputting scheduling requirement parameters into the scheduling model, so that the scheduling model can configure the tasks to be executed based on the scheduling requirement parameters, wherein the historical scheduling tasks include demand information, transportation plan information, transportation task information, ride-hailing task information, and vehicle dispatch task information.

[0099] In this embodiment, as another way to configure tasks to be executed based on a scheduling model, a large number of historical scheduling tasks are acquired, and a model is trained based on these historical scheduling tasks to obtain a scheduling model. When it is necessary to configure tasks to be executed, only one specific data among demand information, transportation plan information, transportation task information, ride-hailing task information, and dispatch task information needs to be input, and the corresponding task to be executed can be output. The scheduling model obtained through model training can make the output task to be executed highly reliable and executable.

[0100] In one embodiment, the method further includes: marking historical scheduling tasks that identify the scheduling line so as to skip the marked historical scheduling tasks when configuring the next task to be executed.

[0101] In one embodiment, when the delivery condition is detected to be met, a task to be executed is delivered to the scheduling terminal so that the scheduling terminal can generate a scheduling task based on the task to be executed. Specifically, when the current time is detected to have reached a preset time, it is determined that the delivery condition is met so that the scheduling terminal delivers the task to be executed.

[0102] In this embodiment, by setting a preset time, the scheduled tasks are sent on a timed basis, thereby ensuring the timeliness of the tasks to be executed.

[0103] like Figure 6 As shown, the transportation scheduling scheme of the embodiments of this disclosure is specifically described based on multiple dimensions. Among them, one dimension of the intelligent scheduling system 600 includes a delayed task triggering module 602, a scheduling model 604, and a scheduling terminal 606, while another dimension includes a task generation module 608 and a task creation module 610.

[0104] The processes corresponding to the delayed task triggering module 602 and the task generation module 608 include:

[0105] Issue the analysis task.

[0106] The process corresponding to scheduling model 604 and task generation module 608 includes:

[0107] Analyze historical scheduling tasks over M days.

[0108] Create a task to be executed.

[0109] The processes corresponding to the delayed task triggering module 602 and the task generation module 608 also include:

[0110] Receive tasks to be executed.

[0111] The processes corresponding to the delayed task triggering module 602 and the task creation module 610 also include:

[0112] Issue tasks to be executed.

[0113] The process corresponding to the scheduling terminal 606 and the task creation module 610 also includes:

[0114] Create a scheduled task.

[0115] like Figure 7 As shown, a transportation scheduling method according to an embodiment of the present disclosure includes:

[0116] Step S702: Retrieve historical M-day authorization forms in reverse chronological order.

[0117] Specifically, historical M-days of authorization letters are obtained by reversing the planned departure time. The authorization letter information includes the route and vehicle type, such as route L1_V1. One of these routes is then selected to obtain route L1_V1.

[0118] Step S704: Calculate the complete routes included in each authorization letter.

[0119] Specifically, using the dispatch order number of this dispatch order, query all dispatch orders to obtain the complete route of this dispatch order, such as L1_L2_V1.

[0120] Step S706: Statistically analyze and summarize all dispatched lines within the past M days.

[0121] Specifically, obtain all complete routes within M days.

[0122] Step S708: Calculate the scheduling probability of each scheduling line.

[0123] Specifically, if the scheduling probability is greater than the configured probability threshold, then the condition for creating a task to be executed is met.

[0124] Step S710: Configure the tasks to be executed based on the scheduling lines whose scheduling probability is greater than the probability threshold.

[0125] Specifically, the relevant parameters for the scheduling task are constructed, and the task to be executed is sent to the scheduling terminal.

[0126] Step S712: Mark the historical scheduling tasks of the identified scheduling lines so that the marked historical scheduling tasks can be skipped when configuring the next task to be executed.

[0127] Specifically, when performing the analysis of the next mandate, the marked historical scheduling tasks are automatically skipped.

[0128] It should be noted that the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Furthermore, it is readily understood that these processes may, for example, be executed synchronously or asynchronously in multiple modules.

[0129] Those skilled in the art will understand that various aspects of the present invention can be implemented as systems, methods, or program products. Therefore, various aspects of the present invention can be specifically implemented in the following forms: entirely hardware implementations, entirely software implementations (including firmware, microcode, etc.), or implementations combining hardware and software aspects, collectively referred to herein as “circuits,” “modules,” or “systems.”

[0130] The following reference Figure 8 The transportation scheduling device 800 according to this embodiment of the present invention will be described. Figure 8 The transportation scheduling device 800 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0131] The transportation scheduling device 800 is manifested in the form of hardware modules. The components of the transportation scheduling device 800 may include, but are not limited to: an acquisition module 802, used to acquire multiple historical scheduling tasks; a configuration module 804, used to generate a scheduling model based on multiple historical scheduling tasks, and to configure the tasks to be executed based on the scheduling model; and a distribution module 806, used to distribute the tasks to be executed to the scheduling terminal when the distribution conditions are detected, so that the scheduling terminal can generate scheduling tasks based on the tasks to be executed.

[0132] In one embodiment, the configuration module 804 includes: a traversal submodule 8042, used to traverse multiple historical scheduling tasks and identify multiple scheduling routes, the scheduling routes including running routes and transport vehicle types; a configuration submodule 8044, used to configure a scheduling model based on multiple scheduling routes and probability thresholds; a statistics submodule 8046, used to calculate the scheduling probability of each scheduling route based on the scheduling model; and a second configuration submodule 8048, used to configure tasks to be executed based on scheduling routes whose scheduling probability is greater than the probability threshold.

[0133] In one embodiment, the traversal submodule 8042 is further configured to: traverse multiple historical scheduling tasks to extract the starting point, ending point, and vehicle type of historical routes in the historical scheduling tasks; identify historical routes whose starting point is within a first preset range and whose ending point is within a second preset range as the same fusion route; and identify historical routes that belong to the same fusion route and have the same vehicle type as a scheduling route.

[0134] In one embodiment, the acquisition module 802 is further configured to: trigger a timed analysis task based on the timed task rules; and acquire historical scheduled tasks that have passed through a preset number of unit time periods based on the timed analysis task.

[0135] In one embodiment, the statistics submodule 8046 is further configured to: detect whether a scheduling line appears in each unit of time based on the scheduling model; when a scheduling line is detected to appear in a unit of time, count it so as to obtain the number of scheduled lines after a preset number of unit time detections are completed; calculate the ratio between the number of scheduled lines and the preset number, and determine the ratio as the scheduling probability.

[0136] In one embodiment, the configuration module 804 further includes: a training submodule 8050, used to train a scheduling model based on multiple historical scheduling tasks to generate a scheduling model; and a third configuration submodule 8052, used to input scheduling requirement parameters into the scheduling model so that the scheduling model can configure the tasks to be executed based on the scheduling requirement parameters, wherein the historical scheduling tasks include requirement information, transportation plan information, transportation task information, ride-hailing task information, and vehicle dispatch task information.

[0137] In one embodiment, the system further includes a marking module 808, which marks historical scheduling tasks that have identified the scheduling line so as to skip the marked historical scheduling tasks when configuring the next task to be executed.

[0138] In one embodiment, the sending module 806 is further configured to: determine that the sending conditions are met when the current time is detected to have reached a preset time, so as to send the task to be executed to the scheduling terminal.

[0139] The following reference Figure 9 To describe an electronic device 900 according to this embodiment of the present invention. Figure 9 The electronic device 900 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0140] like Figure 9 As shown, the electronic device 900 is manifested in the form of a general-purpose computing device. The components of the electronic device 900 may include, but are not limited to: at least one processing unit 910, at least one storage unit 920, and a bus 930 connecting different system components (including the storage unit 920 and the processing unit 910).

[0141] The storage unit stores program code, which can be executed by the processing unit 910 to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of the present invention. For example, the processing unit 910 can perform actions such as... Figure 2Steps S202, S204, and S206 shown herein, as well as other steps defined in the transportation scheduling method of this disclosure.

[0142] Storage unit 920 may include readable media in the form of volatile storage units, such as random access memory (RAM) 9201 and / or cache memory 9202, and may further include read-only memory (ROM) 9203.

[0143] Storage unit 920 may also include a program / utility 9204 having a set (at least one) program module 9205, such program module 9205 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0144] Bus 930 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0145] Electronic device 900 can also communicate with one or more external devices 960 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with the electronic device, and / or with any device that enables the electronic device 900 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 950. Furthermore, electronic device 900 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 950. As shown, network adapter 950 communicates with other modules of electronic device 900 via bus 930. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0146] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, terminal device, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0147] In exemplary embodiments of this disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the methods described above is stored. In some possible embodiments, various aspects of the present invention may also be implemented as a program product comprising program code that, when the program product is run on a terminal device, causes the terminal device to perform the steps of the various exemplary embodiments of the present invention described in the "Exemplary Methods" section above.

[0148] According to embodiments of the present invention, a program product for implementing the above-described method may employ a portable compact disc read-only memory (CD-ROM) and include program code, and may run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto. In this document, a readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0149] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying 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. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting programs for use by or in conjunction with an instruction execution system, apparatus, or device.

[0150] The program code contained on the readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0151] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0152] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0153] Furthermore, although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps.

[0154] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, mobile terminal, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0155] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the appended claims.

Claims

1. A method of dispatching transportation, characterized by, The method comprises the following steps: acquiring a plurality of historical scheduling tasks; generating a scheduling model based on the plurality of historical scheduling tasks, comprising: traversing the plurality of historical scheduling tasks, identifying a plurality of scheduling routes, the scheduling route comprising a running route and a transportation vehicle type, configuring the scheduling model based on the plurality of scheduling routes and a probability threshold, and configuring a to-be-executed task based on the scheduling model, the to-be-executed task being a task that can be executed again from the plurality of historical scheduling tasks based on the screening rule; when it is detected that a delivery condition is met, delivering the to-be-executed task to a scheduling end, so that the scheduling end generates a scheduling task based on the to-be-executed task.

2. The transport dispatch method of claim 1, wherein, The method for configuring a to-be-executed task based on the scheduling model comprises the following steps: statistically determining a scheduling probability of each of the scheduling routes based on the scheduling model; configuring the to-be-executed task according to the scheduling route whose scheduling probability is greater than a probability threshold.

3. The transport dispatch method of claim 2, wherein, The method for traversing the plurality of historical scheduling tasks to identify a plurality of scheduling routes comprises the following steps: traversing the plurality of historical scheduling tasks to extract a line starting point, a line ending point and the transportation vehicle type of a historical route in the historical scheduling task; determining the historical route whose line starting point is in a first preset range and whose line ending point is in a second preset range as a same fusion route; identifying the historical route belonging to the same fusion route and having the same transportation vehicle type as one of the scheduling routes.

4. The transportation scheduling method of claim 2, wherein, The method for acquiring a plurality of historical scheduling tasks comprises the following steps: triggering a timing analysis task based on a timing task rule; acquiring the historical scheduling task within a preset number of unit times based on the timing analysis task.

5. The transportation scheduling method of claim 4, wherein, The scheduling route further comprises a running frequency in each of the unit times, and the method for statistically determining a scheduling probability of each of the scheduling routes based on the scheduling model comprises the following steps: detecting whether the scheduling route appears in each of the unit times based on the scheduling model; when it is detected that the scheduling route appears in one of the unit times, counting, so that a statistical scheduling number is obtained when detection on the preset number of unit times is completed; calculating a ratio between the statistical scheduling number and the preset number, and determining the ratio as the scheduling probability.

6. The transportation scheduling method of claim 1, wherein, The method for generating a scheduling model based on the plurality of historical scheduling tasks to configure a to-be-executed task based on the scheduling model comprises the following steps: performing model training based on the plurality of historical scheduling tasks to generate the scheduling model; inputting a scheduling demand parameter into the scheduling model, so that the to-be-executed task is configured by the scheduling model based on the scheduling demand parameter, wherein the historical scheduling task comprises demand information, transportation plan information, transportation task information, car booking task information and car dispatching task information.

7. The transportation scheduling method of claim 2, wherein, The method further comprises the following steps: labeling the historical scheduling task in which the scheduling route is identified, so that the labeled historical scheduling task is skipped when a next to-be-executed task is configured. 8.The transportation scheduling method according to any one of claims 1 to 7, characterized in that, The method for delivering the to-be-executed task to the scheduling end when it is detected that a delivery condition is met, so that the scheduling end generates a scheduling task based on the to-be-executed task, comprises the following steps: When it is detected that the current time reaches the preset time, it is determined that the dispatching end meets the issuing condition to issue the to-be-executed task.

9. A transport dispatching device, characterized by Comprising: An acquisition module, configured to acquire a plurality of historical scheduling tasks; A configuration module, configured to generate a scheduling model based on the plurality of historical scheduling tasks, comprising: traversing the plurality of historical scheduling tasks, identifying a plurality of scheduling routes, the scheduling routes comprising running routes and transportation vehicle types, and configuring the scheduling model based on the plurality of scheduling routes and a probability threshold, to configure a to-be-executed task based on the scheduling model, the to-be-executed task being a task that can be executed again from the plurality of historical scheduling tasks based on the screening rule; An issuing module, configured to issue the to-be-executed task to the dispatching end when it is detected that the issuing condition is met, to generate a scheduling task by the dispatching end based on the to-be-executed task.

10. An electronic device, comprising: Comprising: A processor; And A memory, configured to store executable instructions of the processor; Wherein, the processor is configured to execute the executable instructions to perform the transportation scheduling method in any one of claims 1-8.

11. A computer readable storage medium having stored thereon a computer program, characterized in that The computer program is executed by the processor to implement the transportation scheduling method in any one of claims 1-8.

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