Method, device, medium and product for determining a waybill task planning scheme

Abstract

The application provides a method, device, medium and product for determining a shipping order task planning scheme. The method comprises: obtaining at least one candidate task planning scheme of a shipping order task operation object in a logistics service network, the candidate task planning scheme comprising a shipping order task allocated to the shipping order task operation object, a planned path of the shipping order task operation object, and a planned time of the shipping order task operation object on the planned path; simulating the at least one candidate task planning scheme through a digital twin model corresponding to the logistics service network to obtain a simulation result corresponding to the at least one candidate task planning scheme; determining a shipping order task planning scheme of the shipping order task operation object based on the simulation result, and sending the shipping order task planning scheme to the shipping order task operation object. Thus, the digital twin model is simulated to determine a more optimal shipping order task planning scheme, reduce resource waste in the shipping order task operation process, and improve the quality of logistics services.

Description

Technical Field

[0001] This application relates to the field of logistics technology, specifically to a method, equipment, medium, and product for determining a waybill task planning scheme. Background Technology

[0002] With the development of online business in the logistics industry, the logistics service network is facing an increasing number of order tasks and greater service pressure.

[0003] In related technologies, logistics station managers formulate a waybill task allocation plan based on their own experience; according to the allocation plan, the waybill tasks are assigned to the corresponding couriers; and the couriers plan their assigned waybill tasks according to the actual situation.

[0004] However, as the number of waybill tasks increases, the above method is prone to problems such as unreasonable waybill task planning, resulting in waste of resources and decline in service quality. Summary of the Invention

[0005] In view of this, embodiments of the present invention aim to provide a device, medium, and product for determining a waybill task planning scheme, so as to generate a better waybill task planning scheme and improve resource utilization and service quality in the waybill task operation process.

[0006] According to a first aspect of the embodiments of this application, a method for determining a waybill task planning scheme is provided, comprising: obtaining at least one candidate task planning scheme for a waybill task operation object in a logistics service network, the candidate task planning scheme including: a waybill task to be allocated to the waybill task operation object, a planned path for the waybill task operation object, and a planned time for the waybill task operation object on the planned path; simulating the at least one candidate task planning scheme using a digital twin model corresponding to the logistics service network to obtain simulation results corresponding to the at least one candidate task planning scheme; determining the waybill task planning scheme for the waybill task operation object based on the simulation results, and sending the waybill task planning scheme to the waybill task operation object.

[0007] According to a second aspect of the present application, an electronic device is provided, including a memory and a processor; the memory is connected to the processor and is used to store a program; the processor is used to implement the method for determining a waybill task planning scheme as described in the first aspect of the present application by running the program in the memory.

[0008] According to a third aspect of the embodiments of this application, a storage medium is provided, on which a computer program is stored. When the computer program is run by a processor, it implements the method for determining a waybill task planning scheme as described in the first aspect of the embodiments of this application.

[0009] According to a fourth aspect of the embodiments of this application, a computer program product is provided, including computer program instructions, which, when executed by a processor, cause the processor to perform the method for determining a waybill task planning scheme as described in the first aspect of the embodiments of this application.

[0010] According to the technical solution of this application embodiment, at least one candidate task planning scheme is obtained for a waybill task operation object in a logistics service network. Each candidate task planning scheme includes the waybill task to be allocated to the waybill task operation object, the planned path of the waybill task operation object, and the planned time for the waybill task object on the planned path. The at least one candidate task planning scheme is simulated using a digital twin model corresponding to the logistics service network to obtain simulation results. Based on the simulation results, a waybill task planning scheme is determined for the waybill task operation object and sent to it. Thus, by simulating the candidate task planning schemes using a digital twin model corresponding to the logistics service network, the task execution situation of waybill tasks executed according to the candidate task planning schemes is accurately predicted, the superior waybill task planning scheme is selected, and by sending the superior waybill task planning scheme to the waybill task operation object, resource utilization and service quality during the waybill task operation process are improved. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0012] Figure 1 A flowchart illustrating the method for determining a waybill task planning scheme provided in an embodiment of this application;

[0013] Figure 2 A schematic diagram illustrating the process of establishing a digital twin model provided in an embodiment of this application;

[0014] Figure 3 A flowchart illustrating the generation of candidate task planning schemes provided in this application embodiment;

[0015] Figure 4 A schematic diagram of the device for determining a waybill task planning scheme provided in an embodiment of this application;

[0016] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0017] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0018] First, the technical terms involved in the embodiments of this application will be explained:

[0019] Digital Twin Technology is a simulation process that integrates multiple disciplines, multiple physical quantities, multiple scales, and multiple probabilities. It reflects the entire life cycle of the corresponding physical equipment by completing mapping in virtual space.

[0020] A logistics service network refers to an organic whole composed of multiple interacting and interconnected logistics entities that possess logistics service functions. Logistics entities may include logistics stations (also known as logistics nodes), logistics equipment (such as logistics vehicles, weighing equipment, and barcode scanning equipment), logistics personnel, logistics customers, and surrounding map elements of the logistics stations (such as buildings, roads, and road facilities).

[0021] Waybill tasks: These are order tasks that require logistics transportation, including online waybill tasks (such as customers placing orders on the online platform of logistics institutions) and offline waybill tasks (such as customers placing orders at nearby logistics stations or after contacting a courier to place an order).

[0022] The objects of waybill task operations may include waybill task operation devices and / or waybill task operation personnel. Waybill task operation devices may include waybill task operation vehicles, i.e., logistics vehicles, such as a courier's delivery tricycle, and waybill task operation personnel may include couriers (or logistics drivers). With the development of artificial intelligence technology, AI robots may replace waybill task operation personnel, so waybill task operation objects may include AI robots.

[0023] Secondly, the relevant technologies and technical issues of waybill task planning will be introduced:

[0024] In the relevant technologies of logistics service networks, a waybill task allocation strategy is determined based on personnel experience and fixed rules. According to the waybill task allocation strategy, waybill tasks are assigned to couriers, and couriers plan and execute the waybill tasks assigned to them.

[0025] However, relying on personnel experience and fixed rules has significant limitations. Generating waybill task allocation strategies based on personnel experience and fixed rules has at least the following drawbacks:

[0026] (1) The flexibility and adaptability of the waybill task allocation strategy are poor, and it is unable to adapt to the real-time changes in the logistics environment in the logistics service network, such as traffic conditions, customer needs, and courier status, which leads to a decline in the quality of logistics services (including logistics service efficiency, the ability to handle emergencies in logistics services, and the condition of goods); (2) The allocation of logistics resources (including human resources, vehicle resources and / or time resources) is unreasonable, resulting in resource waste and adversely affecting the quality of logistics services; (3) There is a lack of effective prediction mechanisms, which makes it impossible to accurately predict potential problems in the logistics service process, such as package delays, traffic congestion, and excessive task allocation to couriers; (4) Logistics institutions have accumulated a large amount of logistics data, but the above methods have failed to make full use of this logistics data and explore its potential value in order to improve the rationality of waybill task planning.

[0027] To address the aforementioned problems, this application provides a method, apparatus, medium, and product for determining a waybill task planning method. In the waybill task planning method, based on a digital twin model corresponding to a logistics service network, at least one candidate task planning scheme for a waybill task operation object in the logistics service network is simulated to obtain simulation results for at least one candidate task planning scheme; based on the simulation results of the at least one candidate task planning scheme, a waybill task planning scheme for the waybill task operation object is determined; and the waybill task planning scheme is sent to the waybill task operation object so that the waybill task operation object can execute the waybill task according to the waybill task planning scheme.

[0028] On the one hand, digital twin models simulate logistics service networks, simulating logistics resources and the real-time changing logistics environment within these networks. Candidate task planning schemes are simulated within the digital twin model, yielding simulation results that fully consider both logistics resources and the dynamic logistics environment. Therefore, the final determined waybill task planning scheme possesses flexibility and adaptability, capable of accommodating the real-time changes in the logistics service network, improving the rationality of logistics resource allocation, and reducing resource waste.

[0029] On the other hand, simulating candidate task planning schemes using digital twin models is essentially predicting the execution of these schemes. Since digital twin models simulate logistics service networks, they can accurately simulate potential problems in executing waybill tasks according to candidate plan schemes, thereby determining more reasonable waybill task planning schemes and improving logistics service quality.

[0030] On the other hand, the use of digital twin models requires making full use of logistics data in the logistics service network and mining the potential value of logistics data, so that logistics data in the logistics service network can play a role in the determination of waybill task planning schemes, thereby improving the rationality of waybill task planning schemes.

[0031] As can be seen, the embodiments of this application combine digital twin technology with waybill task planning in the logistics service network, and determine a better waybill task planning scheme for the logistics service network. Through the better waybill task planning scheme, the resource utilization rate and service quality in the waybill task operation process are improved.

[0032] The following describes in detail, with reference to the accompanying drawings, the method for determining the waybill task planning scheme provided in the embodiments of this application.

[0033] Exemplary methods

[0034] Figure 1 This is a flowchart illustrating the method for determining a waybill task planning scheme provided in an embodiment of this application. Please refer to... Figure 1 In an exemplary embodiment, the method for determining the provided waybill task planning scheme may include the following steps 100 to 120:

[0035] Step 100: Obtain at least one candidate task planning scheme for a waybill task operation object in the logistics service network. The candidate task planning scheme includes: the waybill task to be allocated to the waybill task operation object, the planned route of the waybill task operation object, and the planned time of the waybill task operation object on the planned route. Step 110: Simulate the at least one candidate task planning scheme using a digital twin model corresponding to the logistics service network to obtain simulation results for the at least one candidate task planning scheme. Step 120: Based on the simulation results, determine the waybill task planning scheme for the waybill task operation object and send the waybill task planning scheme to the waybill task operation object.

[0036] First, the terms used in steps 100 to 120 will be explained:

[0037] Among them, the digital twin model corresponding to the logistics service network is a virtual model built based on the real logistics service network. It is a mapping of the real logistics service network in virtual space and is used to restore (reflect) the real logistics service process.

[0038] The logistics service network includes at least one waybill task operation object, which may be at least one waybill task operation personnel and / or at least one waybill task operation device, and the waybill task operation device may include waybill task operation vehicle.

[0039] In a logistics service network, a one-to-one binding relationship can be established between waybill task operators and waybill task operation devices. The waybill task assigned to the waybill task operator is the waybill task assigned to the waybill task operation device bound to that waybill task operator. The planned path of the waybill task operation object is the planned path of the waybill task operation device bound to that waybill task operator. The planned time of the waybill task operation object on the planned path is the planned path of the waybill task operation device bound to that waybill task operator.

[0040] For example, each courier is linked to a delivery tricycle, and the package delivery task assigned to a courier is the package delivery task assigned to the delivery tricycle linked to that courier.

[0041] In one example, the logistics service network includes a logistics delivery network, waybill tasks include delivery (or distribution) tasks, waybill task operation objects include delivery task operation objects, delivery task operation objects include delivery task personnel and / or delivery task operation equipment (furthermore, delivery task operation equipment includes delivery task operation vehicles), and waybill task planning schemes include delivery task planning schemes. Thus, through a digital twin model, a better delivery task planning scheme is generated for the logistics delivery network, improving resource utilization and delivery efficiency during the delivery task operation process.

[0042] In another example, the logistics service network includes a logistics collection network, waybill tasks include collection tasks, waybill task operation objects include collection task operation objects, collection task operation objects include collection task personnel and / or collection task operation devices (furthermore, collection task operation devices include collection task operation vehicles), and waybill task planning schemes include collection task planning schemes. Thus, through a digital twin model, a better collection task planning scheme is generated for the logistics collection network, improving resource utilization and delivery efficiency during the collection task operation process.

[0043] The planned path of a waybill task refers to the planned walking and / or driving path of the waybill task during the waybill task operation process. The planned path contains at least one path node, and the planned time for the waybill task on the planned path may include the planned time of the waybill task at that at least one path node, such as the arrival time, operation time, and / or departure time of the waybill task at that at least one path node.

[0044] In one example, where the logistics service network includes a logistics delivery network and / or a logistics pickup network, the planned path for a waybill task operation object includes the delivery planned path for the delivery task operation object and / or the pickup planned path for the pickup task operation object.

[0045] The delivery planning route for the delivery task may include at least one logistics station and / or at least one delivery destination. The planning time for the delivery task on the delivery planning route may include the planning time of the delivery task at at least one logistics station and / or the planning time of the delivery task at at least one delivery destination.

[0046] Taking a delivery task that includes both the delivery personnel and the delivery vehicle bound to them as an example, the planned time for the delivery task at the logistics station may include at least one of the following: the time when the delivery vehicle arrives at the logistics station (or the time when the delivery vehicle arrives at the corresponding electronic fence of the logistics station, where the electronic fence is a geographical area set on an electronic map, and the setting of the electronic fence can, to some extent, solve the problem that insufficient positioning accuracy of the delivery vehicle leads to the inability to accurately detect whether the vehicle has arrived at the logistics station), and the time when the delivery vehicle stops at the checkpoint at the logistics station. Alternatively, the time during which the relative distance between the delivery vehicle and the logistics station is less than or equal to a distance threshold after the delivery vehicle arrives at the electronic fence corresponding to the logistics station; the time during which the delivery personnel load the package at the logistics station; the time during which the delivery personnel seal the delivery vehicle; and the time during which the delivery vehicle leaves the logistics station; and the planned time for the delivery object to reach the delivery destination, may include at least one of the following: the time when the delivery vehicle arrives at the delivery destination; the time when the delivery vehicle completes delivery at the delivery destination; and the time when the delivery vehicle leaves the delivery destination.

[0047] The planned route for a pickup task may include at least one logistics station and / or at least one pickup destination. The planned time for the pickup task on the planned route may include the planned time for the pickup task at at least one logistics station and / or the planned time for the pickup task at at least one pickup destination. The planned time for the pickup task on the planned route can be referred to the above description of the planned time for the delivery task on the planned route, and will not be listed here.

[0048] In cases where there are multiple candidate task planning schemes, each scheme corresponds to its own simulation results. These simulation results, also known as simulation or verification results, specifically refer to the simulated operation status and / or simulation results obtained by performing a waybill task according to the candidate scheme in a digital twin model. The simulated operation status can refer to the simulated operation status of the waybill task object, such as whether the waybill task object exhibits too many or too few tasks, too many timeouts, or missing tasks. It can also refer to the simulated operation status of the logistics station, such as whether the loading / unloading time for the waybill task is too long, or whether the vehicle's parking time for the waybill task is too long. The simulation results represent the completion status of the simulated waybill task after performing the task according to the candidate scheme. Therefore, the simulation status and / or simulation results reflect the merits of the candidate task planning schemes.

[0049] In one example, the simulation results corresponding to the candidate task planning scheme may include: simulated index values ​​of the candidate task planning scheme under at least one task performance indicator. The at least one task performance indicator may include: labor cost indicator, time cost indicator, equipment cost indicator, operational efficiency indicator, resource utilization rate indicator, and / or customer satisfaction indicator. The simulated index values ​​under the at least one task performance indicator indicate the operational statistics of the candidate task planning scheme under that at least one task performance indicator. Simulated index values ​​can be expressed numerically (e.g., the index value of the time cost indicator could be the completion time of the simulated task, and the index value of the equipment cost indicator could be the estimated equipment cost of the simulated task) or in textual form (e.g., the index value of the customer satisfaction indicator may include levels such as very satisfied, moderately satisfied, and dissatisfied). Simulation using a digital twin model can simulate the interactions between task performance indicators, thereby improving the accuracy of predicting the index values ​​of the candidate task planning scheme under the task performance indicators and more accurately reflecting the merits of the candidate task planning scheme from one or more dimensions.

[0050] Furthermore, the evaluation method (i.e., the statistical method of indicators) corresponding to the task performance indicators can be flexibly adjusted; when there are multiple task performance indicators, the priority of multiple task performance indicators can also be flexibly adjusted to achieve the best allocation of waybill tasks and the best planning of routes in the logistics service process, thereby improving the quality of waybill task operations from multiple dimensions.

[0051] In another example, the simulation results corresponding to the candidate task planning scheme may include: operational anomalies that occur during the simulation of the candidate task planning scheme, such as failure to complete the waybill task on time, unreachable path nodes, or uncompleted waybill tasks after simulating the operation according to the candidate task planning scheme. Thus, from the perspective of simulated operational anomalies, the superiority or inferiority of the candidate task planning scheme can be accurately reflected.

[0052] Next, the implementation process of steps 100 to 120 will be described:

[0053] In step 100, at least one candidate task planning scheme for the waybill task operation object in the logistics service network can be obtained from the user's input data; or, at least one candidate task planning scheme for the waybill task operation object in the logistics service network can be obtained from the scheme database; or, at least one candidate task planning scheme for the waybill task operation object in the logistics service network can be obtained from the output data of the strategy module (also known as the decision module), and the strategy module is responsible for generating corresponding candidate task planning schemes for the waybill task operation object.

[0054] In step 110, the waybill task can be simulated in the digital twin model according to the candidate task planning scheme. Based on the simulation results of the waybill task, the simulation results corresponding to the candidate task planning scheme can be obtained. For multiple candidate task planning schemes corresponding to the same batch of waybill tasks to be processed, batch simulation can be performed in the digital twin model to obtain simulation results corresponding to each candidate task planning scheme, thereby improving the simulation efficiency of the candidate task planning scheme.

[0055] In step 120, the simulation results corresponding to at least one candidate task planning scheme can be used as reference data for selecting the waybill task planning scheme. From the at least one candidate task planning scheme, the waybill task planning scheme for the waybill task operation object is selected. Then, the waybill task planning scheme is sent to the waybill task operation object to instruct the waybill task operation object to perform the waybill task according to the waybill task operation planning scheme.

[0056] In one example, when there are multiple candidate task planning schemes, the simulation results corresponding to the multiple candidate task planning schemes can be compared. Based on the comparison results, the candidate task planning scheme with the best simulation result is selected and determined as the waybill task planning scheme for the waybill task operation object.

[0057] In another example, from at least one candidate task planning scheme, the one whose simulation result is closest to the waybill task planning objective can be selected, and this candidate task planning scheme can be determined as the waybill task operation object's waybill task planning scheme. The waybill task planning objective may include at least one indicator threshold corresponding to a task performance indicator, or it may include at least one of the following: the planning objective of waybill task planning, the allocation objective of the task allocation algorithm, and the planning objective of path planning. The planning objectives of waybill task planning, the allocation objective of the task allocation algorithm, and the planning objective of path planning can be referred to in subsequent embodiments and will not be elaborated further.

[0058] According to the technical solution of this embodiment, by using a digital twin model corresponding to the logistics service network, at least one candidate task planning scheme for the waybill task operation object is simulated, which improves the accuracy of predicting the operation status of waybill tasks according to the candidate task planning scheme. The simulation results can accurately reflect the operation status. Based on the simulation results, a better waybill task planning scheme is determined for the waybill task operation object, and the better waybill task planning scheme is sent to the waybill task operation object, thereby improving the resource utilization rate and service quality in the waybill task operation process.

[0059] Below, examples are provided for digital twin models and their construction.

[0060] In some embodiments, the digital twin model includes sub-models corresponding to logistics entities in the logistics service network. These sub-models include various types, such as: a task operation model corresponding to a waybill, a station model corresponding to a logistics station, a customer model corresponding to a logistics customer, and a road model corresponding to a logistics route. The task operation model, station model, customer model, and road model are virtual mappings of the waybill task operation object, logistics station, logistics customer, and logistics route in the digital twin model, respectively. The task operation model may include a personnel model (e.g., a courier model) corresponding to the waybill task operator and / or a device model (e.g., a vehicle model) corresponding to the waybill task operation equipment. Therefore, by combining the task operation object model, station model, customer model, and road model in the digital twin model, the simulation (or verification) of the waybill task operation process in the logistics service network can be accurately achieved.

[0061] To establish a digital twin model for simulating waybill task operation schemes, this application provides an optional embodiment that can establish a digital twin model. For example... Figure 2 As shown, its specific implementation is as follows, including steps 200 to 210:

[0062] Step 200: Obtain historical logistics data from the logistics service network. Step 210: Generate sub-models in the digital twin model based on the historical logistics data.

[0063] Historical logistics data refers to logistics data collected in the past.

[0064] In step 200, historical logistics data can be obtained from the database. The historical logistics data is obtained by collecting logistics data from the logistics service network over the past time through data collection devices deployed on logistics entities (e.g., positioning sensors and camera devices deployed on logistics vehicles, camera devices and weighing devices deployed at logistics stations) and / or data collection devices carried by logistics entities (e.g., barcode scanning devices, positioning devices and other terminal devices carried by waybill task operators).

[0065] In step 210, historical logistics data can be preprocessed to obtain historical attribute information of logistics entities in the logistics service network; based on the historical attribute information of logistics entities, a sub-model corresponding to the logistics entity is constructed; based on the constructed sub-model, a constructed digital twin model is obtained.

[0066] Preprocessing historical logistics data can include at least one of the following: data cleaning, data integration, and data analysis. Data cleaning includes, for example, standardizing the format of historical logistics data from different formats, performing integrity checks and processing missing data, and performing duplicate checks and deleting duplicate data. Data integration can include integrating data from different data acquisition devices and / or integrating data from different time periods. For example, if there are conflicting data from two data acquisition devices, the more reliable one can be trusted; or, if data is missing from a certain time period, data from adjacent time periods can be used to fill in the gaps. Data analysis refers to mining the potential value of historical logistics data. Big data analytics techniques (such as large language models) can be used to analyze historical logistics data to obtain more accurate historical attribute information about physical entities.

[0067] Based on the historical attribute information of logistics entities, sub-models corresponding to logistics entities can be constructed, which may include: constructing an operation object model corresponding to a waybill task operation object based on the historical attribute information of the waybill task operation object; constructing a site model corresponding to a logistics station based on the historical attribute information of the logistics station; constructing a customer model corresponding to a logistics customer based on the historical attribute information of the logistics customer; and constructing a road model corresponding to a logistics road based on the historical attribute information of the logistics road.

[0068] For example, the historical attribute information of a logistics station may include at least one of the following: the logistics station's past working hours, the logistics station's location information, and the logistics station's past loading and unloading time; the historical attribute information of the waybill task operation object may include at least one of the following: the work experience of the waybill task operation personnel, the operation efficiency of the waybill task operation personnel in the past operation, and the driving speed of the waybill task operation vehicle in the past operation; the historical attribute information of a logistics road may include at least one of the following: the road location of the logistics road (such as the starting point and ending point of the road), the number of traffic lights on the logistics road, and the time required for vehicles to pass through the logistics road in the past.

[0069] In one example, during the process of constructing a sub-model corresponding to a logistics entity based on its historical attribute information, statistical analysis can be performed on the historical attribute information of the logistics entity to obtain the data distribution of the logistics entity in one or more attribute dimensions. For example, for a waybill task operation object, the time when the waybill task operation object arrives at the logistics station follows a normal distribution.

[0070] In another example, during the process of constructing a sub-model corresponding to a logistics entity based on its historical attribute information, a deep learning model can be trained using this information to obtain the sub-model. This sub-model is essentially a trained deep learning model. Thus, by utilizing a deep learning model with strong learning capabilities, the abilities, behaviors, or other characteristics of the corresponding logistics entity can be simulated more accurately in virtual space.

[0071] According to the technical solution of this embodiment, based on the historical logistics data of the logistics service network, the historical attribute information of the logistics entity is analyzed, and based on the historical attribute information of the logistics entity, a sub-model in the digital twin model is constructed, so that the sub-model in the digital twin model can simulate the corresponding logistics entity, and the digital twin model can simulate the logistics service network.

[0072] In some embodiments, the digital twin model also includes environmental information about the area where the logistics service network is located. After constructing the digital twin model, the following steps can be performed: real-time logistics data and real-time environmental information about the area where the logistics service network is located are collected using data acquisition devices deployed on the logistics entity and / or carried by the logistics entity; the sub-models in the digital twin model and the environmental information in the digital twin model are updated based on the real-time logistics data and real-time environmental information. Thus, the digital twin model can monitor the logistics service network in real time, more accurately simulate the logistics service network in real-time, and improve the accuracy of simulating waybill task planning schemes using the digital twin model.

[0073] The environmental information of the area where the logistics service network is located may include at least one of the following: geographical information of the area where the logistics service area is located (such as latitude and longitude information, administrative division information, and the location of areas with high delivery frequency), weather information, and traffic conditions (such as real-time congestion of logistics roads, real-time construction of logistics roads, and whether logistics roads are temporarily closed to traffic).

[0074] The data acquisition devices deployed on the logistics entity and / or carried by the logistics entity can be referred to the description in the foregoing embodiments, and will not be repeated here. The difference between real-time logistics data and historical logistics data lies in the time frame; the remaining details can be referred to historical logistics data, and will not be repeated here.

[0075] In this embodiment, real-time logistics data can be preprocessed to obtain real-time attribute information of logistics entities in the logistics service network. Based on the real-time attribute information of the logistics entities, the sub-models in the digital twin model can be updated (this may include updating the data distribution of the sub-models or retraining the sub-models). Real-time environmental information can also be preprocessed (e.g., cleaned, integrated, and analyzed), and the preprocessed real-time environmental information can be written into the digital twin model to update the environmental information in the digital twin model.

[0076] Below, corresponding embodiments are provided for generating candidate task planning schemes.

[0077] To obtain feasible candidate task planning schemes, this application provides an optional embodiment that can generate candidate task planning schemes, such as... Figure 3 As shown, its specific implementation is as follows, including steps 300 to 310:

[0078] Step 300: Obtain task information of the pending waybill tasks in the logistics service network and attribute information of the logistics entities in the logistics service network; Step 310: Generate at least one candidate task planning scheme based on the task information and attribute information using a task planning algorithm.

[0079] The task information for the pending waybill tasks may include the identification information (such as the waybill number), address information (which may include the sender's address and / or the recipient's address), and time information (which may include at least one of the following: order placement time, required pickup time, required delivery time, and the time when the package corresponding to the waybill task arrives at the logistics station).

[0080] The attribute information of logistics entities in the logistics service network may include: historical attribute information and / or real-time attribute information of logistics entities. The historical attribute information and entity attribute information can be referred to the description in the foregoing embodiments, and will not be repeated here.

[0081] In step 300, a task processing request message can be received, and based on the task processing request message, task information of the pending waybill task can be obtained; alternatively, task information of the pending waybill task can be obtained from the task processing queue. The digital twin model corresponding to the logistics service network can contain attribute information of logistics entities, so attribute information of logistics entities in the logistics service network can be obtained from the digital twin model corresponding to the logistics service network.

[0082] In step 310, after obtaining the task information of the waybill task to be processed in the logistics service network and the attribute information of the logistics entity in the logistics service network, the task information and attribute information can be used as input to the task planning algorithm. The task planning algorithm is used to perform task planning for the waybill task to be processed, and at least one candidate task planning scheme corresponding to the waybill task operation object is generated.

[0083] In one example, the planning objectives and constraints for waybill planning can be determined based on the task information of unprocessed waybill tasks in the logistics service network and the attribute information of logistics entities in the logistics service network. Under the constraints, the planning objectives are optimized and solved using a task planning algorithm to obtain at least one candidate task planning scheme. Thus, by establishing the planning objectives and constraints for waybill task planning and optimizing the solution, the quality of generated candidate task planning schemes can be improved.

[0084] The planning objective of waybill task planning refers to the goals or standards that are desired to be achieved during waybill task operations. The planning objectives of waybill task planning may include one or more of the following: maximizing waybill task operation efficiency, minimizing waybill task operation costs, ensuring the safety of goods corresponding to the waybill task, and improving customer satisfaction with the waybill task.

[0085] The constraints of waybill task planning refer to the conditions that must be met while achieving the objectives during waybill task operations. These constraints may include one or more of the following: the planned time for the waybill task on the planned path must meet a specified time window; the number of waybills allocated to the waybill task must meet the load capacity of the waybill task; the path length of the waybill task must be less than or equal to a set path length threshold; and the operation time of the waybill task must meet the waybill task's commuting time range.

[0086] In this example, an objective function for the task planning algorithm can be constructed based on the goals to be achieved in the waybill task planning; a conditional function can be constructed based on the conditions to be satisfied in the waybill task planning; the input data required by the objective function and the input data required by the conditional function are extracted from the task information of the waybill to be processed and the attribute information of the logistics entity; the input data required by the objective function is input into the objective function to obtain the planning goal of the waybill task planning; the input data required by the conditional function is input into the conditional function to obtain the constraints of the waybill task planning. Then, under the constraints, the planning goal is optimized and solved using an optimization algorithm in the task planning algorithm (such as genetic algorithm, ant colony algorithm, machine learning, and other intelligent algorithms) to obtain at least one candidate task planning scheme.

[0087] In one example, the task planning algorithm may include a task allocation algorithm and a path planning algorithm, such as Figure 3 As shown, step 310 may include: step 311, assigning tasks to the pending waybill tasks using a task allocation algorithm based on the task information of the waybill tasks to be processed and the attribute information of the logistics entities, to determine the waybill tasks planned to be assigned to the waybill task operation objects; step 312, performing path planning for the waybill task operation objects using a path planning algorithm based on the waybill tasks planned to be assigned to the waybill task operation objects and the attribute information of the logistics entities, to obtain at least one candidate task planning scheme. Thus, task planning is implemented in stages: first, the rationality of task allocation is improved in the task allocation stage, and then reasonable operation path information is generated for the waybill task operation objects at the path planning node, so as to provide the digital twin model with candidate task planning schemes of acceptable quality.

[0088] In this example, the allocation objective and constraints for waybill task assignment can be determined based on the task information of the pending waybill tasks in the logistics service network and the attribute information of the logistics entities in the logistics service network. Under these constraints, the allocation objective is optimized using a task assignment algorithm to obtain the waybill tasks planned to be assigned to the waybill task operation objects. The planning objective and constraints for path planning can be determined based on the waybill tasks planned to be assigned to the waybill task operation objects and the attribute information of the logistics entities. Under these constraints, the planning objective is optimized using a path planning algorithm to obtain at least one candidate task planning scheme for the waybill task operation objects.

[0089] The allocation objectives of waybill tasks may include one or more of the following: maximizing waybill task operation efficiency, minimizing waybill task operation costs, ensuring the safety of goods corresponding to waybill tasks, and improving customer satisfaction with waybill tasks.

[0090] The constraints for waybill task allocation may include one or more of the following: the number of waybill tasks allocated to the waybill task operation object must meet the load capacity of the waybill task operation object, and the distance between the task address of the waybill task allocated to the waybill task object and the location of the waybill task object is less than a set distance threshold.

[0091] The planning objectives of route planning may include one or more of the following: maximizing the efficiency of waybill operations, minimizing the cost of waybill operations, ensuring the safety of goods corresponding to waybill operations, and improving customer satisfaction with waybill operations.

[0092] In designing the constraints for route planning, factors such as real-time traffic conditions and urgent orders need to be considered. Therefore, route planning constraints may include one or more of the following: the total time spent by the delivery task on the planned route must be less than or equal to a set time threshold; the congestion probability of the delivery task on the planned route must be less than or equal to a set probability threshold; the planned time of the delivery task on the planned route must meet a specified time window (e.g., the delivery time range for urgent orders); the path length of the planned route of the delivery task must be less than or equal to a set path length threshold; and the operation time of the delivery task on the planned route must meet the commuting time range of the delivery task.

[0093] The process of determining the allocation objectives and constraints of waybill task allocation, the planning objectives and constraints of route planning can refer to the process of determining the planning objectives and constraints of waybill task planning in the aforementioned embodiments, and will not be repeated here.

[0094] According to the technical solution of this embodiment, based on the task information of the waybill task to be processed in the logistics service network and the attribute information of the logistics entity, a candidate task planning scheme is generated through a task planning algorithm, thereby improving the generation efficiency and quality of the candidate task planning scheme.

[0095] In some embodiments, after simulating at least one candidate task planning scheme using a digital twin model corresponding to the logistics service network and obtaining simulation results for at least one candidate task planning scheme, if none of the simulation results for the candidate task planning schemes meet the requirements (e.g., the difference between the simulation results for the candidate task planning schemes and the waybill task planning target is greater than a set threshold), at least one candidate task planning scheme for the waybill task operation object can be re-acquired. The newly acquired candidate task planning scheme is then simulated again using the digital twin model until at least one candidate task planning scheme with satisfactory simulation results is obtained. Finally, the waybill task planning scheme for the waybill task operation object is selected from these at least one candidate task planning schemes. Thus, by acquiring candidate tasks multiple times and conducting multiple simulations, the quality of the candidate task planning schemes is ensured.

[0096] Optionally, if the simulation results corresponding to the candidate task planning schemes do not meet the requirements, at least one candidate task planning scheme for the waybill task operation object is generated again through the task planning algorithm. Thus, through multiple collaborations and interactions between the task attribution algorithm and the digital twin model, the quality of the candidate task planning schemes is ensured.

[0097] In some embodiments, after sending the waybill task planning scheme to the waybill task operation object, the method further includes: optimizing the waybill task planning scheme based on a task planning algorithm when scheme optimization is required, thereby adjusting the waybill task planning scheme; wherein, the situations requiring scheme optimization include at least one of the following: the difference between the real-time waybill task operation progress of the waybill task operation object and the waybill task planning scheme meets the scheme optimization conditions, changes occur in the logistics service network, or task anomaly information is received from the waybill task operation object. Thus, the method adapts to the real-time situation of waybill task planning, responds promptly and adjusts the waybill task planning scheme, and improves the flexibility and adaptability of the waybill task planning scheme.

[0098] This involves processing real-time logistics data collected by data acquisition devices deployed on and / or carried by the logistics entity to obtain the real-time progress of the waybill task. This real-time progress is then compared with the waybill task planning scheme. For example, the actual time the waybill task reaches a certain path node in the real-time progress is compared with the planned time for that path node in the waybill task planning scheme. Similarly, the actual operation path in the real-time progress is compared with the planned path in the waybill task planning scheme. If the difference between the real-time progress and the waybill task planning scheme exceeds a threshold, the optimization conditions are met, and the waybill task planning scheme is optimized based on the task planning algorithm.

[0099] Changes in the logistics service network can occur, such as changes in weather conditions, road congestion, or the location of logistics stations in the network's area. In such cases, the digital twin model can be adjusted based on real-time logistics information from the network. The adjusted digital twin model can then be used to simulate revised waybill task planning schemes, thereby improving simulation accuracy.

[0100] Among them, the abnormal information reported by the task operator of the waybill includes, for example, damage to the logistics vehicle, change of customer address, road congestion, and discrepancy between the planned route and the actual road.

[0101] In some embodiments, customer-input waybill task reservation information (such as scheduled delivery time and scheduled pickup time) can be obtained; this waybill business reservation information can be used in the process of generating candidate planning routes or in the process of simulating candidate planning routes, so that the waybill business planning route conforms to the waybill business reservation information.

[0102] In this embodiment, an interactive interface can be provided to the customer to obtain the waybill task reservation information input by the user on the interface. During the generation of candidate planning paths, the waybill business reservation information can be input into the corresponding optimization algorithm (such as the aforementioned task planning algorithm, task allocation algorithm, and path planning algorithm), or the waybill business reservation information can be applied to the construction of optimization objectives or constraints in the optimization algorithm. Alternatively, the waybill business reservation information can be used as real-time attribute information of the sub-model corresponding to the logistics customer and input into the digital twin model, so that the digital twin model takes the waybill business reservation information into account during the simulation of candidate task planning schemes.

[0103] Exemplary device

[0104] Accordingly, embodiments of this application also provide a device for determining a waybill task planning scheme, such as... Figure 4 As shown, the waybill task planning scheme determination device 400 provided in this embodiment may include: an acquisition module 410, a simulation module 420, a determination module 430, and a sending module 440.

[0105] The system includes the following modules: Acquisition module 410, which acquires at least one candidate task planning scheme for a waybill task operation object in the logistics service network. The candidate task planning scheme includes: the waybill task to be allocated to the waybill task operation object, the planned path of the waybill task operation object, and the planned time for the waybill task operation object on the planned path. Simulation module 420, which simulates the at least one candidate task planning scheme using a digital twin model corresponding to the logistics service network, obtains simulation results for the at least one candidate task planning scheme. Determination module 430, which determines the waybill task planning scheme for the waybill task operation object based on the simulation results. Sending module 440, which sends the waybill task planning scheme to the waybill task operation object.

[0106] In some embodiments, the digital twin model includes sub-models corresponding to logistics entities in the logistics service network. The sub-models include the following: operation object model corresponding to waybill task operation object, station model corresponding to logistics station, customer model corresponding to logistics customer, and road model corresponding to logistics road.

[0107] In some embodiments, the process of constructing a digital twin model includes: acquiring historical logistics data from a logistics service network; and generating a sub-model based on the historical logistics data.

[0108] In some embodiments, the digital twin model further includes environmental information of the area where the logistics service network is located, and the waybill task planning device further includes: a collection module (not shown in the figure), used to collect real-time logistics data in the logistics service network and real-time environmental information of the area where the logistics service network is located through a data collection device deployed on the logistics entity and / or a data collection device carried by the logistics entity; and an update module (not shown in the figure), used to update the sub-models in the digital twin model and the environmental information in the digital twin model according to the real-time logistics data and the real-time environmental information.

[0109] In some embodiments, at least one candidate task planning scheme is generated through the following process: obtaining task information of unprocessed waybill tasks in the logistics service network and attribute information of logistics entities in the logistics service network; generating at least one candidate task planning scheme based on the task information and attribute information using a task planning algorithm.

[0110] In some embodiments, the task planning algorithm includes a task allocation algorithm and a path planning algorithm. Based on task information and attribute information, the task planning algorithm generates at least one candidate task planning scheme, including: allocating tasks to be processed waybill tasks according to the task information and attribute information using the task allocation algorithm to determine the waybill tasks to be allocated to the waybill task operation objects; and performing path planning for the waybill task operation objects according to the waybill tasks to be allocated to the waybill task operation objects and attribute information using the path planning algorithm to obtain at least one candidate task planning scheme.

[0111] In some embodiments, the waybill task planning device further includes: an optimization module (not shown in the figure), used to optimize the waybill task planning scheme based on the task planning algorithm when scheme optimization is required, so as to adjust the waybill task planning scheme; wherein, the situations requiring scheme optimization include at least one of the following: the degree of difference between the real-time waybill task operation progress of the waybill task operation object and the waybill task planning scheme meets the scheme optimization conditions, the logistics service network changes, and task abnormality information is received from the waybill task operation object.

[0112] The device for determining the waybill task planning scheme provided in this embodiment belongs to the same application concept as the method for determining the waybill task planning scheme provided in the above embodiments of this application. It can execute the method for determining the waybill task planning scheme provided in any of the above embodiments of this application, and has the corresponding functional modules and beneficial effects of the method for determining the waybill task planning scheme. Technical details not described in detail in this embodiment can be found in the specific processing content of the method for determining the waybill task planning scheme provided in the above embodiments of this application, and will not be repeated here.

[0113] It should be understood that the modules in the above device can be implemented by a processor calling software. For example, the device includes a processor connected to a memory containing instructions. The processor calls the instructions stored in the memory to implement any of the above methods or to implement the functions of each unit of the device. The processor can be a general-purpose processor, such as a CPU or microprocessor, and the memory can be internal or external to the device. Alternatively, the units in the device can be implemented as hardware circuits. By designing the hardware circuits, some or all of the unit functions can be implemented. The hardware circuits can be understood as one or more processors. For example, in one implementation, the hardware circuit is an ASIC, and the functions of some or all of the above units are implemented by designing the logical relationships between the components within the circuit. In another implementation, the hardware circuit can be implemented by a PLD, such as an FPGA, which can include a large number of logic gates. The connection relationships between the logic gates are configured through configuration files to implement the functions of some or all of the above units. All units of the above device can be implemented entirely by a processor calling software, entirely by hardware circuits, or partially by a processor calling software with the remaining parts implemented by hardware circuits.

[0114] In this application embodiment, a processor is a circuit with signal processing capabilities. In one implementation, the processor can be a circuit with instruction reading and execution capabilities, such as a CPU, microprocessor, GPU, or DSP. In another implementation, the processor can implement certain functions through the logical relationships of hardware circuits. These logical relationships are fixed or reconfigurable. For example, the processor may be a hardware circuit implemented as an ASIC or PLD, such as an FPGA. In a reconfigurable hardware circuit, the process of the processor loading a configuration document and configuring the hardware circuit can be understood as the processor loading instructions to implement the functions of some or all of the above units. Furthermore, it can also be a hardware circuit designed for artificial intelligence, which can be understood as an ASIC, such as an NPU, TPU, or DPU.

[0115] As can be seen, each unit in the above device can be one or more processors (or processing circuits) configured to implement the above methods, such as: CPU, GPU, NPU, TPU, DPU, microprocessor, DSP, ASIC, FPGA, or a combination of at least two of these processor forms.

[0116] Furthermore, the units in the above devices can be integrated in whole or in part, or they can be implemented independently. In one implementation, these units are integrated together and implemented in the form of a System-on-Chip (SoC). The SoC may include at least one processor for implementing any of the above methods or implementing the functions of the units in the device. The at least one processor may be of different types, such as CPU and FPGA, CPU and artificial intelligence processor, CPU and GPU, etc.

[0117] Exemplary electronic devices

[0118] This application provides an electronic device, see [link to relevant documentation] Figure 5 As shown, the electronic device includes a memory 500 and a processor 510 connected to the memory 500.

[0119] The memory 500 is used to store programs.

[0120] The processor 510 is configured to perform each step of the method for determining any waybill task planning scheme in any of the above embodiments.

[0121] For details on the specific processing procedure of the processor 510 described above, please refer to the description of the above method embodiments. For details on the specific implementation of the processor 510, please refer to the description of the above embodiments.

[0122] Specifically, the aforementioned electronic device may also include: a bus, a communication interface 520, an input device 530, and an output device 540.

[0123] The processor 510, memory 500, communication interface 520, input device 530, and output device 540 are interconnected via a bus. Among them:

[0124] A bus can include a pathway for transmitting information between various components of a computer system.

[0125] The processor 510 can be a general-purpose processor, such as a general-purpose central processing unit (CPU), a microprocessor, etc., or an application-specific integrated circuit (ASIC), or one or more integrated circuits used to control the execution of the program of the present invention. It can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0126] The processor 510 may include a main processor, as well as a baseband chip, modem, etc.

[0127] The memory 500 stores a program that executes the technical solution of this invention, and may also store an operating system and other key business functions. Specifically, the program may include program code, which includes computer operation instructions. More specifically, the memory 500 may include read-only memory (ROM), other types of static storage devices capable of storing static information and instructions, random access memory (RAM), other types of dynamic storage devices capable of storing information and instructions, disk storage, flash memory, etc.

[0128] Input device 530 may include a device for receiving user input data and information, such as an error microphone, keyboard, mouse, camera, scanner, light pen, voice input device, touch screen, pedometer, or gravity sensor.

[0129] Output device 540 may include devices that allow information to be output to a user, such as a speaker, display screen, printer, etc.

[0130] The communication interface 520 may include a device that uses any transceiver to communicate with other devices or communication networks, such as Ethernet, Radio Access Network (RAN), Wireless Local Area Network (WLAN), etc.

[0131] The processor 510 executes the program stored in the memory 500 and calls other devices, which can be used to implement the various steps of the method for determining any waybill task planning scheme provided in the above embodiments of this application.

[0132] This application also proposes a chip, which includes a processor and a data interface. The processor reads and runs a program stored in the memory through the data interface to execute the method for determining the waybill task planning scheme described in any of the above embodiments. For the specific processing procedure and its beneficial effects, please refer to the above embodiments of the method for determining the waybill task planning scheme.

[0133] Exemplary computer program products and storage media

[0134] In addition to the methods and devices described above, embodiments of this application may also be computer program products, which include computer program instructions that, when executed by a processor, cause the processor to perform the steps in the method for determining a waybill task planning scheme according to various embodiments of this application as described in any of the above embodiments of this specification.

[0135] Computer program products can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of this application. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's computing 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.

[0136] Furthermore, embodiments of this application may also be storage media storing a computer program, which is executed by a processor in the steps of the method for determining a waybill task planning scheme according to various embodiments of this application as described in any of the above embodiments of this specification.

[0137] For the foregoing method embodiments, in order to simplify the description, they are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to this application, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0138] It should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For apparatus embodiments, since they are basically similar to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0139] The steps in the methods of the various embodiments of this application can be adjusted, merged, or deleted in order according to actual needs, and the technical features described in each embodiment can be replaced or combined.

[0140] The modules of the apparatus in the various embodiments of this application can be merged, divided, and deleted according to actual needs.

[0141] It should be understood that the disclosed terminals, devices, and methods can be implemented in other ways, given the several embodiments provided in this application. For example, the terminal embodiments described above are merely illustrative. For instance, the division of modules or sub-modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple sub-modules or modules may be combined or integrated into another module, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or modules, and may be electrical, mechanical, or other forms.

[0142] The modules or submodules described as separate components may or may not be physically separate. The components that constitute a module or submodule may or may not be physical modules or submodules; that is, they may be located in one place or distributed across multiple network modules or submodules. Some or all of the modules or submodules can be selected to achieve the purpose of this embodiment's solution, depending on actual needs.

[0143] Furthermore, the functional modules or sub-modules in the various embodiments of this application can be integrated into one processing module, or each module or sub-module can exist physically separately, or two or more modules or sub-modules can be integrated into one module. The integrated modules or sub-modules described above can be implemented in hardware or in the form of software functional modules or sub-modules.

[0144] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0145] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software unit executed by a processor, or a combination of both. The software unit can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0146] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0147] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for determining a waybill task planning scheme, characterized in that, include: Obtain at least one candidate task planning scheme for a waybill task operation object in a logistics service network, wherein the candidate task planning scheme includes: a waybill task to be allocated to the waybill task operation object, a planned path for the waybill task operation object, and a planned time for the waybill task operation object on the planned path; The at least one candidate task planning scheme is simulated using a digital twin model corresponding to the logistics service network, and the simulation results corresponding to the at least one candidate task planning scheme are obtained. Based on the simulation results, a waybill task planning scheme is determined for the waybill task operation object, and the waybill task planning scheme is sent to the waybill task operation object.

2. The method for determining the waybill task planning scheme according to claim 1, characterized in that, The digital twin model includes sub-models corresponding to logistics entities in the logistics service network. The sub-models include the following: operation object model corresponding to the waybill task operation object, station model corresponding to the logistics station, customer model corresponding to the logistics customer, and road model corresponding to the logistics road.

3. The method for determining the waybill task planning scheme according to claim 2, characterized in that, The construction process of the digital twin model includes: Obtain historical logistics data from the logistics service network; The sub-model is generated based on the historical logistics data.

4. The method for determining the waybill task planning scheme according to claim 2, characterized in that, The digital twin model also includes environmental information about the area where the logistics service network is located, and the waybill task planning method further includes: Real-time logistics data and real-time environmental information of the area where the logistics service network is located are collected by data acquisition devices deployed on the logistics entity and / or carried by the logistics entity. Based on the real-time logistics data and the real-time environmental information, the sub-models in the digital twin model and the environmental information in the digital twin model are updated.

5. The method for determining the waybill task planning scheme according to any one of claims 1-4, characterized in that, The at least one candidate task planning scheme is generated through the following process: Obtain task information of pending waybill tasks in the logistics service network and attribute information of logistics entities in the logistics service network; Based on the task information and the attribute information, at least one candidate task planning scheme is generated using a task planning algorithm.

6. The method for determining the waybill task planning scheme according to claim 5, characterized in that, The task planning algorithm includes a task allocation algorithm and a path planning algorithm. The step of generating at least one candidate task planning scheme based on the task information and the attribute information using the task planning algorithm includes: Based on the task information and the attribute information, the task allocation algorithm is used to allocate the pending waybill tasks to determine the waybill tasks to be assigned to the waybill task operation objects. Based on the waybill tasks assigned to the waybill task operation object according to the plan and the attribute information, the path planning algorithm is used to perform path planning for the waybill task operation object to obtain at least one candidate task planning scheme.

7. The method for determining the waybill task planning scheme according to claim 5, characterized in that, After sending the waybill task planning scheme to the waybill task operation object, the method further includes: If the solution needs to be optimized, the waybill task planning solution is optimized based on the task planning algorithm to adjust the waybill task planning solution. The situations requiring scheme optimization include at least one of the following: the difference between the real-time progress of the waybill task and the waybill task planning scheme meets the scheme optimization conditions; the logistics service network changes; or the waybill task operation object receives task anomaly information.

8. An electronic device, characterized in that, Including memory and processor; The memory is connected to the processor and is used to store programs; The processor is used to implement the method for determining a waybill task planning scheme as described in any one of claims 1-7 by running a program in the memory.

9. A storage medium, characterized in that... The storage medium stores a computer program, which, when executed by a processor, implements the method for determining a waybill task planning scheme as described in any one of claims 1-7.

10. A computer program product, characterized in that, It includes computer program instructions, which, when executed by a processor, cause the processor to perform the method for determining a waybill task planning scheme as described in any one of claims 1-7.

Images