A method, device and medium for distributing logistics data packets.

Abstract

This invention relates to the field of data distribution technology, and in particular to a method, device, and medium for distributing logistics data packets. The method involves initially allocating logistics tasks based on a first priority, forming a first task queue by combining an initial task queue with the predicted completion time, marking tasks to be optimized, and generating reference numbers. A second priority is calculated by combining the first priority and the predicted completion time, dynamically optimizing target service providers. By bidirectionally updating the service provider's task queue, the predicted completion time and tasks to be optimized are updated synchronously, ensuring that new timeout risks are promptly captured after logistics task transfers, thus guaranteeing the dynamic effectiveness of the optimization process. The optimization of logistics service providers and the distribution of data packets are progressively advanced using reference numbers, gradually eliminating the timeout risk for each logistics service provider, improving the matching accuracy between logistics data packets and logistics service providers, and consequently improving the service quality of the logistics service providers.

Description

Technical Field

[0001] This invention relates to the field of data distribution technology, and in particular to a method, device and medium for distributing logistics data packets. Background Technology

[0002] The distribution of logistics data packages is a key link connecting logistics tasks and logistics service providers, and is of great significance for improving logistics efficiency, ensuring service quality, optimizing resource allocation, and promoting industry intelligence.

[0003] In existing technologies, intermediary service platforms typically allocate logistics tasks and distribute corresponding logistics data packages based on single attributes such as distance or historical cooperation relationships, neglecting the multi-dimensional needs of logistics tasks and the dynamic adaptation to the capabilities of logistics service providers. Furthermore, logistics task allocation relies on a fixed allocation relationship between the task and an individual logistics service provider, lacking a dynamic adjustment mechanism and failing to adapt to the timely delivery of a large number of tasks. For example, if a logistics service provider experiences overload, causing some tasks to time out, and the predicted completion time of new tasks exceeds the deadline, the intermediary service platform may still distribute logistics data packages to that service provider based on a fixed allocation relationship, leading to delivery delays.

[0004] Therefore, improving the matching accuracy between logistics data packages and logistics service providers, and thus improving the service quality of logistics service providers, has become an urgent problem to be solved. Summary of the Invention

[0005] To address the aforementioned technical problems, the present invention provides a method for distributing logistics data packets, which includes the following steps:

[0006] S1, based on the task attributes of each logistics task, obtains the first priority between each logistics task and each logistics service provider.

[0007] S2, based on the first priority and the initial task queue corresponding to each logistics service provider, obtain the first task queue corresponding to each logistics service provider, the predicted completion time returned by each logistics service provider for each logistics task in the first task queue, and several tasks to be optimized and reference sequence numbers corresponding to each logistics service provider.

[0008] S3, initialize reference number j=1.

[0009] S4. For the logistics service provider with reference number j, based on the first priority between the first task to be optimized and each logistics service provider, and the predicted completion time of the first task to be optimized for each logistics service provider, obtain the target service provider corresponding to the first task to be optimized.

[0010] S5, based on the target service provider corresponding to the first task to be optimized, update the first task queue corresponding to the current logistics service provider and the current target service provider, the predicted completion time returned for each logistics task in the updated first task queue, and the corresponding number of tasks to be optimized.

[0011] S6. Repeat step S4 until the number of tasks to be optimized for the current logistics service provider is 0.

[0012] S7, update j=j+1, repeat step S4 until j=N, obtain the updated first task queue for each logistics service provider, where N is the total number of logistics service providers.

[0013] S8 distributes the data packets corresponding to each logistics task through the intermediate service platform based on the updated first task queue of each logistics service provider.

[0014] The present invention also provides a non-transitory computer-readable storage medium storing at least one instruction or at least one program, wherein the at least one instruction or at least one program is loaded and executed by a processor to implement the above-described method for distributing logistics data packets.

[0015] The present invention also provides an electronic device, including a processor and the aforementioned non-transitory computer-readable storage medium.

[0016] The present invention has at least the following beneficial effects: By comprehensively calculating the multi-dimensional matching degree between logistics tasks and logistics service providers based on task attributes and generating the first priority, the logistics tasks are initially allocated according to the first priority, and the initial tasks of the logistics service providers and the newly allocated logistics tasks are integrated to form the first task queue. The tasks to be optimized are marked based on the predicted completion time, and reference numbers are generated by sorting according to the number of tasks to be optimized, so that the allocation of logistics tasks combines the historical load of logistics service providers, can accurately locate the timeout risk, and at the same time clarifies the optimization priority, providing a data basis for subsequent optimization; By initializing the reference number to 1, the optimization process starts from the logistics service provider with the largest number of tasks to be optimized, ensuring that resources are preferentially invested in the most prominent risk links; By comprehensively calculating the second priority based on the first priority and the predicted completion time, the optimal logistics service provider is selected as the target service provider, so that the transfer of tasks to be optimized takes into account both adaptability and time feasibility, and realizes dynamic adjustment on the basis of the initial allocation, improving the probability of on-time completion of logistics tasks; By updating the task queues of the current logistics service provider and the target service provider bidirectionally, synchronously updating the predicted completion time and re-identifying the tasks to be optimized, the data after the transfer of logistics tasks is always kept accurate, and the newly generated timeout risk is captured in time, ensuring the dynamic effectiveness of the optimization process; By repeatedly processing the tasks to be optimized of the current logistics service provider until the number is 0, and then advancing to the next logistics service provider according to the reference number until all logistics service providers complete the optimization, the timeout risk of each logistics service provider is gradually eliminated, and it is ensured that the resource allocation matches the severity of the risk, avoiding incomplete optimization or resource waste; By determining the final target service provider based on the optimized task queue and accurately distributing data packets by the intermediate platform, the previous optimization results are transformed into actual execution instructions, ensuring that the logistics tasks are started according to the optimal plan, improving the matching accuracy between logistics data packets and logistics service providers, and thus improving the service quality of logistics service providers. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained according to these drawings.

[0018] Figure 1 It is a flowchart of a method for distributing logistics data packets provided in Embodiment 1 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It is understood that, where appropriate, the terms used to distinguish similar objects can be interchanged so that the invention can also be implemented in other embodiments besides the illustrated or described embodiments. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products, or devices.

[0021] Example 1

[0022] This embodiment provides a method for distributing logistics data packets. The method includes the following steps: Figure 1 As shown:

[0023] S1, based on the task attributes of each logistics task, obtains the first priority between each logistics task and each logistics service provider.

[0024] Among these, task attributes are key characteristic information inherent in the logistics task itself, used to clarify the specific requirements of the logistics task and provide demand data support for matching the capabilities of logistics service providers. For example, the task attribute of "cold chain products" determines that a logistics service provider with cold chain transportation capabilities must be matched.

[0025] The first priority is the result of ranking all logistics service providers from highest to lowest suitability for a single logistics task. This ranking provides an initial decision-making basis for task allocation, avoiding mismatches caused by random allocation or single-dimensional decision-making. Specifically, the higher the ranking, the higher the match between the logistics service provider and the logistics task, and the higher the priority it will be selected in the initial allocation stage.

[0026] In one specific implementation, the task attributes include the coordinates of the sending and receiving locations, the type of goods, and the task time limit. S1 includes the following steps:

[0027] S11, obtain the service capability indicators corresponding to each logistics service provider. The service capability indicators include service coverage, type support range, average task completion time and historical service quality score.

[0028] S12, match the task attributes of each logistics task with the service capability indicators of each logistics service provider to obtain a set of matching degrees between each logistics task and each logistics service provider. The set of matching degrees includes location matching degree, type matching degree, and time limit matching degree.

[0029] S13. Based on the preset weight set, the matching degree set between each logistics task and each logistics service provider, and the historical service quality score corresponding to each logistics service provider, obtain the first priority between each logistics task and each logistics service provider. The preset weight set includes the preset weights corresponding to the location matching degree, type matching degree, and time limit matching degree, respectively.

[0030] The specific task attributes include the coordinates of the sending and receiving locations (the spatial distance between the sending and receiving locations), the type of goods (e.g., fragile goods, cold chain goods), and the task time limit (the latest time required to complete the logistics task, e.g., 24-hour delivery, 48-hour delivery), which are used to clarify the specific requirements of the logistics task.

[0031] The service capability indicators for logistics service providers include service coverage (deliverable areas), type support range (types of goods that can be transported), average task completion time (timeliness requirements that can be met), and historical service quality score (the quality of past logistics task completion, such as on-time rate and damage rate). By systematically collecting service capability data from logistics service providers, the matching of logistics tasks with logistics service providers has a clear supply-side basis, avoiding blind allocation due to incomplete information.

[0032] By comparing task attributes with the capabilities of logistics service providers one by one, the matching degree of location, type, and time limit is obtained. This multi-dimensional matching quantifies the suitability of logistics tasks and logistics service providers, avoiding one-sided decisions caused by a single dimension.

[0033] Specifically, location matching degree measures the degree of fit between the coordinates of the origin and destination of a logistics task and the service coverage of the logistics service provider. It can be obtained by determining whether the coordinates of the origin and destination are within the distance covered by the logistics service provider. If the coordinates of the origin or destination are not within the service coverage of the logistics service provider, the corresponding location matching degree is 0; if both the origin and destination coordinates are within the service coverage of the logistics service provider, the corresponding location matching degree is 1.

[0034] Type matching degree measures the degree of match between the type of goods in a logistics task and the type range supported by the logistics service provider. The type support range includes core types, non-core types, outsourced types, and types outside the supported range. If the type of goods in the logistics task belongs to a core type within the type support range of the logistics service provider, the corresponding location matching degree is 1. If the type of goods in the logistics task belongs to a non-core type within the type support range of the logistics service provider, the corresponding location matching degree is 0.7. If the type of goods in the logistics task belongs to an outsourced type within the type support range of the logistics service provider, the corresponding location matching degree is 0.4. If the type of goods in the logistics task belongs to a type outside the supported range of the type support range of the logistics service provider, the corresponding location matching degree is 0.

[0035] Time-limit matching degree is used to measure whether the timeliness capability of a logistics service provider matches the time limit required by the logistics task. Specifically, based on the origin coordinates and cargo type of the logistics task, the average task completion time t2 of the logistics service provider for that task is obtained. Then, combined with the corresponding task time limit t1, the time-limit matching degree P between the logistics task and the logistics service provider is obtained. t =(t1-t2) / t2.

[0036] Historical service quality score refers to a comprehensive score that quantifies the service performance of a logistics service provider based on past logistics task data. It is a sum of scores for on-time delivery rate, goods integrity rate, customer complaint rate, and exception handling efficiency, and is used to characterize the logistics service provider's fulfillment capability, reliability, and customer satisfaction in actual operation, serving as the basis for logistics task allocation.

[0037] For example, the first priority between the logistics task and the logistics service provider is Y1=F×(α1×P). d +α2×P x +α3×P t ), where F is the historical service quality score corresponding to the logistics service provider, and P d P represents the degree of location matching between the logistics task and the logistics service provider. x P represents the degree of type matching between the logistics task and the logistics service provider. t To determine the time-limit matching degree between the logistics task and the logistics service provider, α1 is the preset weight corresponding to the location matching degree, α2 is the preset weight corresponding to the type matching degree, and α3 is the preset weight corresponding to the time-limit matching degree. The specific values ​​of α1, α2, and α3 can be set by the implementer according to the actual situation. For example, based on the importance, α1 can be set to 0.3, α2 to 0.4, and α3 to 0.3.

[0038] The above-mentioned multi-dimensional matching analysis, which considers location matching degree, type matching degree, time limit matching degree and historical service quality score, quantifies the suitability of logistics tasks and logistics service providers, avoids one-sided decision-making caused by a single dimension, and improves the rationality and reliability of the first priority.

[0039] S2, based on the first priority and the initial task queue corresponding to each logistics service provider, obtain the first task queue corresponding to each logistics service provider, the predicted completion time returned by each logistics service provider for each logistics task in the first task queue, and several tasks to be optimized and reference sequence numbers corresponding to each logistics service provider.

[0040] In one specific embodiment, S2 includes the following steps:

[0041] S21, For any logistics task, the logistics service provider with the highest first priority corresponding to the current logistics task is determined as the first candidate service provider corresponding to the current logistics task.

[0042] S22, obtain the initial task queue corresponding to each logistics service provider, wherein the initial task queue includes several initial tasks.

[0043] S23. Based on the initial task queue corresponding to each logistics service provider and the several logistics tasks corresponding to each logistics service provider as the first candidate service provider, obtain the first task queue corresponding to each logistics service provider, the predicted completion time returned by each logistics service provider for each logistics task in the first task queue, and the several tasks to be optimized and reference sequence numbers corresponding to each logistics service provider.

[0044] The initial task queue is a set of unfinished logistics tasks that the logistics service provider has already accepted before this logistics task allocation. It is used to reflect the current load status of the logistics service provider and is the basic data when integrating new logistics tasks.

[0045] The first task queue is an ordered set of logistics tasks for each logistics service provider, formed by merging its initial task queue with the newly allocated logistics tasks based on the first priority, and sorting them by task start time. It serves as the formal logistics task list received by the logistics service provider, is the basis for the logistics service provider to calculate and predict the completion time, and is also the benchmark queue for subsequent optimization.

[0046] Predicted completion time is the estimated time from start to finish for each logistics task by the logistics service provider based on its own resources (e.g., vehicles, staff) and the order of logistics tasks in the first task queue. It is used to determine whether the logistics task can be completed on time.

[0047] The tasks to be optimized are logistics tasks in the first task queue whose predicted completion time is later than the task deadline, i.e., logistics tasks with the risk of timeout. They are used to clarify the specific targets for subsequent optimization and avoid blind adjustments without a clear target.

[0048] The reference number is a sorting number assigned to all logistics service providers based on the number of tasks to be optimized, in descending order. This number is used to determine the priority of subsequent optimizations, ensuring that resources are allocated first to the logistics service providers with the most prominent issues. For example, the logistics service provider with the most tasks to be optimized would have a reference number of 1.

[0049] As described above, by integrating the initial task list with newly assigned logistics tasks, the task queues of logistics service providers include both historical loads and new demands, achieving comprehensive logistics task data and avoiding allocation imbalances caused by incomplete information. Identifying tasks to be optimized based on predicted completion times provides clear objectives for subsequent optimization. Assigning reference sequence numbers to logistics service providers ensures a sequential optimization process for multiple providers, preventing inefficiencies caused by disordered processing and ensuring that high-load logistics service providers are prioritized for adjustment. Generating an ordered first task queue allows logistics service providers to plan their execution order according to unified rules, guaranteeing the accuracy of subsequent predicted completion times and the consistency of logistics task adjustments.

[0050] In one specific embodiment, S23 includes the following steps:

[0051] S231, based on the initial task queue corresponding to each logistics service provider and the several logistics tasks corresponding to each logistics service provider as the first candidate service provider, obtain the first task queue corresponding to the current logistics service provider.

[0052] S232, send the task attributes of each initial task and each logistics task in the first task queue corresponding to each logistics service provider to the corresponding logistics service provider.

[0053] S233, receive the predicted completion time returned by each logistics service provider for each logistics task in the first task queue.

[0054] S234, for any logistics task corresponding to any logistics service provider, if the predicted completion time of the current logistics task is later than the corresponding task deadline, then the current logistics task is identified as the task to be optimized for the current logistics service provider.

[0055] S235, based on the number of tasks to be optimized for each logistics service provider, sort all logistics service providers in descending order of quantity, and determine the reference number for each logistics service provider.

[0056] In one specific embodiment, the initial task queue also includes the task start time corresponding to each initial task, and the task attributes of each logistics task also include the task start time. S231 includes the following steps:

[0057] S2311, For any logistics service provider, each initial task in the initial task queue corresponding to the current logistics service provider, as well as each logistics task corresponding to the current logistics service provider as the first candidate service provider, are all treated as tasks to be screened.

[0058] S2312, based on the task start time corresponding to each task to be screened, sort all the tasks to be screened in order from earliest to latest, and obtain the first task queue corresponding to the current logistics service provider.

[0059] Each logistics service provider calculates the estimated completion time for each logistics task based on the order of the first task queue and its own resources, and provides feedback to form a quantitative time assessment result, which serves as a direct basis for judging whether a logistics task is overdue. For example, by comparing the "predicted completion time of logistics task A is 26 hours" returned by the logistics service provider with the "24-hour time limit" of logistics task A, it is used to identify whether it is a task that needs to be optimized.

[0060] The above-mentioned methods, by merging historical and newly assigned logistics tasks and sorting them by task start time, clarify the execution order of logistics tasks for logistics service providers. This provides an orderly calculation basis for the accuracy of subsequent completion time predictions, avoiding time estimation deviations caused by chaotic task order. By fully synchronizing task attributes, logistics service providers can calculate and predict completion times based on comprehensive information, avoiding time estimation errors caused by missing information and improving data accuracy. By collecting real-time estimated times from logistics service providers, the feasibility of logistics task execution can be objectively assessed, providing quantitative data for subsequent risk identification and avoiding reliance on subjective experience to judge timeout risks. By marking tasks to be optimized according to the rule that the predicted completion time is greater than the task deadline, subsequent optimization has clear targets, avoiding wasting resources on logistics tasks that do not need adjustment and improving the targeting of optimization. By sorting by the number of tasks to be optimized, the intermediate service platform can allocate optimization resources according to the severity of the problem, avoiding inefficiency caused by disordered processing, ensuring that high-risk logistics service providers are adjusted first, and improving overall optimization efficiency.

[0061] S3, initialize reference number j=1.

[0062] Specifically, by initializing the reference number j to 1, the starting point of the subsequent optimization process is clarified, that is, the allocation and optimization of logistics tasks begins with the logistics service provider with reference number 1, providing an initial benchmark for the iterative process of subsequent steps.

[0063] S4. For the logistics service provider with reference number j, based on the first priority between the first task to be optimized corresponding to the current logistics service provider and each logistics service provider, and the predicted completion time of the first task to be optimized for each logistics service provider, obtain the target service provider corresponding to the first task to be optimized. The first task to be optimized corresponding to the current logistics service provider is the logistics task with the earliest task start time among all the tasks to be optimized corresponding to the current logistics service provider.

[0064] In one specific embodiment, S4 includes the following steps:

[0065] S41. Based on the first priority between the first task to be optimized corresponding to the current logistics service provider and each logistics service provider, and the predicted completion time of the first task to be optimized for each logistics service provider, obtain the second priority between the first task to be optimized and each logistics service provider.

[0066] S42, determine the logistics service provider corresponding to the highest second priority of the current first task to be optimized as the target service provider of the current first task to be optimized.

[0067] The second priority is a recalculated priority ranking result for tasks to be optimized, taking into account their initial compatibility with each logistics service provider and the time feasibility of each logistics service provider executing the logistics task. This ranking serves as the direct decision-making basis for transferring logistics tasks.

[0068] The target service provider is selected as the logistics service provider to undertake the current task to be optimized after being sorted by the second priority. This is used to clarify the final destination of the logistics task and serves as the core result of the logistics task transfer.

[0069] For example, based on the corresponding first priority Y1, predicted completion time T, and task deadline t1, and combining the first preset weight coefficient β1 for the first priority dimension and the second preset weight coefficient β2 for the time dimension, a new priority score is generated as the second priority Y2 = β1 × Y1 + β2 × (t1 - T) / t1. The specific values ​​of β1 and β2 can be set by the implementer according to the actual situation; for example, based on the importance, β1 can be set to 0.6 and β2 to 0.4.

[0070] The above approach prioritizes tasks with the earliest start times, reducing backlogged logistics tasks and minimizing their dwell time, thus preventing cascading delays caused by task accumulation. By integrating adaptability and time feasibility calculations as a second priority, logistics task allocation considers not only the static capabilities of logistics service providers but also their dynamic load, allowing for dynamic adjustments based on the initial allocation and improving its rationality. Furthermore, by clearly defining target service providers, the approach provides clear direction for subsequent logistics task transfers, task queues, predicted completion times, and updates to tasks awaiting optimization.

[0071] S5, based on the target service provider corresponding to the first task to be optimized, update the first task queue corresponding to the current logistics service provider and the current target service provider, the predicted completion time returned for each logistics task in the updated first task queue, and the corresponding number of tasks to be optimized.

[0072] In one specific embodiment, S5 includes the following steps:

[0073] S51, if the target service provider corresponding to the first task to be optimized is the same as the current logistics service provider, then the first task queue corresponding to the current logistics service provider is determined as the updated first task queue corresponding to the current logistics service provider.

[0074] S52, if the target service provider corresponding to the first task to be optimized is different from the current logistics service provider, then remove the first task to be optimized from the first task queue corresponding to the current logistics service provider and obtain the updated first task queue corresponding to the current logistics service provider.

[0075] S53. Based on the task start time of the first task to be optimized and the task start time of each initial task and each logistics task in the first task queue of the current target service provider, sort the first task to be optimized and each initial task and each logistics task in the first task queue of the current target service provider in order from earliest to latest, and obtain the updated first task queue of the current target service provider.

[0076] S54, send the task attributes of each initial task and each logistics task in the updated first task queue corresponding to the current logistics service provider to the current logistics service provider, and update the predicted completion time returned by the current logistics service provider for each logistics task in the updated first task queue.

[0077] S55, based on any logistics task in the updated first task queue corresponding to the current logistics service provider, if the updated predicted completion time of the current logistics task is later than the corresponding task deadline, then the current logistics task is identified as the task to be optimized for the current logistics service provider.

[0078] S56, send the task attributes of each initial task and each logistics task in the updated first task queue corresponding to the current target service provider to the current target service provider, and update the predicted completion time returned by the current target service provider for each logistics task in the updated first task queue.

[0079] S57. Based on any logistics task in the updated first task queue corresponding to the current target service provider, if the updated predicted completion time of the current logistics task is later than the corresponding task deadline, then the current logistics task is identified as the task to be optimized corresponding to the current target service provider.

[0080] The above-mentioned methods, by distinguishing whether the target service provider is the current logistics service provider, ensure that the transfer of logistics tasks is both accurate and efficient, avoiding meaningless queue adjustments and reducing redundant system operations; by reordering the queue according to the task start time, ensure that the execution order of the target service provider's logistics tasks conforms to time logic, providing a basis for the accuracy of predicted completion time and avoiding timeliness assessment errors caused by disordered order; by synchronously updating the predicted completion time, the intermediate service platform always has the latest timeliness data, avoiding subsequent optimization based on outdated information and ensuring the timeliness of decision-making; by re-identifying tasks to be optimized, new risks caused by the transfer of logistics tasks are captured in a timely manner, ensuring that all potential problems are included in the subsequent optimization scope and improving the robustness of the overall process; by bidirectionally updating the data of the current logistics service provider and the target service provider, the task queues, predicted times, and lists of tasks to be optimized of both are completely synchronized, avoiding optimization deviations caused by data inconsistencies.

[0081] S6. Repeat step S4 until the number of tasks to be optimized for the current logistics service provider is 0.

[0082] S7, update j=j+1, repeat step S4 until j=N, obtain the updated first task queue for each logistics service provider, where N is the total number of logistics service providers.

[0083] If the number of tasks to be optimized for the current logistics service provider is 0, it means that all logistics tasks of the current logistics service provider can be completed on time, and then the optimization of logistics tasks for the next logistics service provider can begin.

[0084] By updating j=j+1, j increments by 1 after each logistics service provider optimization is completed, ensuring that the optimization order is consistent with the risk severity of the logistics service providers, in line with the principle of solving the most urgent problems first, and achieving efficient use of resources.

[0085] The above process involves continuous iteration until the number of tasks to be optimized is zero, ensuring that all timeout risks of each logistics service provider are identified and adjusted to avoid lingering problems. Processing is done in order of reference number to prioritize resources for high-risk logistics service providers and minimize the overall logistics delay rate.

[0086] S8 distributes the data packets corresponding to each logistics task through the intermediate service platform based on the updated first task queue of each logistics service provider.

[0087] In one specific embodiment, S8 includes the following steps:

[0088] S81, based on the updated first task queue of each logistics service provider, determine the updated target service provider corresponding to each logistics task.

[0089] S82 distributes the data packets corresponding to each logistics task to the corresponding updated target service provider through the intermediate service platform.

[0090] The intermediary service platform is a third-party information exchange system connecting cargo owners, e-commerce platforms, and other logistics task initiators with logistics service providers. It possesses functions such as data storage, logistics task matching, and information push. It acts as a hub for data packet distribution, responsible for transmitting logistics task information from the system end to the logistics service provider end, ensuring the security, timeliness, and accuracy of information transmission. For example, the intermediary platform can encrypt logistics task data packets to prevent information leakage, while simultaneously providing real-time feedback on the logistics service provider's reception status.

[0091] As described above, by updating the target service provider based on the optimized task queue, the data packet distribution result is completely consistent with the optimization decision, ensuring that the optimal allocation scheme is implemented and avoiding the optimization results being negated by invalid transmission.

[0092] As described above, the multi-dimensional matching degree between the logistics task and the logistics service provider is calculated through comprehensive task attributes, and the first priority is generated. The logistics task is initially allocated according to the first priority, and the initial tasks of the logistics service provider and the newly allocated logistics tasks are integrated to form the first task queue. The tasks to be optimized are marked based on the predicted completion time, and the reference numbers are generated by sorting according to the number of tasks to be optimized, so that the logistics task allocation not only combines the historical load of the logistics service provider, but also can accurately locate the overtime risk, and at the same time clarify the optimization priority, providing a data basis for subsequent optimization; By initializing the reference number to 1, the optimization process starts from the logistics service provider with the largest number of tasks to be optimized, ensuring that resources are preferentially invested in the most prominent risk link; By calculating the second priority through comprehensive the first priority and the predicted completion time, the optimal logistics service provider is selected as the target service provider, so that the transfer of the tasks to be optimized takes into account both adaptability and time feasibility, and realizes dynamic adjustment on the basis of the initial allocation, improving the probability of on-time completion of logistics tasks; By updating the task queues of the current logistics service provider and the target service provider bidirectionally, synchronously updating the predicted completion time and re-identifying the tasks to be optimized, the data after the transfer of logistics tasks always remains accurate, timely capturing the newly generated overtime risk, and ensuring the dynamic effectiveness of the optimization process; By repeatedly processing the tasks to be optimized of the current logistics service provider until the number is 0, and then advancing to the next logistics service provider according to the reference number until all logistics service providers complete the optimization, the overtime risk of each logistics service provider is gradually eliminated, and the resource allocation is ensured to match the severity of the risk, avoiding incomplete optimization or resource waste; By determining the final target service provider based on the optimized task queue and accurately distributing the data packet by the intermediate platform, the previous optimization results are transformed into actual execution instructions, ensuring that the logistics task starts according to the optimal plan, improving the matching accuracy between the logistics data packet and the logistics service provider, and further improving the service quality of the logistics service provider.

[0093] Embodiment 2

[0094] Embodiment 2 of the present invention provides a non-transitory computer-readable storage medium, which can be set in an electronic device to store at least one instruction or at least one program related to a method in the method embodiment. The at least one instruction or the at least one program is loaded and executed by the processor to implement the distribution method of the logistics data packet provided in the above embodiment.

[0095] Embodiment 3

[0096] Embodiment 3 of the present invention provides an electronic device, which includes a processor and the non-transitory computer-readable storage medium in Embodiment 2 of the present invention.

[0097] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for distributing logistics data packets, characterized in that, The method includes the following steps: S1, based on the task attributes of each logistics task, obtain the first priority between each logistics task and each logistics service provider; S2, based on the first priority and the initial task queue corresponding to each logistics service provider, obtain the first task queue corresponding to each logistics service provider, the predicted completion time returned by each logistics service provider for each logistics task in the first task queue, and several tasks to be optimized and reference numbers corresponding to each logistics service provider. The initial task queue includes several initial tasks. The step of obtaining the first task queue corresponding to each logistics service provider, the predicted completion time returned by each logistics service provider for each logistics task in the first task queue, and several tasks to be optimized and reference numbers corresponding to each logistics service provider based on the first priority and the initial task queue corresponding to each logistics service provider includes: The logistics service provider with the highest first priority for each logistics task is determined as the first candidate service provider for each logistics task. Based on the initial task queue corresponding to each logistics service provider and the several logistics tasks corresponding to each logistics service provider as the first candidate service provider, the first task queue corresponding to each logistics service provider is obtained. Send the task attributes of each initial task and each logistics task in the first task queue corresponding to each logistics service provider to the corresponding logistics service provider; Receive the predicted completion time returned by each logistics service provider for each logistics task in the first task queue; For any logistics task corresponding to any logistics service provider, if the predicted completion time of the current logistics task is later than the corresponding task deadline, then the current logistics task is identified as a task to be optimized for the current logistics service provider. Based on the number of tasks to be optimized for each logistics service provider, all logistics service providers are sorted in descending order of quantity, and a reference number is determined for each logistics service provider. S3, initialize reference index j=1; S4. For the logistics service provider with reference number j, based on the first priority between the first task to be optimized and each logistics service provider, and the predicted completion time of the first task to be optimized for each logistics service provider, obtain the target service provider corresponding to the first task to be optimized. S5, based on the target service provider corresponding to the first task to be optimized, update the first task queue corresponding to the current logistics service provider and the current target service provider, return the predicted completion time for each logistics task in the updated first task queue, and the corresponding number of tasks to be optimized. S6. Repeat step S4 until the number of tasks to be optimized for the current logistics service provider is 0. S7, update j=j+1, repeat step S4 until j=N, obtain the updated first task queue for each logistics service provider, where N is the total number of logistics service providers; S8 distributes data packets corresponding to each logistics task through an intermediate service platform based on the updated first task queue of each logistics service provider. The intermediate service platform has data storage, logistics task matching, and information push functions. As the hub for data packet distribution, it transmits logistics task information from the system end to the logistics service provider end, so that the data packet distribution result is consistent with the optimization decision.

2. The method for distributing logistics data packets according to claim 1, characterized in that, The task attributes include the coordinates of the origin and destination, the type of goods, and the task time limit. S1 includes the following steps: S11, obtain the service capability indicators corresponding to each logistics service provider. The service capability indicators include service coverage, type support range, average task completion time and historical service quality score. S12, match the task attributes of each logistics task with the service capability indicators of each logistics service provider to obtain a set of matching degrees between each logistics task and each logistics service provider. The set of matching degrees includes location matching degree, type matching degree and time limit matching degree. S13. Based on the preset weight set, the matching degree set between each logistics task and each logistics service provider, and the historical service quality score corresponding to each logistics service provider, obtain the first priority between each logistics task and each logistics service provider. The preset weight set includes the preset weights corresponding to the location matching degree, type matching degree, and time limit matching degree, respectively.

3. The method for distributing logistics data packets according to claim 1, characterized in that, The initial task queue also includes the task start time corresponding to each initial task, and the task attributes of each logistics task also include the task start time. The step of obtaining the first task queue corresponding to each logistics service provider based on the initial task queue corresponding to each logistics service provider and the several logistics tasks corresponding to each logistics service provider as the first candidate service provider includes: S2311, For any logistics service provider, each initial task in the initial task queue corresponding to the current logistics service provider, and each logistics task corresponding to the current logistics service provider as the first candidate service provider, are all regarded as tasks to be screened. S2312, based on the task start time corresponding to each task to be screened, sort all the tasks to be screened in order from earliest to latest, and obtain the first task queue corresponding to the current logistics service provider.

4. The method for distributing logistics data packets according to claim 3, characterized in that, S4 includes the following steps: S41. Based on the first priority between the first task to be optimized corresponding to the current logistics service provider and each logistics service provider, and the predicted completion time of the first task to be optimized for each logistics service provider, obtain the second priority between the first task to be optimized and each logistics service provider. S42, determine the logistics service provider corresponding to the highest second priority of the current first task to be optimized as the target service provider of the current first task to be optimized.

5. The method for distributing logistics data packets according to claim 4, characterized in that, S5 includes the following steps: S51, if the target service provider corresponding to the first task to be optimized is the same as the current logistics service provider, then the first task queue corresponding to the current logistics service provider is determined as the updated first task queue corresponding to the current logistics service provider. S52, if the target service provider corresponding to the first task to be optimized is different from the current logistics service provider, then remove the first task to be optimized from the first task queue corresponding to the current logistics service provider and obtain the updated first task queue corresponding to the current logistics service provider. S53, based on the task start time of the first task to be optimized and the task start time of each initial task and each logistics task in the first task queue of the current target service provider, sort the first task to be optimized and each initial task and each logistics task in the first task queue of the current target service provider in order from early to late, and obtain the updated first task queue of the current target service provider. S54, send the task attributes of each initial task and each logistics task in the updated first task queue corresponding to the current logistics service provider to the current logistics service provider, and update the predicted completion time returned by the current logistics service provider for each logistics task in the updated first task queue; S55, based on any logistics task in the updated first task queue corresponding to the current logistics service provider, if the updated predicted completion time of the current logistics task is later than the corresponding task time limit, then the current logistics task is identified as the task to be optimized corresponding to the current logistics service provider. S56, send the task attributes of each initial task and each logistics task in the updated first task queue corresponding to the current target service provider to the current target service provider, and update the predicted completion time returned by the current target service provider for each logistics task in the updated first task queue. S57. Based on any logistics task in the updated first task queue corresponding to the current target service provider, if the updated predicted completion time of the current logistics task is later than the corresponding task deadline, then the current logistics task is identified as the task to be optimized corresponding to the current target service provider.

6. The method for distributing logistics data packets according to claim 1, characterized in that, S8 includes the following steps: S81, based on the updated first task queue of each logistics service provider, determine the updated target service provider corresponding to each logistics task; S82, the data packets corresponding to each logistics task are distributed to the corresponding updated target service provider through the intermediate service platform.

7. A non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores at least one instruction or at least one program, characterized in that, The at least one instruction or the at least one program segment is loaded and executed by the processor to implement the logistics data packet distribution method as described in any one of claims 1-6.

8. An electronic device, characterized in that, Includes a processor and the non-transitory computer-readable storage medium as described in claim 7.

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