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CN-121998309-A - Self-marketing and crowdsourcing power integration distribution method of E-commerce order and electronic equipment

CN121998309ACN 121998309 ACN121998309 ACN 121998309ACN-121998309-A

Abstract

The application provides a self-marketing and crowdsourcing power set distribution method of an electronic commerce order and electronic equipment. The method comprises the steps of obtaining an order set to be distributed, a commodity set, inventory data, capacity data and time window data of an e-commerce platform, constructing a cost optimization model of the order set to be distributed, determining a decision variable and a target constraint condition of the cost optimization model, solving the decision variable of the cost optimization model based on the order set to be distributed, the commodity set, the inventory data, the capacity data and the target constraint condition to obtain a target variable value of the decision variable, and outputting a target distribution strategy of the order set to be distributed based on the target variable value of the decision variable. The application can improve the overall timeliness of the order set, reduce the overall distribution cost and reduce the problem of order timeliness default.

Inventors

  • Jiang Dapei
  • YANG WEI
  • HU SHAOLONG
  • LIN SHUPENG

Assignees

  • 深圳信息职业技术大学

Dates

Publication Date
20260508
Application Date
20251231

Claims (10)

  1. 1. A self-camping and crowdsourcing capability set allocation method for an e-commerce order, the method comprising: Acquiring an order set to be distributed, a commodity set, inventory data, transport capacity data and time window data of an e-commerce platform, wherein the transport capacity data comprise front transport capacity data of all front bins, self-operating capacity data of all distribution centers and crowdsourcing transport capacity data, the inventory data comprise the inventory of all commodities of all front bins, and the commodity set is a set of commodities related to the order set to be distributed; constructing a cost optimization model of the to-be-allocated order set, wherein the cost optimization model comprises a prepositive capacity cost parameter, a self-operating capacity cost parameter and a crowdsourcing capacity cost parameter, and the crowdsourcing capacity cost parameter comprises crowdsourcing transportation cost, crowdsourcing capacity cost and crowdsourcing time cost; determining decision variables and target constraints of the cost optimization model, wherein the decision variables comprise a prepositioned route variable, a self-contained route variable, a crowdsourcing membership variable, a self-contained transit variable, a crowdsourcing transit variable, a prepositioned starting time variable, a self-contained starting time variable and a crowdsourcing starting time variable, and the target constraints comprise a time constraint based on the time window data, a first constraint of prepositioned capacity, a second constraint of self-contained capacity and a third constraint of crowdsourcing capacity; solving a decision variable of the cost optimization model based on the to-be-allocated order set, the commodity set, the inventory data, the capacity data and the target constraint condition to obtain a target variable value of the decision variable; and outputting the target allocation strategy of the to-be-allocated order set based on the target variable value of the decision variable.
  2. 2. The method of claim 1, wherein solving the decision variables of the cost optimization model based on the to-be-allocated order set, the commodity set, the inventory data, the capacity data, and the target constraint conditions to obtain target variable values of the decision variables comprises: Clustering distribution centers of the to-be-distributed order sets to obtain distribution center constraint conditions of the to-be-distributed order sets; Acquiring a target variable value of the self-contained route variable, a target variable value of a crowdsourcing membership variable, a target variable value of a self-contained start time variable and a target variable value of a crowdsourcing start time variable based on the distribution center constraint, the commodity set, the inventory data, the capacity data, the time constraint, the second constraint and the third constraint; Determining a target variable value of the lead route variable, a target variable value of a self-contained transit variable, a target variable value of a crowd-sourced transit variable, and a target variable value of the lead start time variable based on the target variable value of the self-contained route variable, the target variable value of the crowd-sourced membership variable, the target variable value of the self-contained start time variable, the target variable value of the crowd-sourced start time variable, and the first constraint condition.
  3. 3. The method of claim 1, wherein outputting the target allocation policy for the set of orders to be allocated based on the target variable value of the decision variable comprises: determining an initial allocation strategy of the to-be-allocated order set based on the target variable value of the decision variable; Acquiring a distribution strategy before terminal optimization at this time based on the initial distribution strategy; removing the distribution strategy before the terminal optimization to obtain a distribution strategy after the terminal removal; Performing terminal optimization processing on the terminal-removed distribution strategy based on the first constraint condition, the second constraint condition and the third constraint condition to obtain the terminal-optimized distribution strategy; and outputting the target allocation strategy according to the allocation strategy after the terminal is optimized.
  4. 4. The method for allocating self-camping and crowdsourcing power sets of e-commerce orders according to claim 3, wherein the removing the pre-end allocation policy to obtain the post-end allocation policy comprises: determining a current end removal order of the distribution strategy before the current end optimization; and removing the distribution strategy before the end optimization based on the end removal order to obtain the distribution strategy after the end removal.
  5. 5. The method of self-camping and crowdsourcing capacity set allocation for an e-commerce order of claim 4, wherein said determining the current end-removal order for the current end-pre-optimization allocation policy comprises: Determining a target front-end service bin with the least service commodity quantity from front-end service bins indicated by the front-end allocation strategy, wherein the allocation strategy is used for indicating the front-end service bin, front-end service capacity, self-service capacity, crowdsourcing service capacity of the to-be-allocated order, and service orders of the front-end service bin, loading orders of the front-end service capacity, loading orders of self-service capacity and loading orders of crowdsourcing service capacity of the front-end service bin; or determining the target front-end service capacity with the minimum loading capacity from the front-end service capacity indicated by the front-end allocation strategy for the end optimization, and taking the loading order corresponding to the target front-end service capacity as the end removal order; Or determining a target self-service capacity with the minimum loading capacity from the self-service capacity indicated by the distribution strategy before the terminal optimization, wherein the loading order corresponding to the target self-service capacity is used as the terminal removal order; Or randomly selecting the self-service operation capacity from the self-service operation capacity indicated by the distribution strategy before the terminal optimization, taking the loading order corresponding to the randomly selected self-service operation capacity as the terminal removal order; or randomly selecting the crowdsourcing service capacity from the crowdsourcing service capacity indicated by the distribution strategy before the terminal optimization, and taking the loading order corresponding to the randomly selected crowdsourcing service capacity as the terminal removal order; or acquiring a first order cluster with the commodity similarity between orders greater than a first preset similarity threshold value from the orders to be distributed; Or obtaining a second order cluster with the space similarity between orders larger than a second preset similarity threshold value from the orders to be distributed, and taking each order of the second order cluster as the end removal order.
  6. 6. The method for self-camping and crowdsourcing capacity set allocation of e-commerce orders according to claim 4, wherein the performing terminal optimization processing on the terminal post-removal allocation policy based on the first constraint condition, the second constraint condition and the third constraint condition to obtain the terminal post-optimization allocation policy includes: Detecting a first end service capacity which enables the insertion cost of the end removal order to be the lowest from the distribution strategy after the end removal according to the first constraint condition, the second constraint condition and the third constraint condition; If the first terminal service capacity exists in the terminal removal distribution strategy, adding the terminal removal order into the first terminal service capacity to finish terminal optimization processing of the terminal removal distribution strategy, so as to obtain the terminal optimization distribution strategy; or if the first terminal service capacity does not exist in the terminal removal distribution strategy, creating a second terminal service capacity to be added into the terminal pre-optimization distribution strategy, and adding the terminal removal order into the first terminal service capacity to complete terminal optimization processing of the terminal removal distribution strategy and obtain the terminal post-optimization distribution strategy.
  7. 7. The method of self-camping and crowdsourcing capacity set allocation for an e-commerce order of claim 3, wherein outputting the target allocation policy according to the post-end-of-time optimized allocation policy comprises: Determining a distribution strategy before the transfer optimization according to the distribution strategy after the terminal optimization; removing the allocation strategy before the transfer optimization to obtain an allocation strategy after the transfer is removed; carrying out transfer optimization treatment on the transfer post-removal allocation strategy based on the first constraint condition, the second constraint condition and the third constraint condition to obtain the transfer post-optimization allocation strategy; and outputting the target allocation strategy according to the allocation strategy after the transfer optimization.
  8. 8. The method for allocating self-camping and crowdsourcing capacity sets of e-commerce orders according to claim 7, wherein the removing the allocation policy before the current diversion optimization to obtain the allocation policy after the current diversion removal comprises: Determining the removing capacity from all terminal service capacities indicated by the distribution strategy before the transfer optimization, wherein the terminal service capacities comprise self-service capacity and crowdsourcing service capacity; And removing the allocation strategy before the transfer optimization based on the current order group with the current removal capacity to obtain an allocation strategy after the transfer removal, wherein each order in the current order group belongs to the same front capacity, and the total number of commodity categories in the current order group is smaller than the total number of preset categories.
  9. 9. The method for self-camping and crowdsourcing capacity set allocation of e-commerce orders according to claim 8, wherein the performing a transfer optimization process on the transfer post-removal allocation policy based on the first constraint condition, the second constraint condition and the third constraint condition to obtain the transfer post-optimization allocation policy includes: detecting a first front-end service capacity which enables the insertion cost of the current order group to be lowest from the distribution strategy after the current transfer and removal according to the first constraint condition, the second constraint condition and the third constraint condition; And if the first front service capacity exists in the distribution strategy after the transfer removal, adding the order group into the first front service capacity to finish the transfer optimization processing of the distribution strategy after the transfer removal, so as to obtain the distribution strategy after the transfer optimization.
  10. 10. An electronic device comprising a processor and a memory, the memory having stored therein a computer program, the processor executing the method of self-camping and crowdsourcing power set allocation for an e-commerce order of any one of claims 1 to 9 when invoking the computer program in the memory.

Description

Self-marketing and crowdsourcing power integration distribution method of E-commerce order and electronic equipment Technical Field The application relates to the technical field of digital scheduling processing, in particular to a self-marketing and crowdsourcing power integration component of an electronic commerce order and electronic equipment. Background With the development of electronic commerce and instant retail, sellers continue to increase the demand for time-efficient services such as "arrive on the day" and "arrive on the next day" for distribution services. However, the flat peak order quantity and the peak order quantity of the e-commerce platform have larger fluctuation, and the self-operating capacity is difficult to simultaneously consider the flat peak cost and the peak aging guarantee, so that the problems of high distribution service performance pressure in the peak period, idle capacity in the flat peak period and resource waste are caused. In the related art, a crowdsourcing distribution mode is introduced, namely, self-operating capacity and crowdsourcing operation capacity are cooperated to finish distribution service, so that the problems of high performance pressure, idle capacity and resource waste of the distribution service in peak period are solved to a certain extent. However, the inventor finds that, in the actual research and development process, since the self-service capacity and the crowdsourcing capacity in the related technology are usually distributed by orders relatively independently, the order data lacks uniform distribution and synchronization constraint between the self-service capacity and the crowdsourcing capacity, and finally the overall timeliness of the order data is lower and the overall distribution cost is higher. Disclosure of Invention The application provides a self-marketing and crowdsourcing capacity set distribution method of an electronic commerce order and electronic equipment, which can improve the overall timeliness of the order set, reduce the overall distribution cost and reduce the problem of order timeliness and default. In a first aspect, the present application provides a method of self-camping and crowd-sourced performance set distribution for an e-commerce order, the method comprising: acquiring an order set to be distributed, a commodity set, inventory data, transport capacity data and time window data of an e-commerce platform, wherein the transport capacity data comprises front transport capacity data of each front bin Self-operating capacity data of each distribution centerAnd crowd sourcing performance dataThe inventory data comprises the inventory of all commodities in all pre-bins, and the commodity set is a set of commodities related to the to-be-allocated order set; constructing a cost optimization model of the to-be-allocated order set, wherein the cost optimization model comprises a prepositive capacity cost parameter, a self-operating capacity cost parameter and a crowdsourcing capacity cost parameter, and the crowdsourcing capacity cost parameter comprises crowdsourcing transportation cost, crowdsourcing capacity cost and crowdsourcing time cost; Determining decision variables and target constraints of the cost optimization model, wherein the decision variables comprise leading-route variables Route variables of self-campingCrowd-sourced route variablesCrowd-sourced membership variableTransport variable of self-nutrientCrowd-sourced transport variablesPre-start time variableStart time variable of self-nutrientAnd crowd-sourced start time variableThe target constraints include a time constraint based on the time window data, a first constraint of a pre-capacity, a second constraint of a self-capacity, and a third constraint of a crowdsourcing capacity; solving a decision variable of the cost optimization model based on the to-be-allocated order set, the commodity set, the inventory data, the capacity data and the target constraint condition to obtain a target variable value of the decision variable; and outputting the target allocation strategy of the to-be-allocated order set based on the target variable value of the decision variable. In a second aspect, the present application further provides an electronic device, where the electronic device includes a processor and a memory, where the memory stores a computer program, and when the processor invokes the computer program in the memory, the self-camping and crowdsourcing operation integration allocation method of any one of the e-commerce orders provided by the present application is executed. In a third aspect, the present application also provides a computer readable storage medium having stored thereon a computer program to be loaded by a processor for executing the self-camping and crowdsourcing operation set allocation method of an e-commerce order. In the application, according to the first aspect, the self-operating capacity and the crowdsourcing capacity can be con