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CN-121981437-A - Method, device and electronic equipment for determining visiting store order

CN121981437ACN 121981437 ACN121981437 ACN 121981437ACN-121981437-A

Abstract

The embodiment of the application provides a method for determining visiting stores and a visiting store sequence, which comprises the steps of obtaining store information, carrying out multiple screening on the obtained stores to obtain a daily visiting candidate pool, carrying out quantitative evaluation on each store in the daily visiting candidate pool, at least scoring the visiting value and urgency of the stores to obtain the comprehensive score of each store, carrying out constraint condition screening on each store according to the comprehensive score of each store in the daily visiting candidate pool to obtain a first store of the stores, carrying out geographic position distribution rationality check on the first store of the stores to obtain a second store of the stores, and determining the second store of the stores as daily visiting stores. According to the embodiment of the application, the screened stores are determined to be visited every day, and the store list which is finally visited on the same day is selected from the candidate store pool through a series of screening and decision logics, so that the total expected value visited on the same day is maximized on the premise of meeting the constraint.

Inventors

  • ZHANG LONG
  • LIU LIJIA
  • ZHOU JUN

Assignees

  • 先蜂购(上海)网络科技有限公司

Dates

Publication Date
20260505
Application Date
20251225

Claims (15)

  1. 1. A method of determining a visit to a store, the method comprising: acquiring store information and a visit policy rule base, and performing multiple screening on the acquired stores according to the visit policy rule base to obtain a daily visit candidate pool; Carrying out quantitative evaluation on each store in the daily visit candidate pool, wherein the quantitative evaluation at least comprises scoring of visit value and urgency of the store to obtain comprehensive score of each store; According to the comprehensive score of each store in the daily visit candidate pool and the information of personnel to be visited, constraint condition screening is carried out on each store, and a store first set with high score and meeting constraints is obtained; And carrying out geographic position distribution rationality check on the first store set to obtain a second store set which accords with constraint, is reasonable in geographic position and has high score, and determining the second store set as a daily visiting store.
  2. 2. The method of claim 1, wherein the store information at least includes key attributes, sales targets or market information, the visit policy rule base includes at least a fixed visit frequency of each store and a last successful visit date of each store, and the multiple screening of the acquired stores according to the visit policy rule base to obtain a daily visit candidate pool includes: according to the fixed visit frequency of each store in the visit strategy rule library and the last successful visit date of each store, initially screening all first stores which are required to be visited due; Monitoring key attributes of the stores, and obtaining a second store corresponding to the key attributes meeting preset triggering conditions; Identifying a visit store with potentially high value as a third store based on the sales goals or market intelligence; And obtaining a daily visit candidate pool according to the first store, the second store and the third store.
  3. 3. The method of claim 2, wherein the key attributes of the store include at least one or more of sales volume, inventory emphasis, promotional program, and urgent customer complaints or service requests, Correspondingly, the triggering condition at least comprises one or more of sales volume falling beyond a threshold value, important inventory falling below an early warning line, newly initiated sales promotion activities and receipt of urgent customer complaints or service requests.
  4. 4. A method according to any one of claims 1-3, wherein said quantitatively evaluating each store in said daily call candidate pool comprises scoring at least a store call value and urgency to obtain a composite score for each store, comprising: Calculating basic layering score, strategy coincidence degree score, recent performance score, opportunity/risk indicator score, history contribution and potential score and last visit effect score of each store, setting weight for each score, and weighting calculation to obtain store visit value score and urgency score; The store visit value score is calculated according to a basic layering score, a strategy coincidence score, a recent performance score, a history contribution and potential score, and the urgency score is calculated according to an opportunity/risk indicator score and a visit effect score.
  5. 5. The method of claim 4, wherein the personnel information to be visited includes at least a maximum number of stores visited daily and a maximum working time of the visit on the same day, and the screening the constraint condition of each store according to the comprehensive score of each store in the candidate daily visit pool and the personnel information to be visited to obtain a first set of stores with high scores and meeting the constraint comprises: Setting estimated visit time length for each store, and calculating total visit time length of the maximum store number according to the maximum store number; When the longest working time length of the daily visit is longer than the total time length, determining each store in the visit candidate pool as a first set; And when the longest working time of daily visit is less than or equal to the total time, sorting the stores from large to small according to the comprehensive score of each store in the daily visit candidate pool, sequentially calculating the visit time of each store, accumulating the visit time, and determining all stores with the comprehensive score greater than the accumulated stores as a first set when the visit time of the accumulated stores is longer than the longest working time of daily visit.
  6. 6. The method of claim 5, wherein said performing a geographic location distribution plausibility check on said first set of stores results in a constrained, geographically sound, and high scoring second set of stores, comprising: judging whether the total distance of the store with the distance comprehensive score within a certain range exceeds a reasonable range or not; if yes, selecting stores with relatively concentrated geographic positions from stores in the first range; Or will be closer to the store in the first range than the store farther in the first range; Or will be closer to the store in the first range and the composite score is lower, replacing the store farther in the first range and the composite score is higher; The stores or replaced stores in the geographic location relative set are determined to be a second set.
  7. 7. A method of determining the order of visiting stores provided based on the method of determining visiting stores of claims 1-6, the method comprising: and determining the optimal visiting store sequence by adopting a path optimization algorithm for the second set of stores which are in line with the constraint, reasonable in geographic position and high in score.
  8. 8. The method of claim 7, further comprising obtaining a business time window for each store, an estimated time of visit for each store, and a longest time of day visit; correspondingly, the method for determining the optimal visiting store order by adopting the path optimization algorithm comprises the following steps: And taking the total travel distance or total travel time as an optimization target, taking the longest working time visited by the person to be visited on the same day, the business time window of each store and the estimated visiting time of each store as constraint conditions, and outputting the visiting store sequence.
  9. 9. The method of claim 8, further comprising obtaining geographic coordinates of each store in the second set, a current day departure address and a desired end address of a person to be visited, and traffic information; correspondingly, the method for determining the optimal visiting store order by adopting the path optimization algorithm comprises the following steps: The method comprises the steps of taking the total travel distance or the total travel time as an optimization target, taking the longest working time of visiting by a person to be visited on the same day, the business time window of each store, the expected visiting time of each store as constraint conditions, the longest working time of visiting by the person to be visited on the same day, the business time window of each store and the expected visiting time of each store, outputting the visiting store sequence, the expected arrival time and the expected departure time of each store, and the expected total travel distance and the total travel time.
  10. 10. The method of claim 8, wherein the path optimization algorithm alternatively employs a Clarke-write saving algorithm, a simulated annealing algorithm, a tabu search algorithm, a genetic algorithm, and an ant colony optimization algorithm.
  11. 11. The device for determining the visiting store is characterized by comprising an acquisition module, a screening module, a calculation module and a visiting store generation module; The acquisition module is used for acquiring store information and visit policy rules; the screening module is used for carrying out multiple screening on the acquired stores according to the visit policy rule base to obtain a daily visit candidate pool; the computing module is used for quantitatively evaluating each store in the daily visit candidate pool obtained by the screening module, and the computing module at least comprises scoring the visit value and urgency of the store to obtain the comprehensive score of each store; The visit store generation module is used for screening constraint conditions of each store according to the comprehensive score and the information of people to be visited of each store in the daily visit candidate pool to obtain a store first set which is high in score and accords with constraint, carrying out geographic position distribution rationality check on the store first set to obtain a store second set which is reasonable in geographic position and accords with constraint and is high in score, and determining the store second set as a daily visit store.
  12. 12. The device for determining the visiting store sequence is characterized by comprising an acquisition module, a screening module, a calculation module, a visiting store generation module and a visiting store path planning module; the acquisition module is used for acquiring store information; The screening module is used for carrying out multiple screening on the acquired stores to obtain a daily visit candidate pool; the computing module is used for quantitatively evaluating each store in the daily visit candidate pool obtained by the screening module, and the computing module at least comprises scoring the visit value and urgency of the store to obtain the comprehensive score of each store; the visit store generation module is used for screening constraint conditions of each store according to the comprehensive score of each store in the daily visit candidate pool to obtain a store first set with high score and meeting the constraint; performing geographic position distribution rationality check on the first store set to obtain a second store set which accords with constraint, is reasonable in geographic position and has high score, and determining the second store set as a daily visit store; the visit store path planning module is used for determining the optimal visit store sequence by adopting a path optimization algorithm for the second set of stores which are in line with constraint, reasonable in geographic position and high in score.
  13. 13. An electronic device, the electronic device comprising: A processor, an encryptor, a memory and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of determining visiting stores according to any of claims 1-6 or the method of determining visiting store order according to any of claims 7-10 when executing the computer program.
  14. 14. A machine-readable storage medium having stored thereon executable instructions that when executed by a machine cause the method of determining visiting stores according to any of claims 1-6 or the method of determining visiting store order according to any of claims 7-10 to be implemented.
  15. 15. A computer program product containing instructions which, when run on a computer, cause the computer to perform the steps of the method of determining a store of any of the preceding claims 1-6 or the method of determining a store order of any of the claims 7-10.

Description

Method, device and electronic equipment for determining visiting store order Technical Field The embodiment of the application relates to the technical field of computers, in particular to a method, a device and electronic equipment for determining visiting stores and visiting store sequences. Background The end point sales business of the beer industry has its uniqueness and challenges. Each salesman typically needs to manage a large number of stores and to guarantee a high daily call volume. Conventional sales visits typically rely on the personal experience of the business person and are manually planned based on a simple geographical division. At present, some CRM or SFA systems can provide functions of customer information management, visit record tracking and the like, but are limited in the aspects of planning of a visit plan, route optimization and task priority ranking, are mainly generated based on preset fixed rules or simple routes, such as linear distance ranking or regional clustering, lack of comprehensive consideration on complex factors, inaccurate planned routes and more invalid mileage. Moreover, by the adoption of the method based on experience judgment, historical data and real-time performance of stores cannot be fully utilized for dynamic adjustment, so that resource allocation is unreasonable, important stores or high-potential stores are ignored, and visiting efficiency is low. Disclosure of Invention According to the method, the device and the electronic equipment for determining the visiting stores and the visiting store sequence, the visiting efficiency is effectively improved by comprehensively considering store information, personnel information to be visited and visiting strategies. In a first aspect, an embodiment of the present application provides a method for determining a visiting store, the method including: acquiring store information and a visit policy rule base, and performing multiple screening on the acquired stores according to the visit policy rule base to obtain a daily visit candidate pool; Carrying out quantitative evaluation on each store in the daily visit candidate pool, wherein the quantitative evaluation at least comprises scoring of visit value and urgency of the store to obtain comprehensive score of each store; According to the comprehensive score of each store in the daily visit candidate pool and the information of personnel to be visited, constraint condition screening is carried out on each store, and a store first set with high score and meeting constraints is obtained; And carrying out geographic position distribution rationality check on the first store set to obtain a second store set which accords with constraint, is reasonable in geographic position and has high score, and determining the second store set as a daily visiting store. In a second aspect, an embodiment of the present application provides a method for determining a visiting store order, the method including: and determining the optimal visiting store sequence by adopting a path optimization algorithm for the second set of stores which are in line with the constraint, reasonable in geographic position and high in score. In a third aspect, an embodiment of the present application provides an apparatus for determining a visiting store, where the apparatus includes an acquisition module, a screening module, a calculation module, and a visiting store generation module; The acquisition module is used for acquiring store information and a visit policy rule base; the screening module is used for carrying out multiple screening on the acquired stores according to the visit policy rule base to obtain a daily visit candidate pool; the computing module is used for quantitatively evaluating each store in the daily visit candidate pool obtained by the screening module, and the computing module at least comprises scoring the visit value and urgency of the store to obtain the comprehensive score of each store; The visit store generation module is used for screening constraint conditions of each store according to the comprehensive score and the information of people to be visited of each store in the daily visit candidate pool to obtain a store first set which is high in score and accords with constraint, carrying out geographic position distribution rationality check on the store first set to obtain a store second set which is reasonable in geographic position and accords with constraint and is high in score, and determining the store second set as a daily visit store. In a fourth aspect, an embodiment of the present application provides an apparatus for determining a visiting store order, where the apparatus includes an acquisition module, a screening module, a calculation module, a visiting store generation module, and a visiting store path planning module; The acquisition module is used for acquiring store information and a visit policy rule base; the screening module is used for carrying out