CN-121981386-A - Integrated management method and system for interlocking store by adopting modularized design

CN121981386ACN 121981386 ACN121981386 ACN 121981386ACN-121981386-A

Abstract

The invention relates to the technical field of store data management, and particularly discloses a method and a system for comprehensively managing a linked store by adopting a modularized design, wherein the method calculates the operation influence weight of each functional component aiming at a target store by constructing an atomization functional component library and a store history data feature library; dividing all the functional components into a plurality of functional groups according to a historical weight distribution mode, determining macroscopic weight characteristic center points of all the groups, calculating comprehensive suitability and generating a self-adaptive functional component set based on weight data of a target store and macroscopic characteristics of the groups to which the functional components belong, and finally loading and instantiating to form a runtime management module. The invention also discloses a concrete method for calculating the weight based on the historical data comparison analysis, a comprehensive fit degree calculation method based on the spatial clustering and the deviation analysis, and the expanded functions of real-time dynamic adjustment, future trend prediction, high-performance store configuration migration and the like. The intelligent and personalized dynamic configuration of the chain store management system is realized, and the management efficiency and the accuracy are obviously improved.

Inventors

HUANG ZHIYU
Hu Jiejing
QU XIN
YAN GUANGYING
ZHAO FENG
XIONG RUI
SHI BIN
Liao Datian
WANG QINGPENG
HE ZHENBO

Assignees

广东彩惠智能科技有限公司
广东聚彩智能科技股份有限公司

Dates

Publication Date: 20260505
Application Date: 20260120

Claims (10)

1. The utility model provides a comprehensive management method of a chain store adopting a modularized design, which is characterized by comprising the following steps: An atomization function component library and a store history data feature library are built, wherein the atomization function component library stores a plurality of independently packaged function components, and the store history data feature library stores a large number of attribute data, efficiency data and function component identification sets which are associated with the efficiency data and take effect in a historical store; aiming at a target store, acquiring current store attribute data and historical efficiency data; calculating the operation influence weight of each function component in the atomization function component library on the target store based on the current store attribute data and the historical efficacy data of the target store in combination with a store historical data feature library; Dividing all the functional components in the atomization functional component library into a plurality of functional groups according to weight distribution modes shown in a store history data feature library, and determining a macroscopic weight feature center point for each functional group; Calculating the comprehensive adaptation degree between the target store and each functional component based on the operation influence weight of the target store on each functional component and the macroscopic weight characteristic center point of the functional group to which each functional component belongs; Screening out functional components from the atomization functional component library according to comprehensive fit degree to generate a self-adaptive functional component set of a target store; In the operating environment of the management system, the function components in the adaptive function component set are loaded and instantiated to form a runtime management module of the target store.
2. The method for integrated management of interlocking stores in a modular design according to claim 1, wherein calculating the operation impact weight of each function component in the atomized function component library on the target store based on the current store attribute data and the historical performance data of the target store in combination with the store historical data feature library comprises: extracting historical efficiency data of a target store in a preset historical period; According to the current store attribute data and the historical efficiency data of the target store, screening a reference store subset which is similar in attribute data and comparable in historical efficiency data mode from a store historical data feature library; For each functional component to be evaluated in the atomized functional component library, performing the following operations: identifying, in the reference store subset, a first store group in which the functional component under evaluation is enabled for a historical period, and a second store group in which the functional component under evaluation is not enabled; Calculating data distribution difference values between the first store group and the second store group in each performance dimension based on historical performance data sequences of the first store group and the second store group in all performance dimensions; And calculating to obtain the operation influence weight of the functional component to be evaluated on the target store by a weighted aggregation mode based on the numerical value of the historical performance data of the target store in each performance dimension and the data distribution difference value of the first store group and the second store group in the corresponding performance dimension.
3. The method of claim 1, wherein dividing all of the functional components in the library of atomized functional components into a plurality of functional groups according to a weight distribution pattern represented in the library of historic data features, and determining a macroscopic weight feature center point for each functional group comprises: Extracting weight vectors of all historical stores in the store history data feature library to all functional components in the atomization functional component library; Mapping the ownership vector to a feature space formed by key efficiency dimensions or dimension reduction features to obtain a corresponding ownership vector point; in the feature space, carrying out cluster analysis based on the spatial distribution of weight vector points of all historical stores to form a plurality of cluster clusters, wherein each cluster corresponds to a functional component set characterized by weight vectors with close spatial positions and is used as a functional group; and calculating the center point of all weight vector points in each cluster, and taking the center point as the macroscopic weight characteristic center point of the corresponding functional group.
4. The method for integrated management of interlocking stores in a modular design according to claim 1, wherein calculating the integrated fitness between the target store and each functional component based on the operation influence weight of the target store on each functional component and the macroscopic weight feature center point of the functional group to which each functional component belongs comprises: combining the operation influence weights of the target store on each functional component into weight vectors, and mapping the weight vectors into a feature space to obtain target store points; determining the function group to which the target store point belongs or is nearest in the feature space; Calculating a deviation vector between a target store point and a macroscopic weight characteristic center point of the belonging or nearest functional group; for the current functional component in the atomized functional component library, the following operation steps are performed: Extracting the operation influence weight of the current functional component; Extracting a central point component value of a coordinate axis corresponding to the current functional component from a macroscopic weight characteristic central point of a functional group to which the current functional component belongs; Extracting component values of corresponding coordinate axes of the current functional component from the deviation vector to serve as deviation vector component values; and carrying out weighted fusion on the operation influence weight of the current functional component, the corresponding central point component value and the corresponding deviation vector component value to obtain the comprehensive adaptation degree of the current functional component and the target store.
5. The method for integrated management of interlocking stores in a modular design according to claim 1, further comprising, after generating the set of adaptive function components for the target store: Analyzing co-occurrence frequency and cooperative gain effect of any two functional components in the self-adaptive functional component set in a store history data feature library; Based on the co-occurrence frequency and the cooperative gain effect, generating the dependency strength and the execution sequence rule between the functional components; the dependency strength and the execution sequence rule are injected into a runtime management module for optimizing call logic and data flow between functional components.
6. The method for integrated management of interlocking stores in a modular design of claim 1, further comprising the step of dynamically adjusting the set of adaptive function components based on the stream of real-time operational events: Monitoring and converging operation event streams of a target store in real time through a data interface of a runtime management module; Triggering recalculation of operation influence weights of related functional components in the atomization functional component library when a specific type of operation event is identified; According to the recalculated operation influence weight, the comprehensive adaptation degree of the related functional components is recalculated by combining the latest functional group division and macroscopic weight characteristics; And comparing the recalculated comprehensive adaptation degree with the currently loaded self-adaptive function component set, generating and sending a component hot loading or hot unloading instruction to the runtime management module.
7. The method of claim 1, wherein the specific types of operational events include marketing campaign events, peak to valley events, inventory anomalies, and equipment status events.
8. The method for integrated management of interlocking stores in a modular design according to claim 1, further comprising the step of pre-configuring functional components based on future operational trend predictions: Training a store operating state prediction model based on historical performance data of a target store and external time sequence associated data, wherein the external time sequence associated data comprises seasonal indexes, holiday identifications and business district activity schedules; predicting predicted performance data of the target store in a plurality of performance dimensions within a future target period by using a store operation state prediction model; Calculating a predicted overall suitability of each function component in the atomized function component library for a future target period of the target store based on the current store attribute data of the target store and the predicted performance data over a plurality of performance dimensions; Generating a preconfigured function component suggestion set facing to a future target period according to the prediction comprehensive fitness; In the current runtime management module, pre-loaded resources are allocated for the function components in the pre-configured function component suggestion set, or pre-configured prompts are pushed to management system users.
9. The method for integrated management of interlocking stores in a modular design according to any one of claims 1-8, further comprising the step of performing a migration configuration of functional components based on a high performance store configuration mode: Screening out high-performance store groups which are better than a threshold value in a preset efficacy dimension from a store history data feature library, and taking the high-performance store groups as a marker post store set; Analyzing point location distribution of all stores in the marker post store set in a feature space, and identifying association rules among core function groups and groups forming a high-performance configuration mode; packaging association rules among core function groups and the groups into a movable marker post configuration strategy package; when configuring a target store, calculating the matching degree between the current store attribute data and the historical efficiency data of the target store and the mark post configuration strategy package; if the matching degree is higher than the migration threshold, carrying out strategic correction on the current self-adaptive function component set of the target store according to the target pole configuration strategy package, wherein the correction mode comprises the steps of improving the comprehensive adaptation degree of the function component marked as a core in the target pole configuration strategy package in the self-adaptive function component set, or directly marking the function component marked as a core in the target pole configuration strategy package and not contained in the current self-adaptive function component set, and adding the function component into the self-adaptive function component set; And forming a runtime management module of the target store based on the corrected self-adaptive function component set.
10. A chain store integrated management system employing a modular design, comprising: The basic library management module is used for constructing an atomization function component library and a store history data feature library, wherein the atomization function component library is stored with a plurality of independently packaged function components, and the store history data feature library is stored with a large number of historical store attribute data, efficacy data and function component identification sets which are associated with the efficacy data and take effect; The store data acquisition module is used for acquiring current store attribute data and historical efficiency data aiming at a target store; the weight calculation module is used for calculating the operation influence weight of each function component in the atomization function component library on the target store based on the current store attribute data and the historical efficiency data of the target store and combining the store historical data feature library; The group analysis module is used for dividing all the functional components in the atomization functional component library into a plurality of functional groups according to the weight distribution mode shown in the store history data feature library, and determining a macroscopic weight feature center point for each functional group; The adaptation degree calculation module is used for calculating the comprehensive adaptation degree between the target store and each functional component based on the operation influence weight of the target store on each functional component and the macroscopic weight characteristic center point of the functional group to which each functional component belongs; The component screening module is used for screening out functional components from the atomization functional component library according to comprehensive proper matching degree to generate a self-adaptive functional component set of a target store; And the runtime management module is used for loading and instantiating the functional components in the adaptive functional component set to form a runtime management module of the target store.

Description

Integrated management method and system for interlocking store by adopting modularized design Technical Field The invention relates to the technical field of store data management, in particular to a method and a system for comprehensively managing a linked store by adopting a modularized design. Background In the business states of chain retail, catering and the like, enterprises generally deploy a unified information management system for realizing large-scale and standardized operation. In the prior art, such systems mostly adopt a one-piece integrated software architecture or a fixed function module combination mode. Specifically, a system provider or business headquarters will predefine a standardized set of management functions, such as unified inventory, member management, cashing and reporting modules, and deploy them to all stores. Although the uniformity of the basic business flow is guaranteed, the management logic is static and stiff in nature, and depends on preset business rules and fixed authority templates, so that the personalized and dynamic management requirements of different stores due to differences of business state types, scale, regional markets, development stages and the like can not be deeply adapted. The technical defects of the prior art are that firstly, the system function configuration is disjointed with the real operation requirement of a store. The traditional management system cannot intelligently analyze and evaluate the actual influence degree of each functional component (such as refined inventory early warning and specific marketing tools) on the business efficiency of the store based on the actual operation efficiency data of the store, so that the functional configuration is blind and resources are mismatched. Second, there is a lack of dynamic adaptive system reconfiguration capability. When the operation state of a store changes (such as entering a sales promotion season and changing a passenger flow mode) or excellent co-store experience needs to be referred, the conventional system cannot automatically diagnose the requirement and dynamically adjust the function combination through a scientific algorithm model according to real-time or historical data, but relies on manual experience to carry out secondary development or parameter adjustment, and has lag response and high cost. This ultimately results in a stiff and heavy chain of enterprise management systems that are difficult to support the agile operating and fine management needs of the enterprise in complex and diverse market environments. Therefore, the invention provides a method and a system for comprehensively managing interlocking stores by adopting a modularized design. Disclosure of Invention The invention provides a comprehensive management method and system for a chain store by adopting a modularized design, which is innovatively characterized by constructing a set of intelligent configuration method for a data-driven, quantifiable and self-adaptive chain store management system. The invention provides a comprehensive management method of a interlocking store adopting a modularized design, which comprises the following steps: An atomization function component library and a store history data feature library are built, wherein the atomization function component library stores a plurality of independently packaged function components, and the store history data feature library stores a large number of attribute data, efficiency data and function component identification sets which are associated with the efficiency data and take effect in a historical store; aiming at a target store, acquiring current store attribute data and historical efficiency data; calculating the operation influence weight of each function component in the atomization function component library on the target store based on the current store attribute data and the historical efficacy data of the target store in combination with a store historical data feature library; Dividing all the functional components in the atomization functional component library into a plurality of functional groups according to weight distribution modes shown in a store history data feature library, and determining a macroscopic weight feature center point for each functional group; Calculating the comprehensive adaptation degree between the target store and each functional component based on the operation influence weight of the target store on each functional component and the macroscopic weight characteristic center point of the functional group to which each functional component belongs; Screening out functional components from the atomization functional component library according to comprehensive fit degree to generate a self-adaptive functional component set of a target store; In the operating environment of the management system, the function components in the adaptive function component set are loaded and instantiated to form a runtime manageme