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CN-122022823-A - Intelligent management method, device, equipment and storage medium for shoe and clothing clients

CN122022823ACN 122022823 ACN122022823 ACN 122022823ACN-122022823-A

Abstract

The invention relates to the technical field of information processing, in particular to an intelligent management method, device, equipment and storage medium for a shoe service customer, which are used for extracting core characteristics from cooperative application reporting information, calculating reporting priority according to the core characteristics, collecting multidimensional operation data of the shoe service customer based on the reporting priority, calculating customer equity preference according to the multidimensional operation data, matching personalized equity schemes according to the customer equity preference, carrying out association binding on the whole life cycle data of a bill and the personalized equity schemes, pre-judging risk levels according to the whole life cycle data of the bill, generating differential early warning information according to an association mapping table and the risk levels, dynamically generating the personalized equity schemes based on multidimensional data, and realizing real-time association and risk intelligent early warning of the whole life cycle data of the bill and the customer equity, thereby comprehensively improving the management efficiency and the service refinement level of the shoe service customer.

Inventors

  • LAI ZHIJIE

Assignees

  • 上海东普信息科技有限公司

Dates

Publication Date
20260512
Application Date
20260109

Claims (10)

  1. 1. An intelligent management method for a shoe-suit client is characterized by comprising the following steps: Receiving cooperative application report information sent by a shoe suit client, and extracting core features from the cooperative application report information, wherein the core features comprise client history cooperative time length, delivery volume acceleration, history complaint rate, commodity compliance, store qualification and reference volume stability; Calculating a reporting priority according to the core characteristics, and collecting multidimensional operation data of the shoe service clients based on the reporting priority; Calculating client benefit preferences according to the multidimensional operation data, matching personalized benefit schemes according to the client benefit preferences, and sending the personalized benefit schemes to clients corresponding to the footwear clients; Acquiring the whole-life cycle data of the freight bill of the shoe service customer in real time, carrying out association binding on the whole-life cycle data of the freight bill and the personalized equity scheme to obtain an association mapping table, and pre-judging risk level according to the whole-life cycle data of the freight bill; And generating differential early warning information according to the association mapping table and the risk level, and sending the differential early warning information to a management terminal.
  2. 2. The method for intelligently managing shoe-wear clients according to claim 1, wherein the step of receiving the cooperative application report information sent by the shoe-wear clients and extracting core features from the cooperative application report information, wherein the core features include client history cooperative time length, delivery volume acceleration, history complaint rate, commodity compliance, store qualification and reference volume stability, comprises: Receiving cooperative application report information sent by a shoe service client; pre-auditing the information of the information submitted by the cooperative application to obtain an auditing result; And when the auditing result passes, extracting core features from the cooperation application report information, wherein the core features comprise the historical cooperation time of the client, the delivery quantity acceleration, the historical complaint rate, the commodity compliance, the store qualification and the reference quantity stability.
  3. 3. The method according to claim 1, wherein the calculating a reporting priority according to the core feature, and collecting multidimensional operating data of the footwear client based on the reporting priority, comprises: Based on a preset scale method, carrying out importance comparison on the historical cooperation duration of the client, the delivery quantity acceleration rate, the historical complaint rate, the commodity compliance, the store qualification and the reference quantity stability to obtain a comparison result, and constructing a judgment matrix according to the comparison result; Calculating the feature vector of the judgment matrix, and carrying out normalization processing on the feature vector to obtain weight distribution corresponding to each type of core features; Calculating the consistency ratio of the judgment matrix, and judging that the weight allocation is effective when the consistency ratio is smaller than a preset threshold value; based on the weight distribution, carrying out weighted summation on each core characteristic value to obtain a reporting comprehensive score, and determining a corresponding reporting priority level according to the reporting comprehensive score; and distributing the cooperative application report information to a corresponding data processing priority queue according to the report priority level, and collecting the multidimensional operation data of the shoe service client according to the sequence of the data processing priority queue.
  4. 4. The method for intelligently managing a footwear client according to claim 1, wherein the calculating a client benefit preference according to the multidimensional operation data, matching a personalized benefit scheme according to the client benefit preference, and transmitting the personalized benefit scheme to a client corresponding to the footwear client includes: normalizing the multi-dimensional operation data, and calculating a customer value comprehensive score according to the normalized multi-dimensional operation data; dividing the footwear clients into corresponding target groups by adopting a K-means clustering algorithm based on the client value comprehensive score; based on historical data, counting the utilization rate and satisfaction of the target group on various rights and interests to obtain a counting result; and according to the statistics result, matching the optimal equity combination as a personalized equity scheme, and sending the personalized equity scheme to a client corresponding to the footwear client.
  5. 5. The method for intelligently managing shoe-wear clients according to claim 1, wherein the collecting the bill-of-freight full life cycle data of the shoe-wear clients in real time, performing association binding on the bill-of-freight full life cycle data and the personalized equity scheme to obtain an association mapping table, and predicting risk levels according to the bill-of-freight full life cycle data comprises: acquiring the whole-life cycle data of the waybill of the shoe-wear client in real time, wherein the whole-life cycle data of the waybill comprise the waybill type, the commodity category, the transportation route, the historical retention data, the weather condition, the website processing capacity and the delivery time window; carrying out data preprocessing on the waybill full life cycle data; based on the customer identification and the equity scheme identification of the shoe service customer, carrying out association binding on the whole life cycle data of the freight bill and the personalized equity scheme, and establishing an association mapping table; And predicting the risk level according to the waybill full life cycle data.
  6. 6. The method for intelligently managing shoe-wear clients according to claim 1, wherein generating differential early-warning information according to the association mapping table and the risk level, and transmitting the differential early-warning information to a management terminal comprises: matching a preset early warning template according to the association mapping table and the risk level; generating differential early warning information comprising a bill number, a risk type, an early warning level and suggested measures according to the early warning template and the bill full life cycle data; And sending the differential early warning information to a management terminal.
  7. 7. The intelligent shoe-wear client management method according to claim 1, wherein the generating the differential early-warning information according to the risk level, and after sending the differential early-warning information to a management terminal, further comprises: summarizing early warning processing records, the freight bill completion situation and customer feedback data to obtain summarized information, and generating a management report according to the summarized information; encrypting the management report by using an encryption algorithm to obtain an encrypted management report; Uploading the encryption management report to a blockchain network for certification.
  8. 8. An intelligent management device for a footwear customer, comprising: The receiving and extracting module is used for receiving the cooperative application report information sent by the shoe suit client, and extracting core features from the cooperative application report information, wherein the core features comprise client history cooperative time length, delivery quantity speed increasing, history complaint rate, commodity compliance, store qualification and reference quantity stability; the calculation and collection module is used for calculating a reporting priority level according to the core characteristics and collecting multidimensional operation data of the shoe service client based on the reporting priority level; The calculation matching sending module is used for calculating client benefit preference according to the multidimensional operation data, matching personalized benefit schemes according to the client benefit preference and sending the personalized benefit schemes to the clients corresponding to the footwear clients; The binding pre-judging module is used for collecting the whole life cycle data of the freight bill of the shoe service customer in real time, carrying out association binding on the whole life cycle data of the freight bill and the personalized equity scheme to obtain an association mapping table, and pre-judging the risk level according to the whole life cycle data of the freight bill; and the generation and transmission module is used for generating differentiated early warning information according to the association mapping table and the risk level and transmitting the differentiated early warning information to a management terminal.
  9. 9. The intelligent management device for the shoe and clothing clients is characterized by comprising a memory and at least one processor, wherein instructions are stored in the memory; at least one of the processors invokes the instructions in the memory to cause the footwear customer intelligent management device to perform the steps of the footwear customer intelligent management method according to any of claims 1-7.
  10. 10. A computer readable storage medium having instructions stored thereon, which when executed by a processor, perform the steps of the footwear customer intelligent management method according to any of claims 1-7.

Description

Intelligent management method, device, equipment and storage medium for shoe and clothing clients Technical Field The present invention relates to the field of information processing technologies, and in particular, to a method, an apparatus, a device, and a storage medium for intelligent management of a footwear client. Background With the rapid development of the shoe and clothing electronics industry, the importance of logistic services in the shoe and clothing supply chain is increasingly highlighted. The goods of the footwear and the clothing have the characteristics of multiple styles, strong seasonality, high return rate and the like, and higher requirements are put on timeliness, reliability and personalized response capability of customers of logistics service. Therefore, how to perform efficient and intelligent full life cycle management on the shoe service clients becomes a key for improving the operation efficiency and the service quality of the logistics service provider. At present, in the aspects of admission audit and rights management of shoe-suit clients, the following defects generally exist that firstly, the processing of client cooperation application mostly depends on manual audit. The auditor needs to check the qualification information submitted by the clients one by one and combine the limited historical cooperation data to carry out subjective judgment. The method has low efficiency and long processing period, and is difficult to quantitatively evaluate and scientifically sort the potential value of the clients, so that the request of the high-value clients cannot be processed preferentially, business opportunities are missed, and the cooperation experience of the high-quality clients is also influenced. Secondly, existing client benefit distribution mechanisms often employ fixed or unified standardized schemes, lacking in dynamics and personalization. Rights schemes are typically formulated based on simple customer ratings or historical collaborative volumes and fail to dynamically adjust in deep association with customer real-time multidimensional operational data (e.g., shipping stability, quality of service, rights usage preferences, etc.). This makes the equity resource allocation inaccurate, and the high-value customers may not be able to obtain equity guarantees that match their contributions, and the inefficient equity delivery also reduces the overall resource utilization. In addition, in terms of risk early warning and customer linkage in the logistic performance process, the existing system is often focused on post-tracking and passive processing. The waybill data, the client benefit information, the website operation data and the like are in an island state, and effective association and deep analysis are lacked. The manager is difficult to predict the potential risks (such as retention and delay) of the transportation link in time, and different early warning and intervention measures are implemented according to different risk levels and client rights and interests, so that the response of the problems is delayed, and the customer satisfaction is damaged. Disclosure of Invention In order to overcome the defects of the prior art, the invention aims to provide the shoe and clothing customer intelligent management method, device, equipment and storage medium which can realize automatic evaluation and grading treatment of customer qualification, dynamically generate a personalized equity scheme based on multidimensional data, realize real-time association of the whole life cycle data of a freight bill and customer equity and intelligent risk early warning, and comprehensively improve the management efficiency and the service refinement level of shoe and clothing customers. The first aspect of the invention provides an intelligent management method for shoe and suit clients, which comprises the steps of receiving cooperative application reporting information sent by a shoe and suit client, extracting core features from the cooperative application reporting information, wherein the core features comprise client history cooperative time length, delivery volume acceleration, history complaint rate, commodity compliance, store qualification and reference volume stability, calculating reporting priority according to the core features, collecting multidimensional operation data of the shoe and suit client based on the reporting priority, calculating client benefit preference according to the multidimensional operation data, matching personalized benefit schemes according to the client benefit preference, sending the personalized benefit schemes to clients corresponding to the shoe and suit client, collecting the bill full life cycle data of the shoe and suit client in real time, carrying out association binding on the bill full life cycle data and the personalized benefit schemes to obtain an association mapping table, and generating differential pre-warning risk information acc