Tobacco retail operation subject credit intelligent scoring system and method based on dynamic index library

Abstract

The invention discloses a tobacco retail operation subject credit intelligent scoring system and method based on a dynamic index library, and relates to the technical field of data credit management. Comprising the steps of 1, constructing a tobacco retail operation subject credit intelligent scoring system, 2, constructing a two-stage index library through a dynamic index management module, and visually configuring credit evaluation indexes and rules according to needs; 3, through a multi-source data integration module, docking a marketing system, determining customer grades and order data, docking a monopoly system, obtaining case information and license states, docking a logistics system, monitoring signing abnormity and real cigarette outflow data, and collecting operation behavior data in real time; step 4, automatically calculating credit scores and dividing credit grades according to credit evaluation indexes and rules based on the two-stage index library through an intelligent scoring module, and step 5, realizing linkage application of the credit scores in goods source putting and customer grading scenes through a credit application module; and step 6, carrying out natural restoration and application restoration of the two channels through a credit restoration module.

Description

Technical Field

[0001] This invention discloses a credit intelligent scoring system and method for tobacco retail operators based on a dynamic indicator library, which relates to the field of data credit management technology. Background Technology

[0002] In the tobacco industry, retail customer credit management is a crucial means of ensuring market order and optimizing resource allocation. Current credit scoring methods largely rely on static indicators and manual review, which presents the following problems:

[0003] Fixed indicators: Credit rating indicators are fixed and cannot be dynamically updated according to market changes or policy adjustments.

[0004] Data silos: Business data such as marketing, specialty stores, and logistics are scattered, making it difficult to achieve efficient integration and analysis.

[0005] Inefficient scoring: Traditional methods rely on manual review, which is inefficient when processing massive amounts of master-slave relationship data and makes it difficult to quickly identify abnormal credit behavior.

[0006] Delayed repair: The credit repair process is complex and lacks automation, which affects customer experience and market responsiveness. Summary of the Invention

[0007] This invention addresses the problems of existing technologies by providing a credit intelligent scoring system and method for tobacco retail operators based on a dynamic indicator library. Through dynamic indicator library management, multi-source data integration, intelligent scoring algorithms, and credit repair mechanisms, it achieves full lifecycle management of retail customer credit.

[0008] The specific solution proposed in this invention is as follows:

[0009] This invention provides a method for intelligent credit scoring of tobacco retail operators based on a dynamic indicator library, comprising:

[0010] Step 1: Construct an intelligent credit scoring system for tobacco retail operators. This system includes a dynamic indicator management module, a multi-source data integration module, an intelligent scoring module, a credit application linkage module, and a dual-channel repair module.

[0011] Step 2: Build a two-level indicator library through the dynamic indicator management module, and configure credit evaluation indicators and rules visually as needed;

[0012] Step 3: Through the multi-source data integration module, connect to the marketing system to determine customer tiers and order data, connect to the franchise system to obtain case information and license status, and connect to the logistics system to monitor abnormal signing and outflow of genuine cigarettes, and collect business behavior data in real time;

[0013] Step 4: The intelligent scoring module automatically calculates credit scores and classifies credit levels based on a two-level indicator library and according to credit evaluation indicators and rules. The intelligent scoring engine of the intelligent scoring module initiates the credit rating calculation process. When triggering level updates and public announcement operations, the intelligent scoring engine backs up the current level record, clears previous backup data, and updates the status. When triggering confirmation operations, it creates a running record and establishes a security protection mechanism. After completing the level calculation, it saves the current level, generates a new level, updates the master level, and simultaneously updates the record status; data synchronization is also performed.

[0014] Step 5: Through the credit application module, realize the linkage application of credit score in scenarios such as goods allocation and customer grading;

[0015] Step 6: Perform natural credit repair and application repair through the credit repair module.

[0016] Furthermore, in step 2 of the aforementioned intelligent credit scoring method for tobacco retail operators based on a dynamic indicator library, credit evaluation indicators and rules are configured on demand and visually through the dynamic indicator management module, including:

[0017] The rules engine dynamically adjusts the indicator weights, evaluation periods, and credit score calculation formulas.

[0018] Configure nested rules, including rules for specifying integrity levels.

[0019] Furthermore, in step 3 of the aforementioned intelligent credit scoring method for tobacco retail operators based on a dynamic indicator library, the multi-source data integration module utilizes ETL to clean and standardize the collected multi-source heterogeneous data, generating a credit-themed dataset.

[0020] Furthermore, in step 6 of the aforementioned intelligent credit scoring method for tobacco retail operators based on a dynamic indicator library, the credit repair module automatically restores the score upon expiration in the natural repair channel.

[0021] Through the credit repair module, you can submit a repair application via the mobile app, record the repair process, and ensure that the process is traceable.

[0022] This invention also provides a credit intelligent scoring system for tobacco retail operators based on a dynamic indicator library, including a dynamic indicator management module, a multi-source data integration module, an intelligent scoring module, a credit application linkage module, and a dual-channel repair module.

[0023] The dynamic indicator management module constructs a two-level indicator library, allowing for the visual configuration of credit evaluation indicators and rules as needed.

[0024] The multi-source data integration module connects to the marketing system to determine customer tiers and order data, connects to the franchise system to obtain case information and license status, and connects to the logistics system to monitor abnormal receipts and the outflow of genuine cigarettes, and collects business behavior data in real time.

[0025] The intelligent scoring module, based on a two-level indicator library, automatically calculates credit scores and classifies credit levels according to credit evaluation indicators and rules. The intelligent scoring engine initiates the credit rating calculation process. When triggering level updates and public announcement operations, the intelligent scoring engine backs up the current level record, clears previous backup data, and updates the status. When triggering confirmation operations, it creates a running record and implements a security protection mechanism. After completing the level calculation, it saves the current level, generates a new level, updates the master level, and simultaneously updates the record status; data synchronization is also performed.

[0026] The credit application module enables the coordinated application of credit scores in scenarios such as product allocation and customer segmentation.

[0027] The credit repair module offers two channels: natural repair and application repair.

[0028] Furthermore, the dynamic indicator management module of the intelligent credit scoring system for tobacco retail operators based on a dynamic indicator library allows for on-demand, visual configuration of credit evaluation indicators and rules, including:

[0029] The rules engine dynamically adjusts the indicator weights, evaluation periods, and credit score calculation formulas.

[0030] Configure nested rules, including rules for specifying integrity levels.

[0031] Furthermore, the multi-source data integration module of the intelligent credit scoring system for tobacco retail operators based on a dynamic indicator library utilizes ETL to clean and standardize the collected multi-source heterogeneous data, generating a credit-themed dataset.

[0032] Furthermore, the credit repair module of the intelligent credit scoring system for tobacco retail operators based on a dynamic indicator library has a natural repair channel: the score is automatically restored upon expiration.

[0033] Through the credit repair module, you can submit a repair application via the mobile app, record the repair process, and ensure that the process is traceable.

[0034] The advantages of this invention are:

[0035] Achieving dynamic adaptability: Through a dynamic indicator library, credit scoring rules can be adjusted in real time as policies or markets change, enhancing system flexibility.

[0036] Enables multi-dimensional applications: Credit scoring results can be applied to customer segmentation, product distribution, terminal construction, and other scenarios to optimize resource allocation.

[0037] Risk prevention and control: Strengthen the control of dishonest behavior and maintain market order through credit constraint and repair mechanisms. Attached Figure Description

[0038] Figure 1 This is a schematic diagram of the automatic calculation process of credit score by the method of the present invention.

[0039] Figure 2 This is a schematic diagram illustrating the process of applying credit to the supply of goods using the method of this invention. Detailed Implementation

[0040] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand and implement the present invention. However, the embodiments described are not intended to limit the present invention.

[0041] Example 1

[0042] This invention provides a method for intelligent credit scoring of tobacco retail operators based on a dynamic indicator library, comprising:

[0043] Step 1: Construct an intelligent credit scoring system for tobacco retail operators. This system includes a dynamic indicator management module, a multi-source data integration module, an intelligent scoring module, a credit application linkage module, and a dual-channel repair module.

[0044] Step 2: Construct a two-level indicator library through the dynamic indicator management module, and configure credit evaluation indicators and rules visually as needed. This may specifically include:

[0045] The rules engine dynamically adjusts the indicator weights, evaluation periods, and credit score calculation formulas.

[0046] Set up nested rules, including rules for specifying integrity levels, such as limiting blacklisted users to a maximum level of A.

[0047] Step 3: Through the multi-source data integration module, connect to the marketing system to determine customer tiers and order data; connect to the franchise system to obtain case information and license status; connect to the logistics system to monitor abnormal delivery and genuine cigarette outflow data, and collect business behavior data in real time. The multi-source data integration module can utilize ETL to clean and standardize the collected heterogeneous data from multiple sources, generating a credit-themed dataset.

[0048] Step 4: The intelligent scoring module automatically calculates credit scores and classifies credit levels based on a two-level indicator library and according to credit evaluation indicators and rules. The intelligent scoring engine of the intelligent scoring module initiates the credit rating calculation process.

[0049] When triggering grade updates and public announcements via the intelligent scoring engine:

[0050] Back up the current grade records, and back up the data in the `crit_grade_publicity_reserved` table to the main `crit_cust` table.

[0051] Clear prior backup data and delete records from the crit_grade_publicity_reserved table.

[0052] And update the status, setting the record table status to 98.

[0053] When the confirmation operation is triggered,

[0054] Create a run record: Insert a new record into the crit_grade_cal_record table.

[0055] Security protection mechanism:

[0056] Error detection before it takes effect

[0057] Rollback in case of loss of control: Delete tags, update crit_cust_line data,

[0058] Update credit score: Write the latest score to crit_cust.crit_mark.

[0059] Perform grade update: Call the grade calculation method to update crit_cust.crit_grade, and update the record status: Set the status of crit_grade_cal_record to 01, indicating that it has been run.

[0060] After completing the grade calculation, save the current grade and back up the grades from the main table to crit_grade_publicity_reserved.

[0061] Generate New Rating: Based on the score, call the preview method to generate the latest credit rating.

[0062] Update major grade: Write the new grade to crit_cust.crit_grade.

[0063] Simultaneously update the record status: set the crit_grade_cal_record status to 10, and display the status.

[0064] Perform data synchronization:

[0065] Execute data synchronization in the middle platform:

[0066] Prepare row data: Insert data from the main table into the row table crit_grade_update_line.

[0067] Record change log: Update the timestamp of the change in crit_grade_update_log.

[0068] Calculate the latest grade: Update `crit_cust.crit_grade` by calling the grade calculation method.

[0069] Update row table data: Write the new grade to crit_grade_update_line.crit_grade_new.

[0070] Clear backup: Delete data from the crit_grade_publicity_reserved table.

[0071] Update record status: Set the status of crit_grade_cal_record to 99, confirming the status.

[0072] For middleware synchronization, complete the data synchronization:

[0073] Update status: The status of crit_grade_cal_record has been set to 03 and is in effect.

[0074] Calling the middleware interface: Synchronize the following fields to the middleware customer profile:

[0075] credit_class_code,

[0076] credit_class_id,

[0077] credit_class_name,

[0078] std_credit_class_code,

[0079] Updated final status: The status of crit_grade_cal_record has been set to 02, and the change has taken effect.

[0080] Step 5: Through the credit application module, realize the linkage application of credit score in the scenarios of goods allocation and customer segmentation. For example, it can be applied to the goods allocation controller and customer segmentation optimizer, and use credit rating as the core parameter for segmentation, with a weight ratio of ≥30%.

[0081] Step 6: Perform natural credit repair and application repair through the credit repair module.

[0082] Through the credit repair module in the natural repair channel: scores will automatically recover upon expiration.

[0083] Through the credit repair module, you can submit a repair application via the mobile app, record the repair process, and ensure that the process is traceable.

[0084] Example 2

[0085] This invention also provides a credit intelligent scoring system for tobacco retail operators based on a dynamic indicator library, including a dynamic indicator management module, a multi-source data integration module, an intelligent scoring module, a credit application linkage module, and a dual-channel repair module.

[0086] The dynamic indicator management module constructs a two-level indicator library, allowing for the visual configuration of credit evaluation indicators and rules as needed.

[0087] The multi-source data integration module connects to the marketing system to determine customer tiers and order data, connects to the franchise system to obtain case information and license status, and connects to the logistics system to monitor abnormal receipts and the outflow of genuine cigarettes, and collects business behavior data in real time.

[0088] The intelligent scoring module, based on a two-level indicator library, automatically calculates credit scores and classifies credit levels according to credit evaluation indicators and rules. The intelligent scoring engine initiates the credit rating calculation process. When triggering level updates and public announcement operations, the intelligent scoring engine backs up the current level record, clears previous backup data, and updates the status. When triggering confirmation operations, it creates a running record and implements a security protection mechanism. After completing the level calculation, it saves the current level, generates a new level, updates the master level, and simultaneously updates the record status; data synchronization is also performed.

[0089] The credit application module enables the coordinated application of credit scores in scenarios such as product allocation and customer segmentation.

[0090] The credit repair module offers two channels: natural repair and application repair.

[0091] The information interaction and execution process between the modules in the above system are based on the same concept as the method embodiment of the present invention, and the specific details can be found in the description in the method embodiment of the present invention, and will not be repeated here.

[0092] Similarly, the advantages of the system of the present invention are:

[0093] Achieving dynamic adaptability: Through a dynamic indicator library, credit scoring rules can be adjusted in real time as policies or markets change, enhancing system flexibility.

[0094] Enables multi-dimensional applications: Credit scoring results can be applied to customer segmentation, product distribution, terminal construction, and other scenarios to optimize resource allocation.

[0095] Risk prevention and control: Strengthen the control of dishonest behavior and maintain market order through credit constraint and repair mechanisms.

[0096] It should be noted that not all steps and modules in the above processes and system structures are mandatory; some steps or modules can be omitted as needed. The execution order of the steps is not fixed and can be adjusted as required. The system structures described in the above embodiments can be physical or logical structures. That is, some modules may be implemented by the same physical entity, or some modules may be implemented by multiple physical entities, or they may be implemented by certain components in multiple independent devices.

[0097] The above-described embodiments are merely preferred embodiments provided to fully illustrate the present invention, and the scope of protection of the present invention is not limited thereto. Equivalent substitutions or modifications made by those skilled in the art based on the present invention are all within the scope of protection of the present invention. The scope of protection of the present invention is defined by the claims.

Claims

1. A method for intelligent credit scoring of tobacco retail operators based on a dynamic indicator library, characterized by: include: Step 1: Construct an intelligent credit scoring system for tobacco retail operators. This system includes a dynamic indicator management module, a multi-source data integration module, an intelligent scoring module, a credit application linkage module, and a dual-channel repair module. Step 2: Build a two-level indicator library through the dynamic indicator management module, and configure credit evaluation indicators and rules visually as needed; Step 3: Through the multi-source data integration module, connect to the marketing system to determine customer tiers and order data, connect to the franchise system to obtain case information and license status, and connect to the logistics system to monitor abnormal signing and outflow of genuine cigarettes, and collect business behavior data in real time; Step 4: The intelligent scoring module automatically calculates credit scores and classifies credit levels based on a two-level indicator library and according to credit evaluation indicators and rules. The intelligent scoring engine of the intelligent scoring module initiates the credit rating calculation process. When triggering level updates and public announcement operations, the intelligent scoring engine backs up the current level record, clears previous backup data, and updates the status. When triggering confirmation operations, it creates a running record and establishes a security protection mechanism. After completing the level calculation, it saves the current level, generates a new level, updates the master level, and simultaneously updates the record status; data synchronization is also performed. Step 5: Through the credit application module, realize the linkage application of credit score in scenarios such as goods allocation and customer grading; Step 6: Perform natural credit repair and application repair through the credit repair module.

2. The intelligent credit scoring method for tobacco retail operators based on a dynamic indicator library as described in claim 1, characterized in that: Step 2 involves configuring credit rating indicators and rules visually as needed through the dynamic indicator management module, including: The rules engine dynamically adjusts the indicator weights, evaluation periods, and credit score calculation formulas. Configure nested rules, including rules for specifying integrity levels.

3. The intelligent credit scoring method for tobacco retail operators based on a dynamic indicator library as described in claim 1, characterized in that: In step 3, the multi-source data integration module uses ETL to clean and standardize the collected multi-source heterogeneous data to generate a credit-themed dataset.

4. The intelligent credit scoring method for tobacco retail operators based on a dynamic indicator library as described in claim 1, characterized in that: In step 6, the credit repair module uses the natural repair channel: the score will automatically recover upon expiration. Through the credit repair module, you can submit a repair application via the mobile app, record the repair process, and ensure that the process is traceable.

5. A credit intelligent scoring system for tobacco retail operators based on a dynamic indicator library, characterized by: It includes a dynamic indicator management module, a multi-source data integration module, an intelligent scoring module, a credit application linkage module, and a dual-channel repair module. The dynamic indicator management module constructs a two-level indicator library, allowing for the visual configuration of credit evaluation indicators and rules as needed. The multi-source data integration module connects to the marketing system to determine customer tiers and order data, connects to the franchise system to obtain case information and license status, and connects to the logistics system to monitor abnormal receipts and the outflow of genuine cigarettes, and collects business behavior data in real time. The intelligent scoring module, based on a two-level indicator library, automatically calculates credit scores and classifies credit levels according to credit evaluation indicators and rules. The intelligent scoring engine initiates the credit rating calculation process. When triggering level updates and public announcement operations, the intelligent scoring engine backs up the current level record, clears previous backup data, and updates the status. When triggering confirmation operations, it creates a running record and implements a security protection mechanism. After completing the level calculation, it saves the current level, generates a new level, updates the master level, and simultaneously updates the record status; data synchronization is also performed. The credit application module enables the coordinated application of credit scores in scenarios such as product allocation and customer segmentation. The credit repair module offers two channels: natural repair and application repair.

6. The intelligent credit scoring system for tobacco retail operators based on a dynamic indicator database as described in claim 5, characterized in that: The dynamic indicator management module allows for on-demand, visual configuration of credit rating indicators and rules, including: The rules engine dynamically adjusts the indicator weights, evaluation periods, and credit score calculation formulas. Configure nested rules, including rules for specifying integrity levels.

7. A credit intelligent scoring system for tobacco retail operators based on a dynamic indicator library as described in claim 5, characterized in that: The multi-source data integration module uses ETL to clean and standardize the collected heterogeneous data from multiple sources, generating a credit-themed dataset.

8. A credit intelligent scoring system for tobacco retail operators based on a dynamic indicator library as described in claim 5, characterized in that credit... The repair module is in the natural repair channel: scores will automatically recover upon expiration. Through the credit repair module, you can submit a repair application via the mobile app, record the repair process, and ensure that the process is traceable.

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