Baijiu industry problem terminal early warning and checking system and method

Abstract

This invention primarily relates to the field of data analysis technology. To improve the accuracy of monitoring the situation of liquor products in terminal stores, this invention provides a system and method for early warning and verification of problematic terminals in the liquor industry. First, by connecting to multiple data sources, it automatically collects multi-dimensional data from terminals, including bottle opening, barcode scanning for warehousing, customer management, and product traceability code verification. Then, based on preset parallel judgment rules, it automatically identifies terminals that may have cross-selling or distribution anomalies, and combines a regional sales data benchmark pool to perform multi-algorithm collaborative verification, including data feature extraction, deviation calculation, and correlation matching verification, to accurately verify abnormal signals. Finally, the system generates a structured early warning list with risk types and evidence, and pushes it to the verification module to efficiently scan and compare the inventory products of the target terminals, achieving non-intrusive evidence collection. Based on the verification results, it optimizes the judgment rules and benchmark pool, improving the efficiency and coverage of traditional market supervision.

Description

Technical Field

[0001] This invention mainly relates to the field of data analysis technology, and in particular to a problem terminal early warning and verification system and method for the liquor industry. Background Technology

[0002] Baijiu, a unique distilled spirit in China, embodies traditional food culture and is a core segment of the domestic consumer goods market. It has formed a complete industrial ecosystem covering planting, brewing, and sales, consistently maintaining a trillion-yuan market size. Currently, the market exhibits clear brand stratification, with leading brands dominating the high-end market and regional brands competing in the mid-to-low-end market. The development of e-commerce and new retail has shifted sales channels from the traditional distributor system to a hybrid "online + offline" structure, placing higher demands on market management.

[0003] Currently, the traditional regulatory model for terminal stores in the industry relies primarily on manual inspections and experience-based judgment. As a crucial link in product delivery to consumers, the operational standardization of terminals directly impacts brand image and performance. Brands or distributors typically form inspection teams to conduct regular visits, verifying product authenticity, price adherence, and other issues, and relying on experience to assess sales and inventory. This model was effective in the early stages of industry development, but with market expansion, intensified competition, and more complex channels, its inherent flaws have become increasingly apparent: manual inspections are inefficient and costly, unable to cover the vast terminal network, and exhibit blind spots and delays in regulation, with up to 95% of terminals falling outside the regulatory blind spot; experience-based judgment depends on individual ability, lacks data support, is prone to bias, and struggles to identify hidden problems; the collected information is scattered and disorganized, hindering in-depth analysis and value extraction, resulting in a lack of scientific basis for corporate market decisions; furthermore, problematic terminals (such as those involved in cross-selling or unauthorized distribution) are often highly concealed. Products sold through cross-border channels may be concealed, and merchants generally prefer to sell to familiar customers rather than new ones, making it difficult for inspectors to discover and obtain effective evidence during routine visits. Furthermore, traditional methods of evidence collection mainly rely on purchasing, which not only has a low success rate (merchants may refuse to sell or provide legitimate sources of goods) but is also extremely time-consuming. The judgment of problematic terminals heavily depends on the personal experience of inspectors, lacking unified and objective quantitative standards, making it difficult to standardize and scale up regulatory work. Summary of the Invention

[0004] This invention provides a system and method for early warning and verification of problematic terminals in the liquor industry, aiming to improve the accuracy of supervision over the liquor situation in terminal stores.

[0005] The technical solution adopted by the present invention to solve the above-mentioned technical problems is as follows: On the one hand, this invention provides a problem terminal early warning and verification system for the liquor industry, including: The data acquisition module is used to acquire terminal bottle opening data, customer management data, barcode scanning and warehousing data, and product traceability code verification data; The abnormal terminal identification module performs rule matching on the data collected by the data acquisition module based on preset judgment rules. If the corresponding terminal meets the preset judgment rules, an abnormal identification signal is generated. The early warning module receives anomaly identification signals, calls the regional sales data benchmark pool of the corresponding terminal's region for comparison and verification, and generates a structured early warning list after the verification is passed and pushes it to the verification module. The verification module obtains the electronic tag scanning results of terminal inventory products, automatically compares them with the information in the early warning list, and outputs the comparison results.

[0006] Furthermore, the preset judgment rules in the abnormal terminal identification module include: First rule: Among the consumers bound to the terminal, the number of people scanning goods from other places reaches a preset threshold, or the number of out-of-town goods products verified by the corresponding terminal reaches a preset threshold. Second rule: Among the consumers bound to the corresponding terminal, the number of terminals that open the bottle locally but not on the terminal itself reaches the preset terminal number threshold. Third rule: The corresponding terminal meets the condition that the number of bottles received does not match the number of bottles opened, or the bottle opening rate declines by a preset percentage within a preset time period. Fourth rule: The cross-regional bottle opening rate of the corresponding terminal reaches the preset cross-regional bottle opening rate threshold, or the number of other terminals bound to the consumers and the number of cross-regional bottle openings both exceed the corresponding preset threshold.

[0007] On the other hand, the present invention also provides a method for early warning and verification of problematic terminals in the liquor industry, the method comprising: Step S1: The data acquisition module obtains terminal bottle opening data, customer management data, barcode scanning and warehousing data, and product traceability code verification data from multiple data sources respectively; Step S2: The abnormal terminal identification module performs rule matching on the data collected by the data acquisition module based on preset judgment rules. If the corresponding terminal meets the preset judgment rules, an abnormal identification signal is generated. Step S3: After receiving the abnormal identification signal, the early warning module automatically calls the regional sales data benchmark pool of the corresponding terminal's region and compares and verifies the individual data of the corresponding terminal with the regional average data in the benchmark pool; after the verification is passed, a structured early warning list containing the terminal name, address, risk type and core comparison data is generated. Step S4: The early warning module pushes the early warning list to the verification module; Step S5: The verification module receives the warning list, obtains the electronic tag scanning results of the inventory products, automatically compares them with the information in the warning list, and outputs the comparison results as evidence.

[0008] Furthermore, the preset determination rules in step S2 include: First rule: Among the consumers bound to the terminal, the number of people scanning goods from other places reaches a preset threshold, or the number of out-of-town goods products verified by the corresponding terminal reaches a preset threshold. Second rule: Among the consumers bound to the corresponding terminal, the number of terminals that open the bottle locally but not on the terminal itself reaches the preset terminal number threshold. Third rule: The corresponding terminal meets the condition that the number of bottles received does not match the number of bottles opened, or the bottle opening rate declines by a preset percentage within a preset time period. Fourth rule: The cross-regional bottle opening rate of the corresponding terminal reaches the preset cross-regional bottle opening rate threshold, or the number of other terminals bound to the consumers and the number of cross-regional bottle openings both exceed the corresponding preset threshold.

[0009] Furthermore, step S3 includes; Key feature data is extracted from the individual data of the corresponding terminal based on the rule type number carried by the anomaly identification signal; Compare the key feature data of the corresponding terminal with the feature mean of the corresponding region in the regional sales data benchmark pool, calculate the deviation value of each dimension and output the deviation vector; Based on the deviation vector, the correlation between the abnormal behavior of the corresponding terminal and other related terminals in the regional sales data benchmark pool is evaluated, and a confidence score is output. When the deviation values ​​of each dimension exceed the preset deviation threshold and the confidence score exceeds the preset confidence threshold, an early warning list is generated.

[0010] Furthermore, the rule type number carried by the anomaly identification signal is initially labeled based on the rule engine, and the labeling results are verified and corrected by the random forest classification algorithm to output the final rule type number.

[0011] Furthermore, the early warning list in step S3 includes: terminal name and actual business address, risk type, deviation value between individual and regional mean, correlation matching confidence, time series prediction deviation rate, and source identification.

[0012] Furthermore, step S3 also includes: deduplicating the received abnormal identification signals to prevent the same terminal from triggering the warning repeatedly within a preset time window, and detecting whether the corresponding terminal is in a verification waiting state or a confirmed state. If so, the warning trigger is blocked.

[0013] Furthermore, in step S4, the early warning module pushes the early warning list to the verification module, and the system enters the verification waiting state; when the verification results are fed back, the verification module automatically cross-verifies the scan comparison results with the original early warning information in the early warning list; if the verification is consistent, the status is updated to confirmed; if inconsistent, a second review process is triggered.

[0014] Furthermore, step S4 also includes: periodically transmitting the comparison results and confirmation status output by the verification module back to the abnormal terminal identification module and the early warning module at a preset cycle; the abnormal terminal identification module automatically adjusts the threshold parameters of the judgment rules according to the transmitted results; and the early warning module updates the statistical data in the regional sales data benchmark pool according to the transmitted results.

[0015] The advantages of this invention are: (1) Significantly improved identification accuracy: Based on objective data and quantitative models, it eliminates the reliance on subjective experience. Practice has proven that the overall identification accuracy of problematic terminals has been significantly improved, enabling precise strikes against high-risk targets.

[0016] (2) Realize a digital closed loop for market supervision: This invention connects the entire chain of data analysis, intelligent early warning, precise verification and evidence fixation, forming a replicable and scalable new model of digital market supervision, enabling enterprises to allocate inspection resources more scientifically and efficiently.

[0017] (3) Enhance market deterrence: Efficient and accurate regulatory capabilities have a strong deterrent effect on cross-selling and illegal distribution in the market, which helps to maintain the stability of the price system, purify the market environment, and protect the interests of compliant channel merchants. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the architecture of the problem terminal early warning and inspection system for the liquor industry as described in this invention; Figure 2 This is a flowchart of the preset judgment rules in the abnormal terminal identification module. Detailed Implementation

[0019] The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings. These embodiments are only used to more clearly illustrate the technical solutions of the present invention and should not be construed as limiting the scope of protection of the present invention.

[0020] like Figure 1As shown, the data acquisition module obtains terminal-generated bottle opening data, customer management data, barcode scanning and warehousing data, and product traceability code verification data from multiple data sources. Bottle opening data includes opening time, opening location, and product traceability information. Customer management data is obtained through a combination of scheduled and real-time synchronization with a customer relationship management system, acquiring anonymized and standardized data related to customer registration, purchases, and memberships. Barcode scanning and warehousing data is collected by warehouse barcode scanners, collecting product identification codes, warehouse location codes, and other warehousing information, and uploading them to the warehouse management system. The data acquisition module interfaces with this system to obtain data and filter invalid data. Product traceability code verification data is collected by the verification terminal, collecting product traceability codes, verification time, corresponding product and customer information, and synchronizing it to the brand rights management system. The data acquisition module interfaces with this system to obtain data and record the verification status.

[0021] The data acquisition module aggregates the aforementioned data using the unique identifier of each terminal, forming the data to be judged before outputting it to the abnormal terminal identification module. The data acquisition module filters invalid data, standardizes the format, and de-identifies data from each data source to ensure that the data input to the abnormal terminal identification module is in a uniform format and does not contain sensitive information.

[0022] After receiving the judgment data, the abnormal terminal identification module performs rule-by-rule matching on each terminal based on preset judgment rules. The preset judgment rules consist of multiple parallel logical rules, including rules for identifying terminals involved in cross-selling, rules for identifying terminals involved in cross-selling or distribution, rules for identifying invalid terminals involved in cross-selling or distribution, and rules for identifying distribution terminals. The judgment criteria for each rule are as follows: Figure 2 As shown: The specific rules for determining cross-regional sales terminals are as follows: if the number of consumers who scanned cross-regional goods on the terminal is ≥3 or the number of bottles of cross-regional goods verified is ≥15, the terminal will be marked as a cross-regional sales terminal. The specific rules for determining a sales and distribution terminal are as follows: when the number of consumers bound to a terminal who open bottles locally but not on that terminal reaches a preset terminal number threshold, for example, when the number of terminals bound to a terminal that open bottles locally but not on that terminal is ≥10, the terminal is marked as a sales and distribution terminal. The rules for determining invalid terminals for cross-selling and cross-distribution include multiple sub-conditions. When the number of bottles received by a terminal reaches the preset receiving threshold (30 bottles) but the number of bottles opened is zero, or when the number of bottles opened reaches the preset opening threshold (30 bottles) but the number of bottles received is zero, or when the number of bottles received increases month-on-month within a preset time period and the opening rate decreases month-on-month to a preset decrease ratio (20%), the terminal will be marked as an invalid terminal for cross-selling and cross-distribution. The specific rules for determining a distribution terminal are as follows: when the cross-county bottle opening rate of a certain terminal reaches the preset cross-county bottle opening rate threshold (30%), or when the number of other terminals bound to the consumers radiated by the terminal reaches the preset bound terminal number threshold (30) and the number of bottles opened across the county exceeds the preset cross-county bottle opening number threshold (20), the terminal is marked as a distribution terminal.

[0023] The abnormal terminal identification module judges each terminal according to the above rules in sequence or in parallel. When any rule is triggered, the module immediately generates a corresponding abnormal identification signal, carries the triggered rule type number in the abnormal identification signal, and then outputs the signal to the early warning module.

[0024] The early warning module receives anomaly identification signals from the abnormal terminal identification module in real time. The early warning module first deduplicates the received anomaly identification signals to prevent the same terminal from repeatedly triggering early warnings within a preset time window. Simultaneously, the early warning module checks whether the terminal is in a verification waiting state or a confirmed state; if it is in either state, the early warning trigger is blocked.

[0025] After validity verification, the early warning module initiates a multi-algorithm collaborative verification process within a preset time after receiving an anomaly indicator signal. The early warning module automatically calls the regional sales data benchmark pool for the area where the terminal is located. This benchmark pool is jointly maintained by the brand sales management system and the data acquisition module. During the initial construction phase, it collects bottle opening data, verification data, and inventory data for each region within a preset historical period, calculates statistical benchmark values ​​and normal fluctuation ranges by region, and adds new data to the benchmark pool at preset update intervals during the dynamic maintenance phase, recalculating the statistical benchmark values ​​using a sliding time window.

[0026] The multi-algorithm collaborative verification process sequentially includes data feature extraction, deviation calculation with the baseline pool data, and association matching confidence verification. The data feature extraction algorithm extracts key features related to the anomaly type from the judgment data, including the terminal's bottle opening frequency, redemption distribution, and the ratio of warehousing to bottle opening. The deviation calculation algorithm compares the terminal's individual feature data with the mean feature value of the corresponding region in the regional sales data baseline pool to calculate the deviation value. The association matching confidence verification algorithm assesses the degree of correlation between the terminal's abnormal behavior and other relevant terminals, including whether the source distribution of goods from different regions is concentrated and whether the flow of cross-regional bottle openings is abnormal.

[0027] The warning module will only trigger the generation of a warning list if all the output results of the above verification process exceed the preset threshold. If any verification result is lower than the preset threshold, it is considered a normal fluctuation, and no warning list will be generated.

[0028] When the warning list is generated, the terminal name and actual operating address are retrieved from the terminal operation filing file in the customer management data module. A fuzzy matching algorithm is used to correct minor discrepancies between the file information and the real-time collected address. Risk types are accurately labeled based on the triggered rule type number, using a rule engine combined with a random forest classification algorithm. Specific labels include predatory pricing, cross-border sales, and illegal verification of product traceability codes. Relevant data evidence is automatically extracted using feature extraction algorithms to capture core comparative data during the verification process, including deviations between individual and regional means, association matching confidence levels, and time-series prediction bias rates. These are then transformed into visual data tables using a standardized formatting algorithm.

[0029] Each warning list includes a unique traceability identifier, generated based on a universally unique identification algorithm, used to link log data throughout the entire warning generation process. After generation, the warning list is stored in a dedicated database and simultaneously pushed in real-time to the brand's terminal management backend warning dashboard. It is then further pushed to the mobile devices of market supervisors via the company's internal collaboration system.

[0030] After receiving the warning list pushed to their mobile terminals, market inspectors will carry radio frequency identification (RFID) devices to the target terminals for on-site verification, based on the terminal name, actual business address, risk type, and core comparative data provided in the list.

[0031] Once inspectors arrive at the target terminal, they use RFID devices to quickly scan the electronic tags of the products in the terminal's inventory to obtain traceability information such as batch numbers, production dates, shipping destinations, and distribution paths for multiple products.

[0032] After scanning is complete, the verification module receives the scan results and automatically compares them with the warning information in the warning list. The comparison includes whether the batch source of the inventory products is consistent with the off-site source characteristics in the warning information, whether the product circulation path contains abnormal nodes, and whether the product's write-off status matches the warning information.

[0033] When inspectors report the initial verification results via mobile terminal, the verification module automatically cross-verifies the scan comparison results with the original warning information in the warning list. If the cross-verification matches, the system updates the verification status to "confirmed." If the cross-verification does not match, the system automatically triggers a second verification process, sending a verification request to another inspector. This inspector then conducts an independent verification at the target terminal, and the result of the second verification serves as the final judgment.

[0034] The verification module outputs the comparison results as evidence, updates the verification status to "verified", and records information such as verification time, verification personnel, and verification results, storing them in a dedicated database.

[0035] The comparison results and confirmation status output by the verification module are periodically returned to the abnormal terminal identification module and the early warning module at preset intervals. The abnormal terminal identification module automatically adjusts the threshold parameters of the judgment rules based on the returned results. If the false alarm rate of a certain rule is consistently high, the trigger threshold for that rule is automatically increased; if the false alarm rate of a certain rule is high, the threshold is automatically decreased. The early warning module updates the statistical data in the regional sales data benchmark pool based on the returned results, incorporating newly added real abnormal case data into the statistical calculations of the benchmark pool, making the benchmark values ​​closer to the actual market situation.

[0036] Through the above steps, the system completes a closed-loop operation from data collection, anomaly identification, early warning push, on-site verification to result feedback.

Claims

1. A problem-oriented terminal early warning and verification system for the liquor industry, characterized in that: include: The data acquisition module is used to acquire terminal bottle opening data, customer management data, barcode scanning and warehousing data, and product traceability code verification data; The abnormal terminal identification module performs rule matching on the data collected by the data acquisition module based on preset judgment rules. If the corresponding terminal meets the preset judgment rules, an abnormal identification signal is generated. The early warning module receives anomaly identification signals, calls the regional sales data benchmark pool of the corresponding terminal's region for comparison and verification, and generates a structured early warning list after the verification is passed and pushes it to the verification module. The verification module obtains the electronic tag scanning results of terminal inventory products, automatically compares them with the information in the early warning list, and outputs the comparison results.

2. The problem terminal early warning and verification system for the liquor industry according to claim 1, characterized in that, The preset judgment rules in the abnormal terminal identification module include: First rule: Among the consumers bound to the terminal, the number of people scanning goods from other places reaches a preset threshold, or the number of out-of-town goods products verified by the corresponding terminal reaches a preset threshold. Second rule: Among the consumers bound to the corresponding terminal, the number of terminals that open the bottle locally but not on the terminal itself reaches the preset terminal number threshold. Third rule: The corresponding terminal meets the condition that the number of bottles received does not match the number of bottles opened, or the bottle opening rate declines by a preset percentage within a preset time period. Fourth rule: The cross-regional bottle opening rate of the corresponding terminal reaches the preset cross-regional bottle opening rate threshold, or the number of other terminals bound to the consumers and the number of cross-regional bottle openings both exceed the corresponding preset threshold.

3. A method for early warning and verification of problematic terminals in the liquor industry, applied to the early warning and verification system for problematic terminals in the liquor industry as described in claim 1 or 2, characterized in that, The method includes: Step S1: The data acquisition module obtains terminal bottle opening data, customer management data, barcode scanning and warehousing data, and product traceability code verification data from multiple data sources respectively; Step S2: The abnormal terminal identification module performs rule matching on the data collected by the data acquisition module based on preset judgment rules. If the corresponding terminal meets the preset judgment rules, an abnormal identification signal is generated. Step S3: After receiving the abnormal identification signal, the early warning module automatically calls the regional sales data benchmark pool of the corresponding terminal's region and compares and verifies the individual data of the corresponding terminal with the regional average data in the benchmark pool; after the verification is passed, a structured early warning list containing the terminal name, address, risk type and core comparison data is generated. Step S4: The early warning module pushes the early warning list to the verification module; Step S5: The verification module receives the warning list, obtains the electronic tag scanning results of the inventory products, automatically compares them with the information in the warning list, and outputs the comparison results as evidence.

4. The method for early warning and verification of problematic terminals in the liquor industry according to claim 3, characterized in that, The preset determination rules mentioned in step S2 include: First rule: Among the consumers bound to the terminal, the number of people scanning goods from other places reaches a preset threshold, or the number of out-of-town goods products verified by the corresponding terminal reaches a preset threshold. Second rule: Among the consumers bound to the corresponding terminal, the number of terminals that open the bottle locally but not on the terminal itself reaches the preset terminal number threshold. Third rule: The corresponding terminal meets the condition that the number of bottles received does not match the number of bottles opened, or the bottle opening rate declines by a preset percentage within a preset time period. Fourth rule: The cross-regional bottle opening rate of the corresponding terminal reaches the preset cross-regional bottle opening rate threshold, or the number of other terminals bound to the consumers and the number of cross-regional bottle openings both exceed the corresponding preset threshold.

5. The method for early warning and verification of problematic terminals in the liquor industry according to claim 3, characterized in that, Step S3 includes: Key feature data is extracted from the individual data of the corresponding terminal based on the rule type number carried by the anomaly identification signal; Compare the key feature data of the corresponding terminal with the feature mean of the corresponding region in the regional sales data benchmark pool, calculate the deviation value of each dimension and output the deviation vector; Based on the deviation vector, the correlation between the abnormal behavior of the corresponding terminal and other related terminals in the regional sales data benchmark pool is evaluated, and a confidence score is output. When the deviation values ​​of each dimension exceed the preset deviation threshold and the confidence score exceeds the preset confidence threshold, an early warning list is generated.

6. The method for early warning and verification of problematic terminals in the liquor industry according to claim 4, characterized in that, The rule type number carried by the anomaly identification signal is initially labeled based on the rule engine, and the labeling results are verified and corrected by the random forest classification algorithm to output the final rule type number.

7. The method for early warning and verification of problematic terminals in the liquor industry according to claim 3, characterized in that, The early warning list mentioned in step S3 includes: terminal name and actual business address, risk type, deviation value between individual and regional mean, correlation matching confidence, time series prediction deviation rate, and source identification.

8. The method for early warning and verification of problematic terminals in the liquor industry according to claim 3, characterized in that, Step S3 also includes: deduplicating the received abnormal identification signals to prevent the same terminal from triggering the warning repeatedly within the preset time window, and detecting whether the corresponding terminal is in the verification waiting state or the confirmed state. If so, the warning trigger is blocked.

9. The method for early warning and verification of problematic terminals in the liquor industry according to claim 3, characterized in that, In step S4, the early warning module pushes the early warning list to the verification module, and the system enters the verification waiting state. When the verification results are fed back, the verification module automatically cross-verifies the scan comparison results with the original early warning information in the early warning list. If the verification is consistent, the status is updated to confirmed; if inconsistent, a second review process is triggered.

10. The method for early warning and verification of problematic terminals in the liquor industry according to claim 3, characterized in that, Step S4 also includes: periodically transmitting the comparison results and confirmation status output by the verification module back to the abnormal terminal identification module and the early warning module at a preset cycle; the abnormal terminal identification module automatically adjusts the threshold parameters of the judgment rules according to the transmitted results; and the early warning module updates the statistical data in the regional sales data benchmark pool according to the transmitted results.

Images