Cigarette specification health rating method and system

Abstract

This application relates to a method and system for rating the health of cigarette specifications. The method includes: acquiring multi-source cigarette specification dimension data; performing concurrent matching calculations between the cigarette specification dimension data and health assessment rules in a pre-built dynamic rule base to determine the specification layout health; and determining the health status level based on the specification layout health. The health status level includes healthy status, concern status, warning status, and risk status. This application solves the problem in related technologies of being unable to comprehensively quantify and rate multi-dimensional heterogeneous indicator data, achieving comprehensive quantitative rating of heterogeneous indicator data across different dimensions.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of industrial informatization, in particular to a cigarette specification health degree rating method and system. BACKGROUND

[0002] Under the background of the tobacco industry moving towards informatization and data-driven, accurate and quantitative health status assessment of cigarette specification data has become a key technical requirement for industry informatization management. Therefore, a timely and efficient cigarette specification health degree rating technology plays a crucial role in the informatization management of cigarette products.

[0003] The existing technology relies on isolated judgment of a single or a few indicators and outputs a binary evaluation result. Its defects are that when there are multiple dimensional indicator data, it cannot comprehensively quantify and rate the multi-dimensional heterogeneous indicator data.

[0004] In view of the problem in the related art that the multi-dimensional heterogeneous indicator data cannot be comprehensively quantified and rated, no effective solution has been proposed so far. SUMMARY

[0005] A cigarette specification health degree rating method and system are provided in the present embodiment to solve the problem in the related art that the multi-dimensional heterogeneous indicator data cannot be comprehensively quantified and rated.

[0006] In a first aspect, a cigarette specification health degree rating method is provided in the present embodiment, comprising:

[0007] Obtaining multi-source cigarette specification dimensional data;

[0008] Concurrently matching and calculating the cigarette specification dimensional data and the health degree evaluation rules in the pre-constructed dynamic rule library to determine the specification layout health degree;

[0009] Determining a health status level according to the specification layout health degree; the health status level includes a health status, an attention status, a pre-warning status and a risk status.

[0010] In some embodiments, the concurrently matching and calculating the cigarette specification dimensional data and the health degree evaluation rules in the pre-constructed dynamic rule library to determine the specification layout health degree comprises:

[0011] Generating a specification indicator wide table according to the cigarette specification dimensional data;

[0012] Concurrently matching and calculating the specification data in the specification indicator wide table and the health degree evaluation rules in the dynamic rule library to obtain the specification layout health degree.

[0013] In some embodiments, the generating a specification index wide table according to the cigarette specification dimension data comprises:

[0014] The cigarette specification dimension data is cleaned, aligned and fused to generate the specification index wide table.

[0015] In some embodiments, the concurrent matching calculation of the specification data in the specification index wide table and the health degree evaluation rules in the dynamic rule library to obtain the specification layout health degree comprises:

[0016] The specification data is matched with the health degree evaluation rules to obtain a matching result; the health degree evaluation rules include general rules and a plurality of configurable personalized rules;

[0017] According to the matching result and a preset rule contribution score, the specification layout health degree is calculated.

[0018] In some embodiments, the determining a health state level according to the specification layout health degree comprises:

[0019] If the specification layout health degree belongs to a preset first numerical interval, the health state level is determined as a healthy state;

[0020] If the specification layout health degree belongs to a preset second numerical interval, the health state level is determined as an attention state;

[0021] If the specification layout health degree belongs to a preset third numerical interval, the health state level is determined as a pre-warning state;

[0022] If the specification layout health degree belongs to a preset fourth numerical interval, the health state level is determined as a risk state.

[0023] In some embodiments, after the determining a health state level according to the specification layout health degree, the method further comprises:

[0024] According to the health state level, a corresponding visual diagnosis result is generated.

[0025] In some embodiments, the generating a corresponding visual diagnosis result according to the health state level comprises:

[0026] If the health state level is a healthy state or an attention state, a corresponding visual diagnosis result is generated;

[0027] If the health state level is a pre-warning state or a risk state, pre-warning information is pushed to a designated manager, and a corresponding visual diagnosis result is generated.

[0028] In some embodiments, after determining the health status level based on the health status of the layout according to the specifications, the method further includes:

[0029] Based on the health status level, match and output the corresponding optimization strategy from the preset strategy knowledge base; and execute the optimization strategy.

[0030] Receive and record the execution feedback data of the optimization strategy, and optimize and update the dynamic rule base based on the execution feedback data.

[0031] Secondly, this embodiment provides a cigarette specification health rating system, including a multi-source heterogeneous data integration and fusion module, a dynamic rule base construction and management module, and a real-time streaming diagnostic engine module;

[0032] The multi-source heterogeneous data integration and fusion module is used to acquire multi-source cigarette specification dimension data;

[0033] The dynamic rule base construction and management module is used to store and configure different types of health assessment rules;

[0034] The real-time streaming diagnostic engine module is used to determine the health of the specification layout and the health status level.

[0035] In some embodiments, the system further includes a diagnostic visualization and early warning push module and a strategy suggestion and closed-loop feedback module;

[0036] The diagnostic visualization and early warning push module is used to present visualized diagnostic results and push early warning information;

[0037] The strategy suggestion and closed-loop feedback module is used to output optimization strategies based on the health status level, and to optimize and update the dynamic rule base based on the execution feedback data of the optimization strategies.

[0038] Compared with related technologies, the cigarette specification health rating method and system provided in this embodiment acquires multi-source cigarette specification dimension data; performs concurrent matching calculations between the cigarette specification dimension data and health assessment rules in a pre-built dynamic rule base to determine the specification layout health; and determines the health status level based on the specification layout health. The health status level includes healthy status, attention status, warning status, and risk status. By constructing a dynamic rule base and employing a concurrent matching calculation mechanism, efficient fusion analysis and comprehensive quantitative scoring of multi-dimensional heterogeneous indicator data are achieved. This solves the problem in related technologies that cannot perform comprehensive quantitative rating of multi-dimensional heterogeneous indicator data, and realizes comprehensive quantitative rating of heterogeneous indicator data of different dimensions.

[0039] Details of one or more embodiments of this application are set forth in the following drawings and description to make other features, objects and advantages of this application more readily apparent. Attached Figure Description

[0040] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0041] Figure 1 This is a structural block diagram of a cigarette specification health rating system provided in an embodiment of this application;

[0042] Figure 2 This is a flowchart of a cigarette specification health rating method provided in an embodiment of this application;

[0043] Figure 3 This is a schematic diagram of the structure of a dynamic rule base provided in an embodiment of this application;

[0044] Figure 4 This is a schematic diagram of the process for determining the health of a specification layout according to an embodiment of this application;

[0045] Figure 5 This is a schematic diagram of the structure for visualizing diagnostic results provided in one embodiment of this application;

[0046] Figure 6 This is a flowchart illustrating the process of determining an optimization strategy according to an embodiment of this application;

[0047] Figure 7 This is a schematic diagram of the structure of a cigarette specification health rating system provided in an embodiment of this application.

[0048] In the diagram: 210, Multi-source heterogeneous data integration and fusion module; 220, Dynamic rule base construction and management module; 230, Real-time streaming diagnostic engine module; 240, Diagnostic visualization and early warning push module; 250, Strategy suggestion and closed-loop feedback module. Detailed Implementation

[0049] To better understand the purpose, technical solution, and advantages of this application, the application is described and illustrated below in conjunction with the accompanying drawings and embodiments.

[0050] Unless otherwise defined, the technical or scientific terms used in this application shall have the general meaning understood by one of ordinary skill in the art to which this application pertains. Words such as “a,” “an,” “an,” “the,” “the,” and “these” used in this application do not indicate quantitative limitation and may be singular or plural. The terms “comprising,” “including,” “having,” and any variations thereof used in this application are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that comprises a series of steps or modules (units) is not limited to the listed steps or modules (units) but may include steps or modules (units) not listed, or may include other steps or modules (units) inherent to these processes, methods, products, or devices. Words such as “connected,” “linked,” and “coupled” used in this application are not limited to physical or mechanical connections but may include electrical connections, whether direct or indirect. “Multiple” used in this application refers to two or more. “And / or” describes the relationship between related objects, indicating that three relationships may exist; for example, “A and / or B” can represent: A alone, A and B simultaneously, and B alone. Normally, the character " / " indicates that the objects before and after it are in an "or" relationship. The terms "first," "second," "third," etc., used in this application are merely to distinguish similar objects and do not represent a specific order of objects.

[0051] The method embodiments provided in this example can be executed on a terminal, computer, or similar computing device. Figure 1 This is a structural block diagram of the cigarette specification health rating system in this embodiment. Figure 1As shown, the system includes a multi-source heterogeneous data integration and fusion module 210, a dynamic rule base construction and management module 220, a real-time streaming diagnostic engine module 230, a diagnostic visualization and early warning push module 240, and a strategy suggestion and closed-loop feedback module 250. The multi-source heterogeneous data integration and fusion module 210 cleans, aligns, and fuses the collected cigarette specification dimension data to form a unified specification index wide table, which is then output to the real-time streaming diagnostic engine module 230. The dynamic rule base construction and management module 220 provides real-time rule loading and matching support for the real-time streaming diagnostic engine module 230. The real-time streaming diagnostic engine module 230 performs real-time matching and calculation between the fused data stream and the rule base to generate specification layout health and status levels, and synchronously sends the results to the diagnostic visualization and early warning push module 240 and the strategy suggestion and closed-loop feedback module 250. The diagnostic visualization and early warning push module 240 visualizes the results and triggers real-time information push for early warnings and risk statuses. The strategy suggestion and closed-loop feedback module 250 matches and optimizes strategies based on the diagnostic results and collects the effect feedback data after strategy execution, which is used to iteratively optimize the rules in the dynamic rule base construction and management module 220, thereby forming a closed-loop data stream from data input, real-time diagnosis, result presentation to strategy feedback. As will be understood by those skilled in the art, Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the terminal described above. For example, the system may also include... Figure 1 The more or fewer modules shown, or having the same Figure 1 The different configurations shown are illustrated.

[0052] This embodiment provides a method for rating the healthiness of cigarette specifications. Figure 2 This is a flowchart of the cigarette specification health rating method in this embodiment, as shown below. Figure 2 As shown, the process includes the following steps:

[0053] Step S210: Obtain multi-source cigarette specification dimension data.

[0054] Specifically, obtaining multi-source cigarette specification data can be achieved by accessing and collecting production and inventory data from industrial enterprise resource planning (ERP) systems; it can also be achieved by integrating order, sales, and sales data from a platform; and it can also be achieved by obtaining status data such as prices and social inventory from retail terminal information collection systems. No restrictions are placed on the specific methods for collecting cigarette specification data.

[0055] Step S220: Perform concurrent matching calculations between the cigarette specification dimension data and the health assessment rules in the pre-built dynamic rule base to determine the health of the specification layout.

[0056] Specifically, the health of cigarette specification layout is determined by concurrently matching and calculating the cigarette specification dimension data with the health assessment rules in the pre-built dynamic rule base. This can be achieved by parallel processing and rule matching of real-time data streams based on a streaming computing framework; alternatively, it can be achieved by loading the data into a self-developed rule engine and using its inference mechanism to synchronously evaluate batch specification data; or it can be achieved by using complex event processing technologies based on Drools or Flink CEP to perform pattern detection and multi-rule trigger judgment on time-series data streams. No specific method is limited in the method used to determine the health of the specification layout.

[0057] Step S230: Determine the health status level based on the health of the specification layout; the health status level includes healthy status, attention status, warning status and risk status.

[0058] Specifically, the health status level is determined based on the health of the specification layout. This can be achieved by comparing the health score with multiple preset threshold intervals to map continuous scores to discrete status levels; alternatively, a classification model can be used to predictively determine the status level by comprehensively considering scoring trends, rule trigger combinations, and historical performance; or a combination of multi-rule weighted aggregation and expert experience correction can be employed to generate the final status level result. No specific restrictions are placed on the specific method used to determine the health status level.

[0059] Through the above steps, multi-source cigarette specification dimension data is acquired, and the specification layout health score is calculated based on the cigarette specification dimension data and a dynamic rule base. The corresponding health status level is determined according to the specification layout health score. This constructs a complete technical closed loop from real-time acquisition of multi-source cigarette specification dimension data, dynamic rule matching calculation to intelligent health status grading, solving the problem in related technologies that cannot comprehensively quantify and rate multi-dimensional heterogeneous indicator data, and achieving comprehensive quantitative rating of heterogeneous indicator data across different dimensions.

[0060] The above steps are explained in detail below:

[0061] In some embodiments, step S220, which involves concurrently matching and calculating the specification dimension data with the health assessment rules in a pre-built dynamic rule base to determine the specification layout health, includes the following steps:

[0062] Generate a wide table of specification indicators based on cigarette specification data;

[0063] The specification layout health score is obtained by concurrently matching and calculating the specification data in the specification index wide table with the health assessment rules in the dynamic rule base.

[0064] Specifically, a wide table of specifications can be generated based on cigarette specification dimension data. This can be achieved by using an ETL (Extract-Transform-Load) tool to fuse multi-source heterogeneous cigarette specification dimension data from industrial or retail systems; or by using streaming data processing technology to perform windowed aggregation and correlation of real-time collected data before placing it into a table. There are no restrictions on the method used to generate the wide table of specifications.

[0065] The specification layout health score is obtained by concurrently matching specification data in the specification indicator wide table with health assessment rules in the dynamic rule base. This can be achieved by using a streaming computing engine to consume wide table change logs in real time and execute multiple rule matching in parallel; or by loading wide table data into distributed memory and using the rule engine's inference mechanism for batch concurrent evaluation. There are no restrictions on the matching method between specification data in the wide table and health assessment rules.

[0066] In this embodiment, the system effectively integrates multi-source, heterogeneous cigarette specification dimension data by constructing a unified wide table of specification indicators, providing structurally consistent and dimensionally complete input data for subsequent evaluation. Furthermore, through a concurrent matching and calculation mechanism, it achieves efficient, real-time rule-based judgment and health quantification of massive specification data, reducing the diagnostic cycle from days / weeks to minutes / hours.

[0067] In some embodiments, a wide table of specification indicators is generated based on cigarette specification dimension data, including the following steps:

[0068] The data on cigarette specifications were cleaned, aligned, and merged to generate a wide table of specifications.

[0069] Specifically, firstly, the cigarette specification data is deduplicated, removing duplicate transaction records and filtering out outliers that clearly exceed reasonable limits. Secondly, the cleaned data is aligned and correlated based on a unified specification ID, time granularity, and regional code. Finally, the indicators of each dimension are integrated and structured according to the aligned key-value pairs, merging them into a wide table record containing multi-dimensional indicators such as sales volume, inventory, price, and public opinion. This results in data with unified specifications and complete time series, providing a consistent and reliable data foundation for subsequent rule matching and health calculation.

[0070] In this embodiment, the system transforms raw data from various sources and in different formats into a comprehensive and interconnected table of specifications and indicators through a standardized data processing workflow, providing a high-quality structured data foundation for subsequent health assessment rule calculations.

[0071] In some embodiments, the specification data in the specification index wide table and the health assessment rules in the dynamic rule base are concurrently matched and calculated to obtain the specification layout health score, including the following steps:

[0072] The specification data is matched with the health assessment rules to obtain the matching results; the health assessment rules include general rules and several configurable personalized rules;

[0073] Based on the matching results and the contribution scores of the preset rules, the health of the specification layout is calculated.

[0074] Specifically, the specification data is compared and logically judged against the health assessment rules in the dynamic rule base. For example... Figure 3 As shown, the general rules are basic evaluation criteria applicable to the entire industry, such as an inventory-to-sales ratio greater than a threshold Y for X consecutive weeks, a retail price being more than Z% lower than the suggested retail price, and a continuous decline in market coverage. For example, rule R001 is triggered if the inventory-to-sales ratio is greater than 1.8 and the sales turnover rate is less than 0.3, resulting in a contribution score of -40 points. Rule R002 is triggered if the percentage of days with a retail price inversion is greater than 40%, resulting in a contribution score of -50 points.

[0075] Multiple configurable personalized rules support flexible setting of thresholds and logical combinations based on different regions or strategies. Personalized rules allow users to add rules and edit thresholds or weights based on specific regional market characteristics, specification lifecycles (new product, growth stage, maturity stage, decline stage), brand strategies, etc. The rule parameter setting panel supports configuring the effective time range, applicable regions, and applicable specifications. Rule conditions can be set based on the absolute value of the indicator, month-on-month comparison, year-on-year comparison, trend, and ranking. It also supports enabling, disabling, or rolling back versions. The saved rules list stores rules in different states such as active, disabled, and editing.

[0076] After matching is complete, the system sums the contribution scores corresponding to each triggering rule, and finally outputs the quantified value of the layout health for that specification. The expression for calculating the layout health of a specification is:

[0077] ;

[0078] Where S represents the health of the specification layout, Q represents the initial score, and W represents the... i This is the contribution score corresponding to the i-th triggered rule. For example, if the product specifications have an inventory-to-sales ratio of 2.1, a sales rate of 0.25, and a retail price inversion period of 35%, triggering rule R001 results in a rule contribution score of -40 points. Given an initial score of 100 points, the final score is 60 points. Furthermore, the expression can include weight values ​​to constrain the rule contribution score.

[0079] It is understood that the range listed above can be configured and adjusted according to the actual application environment, and is not limited to the fixed values ​​listed.

[0080] In this embodiment, the system achieves dynamic and differentiated assessment of the health status of cigarette specifications through structured rule matching and configurable weight calculation, thereby improving the accuracy of health status rating.

[0081] In some embodiments, determining the health status level based on the specification layout health in step S230 includes the following steps:

[0082] If the health status of the specification layout falls within the preset first value range, then the health status level is determined to be healthy.

[0083] If the health status of the specification layout falls within the preset second value range, then the health status level is determined to be a state of concern.

[0084] If the health status of the specification layout falls within the preset third value range, then the health status level is determined to be a warning state.

[0085] If the health status of the specification layout falls within the preset fourth value range, then the health status level is determined to be a risky state.

[0086] Specifically, such as Figure 4 As shown, when the health score of the specification layout falls within the preset first range (80 to 100 points), the health status is considered healthy, and the visualization color is green. When the health score falls within the preset second range (60 to 80 points), the health status is considered "attention," and the visualization color is yellow. When the health score falls within the preset third range (40 to 60 points), the health status is considered "warning," and the visualization color is orange. When the health score falls within the preset fourth range (0 to 40 points), the health status is considered "risk," and the visualization color is red.

[0087] After obtaining the health status level, the system outputs the diagnostic results, such as triggering rule R001, scoring 60 points, and identifying the risk points as sluggish sales and high inventory. The diagnostic results are then pushed to the diagnostic visualization and early warning push module and the strategy suggestion and closed-loop feedback module.

[0088] It is understood that the range listed above can be configured and adjusted according to the actual application environment, and is not limited to the fixed values ​​listed.

[0089] This embodiment establishes a clear and configurable mapping mechanism between continuous layout health scores and discrete health status levels, transforming quantitative assessment results into standardized management signals. This provides a direct and reliable input basis for subsequent risk warnings, visualization, and differentiated strategy triggering.

[0090] In some embodiments, after determining the health status level based on the specification layout health, the following steps are also included:

[0091] Based on the health status level, generate corresponding visual diagnostic results.

[0092] Specifically, based on health status levels and related detailed indicator data, a data visualization engine automatically generates and updates various interactive views, including specification layout heatmaps, health trend charts, and risk specification rankings. Simultaneously, it supports dynamic drill-down and linked analysis based on user-selected time ranges, regional dimensions, or brand categories, thereby transforming quantified health status levels into intuitive and interactive graphical representations.

[0093] For example, such as Figure 5 As shown, the top control bar of the visual dashboard displays the system title and supports multi-dimensional filtering by time, region, brand, specification, etc., as well as data refresh and export. The left dashboard quickly presents a global overview through overall health, total number of monitored specifications, number of risk specifications, number of warning specifications, today's warning events, and the central main area. The central main area map intuitively displays the risk distribution of each area in red / orange / yellow / green, with red indicating a risk status, orange indicating a warning status, yellow indicating a concern status, and green indicating a healthy status. Hovering the mouse over the area allows you to view the area details. The risk specification list on the right lists the problematic specifications and core risk points according to priority. The bottom details panel displays specifications, health trends, indicator performance, trigger rules, and business suggestions, forming a complete diagnostic loop from macro to micro, helping managers efficiently identify layout problems, locate risks, and formulate response strategies.

[0094] Through this embodiment, the system centrally displays and interacts with the health status level and specific indicators obtained from internal analysis and calculation through rich and intuitive visualization, which significantly reduces the threshold for data understanding and decision analysis, enabling managers to quickly and comprehensively grasp the overall situation of the specification layout.

[0095] In some embodiments, a corresponding visual diagnostic result is generated based on the health status level, including the following steps:

[0096] If the health status level is healthy or concerned, a corresponding visual diagnostic result will be generated.

[0097] If the health status level is a warning or risk status, a warning message will be sent to the designated management personnel, and a corresponding visual diagnostic result will be generated.

[0098] Specifically, when a specification's health status is determined to be either healthy or concerning, the system will update the corresponding icon color and status indicator in the visualization dashboard. Green indicates a healthy status, and yellow indicates a concerning status. This information will be incorporated into comprehensive views such as the overall layout heatmap and historical trend curves for regular display, allowing managers to conduct daily monitoring and periodic analysis.

[0099] If the level is determined to be in a warning or risk state, the system will highlight the specification in a visual dashboard with a bright color: red for risk and orange for warning. Simultaneously, the system will automatically push a warning notification to designated market management personnel via message center, SMS, email, or collaborative office software. This notification includes, but is not limited to, the specification name, current health score, details of triggering major risk rules, snapshots of key indicators, and preliminary handling suggestions, thus achieving an upgraded response from static visualization to proactive risk outreach. There are no restrictions on the data types included in the notification.

[0100] Through this embodiment, the system realizes the hierarchical output and response management of diagnostic results. While providing comprehensive and intuitive visualization and monitoring capabilities, it establishes an active early warning and direct information delivery mechanism for medium and high-risk specifications, effectively shortening the response cycle from risk identification to management intervention and improving the timeliness of specification health management.

[0101] In some embodiments, after determining the health status level based on the specification layout health, the following steps are also included:

[0102] Based on the health status level, match and output the corresponding optimization strategy from the preset strategy knowledge base; and execute the optimization strategy.

[0103] Receive and record the execution feedback data of the optimization strategy, and optimize and update the dynamic rule base based on the execution feedback data.

[0104] Specifically, after generating visualized diagnostic results based on the health status level, the system automatically matches and outputs corresponding optimization strategies from a pre-defined "diagnosis-strategy" mapping knowledge base based on the specific triggered risk rules (such as excessively high inventory-to-sales ratio or inverted retail prices). For example, for excessively high inventory-to-sales ratios, the system controls the delivery pace. For inverted retail prices, the system checks channel order and adjusts the strategy. Managers can adopt, adjust, and record the implementation process of the system's suggestions. Subsequently, the strategy execution effect data is fed back to the system as feedback for optimizing and updating the rule thresholds, weights, or logic in the dynamic rule base.

[0105] For example, such as Figure 6As shown, after obtaining the health status level, a suggestion card is generated by querying the knowledge base and matching rules. After manual review, a task order is generated and issued for execution. Through tracking records and effect monitoring and evaluation, if the strategy is effective, it is marked as a success case and fed back to the dynamic rule base to update rule weights or parameters. If the strategy is ineffective, it is marked as a case requiring optimization and fed back to the dynamic rule base to add or modify strategy rules. Simultaneously, the knowledge base is enhanced, making subsequent diagnostic processes more accurate. The strategy suggestion card includes, but is not limited to, information such as target specifications, diagnostic conclusions, recommended measures, priorities, expected effects, and responsible departments. There are no restrictions on the data types included in the strategy suggestion card. The strategy knowledge base contains strategies corresponding to different health status levels and specifications. For example, when the status level is risky and the risk point is slow sales, it is recommended to control advertising. When the status level is warning, it is recommended to conduct channel checks. When the status level is under observation and the region is a new product incubation area, it is recommended to increase activities.

[0106] Through this embodiment, the system, based on diagnosis and early warning, realizes a business closed loop from problem identification to strategy execution and rule optimization, enabling the health rating system to continuously improve itself based on actual business results and enhancing the adaptability of specification management.

[0107] The present embodiment will now be described and illustrated through preferred embodiments.

[0108] First, after acquiring the cigarette specification data for specification A, the system matches it against the dynamic rule base, triggering two rules simultaneously: "inventory-to-sales ratio > 1.2 for two consecutive weeks" and "week-on-week retail price decline > 3%". The calculated health score is 52 points, and the system automatically classifies it as an orange alert based on preset mapping rules. Second, the diagnostic visualization and alert push module highlights the specification in orange on the global dashboard and automatically pushes an alert message, including risk details, data snapshots, and preliminary strategy suggestions. The strategy suggestion and closed-loop feedback module further generates actionable suggestions such as "check channel inventory and plan a tasting event". Finally, the system collects the effect data after strategy execution (such as a decrease in inventory-to-sales ratio and a price rebound) and feeds it back to the dynamic rule base for adaptive optimization of relevant rule thresholds or logic, thus completing a full business closed loop from real-time diagnosis, alert push, strategy execution to effect feedback.

[0109] This embodiment provides a cigarette specification health rating system, including a multi-source heterogeneous data integration and fusion module, a dynamic rule base construction and management module, and a real-time streaming diagnostic engine module;

[0110] The multi-source heterogeneous data integration and fusion module is used to acquire multi-source cigarette specification dimension data;

[0111] The dynamic rule base building and management module is used to store and configure different types of health assessment rules;

[0112] The real-time streaming diagnostic engine module is used to determine the health of the specification layout and the health status level.

[0113] Specifically, such as Figure 7 As shown, the multi-source heterogeneous data integration and fusion module is used to acquire multi-dimensional cigarette specification data from sources such as industrial ERP systems, marketing platforms, retail terminal data collection systems, and consumer / public opinion platforms. The acquisition method for cigarette specification data is not restricted. The dynamic rule base construction and management module builds and maintains a rule system containing industry-wide common rules and configurable individual rules. Common rules define general diagnostic thresholds, while individual rules allow adjustments to thresholds and logic based on dimensions such as region and lifecycle. The real-time streaming diagnostic engine module, based on a streaming computing framework, performs parallel matching and conditional judgments on cigarette specification data in real time, matching it with rules in the rule system to calculate a quantitative score for the layout health of each specification, and mapping it to the corresponding health status level according to a preset score range.

[0114] Through this embodiment, the system realizes the automated collection and fusion of multi-source cigarette specification data, supports differentiated diagnosis through a dynamically configurable rule base, and completes real-time, concurrent health assessment and classification using a streaming computing engine, providing core computing capabilities for the precise management of cigarette specifications.

[0115] In some embodiments, the cigarette specification health rating system also includes a diagnostic visualization and early warning push module and a strategy suggestion and closed-loop feedback module;

[0116] The diagnostic visualization and early warning push module is used to present visualized diagnostic results and push early warning information;

[0117] The strategy suggestion and closed-loop feedback module is used to output optimization strategies based on the health status level, and to optimize and update the dynamic rule base based on the execution feedback data of the optimization strategies.

[0118] Specifically, such as Figure 7As shown, the diagnostic visualization and early warning push module is used to centrally display health status levels and diagnostic details through color-coded maps / visual dashboards and push early warning messages. The visual dashboards offer various views, including but not limited to specification layouts such as "heatmaps," "health trend charts," and "risk specification rankings." There are no restrictions on the types of views provided by the visual dashboards. The visual dashboards support drill-down analysis by region, time, brand, and other dimensions. The strategy suggestion and closed-loop feedback module automatically matches and outputs corresponding optimization strategies from the built-in "diagnostic results - strategy suggestions" mapping knowledge base based on the health status level. Optimization strategies include strategy suggestion reports and R&D / marketing task cards. After the optimization strategy is executed, the execution feedback data flows back into the system. Based on the returned execution feedback data, the dynamic rule base is further optimized and updated.

[0119] In this embodiment, the system, based on automatic health rating, further achieves real-time risk perception and delivery through diagnostic visualization and early warning push modules, and intelligently associates diagnostic results with optimization strategies through strategy suggestion and closed-loop feedback modules. Finally, the rule base is iteratively updated based on strategy execution effect data, thereby constructing an adaptive management closed loop that integrates real-time monitoring, intelligent diagnosis, strategy recommendation and effect feedback.

[0120] It should be noted that the above modules can be functional modules or program modules, and can be implemented through software or hardware. For modules implemented through hardware, the above modules can reside in the same processor; or the above modules can be located in different processors in any combination.

[0121] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementations, and will not be repeated in this embodiment.

[0122] It should be noted that all information and data involved in this application are authorized by the user or fully authorized by all parties and will be used legally.

[0123] It should be understood that the specific embodiments described herein are merely illustrative of the application and not intended to limit it. All other embodiments derived by those skilled in the art based on the embodiments provided in this application without inventive effort are within the scope of protection of this application.

[0124] Obviously, the accompanying drawings are merely some examples or embodiments of this application. Those skilled in the art can apply this application to other similar situations based on these drawings without any creative effort. Furthermore, it is understood that although the work done in this development process may be complex and lengthy, for those skilled in the art, certain design, manufacturing, or production modifications made based on the technical content disclosed in this application are merely conventional technical means and should not be considered as insufficient disclosure of this application.

[0125] The term "embodiment" in this application refers to a specific feature, structure, or characteristic described in connection with an embodiment that may be included in at least one embodiment of this application. The appearance of this phrase in various places in the specification does not necessarily imply the same embodiment, nor does it imply that it is mutually exclusive with or independent of other embodiments. It will be clearly or implicitly understood by those skilled in the art that the embodiments described in this application may be combined with other embodiments without conflict.

[0126] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of patent protection. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the appended claims.

Claims

1. A method for rating the healthiness of cigarette specifications, characterized in that, include: Acquire multi-source cigarette specification data; The cigarette specification dimension data and the health assessment rules in the pre-built dynamic rule base are concurrently matched and calculated to determine the health of the specification layout. Based on the health status of the specified layout, determine the health status level; The health status levels include healthy status, attention status, early warning status, and risk status.

2. The method for rating the health of cigarette specifications according to claim 1, characterized in that, The step of concurrently matching and calculating the health assessment rules in the pre-built dynamic rule base to determine the health of the cigarette specification layout includes: Based on the cigarette specification dimension data, a wide table of specification indicators is generated; The specification layout health score is obtained by concurrently matching and calculating the specification data in the specification index wide table and the health assessment rules in the dynamic rule base.

3. The method for rating the health of cigarette specifications according to claim 2, characterized in that, The step of generating a wide table of specification indicators based on the cigarette specification dimension data includes: The cigarette specification dimension data is cleaned, aligned, and merged to generate the specification index wide table.

4. The method for rating the health status of cigarette specifications according to claim 2, characterized in that, The step of concurrently matching and calculating the specification data in the specification index wide table and the health assessment rules in the dynamic rule base to obtain the specification layout health score includes: The specification data is matched with the health assessment rules to obtain the matching results; the health assessment rules include general rules and multiple configurable personalized rules. The health of the specification layout is calculated based on the matching results and the preset rule contribution scores.

5. The method for rating the health status of cigarette specifications according to claim 1, characterized in that, The process of determining the health status level based on the health of the specified layout includes: If the health status of the specification layout falls within a preset first value range, then the health status level is determined to be healthy. If the health status of the specification layout falls within the preset second numerical range, then the health status level is determined to be a state of concern. If the health status of the specification layout falls within the preset third value range, then the health status level is determined to be a warning state. If the health status of the specified layout falls within the preset fourth numerical range, then the health status level is determined to be a risky state.

6. The method for rating the health of cigarette specifications according to claim 1, characterized in that, After determining the health status level based on the health of the layout according to the specifications, the method further includes: Based on the stated health status level, a corresponding visual diagnostic result is generated.

7. The method for rating the health of cigarette specifications according to claim 6, characterized in that, The step of generating corresponding visual diagnostic results based on the health status level includes: If the health status level is healthy or concerned, then the corresponding visual diagnostic result is generated; If the health status level is a warning state or a risk state, a warning message will be pushed to the designated manager, and the corresponding visual diagnostic result will be generated.

8. The method for rating the health status of cigarette specifications according to claim 1, characterized in that, After determining the health status level based on the health of the layout according to the specifications, the method further includes: Based on the health status level, match and output the corresponding optimization strategy from the preset strategy knowledge base; and execute the optimization strategy. Receive and record the execution feedback data of the optimization strategy, and optimize and update the dynamic rule base based on the execution feedback data.

9. A cigarette specification health rating system, characterized in that, It includes a multi-source heterogeneous data integration and fusion module, a dynamic rule base construction and management module, and a real-time streaming diagnostic engine module; The multi-source heterogeneous data integration and fusion module is used to acquire multi-source cigarette specification dimension data; The dynamic rule base construction and management module is used to store and configure different types of health assessment rules; The real-time streaming diagnostic engine module is used to determine the health of the specification layout and the health status level.

10. The cigarette specification health rating system according to claim 9, characterized in that, The system also includes a diagnostic visualization and early warning push module and a strategy suggestion and closed-loop feedback module; The diagnostic visualization and early warning push module is used to present visualized diagnostic results and push early warning information; The strategy suggestion and closed-loop feedback module is used to output optimization strategies based on the health status level, and to optimize and update the dynamic rule base based on the execution feedback data of the optimization strategies.

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