Internal supervision system of tobacco unified monopoly supervision platform

Abstract

The invention relates to the technical field of cigarette production, operation and supervision, in particular to an internal supervision system of a tobacco unified monopoly supervision platform, which comprises an evaluation layer, a behavior layer, a system layer and a digital platform, and forms a complete internal supervision architecture. The behavior layer is used for monitoring system execution and supporting multi-terminal management to enhance the quick response capability, and the system layer is used for providing system first draft examination and management functions and ensuring the compliance of system execution; according to the tobacco monopoly management system, a systematic internal control and risk management mechanism is established through comprehensive supervision of links such as monopoly, marketing and logistics, in addition, the system has dynamic feedback and intelligent decision support functions, the management level of tobacco monopoly can be improved, and the management efficiency of tobacco monopoly can be improved. And optimal configuration of resources and effective control of risks are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of cigarette production and operation supervision, and in particular to an internal supervision system of a unified tobacco monopoly supervision platform. Background Art

[0002] With the rapid development of the tobacco industry and the continuous changes in the market environment, the demand for tobacco monopoly supervision is increasing. In order to adapt to this change, the tobacco monopoly department urgently needs to establish an efficient and intelligent internal supervision system to ensure the effective implementation of the monopoly policy and compliance with industry norms. At the same time, with the rapid development of information technology, digitalization and dataization have become important means to improve management efficiency and decision-making support. By building a unified supervision platform, centralized management, real-time monitoring and analysis of data can be achieved, thus providing guarantees for the sustainable development of the tobacco industry.

[0003] However, there are many problems with the existing internal regulatory technology for tobacco monopolies. First, the traditional regulatory model often relies on manual inspection and decentralized data management, resulting in low regulatory efficiency and failure to detect and respond to risks in a timely manner. Second, the existing system often lacks cross-departmental collaboration capabilities, leading to the formation of information islands, making it impossible to share and circulate regulatory information, and affecting the scientificity and effectiveness of decision-making. In addition, the compliance issues of system implementation are becoming increasingly prominent, many systems have not been effectively implemented, and there is a lack of systematic monitoring and evaluation mechanisms. Summary of the invention

[0004] Based on the above objectives, the present invention provides an internal supervision system for a unified tobacco monopoly supervision platform.

[0005] The internal supervision system of the unified tobacco monopoly supervision platform includes the evaluation layer, behavior layer, system layer and digital middle platform, among which:

[0006] The evaluation layer is used to evaluate the internal management effectiveness display, including the home page dashboard, unit business status portrait, retailer portrait, cigarette portrait, statistical analysis, assessment and evaluation, and internal control maturity;

[0007] The behavior layer is used to supervise the implementation of the system and to manage problems and risks based on PC and mobile terminals, including abnormal investigation and processing, supervision management, internal inspection, early warning monitoring and collaborative supervision;

[0008] The system layer is used for basic system management, providing review, registration, maintenance and query functions for the draft systems of monopoly, marketing and logistics that require the participation of internal management departments for review, and realizing the filing review of the system;

[0009] The digital middle platform includes a business middle platform and a data middle platform. The business middle platform covers the user center, customer center, product center, transaction center, message center, certificate center, case center and map center, which facilitates the unified management of the business. The data middle platform contains exclusive data, marketing data, logistics data, industrial data and market abnormality information feedback, and supports data analysis and decision optimization.

[0010] Optionally, the evaluation layer includes:

[0011] Unit business status portrait generation: By connecting to the data source in the business center, we collect real-time business data of each unit, including sales volume, inventory level and customer feedback. At the same time, we extract the corresponding historical business data from the data center to ensure the integrity and comparability of the data. We use data analysis algorithms to compare historical data with real-time data, calculate the change rate and trend of various indicators, and form a dynamic business status portrait.

[0012] Evaluation report generation: Based on the generated unit business status portrait, an evaluation report is automatically generated, including quantitative analysis and qualitative evaluation of the business situation. The evaluation report contains abnormal indicator reminders, trend analysis charts and recommended measures for managers' reference;

[0013] Feedback on evaluation results: After the evaluation report is completed, the manager will comment and provide feedback on the report content online. The system will record the feedback information. If the evaluation results show that the operating status is abnormal, the system will automatically generate rectification tasks and assign the tasks to the relevant responsible departments;

[0014] Rectification task tracking: After receiving the rectification task in the system, the responsible department fills in the rectification plan and uploads the implementation progress. The system tracks and monitors the rectification progress to ensure that the task is completed within the specified time. After the rectification is completed, the responsible department submits the rectification results, and the system reviews and provides feedback on the results.

[0015] Data visualization: The evaluation layer uses data visualization technology to display the operating status of each unit in the form of charts;

[0016] Decision support steps: Establish data flow between the assessment layer and the behavior layer to ensure that the assessment results can be fed back to the behavior layer in real time. The behavior layer uses the assessment results to optimize supervision and management measures, formulate corresponding rectification measures, and form a closed-loop management.

[0017] Optionally, the behavior layer includes:

[0018] Real-time problem reporting: When an abnormality or problem is found, the relevant personnel submit the problem report through the mobile or PC interface in the system, fill in the problem description, occurrence time, location and related attachments, etc. The system will conduct a preliminary review of the report content;

[0019] Automatically assign investigation tasks: The system automatically assigns investigation tasks to designated investigation teams based on the preset rules of problem type, severity and responsible department. Investigation tasks include task name, task description, deadline and priority, etc. The system will send task notifications to relevant personnel through the message center;

[0020] Investigation progress tracking: After receiving the task, the investigation team needs to register the investigation progress in the system, including the time when the investigation started, the current investigation status, the preliminary findings, and matters that need further investigation;

[0021] Result feedback mechanism: After the investigation is completed, the investigation team fills in the investigation report in the system, which includes the investigation results, rectification suggestions and follow-up measures;

[0022] Implementation of corrective measures: Based on the corrective suggestions in the investigation report, the responsible department formulates a specific corrective plan and registers it in the system;

[0023] Verification of rectification results: After the rectification measures are implemented, the responsible department submits a rectification result report in the system, including rectification effect evaluation and follow-up plan;

[0024] Problem archiving and analysis: All processed problems will be automatically archived, and the system will conduct data statistics and analysis on each link of problem handling.

[0025] Optionally, the system layer includes:

[0026] Steps for entering the draft of the system: The relevant departments will enter the draft of the system to be reviewed into the system, including the system name, version number, drafting department and drafting date;

[0027] Compliance automatic reminder step: The system automatically identifies the legal and regulatory clauses involved in the draft system based on the set legal and regulatory database;

[0028] Comparison of laws and regulations: Auditors open the draft of the system in the system. The system provides a comparison function for laws and regulations and generates a compliance inspection report through automatic comparison.

[0029] Audit opinion records: Auditors record audit opinions during the audit process, including compliance evaluation, modification suggestions and audit questions;

[0030] Version control: For each review and modification, the system automatically generates a new version of the draft system and retains the historical version;

[0031] Audit result confirmation: After completing the audit, the auditor confirms the audit results in the system, including compliance assessment and whether the modification opinions are adopted;

[0032] Final review before release: After the draft of the system is confirmed by the auditor, the system will conduct a final compliance review. After the final review is completed, the system will generate a final review report;

[0033] System release: The first draft of the system that passes the final review will enter the release process, and the system will notify the relevant departments to release the system. After the release, the system will automatically update the system library to ensure that all departments can obtain the latest system information, and can query and download the system.

[0034] Optionally, the system layer has clause requirement management functions, specifically including:

[0035] Establishment of clause classification structure: The system sets multi-dimensional classification standards, including regulatory categories, department classifications, document importance and implementation stages, creates a classification tree structure, and supports users to select classification dimensions according to their needs;

[0036] Clause input step: relevant departments input clause information in the system documents into the system, including clause content, corresponding laws and regulations, scope of application and responsible departments;

[0037] Maintenance and update of clauses: For entered clauses, the system supports modification and update functions, and supports users to edit, review and confirm the content of clauses. When updating, the system automatically records the change history and retains the old version for query;

[0038] Accurate search function: supports users to conduct quick search by combining multiple conditions (such as keywords, clause categories, responsible departments, etc.);

[0039] Clause association function: The system supports associating clauses with relevant documents, laws and regulations, and institutional matters to form a network connection between clauses. It supports users to directly view the associated clause information by clicking on a clause, making it easier to understand the institutional background and requirements.

[0040] Export and report generation: Support users to select specific clauses for batch export. The system supports export options in multiple formats (such as PDF, Excel, etc.) for easy offline viewing and sharing. The generated export report contains the classification information, review status and change history of the clauses to ensure information integrity;

[0041] Department collaborative management: Support departments to view the terms and conditions related to themselves in the system and make comments and suggestions on the applicability of the terms and conditions;

[0042] Regular evaluation and optimization: The system sets up a regular evaluation mechanism to evaluate the effectiveness and applicability of the published terms and conditions, and update and optimize them in a timely manner.

[0043] Optionally, the risk management in the execution of the supervision system at the behavior layer includes:

[0044] Data collection and integration: The system collects basic data of retailers through multiple channels, including business licenses, transaction records, inventory information and customer feedback, and uses real-time monitoring systems to obtain market status, sales data and abnormal flow data, and integrates historical data with real-time monitoring data to form a data pool;

[0045] Risk indicator setting: According to business needs and industry standards, a series of risk indicators are set, such as order anomalies, frequent changes, customer complaint rates, etc. The system supports user-defined risk indicators and makes regular adjustments based on industry dynamics;

[0046] Steps to build an outflow risk assessment model: Use big data analysis technology to build an outflow risk assessment model based on historical data and real-time data. The outflow risk assessment model combines machine learning algorithms to analyze the relationship between different variables and identify key factors affecting risks;

[0047] Model training and validation steps: Use the labeled historical data to train the outflow risk assessment model, perform cross-validation, evaluate the performance of the outflow risk assessment model on different data sets, and continuously optimize the model parameters based on the validation results;

[0048] Risk identification and early warning steps: Apply the established outflow risk assessment model to analyze the transaction behavior and market dynamics of retailers in real time, automatically identify potential risk customers and risky behaviors, and generate early warning information;

[0049] Risk management strategy formulation steps: formulate corresponding risk management strategies based on the identified risks, such as strengthening supervision, restricting transactions, conducting customer return visits, etc., and feedback the strategies to the regulatory authorities;

[0050] Continuous monitoring and feedback steps: The system continuously monitors the effectiveness of the implemented risk management strategies, evaluates the effectiveness through real-time data feedback, and adjusts and optimizes the risk management strategies based on the feedback results to form a closed-loop management mechanism;

[0051] Report generation and analysis steps: The system regularly generates risk management reports that list risk identification, management measures and effectiveness evaluation. The reports provide decision support to help management understand the risk status in a timely manner and make corresponding adjustments.

[0052] Optionally, the evaluation layer includes an internal control maturity management unit, which is based on the CMMI model, scores various indicators of internal control, regularly generates maturity reports and provides optimization suggestions, specifically including:

[0053] Establishment of maturity indicator system: internal control maturity is divided into five levels, namely initial level, management level, clear level, management level and optimization level. For each level, corresponding evaluation indicators are formulated, including system perfection, implementation compliance, risk control ability and problem rectification efficiency;

[0054] Data collection: Regularly collect internal control document data, including internal control system documents, historical assessment reports, risk assessment data, supervision and inspection records, problem rectification records, and training and assessment records;

[0055] Application of scoring mechanism: Use the pre-trained CMMI model to score the internal control document data, use quantitative methods to score each indicator, and calculate the scoring results based on the indicator weights to obtain the overall internal control maturity score;

[0056] Maturity report generation: Based on the scoring results, internal control maturity reports are generated regularly, including maturity scores, analysis of various indicators, evaluation results and existing problems. The reports are presented in the form of charts and data analysis to facilitate understanding and analysis;

[0057] Optimization suggestions: Based on the evaluation results of the maturity report, corresponding optimization suggestions are put forward, including system improvement, process optimization, training needs, etc.;

[0058] Regular review and adjustment: Review and reassess the internal control maturity periodically (e.g. quarterly or annually), and update the maturity score and optimization suggestions based on newly collected internal control document data and improvement measures.

[0059] Optionally, the system layer includes a problem risk item management unit, and the problem risk item management unit specifically includes:

[0060] Creation of checklists: Automatically generate checklists based on internal control documents, laws, regulations and industry standards, and support users to manually add or modify checklist items based on actual needs;

[0061] Multi-level checklist management: supports dividing the checklist into multiple levels according to the complexity and importance of regulatory matters;

[0062] Online maintenance and feedback function: Provide online maintenance function for the list, support users to modify, update, supplement or delete the list, ensure the timeliness and accuracy of information, and set up a feedback mechanism. After completing the inspection, the relevant responsible person submits inspection feedback in the system, recording the problems found, rectification suggestions and rectification deadlines;

[0063] Data recording and analysis: The system records the results of each inspection, including the inspection time, inspector, problems found and rectification status, forming a complete inspection file. Through data analysis, statistics and analysis are conducted on areas with frequent problems, and analysis reports are generated to provide a basis for subsequent regulatory decisions;

[0064] Real-time early warning mechanism: Set early warning rules based on the risk indicators in the checklist. If the risk items are not rectified within the specified time, an early warning will be automatically triggered and the relevant responsible persons will be notified. The early warning information can be pushed simultaneously through the PC and mobile terminals to ensure that the management can take timely measures;

[0065] Regular evaluation and optimization: Regularly evaluate the checklist and its implementation, identify areas that need improvement, and adjust the content and frequency of inspections to adapt to changing regulatory needs.

[0066] Optionally, the data center includes a data analysis module, and the data analysis module specifically includes:

[0067] Data collection: Collect relevant data in real time from the evaluation layer, behavior layer and institutional layer within the system, including past execution records, problem rectification status, current sales data, inventory status and abnormal behavior;

[0068] Data preprocessing: Clean and preprocess the collected data, including removing redundant information, processing missing values, unifying data formats, etc., to improve data quality;

[0069] Multi-dimensional analysis model construction: Based on analysis requirements, a multi-dimensional data analysis model is constructed to support data stratification, grouping and aggregation. The analysis dimensions include time, region, product category and sales channel, so as to conduct in-depth analysis from different angles.

[0070] Report generation: The system automatically generates data analysis reports, including indicator statistics, trend analysis, and identification of abnormal situations;

[0071] Decision support function: Provide intelligent decision support function and give corresponding optimization suggestions based on analysis results;

[0072] Result feedback and optimization: A feedback mechanism is established, and the management will provide feedback on the results after implementing the optimization suggestions. The system will compare the data before and after implementation to evaluate the optimization effect.

[0073] Optionally, the data center includes a multi-dimensional dynamic association analysis algorithm module, and the multi-dimensional dynamic association analysis algorithm module specifically includes:

[0074] Data preprocessing and standardization: Collect and integrate multi-dimensional data from exclusive sales, marketing and logistics, and standardize the multi-dimensional data;

[0075] Feature extraction and hierarchical analysis: Use feature selection and hierarchical clustering algorithms to process data of different dimensions, extract features and identify correlations;

[0076] Granger causality analysis: Analyze the temporal relationship between different data dimensions and use Granger causality test to identify the impact of external factors on the target variable;

[0077] Recurrent Neural Network and Long Short-Term Memory Network Model: In order to capture the time dependency in the data, recurrent neural network and long short-term memory network are used to establish a multi-dimensional correlation analysis model for dynamic data;

[0078] Adaptive learning and optimization: Introduce an adaptive learning mechanism to continuously adjust model parameters by introducing an optimization algorithm based on gradient descent;

[0079] Decision support and risk prediction: Utilize the output results of the multi-dimensional correlation analysis model to provide decision support for managers and generate analysis reports, which include risk assessment, trend prediction and optimization suggestions.

[0080] Beneficial effects of the present invention:

[0081] The present invention realizes comprehensive monitoring and evaluation of internal management through the organic combination of the evaluation layer, behavior layer, system layer and digital middle platform. The interrelationship between the modules enhances the response speed of the system, enables managers to identify and solve problems in internal control in a timely manner, greatly improves the efficiency and effectiveness of internal supervision, and thus reduces management loopholes and potential risks.

[0082] The present invention, through the construction of data analysis modules, can collect and analyze historical data and real-time data in real time, thereby generating multi-dimensional analysis reports. These reports provide key performance indicators and trend charts to help management gain an in-depth understanding of market dynamics and internal management effects, making decisions more scientific and reasonable. The introduction of decision support functions provides management with intelligent optimization suggestions and risk assessments, which helps to formulate more effective business strategies.

[0083] In this invention, the management function of the system layer ensures that all internal control systems are strictly reviewed before release, thereby improving the compliance of system implementation. Through the management of drafts, clause requirements, problem risk items, and things to know and do, the system not only strengthens the supervision of system implementation, but also promotes collaboration between departments and breaks down information barriers between business lines. This series of measures ensures the integrity and effective implementation of the internal control system, thereby providing a strong guarantee for the compliance operation of tobacco monopoly. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0085] Figure 1 A schematic diagram of a system flow of an embodiment of the present invention;

[0086] Figure 2 This is a schematic diagram of the evaluation layer process of an embodiment of the present invention;

[0087] Figure 3 The figure is a schematic diagram of a behavior layer flow of an embodiment of the present invention. DETAILED DESCRIPTION

[0088] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0089] It should be noted that the references to "one embodiment", "an embodiment", "an exemplary embodiment", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0090] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0091] like Figure 1-Figure 3 As shown in the figure, the internal supervision system of the unified tobacco monopoly supervision platform includes the evaluation layer, behavior layer, system layer and digital middle platform, among which:

[0092] The evaluation layer is used to evaluate the internal management effectiveness display, including the home page dashboard, unit business status portrait, retailer portrait, cigarette portrait, statistical analysis, assessment and evaluation, and internal control maturity. The evaluation layer helps managers to understand the effectiveness and existing problems of internal control in a timely manner through real-time data display and analysis;

[0093] The behavior layer is used to supervise the implementation of the system and to manage problems and risks based on PC and mobile terminals, including abnormal investigation and processing, supervision management, internal inspection, early warning monitoring and collaborative supervision. Through multi-terminal management, the ability to respond quickly to problems is enhanced;

[0094] The system layer is used for basic system management. It provides review, registration, maintenance and query functions for the draft systems of monopoly, marketing and logistics that require the participation of internal management departments for review, and realizes the filing review of the system. The system layer ensures that all systems are rigorously reviewed before release to improve the effectiveness of system implementation.

[0095] The digital middle platform includes the business middle platform and the data middle platform. The business middle platform covers the user center, customer center, product center, transaction center, message center, certificate center, case center and map center, which facilitates the unified management of the business. The data middle platform contains exclusive data, marketing data, logistics data, industrial data and market abnormal information feedback, and supports data analysis and decision optimization.

[0096] The assessment layers include:

[0097] Unit business status portrait generation: By connecting to the data source in the business center, we collect real-time business data of each unit, including sales volume, inventory level and customer feedback. At the same time, we extract the corresponding historical business data from the data center to ensure the integrity and comparability of the data. We use data analysis algorithms to compare historical data with real-time data, calculate the change rate and trend of various indicators, and form a dynamic business status portrait.

[0098] Evaluation report generation: Based on the generated unit business status portrait, an evaluation report is automatically generated, including quantitative analysis and qualitative evaluation of the business situation. The evaluation report contains abnormal indicator reminders, trend analysis charts and recommended measures for managers to refer to. After the report is generated, it is automatically pushed to the manager's personal center and a reminder is sent through the message center;

[0099] Feedback on evaluation results: After the evaluation report is completed, the manager will comment and provide feedback on the report content online. The system will record the feedback information. If the evaluation results show that the operating status is abnormal, the system will automatically generate rectification tasks and assign the tasks to the relevant responsible departments;

[0100] Rectification task tracking: After receiving the rectification task in the system, the responsible department fills in the rectification plan and uploads the implementation progress. The system tracks and monitors the rectification progress to ensure that the task is completed within the specified time. After the rectification is completed, the responsible department submits the rectification results, and the system reviews and provides feedback on the results.

[0101] Data visualization: The evaluation layer uses data visualization technology to display the operating status of each unit in the form of charts, which is convenient for managers to intuitively understand the operating conditions of each unit. The system supports customized report generation, and users can choose display indicators according to their own needs;

[0102] Decision support steps: Establish data flow between the assessment layer and the behavior layer to ensure that the assessment results can be fed back to the behavior layer in real time. The behavior layer uses the assessment results to optimize supervision and management measures, formulate corresponding rectification measures, and form a closed-loop management.

[0103] The behavioral layer includes:

[0104] Real-time problem reporting: When an abnormality or problem is found, the relevant personnel submit the problem report through the mobile or PC interface in the system, fill in the problem description, time and location of occurrence, and related attachments. The system conducts a preliminary review of the report content to ensure the completeness and accuracy of the information, and prompts the user to supplement the information when necessary;

[0105] Automatically assign investigation tasks: The system automatically assigns investigation tasks to designated investigation teams based on the preset rules of problem type, severity and responsible department. Investigation tasks include task name, task description, deadline and priority, etc. The system will send task notifications to relevant personnel through the message center;

[0106] Investigation progress tracking: After receiving the task, the investigation team needs to register the investigation progress in the system, including the time when the investigation started, the current investigation status, the preliminary findings, and matters that need further investigation.

[0107] The system supports real-time progress updates and automatically sends progress reminders to relevant responsible persons and managers to ensure that all parties are aware of the investigation dynamics;

[0108] Result feedback mechanism: After the investigation is completed, the investigation team fills in the investigation report in the system, which includes the investigation results, rectification suggestions and follow-up measures;

[0109] The system will push the investigation report to the relevant responsible departments and managers, and send the result notification through the message center;

[0110] Implementation of corrective measures: Based on the corrective suggestions in the investigation report, the responsible department formulates a specific corrective plan and registers it in the system;

[0111] The system will review the rectification plan to ensure its feasibility and effectiveness. After the review is passed, the rectification measures will be implemented;

[0112] Verification of rectification results: After the rectification measures are implemented, the responsible department submits a rectification result report in the system, including rectification effect evaluation and follow-up plan. The system will automatically notify the investigation team to review the rectification results to ensure the effective implementation of the rectification measures;

[0113] Problem archiving and analysis: All processed problems will be automatically archived, and the system will collect data and analyze each link of problem processing;

[0114] The system can generate problem handling reports for reference in subsequent decision-making. It also supports regular summary of abnormal problem types and handling status, providing a basis for internal management optimization.

[0115] The institutional layer includes:

[0116] Steps for entering the draft of the system: The relevant departments will enter the draft of the system to be reviewed into the system, including the system name, version number, drafting department and drafting date;

[0117] The system generates a unique identifier for the first draft and stores the first draft information in the database;

[0118] Automatic compliance reminder step: The system automatically identifies the legal and regulatory clauses involved in the draft of the system based on the set legal and regulatory database. After the draft is entered, the system automatically sends a compliance review reminder to the personnel involved in the review. The notification content includes the legal and regulatory clauses to be reviewed and the review deadline;

[0119] Comparison of laws and regulations: Auditors open the draft of the system in the system. The system provides a comparison function for laws and regulations, and generates a compliance inspection report through automatic comparison. The report lists the degree of compliance, potential conflicts and omissions between the draft and laws and regulations, and marks the corresponding clauses for the auditors' reference;

[0120] Audit opinion record: Auditors record audit opinions during the audit process, including compliance evaluation, modification suggestions and audit questions. The system automatically saves the audit opinions and forms audit records to facilitate subsequent tracking and backtracking;

[0121] Version control: For each review and modification, the system automatically generates a new version of the draft of the system and retains the historical version to ensure traceability. The personnel involved in the review can view the modification records of the historical version through the system to understand the evolution process of the draft;

[0122] Confirmation of audit results: After completing the audit, the auditor confirms the audit results in the system, including compliance assessment and whether the modification opinions are adopted. The system automatically pushes audit feedback to the drafting department based on the audit results to ensure timely communication and feedback;

[0123] Final review before release: After the first draft of the system is confirmed by the auditor, the system will conduct a final compliance review to ensure that all modifications and comments have been implemented. After the final review is completed, the system will generate a final review report and record the confirmation information of each participant in the report;

[0124] System release: The first draft of the system that passes the final review will enter the release process, and the system will notify the relevant departments to release the system. After the release, the system will automatically update the system library to ensure that all departments can obtain the latest system information, and can query and download the system.

[0125] The system layer has the function of managing clause requirements, including:

[0126] Establishment of clause classification structure: The system sets multi-dimensional classification standards, including regulatory categories, department classifications, document importance and implementation stages, creates a classification tree structure, supports users to select classification dimensions according to their needs, and facilitates the classification and management of subsequent clauses;

[0127] Clause entry step: The relevant departments enter the clause information in the system documents, including clause content, corresponding laws and regulations, scope of application and responsible departments. The system generates a unique identifier for each clause to ensure the traceability and management convenience of the clause information;

[0128] Maintenance and update of clauses: For entered clauses, the system supports modification and update functions, and supports users to edit, review and confirm the content of clauses. When updating, the system automatically records the change history to ensure that each modification can be traced back and retains the old version for query;

[0129] Accurate search function: supports users to conduct quick searches through multiple condition combinations (such as keywords, clause categories, responsible departments, etc.). The system provides real-time search feedback and displays the clause information that meets the conditions in a list format, allowing users to click to view detailed content.

[0130] Clause association function: The system supports associating clauses with relevant documents, laws and regulations, and institutional matters to form a network connection between clauses. It supports users to directly view the associated clause information by clicking on a clause, making it easier to understand the institutional background and requirements.

[0131] Export and report generation: Support users to select specific clauses for batch export. The system supports export options in multiple formats (such as PDF, Excel, etc.) for easy offline viewing and sharing. The generated export report contains the classification information, review status and change history of the clauses to ensure information integrity;

[0132] Department collaborative management: Support departments to view the terms and conditions related to themselves in the system, and put forward opinions and suggestions on the applicability of the terms. The system records the communication and feedback between departments, forms a collaborative management mechanism, and improves the effectiveness of the implementation of the terms.

[0133] Regular evaluation and optimization: The system sets up a regular evaluation mechanism to evaluate the effectiveness and applicability of the published terms, and update and optimize them in a timely manner. The evaluation results can generate reports, and the system automatically notifies relevant departments to make necessary adjustments and modifications.

[0134] Risk management in the implementation of the supervision system at the behavioral level includes:

[0135] Data collection and integration: The system collects basic data of retailers through multiple channels, including business licenses, transaction records, inventory information and customer feedback, and uses real-time monitoring systems to obtain market status, sales data and abnormal flow data to ensure the comprehensiveness and timeliness of data. It integrates historical data with real-time monitoring data to form a data pool to support subsequent analysis and modeling;

[0136] Historical data includes past transaction records, customer information, sales data, transaction records, inventory information, and customer feedback, etc., reflecting the business status and trends in different time periods;

[0137] Examples of historical data: including retail sales volume, complaint records, policy implementation, etc. for the past year or longer;

[0138] Real-time data refers to the dynamic information obtained by the system at the current moment, including real-time transaction data, market status, instant customer feedback, inventory changes, sales data, abnormal flow data, etc., which are collected instantly through sensors, monitoring systems, mobile devices, etc.

[0139] Examples of real-time data: sales data for the day, real-time inventory levels, latest customer complaints, etc.

[0140] Risk indicator setting: According to business needs and industry standards, a series of risk indicators are set, such as order anomalies, frequent changes, customer complaint rates, etc. The system supports users to define risk indicators and makes regular adjustments based on industry dynamics to maintain the applicability of the model;

[0141] Steps to build an outflow risk assessment model: Use big data analysis technology to build an outflow risk assessment model based on historical data and real-time data. The outflow risk assessment model combines machine learning algorithms to analyze the relationship between different variables and identify key factors affecting risks;

[0142] Model training and validation steps: Use labeled historical data to train the outflow risk assessment model to ensure the accuracy and reliability of the model, perform cross-validation, evaluate the performance of the outflow risk assessment model on different data sets, and continuously optimize the model parameters based on the validation results;

[0143] Risk identification and early warning steps: Apply the established outflow risk assessment model to analyze the transaction behavior and market dynamics of retailers in real time, automatically identify potential risk customers and risky behaviors, and generate early warning information to identify high-risk customers and abnormal behaviors;

[0144] Risk management strategy formulation steps: formulate corresponding risk management strategies based on the identified risks, such as strengthening supervision, restricting transactions, conducting customer return visits, etc., and feedback the strategies to the regulatory authorities, provide data support, and ensure the scientific nature and effectiveness of management measures;

[0145] Continuous monitoring and feedback steps: The system continuously monitors the effectiveness of the implemented risk management strategies, evaluates the effectiveness through real-time data feedback, and adjusts and optimizes the risk management strategies based on the feedback results to form a closed-loop management mechanism;

[0146] Report generation and analysis steps: The system regularly generates risk management reports that list risk identification, management measures and effectiveness evaluation. The reports provide decision support to help management understand the risk status in a timely manner and make corresponding adjustments.

[0147] The assessment layer includes the internal control maturity management unit. The internal control maturity management unit is based on the CMMI model. It scores various indicators of internal control, generates maturity reports regularly and provides optimization suggestions, including:

[0148] Establishment of maturity indicator system: internal control maturity is divided into five levels, namely initial level, management level, clear level, management level and optimization level. For each level, corresponding evaluation indicators are formulated, including system perfection, implementation compliance, risk control ability and problem rectification efficiency;

[0149] Data collection: Regularly collect internal control document data, including internal control system documents, historical assessment reports, risk assessment data, supervision and inspection records, problem rectification records, and training and assessment records, among which;

[0150] Internal control system documents: including various management regulations, policies and operating procedures;

[0151] Historical evaluation report: Documentation on the evaluation of the effectiveness of internal controls in the past;

[0152] Risk assessment data: information related to identified and assessed risks;

[0153] Supervision and inspection records: inspection and feedback records on the implementation of internal controls;

[0154] Problem rectification records: problems found during the internal control process and their rectification status;

[0155] Training and assessment records: training content and assessment results related to internal control;

[0156] Obtain real-time and historical internal control document data through system automation tools to ensure data accuracy and comprehensiveness;

[0157] Application of scoring mechanism: Use the pre-trained CMMI model to score the internal control document data, use quantitative methods to score each indicator, and calculate the scoring results based on the indicator weights to obtain the overall internal control maturity score;

[0158] Maturity report generation: Based on the scoring results, internal control maturity reports are generated regularly, including maturity scores, analysis of various indicators, evaluation results and existing problems. The reports are presented in the form of charts and data analysis to facilitate understanding and analysis;

[0159] Optimization suggestions: Based on the evaluation results of the maturity report, corresponding optimization suggestions are put forward, including system improvement, process optimization, training needs, etc., and a feedback mechanism is established to encourage relevant departments to evaluate and implement the suggestions and record the implementation status;

[0160] Regular review and adjustment: Review and reassess the internal control maturity periodically (e.g. quarterly or annually), update the maturity score and optimization suggestions based on newly collected internal control document data and improvement measures, and dynamically adjust indicators and evaluation criteria based on implementation effect feedback to ensure continuous optimization and adaptability of the system.

[0161] The system layer includes the problem risk management unit, which specifically includes:

[0162] Creation of checklists: Checklists are automatically generated based on internal control documents, laws, regulations and industry standards. The checklists include key regulatory matters, implementation standards, risk indicators, etc. Users are supported to manually add or modify checklist items according to actual needs to ensure that they are consistent with the current regulatory environment and requirements;

[0163] Multi-level checklist management: Supports dividing the checklist into multiple levels according to the complexity and importance of regulatory matters, such as the first-level checklist (main risk matters), the second-level checklist (sub-matters), etc. Each level of the checklist can be set with different reviewers and review cycles to ensure clear regulatory responsibilities;

[0164] Online maintenance and feedback function: Provide online maintenance function for the list, support users to modify, update, supplement or delete the list, ensure the timeliness and accuracy of information, and set up a feedback mechanism. After completing the inspection, the relevant responsible person submits inspection feedback in the system, recording the problems found, rectification suggestions and rectification deadlines;

[0165] Data recording and analysis: The system records the results of each inspection, including the inspection time, inspector, problems found and rectification status, forming a complete inspection file. Through data analysis, statistics and analysis are conducted on areas with frequent problems, and analysis reports are generated to provide a basis for subsequent regulatory decisions;

[0166] Real-time early warning mechanism: Set early warning rules based on the risk indicators in the checklist. If the risk items are not rectified within the specified time, an early warning will be automatically triggered and the relevant responsible persons will be notified. The early warning information can be pushed simultaneously through the PC and mobile terminals to ensure that the management can take timely measures;

[0167] Regular evaluation and optimization: Regularly evaluate the inspection checklist and its implementation, identify areas that need improvement, adjust the inspection content and frequency to adapt to changing regulatory needs, optimize the checklist structure through analysis of historical data, and improve management efficiency and supervision effectiveness.

[0168] The data center includes a data analysis module, which specifically includes:

[0169] Data collection: collect relevant data in real time from the evaluation layer, behavior layer and institutional layer within the system, including past execution records, problem rectification status, current sales data, inventory status and abnormal behavior. Data collection should use standardized interfaces to ensure that data from different sources can be efficiently integrated;

[0170] Data preprocessing: Clean and preprocess the collected data, including removing redundant information, processing missing values, unifying data formats, etc., to improve data quality, and establish data verification rules to ensure data accuracy and completeness;

[0171] Multi-dimensional analysis model construction: Based on analysis requirements, a multi-dimensional data analysis model is constructed to support data stratification, grouping and aggregation. The analysis dimensions include time, region, product category and sales channel, so as to conduct in-depth analysis from different angles.

[0172] Report generation: The system automatically generates data analysis reports, including indicator statistics, trend analysis, and identification of abnormal situations. The report format supports visual display, including charts, dashboards, etc., so that management can intuitively understand the data;

[0173] Decision support function: Provide intelligent decision support function, give corresponding optimization suggestions based on analysis results, such as adjusting marketing strategies, optimizing inventory management, etc. Users can choose implementation plans based on the suggestions in the analysis report and record the decision-making process for subsequent review;

[0174] Results feedback and optimization: A feedback mechanism is established. Management will provide feedback on the results after implementing the optimization suggestions. The system will compare the data before and after implementation to evaluate the optimization effect. Based on the feedback results, the data analysis model will be regularly optimized, and the analysis dimensions and methods will be adjusted to continuously improve the accuracy and effectiveness of data analysis.

[0175] Regular updates and maintenance: Regularly update data analysis units, add new analysis indicators and dimensions, ensure that the analysis content is consistent with industry trends and corporate needs, maintain access to and management of data sources, and ensure timely updating and accuracy of data.

[0176] The data center includes a multi-dimensional dynamic correlation analysis algorithm module, which specifically includes:

[0177] Data preprocessing and standardization: Collect and integrate multi-dimensional data from exclusive sales, marketing and logistics, and standardize the multi-dimensional data to remove noise and fill in missing values ​​to ensure data integrity and consistency;

[0178] Feature extraction and hierarchical analysis: Use feature selection and hierarchical clustering algorithms to process data of different dimensions, extract features and identify correlations. For example, the similarity between data points is calculated using the Euclidean distance formula, expressed as:

[0179]

[0180] Where x and y are two sample points in the data matrix, n is the number of features, and d(x,y) represents the Euclidean distance between sample points x and y, which measures their similarity in the multidimensional feature space.

[0181] Granger causality analysis: Analyze the temporal relationship between different data dimensions and use the Granger causality test to identify the impact of external factors on the target variable. Suppose there are two time series Y t and X t , and its regression equation is expressed as:

[0182]

[0183] Among them, Y t is the target variable (e.g., retail sales volume), X t is an external influencing factor (e.g., market advertising expenditure), α i and βi are the coefficients of the autoregressive term and external factors, ∈ t is a white noise term, representing the error;

[0184] The hypothesis test of Granger causality is expressed as:

[0185] H0:β1=β2=...=β q =0 (i.e. X t Y t No impact);

[0186] If the null hypothesis is rejected by the F test, then X t Y t Have a significant impact;

[0187] Recurrent neural network and long short-term memory network model: In order to capture the time dependency in the data, a recurrent neural network and long short-term memory network are used to establish a multi-dimensional correlation analysis model for dynamic data, which is expressed as:

[0188] h t =σ(W h h t-1 +W x x t +b h );

[0189] y t =W y h t +b y ;

[0190] Among them, h t is the hidden state, x t is the input data, W h ,W x ,W y is the weight matrix, σ is the activation function, y t is the output predicted by the model;

[0191] Adaptive learning and optimization: Introduce an adaptive learning mechanism, and continuously adjust model parameters by introducing an optimization algorithm based on gradient descent to ensure that the algorithm can adapt to market changes and data trends;

[0192] Decision support and risk prediction: Utilize the output results of the multi-dimensional correlation analysis model to provide managers with decision support, including predicting potential risks, optimizing marketing strategies, adjusting inventory management, etc., and generate analysis reports including risk assessment, trend prediction and optimization suggestions.

[0193] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0194] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. The internal supervision system of the unified tobacco monopoly supervision platform is characterized by: It includes the evaluation layer, behavior layer, system layer and digital middle platform, among which: The evaluation layer is used to evaluate the internal management effectiveness display, including the home page dashboard, unit business status portrait, retailer portrait, cigarette portrait, statistical analysis, assessment and evaluation, and internal control maturity; The behavior layer is used to supervise the implementation of the system and to manage problems and risks based on PC and mobile terminals, including abnormal investigation and processing, supervision management, internal inspection, early warning monitoring and collaborative supervision; The system layer is used for basic system management, providing review, registration, maintenance and query functions for the draft systems of monopoly, marketing and logistics that require the participation of internal management departments for review, and realizing the filing review of the system; The digital middle platform includes a business middle platform and a data middle platform. The business middle platform covers the user center, customer center, product center, transaction center, message center, certificate center, case center and map center, which facilitates the unified management of the business. The data middle platform contains exclusive data, marketing data, logistics data, industrial data and market abnormality information feedback, and supports data analysis and decision optimization.

2. The internal supervision system of the unified tobacco monopoly supervision platform according to claim 1 is characterized in that: The evaluation layer includes: Unit business status portrait generation: By connecting to the data source in the business platform, we collect real-time business data of each unit, including sales volume, inventory level and customer feedback. At the same time, we extract the corresponding historical business data from the data platform, use data analysis algorithms to compare historical data with real-time data, calculate the change rate and trend of various indicators, and form a dynamic business status portrait; Evaluation report generation: Based on the generated unit business status portrait, an evaluation report is automatically generated, including quantitative analysis and qualitative evaluation of the business situation. The evaluation report contains abnormal indicator reminders, trend analysis charts and recommended measures for managers' reference; Feedback on evaluation results: After the evaluation report is completed, the manager will comment and provide feedback on the report content online. The system will record the feedback information. If the evaluation results show that the operating status is abnormal, the system will automatically generate rectification tasks and assign the tasks to the relevant responsible departments; Rectification task tracking: After receiving the rectification task in the system, the responsible department fills in the rectification plan and uploads the implementation progress. The system tracks and monitors the rectification progress. After the rectification is completed, the responsible department submits the rectification results, and the system reviews and provides feedback on the results. Data visualization: The evaluation layer uses data visualization technology to display the operating status of each unit in the form of charts; Decision support: Establish data flow between the assessment layer and the behavior layer, and feed back the assessment results to the behavior layer in real time. The behavior layer uses the assessment results to optimize supervision and management measures and formulate corresponding rectification measures.

3. The internal supervision system of the unified tobacco monopoly supervision platform according to claim 1 is characterized in that: The behavior layer includes: Real-time problem reporting: When an abnormality or problem is found, the relevant personnel submit the problem report through the mobile or PC interface in the system, and the system conducts a preliminary review of the report content; Automatically assign investigation tasks: The system automatically assigns investigation tasks to designated investigation teams based on the preset rules of problem type, severity and responsible department. Investigation tasks include task name, task description, deadline and priority, etc. The system will send task notifications to relevant personnel through the message center; Investigation progress tracking: After receiving the task, the investigation team needs to register the investigation progress in the system, including the time when the investigation started, the current investigation status, the preliminary findings and the matters under investigation; Result feedback mechanism: After the investigation is completed, the investigation team fills in the investigation report in the system, which includes the investigation results, rectification suggestions and follow-up measures; Implementation of corrective measures: Based on the corrective suggestions in the investigation report, the responsible department formulates a specific corrective plan and registers it in the system; Verification of rectification results: After the rectification measures are implemented, the responsible department submits a rectification result report in the system, including rectification effect evaluation and follow-up plan; Problem archiving and analysis: All processed problems will be automatically archived, and the system will conduct data statistics and analysis on each link of problem handling.

4. The internal supervision system of the unified tobacco monopoly supervision platform according to claim 1 is characterized in that: The institutional layer includes: Steps for entering the draft of the system: The relevant departments will enter the draft of the system to be reviewed into the system, including the system name, version number, drafting department and drafting date; Compliance automatic reminder step: The system automatically identifies the legal and regulatory clauses involved in the draft system based on the set legal and regulatory database; Comparison of laws and regulations: Auditors open the draft of the system in the system. The system provides a comparison function for laws and regulations and generates a compliance inspection report through automatic comparison. Audit opinion records: Auditors record audit opinions during the audit process, including compliance evaluation, modification suggestions and audit questions; Version control: For each review and modification, the system automatically generates a new version of the draft system and retains the historical version; Audit result confirmation: After completing the audit, the auditor confirms the audit results in the system, including compliance assessment and whether the modification opinions are adopted; Final review before release: After the draft of the system is confirmed by the auditor, the system will conduct a final compliance review. After the final review is completed, the system will generate a final review report; System release: The first draft of the system that passes the final review will enter the release process, and the system will notify the relevant departments to release the system. After the release, the system will automatically update the system library to ensure that all departments can obtain the latest system information.

5. The internal supervision system of the unified tobacco monopoly supervision platform according to claim 1 is characterized in that: The system layer has the function of managing clause requirements, including: Establishment of clause classification structure: The system sets multi-dimensional classification standards, including regulatory categories, department classifications, document importance and implementation stages, creates a classification tree structure, and supports users to select classification dimensions according to their needs; Clause input step: relevant departments input clause information in the system documents into the system, including clause content, corresponding laws and regulations, scope of application and responsible departments; Maintenance and update of clauses: For entered clauses, the system supports modification and update functions, and supports users to edit, review and confirm the content of clauses. When updating, the system automatically records the change history and retains the old version for query; Accurate search function: supports users to search by combining multiple conditions; Clause association function: The system supports associating clauses with relevant documents, laws and regulations, and institutional matters to form a network connection between clauses, and supports users to directly view the associated clause information by clicking on a clause; Export and report generation: Support users to select specific terms for batch export. The system supports export options in multiple formats. The generated export report contains the classification information, review status and change history of the terms. Department collaborative management: Support departments to view the terms and conditions related to themselves in the system and make comments and suggestions on the applicability of the terms and conditions; Regular evaluation and optimization: The system sets up a regular evaluation mechanism to evaluate the effectiveness and applicability of the published terms and conditions, and update and optimize them in a timely manner.

6. The internal supervision system of the unified tobacco monopoly supervision platform according to claim 1 is characterized in that: Risk management in the implementation of the supervision system at the behavioral level includes: Data collection and integration: The system collects basic data of retailers through multiple channels, including business licenses, transaction records, inventory information and customer feedback, and uses real-time monitoring systems to obtain market status, sales data and abnormal flow data, and integrates historical data with real-time monitoring data to form a data pool; Risk indicator setting: A series of risk indicators are set according to business needs and industry standards. The system supports users to customize risk indicators and make regular adjustments based on industry dynamics. Steps to build an outflow risk assessment model: Use big data analysis technology to build an outflow risk assessment model based on historical data and real-time data. The outflow risk assessment model combines machine learning algorithms to analyze the relationship between different variables and identify key factors affecting risks; Model training and validation steps: Use the labeled historical data to train the outflow risk assessment model, perform cross-validation, evaluate the performance of the outflow risk assessment model on different data sets, and continuously optimize the model parameters based on the validation results; Risk identification and early warning steps: Apply the established outflow risk assessment model to analyze the transaction behavior and market dynamics of retailers in real time, automatically identify potential risk customers and risky behaviors, and generate early warning information; Risk management strategy formulation steps: formulate corresponding risk management strategies based on the identified risks and feedback the strategies to the regulatory authorities; Continuous monitoring and feedback steps: The system continuously monitors the effectiveness of the implemented risk management strategies, evaluates the effectiveness through real-time data feedback, and adjusts and optimizes the risk management strategies based on the feedback results to form a closed-loop management mechanism; Report generation and analysis steps: The system regularly generates risk management reports that list risk identification, management measures and effectiveness evaluation.

7. The internal supervision system of the unified tobacco monopoly supervision platform according to claim 1 is characterized in that: The evaluation layer includes an internal control maturity management unit, which is based on the CMMI model, scores various indicators of internal control, regularly generates maturity reports and provides optimization suggestions, including: Establishment of maturity indicator system: internal control maturity is divided into five levels, namely initial level, management level, clear level, management level and optimization level. For each level, corresponding evaluation indicators are formulated, including system perfection, implementation compliance, risk control ability and problem rectification efficiency; Data collection: Regularly collect internal control document data, including internal control system documents, historical assessment reports, risk assessment data, supervision and inspection records, problem rectification records, and training and assessment records; Application of scoring mechanism: Use the pre-trained CMMI model to score the internal control document data, use quantitative methods to score each indicator, and calculate the scoring results based on the indicator weights to obtain the overall internal control maturity score; Maturity report generation: Based on the scoring results, internal control maturity reports are generated regularly, including maturity scores, analysis of various indicators, evaluation results and existing problems; Optimization suggestion provision: According to the evaluation results of the maturity report, corresponding optimization suggestions are put forward; Regular review and adjustment: Regularly review and reassess the internal control maturity, and update the maturity score and optimization suggestions based on newly collected internal control document data and improvement measures.

8. The internal supervision system of the unified tobacco monopoly supervision platform according to claim 1 is characterized in that: The system layer includes a problem risk management unit, which specifically includes: Creation of checklists: Automatically generate checklists based on internal control documents, laws, regulations and industry standards, and support users to manually add or modify checklist items based on actual needs; Multi-level checklist management: supports dividing the checklist into multiple levels according to the complexity and importance of regulatory matters; Online maintenance and feedback function: Provide online maintenance function for the checklist, support users to modify, update, supplement or delete the checklist, and set up a feedback mechanism. After completing the inspection, the relevant responsible person submits inspection feedback in the system, recording the problems found, rectification suggestions and rectification deadlines; Data recording and analysis: The system records the results of each inspection, including the inspection time, inspector, problems found and rectification status, and forms an inspection file. Through data analysis, statistics and analysis are conducted on areas where problems frequently occur, and analysis reports are generated; Real-time early warning mechanism: set early warning rules based on the risk indicators in the checklist. If the risk items are not rectified within the specified time, an early warning will be automatically triggered and the relevant responsible persons will be notified; Regular evaluation and optimization: Regularly evaluate the checklist and its implementation, identify areas that need improvement, and adjust the content and frequency of inspections to adapt to changing regulatory needs.

9. The internal supervision system of the unified tobacco monopoly supervision platform according to claim 1 is characterized in that: The data center includes a data analysis module, which specifically includes: Data collection: Collect relevant data in real time from the evaluation layer, behavior layer and institutional layer within the system, including past execution records, problem rectification status, current sales data, inventory status and abnormal behavior; Data preprocessing: Clean and preprocess the collected data; Multi-dimensional analysis model construction: Based on analysis requirements, a multi-dimensional data analysis model is constructed to support data stratification, grouping and aggregation. The analysis dimensions include time, region, product category and sales channel. Report generation: The system automatically generates data analysis reports, including indicator statistics, trend analysis, and identification of abnormal situations; Decision support function: Provide intelligent decision support function and give corresponding optimization suggestions based on analysis results; Result feedback and optimization: A feedback mechanism is established, and the management will provide feedback on the results after implementing the optimization suggestions. The system will compare the data before and after implementation to evaluate the optimization effect.

10. The internal supervision system of the unified tobacco monopoly supervision platform according to claim 1 is characterized in that: The data center includes a multi-dimensional dynamic correlation analysis algorithm module, which specifically includes: Data preprocessing and standardization: Collect and integrate multi-dimensional data from exclusive sales, marketing and logistics, and standardize the multi-dimensional data to remove noise and fill in missing values ​​to ensure data integrity and consistency; Feature extraction and hierarchical analysis: Use feature selection and hierarchical clustering algorithms to process data of different dimensions, extract features and identify correlations. For example, the similarity between data points is calculated using the Euclidean distance formula, expressed as: Where x and y are two sample points in the data matrix, n is the number of features, and d(x,y) represents the Euclidean distance between sample points x and y, which measures their similarity in the multidimensional feature space. Granger causality analysis: Analyze the temporal relationship between different data dimensions and use the Granger causality test to identify the impact of external factors on the target variable. Suppose there are two time series Y t and X t , and its regression equation is expressed as: Among them, Y t is the target variable (e.g., retail sales volume), X t is an external influencing factor (e.g., market advertising expenditure), α i and β i are the coefficients of the autoregressive term and external factors, ∈ t is a white noise term, representing the error; The hypothesis test of Granger causality is expressed as: H0:β1=β2=...=β q =0 (i.e. X t Y t No impact); If the null hypothesis is rejected by the F test, then X t Y t Have a significant impact; Recurrent neural network and long short-term memory network model: In order to capture the time dependency in the data, a recurrent neural network and long short-term memory network are used to establish a multi-dimensional correlation analysis model for dynamic data, which is expressed as: h t =σ(W h h t-1 +W x x t +b h ); y t =W y h t +b y ; Among them, h t is the hidden state, x t is the input data, W h ,W x ,W y is the weight matrix, σ is the activation function, y t is the output predicted by the model; Adaptive learning and optimization: Introduce an adaptive learning mechanism, and continuously adjust model parameters by introducing an optimization algorithm based on gradient descent to ensure that the algorithm can adapt to market changes and data trends; Decision support and risk prediction: Utilize the output results of the multi-dimensional correlation analysis model to provide managers with decision support, including predicting potential risks, optimizing marketing strategies, adjusting inventory management, etc., and generate analysis reports including risk assessment, trend prediction and optimization suggestions.

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