Label management method and system for enterprise management and government affair service

Abstract

The invention discloses a label management method and system for enterprise management and government affair service, and the method comprises the steps: obtaining target management data, carrying out the preprocessing of the target management data, building a multi-dimensional model for simulating an enterprise operation environment according to the preprocessed target management data, and generating an initial label, the multi-dimensional model for simulating the enterprise operation environment is constructed for the target management data to generate the initial label, the multi-dimensional change frequency of the target management data is monitored in real time based on the generated label, intelligent management of the label can be realized, the update period of the label is dynamically adjusted through the data change frequency of each dimension, and the user experience is improved. The state of the current label is evaluated according to the real-time state of the target management data, and the change trend of the label is predicted in combination with multi-modal time sequence prediction analysis and a self-adaptive threshold adjustment mechanism, so that the accuracy and timeliness of the label are improved, the management difficulty caused by the complexity of the data is reduced, and the user experience is improved. And the data management efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to a label management method and system for enterprise management and government services. Background Art

[0002] With the rapid development of information technology, enterprises and government departments are facing the need to process and analyze massive amounts of data. Traditional label management systems usually rely on static rules and manual intervention, making it difficult to cope with the complex and ever-changing market environment and policy changes. Specific problems include information silos, cold start methods, high operation and maintenance costs, and insufficient personalized needs. Therefore, the present invention proposes a label management and government service dynamic evaluation method for enterprises based on multi-source data fusion and adaptive learning, aiming to improve the accuracy and efficiency of label management through intelligent means, and provide more personalized and forward-looking service support for enterprises and the government. Summary of the Invention

[0003] The technical problem to be solved by the present invention is that due to the complexity of data, it is difficult to effectively manage data by labeling. The purpose is to provide a label management method and system for enterprise management and government services. By constructing a multi-dimensional model that simulates the enterprise operation environment for target management data to generate initial labels, and based on the generated labels, real-time monitoring the multi-dimensional change frequency of target management data, intelligent management of labels can be achieved. The update period of labels is dynamically adjusted according to the data change frequency of each dimension. The status of the current label is evaluated according to the real-time status of the target management data, and combined with multi-modal time series prediction analysis and adaptive threshold adjustment mechanism, the change trend of the label is predicted, improving the accuracy and timeliness of the label, reducing the management difficulty caused by the complexity of data, and improving the data management efficiency.

[0004] The present invention is realized through the following technical solutions:

[0005] Obtain target management data, preprocess the target management data, and construct a multi-dimensional model that simulates the enterprise operation environment according to the preprocessed target management data to generate initial labels;

[0006] Real-time monitor the multi-dimensional change frequency of target management data, and dynamically adjust the update period of labels according to the data change frequency of each dimension;

[0007] Evaluate the status of the current label according to the real-time status of the target management data, and combine multi-modal time series prediction analysis and adaptive threshold adjustment mechanism to predict the change trend of the label;

[0008] Manage the target management data according to the predicted change trend of the label.

[0009] Further, the obtaining of the target management data and the preprocessing of the target management data specifically include:

[0010] Using a distributed data acquisition framework to capture and process data streams from different channels in real time to obtain target management data;

[0011] Performing data cleaning on the target management data. During the data cleaning process, obtaining structured data, identifying and filling in missing data values;

[0012] Performing standardization processing on the cleaned data to obtain unstructured data, and using NLP technology to convert the unstructured data into a structured format;

[0013] Associating and integrating the formatted data to obtain the preprocessed data.

[0014] Further, the construction of a multi-dimensional model for simulating the enterprise operation environment to generate initial labels specifically includes:

[0015] Constructing a multi-dimensional model for simulating the enterprise operation environment, where the model includes multiple dimensions and is used to simulate the behaviors and changes of the enterprise in different scenarios;

[0016] Based on the multi-dimensional model for simulating the enterprise operation environment, using a deep reinforcement learning algorithm to learn the optimal label management strategy, and finding the label update period and trigger conditions within the set threshold through iterative trial and error;

[0017] Real-time obtaining of newly inflowing data, and automatically adjusting the definition and classification criteria of labels according to the latest information.

[0018] Further, the real-time monitoring of the multi-dimensional change frequency of the target management data and the dynamic adjustment of the label update period according to the data change frequency of each dimension specifically include:

[0019] Calculating the single-dimensional data change frequency of the target management data;

[0020] Constructing a contribution degree evaluation model, and calculating the data fluctuation value of each dimension according to the standard deviation of the portrait data of a single dimension within a set time period to obtain the benchmark weight of the corresponding dimension;

[0021] According to the benchmark weights of each dimension, combined with the single-dimensional data change frequency, determining the comprehensive change rate;

[0022] Dynamically adjusting the label update period according to the comprehensive change rate.

[0023] Further, the dynamic adjustment of the label update period according to the comprehensive change rate specifically includes:

[0024] High change rate: When the change rate exceeds 10%, the label update period for this dimension is shortened to daily;

[0025] Medium change rate: When the change rate is between 5% - 10%, the label update period for this dimension is weekly;

[0026] Low change rate: When the change rate is below 5%, the label update period for this dimension is extended to monthly.

[0027] Furthermore, after constructing the contribution degree evaluation model, obtain the historical data change trend of the target management data, and adopt an adaptive learning algorithm to optimize the contribution degree evaluation model;

[0028] Quantitatively evaluate the impact of each dimension on the overall performance of the target management data to obtain the contribution degree of each dimension;

[0029] Regularly update and evaluate the contribution degree of each dimension, adjust the data collection strategy according to the evaluation results, retain the contribution degree dimensions within the set threshold, and remove the contribution degree dimensions outside the set threshold.

[0030] Furthermore, the update process of the label specifically includes:

[0031] Judge whether the data change rate triggers the update condition according to the preset threshold. If it triggers, enter the update process; among them, when the time difference from the last update time exceeds the preset threshold and meets the minimum waiting interval duration, trigger a single update of the label;

[0032] Adjust the update period of the corresponding dimension label, and set the update frequency according to the corresponding change rate;

[0033] Construct a dimension correlation matrix, obtain the correlation between different dimensions, and perform multi-dimensional joint update.

[0034] Furthermore, the prediction of the change trend of the label specifically includes:

[0035] Evaluate the status of the current label according to the real-time status of the target management data to obtain the evaluation result;

[0036] Combine multi-modal time series prediction analysis, integrate information from different types of data sources, and predict the change trend of the target management data;

[0037] Adopt an adaptive threshold adjustment mechanism, and adjust the threshold range of the data for each dimension of the target management data according to the change trend of the target management data;

[0038] Based on the change trend of the target management data and the adjustment of the threshold range of the data for each dimension of the target management data, obtain the prediction result of the change trend of the label;

[0039] Automatically adjust the definition and classification criteria of tags according to the prediction results.

[0040] Furthermore, when operating on tags, it also includes constructing a distributed ledger based on blockchain to record each tag operation as a transaction, specifically including: automatically triggering the operation and writing it into the blockchain for storage when the creation conditions, modification permissions, and deletion processes of the tags meet the preset conditions.

[0041] The second aspect of the present invention provides a tag management system for enterprise management and government affairs services, including:

[0042] A data acquisition layer for obtaining target management data;

[0043] A data processing layer for preprocessing the target management data;

[0044] An intelligent analysis layer for constructing a multi-dimensional model simulating the enterprise operation environment based on the preprocessed target management data to generate initial tags, where the initial tags include enterprise characteristic tags, industry characteristic tags, and production and operation tags;

[0045] It is also used to describe the relevant content of tag classification management, including defining tags for enterprise characteristic tags, industry characteristic tags, and production and operation tags;

[0046] It is also used to evaluate the current state of tags according to the real-time state of target management data, and predict the change trend of tags by combining multi-modal time series prediction analysis and an adaptive threshold adjustment mechanism;

[0047] A tag management layer for creating, modifying, deleting, and maintaining tags;

[0048] A service providing layer for managing target management data according to the predicted change trend of tags.

[0049] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0050] Intelligent tag management: By introducing multi-source data fusion, deep learning algorithms, and blockchain technology support, the system realizes the comprehensive intelligence and automation of tag management; not only improves the accuracy and timeliness of tags, but also reduces the operation and maintenance costs, and enhances the transparency and credibility of the system;

[0051] Adaptive adjustment and predictive maintenance: By adaptively adjusting the tag collection dimension and predictive maintenance, it can better adapt to the changes in enterprise needs;

[0052] User participation and transparent operation and maintenance: It provides an interactive tag management platform, allowing multiple departments to jointly participate in the review and correction process of tags, improving the transparency and participation of tag management. Brief Description of the Drawings

[0053] To more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the drawings required for the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts. In the drawings:

[0054] Figure 1 It is a label management method for enterprise management and government affairs services in the embodiments of the present invention. Detailed Embodiments

[0055] To make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in combination with the embodiments and the drawings. The illustrative embodiments of the present invention and their descriptions are only used to explain the present invention and are not intended to limit the present invention.

[0056] As a possible embodiment, as Figure 1 shown, this embodiment provides a label management method for enterprise management and government affairs services, specifically including:

[0057] S1. Operation and Maintenance of Labels

[0058] 1. Dynamic Update Driver:

[0059] (1) Update Cycle Determination: Adopt a dynamic update mechanism to ensure the efficiency and accuracy of the label management system. Combine the regular update and single-trigger update methods, and adjust the update cycle of the label according to the data change frequency of each dimension of the enterprise (such as financial status, market performance, policy response, etc.). Specifically, the system will regularly calculate the data change rate of each dimension and compare it with a preset threshold. If the change rate of a certain dimension exceeds the threshold, the label corresponding to this dimension will have a shorter update cycle; otherwise, the update cycle will be extended. In addition, the system will also consider the correlation between different dimensions and set a more tightly synchronized update mechanism for those dimensions that have a greater impact on each other. The specific steps are as follows:

[0060] (1) Analysis of Data Change Frequency

[0061] Dynamically adjust the update cycle of the label according to the data change frequency of each dimension of the enterprise to ensure that the label can timely reflect the latest status of the enterprise while avoiding unnecessary frequent updates.

[0062] ①Data collection and preprocessing: The system obtains relevant information of enterprises from multiple channels, such as financial statements, market performance, policy responses, etc. The collected raw data is cleaned, de-duplicated, error-corrected and standardized to ensure the accuracy and consistency of the data.

[0063] ②Calculating the data change rate: The system calculates the data change rate for each dimension at regular intervals (e.g., daily, weekly or monthly). Traditional change rate calculation formulas are only based on the difference between the current value and the value in the previous period, ignoring the influence of time factors. However, in actual business scenarios, the impact of data changes in different time periods on enterprises is different. For example, recent changes are often more representative than long-term changes. The specific technical method is as follows:

[0064] A time decay factor is introduced, so that newer data has a higher weight in the change rate calculation, while older data gradually loses its influence.

[0065] Calculate the data change frequency for each dimension of the target management data. The calculation steps include:

[0066]

[0067] where v t represents the value in the t-th period, v t-1 represents the value in the (t - 1)-th period, w t represents the time decay weight in the t-th period, and T represents the length of the time window, that is, the number of historical periods considered.

[0068] By introducing the time decay factor, the system can pay more attention to recent changes, avoid the excessive influence of long-term data on the current change rate, and thus improve the sensitivity and accuracy of the change rate.

[0069] ③Changes in the financial situation may affect market performance, and changes in policy responses may also indirectly affect the operation of enterprises. To more comprehensively evaluate the dynamic changes of enterprises, the present invention introduces multi-dimensional correlation analysis, comprehensively considering the change rates of multiple related dimensions to form a more comprehensive indicator.

[0070]

[0071] where N represents the number of dimensions participating in the calculation; the change rate i represents the change rate of the i-th dimension; a i represents the weight of the i-th dimension, and the weight can be dynamically adjusted according to the importance of each dimension to the overall performance of the enterprise.

[0072] To determine the weight a of each dimension i, a contribution evaluation model is adopted. By analyzing the data fluctuation values of each dimension, the influence degree of this dimension on the overall enterprise portrait is evaluated. The system calculates the data fluctuation value of a dimension according to the standard deviation of the portrait data of a single dimension within the set time period, and adjusts the benchmark weight of the corresponding dimension accordingly. Multidimensional correlation analysis can more comprehensively reflect the dynamic changes of the enterprise and reveal the mutual influence between different dimensions.

[0073] ④ Introduce anomaly detection and adaptive threshold adjustment. In actual business scenarios, some extreme situations (such as sudden market fluctuations or policy changes) may lead to large fluctuations in the change rate, but this does not necessarily mean that the true state of the enterprise has changed significantly. To avoid the interference of these extreme situations on the change rate calculation, by introducing anomaly detection algorithms, abnormal data points can be identified and filtered out to ensure the stability of the change rate. Specifically, the system uses anomaly detection algorithms such as Isolation Forest and Local Outlier Factor to identify the outliers that do not conform to the normal data pattern. For the detected outliers, the system can adopt the following several processing methods: Remove outliers: directly ignore the outliers and do not include them in the change rate calculation; Smoothing processing: smooth the outliers to minimize their impact on the change rate; Mark as special events: mark the outliers as special events for subsequent analysis reference. In addition, an adaptive threshold adjustment mechanism is introduced, and the threshold of the change rate is dynamically adjusted according to the change trend of historical data. For example, if the historical change rate of a certain dimension has been relatively low, the system may appropriately relax the threshold to reduce unnecessary updates; on the contrary, if the change rate of a certain dimension fluctuates greatly, the system will tighten the threshold to increase the update frequency. This adaptive adjustment mechanism not only improves the flexibility of the change rate calculation but also better adapts to the changing market environment.

[0074] ③ Adjust the update cycle: If the change rate of a certain dimension exceeds the preset threshold, the label corresponding to this dimension will have a shorter update cycle. High change rate: If the change rate exceeds 10%, the label update cycle of this dimension is shortened to daily or hourly. Medium change rate: If the change rate is between 5% - 10%, the label update cycle of this dimension is weekly. Low change rate: If the change rate is below 5%, the label update cycle of this dimension is extended to monthly or quarterly. Considering the correlation between different dimensions, for those dimensions with greater mutual influence, a more tightly synchronized update mechanism is set. For example, if there is a strong correlation between the financial situation and the market performance, when one dimension changes, the label of the other dimension will also be updated synchronously.

[0075] ⑤ Dynamic adjustment mechanism: The system dynamically adjusts the thresholds and update cycles for each dimension according to the changing trends of historical data. For example, if the historical change rate of a certain dimension has been relatively low, the system may appropriately relax the threshold and reduce unnecessary updates; conversely, if the change rate of a certain dimension fluctuates greatly, the system will tighten the threshold and increase the update frequency. The system also supports adaptive learning, and automatically optimizes the thresholds and update cycles through machine learning algorithms (such as decision trees, random forests, etc.) to ensure that they always adapt to the latest market environment.

[0076] (2) Consider the correlation between dimensions. For dimensions with greater mutual influence, set a more tightly synchronized update mechanism to ensure that the labels can comprehensively reflect the overall state of the enterprise:

[0077] ① Construct a dimension correlation matrix: Construct a dimension correlation matrix to represent the correlation between different dimensions.

[0078] ② Define the synchronous update rules: For dimensions with high correlation, the system sets synchronous update rules. If the change rate of the financial situation exceeds the threshold, check the change of the market performance and update the relevant labels at the same time. The system can also automatically identify which dimensions have strong correlation based on historical data and set a more tightly synchronized update mechanism for these dimensions.

[0079] ③ Multi-dimensional joint update: If changes occur simultaneously in the three dimensions of financial situation, market performance, and policy response, the system will update all labels related to these three dimensions at one time, rather than performing three independent updates separately. This joint update mechanism can not only improve the efficiency of the system, but also ensure the logical consistency between labels.

[0080] (2) Single-trigger update timing: In addition to regular updates, the system also monitors the change rate of the enterprise's key data in real time. Once the change rate of the data in a certain dimension exceeds the preset threshold, the system will immediately trigger the update of the label for that dimension. To prevent overly frequent updates, the system sets a minimum waiting interval to ensure that each update is necessary and effective. Only when the current judgment meets the data conditions for single-trigger update timing and the time interval since the previous label update exceeds the preset waiting interval will the update operation be executed. The system monitors the change rate of the enterprise's key data in real time. Once the change rate of the data in a certain dimension exceeds the preset threshold, it immediately triggers the update of the label for that dimension to ensure that the label can reflect the latest state of the enterprise in a timely manner. The specific steps are as follows:

[0081] (1) Real-time monitoring and anomaly detection:

[0082] The system monitors the change rate of the enterprise's key data in real time. Once the change rate of the data in a certain dimension exceeds the preset threshold, the update of the label for that dimension is immediately triggered to ensure that the label can reflect the latest status of the enterprise in a timely manner.

[0083] ① Real-time data stream processing: The system uses a distributed stream processing framework to process real-time data to ensure rapid response to the dynamic changes of the enterprise. The specific methods are as follows:

[0084] To ensure rapid response to the dynamic changes of the enterprise, the system uses a distributed stream processing framework to process real-time data. Specifically, the system integrates multiple real-time data sources, including the enterprise's sales data, inventory data, social media comments, news reports, etc. These data are captured in real time through methods such as APIs, Webhooks, and web crawlers, and are preprocessed and cleaned before entering the system to remove noise and invalid information to ensure the accuracy of subsequent processing.

[0085] Data transmission is achieved through Apache Kafka. As a distributed message queue, Kafka is responsible for decoupling the producer (data source) and the consumer (label management system), supporting high-throughput and low-latency data transmission. Kafka divides the data stream into multiple partitions, and each partition can be processed independently, thus achieving parallel processing and horizontal expansion. The system can flexibly configure the number of partitions according to the type of data source or business requirements to ensure the efficiency and performance of data processing. Kafka also supports the persistent storage of data to ensure that data will not be lost even if the system fails, and provides a replication and automatic failover mechanism to ensure the high availability and reliability of data.

[0086] Apache Flink, as a stream processing engine, is responsible for the low-latency processing and analysis of real-time data received from Kafka. The design of Flink allows it to process unbounded data streams and supports event-driven processing patterns. Flink provides rich windowing functions, enabling the system to perform aggregation and statistical analysis on real-time data within time windows. For example, the system can set time windows of every minute, hour, or day to calculate metrics such as the average, maximum, and minimum of sales data, or to analyze the sentiment of social media comments. Such windowing operations help the system capture short-term trends and long-term changes, providing more accurate decision-making support for enterprises. In addition, Flink has a built-in powerful state management mechanism that can save intermediate calculation results to ensure quick recovery in case of system failures. Flink also supports Checkpoint and Savepoint mechanisms, which regularly save snapshots of the system to ensure data consistency and integrity. Flink's Complex Event Processing (CEP) function can identify and respond to complex business events. For example, when the data change rate in a certain dimension is detected to exceed a preset threshold, it triggers corresponding label updates or service pushes. CEP can also be used to detect abnormal patterns or rare events, helping enterprises discover potential risks or opportunities in advance.

[0087] The data processed by Flink is passed to the label management system, which automatically updates relevant labels according to preset rules and conditions. All label operations (creation, modification, deletion) are recorded on the blockchain to ensure that every step is traceable and immutable. Based on the analysis results of real-time data, the system generates personalized service push content. For example, when a company's sales suddenly decline, the system immediately pushes relevant promotional activities or financial optimization suggestions to help the enterprise quickly respond to the change. The content of the service push can be sent to enterprise managers or relevant departments through APIs or message queues (such as Kafka, RabbitMQ) to ensure timely information transmission.

[0088] The system also allows administrators and users to view the processing status of data streams, the update situation of labels, and the effectiveness of service pushes. Monitoring tools can display the changing trends of key metrics to help users detect problems in a timely manner and take actions. The system integrates anomaly detection algorithms (such as Isolation Forest, LOF) that can identify outliers that do not conform to the normal data pattern in real time. When outliers are detected, the system immediately triggers an alarm to notify relevant personnel for handling. For example, if a company's sales decline sharply within a short period, the system automatically sends an alarm message to remind enterprise managers to take measures.

[0089] Through the comprehensive technical solution, not only the efficient and low-latency real-time data processing is achieved, but also the transparency and immutability of label management are ensured, providing personalized and timely service push.

[0090] ② Real-time calculation of change rate: The system calculates the change rate of data for each dimension in real time and compares it with a preset threshold. If the change rate of a certain dimension exceeds the threshold, the system will immediately trigger the update of the label for that dimension. To improve the calculation efficiency, the system uses the sliding window technique to calculate the change rate only for the most recent period of time, rather than recalculating the entire historical data each time. The specific method is as follows: Fixed time window: The system can set a fixed time window of 5 minutes and slide it every 1 minute. Each time it slides, the system only considers the data within the past 5 minutes and discards the historical data outside the window range. Event-based time window: For some scenarios, the system can define the window size based on the number of events. The system can set every 1000 pieces of data as a window and trigger a change rate calculation when 1000 pieces of data are reached. Within each sliding window, the system calculates the change rate according to the following formula: Change rate = (|Vcurrent - Vprevious|) / Vprevious, where Vcurrent represents the latest value within the current window; Vprevious represents the latest value within the previous window.

[0091] ③ Anomaly detection and warning: The system introduces anomaly detection algorithms (such as Isolation Forest, LOF, DBSCAN, etc.) that can identify abnormal patterns in enterprise behavior and issue warnings in a timely manner. For example, if the sales volume of a certain enterprise suddenly drops significantly, the system will trigger an emergency update, re-evaluate the financial health of the enterprise, and adjust its label accordingly. Anomaly detection can not only be used to trigger label updates, but also help enterprises discover potential risks in advance and take countermeasures.

[0092] ④ Minimum waiting interval duration: To avoid overly frequent updates, the system sets a minimum waiting interval duration (such as 24 hours). Only when the current judgment meets the data conditions for a single trigger update and the time interval between the judgment result of this data condition and the previous label update exceeds the preset waiting interval duration, will the update operation be actually executed. This mechanism can effectively prevent the system from triggering updates multiple times in a short period, ensuring that each update is necessary and effective.

[0093] (2) Execution process of single-trigger update: Ensure that the operation of single-trigger update can be completed efficiently and accurately, and record all update operations to ensure the transparency and traceability of the system.

[0094] ① Trigger condition judgment: The system monitors the data change rate of each dimension in real time. Once the change rate of a certain dimension exceeds the preset threshold, the system will enter the trigger condition judgment stage. The system will check whether this dimension meets the following two conditions:

[0095] Change rate exceeding the threshold: Whether the change rate of the current dimension exceeds the preset threshold.

[0096] Minimum waiting interval duration: Whether the current time exceeds the preset minimum waiting interval duration (such as 24 hours) since the last label update. If both conditions are met, the system will trigger the update of the label for this dimension.

[0097] ② Smart contract-driven label update: To ensure the transparency and immutability of the label management process, the system is supported by blockchain technology. All operations for creating, modifying, and deleting labels will be recorded on the blockchain and automatically executed through smart contracts. The smart contract will automatically verify the legality of the operation and write the relevant information to the blockchain. This not only enhances the credibility of the system but also reduces the risk of human operation. Each time an update is triggered, the smart contract will generate a new record containing the update time, dimension, change rate, new label, etc., ensuring that all operations are traceable.

[0098] ③ Notification and feedback: The system will send notifications to relevant users (such as enterprise managers and government department staff) to inform them of the label update situation. The notifications can be pushed through various methods such as email, SMS, and mobile applications.

[0099] Users can view the detailed information of the label update through the system and provide feedback. If users think that some labels are inaccurate or unreasonable, they can submit correction requests through the interactive label review platform to further optimize the label system.

[0100] ④ Log record and audit: The system will record all label update operations, including who performed what operation at what time and the result of the operation. These logs can not only be used to trace historical operations but also serve as legal evidence for resolving disputes when necessary. The system is also equipped with a perfect audit function to regularly review the label update process to ensure that all operations comply with the requirements of relevant laws and regulations.

[0101] 2. Intelligent label analysis and adjustment:

[0102] (1) Intelligent Tag Analysis: To ensure a quick response to the dynamic changes of enterprises, the system adopts a distributed stream processing framework (such as Apache Kafka and Apache Flink) to process real-time data. Specifically, the system integrates multiple real-time data sources, including the enterprise's sales data, inventory data, social media comments, news reports, etc. These data are captured in real time through methods such as APIs, Webhooks, and web crawlers, and are preprocessed and cleaned before entering the system to remove noise and invalid information to ensure the accuracy of subsequent processing.

[0103] (2) Intelligent Tag Adjustment

[0104] After being processed by the intelligent tag analysis module, the system can capture the dynamic changes of enterprises in real time and automatically update relevant tags according to preset rules and conditions. All tag operations (creation, modification, deletion) will be recorded on the blockchain to ensure that every step is traceable and tamper-proof. Specifically, the tag adjustment process is as follows:

[0105] First, the system automatically evaluates the current status of tags based on the results of real-time data analysis. For example, when the sales data suddenly drops, the system will trigger an update of the "sales volume" tag and mark it as "needs attention". Tag updates are not only based on changes in a single data source but can also be comprehensively evaluated by combining data from multiple dimensions. For example, the system may consider factors such as sales data, inventory levels, and market dynamics simultaneously to ensure more comprehensive and accurate tag adjustments.

[0106] Second, the system will dynamically adjust the definition and classification criteria of tags according to the actual needs and business scenarios of the enterprise. For example, with the change of policies or the evolution of the market environment, some tags may need to be updated or redefined. Government departments can initiate tag adjustment requests on the platform. After review, the new tag definitions will come into effect officially and be applied to the subsequent tag management process. This flexible adjustment mechanism ensures the timeliness and adaptability of the tag system, and can reflect the latest policies and market demands in a timely manner.

[0107] To ensure the transparency and engagement of label adjustments, the system introduces a multi-role permission management mechanism to ensure that different types of users can only access and operate functions related to their roles. Enterprise users can submit label feedback and requests, while government department reviewers can review and approve these requests. The system will automatically generate personalized service push content based on the results of label adjustments. For example, when an enterprise's sales suddenly decline, the system will immediately push relevant promotional activities or financial optimization suggestions to help the enterprise quickly respond to changes. The content of service push can be pushed to enterprise managers or relevant departments through APIs or message queues (such as Kafka, RabbitMQ) to ensure the timely transmission of information. The system also provides real-time monitoring and visualization tools, allowing administrators to view the update status of labels and the effects of service push, ensuring the transparency and traceability of the entire label adjustment process.

[0108] 3. Adaptive Optimization of Label Collection Dimensions:

[0109] To ensure the efficiency and accuracy of the label management system, the system dynamically adjusts the data collection strategy through an adaptive label collection dimension optimization mechanism. This mechanism is based on a variety of specific technologies and methods to ensure that the label system always focuses on the most valuable information and avoids the interference of redundant data. This mechanism combines a variety of specific technologies and methods, including contribution degree evaluation models, dynamic adjustment mechanisms, adaptive learning and feedback mechanisms, multi-dimensional joint optimization, and storage cost optimization, providing more intelligent and personalized service support. The specific steps are as follows:

[0110] (1) Contribution Degree Evaluation Model

[0111] The system introduces a contribution degree evaluation model to quantify the impact of each dimension on the overall performance of the enterprise. Contribution degree evaluation is mainly carried out through the following methods: Correlation analysis, using statistical methods such as Pearson correlation coefficient and Spearman rank correlation coefficient to evaluate the correlation between a certain dimension and key enterprise indicators (such as sales, profit, market share). Feature importance evaluation, based on machine learning models, calculates the importance score of each dimension when predicting enterprise performance, reflecting its contribution degree to the model prediction result. Information gain, through methods such as information gain or mutual information, evaluates the additional information provided by a certain dimension. The dimension with a larger information gain usually has a greater impact on the overall performance of the enterprise. Business rule evaluation, combining expert experience and business rules, manually sets the importance weights of certain dimensions.

[0112] (2) Dynamic adjustment mechanism: The system periodically re-evaluates the contribution degrees of each dimension and adjusts the data collection strategy according to the evaluation results: Retain the dimensions with high contribution degrees. For dimensions with relatively high contribution degrees, the system will continue to retain and strengthen the data collection frequency to ensure the timeliness and accuracy of the data. If a certain market performance indicator has a significant impact on the enterprise's sales, the system will increase the collection frequency of this dimension. Reduce the collection frequency of dimensions with low contribution degrees. For dimensions with relatively low contribution degrees, the system can appropriately reduce the collection frequency or even stop collecting. If the data fluctuation of a certain production indicator remains within a very small range for a long time and has little impact on the overall performance of the enterprise, the system may decide to stop collecting this data. Completely remove useless dimensions: For those dimensions that have been non-contributing or have hardly changed for a long time, the system will completely remove their data collection tasks to free up storage space and computing resources. Some legacy dimensions may no longer be applicable to the current enterprise operation model, and the system will automatically identify and remove these dimensions.

[0113] (3) Adaptive learning and feedback mechanism: The system adopts an adaptive learning algorithm to automatically optimize the contribution degree evaluation model according to the change trends of historical data and business requirements. As the enterprise's business expands or the market environment changes, the contribution degrees of some dimensions may change, and the system will timely adjust the evaluation model to ensure that it always adapts to the latest business scenarios. In addition, the system supports a user feedback mechanism, allowing enterprise managers and government department staff to submit suggestions or correction opinions through an interactive platform to further optimize the data collection strategy and ensure that the label system better meets the actual needs.

[0114] (4) Multi-dimensional joint optimization: The system not only evaluates the contribution degree of each dimension separately, but also identifies through multivariate analysis the dimensions with relatively low individual contribution degrees but having an important impact on the enterprise's performance when combined with other dimensions. For dimensions with strong mutual influence, the system sets a more tightly synchronized collection mechanism to ensure the consistency and integrity of the data. For example, there is a strong correlation between the financial situation and market performance. The system will collect market performance data synchronously while collecting financial data to avoid misjudgment caused by inconsistent data.

[0115] (5) Storage cost optimization: By dynamically adjusting the collection dimensions, the system can significantly reduce the storage cost. For dimensions that no longer need to be collected, the system will automatically clean up the relevant historical data to free up storage space. In addition, the system can also adopt data compression technology to compress and store the dimensions with low-frequency collection to further save storage resources. The system adopts a hierarchical storage strategy, storing the frequently used data in high-performance storage media (such as SSDs), while storing the historical data or low-frequency used data in low-cost cold storage media (such as tape libraries) to ensure the efficiency and economy of data access.

[0116] 4. Predictive Tag Maintenance and Proactive Service Push:

[0117] To ensure the forward-looking and intelligent nature of the tag management system, the system not only relies on traditional static tag update mechanisms but also introduces innovative technologies for predictive tag maintenance and proactive service push. By combining advanced machine learning models, real-time data stream processing, and adaptive feedback mechanisms, these technologies can identify future change trends of enterprises in advance and dynamically adjust tags accordingly to provide personalized service push. The following are the specific technical implementations and innovations:

[0118] (I) Predictive Tag Maintenance: Existing tag management systems usually perform static updates based on historical data, unable to reflect the future change trends of enterprises in a timely manner, which easily leads to lagging or inaccurate tags. This embodiment adopts a multi-modal time series prediction model that can simultaneously process various different types of time series data (sales data, inventory data, social media comments) and predict future change trends. This model combines deep learning and traditional statistical methods and can capture complex non-linear relationships and periodic changes. In addition, the model also supports cross-modal fusion, that is, jointly modeling different types of heterogeneous data (text, image, numerical). The introduction of context-aware prediction technology can dynamically adjust the parameters of the prediction model according to factors such as the current market environment, policy changes, and competitor dynamics. When there is a major policy adjustment in the market, the system will automatically identify and adjust the weights of relevant dimensions to ensure that the prediction results are closer to the actual situation.

[0119] The system not only triggers tag updates based on preset fixed thresholds but also introduces an adaptive threshold adjustment mechanism. By analyzing the change trends and volatility of historical data, the system can automatically adjust the threshold range for each dimension to ensure the sensitivity and accuracy of tag updates. For example, for dimensions with large fluctuations, the system will appropriately relax the threshold; while for dimensions with high stability, the system will tighten the threshold to avoid unnecessary frequent updates. The system integrates an anomaly detection algorithm that can real-time identify outliers that do not conform to the normal data pattern and trigger an alarm immediately when an anomaly occurs.

[0120] (II) Proactive Service Push

[0121] To ensure the efficiency and accuracy of forward-looking service push, the system adopts a comprehensive technical solution integrating real-time data stream processing, multi-modal time series prediction, personalized recommendation engine, and adaptive feedback mechanism. Specifically, the system processes real-time data streams from multiple sources such as sales, inventory, and social media through an event-driven architecture, and immediately triggers the service push process when detecting key events (such as a sudden drop in sales, a competitor launching a new product, policy changes, etc.). Using a distributed stream processing framework, it can process large-scale real-time data with millisecond-level latency, ensuring the timeliness of service push.

[0122] The core of the system is a multi-modal time series prediction model, which combines deep learning and traditional statistical methods, can simultaneously process various types of time series data (such as sales data, inventory data, social media comments, etc.), and predict future change trends. In addition, the model supports cross-modal fusion, jointly models different types of heterogeneous data (such as text, images, numerical values), and improves the accuracy of prediction. The system also introduces context-aware prediction technology, dynamically adjusts the parameters of the prediction model according to factors such as the current market environment and policy changes, and ensures that the prediction results are close to the actual situation.

[0123] Based on the enterprise's historical behavior and real-time status, the system constructs a detailed user profile and generates highly personalized service recommendations through collaborative filtering, content recommendation, and deep learning models. The personalized recommendation engine not only relies on the static user profile but also dynamically adjusts the recommended content according to the enterprise's real-time data. For example, when the enterprise's sales suddenly drop, the system will immediately push relevant promotional activities or financial optimization suggestions to help the enterprise quickly respond to changes. The system also supports multi-objective optimization, can simultaneously consider multiple business objectives (increase sales, reduce costs, optimize the supply chain), and generate optimal service recommendations for each objective.

[0124] To continuously optimize the prediction model and service push strategy, the system adopts an adaptive feedback mechanism and adaptive learning algorithms. Users can submit feedback through an interactive platform, and the system will automatically optimize the model parameters of the recommendation engine according to the user's feedback, ensuring that the pushed content always meets the user's needs. The adaptive learning algorithms automatically adjust the prediction model and service push strategy according to the latest business scenarios and market changes, forming a closed-loop feedback cycle. The system supports incremental learning, gradually updates the model parameters without retraining the entire model, and quickly adapts to new business scenarios and market changes.

[0125] Finally, through multi-dimensional joint prediction and comprehensive decision-making support tools, the system not only evaluates the change trends of each dimension individually but also considers the correlations between multiple dimensions, providing comprehensive decision-making support and risk assessment. For example, the system may simultaneously push financial optimization suggestions, market expansion strategies, and supply chain management solutions to help enterprises improve their overall performance in multiple aspects. The scenario simulation tool allows enterprise managers to input different hypothetical conditions (such as market changes, policy adjustments, and competitor dynamics) and generate future scenario simulation results based on the prediction model, evaluating the potential impacts of different decision-making options in advance and making more scientific and reasonable decisions.

[0126] Through this comprehensive technical solution, the system can provide timely, accurate, and personalized forward-looking service push in a complex and changing market environment, helping enterprises identify future change trends in advance, optimize resource allocation, make scientific and reasonable decisions, and maintain a competitive advantage.

[0127] 5. Transparent operation and maintenance supported by blockchain technology:

[0128] Label update driven by smart contracts: To ensure the transparency and immutability of the label management process, the present invention adopts blockchain technology. All operations of label creation, modification, and deletion will be recorded on the blockchain and automatically executed through smart contracts, ensuring that every step is traceable and publicly transparent.

[0129] Specifically, as a distributed ledger, blockchain utilizes its decentralized, immutable, and transparent characteristics to record each label operation as a transaction and verifies and confirms each transaction through a consensus mechanism to ensure the authenticity and consistency of the data. Smart contracts define the specific rules for label management, including creation conditions, modification permissions, and deletion processes. Whenever the preset conditions are met, the smart contract will automatically trigger the corresponding operation and write the operation record into the blockchain, realizing full-process automation. The system introduces role-based permission control and multi-signature verification mechanisms to ensure that only authorized users can initiate label operations, enhancing the security and controllability of the system. All operation logs are completely recorded on the blockchain, including timestamps, initiators, and specific contents, ensuring that every operation step can be traced. At the same time, using the immutable characteristics of the blockchain, any tampering or deletion behavior is prevented. To protect privacy, the system adopts data encryption and zero-knowledge proof technologies to ensure that sensitive data is not leaked during transmission and storage, while allowing enterprises to prove the legality of operations without disclosing specific data. In addition, smart contracts can automatically adjust label management rules according to the latest business requirements and market changes to ensure that the label system always adapts to the latest business scenarios. Through this comprehensive technical solution, the present invention not only improves the intelligent level of label management but also provides enterprises with more transparent and reliable service support, helping enterprises maintain a competitive advantage in a complex and changing market environment.

[0130] The present invention provides an interactive label management platform, aiming to facilitate the joint participation of enterprises and government departments in the review and amendment process of labels. The platform integrates a dialogue system and chatbot technology, provides real-time assistance and support to users, and continuously improves the label system by collecting user feedback. Specifically, enterprises can submit feedback on label accuracy or requests to add new labels through the platform, while government departments can adjust the definition and classification criteria of labels according to actual needs.

[0131] The core functions and technical implementations of the platform are as follows:

[0132] All operations on labels will be recorded on the blockchain, ensuring that every step is traceable and publicly transparent. Whenever a user initiates a label-related request, such as reviewing existing labels, proposing modification suggestions, or applying to add new labels, these requests will be recorded as a task and assigned to the corresponding reviewers. Reviewers can view the task details, evaluate the reasonableness of the request, and decide whether to approve or reject. All review operations will be recorded on the blockchain, ensuring transparency and immutability.

[0133] The platform integrates advanced dialogue system and chatbot technology, which can provide real-time assistance and support to users. Users can interact with the chatbot in natural language to obtain guidance on label management and answer common questions. The chatbot is based on natural language processing and machine learning models, and can understand the user's intention and provide personalized responses. For example, when an enterprise user asks how to submit label feedback, the chatbot will guide the user through the whole process to ensure simplicity and accuracy of the operation.

[0134] The platform is built with a user feedback mechanism, allowing enterprises and government departments to put forward opinions and suggestions on various aspects of the label system. Users can submit feedback through the platform, pointing out problems with labels or proposing improvement suggestions. The system will automatically collect these feedbacks, classify and organize them for the development team to refer to. Based on user feedback, the system can regularly update label rules, optimize the review process, and introduce new functions to ensure the continuous improvement and optimization of the label system.

[0135] The platform introduces a multi-role permission management mechanism to ensure that different types of users (such as enterprise administrators, government department reviewers, ordinary users, etc.) can only access and operate functions related to their roles. Enterprise users can submit label feedback and requests, while government department reviewers can review and approve these requests. In addition, the platform provides collaboration tools, allowing users from different departments to communicate and collaborate in real time. For example, enterprise users can have an online discussion with government department reviewers to explain the background and requirements of label requests, ensuring that both sides reach a consensus.

[0136] To protect privacy, the platform has adopted strict data privacy and security protection measures. All data will be encrypted during transmission and storage to ensure data integrity and confidentiality. In addition, the platform has introduced zero-knowledge proof technology, allowing enterprises to prove the legality of label operations without disclosing specific data, further protecting the business secrets of enterprises.

[0137] The definition and classification criteria of labels can be dynamically adjusted through the platform according to actual needs. For example, as various data evolves, certain labels may need to be updated or redefined. That is, a label adjustment request can be initiated on the platform. After review, the new label definition will be officially effective and applied to subsequent label management processes. This flexible adjustment mechanism ensures the timeliness and adaptability of the label system, and can reflect the latest policies and market demands in a timely manner. The platform integrates a user feedback mechanism to ensure the continuous improvement and optimization of the label system. At the same time, through multi-role permission management and collaboration tools, it promotes communication and collaboration between the government and enterprises. All label operations are recorded on the blockchain to ensure transparency and traceability, enhancing users' trust. This innovative technical solution not only improves the intelligent level of label management, but also provides an efficient, transparent and secure cooperation platform for enterprises and government departments, helping both sides better meet complex label management needs.

[0138] S2. Data Fusion and Multi-source Information Integration

[0139] 1. Multi-source Data Fusion Engine

[0140] In order to build an efficient, accurate and intelligent label management system, the present invention has introduced a number of innovative technical means in the multi-source data fusion engine, significantly improving the capabilities of data collection, cleaning, standardization and correlation integration. Compared with the prior art, the present invention not only covers conventional data collection methods (such as API interfaces, web crawlers, manual entry, etc.), but also combines advanced natural language processing, image recognition, sentiment analysis, graph databases and other technologies to ensure that the system can obtain high-quality data from multiple channels and perform in-depth processing and correlation analysis on it.

[0141] In the data collection stage, the present invention not only supports traditional API interfaces, web crawlers, and manual entry methods, but also cooperates with multiple third-party data providers to ensure the diversity and reliability of data sources. In addition, the system introduces distributed data collection frameworks such as Apache Nifi and Apache Kafka, which can capture and process data streams from different channels in real time, ensuring the timeliness and integrity of data. Through these distributed frameworks, the system can efficiently process large-scale real-time data and quickly respond to new dynamics of enterprises. At the same time, the system adopts an intelligent data screening mechanism based on rules and machine learning, which can automatically identify and filter out low-quality or irrelevant data. For example, through text classification algorithms (such as TF-IDF, BERT) and image recognition algorithms (such as CNN), the system can automatically judge the relevance and value of data, avoiding interference from invalid data. This intelligent screening mechanism not only improves the quality of data, but also reduces the burden of subsequent processing.

[0142] Existing data collection methods usually rely on a single or a few data sources, and the data collection methods are relatively fixed, making it difficult to meet complex and changing business requirements. The present invention ensures the wide range and timeliness of data sources by introducing multi-channel data access and distributed data collection frameworks, and can better adapt to the needs of different enterprise and government affairs scenarios. In addition, existing technologies often rely on manual intervention in data screening, with low efficiency and prone to errors, while the present invention realizes automated and intelligent data filtering through an intelligent data screening mechanism, greatly improving the quality and processing efficiency of data.

[0143] In the data cleaning and preprocessing stage, an adaptive data cleaning algorithm is adopted, which can automatically adjust the cleaning strategy according to the characteristics of the data. For structured data, the system will automatically identify and fill in missing values; for unstructured data, the system will use NLP techniques such as word segmentation, named entity recognition, and relation extraction to convert it into a structured format. In addition, the system also supports incremental data cleaning, which can gradually repair the errors in the newly incoming data without affecting the existing data. Through this adaptive cleaning mechanism, the system can flexibly handle different types of data, ensuring the efficiency and accuracy of data cleaning. To further improve the quality of the data, the system introduces an abnormal data detection algorithm based on statistics and machine learning, which can automatically identify and correct the outliers in the data. For example, the system can detect outliers through statistical methods such as Z-Score and IQR, or use machine learning algorithms such as Isolation Forest and LOF to identify abnormal patterns. For the detected abnormal data, the system will automatically correct or mark it to ensure the integrity and accuracy of the data. This abnormal detection and correction mechanism not only improves the quality of the data, but also enhances the robustness of the system, enabling it to handle complex business environments. Compared with the existing technologies, the data cleaning process of the existing technologies is usually static, with fixed cleaning strategies and difficult to adapt to different types of data. However, through the adaptive data cleaning algorithm of the present invention, the cleaning strategy can be dynamically adjusted according to the characteristics of the data, ensuring the flexibility and efficiency of data cleaning. In addition, the existing technologies often rely on simple statistical methods in abnormal data detection, which are prone to misjudgment or missed detection. However, by combining statistics and machine learning methods, the present invention can more accurately identify abnormal data and automatically correct it to ensure the integrity and accuracy of the data.

[0144] In the data standardization stage, the present invention adopts an intelligent data standardization mechanism based on rules and machine learning, which can automatically convert data from different sources into a unified standard format. The system can define a series of standard fields (such as "operating income", "market share", "customer satisfaction", etc.) according to the specific needs of the enterprise, and use NLP technology to map unstructured text data onto these standard fields. In addition, the system also supports dynamic field expansion, allowing users to customize new fields according to actual needs, ensuring the flexibility and scalability of the data. Through this intelligent standardization mechanism, the system can automatically process data from different sources to ensure its consistency and comparability. To further improve the data consistency, the system introduces a semantic consistency check mechanism based on the knowledge graph, which can automatically verify the semantic consistency between data from different sources. For example, the system can compare information such as enterprise names, addresses, and contact methods from different sources through knowledge graphs (such as DBpedia, Wikidata) to ensure the consistency and comparability of the data. This semantic consistency check mechanism not only improves the accuracy of the data but also enhances the intelligence level of the system, enabling it to better understand the meaning and relationship of the data. Compared with the prior art, the data standardization process of the prior art usually manually defines rules, lacking intelligence and flexibility and being difficult to cope with complex business requirements. While the present invention can automatically convert data from different sources into a unified standard format through the intelligent data standardization mechanism to ensure the consistency and comparability of the data. In addition, the prior art often relies on simple string matching in semantic consistency checking, which is prone to misjudgment or missed detection. While the present invention can more accurately verify the semantic relationship between data through the semantic consistency check mechanism based on the knowledge graph to ensure the accuracy and consistency of the data.

[0145] In the data association and integration stage, the present invention adopts graph database technology (such as Neo4j, ArangoDB) to construct an enterprise relationship network, which can deeply associate and integrate data from different channels. For example, the system can combine the annual reports published by an enterprise on its official website with its interactions on social media to comprehensively evaluate the enterprise's market performance and development potential. In addition, the graph database supports multi-hop queries, which can uncover the relationships hidden behind the data and help enterprises discover potential opportunities and risks. Through this graph database technology, the system can build a more flexible and powerful enterprise relationship network and provide a more comprehensive enterprise portrait. To further optimize the effect of data association, the system not only evaluates the contribution degree of each dimension individually but also considers the correlation between multiple dimensions for multi-dimensional joint optimization. For example, although the contribution degree of some dimensions is relatively low when viewed individually, they may have an important impact on the enterprise's performance when combined with other dimensions. The system will identify these potential associated dimensions through multivariate analysis (such as principal component analysis, factor analysis, etc.) and include them in the key collection scope. This multi-dimensional joint optimization mechanism not only improves the accuracy of data association but also enhances the intelligence level of the system, enabling it to better understand the overall performance of the enterprise.

[0146] Compared with the prior art, the prior art usually relies on traditional relational databases in data association and integration and is difficult to handle complex relationship networks and multi-dimensional data. However, the present invention can build a more flexible and powerful enterprise relationship network by introducing graph database technology, uncover the relationships hidden behind the data, and provide a more comprehensive enterprise portrait. In addition, the prior art often adopts a single-dimensional analysis method in multi-dimensional data analysis and is difficult to discover the correlation between different dimensions. However, through multi-dimensional joint optimization, the present invention can more comprehensively evaluate the overall performance of the enterprise and provide a more accurate label management service.

[0147] 2. Cross-platform data synchronization

[0148] Real-time data stream processing: To meet the requirements of large-scale real-time data, the system adopts a distributed stream processing framework such as Apache Kafka and Apache Flink. These frameworks can efficiently process massive real-time data streams to ensure that the system can quickly respond to the new dynamics of the enterprise. For example, when an enterprise publishes an important announcement on social media, the system can capture this information within seconds and immediately update the corresponding labels. In addition, the system also supports an event-driven architecture and can automatically trigger specific operations according to preset rules, such as sending notifications, generating reports, etc.

[0149] S3. Application of deep learning models

[0150] 1. Deep reinforcement learning model

[0151] Environmental Modeling: The system first constructs a model that simulates the enterprise operation environment, which includes multiple dimensions such as the financial status, market performance, and policy response of the enterprise. Through this model, the system can simulate the behaviors and changes of the enterprise under different scenarios.

[0152] Strategy Learning: Based on the above environmental model, the system uses deep reinforcement learning algorithms (such as DQN, PPO, etc.) to learn the optimal label management strategy. For example, the system can find the most suitable label update cycle and trigger conditions through continuous trial and error to maximize the accuracy and timeliness of the labels.

[0153] Online Learning and Adaptive Adjustment: To cope with the constantly changing market environment, the system adopts an online learning mechanism, enabling the model to continuously optimize its own parameters during actual operation. For example, when new trends emerge in the market or policies change, the system can automatically adjust the definition and classification criteria of the labels to ensure that the labels always match the current economic environment.

[0154] Anomaly Detection and Early Warning: The system also introduces an anomaly detection algorithm that can identify abnormal patterns in enterprise behaviors and issue early warnings in a timely manner. For example, if the sales volume of a certain enterprise suddenly drops significantly, the system will trigger an emergency update, re-evaluate the financial health status of the enterprise, and adjust its labels accordingly.

[0155] The specific implementation methods are as follows:

[0156] To build an intelligent label management system, the present invention adopts deep reinforcement learning technology, combined with multiple modules such as environmental modeling, strategy learning, online learning and adaptive adjustment, and anomaly detection and early warning, to ensure that the system can efficiently and accurately manage enterprise labels and respond to market changes in real time. In terms of environmental modeling, the system first constructs a model that simulates the enterprise operation environment, which comprehensively considers multiple dimensions of the enterprise, including financial status, market performance, policy response, etc. Through this multi-dimensional environmental model, the system can simulate the behaviors and changes of the enterprise under different scenarios. Specifically, the environmental model includes the following key elements:

[0157] Financial Status: Financial indicators such as the enterprise's revenue, profit, liabilities, and cash flow reflect the economic health status of the enterprise. Market Performance: The enterprise's market share, competitor dynamics, consumer feedback, etc. reveal the enterprise's competitiveness and influence in the market. Policy Response: The enterprise's response to government policies and industry regulations, such as tax policies and environmental protection requirements, affects the enterprise's operation strategy and future development. External Factors: External factors such as the macroeconomic environment, industry trends, and technological progress also affect the operation and development of the enterprise.

[0158] Through these multi-dimensional data, the system can construct a comprehensive model of the enterprise operation environment, simulating the behaviors and changes of the enterprise under different scenarios. This environmental model not only provides a basis for subsequent strategy learning but also enables the system to make precise decisions in complex business scenarios.

[0159] In terms of strategy learning, based on the above environmental model, the system uses deep reinforcement learning algorithms (such as DQN, PPO, etc.) to learn the optimal label management strategy. The core idea of deep reinforcement learning is to find the strategy that can maximize the long-term reward through continuous trial and error. In the application scenario of the label management system, the "reward" of the system can be defined as the accuracy and timeliness of enterprise labels. Specifically, the goal of the system is to find the most suitable label update cycle and trigger conditions to ensure that the labels always reflect the latest state of the enterprise. The system can learn through continuous trial and error that in some cases (such as when the enterprise issues important announcements, major changes occur in the market, etc.), the labels should be updated immediately; while in other cases (such as when the enterprise operates smoothly and there are no obvious fluctuations in the market), the label update cycle can be appropriately extended to reduce unnecessary consumption of computing resources. In this way, the system can automatically optimize the label update strategy to ensure the best balance between the accuracy and timeliness of the labels.

[0160] In terms of online learning and adaptive adjustment, to cope with the ever-changing market environment, the system adopts an online learning mechanism, enabling the model to continuously optimize its own parameters during actual operation. The core idea of online learning is to let the model process newly incoming data in real time and make self-adjustments based on the latest information. Specifically, when new trends emerge in the market or policies change, the system can automatically adjust the definition and classification criteria of the labels to ensure that the labels always match the current economic environment. With the introduction of new technologies or policy adjustments, the label definitions in some industries may need to be updated. The system can use the online learning mechanism to monitor these changes in real time and automatically adjust the label classification criteria. This adaptive adjustment not only improves the flexibility of the system but also ensures the accuracy and timeliness of the labels. In addition, the system also supports incremental learning, allowing the model to gradually absorb the information brought by new data without retraining the entire model, avoiding the problem of "catastrophic forgetting".

[0161] In terms of anomaly detection and early warning, to further improve the intelligence level of the system, the system also introduces anomaly detection algorithms, which can identify abnormal patterns in enterprise behaviors and issue early warnings in a timely manner. The core idea of anomaly detection is to identify abnormal situations that do not conform to normal behaviors by analyzing the historical data and real-time data of enterprises. For example, if the sales volume of a certain enterprise suddenly drops significantly, the system will trigger an emergency update, re-evaluate the financial health of the enterprise, and adjust its label accordingly. Specifically, the system uses a variety of anomaly detection algorithms, which can capture the abnormal behaviors of enterprises from different perspectives and issue early warnings in a timely manner. For example, when the system detects that the sales volume of a certain enterprise has dropped significantly for several consecutive quarters, or its market share has been rapidly eroded by competitors, the system will automatically trigger an emergency update, re-evaluate the financial health of the enterprise, and adjust its label accordingly.

[0162] In addition, the system also supports multi-level anomaly detection, which can not only identify the abnormal behaviors of individual enterprises, but also identify the abnormal trends of the entire industry or market. For example, when there are signs of recession in a certain industry as a whole, the system can give an early warning and recommend corresponding countermeasures for relevant enterprises. This multi-level anomaly detection mechanism enables the system to not only provide personalized label management services for enterprises, but also provide forward-looking decision-making support for enterprises and government departments.

[0163] 2. Transfer learning and pre-trained models: Selection of pre-trained models: The present invention selects pre-trained models that have been trained on a large amount of general data, such as BERT, RoBERTa, etc., as the initial models of the label management system. These pre-trained models have strong feature extraction capabilities and can provide a good foundation for subsequent tasks. Domain adaptation: To make the pre-trained models better adapt to the specific tasks of enterprise-benefiting label management, the system adopts domain adaptation techniques to fine-tune the models. For example, the system can adjust the parameters of the models through a small amount of labeled data to make them more focused on specific issues in enterprise and government affairs scenarios. Continuous learning: The system also supports a continuous learning mechanism, enabling the models to continuously improve their performance during the process of continuously accumulating new data. For example, as more and more enterprises join the label management system, the system can use this newly added data to further optimize the parameters of the models and improve their prediction ability and accuracy.

[0164] The specific implementation methods are as follows:

[0165] To build an efficient and accurate label management system, the present invention selects pre-trained models that have been trained on a large amount of general data as the initial models. These pre-trained models, such as BERT and RoBERTa, are trained on large-scale corpora (such as Internet texts, books, news, etc.) and have powerful feature extraction capabilities, enabling them to capture complex patterns in language and text. Specifically, BERT has learned to understand the meanings of words in different contexts through a bidirectional encoder architecture, while RoBERTa has further improved its performance by optimizing the pre-training strategy. By selecting these pre-trained models, the system can quickly obtain powerful feature representation capabilities, providing a solid foundation for subsequent tasks, while reducing the computational resources and time required to train a model from scratch, improving the generalization ability of the model, and enabling it to perform well on different types of text data. Compared with the prior art, the present invention not only relies on a single pre-trained model, but also considers multiple advanced pre-trained models (such as XLNet, DistilBERT, and Electra), and selects the most suitable model according to specific application scenarios, thus ensuring the flexibility and adaptability of the system.

[0166] To enable the pre-trained model to better adapt to specific problems in enterprise and government scenarios, the system uses domain adaptation techniques to fine-tune the model. First, the system collects a certain amount of labeled data in the enterprise and government domains, covering multiple dimensions such as enterprise basic information, market performance, and policy responses, and after cleaning and standardization processing to ensure the quality and consistency of the data. Based on these labeled data, the system fine-tunes the pre-trained model, only updating the parameters of the last few layers of the model and retaining most of the pre-trained weights, which not only retains the powerful feature extraction capabilities of the model on general data, but also makes it more focused on specific tasks. In addition, the system also adopts multi-task learning and adaptive learning rate scheduling strategies to further improve the adaptability and performance of the model. In this way, the pre-trained model can not only accurately identify the labels of enterprises, but also understand the sentiment tendencies in the news or social media comments released by enterprises, thus showing higher accuracy and robustness in enterprise and government scenarios. Compared with the prior art, the present invention not only relies on a static fine-tuning process, but also introduces a dynamic adjustment mechanism that can automatically optimize the model parameters according to the changes in real-time data to ensure that it always adapts to the latest business needs.

[0167] To cope with the changing market environment and enterprise requirements, the system also supports a continuous learning mechanism, enabling the model to continuously improve its performance as it accumulates new data. The system adopts online learning and incremental learning methods to process newly incoming data in real time, ensuring that the labels always reflect the latest state of the enterprise. Whenever a new enterprise joins the label management system or an enterprise releases new information (such as financial reports, market performance, policy responses, etc.), the system immediately inputs this data into the model for prediction and update. Incremental learning allows the model to gradually absorb the information brought by new data without retraining the entire model, avoiding the problem of "catastrophic forgetting". In addition, the system also explores the extended application of transfer learning, transferring knowledge from different fields to new tasks to help the model adapt to new changes faster. The automatic hyperparameter optimization technology and the user feedback-driven learning mechanism further improve the performance of the model, ensuring that the system can be continuously optimized as more and more enterprises join, providing more personalized and forward-looking service support for enterprises and the government. Compared with existing technologies, the present invention not only supports static batch learning but also realizes true online learning and incremental learning, being able to respond to new dynamics of enterprises in real time and ensuring the accuracy and timeliness of the label management system.

[0168] Compared with the prior art, in terms of multi-model selection and flexibility, the prior art usually relies on a single pre-trained model, such as BERT or RoBERTa. In contrast, the present invention not only selects these commonly used pre-trained models but also considers other advanced models (such as XLNet, DistilBERT, and Electra), and selects the most suitable model according to specific application scenarios. This multi-model selection mechanism greatly improves the flexibility and adaptability of the system, enabling the selection of the optimal model configuration according to the requirements of different tasks. In terms of dynamic fine-tuning and real-time optimization, the fine-tuning process in the prior art is usually static, that is, a one-time fine-tuning is performed on a fixed labeled dataset and is not updated thereafter. The present invention introduces a dynamic adjustment mechanism that can automatically optimize model parameters according to changes in real-time data to ensure that it always adapts to the latest business requirements. This dynamic fine-tuning not only improves the accuracy of the model but also enhances the robustness of the system, enabling it to cope with the ever-changing market environment. In terms of online learning and incremental learning, the prior art usually adopts batch learning, and the model needs to be retrained regularly to adapt to new data. The present invention realizes true online learning and incremental learning, which can process newly incoming data in real time to ensure that the labels always reflect the latest status of the enterprise. Incremental learning allows the model to gradually absorb the information brought by new data without retraining the entire model, avoiding the problem of "catastrophic forgetting". This real-time learning mechanism not only improves the efficiency of the system but also ensures the accuracy and timeliness of the label management system. In terms of user feedback-driven learning, the prior art pays little attention to user feedback. The present invention introduces a user feedback-driven learning mechanism that allows enterprises and government departments to submit feedback on label accuracy through an interactive platform. The system will automatically adjust the model parameters according to user feedback to further optimize the label classification results. This user participation method not only improves the transparency of the system but also promotes the continuous improvement of the model to ensure that it always meets the actual needs of users.

[0169] Through these technological innovations, the present invention not only has advantages in the selection and fine-tuning of pre-trained models but also achieves breakthroughs in continuous learning and user feedback mechanisms, ensuring the efficiency, accuracy, and adaptability of the label management system.

[0170] 3. Federated Learning and Privacy Protection

[0171] Federated learning framework: The present invention adopts a federated learning (FL) framework that allows multiple participants to jointly train a global model without sharing raw data. Each participant only trains the model locally and uploads the updated model parameters to a central server. The central server is responsible for aggregating these parameters to generate a new global model and distributing it to each participant.

[0172] Differential Privacy Protection: To further ensure data security and privacy, the system introduces differential privacy (DP) technology. During data collection and analysis, the system adds noise to sensitive information to ensure that no individual's information can be leaked. For example, when calculating statistical data, the system can randomly add noise within a certain range, making it impossible for attackers to reverse-engineer the original data.

[0173] Secure Multi-Party Computation: The system also adopts secure multi-party computation (SMC) technology, allowing multiple participants to collaboratively complete complex computational tasks without exposing their respective data. For example, multiple enterprises can jointly calculate the average growth rate of a certain industry without sharing their financial data.

[0174] S4. User Feedback and Continuous Improvement

[0175] 1. User Feedback Collection Mechanism:

[0176] Multiple Feedback Channels: The system provides multiple feedback channels, including web forms, mobile apps, emails, etc., making it convenient for users to submit opinions and suggestions anytime and anywhere. In addition, the system also supports voice input and image upload, enabling users to express their ideas more intuitively.

[0177] Intelligent Feedback Classification: To improve the efficiency of feedback processing, the system uses natural language processing technology to automatically classify and prioritize the feedback submitted by users. For example, the system can identify which feedback involves issues with label accuracy and which feedback proposes new functional requirements, and assign them to different processing teams respectively.

[0178] Feedback Tracking and Closed-Loop Management: The system establishes a complete feedback tracking mechanism to ensure that each piece of feedback can be responded to and processed in a timely manner. Users can view the status of the feedback they submitted through the system and receive notifications after the problem is solved. In addition, the system will regularly summarize user feedback to form a detailed report, helping the development team understand the advantages and disadvantages of the system and formulate improvement plans.

[0179] 2. Continuous Improvement and Iterative Update:

[0180] Agile Development Process: The system adopts an agile development method, dividing the entire development process into multiple short-cycle iterations. Each iteration includes requirements analysis, design, coding, testing, and deployment, ensuring that new features can be quickly launched and put into use. For example, the development team can complete a small version update within two weeks, fixing known problems and adding new features.

[0181] User Testing and Feedback Loop: To ensure the quality of each update, the system undergoes rigorous user testing before release. The testers include not only internal employees but also some external users to obtain a wider range of feedback. After the testing, the system makes necessary adjustments based on the users' feedback to ensure the stability and reliability of the new features.

[0182] Release Notes and Documentation Support: After each update, the system publishes detailed release notes documenting the specific content and improvements of this update. In addition, the system provides rich documentation support, including user guides, frequently asked questions, video tutorials, etc., to help users quickly get started and fully utilize the potential of the system.

[0183] S5. System Security and Compliance

[0184] 1. Data Encryption and Access Control

[0185] End-to-End Encryption: The system adopts end-to-end encryption technology to ensure that data cannot be stolen or tampered with during transmission and storage. All sensitive information, such as a company's financial data, personal information, etc., will undergo high-strength encryption processing, and only authorized users can decrypt and access this data. The specific implementation methods are as follows:

[0186] In this patent, end-to-end encryption (End-to-End Encryption, E2EE) not only ensures the security of data during transmission but also extends to the data storage stage, forming a comprehensive encryption protection mechanism. The system combines symmetric encryption algorithms (such as AES-256) and asymmetric encryption algorithms (such as RSA, ECC) to ensure that data remains encrypted from the sender to the receiver at all times, and only authorized users can decrypt and read the data. Whether an enterprise uploads ERP system data through an API interface or there is internal data transmission in the label management system, end-to-end encryption ensures that data cannot be stolen or tampered with in the network.

[0187] To further enhance security, the system not only uses encryption during data transmission but also encrypts sensitive information during data storage. All sensitive data stored in the database (such as financial statements, market performance data, etc.) will undergo high-strength symmetric encryption, and the encryption keys will be securely stored in an independent Key Management System (KMS). Each time data is accessed, the system will automatically call the key in the KMS for decryption to ensure that only authenticated users can access the decrypted data. In addition, the system adopts a distributed key management mechanism, combining Hardware Security Modules (HSM) and Multi-Factor Authentication (MFA) to ensure the security and availability of encryption keys. The introduction of the key rotation mechanism enables the system to regularly update encryption keys to prevent security risks brought by long-term use of the same key.

[0188] To address the potential risk of future key leakage, the system also incorporates Forward Secrecy, which generates independent session keys for each communication. This ensures that even if the master key is leaked at some point in the future, the previously encrypted data remains indecipherable. This mechanism is implemented through the Diffie-Hellman key exchange protocol or the Elliptic Curve Diffie-Hellman (ECDH) protocol, ensuring that the keys for each session are independent and do not affect the security of other sessions.

[0189] In addition, the system adopts a Zero Trust Architecture, assuming that all users and devices are untrusted and must undergo strict authentication and authorization to access system resources. Role-Based Access Control (RBAC) and Attribute-Based Access Control (ABAC), combined with multi-factor authentication (MFA) and behavior analysis techniques, ensure that only strictly verified users and devices can access sensitive data. This architecture not only enhances the security of the system, especially in dealing with internal threats, but also ensures data privacy and integrity when enterprises use the label management system.

[0190] Fine-grained permission management: The system sets up fine-grained permission management rules, and users with different roles can only access data and functions related to their responsibilities. For example, ordinary users can only view label information related to them, while administrators have higher permissions and can perform operations such as creating, modifying, and deleting labels. In addition, the system also supports Role-Based Access Control (RBAC), which can automatically assign corresponding permissions according to the user's role.

[0191] To ensure the security of the system and the confidentiality of data, this patent designs a fine-grained permission management mechanism, specifically implementing Role-Based Access Control (RBAC) and permission grading. The system, through advanced authentication and authorization technologies, combined with multi-factor authentication (MFA), dynamic permission adjustment, the Principle of Least Privilege (PoLP), and detailed logging, ensures that users with different roles can only access data and functions related to their responsibilities. The specific methods are as follows:

[0192] First, the system adopts Role-Based Access Control (RBAC) and automatically assigns corresponding permissions according to the user's position or responsibilities. Each user is assigned one or more roles in the system, and each role corresponds to a set of predefined permissions. For example, ordinary users can only view the label information related to them, while administrators have higher permissions and can perform operations such as creating, modifying, and deleting labels. The core of RBAC lies in binding permissions to roles rather than directly to users, which can simplify permission management and quickly adjust permissions when the user's role changes. To further enhance security, the system supports Multi-Factor Authentication (MFA). When logging in, users need to pass additional authentication methods (such as mobile phone verification codes, fingerprint recognition, etc.) to ensure that only verified users can access the system.

[0193] Secondly, the system implements permission grading to ensure more precise permission allocation. In addition to the basic role division, the system also defines detailed permission levels for each role. For example, ordinary users can only view the label information related to them and cannot perform any editing or deletion operations; department heads can view and edit the label information related to their departments but cannot delete labels or create new labels; administrators have the highest level of permissions and can perform operations such as creating, modifying, and deleting labels and can manage the permissions of other users; auditors can view the historical records and operation logs of all labels but cannot perform any editing or deletion operations. This permission grading mechanism ensures that users can only access the data and functions related to their responsibilities, avoiding the risk of permission abuse.

[0194] In addition, the system supports dynamic permission adjustment and can flexibly update the roles and permissions of users according to their real-time needs or changes in the organizational structure. For example, when an employee is promoted from an ordinary user to a department head, the system can automatically update their role and grant them more permissions. Similarly, if an employee leaves or is transferred, the system can immediately revoke their access rights to sensitive data to ensure data security. Dynamic permission adjustment not only improves the flexibility of the system but also reduces the risks brought by manual permission adjustment. To further enhance security, the system follows the "Principle of Least Privilege" (PoLP), that is, users are only granted the minimum permissions required to complete their work. This means that even if a user account is attacked, the attacker cannot easily obtain sensitive information or perform destructive operations. Through PoLP, the system ensures that even if there are malicious behaviors among internal users, it is difficult to cause significant damage to the system.

[0195] Finally, the system is equipped with a complete permission auditing function to record the permission changes and operation logs of all users. Every permission adjustment or user operation will be recorded in detail, including who performed what operation at what time and what the result of the operation was, etc. These logs can not only be used to trace historical operations but also serve as legal evidence for resolving disputes when necessary. In addition, the system also supports regular reviews of permission settings to ensure that all users' permissions always meet their job requirements. The introduction of the permission auditing function not only enhances the transparency of the system but also provides strong support for security management.

[0196] Compared with the prior art, the fine-grained permission management is not only more refined in permission division but also provides higher flexibility and security. Traditional systems usually rely on a single authentication method (such as username and password) and are vulnerable to attacks. While this patent realizes more refined permission division and higher transparency by introducing multi-factor authentication (MFA), combining role-based access control (RBAC), permission grading, dynamic permission adjustment, the principle of least privilege (PoLP), and a detailed permission auditing function. Traditional systems may lack a complete permission auditing function and it is difficult to trace the operation history of users. While this patent ensures that all operations are traceable through detailed permission change and operation log records, enhancing the transparency and traceability of the system. In addition, when the user role or permission changes in traditional systems, manual adjustment is often required, which is prone to omissions or errors. While this patent supports dynamic permission adjustment and can automatically update the user's role and permission according to the user's real-time needs or changes in the organizational structure, ensuring the flexibility and response speed of the system. Through the combination of these advanced technologies, the system not only improves the fineness of permission management but also greatly enhances the security of the system and the controllability of operations.

[0197] Auditing and Logging: The system is equipped with a complete auditing function that can record all operation logs, including who performed what operation at what time and what the result of the operation was, etc. These logs can not only be used to trace historical operations but also serve as legal evidence for resolving disputes when necessary.

[0198] As a possible implementation, this embodiment provides a label management method for practical application. The actual operation process includes:

[0199] 1. Data collection:

[0200] ① API interface integration: The enterprise's ERP system is connected to the label management system through the API interface to transmit production data, sales data, and financial data in real time. ② Web crawler: The system automatically captures the public information of the enterprise on social media platforms (such as Weibo and WeChat official accounts), as well as relevant news reports published by news media. ③ Manual entry: Internal employees of the enterprise regularly submit some data that cannot be obtained through automated means, such as market research reports and customer feedback, through the web form provided by the system.

[0201] 2. Data cleaning and preprocessing:

[0202] ① Duplicate removal and error correction: The system automatically identifies and removes duplicate data and corrects error information to ensure the accuracy of the data. ② Word segmentation and entity recognition: For unstructured text data (such as press releases and social media comments), the system uses natural language processing techniques for word segmentation, named entity recognition, and relationship extraction to generate structured data tables. ③ Image feature extraction: For product pictures released by the enterprise, the system uses image recognition technology to extract features and generate image tags.

[0203] 3. Data standardization:

[0204] ① Unified format: The system converts all data into a unified standard format. For example, it unifies information such as enterprise names, addresses, and contact information from different sources into a standard format for subsequent analysis and utilization. ② Define standard fields: The system defines a series of standard fields according to the specific needs of the enterprise, such as "operating income", "market share", "customer satisfaction", etc., to ensure the consistency and comparability of the data.

[0205] 4. Intelligent analysis and label creation:

[0206] ① Initial label generation: The system automatically generates initial enterprise characteristic labels, industry characteristic labels and production and operation labels based on the basic information and industry characteristics of the enterprise. Among them, enterprise characteristic labels are created based on the basic information of the enterprise and are preset labels in the system. Industry characteristic labels are maintained and created based on the actual business needs of various industry departments. Some sensitive data are desensitized through data labeling to break through the data islands between the government and enterprises. Production and operation labels are jointly maintained by the government and enterprises to break through the data islands between the government and enterprises. Labels can be "intelligent manufacturing", "high-end equipment manufacturing", "green manufacturing", etc. ② Deep learning model training: The system uses deep reinforcement learning algorithms to build a model that simulates the enterprise operating environment. The model includes multiple dimensions such as the company's financial status, market performance, and policy response. Through continuous trial and error, the system finds the most appropriate label update cycle and trigger conditions to maximize the accuracy and timeliness of labels. ③ Blockchain technology support: All label creation, modification, and deletion operations will be recorded on the blockchain and automatically executed through smart contracts to ensure that each step is traceable and cannot be tampered with.

[0207] 5. Dynamic update and maintenance:

[0208] ① Update cycle adjustment: The system dynamically adjusts the update cycle of labels according to the frequency of data changes in each dimension. For example, for key dimensions such as financial status and market performance, a shorter update cycle (such as once a week) is set, while for some relatively stable dimensions (such as company name and address), the update cycle is extended (such as once a quarter). ② Single trigger update: The system monitors the change rate of key data of the enterprise in real time. Once the change rate of a certain dimension exceeds the preset threshold (such as operating income growth of more than 10%), the system immediately triggers the update of the dimension label. For example, when the operating income of a startup company increases significantly, the system will respond quickly and update the relevant labels. ③ Minimum waiting interval: In order to avoid too frequent updates, the system sets a minimum waiting interval (such as 24 hours). The update operation will only be actually performed when the data condition that meets the single trigger update timing is currently determined and the interval between the judgment result of the data condition and the previous label update exceeds the preset waiting interval.

[0209] 6. Scenario simulation and decision support:

[0210] ① Predictive maintenance: The system utilizes time series analysis techniques to predict the future development trends and potential risks of the enterprise. Based on these prediction results, the system proactively plans the maintenance strategies for tags, such as adding specific tags, adjusting the definitions of existing tags, etc., to help the enterprise seize opportunities and avoid risks. ② Scenario simulation: The system can simulate the impact of different tax incentive policies on the enterprise's profits to assist the government in selecting the policy combination that is most conducive to economic development. Meanwhile, the system can also generate detailed reports to provide intuitive data support for the enterprise's management and assist them in making informed decisions.

[0211] 7. User participation and transparent operation and maintenance:

[0212] ① Tag review platform: The enterprise provides an interactive tag management platform that allows internal employees of the enterprise and government departments to jointly participate in the review and correction process of tags. The platform integrates dialogue systems and chatbot technologies to provide real-time assistance and support for users, and collects user feedback for continuous improvement of the tag system. ② Feedback tracking: Users can view the status of the feedback they submitted through the system and receive notifications after the problems are resolved. In addition, the system will regularly summarize user feedback to form detailed reports to help the development team understand the advantages and disadvantages of the system and formulate improvement plans.

[0213] 8. Effect evaluation:

[0214] ① Improvement in tag accuracy: Through the dynamic update mechanism and intelligent analysis unit, the tag accuracy of the system has been significantly improved. For example, key indicators such as the enterprise's financial health and market performance can be reflected in the tags in real time to help the enterprise promptly adjust its business strategies. ② Enhanced decision-making support: The scenario simulation and decision-making assistance functions of the system provide a scientific and reasonable basis for decision-making for the enterprise. For example, by simulating different market scenarios, the enterprise can formulate countermeasures in advance to reduce business risks. ③ Improvement in market competitiveness: Ultimately, through the tag management system, the enterprise has successfully identified its competitive advantages and potential risks and formulated corresponding countermeasures, significantly enhancing its market competitiveness.

[0215] The above embodiments demonstrate the functions and technical implementations of the system and also highlight its actual application effects in different scenarios. That is, through the introduction of multi-source data fusion, deep learning algorithms, and blockchain technology support, this embodiment realizes the comprehensive intelligence and automation of tag management, featuring high customization, strong scalability, excellent user experience, and long-term value creation.

[0216] As a possible implementation manner, this embodiment provides a system for tag management in enterprise management and government services, including:

[0217] To implement the above technical solution, the present invention proposes a design of a hierarchical architecture, including a data acquisition layer, a data processing layer, an intelligent analysis layer, a label management layer, and a service provision layer. The layers interact through API interfaces to ensure the modularity and scalability of the system.

[0218] Data acquisition layer: Responsible for obtaining relevant information of the enterprise from multiple channels. This layer supports multiple data source access methods, such as API interfaces, web crawlers, manual entry, etc. In addition, the system also cooperates with third-party data providers to ensure the diversity and reliability of data sources. For example, an enterprise can directly transmit the data of its ERP system to the label management system through an API interface, and the system can also automatically crawl the public information of the enterprise on social media through a web crawler.

[0219] Data processing layer: Responsible for cleaning, preprocessing, and standardizing the collected raw data. This layer uses advanced natural language processing (NLP), image recognition, and sentiment analysis technologies to convert unstructured data into a structured format. For example, the system can perform word segmentation, named entity recognition, and relationship extraction on the enterprise's press releases to generate structured text data; at the same time, it can also perform feature extraction and classification on the product pictures released by the enterprise to generate image tags. In addition, the data processing layer is also responsible for removing duplicates, correcting error information, and filling in missing values to ensure the quality of the data.

[0220] Intelligent analysis layer: Used to construct a multi-dimensional model that simulates the enterprise operation environment based on the preprocessed target management data to generate initial tags. The initial tags include enterprise characteristic tags, industry characteristic tags, and production and operation tags. It is also used to describe the relevant content of label classification management, including defining enterprise characteristic tags, industry characteristic tags, and production and operation tags; and is also used to evaluate the current state of the tags according to the real-time state of the target management data, and combine multi-modal time series prediction analysis and adaptive threshold adjustment mechanism to predict the change trend of the tags through deep learning and federated learning technologies. That is, this layer is responsible for constructing and training various prediction models and classifiers. For example, the system can use deep reinforcement learning algorithms to optimize the tag update strategy to ensure that the tags always reflect the latest state of the enterprise; it can also use transfer learning and pre-trained models to improve the generalization ability and applicability of the models. In addition, the intelligent analysis layer also supports real-time data stream processing, can quickly respond to the new dynamics of the enterprise, and update the corresponding tags in a timely manner.

[0221] Label Management Layer: Responsible for the creation, modification, deletion, and maintenance of labels. This layer incorporates blockchain technology support to ensure that all operations are traceable and tamper-proof. For example, whenever a new label is added or an existing label is updated, the smart contract automatically verifies the legality of the operation and writes the relevant information to the blockchain. In addition, the Label Management Layer also provides a label review platform for user participation, allowing enterprises and government departments to jointly participate in the label review and correction process, improving the transparency and participation of label management.

[0222] Service Provision Layer: Responsible for providing personalized label management and government services to users. This layer integrates dialogue systems and chatbot technologies to provide users with real-time assistance and support, and collects user feedback for continuous improvement of the label system. For example, enterprises can submit feedback on label accuracy through the platform or request the addition of new labels; government departments can adjust the definition and classification criteria of labels according to actual needs. In addition, the Service Provision Layer also supports scenario simulation and decision-making assistance functions to help enterprises and governments formulate more scientific and reasonable business strategies.

[0223] The specific implementation manners described above further elaborate on the purpose, technical solutions, and beneficial effects of the present invention. It should be understood that the above are only specific implementation manners of the present invention and are not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A tag management method for enterprise management and government services, characterized in that: The specific steps include: Obtain target management data, pre-process the target management data, and build a multi-dimensional model that simulates the enterprise operating environment to generate initial labels based on the pre-processed target management data; Monitor the multi-dimensional change frequency of target management data in real time, and dynamically adjust the label update cycle according to the data change frequency of each dimension; Evaluate the status of the current tag based on the real-time status of the target management data, and predict the change trend of the tag by combining multimodal time series prediction analysis and adaptive threshold adjustment mechanism; Manage target management data based on predicted tag change trends.

2. The tag management method for enterprise management and government services according to claim 1 is characterized in that: The obtaining of target management data and preprocessing of the target management data specifically include: Use a distributed data collection framework to capture and process data streams from different channels in real time to obtain target management data; Perform data cleaning on target management data. During the data cleaning process, structured data is obtained and missing data values ​​are identified and filled. Standardize the cleaned data, obtain unstructured data, and use NLP technology to convert unstructured data into structured format; The formatted data are associated and integrated to obtain preprocessed data.

3. The tag management method for enterprise management and government services according to claim 1 is characterized in that: The step of constructing a multi-dimensional model simulating the enterprise operating environment to generate initial labels specifically includes: Constructing a multi-dimensional model simulating the enterprise operating environment, wherein the model includes multiple dimensions and is used to simulate the behavior and changes of the enterprise under different scenarios; Based on the multi-dimensional model simulating the enterprise operating environment, a deep reinforcement learning algorithm is used to learn the optimal tag management strategy, and the tag update cycle and trigger conditions within the set threshold are found through iterative trial and error; Ingest new incoming data in real time and automatically adjust tag definitions and classification criteria based on the latest information.

4. The tag management method for enterprise management and government services according to claim 1, characterized in that: The real-time monitoring of the multi-dimensional change frequency of the target management data and the dynamic adjustment of the label update cycle according to the data change frequency of each dimension specifically include: Calculate the frequency of single-dimensional data changes of target management data; Construct a contribution evaluation model, calculate the data fluctuation value of a single dimension based on the standard deviation of the portrait data of the single dimension within a set time period, and obtain the benchmark weight of the corresponding dimension; Determine the comprehensive change rate based on the benchmark weights of each dimension and the frequency of change of single-dimensional data; Dynamically adjust the label update cycle according to the comprehensive change rate.

5. The tag management method for enterprise management and government services according to claim 4 is characterized in that: The dynamically adjusting the label update period according to the comprehensive change rate specifically includes: High change rate: When the change rate exceeds 10%, the label update cycle of this dimension is shortened to daily; Medium change rate: When the change rate is between 5% and 10%, the label update cycle for this dimension is weekly. Low change rate: When the change rate is less than 5%, the label update cycle of this dimension is extended to monthly.

6. The tag management method for enterprise management and government services according to claim 4, characterized in that: After the contribution evaluation model is constructed, the change trend of the historical data of the target management data is obtained, and an adaptive learning algorithm is used to optimize the contribution evaluation model; Quantitatively evaluate the impact of each dimension on the overall performance of target management data and obtain the contribution of each dimension; Regularly update the contribution of each evaluation dimension, adjust the data collection strategy according to the evaluation results, retain the contribution dimensions within the set threshold, and remove the contribution dimensions outside the set threshold.

7. The tag management method for enterprise management and government services according to claim 4 is characterized in that: The label update process specifically includes: Determine whether the data change rate triggers the update condition according to the preset threshold. If it is triggered, enter the update process; when the time difference with the last update exceeds the preset threshold and meets the minimum waiting interval, a single update of the tag is triggered; Adjust the update cycle of the corresponding dimension label and set the update frequency according to the corresponding change rate; Construct a dimension correlation matrix, obtain the correlation between different dimensions, and perform multi-dimensional joint update.

8. The tag management method for enterprise management and government services according to claim 1, characterized in that: The prediction of the change trend of the label specifically includes: Evaluate the status of the current tag according to the real-time status of the target management data to obtain the evaluation result; Combine multimodal time series forecasting analysis, integrate information from different types of data sources, and predict the changing trend of target management data; Adopting an adaptive threshold adjustment mechanism, the threshold range of each dimension of target management data is adjusted according to the changing trend of target management data; According to the change trend of the target management data and the threshold range adjustment of the data in each dimension of the target management data, the prediction result of the change trend of the label is obtained; Automatically adjust label definitions and classification criteria based on prediction results.

9. The tag management method for enterprise management and government services according to claim 1, characterized in that: When operating the tags, it also includes building a distributed ledger based on the blockchain to record each tag operation as a transaction, including: automatically triggering the operation and writing it to the blockchain for storage when the tag creation conditions, modification permissions and deletion process meet the preset conditions.

10. A label management system for enterprise management and government services, characterized in that: include: Data collection layer, used to obtain target management data; Data processing layer, used to pre-process target management data; The intelligent analysis layer is used to construct a multi-dimensional model simulating the enterprise operation environment based on the pre-processed target management data to generate initial labels, wherein the initial labels include enterprise characteristic labels, industry characteristic labels and production and operation labels; It is also used to describe the relevant content of label classification management, including enterprise characteristic labels, industry characteristic labels and production and operation labels for label definition; It is also used to evaluate the current status of the tag according to the real-time status of the target management data, and to predict the changing trend of the tag by combining multimodal time series prediction analysis and adaptive threshold adjustment mechanism; The tag management layer is used to create, modify, delete and maintain tags; The service provision layer is used to manage target management data according to the change trend of the predicted tags.

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