Intelligent adaptation and precise push service platform for tax preferential policy

Abstract

The invention discloses an intelligent adaptation and precise pushing service platform for tax preferential policies, and the platform achieves the precise matching and personalized pushing of enterprises and tax preferential policies through multi-source data collection, enterprise portrait construction, a dynamic policy knowledge graph, an adaptation degree calculation engine, a pushing decision module and a feedback learning module. The adaptation degree calculation formula fully considers multi-dimensional features and weights, the system can continuously optimize the matching effect according to feedback, and the policy landing efficiency and accuracy are improved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of tax informatization and artificial intelligence application, and specifically relates to an intelligent adaptation and accurate push service platform for tax preferential policies, which is particularly suitable for providing personalized preferential matching and real-time push services for enterprises in a complex tax policy environment. BACKGROUND

[0002] Under the background of continuous development of tax informatization, various tax preferential policy push systems have gradually been applied in the daily work of enterprises and tax authorities. However, the existing push systems have many deficiencies in design and implementation, which cannot meet the individual needs of enterprises in a complex tax environment.

[0003] Firstly, the data collection method of the existing system is relatively single, and most of them rely on manual input or single interface to obtain enterprise information, which cannot efficiently integrate multi-source heterogeneous data such as business registration, financial statements, tax records, industry statistics, and regional economic indicators. This limitation leads to incomplete basic data for enterprise portrait construction, which cannot fully reflect the operating status and policy adaptation potential of enterprises.

[0004] Secondly, when constructing the enterprise portrait, the existing system often only uses a few financial indicators, ignoring important dimensions such as industry activity, policy sensitivity, and life cycle stage. The single dimension of the portrait makes the subsequent adaptation calculation too rough, and cannot accurately distinguish the adaptation degree of different enterprises under the same policy.

[0005] Thirdly, the construction of the existing policy library is mostly static structure, and the policy clauses are stored in text form, lacking computable condition association and dynamic updating mechanism. When the tax policy is adjusted, manual intervention is often needed to reflect it in the matching logic, leading to push lag or even errors.

[0006] Fourthly, the calculation method of adaptation degree generally uses simple threshold judgment or linear weighting, without considering the correlation and dimension difference between different feature dimensions, and the accuracy and stability of the matching result are insufficient. At the same time, the matching function design lacks the processing ability of non-linear and non-linear scale, which is difficult to deal with complex enterprise feature distribution.

[0007] Fifthly, the push decision-making link is mostly based on fixed rules, such as uniformly pushing the same policy to all enterprises or pushing according to industry classification, without formulating differentiated push strategies combined with the life cycle stage of enterprises. This not only reduces the relevance of push, but also may cause user information overload or miss key policies.

[0008] Sixthly, the existing system lacks effective feedback learning mechanism, and cannot continuously optimize the matching model and weight distribution according to the feedback data such as click rate and adoption rate of enterprises on the pushed policies, leading to the decline of matching accuracy after long-term operation of the system.

[0009] Seventh, data security and compliance are not well protected. Some systems do not use security algorithms and access control models that meet national standards during data transmission and storage, which poses a risk of information leakage and makes it difficult to meet the strict requirements of the tax field for data security.

[0010] Eighth, existing systems usually only consider single tax matching in adaptive calculation, ignoring the correlation between different taxes. For example, certain preferential policies may affect the final adaptive result when they are superimposed or excluded. The ability of cross-tax joint adaptation is basically missing in existing technology.

[0011] Ninth, policy timeliness management is weak. The system cannot automatically monitor the validity period and update of the policy, and the pushed policy may have expired or been replaced, affecting the timeliness and correctness of enterprise decision-making.

[0012] In summary, the existing tax preferential policy push platform has obvious deficiencies in data collection, portrait construction, policy library management, adaptive calculation, push decision, feedback learning, security protection, cross-tax adaptation, and timeliness management, making it difficult to achieve truly intelligent, precise, and dynamic adaptation and push. Therefore, it is necessary to design a new intelligent adaptive and precise push service platform for tax preferential policies to solve the above problems. SUMMARY

[0013] The purpose of the present application is to provide an intelligent adaptive and precise push service platform for tax preferential policies, especially suitable for providing personalized preferential matching and real-time push services for enterprises in a complex tax policy environment.

[0014] In order to achieve the above object, the present application is realized by the following technical scheme: The intelligent adaptation and accurate pushing service platform for tax preferential policy is composed of multiple function modules that cooperate with each other. The multi-source data acquisition module is responsible for obtaining enterprise-related data from various sources such as industry and commerce, finance, tax, industry statistics, and regional economy, ensuring that the information sources are extensive and diverse. The enterprise portrait construction module cleanses and standardizes the collected raw data, extracts features in multiple dimensions such as business scale index, industry activity, and policy sensitivity, and forms a multi-dimensional portrait of the enterprise to facilitate subsequent accurate matching. The dynamic policy knowledge graph module constructs a computable graph structure of tax policies according to attributes such as tax type, application conditions, time limit, and region, and supports real-time updating to keep the policy library up-to-date. The adaptation degree calculation engine calculates the adaptation degree of the enterprise and the policy based on the enterprise portrait and policy conditions using a multi-dimensional feature matching algorithm, providing a basis for pushing. The pushing decision module generates personalized pushing strategies based on the adaptation degree ranking and the enterprise life cycle stage, ensuring the relevance and timeliness of the pushing content. The feedback learning module collects user responses to the pushing, dynamically adjusts the feature weights, and realizes continuous optimization of the model. The security and compliance module runs throughout the platform to ensure that data acquisition, transmission, and storage comply with national tax information security standards, preventing data leakage and illegal use. The complete functional architecture of the platform is defined, with clear division of labor and close cooperation between modules, forming a closed loop from data acquisition to pushing and realizing intelligent processing throughout the entire process. Through multi-source data acquisition and portrait construction, the platform has the ability to comprehensively understand enterprises; the dynamic policy knowledge graph ensures the real-time and computability of the policy library; the combination of adaptation degree calculation and pushing decision makes the pushing accurate and personalized; feedback learning and security compliance ensure the continuous optimization and legal operation of the system. The overall architecture design avoids the problem of insufficient single-point functions, improving the stability and practicality of the system.

[0015] Further, the multi-source data acquisition module uses a combination of distributed crawlers and API interfaces, which can simultaneously interface with multiple heterogeneous data sources, achieving unified access and incremental updating. During data acquisition, the module performs noise reduction on the raw data, eliminating obvious errors or invalid information; uses a reasonable filling strategy for missing values to ensure data integrity; detects and marks abnormal values to prevent abnormal data from affecting subsequent analysis. The module also supports timed incremental updating of data, ensuring that enterprise information remains up-to-date on the platform, providing a reliable foundation for portrait construction and adaptation calculation. The overall data acquisition is emphasized and quality control is emphasized. The combination of distributed crawlers and API improves the efficiency and coverage of data acquisition; noise reduction, missing value filling, and anomaly detection ensure data quality and reduce matching errors caused by data problems; the incremental updating mechanism ensures the timeliness of information. These features enable the platform to maintain efficient and stable operation when faced with massive heterogeneous data.

[0016] Further, the enterprise portrait construction module adopts a logarithmic normalization formula when calculating the business scale index, converting the enterprise operating income into a dimensionless value between 0 and 1 for comparison between multiple enterprises. This formula effectively compresses the influence of extreme large values by taking the natural logarithm of operating income and normalizing it, allowing different scale enterprises to be reasonably distributed in the portrait space and avoiding matching bias due to large scale differences. The specific normalization method improves the comparability of the portrait and the stability of the matching. Logarithmic normalization reduces the influence of extreme values on the overall distribution, allowing different scale enterprises to be evenly distributed in the same feature space, thereby improving the fairness and accuracy of the adaptation degree calculation.

[0017] Further, the dynamic policy knowledge graph module represents policy terms and conditions as nodes and edges of a weighted directed graph, with nodes representing policy terms or conditions, edges representing logical relationships, and weights reflecting the importance of conditions. The graph structure supports real-time updates based on event triggers, allowing the system to quickly adjust nodes and edges in the graph and update related weights when policies change, ensuring the timeliness and correctness of the matching logic. By graphically representing policy conditions, the policy logical relationships are clear and computable; the weighted design reflects the importance differences of different conditions; the real-time update mechanism ensures that the policy library is synchronized with the latest regulations, avoiding errors caused by policy lag.

[0018] Further, the adaptation degree calculation engine adopts a weighted matching formula in the form of cosine similarity and designs a special matching function to handle feature differences in different dimensions and distributions. The matching function measures the closeness of features and standard values through relative error, avoiding matching bias caused by different dimensions. The weighting mechanism allows the system to dynamically adjust weights based on the importance of different features, making the matching results more consistent with actual conditions. Through precise matching formula and function design, the accuracy and robustness of adaptation degree calculation are improved; the weighting mechanism enhances the flexibility of the system, allowing it to adjust feature importance according to actual needs, making the matching results closer to the real adaptation situation.

[0019] Further, the push decision module uses a Hidden Markov Model based on time series in enterprise life cycle identification, estimates the current development stage of the enterprise by analyzing the time series characteristics of its historical data, and develops differentiated push strategies based on the stage, including push frequency and content depth, to avoid information overload or missing key policies. Through life cycle identification, personalized push is achieved, improving the relevance and effectiveness of the push; differentiated strategies avoid information redundancy while ensuring that important policies are promptly communicated.

[0020] Further, the feedback learning module updates the feature weights using an online gradient descent method, and the loss function is designed based on the mean square error of the actual adoption and the predicted probability. The system continuously optimizes the weight distribution based on user feedback, gradually improving the accuracy of the matching model. Through continuous learning of user feedback, the model is self-optimized, ensuring that the system can maintain high matching accuracy even after long-term operation.

[0021] Further, the security and compliance module uses the SM4 algorithm for data encryption and a role-based access control model for access control, ensuring that different roles can only access authorized data and preventing unauthorized access and data leakage. The dual security mechanism ensures the confidentiality and integrity of the data, meeting the national tax information security standards and enhancing the credibility of the system.

[0022] Further, the platform supports cross-tax type joint adaptation calculation, introduces a tax type correlation matrix, and incorporates the additive or repulsive effect between different tax types into the adaptation degree calculation, making the matching results more comprehensive. Through cross-tax type correlation analysis, the limitations of single tax type matching are avoided, and the adaptation results are more in line with the actual policy environment.

[0023] Further, the platform has a policy timeliness monitoring module that automatically monitors the validity period and update status of the policy through timestamp comparison and version control mechanism. Once the policy is detected to be near expiration or has been updated, the adaptation degree is recalculated and the policy adjustment is pushed. Through timeliness monitoring, the effectiveness of the pushed content is ensured, so that enterprises always obtain the latest and usable policy information, improving the timeliness and accuracy of decision-making.

[0024] The present application provides an intelligent adaptation and precise push service platform for tax preferential policies, which has the following advantages:

[0025] The present application provides an intelligent adaptation and precise push service platform for tax preferential policies, which has the following advantages:

[0026] In terms of data collection, the platform uses a combination of distributed crawlers and API interfaces to support the unified access and incremental update of heterogeneous data sources, and performs noise removal, missing value filling and anomaly detection before the data enters the system. This mechanism ensures the integrity and reliability of the input data, laying a solid foundation for subsequent portrait construction and adaptation calculation.

[0027] In terms of building enterprise profiles, the platform not only integrates financial indicators but also introduces multi-dimensional features such as operating scale index, industry activity, and policy sensitivity. Normalization ensures that features of different dimensions can be compared within the same space. The operating scale index is calculated using logarithmic normalization, effectively compressing the impact of extreme values ​​and ensuring a reasonable distribution of enterprises of different sizes within the profile space. The establishment of multi-dimensional profiles allows the system to understand enterprises from more perspectives, significantly improving the precision of the adaptation.

[0028] In terms of policy database management, the platform constructs a dynamic policy knowledge graph, representing policy clauses and conditions as nodes and edges in a weighted directed graph, and supports real-time updates based on event triggers. This structure not only intuitively expresses the logical relationships between policy conditions, but also allows for rapid adjustment of matching logic when policies change, ensuring the timeliness and accuracy of pushed content.

[0029] In terms of fit calculation, the platform adopts a weighted matching formula in the form of cosine similarity and designs a dedicated matching function to handle feature differences with different dimensions and distributions. This function measures the closeness of features to standard values ​​through relative error, avoiding matching bias caused by different dimensions. The weighting mechanism allows the system to dynamically adjust the weights according to the importance of different features, making the matching results more consistent with reality.

[0030] In terms of push notification decisions, the platform introduces a time-series-based Hidden Markov Model to identify the stages of a company's lifecycle and formulate differentiated push notification strategies accordingly. This strategy can adjust the frequency and depth of push notifications based on the company's development stage, avoiding information overload while ensuring that key policies are communicated in a timely manner.

[0031] In terms of feedback learning, the platform uses online gradient descent to continuously optimize feature weights, and the loss function is designed based on the mean squared error between actual adoption and predicted probability. By continuously learning from user feedback, the system can gradually improve matching accuracy and push effectiveness, achieving self-evolution.

[0032] In terms of security, the platform employs the national standard SM4 algorithm for data encryption and a role-based access control model to ensure that different roles can only access data within their authorized scope. This dual-security mechanism complies with national tax information security standards and effectively prevents data leaks and unauthorized access.

[0033] In terms of cross-tax adaptation, the platform introduces a tax type association matrix, incorporating the superposition or exclusion effects between different taxes into the adaptation calculation. This mechanism enables the system to conduct comprehensive evaluations in a multi-tax environment, avoiding adaptation bias caused by ignoring the relationships between tax types.

[0034] Regarding policy timeliness management, the platform automatically monitors the validity period and update status of policies through timestamp comparison and version control mechanisms. Once it detects that a policy is nearing expiration or has been updated, it immediately triggers a recalculation of adaptability and an adjustment of the push strategy. This ensures that the pushed content is always valid and up-to-date, helping enterprises seize policy opportunities in a timely manner.

[0035] Overall, this platform has achieved breakthroughs in the comprehensiveness of data collection, the multidimensionality of profile construction, the dynamism of policy database management, the accuracy of adaptive calculation, the personalization of decision-making push, the continuity of feedback learning, the rigor of security, the comprehensiveness of cross-tax adaptation, and the proactiveness of timeliness management. It can provide enterprises with truly intelligent, accurate, and dynamic tax incentive policy adaptation and push services, significantly improving the efficiency and effectiveness of policy implementation. Attached Figure Description

[0036] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are merely exemplary, and those skilled in the art can derive other embodiments based on the provided drawings without creative effort.

[0037] Figure 1 This is a diagram illustrating the overall operational logic of the platform of this invention. Detailed Implementation

[0038] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses consistent with some aspects of this disclosure as detailed in the appended claims.

[0039] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0040] How to use:

[0041] Multi-source data acquisition module: After the platform starts, the multi-source data acquisition module is invoked first to obtain relevant data on the target enterprise from business registration information sources, financial data sources, tax data sources, industry statistics sources, and regional economic data sources. During the acquisition process, the system uses a combination of distributed crawlers and API interfaces for unified access and incremental updates, and performs noise reduction, missing value imputation, and anomaly detection on the raw data to ensure that the data entering subsequent modules is complete and reliable.

[0042] Enterprise Profile Building Module: After the collected data enters the enterprise profile building module, it first undergoes standardization processing, and then extracts and calculates multi-dimensional features such as operating scale index, industry activity level, and policy sensitivity. The formula for calculating the operating scale index is as follows: ;

[0043] in For the company's current operating revenue, The maximum operating revenue in the same industry is used, and the calculation result is normalized to the [0,1] interval. Industry activity and policy sensitivity are also quantified based on the collected data to form a multi-dimensional feature vector of enterprises.

[0044] Dynamic Policy Knowledge Graph Module: After the enterprise profile is completed, the dynamic policy knowledge graph module is invoked to construct a weighted directed graph of tax policies according to tax type, applicable conditions, timeliness, and regional attributes. .node Indicates policy terms or conditions, side Represents logical relationships, weights This indicates the importance of conditions. The graph supports real-time updates based on event triggers, automatically adjusting the node and edge structure and weights when policies change.

[0045] Fit Calculation Engine: The fit calculation engine uses enterprise multi-dimensional feature vectors to match policy conditions, and the calculation formula is as follows: ;

[0046] in For the enterprise The feature in the first Quantization value of dimension For policy The condition in the first Standard values ​​of dimensions For the first Dimension weights, the matching function is defined as follows ;

[0047] in To prevent division by zero by small constants, the fit is calculated. Then, sort by value to provide a basis for push decision-making.

[0048] Push Decision Module: The push decision module calls a time-series-based Hidden Markov Model. Identify the stages of the enterprise lifecycle and develop differentiated push strategies based on the identification results, including push frequency and content depth, to avoid information overload or omission of key policies.

[0049] Feedback Learning Module: After a user receives a push notification, the system records feedback data such as click-through rate and acceptance rate. The feedback learning module uses online gradient descent to update the weights. The updated formula is as follows ;

[0050] Where the loss function is ;

[0051] For actual adoption status, To predict the probability of adoption, This is the learning rate. Through continuous learning, the system gradually improves its matching accuracy.

[0052] Security and Compliance Module: Throughout the entire process, the security and compliance module uses the national cryptographic SM4 algorithm to encrypt data transmission. In the access control stage, it adopts the role-based access control (RBAC) model to ensure that different roles can only access data within their authorized scope, preventing unauthorized access and leakage.

[0053] Cross-tax joint adaptation calculation: The platform introduces a tax type association matrix into the adaptation calculation. Matrix elements Indicate tax type With tax The correlation strength and fit calculation take into account the superposition or exclusion effects between tax types to make the matching results more comprehensive.

[0054] Policy Timeliness Monitoring Module: This module automatically detects policy validity and update status through timestamp comparison and version control mechanisms. When a policy is detected as nearing expiration or has been updated, it immediately triggers a recalculation of adaptability and adjusts the push strategy to ensure that pushed content is always valid and up-to-date.

[0055] Example:

[0056] Example 1

[0057] In service scenarios targeting newly established technology companies, the platform initiates a multi-source data collection module to gather data such as business registration information, intellectual property registration information, initial financial statements, industry technology activity statistics, and regional economic innovation indices. It employs a combination of distributed crawlers and API interfaces to achieve unified access and incremental updates. Before data enters the system, it performs noise reduction, missing value imputation, and anomaly detection to ensure data integrity and usability. After the collected data enters the enterprise profile building module, it undergoes standardization processing and extracts multi-dimensional features such as operating scale index, industry activity, and policy sensitivity to form a multi-dimensional feature vector for technology companies. Subsequently, it calls the dynamic policy knowledge graph module to construct a weighted directed graph of tax incentive policies for technology companies, categorized by tax type, applicable conditions, timeliness, and regional attributes. Nodes represent policy clauses or conditions, edges represent logical relationships, and weights reflect the importance of conditions, supporting real-time updates based on event triggers. The fit calculation engine uses a weighted matching formula and matching... The function measures the relative error of features with different dimensions, calculates the fit between enterprises and policies, and sorts them by value. The push decision module identifies the enterprise life cycle stage through a hidden Markov model and formulates a high-frequency, in-depth push strategy for early-stage technology companies to ensure timely delivery of R&D-related preferential policies. The feedback learning module collects user clicks and acceptance feedback on pushes, updates feature weights using online gradient descent, and optimizes the matching model through a loss function. The security and compliance module uses the national cryptographic SM4 algorithm to encrypt data transmission throughout the process and adopts a role-based access control model to ensure that different roles can only access data within their authorized scope. The cross-tax joint fit calculation introduces a tax type association matrix into the fit calculation, incorporating the superposition or exclusion effects between different taxes into the matching process. The policy timeliness monitoring module automatically monitors the policy validity period and version changes. Once it detects that a policy is about to expire or has been updated, it immediately triggers a recalculation of the fit and an adjustment of the push strategy.

[0058] Example 2

[0059] In service scenarios targeting traditional manufacturing enterprises, the platform initiates a multi-source data collection module to gather business registration information, production equipment investment data, energy consumption statistics, industry capacity utilization rates, and regional economic and industrial indices. It employs a combination of distributed web crawling and API interfaces to perform noise reduction, missing value imputation, and anomaly detection to ensure data quality. After standardized processing, the collected data enters the enterprise profile construction module, calculating features such as operating scale index, industry activity, and policy sensitivity to form a multi-dimensional profile of manufacturing enterprises, highlighting production scale and industry cyclical characteristics. The dynamic policy knowledge graph module constructs a weighted directed graph of tax incentives related to energy conservation and emission reduction, equipment purchase, and industrial upgrading in the manufacturing sector, and updates it in real time to stay up-to-date. The system is consistent with regulations; the fit calculation engine uses a weighted matching formula and matching function to calculate the fit, ensuring that different feature dimensions do not affect the matching results, and sorts them by value; the push decision module identifies the life cycle stage of manufacturing enterprises through a hidden Markov model, and formulates a medium-frequency, content-broad coverage push strategy for mature enterprises, taking into account multiple policy types; the feedback learning module collects feedback data and uses online gradient descent to optimize weights and improve the accuracy of the matching model; the security and compliance module uses full encryption and access control to ensure data security and compliance; the cross-tax joint fit calculation introduces a tax type association matrix to comprehensively consider the superposition or exclusion effects between tax types; the policy timeliness monitoring module automatically monitors policy changes and triggers fit recalculation and strategy adjustment.

[0060] Example 3

[0061] In service scenarios targeting cross-border trade enterprises, the platform initiates a multi-source data collection module to gather customs import and export records, foreign exchange settlement data, international logistics information, industry international trade indices, and regional economic openness. It employs a combination of distributed web crawlers and API interfaces to perform noise reduction, data completion, and anomaly detection. After standardized processing, the collected data enters the enterprise profile building module, calculating characteristics such as operating scale index, industry activity, and policy sensitivity to form a multi-dimensional profile of cross-border trade enterprises, highlighting their international operating characteristics. The dynamic policy knowledge graph module covers export tax rebates, tariff preferences, and tax exemptions for cross-border services. The system employs a weighted directed graph of policies, updated in real-time to stay aligned with the latest regulations. The fit calculation engine uses a weighted matching formula and function to calculate fit, ensuring that different feature dimensions do not affect the matching results. The push decision module identifies the lifecycle stages of cross-border trade enterprises through a Hidden Markov Model, developing high-frequency, targeted push strategies for enterprises in the expansion phase, focusing on pushing international trade-related incentives. The feedback learning module collects feedback and optimizes weights. The security and compliance module provides comprehensive protection through encryption and access control. Cross-tax joint fit calculation incorporates a tax type association matrix. The policy timeliness monitoring module automatically monitors and adjusts policies.

[0062] Example 4

[0063] In service scenarios targeting cultural and creative enterprises, the platform initiates a multi-source data collection module to gather copyright registration information, cultural product sales, industry creativity index, and the proportion of regional economic and cultural industries. It employs a combination of distributed web crawling and API interfaces to perform noise reduction, data completion, and anomaly detection. After standardized processing, the collected data enters the enterprise profile construction module, calculating features such as operating scale index, industry activity, and policy sensitivity to form a multi-dimensional profile of cultural and creative enterprises, highlighting creative output and cultural market activity. A dynamic policy knowledge graph module constructs a weighted directed graph involving policies such as tax exemption for cultural products, copyright revenue incentives, and support for the creative industries, and updates it in real time. The fit calculation engine uses weighted matching formulas and matching functions to calculate fit. The push decision module identifies the lifecycle stages of cultural and creative enterprises through a Hidden Markov Model, developing medium-frequency, content-creative-related push strategies for growth-stage enterprises. The feedback learning module collects feedback and optimizes weights. The security and compliance module provides full-process encryption and access control. Cross-tax joint fit calculation introduces a tax-related matrix. The policy timeliness monitoring module automatically monitors and adjusts policies.

[0064] Example 5

[0065] In service scenarios targeting agricultural cooperatives, the platform initiates a multi-source data collection module to gather data on land management area, agricultural output statistics, industry agricultural activity, and regional economic agricultural output. This data is processed using a combination of distributed web crawling and API interfaces to perform noise reduction, data imputation, and anomaly detection. After standardized processing, the collected data enters the enterprise profile construction module, which calculates features such as operating scale index, industry activity, and policy sensitivity to create a multi-dimensional profile of the agricultural cooperative, highlighting the scale and seasonal characteristics of agricultural production. A dynamic policy knowledge graph module constructs a weighted directed graph covering policies such as agricultural subsidies, agricultural machinery purchase incentives, and tax exemptions for agricultural product processing, and updates it in real time. An adaptation calculation engine uses weighted matching formulas and matching functions to calculate adaptation. A push decision module identifies the lifecycle stages of agricultural cooperatives using a Hidden Markov Model, developing high-frequency, agricultural-related push strategies for enterprises during harvest season. A feedback learning module collects feedback and optimizes weights. A security and compliance module provides full-process encryption and access control. A cross-tax joint adaptation calculation introduces a tax-related matrix. A policy timeliness monitoring module automatically monitors and adjusts the data.

[0066] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A smart adaptation and precise push service platform for tax incentive policies, characterized in that: include: The multi-source data acquisition module is used to obtain relevant enterprise data from sources such as business registration, finance, taxation, industry statistics, and regional economic data. The enterprise profile building module is used to standardize the collected data and build a profile that includes an operating scale index. Industry activity Policy sensitivity The system includes: multi-dimensional feature vectors; a dynamic policy knowledge graph module, used to construct a computable graph structure of tax policies according to tax type, applicable conditions, timeliness, and regional attributes, and supporting real-time updates; and an adaptation calculation engine, used to calculate the adaptation degree between enterprises and policies based on multi-dimensional feature vectors and policy conditions. ; The push decision module is used to generate push strategies based on suitability ranking and enterprise lifecycle stage; The feedback learning module is used to dynamically adjust feature weights based on the feedback pushed to the user. The security and compliance module is used to ensure that the entire data processing process complies with national tax information security standards.

2. The intelligent adaptation and precise push service platform for tax incentive policies according to claim 1, characterized in that, The multi-source data acquisition module adopts a combination of distributed crawlers and API interfaces to support unified access and incremental updates of heterogeneous data sources. Before the data enters the system, it performs noise reduction, missing value imputation, and anomaly detection to ensure the integrity and reliability of the input data.

3. The intelligent adaptation and precise push service platform for tax incentive policies according to claim 1, characterized in that, In the enterprise profile construction module, the business scale index The calculation formula is: ； in For the company's current operating revenue, The index represents the highest operating revenue in the same industry, and is normalized to the range of [0,1].

4. The intelligent adaptation and precise push service platform for tax incentive policies according to claim 1, characterized in that, In the dynamic policy knowledge graph module, the relationship between policy conditions and applicable objects is represented by a weighted directed graph. This indicates that the node Indicates policy terms or conditions, side Represents logical relationships, weights To indicate conditional importance, the graph supports a real-time update mechanism based on event triggers.

5. The intelligent adaptation and precise push service platform for tax incentive policies according to claim 1, characterized in that, The adaptation calculation engine uses the following adaptation calculation formula: ； in For the enterprise The feature in the first Quantification value of dimension For policy The condition in the first Standard values ​​of dimensions For the first Dimension weights For the matching function, the matching function is defined as follows: ； in To prevent small constants from being divided by zero.

6. The intelligent adaptation and precise push service platform for tax incentive policies according to claim 1, characterized in that, The push decision module employs a time-series-based Hidden Markov Model in enterprise lifecycle identification. Estimate the stage of enterprise development and select the push frequency and content depth based on the current stage to avoid excessive pushes that cause user fatigue.

7. The intelligent adaptation and precise push service platform for tax incentive policies according to claim 1, characterized in that, The feedback learning module updates the weight wk using an online gradient descent method, with the update formula as follows: ； in For the push effect loss function, Given the learning rate, the loss function is defined as: ； For actual adoption status, To predict the probability of adoption.

8. The intelligent adaptation and precise push service platform for tax incentive policies according to claim 1, characterized in that, The security and compliance module uses the national cryptographic SM4 algorithm for data encryption and a role-based access control (RBAC) model (R,A,P) for access control, ensuring that different roles can only access data within their authorized scope.

9. The intelligent adaptation and precise push service platform for tax incentive policies according to claim 1, characterized in that, The platform supports cross-tax type joint adaptation calculation, that is, it introduces a tax type association matrix into the adaptation calculation. Matrix elements Indicate tax type With tax The correlation strength and fit are calculated by considering the superposition or exclusion effects between tax types.

10. The intelligent adaptation and precise push service platform for tax incentive policies according to claim 1, characterized in that, The platform also has a policy timeliness monitoring module, which uses timestamp comparison and version control mechanisms. When a policy is about to expire or has been updated, it automatically triggers a recalculation of the adaptability and an adjustment of the push strategy to ensure that the pushed content is always valid and up-to-date.

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