One-stop management system for scientific and creative intelligent AI enterprise policy service

Abstract

The invention provides a one-stop management system for scientific and creative intelligent AI enterprise policy services, and belongs to the technical field of computer information technology and artificial intelligence. Aiming at the understanding problem of spoken appeals of users and government affair standardized data, the system collects and enhances spoken samples through a multi-modal semantic understanding training module; training an AI model for accurately understanding the user appeals; the policy intelligent analysis and precise matching module constructs a dynamically updated full-amount policy library, and realizes precise matching of appeals and policies through vocabulary, semantic and regional dimension weighted calculation; the whole-flow online declaration and approval module realizes cross-department data sharing and flow intelligentization; the fund intelligent measurement and calculation and non-inductive cashing module measures and calculates fund based on a multi-factor model and uses a block chain for evidence storage; and the dynamic monitoring and risk early warning module realizes policy timeliness reminding and risk early warning. According to the invention, full-process intelligence of policy service is realized, enterprise service efficiency and government administrative efficiency are improved, and development of the department and creative industry is assisted.

Description

Technical Field

[0001] This invention belongs to the fields of computer information technology and artificial intelligence technology, and involves language processing, knowledge graph and machine learning technologies, especially a one-stop management system for policy services of science and technology innovation-oriented intelligent AI enterprises. Background Technology

[0002] The One-Stop Policy Service Management System for Science and Technology Innovation-Oriented Intelligent AI Enterprises is a digital platform specifically designed for such enterprises, based on technologies such as artificial intelligence, big data, and blockchain. Its goals are to integrate policy resources, optimize application processes, and improve service efficiency, achieving intelligent management across the entire chain from policy interpretation to fund disbursement. The system features intelligent policy analysis and precise matching (e.g., dynamic updates of the full policy database and enterprise profile matching recommendations), fully online application and approval (e.g., one-stop access and cross-departmental data sharing), intelligent fund calculation and seamless disbursement (e.g., policy calculator and blockchain-based evidence storage), and dynamic monitoring and risk warning (e.g., improved policy timeliness). With core functions such as alerts and compliance risk warnings, and supported by key technologies such as large-scale models and NLP, big data and machine learning, and blockchain and data security, it can meet the different needs of startups, medium-sized and large-scale science and technology AI enterprises. It can help enterprises improve efficiency, reduce costs and control risks, and also help the government to implement precise policies, optimize efficiency and achieve data-driven decision-making. Typical cases include Keze Cloud, Puyida, and Changhuida. In the future, it will deepen technology integration and expand the service ecosystem, while also addressing data governance and ethical challenges, and continuously promote the transformation of the government-enterprise interaction model from "enterprises seeking policies" to "policies seeking enterprises", helping to enhance the competitiveness of relevant strategic fields in my country.

[0003] However, existing one-stop policy service management systems developed by science and technology innovation-oriented intelligent AI enterprises often encounter users expressing their needs in colloquial, regionally specific, and non-standardized ways, while government data is mostly in standardized, written language. This makes it difficult for models trained solely on government language corpora to accurately understand user intent. Furthermore, obtaining colloquial sample data for government services is difficult and unevenly distributed, resulting in poor training effects. Therefore, there is an urgent need for a system that integrates artificial intelligence technology to achieve intelligent and integrated management of the entire policy service process, thereby addressing the aforementioned technical pain points. Summary of the Invention

[0004] The purpose of this invention is to address the aforementioned problems in existing technologies by proposing a one-stop management system for policy services of science and technology innovation-oriented intelligent AI enterprises. The technical problem this invention aims to solve is that users typically express their demands in colloquial, regional, and non-standardized ways, while government data is mostly in standardized and written language. This makes it difficult for models trained solely on government language corpora to accurately understand user intentions. Furthermore, it is difficult to obtain colloquial sample data for government services, and the data is unevenly distributed, resulting in poor training effects.

[0005] The objective of this invention can be achieved through the following technical solutions:

[0006] A one-stop management system for policy services for science and technology innovation-oriented intelligent AI enterprises includes the following modules:

[0007] Policy Intelligent Analysis and Precise Matching Module: This module constructs a comprehensive policy database and dynamically updates it. It also establishes enterprise profile models and uses artificial intelligence technology to semantically map and precisely match users' colloquial, regional, and non-standardized requests with standardized government data. The semantic mapping degree S is calculated as follows:

[0008] S=α·S 词汇 +β·S 语义 +γ·S 地域

[0009] In the formula, S 词汇 To measure the vocabulary matching degree between user requests and government data (calculated using the TF-IDF algorithm, with a vocabulary database containing 5000+ government-specific terms), S 语义 For deep semantic similarity (vector dimension 768), S 地域 For the regionalized expression adaptation degree (matched through a regional thesaurus, covering 34 provincial-level administrative regions and major cities across the country), α+β+γ=1 and all of them are weight coefficients;

[0010] The fully online application and approval module provides a one-stop application portal, enables cross-departmental data sharing, and uses big data and machine learning technologies to intelligently streamline and approve the application process. The process efficiency E is calculated as follows:

[0011]

[0012] (The preset process duration is determined based on the average of similar historical application processes; the actual processing time is automatically calculated using the process node timestamps.)

[0013] Intelligent Fund Calculation and Seamless Payment Module: Integrates a policy calculator function and utilizes blockchain technology for fund payment data storage, enabling intelligent calculation and seamless payment of funds. The calculation formula for the payment amount M is as follows:

[0014]

[0015] In the formula, k i Let B be the applicability coefficient of the i-th policy (0≤k_i≤1, obtained by linear mapping of the matching degree S between enterprises and policies). i The policy benchmark amount (automatically extracted from the policy text, with support for manual review and correction), R iThe enterprise qualification coefficient (0≤R_i≤1.5, calculated based on a weighted average of 10+ indicators such as enterprise revenue, R&D investment, and number of patents);

[0016] Dynamic monitoring and risk warning module: Provides reminders about policy timeliness and alerts to enterprise compliance risks. Based on a large-scale model and NLP technology, it achieves dynamic monitoring and risk warning. The compliance risk value R is calculated as follows:

[0017]

[0018] In the formula, w j Let r be the weight of the j-th compliance indicator (determined through the analytic hierarchy process, covering 8 core indicators including qualification validity and document authenticity). j The risk level of the j-th indicator (0≤r_j≤10, determined jointly by the rule engine and the machine learning model);

[0019] Multimodal semantic understanding training module: Collects and constructs a dataset of colloquial samples for government services, employs data augmentation techniques to address uneven sample distribution, and trains an AI model integrating multimodal semantic understanding to accurately understand users' colloquial, regional, and non-standard requests. The optimization objective for sample balance B is:

[0020]

[0021] In the formula, p i The percentage of sample type i (sample categories include 12 categories such as policy consultation, application guidance, and funding calculation). The ideal average proportion is given by n, which is the number of sample categories.

[0022] Preferably, in the policy intelligent analysis and precise matching module, the dynamic update of the full policy database includes real-time capture of policy documents from official government channels (covering the official websites of more than 20 government departments such as the National Development and Reform Commission, the Ministry of Science and Technology, and the Ministry of Industry and Information Technology), automatic analysis and classification of policy texts using NLP technology, and the calculation method for the policy update time T is as follows:

[0023] T = t 发布时间 -t 入库时间

[0024] In the formula, t 发布时间 For the official policy release timestamp, t 入库时间 The timestamp for the system to complete parsing and storing (the parsing process includes word segmentation of policy text, entity recognition, and relation extraction, taking ≤30 minutes).

[0025] Preferably, in the multimodal semantic understanding training module, data augmentation techniques include, but are not limited to, synonym replacement of colloquial expressions (based on the WordNet lexicon, with a replacement ratio ≤30%), word order adjustment (based on syntactic tree analysis, maintaining semantic consistency after adjustment), and regional expression conversion (such as bidirectional mapping between "value-added tax reduction" and "value-added tax incentives"), to expand the scale and diversity of the colloquial sample dataset. The formula for calculating the augmented sample size G is as follows:

[0026]

[0027] In the formula, G0 is the original sample size (≥10000 records), λ k denoted as the sample augmentation coefficient for the k-th augmentation method.

[0028] Preferably, in the fully online application and approval module, cross-departmental data sharing is based on blockchain technology to achieve trusted data storage and access management (using a consortium blockchain architecture, with nodes including government departments, enterprises, and third-party service providers), ensuring the security and consistency of data during cross-departmental transfer. The verification formula for the data sharing credibility C is:

[0029]

[0030] Ensure the security and consistency of data during cross-departmental transfer (block generation time ≤ 10 seconds, data tampering detection accuracy 100%).

[0031] Preferably, in the intelligent fund calculation and seamless payment module, the policy calculator is based on big data analysis of historical data (≥500 policies) and rules for fund payment under various policies, establishing an intelligent calculation model to automatically calculate the fund payment amount. The calculation method for the model's calculation accuracy A is as follows:

[0032]

[0033] In the formula, θ is the preset error threshold (θ≤5%, for major national policies θ≤3%).

[0034] Preferably, in the dynamic monitoring and risk warning module, the policy timeliness reminder is based on the time information in policy documents, combined with the time series analysis capabilities of a large model (using an LSTM model) to achieve accurate policy timeliness reminders; the compliance risk warning is based on enterprise profiles and policy compliance requirements, using machine learning algorithms for risk identification and warning, and the calculation formula for the timeliness warning lead time Δt is:

[0035] Δt=t 截止时间 -t 当前时间 -t 办理耗时

[0036] A reminder is triggered when Δt ≤ Δt0 (preset warning threshold, default Δt0 = 7 days, user-defined is supported); compliance risk warnings are based on enterprise profiles and policy compliance requirements, and use machine learning algorithms to identify and warn of risks (risk identification accuracy ≥ 90%).

[0037] Furthermore, it is characterized by including the following steps:

[0038] Step 1: Train the AI ​​model using the multimodal semantic understanding training module to enable it to accurately understand users' colloquial, regional, and non-standard requests. The model's understanding accuracy P is calculated as follows:

[0039]

[0040] (Test sample size ≥ 5000, covering all sample categories)

[0041] Step 2: Users submit their requests through the system (supporting multiple modal formats such as text, voice, and images). The policy intelligent analysis and precise matching module analyzes the requests and performs precise matching with policies in the full policy database. The matching degree must meet S≥S0 (preset matching threshold, default S0=0.8, which can be adjusted according to policy priority).

[0042] Step 3: After successful matching, users submit policy applications through the online application and approval module. The system automatically handles the process flow and cross-departmental approval. The flow efficiency E must meet E≥E0 (preset efficiency threshold, default E0=30%, for emergency policies E0=50%).

[0043] Step 4: After approval, the intelligent fund calculation and seamless redemption module automatically calculates the fund redemption amount and uses blockchain technology for evidence storage and redemption. The calculation accuracy must meet the requirement of A≤θ.

[0044] Step 5: The dynamic monitoring and risk warning module dynamically monitors and warns about policy timeliness and corporate compliance risks. When the compliance risk value R ≥ R0 (preset risk threshold, default R0 = 6, high-risk policy R0 = 4), an early warning is triggered.

[0045] Furthermore, the model training data in step one includes collected colloquial sample data of government services (≥20,000 records, from actual enterprise consultation records), standardized government data (≥50,000 records, from government information disclosure platforms), and augmented data generated through data augmentation techniques. The training data coverage K is calculated as follows:

[0046]

[0047] Furthermore, when the computer program is executed by the processor, it implements the method of the previous step, wherein during the program operation, it verifies in real time whether each indicator meets the preset formula threshold.

[0048] Compared with existing technologies, the one-stop management system for science and technology innovation-oriented intelligent AI enterprise policy services of the present invention has the following advantages:

[0049] 1. Improve the efficiency of policy acquisition and understanding. The multimodal semantic understanding training module can accurately analyze users' colloquial, regional, and non-standard requests. Combined with the semantic mapping technology of the policy intelligent analysis and accurate matching module, it enables enterprises to quickly match suitable policies, avoiding misunderstandings caused by obscure policy information or differences in expression, and significantly reducing the time cost for enterprises in policy retrieval and interpretation. For example, if a startup uses a colloquial expression such as "We are a small company that does AI image recognition, what subsidies can we apply for?", the system can accurately match policies such as "High-tech Enterprise R&D Subsidy" and "Special Support for Artificial Intelligence Industry".

[0050] 2. Optimize the application process and reduce time and manpower costs. The fully online application and approval module enables one-stop application, cross-departmental data sharing and intelligent process flow. Enterprises do not need to run around to multiple places and submit materials repeatedly. The improvement in process flow efficiency (E) means a shorter application cycle and faster policy implementation.

[0051] 3. Accurately calculate funds to ensure transparent and efficient payment. The policy calculator of the intelligent fund calculation and seamless payment module is based on big data and intelligent models. It can accurately calculate the payment amount M, and the blockchain notarization ensures that the fund payment process is transparent and traceable. Enterprises can clearly know the basis of fund calculation and the progress of fund arrival, avoiding fund losses due to calculation errors or lack of transparency in the process.

[0052] 4. Dynamic risk warning to facilitate compliant operation: The dynamic monitoring and risk warning module can provide real-time reminders of policy expiration and quantitatively assess enterprise compliance risks, helping enterprises to adjust their business strategies in a timely manner and avoid the risk of penalties caused by missed policy expiration or compliance issues. Attached Figure Description

[0053] Figure 1 This is a flowchart of a one-stop management system for policy services of science and technology innovation-oriented intelligent AI enterprises, as described in this invention.

[0054] Figure 2 This is a system module framework diagram of a one-stop management system for policy services of science and technology innovation-oriented intelligent AI enterprises in this invention. Detailed Implementation

[0055] The following are specific embodiments of the present invention, which are described in conjunction with the accompanying drawings. However, the present invention is not limited to these embodiments.

[0056] like Figure 1 - Figure 2 As shown, a one-stop management system for policy services of science and technology innovation-oriented intelligent AI enterprises includes the following modules:

[0057] Policy Intelligent Analysis and Precise Matching Module: This module constructs a comprehensive policy database and dynamically updates it. It also establishes enterprise profile models and uses artificial intelligence technology to semantically map and precisely match users' colloquial, regional, and non-standardized requests with standardized government data. The semantic mapping degree S is calculated as follows:

[0058] S=α·S 词汇 +β·S 语义 +γ·S 地域

[0059] In the formula, S 词汇 To determine the vocabulary matching degree between user requests and government data (using TF-IDF algorithm, with a vocabulary database containing 5000+ government-specific terms, calculated using the formula...) Q represents user requests, D represents government data, W represents a common vocabulary set, and S... 语义 Deep semantic similarity (based on the cosine similarity of sentence vectors in the BERT model, calculated using the formula is) (where S is the sentence vector) 地域 For the localization adaptation (if the user's region and the policy's applicable region are completely matched, then S...), 地域 =1, partial match then S 地域 =0.5, if not matched, then S 地域 =0), α+β+γ=1 and are all weighting coefficients (default α=0.3, β=0.5, γ=0.2);

[0060] The fully online application and approval module provides a one-stop application portal, enables cross-departmental data sharing, and uses big data and machine learning technologies to intelligently streamline and approve the application process. The process efficiency E is calculated as follows:

[0061]

[0062] Intelligent Fund Calculation and Seamless Payment Module: Integrates a policy calculator function and utilizes blockchain technology for fund payment data storage, enabling intelligent calculation and seamless payment of funds. The calculation formula for the payment amount M is as follows:

[0063]

[0064] In the formula, k i Let B be the applicability coefficient of the i-th policy. i As the policy benchmark amount, R i Enterprise qualification coefficient (0≤R)i ≤1.5, by The calculation involves a weighted sum of 10+ indicators, including revenue and R&D investment.

[0065] Dynamic monitoring and risk warning module: Provides reminders about policy timeliness and alerts to enterprise compliance risks. Based on a large-scale model and NLP technology, it achieves dynamic monitoring and risk warning. The compliance risk value R is calculated as follows:

[0066]

[0067] In the formula, w j Let r be the weight of the j-th compliance indicator. j The risk level of the j-th indicator;

[0068] Multimodal semantic understanding training module: Collects and constructs a dataset of colloquial samples for government services, employs data augmentation techniques to address uneven sample distribution, and trains an AI model integrating multimodal semantic understanding to accurately understand users' colloquial, regional, and non-standard requests. The optimization objective for sample balance B is:

[0069]

[0070] In the formula, p i The proportion of the i-th type of sample ( N i Let N be the number of samples in the i-th class. 总 (total sample size) For the ideal average proportion n is the number of sample categories.

[0071] Preferably, in the policy intelligent analysis and precise matching module, the dynamic update of the full policy database includes real-time capture of policy documents from official government channels, automatic analysis and classification of policy texts using NLP technology, and the calculation method for the policy update time T is as follows:

[0072] T = t 发布时间 -t 入库时间

[0073] In the formula, t 发布时间 For the official policy release timestamp, t 入库时间 This is the timestamp indicating when the system completes parsing and data entry.

[0074] Preferably, in the multimodal semantic understanding training module, data augmentation techniques include, but are not limited to, synonym substitution, word order adjustment, and regional expression conversion of colloquial expressions, in order to expand the scale and diversity of the colloquial sample dataset. The formula for calculating the augmented sample size G is as follows:

[0075]

[0076] In the formula, G0 is the original sample size, and λ k λ1 represents the sample augmentation coefficient for the k-th augmentation method (synonym substitution λ1 = 0.5, word order adjustment λ2 = 0.3, and regional conversion λ3 = 0.2).

[0077] Preferably, in the fully online application and approval module, cross-departmental data sharing is based on blockchain technology to achieve trusted data storage and access management, ensuring the security and consistency of data during cross-departmental transfer. The verification formula for the data sharing trustworthiness C is:

[0078]

[0079] Ensure the security and consistency of data during cross-departmental transfers.

[0080] Preferably, in the intelligent fund calculation and seamless payment module, the policy calculator is based on big data analysis of historical data and rules for fund payment under various policies, establishing an intelligent calculation model to automatically calculate the fund payment amount. The calculation method for the model's calculation accuracy A is as follows:

[0081]

[0082] In the formula, θ is the preset error threshold.

[0083] Preferably, in the dynamic monitoring and risk warning module, policy timeliness reminders are based on the time information in policy documents, combined with the time series analysis capabilities of large-scale models, to achieve accurate reminders of policy timeliness; compliance risk warnings are based on enterprise profiles and policy compliance requirements, using machine learning algorithms for risk identification and warnings, and the formula for calculating the lead time Δt for timeliness warnings is:

[0084] Δt=t 截止时 inter-t 当前时间 -t 办理耗时

[0085] The alert is triggered when Δt ≤ Δt0; the compliance risk alert is based on the enterprise profile and policy compliance requirements, and uses machine learning algorithms to identify and alert on risks.

[0086] Furthermore, this includes the following steps:

[0087] Step 1: Train the AI ​​model using the multimodal semantic understanding training module to enable it to accurately understand users' colloquial, regional, and non-standard requests. The model's understanding accuracy P is calculated as follows:

[0088]

[0089] Step 2: Users submit their requests through the system. The policy intelligent analysis and precise matching module analyzes the requests and performs precise matching with policies in the full policy database. The matching degree must meet S≥S0.

[0090] Step 3: After successful matching, users submit policy applications through the online application and approval module. The system automatically handles the process flow and cross-departmental approval. The flow efficiency E must meet the requirement of E≥E0.

[0091] Step 4: After approval, the intelligent fund calculation and seamless redemption module automatically calculates the fund redemption amount and uses blockchain technology for evidence storage and redemption. The calculation accuracy must meet the requirement of A≤θ.

[0092] Step 5: The dynamic monitoring and risk warning module dynamically monitors and warns of policy timeliness and corporate compliance risks. When the compliance risk value R ≥ R0, an early warning is triggered.

[0093] Furthermore, the model training data in step one includes collected colloquial sample data of government services, standardized government data, and augmented data generated through data augmentation techniques. The training data coverage K is calculated as follows:

[0094]

[0095] Furthermore, when the computer program is executed by the processor, it implements the method of the previous step, wherein the program verifies in real time whether various indicators meet the preset formula thresholds during the program's operation.

[0096] The working principle of this invention is as follows: First, the multimodal semantic understanding training module collects colloquial samples of government services and expands the dataset through data augmentation technology to train an artificial intelligence model that integrates multimodal semantic understanding, so as to accurately understand users' colloquial, regional, and non-standardized requests. Next, the policy intelligent parsing and precise matching module constructs a full policy database and updates it dynamically. Based on artificial intelligence technology, it achieves semantic mapping and precise matching between user requests and standardized government data through weighted calculations of lexical matching degree, deep semantic similarity, and regional expression adaptability. Finally, the fully online application and approval module provides... The system provides a one-stop application portal, leveraging big data and machine learning technologies to achieve cross-departmental data sharing and intelligent application process flow. It calculates efficiency by comparing the preset process time with the actual processing time. The intelligent fund calculation and seamless payment module integrates a policy calculator, calculating payment amounts based on a weighted sum of policy applicability coefficients, benchmark amounts, and enterprise qualification coefficients, and using blockchain technology for fund payment data storage. The dynamic monitoring and risk warning module, based on large-scale models and NLP technology, provides policy timeliness reminders and enterprise compliance risk alerts through weighted calculations of compliance indicator weights and risk levels. Through the collaborative operation of these modules, the entire system achieves intelligent management of the entire process of policy services for science and technology innovation-oriented intelligent AI enterprises, from understanding needs, policy matching, application approval, fund payment to risk monitoring.

[0097] In summary, the core beneficial effect of this invention is that the multimodal semantic understanding training module can accurately analyze users' colloquial, regional, and non-standardized requests. Combined with the semantic mapping technology of the policy intelligent analysis and accurate matching module, this allows enterprises to quickly match suitable policies, avoiding misunderstandings caused by obscure policy information or differences in expression, and significantly reducing the time cost for enterprises in policy retrieval and interpretation. For example, if a startup uses a colloquial expression such as "We are a small company doing AI image recognition, what subsidies can we apply for?", the system can accurately match policies such as "High-tech Enterprise R&D Subsidy" and "Special Support for Artificial Intelligence Industry".

[0098] The specific embodiments described herein are merely illustrative of the spirit of the invention. Those skilled in the art to which this invention pertains may make various modifications or additions to the described specific embodiments or use similar methods to substitute them, without departing from the spirit of the invention or exceeding the scope defined by the appended claims.

Claims

1. A one-stop management system for policy services of science and technology innovation-oriented intelligent AI enterprises, characterized in that, Includes the following modules: Policy Intelligent Analysis and Precise Matching Module: This module constructs a comprehensive policy database and dynamically updates it. It also establishes enterprise profile models and uses artificial intelligence technology to semantically map and precisely match users' colloquial, regional, and non-standardized requests with standardized government data. The semantic mapping degree S is calculated as follows: S=α·S 词汇 +β·S 语义 +γ·S 地域 In the formula, S 词汇 To ensure the vocabulary matching between user requests and government data, S 语义 For deep semantic similarity, S 地域 For the regionalized representation adaptation degree, α+β+γ=1 and all of them are weight coefficients; The fully online application and approval module provides a one-stop application portal, enables cross-departmental data sharing, and uses big data and machine learning technologies to intelligently streamline and approve the application process. The process efficiency E is calculated as follows: Intelligent Fund Calculation and Seamless Payment Module: Integrates a policy calculator function and utilizes blockchain technology for fund payment data storage, enabling intelligent calculation and seamless payment of funds. The calculation formula for the payment amount M is as follows: In the formula, k i Let B be the applicability coefficient of the i-th policy. i R is the policy benchmark amount. i For enterprise qualification coefficient; Dynamic monitoring and risk warning module: Provides reminders about policy timeliness and alerts to enterprise compliance risks. Based on a large-scale model and NLP technology, it achieves dynamic monitoring and risk warning. The compliance risk value R is calculated as follows: In the formula, w j Let r be the weight of the j-th compliance indicator. j The risk level of the j-th indicator; Multimodal semantic understanding training module: Collects and constructs a dataset of colloquial samples for government services, employs data augmentation techniques to address uneven sample distribution, and trains an AI model integrating multimodal semantic understanding to accurately understand users' colloquial, regional, and non-standard requests. The optimization objective for sample balance B is: In the formula, p i The proportion of the i-th type of sample. The ideal average proportion is given by n, which is the number of sample categories.

2. The one-stop management system for policy services for science and technology innovation-oriented intelligent AI enterprises according to claim 1, characterized in that, In the policy intelligent analysis and precise matching module, the dynamic update of the full policy database includes real-time capture of policy documents from official government channels, automatic analysis and classification of policy texts using NLP technology, and the calculation method for the policy update time T is as follows: T=t 发布时间 -t 入库时间 In the formula, t 发布时间 For the official policy release timestamp, t 入库时间 This is the timestamp indicating when the system completes parsing and data entry.

3. The one-stop management system for policy services for science and technology innovation-oriented intelligent AI enterprises according to claim 1, characterized in that, In the multimodal semantic understanding training module, data augmentation techniques include, but are not limited to, synonym substitution, word order adjustment, and regional expression conversion for colloquial expressions, in order to expand the scale and diversity of the colloquial sample dataset. The formula for calculating the augmented sample size G is as follows: In the formula, G0 is the original sample size, and λ k denoted as the sample augmentation coefficient for the k-th augmentation method.

4. The one-stop management system for policy services for science and technology innovation-oriented intelligent AI enterprises according to claim 1, characterized in that, In the fully online application and approval module, cross-departmental data sharing utilizes blockchain technology to achieve trusted data storage and access control, ensuring data security and consistency during cross-departmental transfer. The verification formula for the data sharing credibility C is as follows: Ensure the security and consistency of data during cross-departmental transfers.

5. The one-stop management system for policy services for science and technology innovation-oriented intelligent AI enterprises according to claim 1, characterized in that, In the aforementioned intelligent fund calculation and seamless payment module, the policy calculator is based on big data analysis of historical data and rules for fund payments under various policies. It establishes an intelligent calculation model to automatically calculate the fund payment amount. The calculation method for the model's accuracy rate A is as follows: In the formula, θ is the preset error threshold.

6. The one-stop management system for policy services for science and technology innovation-oriented intelligent AI enterprises according to claim 1, characterized in that, In the dynamic monitoring and risk warning module, the policy timeliness reminder is based on the time information in the policy documents and combined with the time series analysis capabilities of the large model to achieve accurate reminders of policy timeliness. Compliance risk warnings are based on enterprise profiles and policy compliance requirements, utilizing machine learning algorithms for risk identification and alerts. The formula for calculating the lead time for early warning, Δt, is as follows: Δt=t 截止时间 -t 当前时间 -t 办理耗时 A reminder is triggered when Δt ≤ Δt0; The compliance risk warning is based on the company profile and policy compliance requirements, and uses machine learning algorithms to identify and warn of risks.

7. A method for intelligent management of the entire policy service process based on the system described in any one of claims 1-6, characterized in that, Includes the following steps: Step 1: Train the AI ​​model using the multimodal semantic understanding training module to enable it to accurately understand users' colloquial, regional, and non-standard requests. The model's understanding accuracy P is calculated as follows: Step 2: Users submit their requests through the system. The policy intelligent analysis and precise matching module analyzes the requests and performs precise matching with policies in the full policy database. The matching degree must meet S≥S0. Step 3: After successful matching, users submit policy applications through the online application and approval module. The system automatically handles the process flow and cross-departmental approval. The flow efficiency E must meet the requirement of E≥E0. Step 4: After approval, the intelligent fund calculation and seamless redemption module automatically calculates the fund redemption amount and uses blockchain technology for evidence storage and redemption. The calculation accuracy must meet the requirement of A≤θ. Step 5: The dynamic monitoring and risk warning module dynamically monitors and warns of policy timeliness and corporate compliance risks. When the compliance risk value R≥R0, an early warning is triggered.

8. The intelligent management method for the entire policy service process according to claim 7, characterized in that, The model training data in Step 1 includes collected colloquial sample data of government services, standardized government data, and augmented data generated through data augmentation techniques. The training data coverage K is calculated as follows:

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method of claim 8, wherein during the program operation, it verifies in real time whether each indicator meets the preset formula threshold.

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