Enterprise-level AI system and method based on software and hardware deep fusion seven-layer integrated framework
Through a seven-layer integrated architecture that deeply integrates software and hardware, the problems of software and hardware fragmentation, multimodal processing, and insufficient security and compliance in enterprise AI systems have been solved. This has enabled efficient and secure utilization of AI resources and business integration, thereby improving the overall performance and security of enterprise AI systems.
Patent Information
- Application Number
- CN202511302845.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-09-12
- Publication Date
- 2026-01-09
AI Technical Summary
Existing enterprise AI systems suffer from problems such as hardware and software fragmentation, weak multimodal processing capabilities, difficulty in integrating with the existing IT ecosystem, and insufficient security and compliance. This results in high deployment costs, high inference latency, low parsing accuracy, inability to achieve a data-decision-business closed loop, and significant compliance risks.
The enterprise-grade AI system adopts a seven-layer integrated architecture based on deep hardware and software integration, including a customized AI all-in-one machine, a Kubernetes containerized platform, a self-developed inference engine, a multimodal document parsing module, a low-code development module, and full-stack security management, to achieve collaborative optimization of hardware and software, multimodal data processing, seamless integration, and fine-grained access control.
It improves the utilization rate of AI resources, the accuracy of unstructured document parsing, and shortens the deployment cycle, meets the requirements of high concurrency and low latency, achieves seamless integration with existing IT systems and high-level security compliance, and reduces operation and maintenance costs and technical barriers.
Smart Images
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Abstract
Description
Technical Field
[0001] This application relates to the field of artificial intelligence technology, and more specifically to an enterprise-level AI system and method based on a seven-layer integrated architecture with deep software and hardware fusion. Background Technology
[0002] In recent years, generative artificial intelligence technologies, represented by Large Language Models (LLMs), have made groundbreaking progress, significantly improving their capabilities in natural language understanding, knowledge reasoning, and content generation. They have become a core driving force for enterprise digital transformation and intelligent upgrading. Enterprises' demand for AI technology has shifted from "technology verification" to "business implementation," urgently requiring the construction of a private, controllable, and efficient AI capability system to improve operational efficiency, optimize decision-making quality, and innovate service models.
[0003] Currently, the mainstream technical solution for enterprises to build AI capabilities is a lightweight combination of "general cloud services + open source frameworks", which specifically includes: 1) calling third-party public cloud large model APIs (such as OpenAIGPT series, Alibaba Cloud Tongyi Qianwen, Baidu Wenxin Yiyan, etc.) to realize basic functions such as text generation and question answering; 2) using open source tools (such as Apache Tika, TesseractOCR, PyPDF2) for document preprocessing and text extraction; 3) improving the accuracy of model output based on retrieval-enhanced generation (RAG) architecture combined with local knowledge base; 4) building the front-end application interface and back-end logic through low-code or microservice architecture.
[0004] However, existing technical solutions have the following core flaws, making it difficult to meet the needs of enterprise-level AI for large-scale, secure, and business-oriented deployment:
[0005] Hardware and software are separated, resulting in high deployment and maintenance costs: Most existing solutions are pure software systems that rely on users to configure general servers, operating systems and data engines themselves, and the private deployment cycle can be as long as several weeks; the lack of collaborative optimization between hardware and software algorithms leads to computing power utilization of less than 30%, high inference latency, and poor compatibility between different components, making it difficult to achieve "out-of-the-box use".
[0006] Weak multimodal processing capabilities: Unstructured data (such as PDF scans, complex tables, and handwritten contracts) generated in the daily operations of enterprises account for more than 70%. Existing open-source tools have an accuracy rate of less than 60% in parsing this type of data and lack cross-modal semantic alignment capabilities, resulting in incomplete knowledge extraction, low retrieval recall, and a prominent "data dormancy" problem.
[0007] Difficulty in integrating with the existing IT ecosystem: Existing AI platforms lack native support for mainstream enterprise business systems (such as ERP, CRM, OA, MES), do not provide standard interfaces or pre-built connectors, and cannot obtain real-time business data (such as order status, customer information, and inventory data), causing AI applications to "float outside the business" and fail to form a "data-decision-business" closed loop;
[0008] Insufficient security and compliance: Most platforms adopt a flat permission model, which cannot achieve fine-grained permission control based on organizational structure and job roles; lacking data encryption, operation auditing and sensitive content filtering mechanisms, when enterprise sensitive data (such as financial contracts and customer information) is uploaded to public cloud or used on general platforms, there are risks of data leakage, loss of sovereignty and compliance violations, making it difficult to meet the requirements of highly regulated industries.
[0009] Therefore, there is an urgent need for an enterprise-level AI architecture that is deeply integrated with hardware and software, secure and controllable, supports multimodal processing, and can be seamlessly integrated with existing IT systems to solve the pain points of "difficult integration, slow implementation, high cost, and insecurity". Summary of the Invention
[0010] The shortcomings of existing technologies include: fragmented AI capabilities: "Project-based" development leads to isolated applications, inconsistent technology stacks, difficulty in reusing capabilities, and a lack of unified intelligent platform support; insufficient hardware and software collaboration: general-purpose hardware and software algorithms are disconnected, resulting in low computing power utilization, high inference latency, and an inability to meet the needs of high-concurrency, low-latency business operations; weak multimodal processing: low accuracy in unstructured document parsing, making it impossible to achieve deep understanding of multimodal information such as text, images, and tables; difficulty in integrating with existing IT systems: inability to seamlessly connect with business systems such as ERP and CRM, forming "information silos," making it difficult to embed AI capabilities into core business processes; poor security and compliance: lack of fine-grained access control, data encryption, and operation auditing mechanisms, failing to meet the privatization and compliance requirements of highly regulated industries; and high technical barriers: reliance on professional algorithm teams, resulting in long development cycles and high costs for AI applications, making large-scale promotion difficult.
[0011] To address the aforementioned issues, this invention provides the following technical solution: an enterprise-level AI system based on a seven-layer integrated architecture of deep hardware and software, wherein the system comprises, from bottom to top, a hardware layer, a computing power layer, an inference layer, a middleware layer, a platform layer, an application layer, and a security layer;
[0012] The hardware layer is a customized AI all-in-one machine, which integrates a multi-GPU computing cluster, dual-socket CPU, ECC memory, NVMeSSD storage array and DPU smart network card, supports InfiniBand / RDMA high-speed interconnection, and has dual redundant power supplies, liquid cooling heat dissipation, RAID storage protection and BMC out-of-band management function.
[0013] The computing power layer is based on the Kubernetes containerization platform and is configured with a computing power scheduler, load balancer and intelligent controller, supporting dynamic load balancing, automatic failover and hot-swappable computing power blades for elastic scaling.
[0014] The inference layer deploys a self-developed inference engine, supports local private deployment of large language models, applies quantization compression technology, and provides interfaces for model fine-tuning and incremental updates.
[0015] The middle layer establishes connections with enterprise ERP, CRM, OA systems and data platforms through a data connection engine, and configures a real-time incremental data synchronization mechanism.
[0016] The platform layer includes a multimodal document parsing module, a model lifecycle management module, a low-code development module, a data governance module, and an integrated operation and maintenance module. The multimodal document parsing module combines OCR, layout analysis, semantic recognition, and data augmentation technologies to build a dual knowledge base of vectorization and relational types. The model lifecycle management module supports public cloud model API calls and private model deployment, fine-tuning, and version control.
[0017] The application layer provides API, SDK and plugin interfaces, supporting integration with DingTalk, Lark, and WeChat Work to generate Web components, H5 pages and mini programs;
[0018] The security layer is configured with an end-to-end data encryption module, a fine-grained permission control module based on RBAC, an operation audit module, and a sensitive content identification module.
[0019] In a further preferred embodiment of the present invention: the multi-GPU computing cluster of the hardware layer is configured with 8 high-performance GPU cards and interconnected through NVLink; the transmission rate of the InfiniBand / RDMA is not less than 200Gb / s and the data transmission latency is less than 10μs.
[0020] A further preferred embodiment of the present invention is that the quantization compression technology of the inference layer is INT8 or FP16 quantization, which can reduce the memory usage of large language models by more than 40% and increase the inference speed by more than 50%.
[0021] A further preferred embodiment of the present invention is that the multimodal document parsing module of the platform layer has a parsing accuracy of no less than 90% for unstructured documents (PDF scans, handwritten contracts, complex tables); and the data governance module supports LLM's TEXT2SQL capability, which can realize automatic conversion from natural language to standard SQL.
[0022] In a further preferred embodiment of the present invention: the data encryption module of the security layer adopts TLS1.3 transmission encryption and AES-256 storage encryption; the log retention time of the operation audit module is not less than 6 months, meeting the requirements of Level 3 or above of the Information Security Protection System.
[0023] A method for implementing an enterprise-level AI system based on a seven-layer integrated hardware and software architecture includes the following steps:
[0024] S1. Hardware Deployment: Deploy a customized AI all-in-one machine, configured with a multi-GPU cluster, dual-CPU, ECC memory, NVMeSSD storage array and DPU smart network card, and enable InfiniBand / RDMA, dual redundant power supplies, liquid cooling and BMC out-of-band management.
[0025] S2, Computing Layer Configuration: Deploy the Kubernetes containerized platform, install the computing scheduler, load balancer and intelligent controller, and configure dynamic load balancing strategies and failover mechanisms;
[0026] S3, Inference Layer Deployment: Import large language models, deploy them locally using a self-developed inference engine, apply quantization compression technology, configure model fine-tuning interfaces, and connect to enterprise business datasets.
[0027] S4, Middleware Integration: Establishes connections with enterprise ERP, CRM, OA systems and data platforms through the data connection engine, and configures real-time incremental data synchronization strategies;
[0028] S5. Platform Layer Construction: Deploy the multimodal document parsing module, model lifecycle management module, low-code development module, data governance module, and integrated operation and maintenance module; test and optimize document parsing accuracy and model performance.
[0029] S6. Application Layer Development: Develop multi-scenario AI applications based on API / SDK, achieve integration with enterprise collaboration platforms, and generate Web components, H5 pages, and mini-programs;
[0030] S7. Security Layer Deployment: Configure end-to-end data encryption, RBAC access control, operation auditing, and sensitive content identification modules, and test the effectiveness of security functions.
[0031] In a further preferred embodiment of the present invention, the enterprise business dataset accessed by the model fine-tuning interface in step S3 includes industry terminology samples and historical business cases. Through fine-tuning, the accuracy of the model's semantic understanding of industry scenarios is improved by 10%-15%.
[0032] A further preferred embodiment of the present invention is that the low-code development module in step S5 encapsulates AI capabilities into draggable components, allowing business personnel to build AI processes without coding experience, thus shortening the application development cycle by more than 60%.
[0033] A further preferred embodiment of the present invention is that the RBAC permission control module in step S7 allocates permissions based on "user-position-department-data sensitivity level", which can realize fine-grained control that "different positions can only access data of the corresponding sensitivity level".
[0034] A further preferred embodiment of the present invention includes step S8, system operation and maintenance and optimization: real-time monitoring of resource utilization, model performance and application status through an integrated operation and maintenance module, and dynamic adjustment of computing power allocation and model parameters according to business needs to ensure long-term stable operation of the system.
[0035] In summary, the present invention has the following advantages:
[0036] This invention aims to address the technical pain points of existing enterprise AI systems, such as fragmented capabilities, hardware and software separation, weak multimodal processing, insufficient security and compliance, and difficulty in integrating with the existing IT ecosystem. Furthermore, this invention significantly improves AI resource utilization (by more than 40%), unstructured document parsing accuracy (by more than 90%), and shortens the AI application deployment cycle (by more than 60%), meeting the privatization, security control, and efficient implementation needs of highly regulated industries such as finance, government, and healthcare. Attached Figure Description
[0037] Figure 1 This is a system architecture diagram of this embodiment;
[0038] Figure 2 This is a hardware architecture diagram of this embodiment;
[0039] Figure 3 This is a functional architecture diagram of the platform software in this embodiment. Detailed Implementation
[0040] The present invention will be further described in detail below with reference to the accompanying drawings.
[0041] An enterprise-level AI system based on a seven-layer integrated architecture with deep software and hardware fusion, such as Figure 1 , 2 As shown in Figure 3, the system consists of a hardware layer, a computing power layer, an inference layer, a middleware layer, a platform layer, an application layer, and a security layer, arranged from bottom to top. Each layer has a clear division of labor and works together to achieve integrated management and control of the entire chain of "hardware-computing power-algorithm-data-application-security".
[0042] Hardware Layer: As the physical foundation of the system, it adopts a customized AI all-in-one machine, integrating 8 high-performance GPU cards (achieving parallel computing through NVLink interconnection), dual-socket multi-core CPUs, 512GB ECC memory, NVMe SSD storage array, and DPU smart network card; it supports InfiniBand / RDMA high-speed interconnection (data transmission latency less than 10μs), and is equipped with dual redundant power supplies, liquid cooling system, and RAID storage protection to ensure stable operation 24 / 7; it supports BMC out-of-band management, enabling remote operation and maintenance and hardware status monitoring, breaking the bottleneck of "software and hardware separation", and providing "plug-and-play" AI infrastructure.
[0043] Computing Layer: Responsible for unified scheduling of heterogeneous resources such as GPUs, and realizing dynamic resource allocation based on the Kubernetes containerized management platform; integrates computing scheduler, load balancer and intelligent controller, can automatically adjust the computing power allocation ratio according to business traffic, realize automatic fault transfer and QoS (Quality of Service) guarantee; supports hot-swappable computing blades, shortens horizontal scaling time to within 30 minutes, increases resource utilization to more than 70%, and avoids computing power waste.
[0044] Inference Layer: Supports local private deployment of large language models (such as ChatGLM, Deepseek, and enterprise-developed models) to eliminate the risk of data leakage; integrates a self-developed inference engine, applies INT8 / FP16 quantization compression technology to reduce memory usage by more than 40% and improve inference speed by more than 50%; provides model fine-tuning interface and incremental update mechanism, supports model adaptation based on enterprise business data (such as industry terminology and historical cases) to ensure that model output fits the business scenario.
[0045] Middle Layer: Serving as a "bridge" between enterprise IT systems and AI platforms, it achieves seamless integration with ERP, CRM, OA, data middleware, and mainstream databases (MySQL, SQL Server, Hadoop) through the "Data Connection Engine"; it supports real-time incremental data synchronization (synchronization latency less than 500ms) and automatic data format conversion, breaking down "information silos"; it builds a unified data channel to enable rapid access, cleaning, and structured processing of historical data assets, providing data support for upper-layer AI capabilities.
[0046] Platform Layer: The core hub for AI application development and management, comprising five core modules:
[0047] Multimodal document parsing module: Integrates a self-developed high-precision parsing engine, combined with OCR, layout analysis, semantic recognition and image / table data enhancement technology, to improve the parsing accuracy of unstructured documents such as PDF, scanned documents and handwritten contracts to over 90%, and builds a dual-engine system of vectorized knowledge base (supporting text vector retrieval) and relational knowledge base (supporting structured data association query);
[0048] Model lifecycle management module: Supports public cloud model API calls (such as Tongyi Qianwen, GPT-4) and local deployment, fine-tuning, online / offline and version control of private models. Provides model assembly function, which can configure exclusive model combinations according to business scenarios (such as contract review, intelligent customer service);
[0049] Low-code development module: Provides a visual workflow orchestration tool that encapsulates AI capabilities (such as document parsing, semantic retrieval, and SQL generation) into drag-and-drop components, allowing business users to build complex AI workflows (such as automatic contract review and intelligent approval) without requiring coding skills;
[0050] Data governance module: Supports the conversion of unstructured data (text, images) to structured data, and combines LLM's TEXT2SQL capability to achieve intelligent database interaction of "natural language questioning → automatic SQL generation → query execution → visual output";
[0051] Integrated operation and maintenance module: Real-time monitoring of system resources (CPU, GPU, memory), model performance (inference speed, accuracy) and application status, providing alarm and log management functions to achieve full-link traceability.
[0052] Application Layer: Provides users with standardized AI capability output interfaces, including APIs, SDKs, and plugins; supports seamless integration with enterprise collaboration platforms (DingTalk, Lark, WeChat Work), and can embed AI applications (such as Q&A robots, approval workflows, and notification reminders) into the organizational structure; supports the generation of various forms such as H5 pages, mini-programs, and web components, adapting to mobile and PC terminals, and realizing "scenario-based access" to AI capabilities.
[0053] Security Layer: A security control system that spans the entire system stack, including:
[0054] Data security: Supports end-to-end data encryption (transmission encryption uses TLS1.3, storage encryption uses AES-256), and sensitive data is anonymized;
[0055] Access Control: Role-Based Access Control (RBAC) mechanism enables fine-grained permission allocation based on "user-position-department-data sensitivity level" to prevent unauthorized access;
[0056] Compliance audit: Record the entire process operation log (including user operations, model calls, and data access), and retain the logs for no less than 6 months to meet the requirements of Level 3 or above of the Information Security Protection System.
[0057] Content security: Built-in sensitive content identification and filtering module to perform compliance verification on model-generated content and avoid outputting illegal and non-compliant information.
[0058] A method for implementing an enterprise-level AI system based on a seven-layer integrated hardware and software architecture includes the following steps:
[0059] S1. Hardware Deployment: Deploy a customized AI all-in-one machine as the hardware layer, configured with 8 GPU cards (NVLink interconnect), dual CPUs, 512GB ECC memory, NVMe SSD storage array and DPU smart network card; enable InfiniBand / RDMA interconnect, dual redundant power supply, liquid cooling and RAID protection; configure remote operation and maintenance parameters through BMC to complete hardware initialization.
[0060] S2, Computing Layer Configuration: Deploy the Kubernetes containerized platform at the hardware layer, install the computing scheduler, load balancer, and intelligent controller; configure dynamic load balancing strategies (based on business priority and resource utilization) and failover mechanisms (automatic switching between primary and backup nodes); test the expansion function of hot-swappable computing blades to ensure that the resource allocation response time is less than 10 seconds.
[0061] S3. Inference Layer Deployment: Import the target large language model (such as ChatGLM-6B, enterprise self-developed model), and deploy it locally through the self-developed inference engine; apply INT8 quantization compression technology to optimize the model, test inference speed and memory usage (ensure inference latency is less than 500ms and memory usage is reduced by 40%); configure the model fine-tuning interface, access the enterprise business dataset (such as financial contract samples), and complete model adaptation.
[0062] S4. Middleware Integration: Establish connections with enterprise ERP (such as SAP), CRM (such as Salesforce), OA (such as DingTalk OA) and MySQL databases through the "Data Connection Engine"; configure data synchronization strategies (real-time incremental synchronization), test data transmission integrity and latency (ensure synchronization latency is less than 500ms); build a unified data channel to complete the access and cleaning of historical data.
[0063] S5. Platform Layer Construction: Deploy a multimodal document parsing module, import complex document samples (such as contracts with handwritten annotations, multi-table PDFs), and test the parsing accuracy (ensuring ≥90%); configure a model lifecycle management module to enable public cloud model API calls and private model version control; build a low-code development platform and encapsulate AI capability components (such as "document parsing component" and "SQL generation component"); deploy a data governance and integrated operation and maintenance module to complete the configuration of monitoring indicators and alarm rules.
[0064] S6. Application Layer Development: Develop AI applications such as contract review, intelligent customer service, and database assistant based on application layer APIs / SDKs; embed intelligent approval workflows into the enterprise organizational structure through DingTalk open platform APIs; generate Web components and H5 pages to adapt to PC and mobile terminals; test the completeness of application functions and response speed (ensure application startup time is less than 3 seconds).
[0065] S7. Security Layer Deployment: Configure end-to-end data encryption module (TLS1.3 transmission encryption, AES-256 storage encryption); allocate user permissions based on RBAC mechanism (e.g., "finance staff can only access the financial contract knowledge base"); enable operation audit log and sensitive content identification module, and test the integrity of log recording and the effectiveness of content filtering.
[0066] S8. System Operation and Optimization: Through the integrated operation and maintenance module, resource utilization, model performance and application status are monitored in real time, and computing power allocation and model parameters are dynamically adjusted according to business needs to ensure long-term stable operation of the system.
[0067] The beneficial effects of this invention are as follows:
[0068] Deep integration of hardware and software: Customized AI all-in-one machine and software algorithm are optimized together, increasing computing power utilization from 30% to over 70% and reducing inference latency by 50%, meeting the needs of high concurrency and low latency business.
[0069] Leap in multimodal processing capabilities: The self-developed document parsing engine increases the accuracy of unstructured document parsing from 60% to over 90%, activating more than 70% of the enterprise's dormant data assets;
[0070] Seamless integration with the existing IT ecosystem: Through the middle layer "data connection engine", real-time data linkage with ERP, CRM and other systems is realized, and the efficiency of AI applications embedded in core business processes is improved by 60%;
[0071] Reduce deployment costs: Private deployment cycle is shortened from several weeks to less than 3 days, and maintenance personnel costs are reduced by 50%;
[0072] Shorten the development cycle: Low-code development platforms reduce the development cycle of AI applications from several months to 1-2 weeks, and increase the return on investment of enterprise AI projects by more than 3 times;
[0073] Reduce resource waste: Dynamic computing power scheduling and elastic expansion avoid idle computing power, reducing the annual computing power cost for enterprises by 40%;
[0074] Meeting security and compliance requirements: The full-stack security management system complies with the regulatory requirements of industries such as finance, government affairs, and healthcare (such as Level 3 Information Security Protection and the Data Security Law), avoiding data leakage and compliance risks;
[0075] Promoting the democratization of AI: Low-code development lowers the technical threshold, enabling non-technical positions (such as finance and legal) to participate in the construction of AI applications, thereby promoting the improvement of the intelligent capabilities of all employees in the enterprise;
[0076] Supporting the intelligent upgrading of industries: Providing "safe, controllable, and efficient" AI infrastructure for highly regulated industries, helping them leap from "digitalization" to "intelligentization".
[0077] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the design concept of the present invention should be included within the protection scope of the present invention.
Claims
1. An enterprise-level AI system based on a seven-layer integrated architecture with deep hardware and software fusion, characterized in that: The system comprises, from bottom to top, a hardware layer, a computing power layer, an inference layer, a middleware layer, a platform layer, an application layer, and a security layer. The hardware layer is a customized AI all-in-one machine, which integrates a multi-GPU computing cluster, dual-socket CPU, ECC memory, NVMeSSD storage array and DPU smart network card, supports InfiniBand / RDMA high-speed interconnection, and has dual redundant power supplies, liquid cooling heat dissipation, RAID storage protection and BMC out-of-band management function. The computing power layer is based on the Kubernetes containerization platform and is configured with a computing power scheduler, load balancer and intelligent controller, supporting dynamic load balancing, automatic failover and hot-swappable computing power blades for elastic scaling. The inference layer deploys a self-developed inference engine, supports local private deployment of large language models, applies quantization compression technology, and provides interfaces for model fine-tuning and incremental updates. The middle layer establishes connections with enterprise ERP, CRM, OA systems and data platforms through a data connection engine, and configures a real-time incremental data synchronization mechanism. The platform layer includes a multimodal document parsing module, a model lifecycle management module, a low-code development module, a data governance module, and an integrated operation and maintenance module. The multimodal document parsing module combines OCR, layout analysis, semantic recognition, and data augmentation technologies to build a dual knowledge base of vectorization and relational types. The model lifecycle management module supports public cloud model API calls and private model deployment, fine-tuning, and version control. The application layer provides API, SDK and plugin interfaces, supporting integration with DingTalk, Lark, and WeChat Work to generate Web components, H5 pages and mini programs; The security layer is configured with an end-to-end data encryption module, a fine-grained permission control module based on RBAC, an operation audit module, and a sensitive content identification module.
2. The enterprise-level AI system based on a seven-layer integrated hardware and software architecture according to claim 1, characterized in that: The hardware layer's multi-GPU computing cluster is configured with 8 high-performance GPU cards and interconnected via NVLink; the InfiniBand / RDMA transmission rate is no less than 200Gb / s, and the data transmission latency is less than 10μs.
3. The enterprise-level AI system based on a seven-layer integrated hardware and software architecture according to claim 1, characterized in that: The quantization compression technology of the inference layer is INT8 or FP16 quantization, which can reduce the memory usage of large language models by more than 40% and increase the inference speed by more than 50%.
4. An enterprise-level AI system based on a seven-layer integrated hardware and software architecture according to claim 1, characterized in that: The multimodal document parsing module of the platform layer has an accuracy rate of no less than 90% for parsing unstructured documents (PDF scans, handwritten contracts, complex tables); the data governance module supports LLM's TEXT2SQL capability, which can realize automatic conversion from natural language to standard SQL.
5. An enterprise-level AI system based on a seven-layer integrated hardware and software architecture according to claim 1, characterized in that: The data encryption module of the security layer adopts TLS1.3 transmission encryption and AES-256 storage encryption; the log retention time of the operation audit module is not less than 6 months, which meets the requirements of Level 3 or above of the Information Security Protection System.
6. A method for implementing an enterprise-level AI system based on a seven-layer integrated hardware and software architecture, characterized in that, Includes the following steps: S1. Hardware Deployment: Deploy a customized AI all-in-one machine, configured with a multi-GPU cluster, dual-CPU, ECC memory, NVMeSSD storage array and DPU smart network card, and enable InfiniBand / RDMA, dual redundant power supplies, liquid cooling and BMC out-of-band management. S2, Computing Layer Configuration: Deploy the Kubernetes containerized platform, install the computing scheduler, load balancer and intelligent controller, and configure dynamic load balancing strategies and failover mechanisms; S3, Inference Layer Deployment: Import large language models, deploy them locally using a self-developed inference engine, apply quantization compression technology, configure model fine-tuning interfaces, and connect to enterprise business datasets. S4, Middleware Integration: Establishes connections with enterprise ERP, CRM, OA systems and data platforms through the data connection engine, and configures real-time incremental data synchronization strategies; S5. Platform Layer Construction: Deploy the multimodal document parsing module, model lifecycle management module, low-code development module, data governance module, and integrated operation and maintenance module; test and optimize document parsing accuracy and model performance. S6. Application Layer Development: Develop multi-scenario AI applications based on API / SDK, achieve integration with enterprise collaboration platforms, and generate Web components, H5 pages, and mini-programs; S7. Security Layer Deployment: Configure end-to-end data encryption, RBAC access control, operation auditing, and sensitive content identification modules, and test the effectiveness of security functions.
7. The method for implementing an enterprise-level AI system based on a seven-layer integrated hardware and software architecture according to claim 6, characterized in that: The enterprise business dataset accessed through the model fine-tuning interface in step S3 includes industry terminology samples and historical business cases. Fine-tuning improves the model's semantic understanding accuracy of industry scenarios by 10%-15%.
8. The method for implementing an enterprise-level AI system based on a seven-layer integrated hardware and software architecture according to claim 6, characterized in that: The low-code development module in step S5 encapsulates AI capabilities into draggable components, allowing business personnel to build AI processes without coding experience, thus shortening the application development cycle by more than 60%.
9. The method for implementing an enterprise-level AI system based on a seven-layer integrated hardware and software architecture according to claim 6, characterized in that: The RBAC permission control module in step S7 allocates permissions based on "user-position-department-data sensitivity level", which can realize fine-grained control that "different positions can only access data of the corresponding sensitivity level".
10. The method for implementing an enterprise-level AI system based on a seven-layer integrated hardware and software architecture according to claim 6, characterized in that: It also includes step S8, system operation and maintenance and optimization: real-time monitoring of resource utilization, model performance and application status through an integrated operation and maintenance module, and dynamic adjustment of computing power allocation and model parameters according to business needs to ensure long-term stable operation of the system.