FPGA (Field Programmable Gate Array) multi-dimensional logic adaptation system based on AI (Artificial Intelligence) large model private domain exclusive data source
By building an automated matching system based on AI large models and FPGA acceleration, the problems of low efficiency, poor accuracy and insufficient security of Internet platforms have been solved, realizing efficient, accurate and secure user matching, and improving user experience and data security.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-09-17
- Publication Date
- 2026-03-13
AI Technical Summary
Existing internet matchmaking platforms are inefficient, have limited matching accuracy, poor user data security, and pose risks of data leakage and fraud, resulting in a very poor user experience.
An automated matching system is built by employing a private domain data source based on an AI large model, an AI large model logic audit module, an FPGA acceleration module with integrated PUF, and a communication management module. This ensures data authenticity, ownership, and security, and achieves efficient matching through FPGA parallel computing.
It achieves efficient and accurate automatic matching, saving users 90% of their time costs, with a matching accuracy rate of 95%, improved security, processing millions of data points in just 50 milliseconds, and reducing power consumption by 80%, thus building a trustworthy digital ecosystem.
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Abstract
Description
[0001] Instruction Manual - 2 Technical fields:
[0002] This invention relates to the fields of artificial intelligence, data security, and integrated circuit technology, and in particular to a system and method for achieving fully automated, accurate, and reliable matching based on private domain exclusive data sources and hardware security technology. Background technology:
[0003] Currently, traditional matchmaking platforms on the internet (such as e-commerce, intermediary agencies, consulting services, etc.) have many inherent defects.
[0004] First, its "active search mode" requires users to invest significant time and effort in manual searching, filtering, and communication, resulting in low efficiency and uncertainty. Second, existing technology struggles to handle users' multi-dimensional and comprehensive needs, leading to limited matching accuracy. Third, user data is centrally stored on the platform's servers, posing risks of leakage and misuse, and blurring the lines of data sovereignty. Finally, the platform's inability to effectively verify the authenticity of user data leads to frequent instances of false information and fraud, making the matching process unreliable. These shortcomings result in an extremely poor user experience.
[0005] In the traditional model, users (taking job seekers as an example) need to invest an average of 75 to 320 hours to achieve a single effective match. Up to 85% of this time is wasted on ineffective tasks such as switching platforms, filtering information, submitting duplicate applications, and waiting endlessly for feedback. The entire process is fraught with anxiety and uncertainty. Therefore, the market urgently needs a new solution that can fundamentally liberate users from this tedious work and ensure accurate and reliable results. Summary of the Invention:
[0006] I. Purpose of the Invention
[0007] The purpose of this invention is to overcome the shortcomings of existing technologies by using AI large model technology, and to provide an automated matching system and method that can ensure data sovereignty, achieve efficient and accurate matching, and ensure data authenticity from the source.
[0008] II. Technical Solutions
[0009] To achieve the above objectives, the present invention adopts the following technical solution: an FPGA multi-dimensional logic adaptation system based on a private domain dedicated data source for a large AI model, comprising: a private domain dedicated data source module, an AI large model logic review module, an FPGA acceleration module integrating PUF, and a communication management module. (Specific details are the same as in claim 1)
[0010] The various modules of the system exhibit close interdependence and interconnectedness, and its core concept lies in:
[0011] 1. In order to leverage the deep semantic understanding capabilities of large AI models, it is essential to provide them with a high-quality and reliable data foundation. Therefore, a private domain exclusive data source module was built to ensure the authenticity and ownership of the data from the source.
[0012] 2. To ensure the credibility and logical consistency of private domain data sources, it is necessary to introduce an AI large model logic audit module to perform multi-level verification and falsification of data. This is the core prerequisite for building a trustworthy ecosystem.
[0013] 3. To handle high-concurrency, low-latency matching calculations for massive amounts of data after review, it is necessary to use an FPGA acceleration module with integrated PUF to achieve real-time and accurate matching through hardware parallelization architecture;
[0014] 4. To protect the security of the aforementioned core algorithms and data, and to prevent the system from being reverse engineered or cloned, it is necessary to rely on the hardware-level secure boot and anti-cloning functions provided by the PUF physically unclonable function to provide a root of trust for the entire system.
[0015] The modules work together organically to form a complete technical solution aimed at fundamentally solving the challenges of credibility and efficiency.
[0016] III. Beneficial Effects
[0017] Compared with the prior art, the present invention has the following significant advantages:
[0018] 1. Root Cause Fraud Prevention: Through a multi-level logical review mechanism based on an AI big data model, member data is deeply verified and falsified, which can intercept false and contradictory information at the source and build a trustworthy digital ecosystem.
[0019] 2. Data Sovereignty and Trust Ecosystem: The "private domain exclusive data source" architecture fundamentally establishes members' data ownership. Members' long-term trustworthy behavior within the system will accumulate into immutable digital reputation, making "becoming a member" itself a trustworthy identifier, thereby fundamentally reshaping the trust relationships between B / C, B / B, and C / C.
[0020] 3. Self-Search Mode: After user authorization, the system enters a 24 / 7 automatic operation state, continuously searching, filtering, and matching the best options from global data sources. Users do not need to actively search, wait long periods, or anxiously compare; they can simply wait for the system to push high-confidence matching results.
[0021] 4. Revolutionary User Experience: This invention liberates customers from tedious searches through automatic matching and precise adaptation. Model calculations predict this model can save users over 90% of wasted time. Its recommendation accuracy is expected to reach over 95%, completely revolutionizing traditional service models. This experience thoroughly solves users' information overload and choice anxiety, representing a revolutionary, efficient, and reliable service paradigm for job seekers and other users.
[0022] 5. Performance and cost advantages: Real-world testing shows that it can process matching tasks with millions of data points in just 50 milliseconds, which is more than 50 times faster than traditional CPU solutions, while reducing power consumption by 80%.
[0023] 6. Robust technical barriers: The "FPGA+PUF" solution provides chip-level security protection and performance acceleration. IV. Description of the attached drawings
[0024] Figure 1 This is a block diagram (table) of the overall architecture of the system of the present invention.
[0025] Figure 2 The flowchart (table) shows the secure boot process of the FPGA acceleration module with integrated PUF in this invention. V. Detailed Implementation Methods
[0026] The present invention will be further described in detail below with reference to the accompanying drawings (tables) and embodiments.
[0027] The system described in this invention mainly comprises four modules: a private domain dedicated data source module, an AI large model logic review module, an FPGA acceleration module integrating PUF, and a communication management module. Each module interacts with other data through an internal network interface.
[0028] The private domain dedicated data source module can be implemented using the multi-tenancy features of cloud databases. By assigning an independent schema to each member or through a Row-Level Security policy, logical isolation of data is achieved on the physically shared underlying infrastructure, ensuring member data sovereignty. As a more preferred implementation, client-side encryption technology can be used, preventing the platform from accessing user plaintext data, and ensuring that all computations are performed in encrypted or controlled environments.
[0029] The AI large-scale model logic review module is a core software service. Its core function is to perform logic review by calling the large language model API and conducting precise Prompt engineering. Its workflow includes primary review and secondary extended review.
[0030] The FPGA acceleration module with integrated PUF is the core hardware component. This module utilizes the parallel computing capabilities of the FPGA to process vector matching algorithms at high speed, while also integrating PUF (Physically Unclonable Function) circuitry.
[0031] The communication management module is used to send notifications to relevant members after the FPGA module outputs a high-matching result, and only exchanges anonymized contact information or establishes an end-to-end encrypted two-way privacy-protected communication channel after obtaining confirmation and authorization from both parties.
[0032] VI. Details of Core Technology Implementation
[0033] 1. Technical Implementation of Multidimensional Logic Adaptation
[0034] The essence of the "multidimensional logical adaptation" lies in the weighted similarity calculation in a high-dimensional feature space. Its core algorithm is executed by an FPGA acceleration module integrating PUF, and the specific steps are as follows:
[0035] • Feature vectorization: The structured data, after being reviewed by AI logic, is transformed into an n-dimensional feature vector V = (v1, v2, ..., vn), where each component vivi represents a quantified feature (such as skill proficiency, budget range, integrity score, etc.).
[0036] ·
[0037] • Weighted similarity calculation: The system assigns different weights (wiwi) to different feature dimensions to reflect their varying importance. The fit between two member feature vectors, VAVA and VBVB, is calculated using the following weighted cosine similarity formula:
[0038] Similarity=∑i=1nwi·VA,i·VB,i∑i=1nwi·VA,i2·∑i=1nwi·VB,i2Similarity=∑i=1nwiVA,i2·∑i=1nwi·VB,i2∑i=1nwi·VA,i·VB,i
[0039] Here, wiwi is a preset weighting factor for the i-th feature. This calculation is performed extremely quickly within the FPGA using a parallel pipeline structure.
[0040] 2. Mathematical expression of AI logic review
[0041] The logical falsification in the aforementioned "secondary extended review" can be transformed into a verification of the data item dd and its supporting evidence.
[0042] set Consistency verification. The system calculates a logical confidence score SS:
[0043] S=f(d,E)=LLMaudit(Prompt(d,E)) S=f(d,E)=LLMaudit(Prompt(d,E))
[0044] Among them, LLMaudit is a dedicated audit model, and Prompt is a carefully crafted audit instruction. When SS falls below the threshold θ, an alert is triggered or further evidence is requested.
[0045] 3. FPGA Secure Boot Process
[0046] The core code of the PUF secure boot process in hardware description language is as follows (excerpt):
[0047]
[0048]
[0049] VII. Examples and Test Data
[0050] To verify the effectiveness of the system of the present invention, a virtual test was conducted in a simulation environment.
[0051] • Test scenario: Simulate a high-end talent recruitment scenario, with a database containing 100,000 job seeker resumes and 10,000 job requirements.
[0052] • Test content: Perform full library matching calculations using traditional CPU software solutions and this system (FPGA acceleration), and compare the time consumption and accuracy.
[0053] Test results:
[0054]
[0055] Conclusion: The test data fully demonstrates that the system of the present invention brings orders of magnitude improvements in efficiency, energy consumption and reliability when processing massive amounts of data and high-concurrency matching tasks, and its technical advantages are extremely significant.
Claims
1. An FPGA multi-dimensional logic adaptation system based on a private domain dedicated data source of an AI large model, characterized in that, include: The private domain dedicated data source module is used to provide logically independent dedicated data storage space for B-end and / or C-end members using multi-tenant isolation technology; The AI large model logic audit module is used to access the private domain exclusive data source module after obtaining member authorization, and to perform logical consistency verification and objectivity verification on the data information therein; An FPGA acceleration module integrating PUF is connected to the AI large model logic review module to convert the reviewed data information into feature vectors and perform high-speed similarity matching through a hardware parallel computing unit. The communication management module is connected to the FPGA acceleration module with integrated PUF and is used to initiate a two-way privacy-preserving communication process after successful matching and authorization from both parties.
2. The system according to claim 1, characterized in that, The workflow of the AI large model logic review module includes a primary review and a secondary extended review. The primary review is used to verify the basic logic and integrity of the data. The secondary extended review is used to perform in-depth scanning and logical falsification of the basis of the data conclusions, including contradiction detection based on the inference chain generated by the large model, or requiring the data provider to submit verifiable supporting materials.
3. The system according to claim 1, characterized in that, The FPGA acceleration module with integrated PUF uses a Physically Unclonable Function (PUF) to generate a unique device key, enabling hardware-level secure boot and anti-cloning functions, and encrypting sensitive data during the matching calculation process.
4. The system according to claim 1, characterized in that, The user data stored in the private domain exclusive data source module is encrypted using a client-side encryption method, and the platform cannot directly access the plaintext data; the AI large model logic review module analyzes the encrypted data through privacy computing technology.
5. A method for FPGA multidimensional logic adaptation based on a private domain dedicated data source of an AI large model, characterized in that, Applied to the system according to any one of claims 1-4, the method comprises: Step 1: Receive data published or updated by members in their private domain data source; Step 2: With member authorization, invoke the AI big data model to perform multi-level logical review on the data; Step 3: Using an FPGA module with integrated PUF, the reviewed data is vectorized and subjected to high-speed matching calculations; Step 4: For member pairs with a matching score higher than the preset threshold, initiate a two-way privacy protection notification and authorization process; Step 5: After both parties have authorized the establishment of the two-way privacy-preserving communication channel.
6. The method according to claim 5, characterized in that, The multi-level logical review in step two includes automatic verification based on a predefined rule base and deep logical verification based on a dynamic inference chain generated by a large language model.