Dynamic weight FPGA acceleration intelligent matching technology based on AI large model
By using a dynamic weighted FPGA-accelerated intelligent matching method based on an AI large model, the problems of low matching accuracy and high latency on the Internet platform are solved, achieving accurate and efficient matching results and meeting the real-time requirements of high-concurrency scenarios.
Patent Information
- Application Number
- CN202511403427.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-09-29
- Publication Date
- 2026-02-03
AI Technical Summary
Existing technologies on internet platforms suffer from limited matching accuracy, difficulty in handling unstructured data, inability to verify information authenticity, and the risk of fraud. Furthermore, large AI models face challenges such as high computational latency and high hardware costs in high-concurrency, low-latency real-time matching scenarios.
We adopt a dynamic weighted FPGA-accelerated intelligent matching method based on AI large model. Through dynamic weighting mechanism and FPGA hardware acceleration, we achieve accurate matching and efficient calculation, including data reception, deep semantic understanding, feature extraction, dynamic weight comparison and result output.
It achieved a matching accuracy improvement of over 35% and a latency reduction of 50 times, meeting the real-time requirements of high-concurrency scenarios and improving user satisfaction and computing efficiency.
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Abstract
Description
Technical Field
[0002] This invention relates to the fields of artificial intelligence and hardware acceleration technology, and in particular to a dynamic weighted intelligent matching technology based on a large AI model. Background Technology
[0003] Currently, most internet platforms use traditional algorithms such as keyword matching and collaborative filtering, which have technical shortcomings such as limited matching accuracy, difficulty in processing unstructured data, and inability to verify the authenticity of information, leading to a high risk of fraud.
[0004] While large AI models possess powerful semantic understanding capabilities, directly applying them to real-time matching scenarios requiring high concurrency and low latency (such as online recruitment and instant e-commerce recommendations) still results in unacceptable computational latency and hardware costs. Simplifying the model to meet real-time requirements, on the other hand, sacrifices its semantic understanding accuracy.
[0005] Therefore, the market urgently needs a new type of dynamic weighted intelligent matching technology to fundamentally improve its matching accuracy, efficiency, and reliability. This invention was created to address this need. Summary of the Invention
[0006] The purpose of this invention is to overcome the shortcomings of existing technologies and provide an intelligent method for achieving accurate and efficient matching. By introducing a context-based dynamic weighting mechanism, the semantic understanding capabilities of large AI models can be more accurately applied to matching decisions without requiring full participation in every real-time computation. This not only overcomes the high latency bottleneck of directly deploying large AI models but also improves matching accuracy through dynamic adaptation. Simultaneously, accelerating computationally bottleneck modules using FPGAs further meets the real-time requirements of high-concurrency scenarios, achieving a synergistic improvement in accuracy and efficiency.
[0007] To achieve the above objectives, this invention employs a dynamic weighted FPGA-accelerated intelligent matching method based on an AI large-scale model. The method includes the following steps: receiving demand data from a first user and a second user; invoking an AI large-scale model to perform deep semantic understanding and feature extraction on the demand data; based on the feature extraction results, using dynamic weighting to perform weighted calculations on the feature vectors to maximize matching accuracy; and outputting the matching results.
[0008] This invention also provides a corresponding system implementation plan, including a data receiving module, an AI processing module, a matching calculation module, and a result output module. Compared with the prior art, this invention has the following advantages: it achieves accurate matching through a dynamic weighting mechanism, effectively solving the technical problems of information asymmetry and low matching accuracy in traditional intermediary services.
[0009] The application realizes significant improvement of matching accuracy and efficiency by introducing a dynamic weight mechanism. Simulation test verification shows that, in a simulated intermediary service scenario, the user satisfaction (success rate) of the matching result of the dynamic weight method is improved by more than 35% compared with the traditional fixed weight method, and the delay of the overall matching process is reduced by more than 50 times due to the hardware acceleration of dynamic weight calculation by FPGA.
[0010] As a preferred embodiment of the application, the generation and calculation process of the dynamic weight factor can be realized by hardware acceleration through a field programmable gate array (FPGA). The parallel computing architecture of FPGA is very suitable for the large number of parallel matrix operations involved in the dynamic weight generation process, which can significantly reduce the calculation delay and meet the real-time requirements in high concurrency scenarios.
[0011] • Schematic diagram of the method flow of the application
[0012] DETAILED DESCRIPTION
[0013] The core of the application is the dynamic weight generation mechanism. This mechanism analyzes the scene context in real time through an AI large model and dynamically generates an optimal weight vector. The specific mathematical expression is as follows:
[0014] Feature vectorization: data is converted into an n-dimensional feature vector V = (v1, v2,..., v□)
[0015] Dynamic weight generation: weight factor w i = f(Context), where Context includes scene tags, user intent information Weighted matching calculation: use the dynamic weighted cosine similarity formula:
[0016] Similarity(A, B) = ∑i(wi × Ai2) × ∑i(wi × Bi2) ∑i(wi × Ai × Bi)
[0017] • Schematic diagram of the dynamic weight generation mechanism flow
[0018]
[0019]
[0020] Simulation test and effect verification
[0021] To verify the effect of the application, a comparative test was conducted in a simulation environment.
[0022] Test scenario: simulate a job recruitment matchmaking scenario with a million people.
[0023] Test group: use the dynamic weight FPGA acceleration method described in the application.
[0024] Control group: using the traditional fixed weight software calculation method.
[0025] Test results:
[0026]
[0027] Conclusion: The test data fully proves that the application brings orders of magnitude improvement in matching speed, success rate and energy efficiency ratio through the synergistic effect of the dynamic weight mechanism and FPGA hardware acceleration. The application realizes accurate matching in the intelligent matching process through the above technical solutions, and provides an effective technical solution for intermediary agencies and e-commerce.
Claims
1. An AI large model-based dynamic weight FPGA acceleration intelligent matching method, characterized in that, Includes the following steps: Receive request data from the first user and the second user; The AI model is invoked to perform deep semantic understanding and feature extraction on the required data. Weighting factors are dynamically generated using an FPGA acceleration module. Based on the feature extraction results, the feature vectors are weighted using the dynamic weight ratio to obtain the matching degree; Output the matching results.
2. The method of claim 1, wherein, The dynamic weighting factor is dynamically generated by the AI big data model after real-time analysis based on the scenario and context of the current matching task, and is calculated by the FPGA acceleration module.
3. The method of claim 1, wherein, The weighted calculation using dynamic weight ratios specifically employs a dynamic weighted cosine similarity algorithm.
4. The method according to any one of claims 1 to 3, characterized in that, The generation and calculation process of the dynamic weighting factor is accelerated by a field-programmable gate array (FPGA).
5. An electronic device comprising a memory, a processor, and a computer program stored on the memory, wherein the computer program comprises instructions that, when executed by the processor, cause the electronic device to perform the method of any one of claims 1-4. When the processor executes the program, it implements the method as described in any one of claims 1-3.
6. A computer-readable storage medium having stored thereon a computer program, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1-3.