Large model performance optimization method based on deep learning
By combining convolutional neural networks and gradient descent algorithms with user feedback to optimize the performance of large models, we solved the adaptability issues of large models in complex tasks and environmental changes, and achieved dynamic performance improvement and cost reduction of the model.
Patent Information
- Application Number
- CN202510687010.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-05-27
- Publication Date
- 2025-09-05
AI Technical Summary
In practical applications, large-scale pre-trained models face problems such as decreased accuracy, distribution drift, trade-offs between response speed and computing resource usage, and difficulty in introducing user feedback into the optimization process. This makes it difficult for model performance to dynamically adapt to complex tasks and environmental changes.
A convolutional neural network is used to conduct a detailed analysis of large models, combined with the gradient descent algorithm and dynamic learning rate, to adjust weights based on task-critical characteristics, and to conduct online learning and parameter updates based on user feedback to achieve dynamic performance optimization of the model.
It improves the adaptability and performance of large models in various mission scenarios, reduces maintenance costs, and provides efficient and intelligent optimization solutions.
Smart Images
Figure CN120596868A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of large model optimization, and in particular to a large model performance optimization method based on deep learning. Background Art
[0002] In recent years, with the rapid development of deep learning technology, large-scale pre-trained models (LPMs), including models such as the GPT series, BERT, and the Vision Transformer, have demonstrated strong performance in fields such as natural language processing, computer vision, and multimodal tasks. These large models, with their large number of parameters and complex architectures, are capable of achieving good generalization performance across a wide range of tasks. However, the high performance of large models also comes with many challenges in practical applications, such as the trade-offs between model accuracy, response speed, and computing resource usage; the diverse demands placed on model performance by data distribution, task complexity, and different task scenarios; and how to efficiently adapt to user feedback and environmental changes during actual deployment to continuously optimize model performance.
[0003] In many application scenarios, complex tasks such as those involving vertical domains, multimodality, multitasking, or multilingualization require large models to dynamically adapt to the characteristics of different tasks. However, with the ever-changing data distribution and fluctuations in the model's operating environment, models face problems such as reduced accuracy and distribution drift, making online optimization and dynamic updates crucial requirements in modern deep learning deployments. Furthermore, developers' subjective satisfaction ratings and expectations for model performance often directly influence the direction of model optimization. Therefore, effectively incorporating user feedback into model optimization and building a feedback-driven performance improvement mechanism are key areas of current research and practice. Summary of the Invention
[0004] In order to solve the above problems, the purpose of the present invention is to provide a large model performance optimization method based on deep learning.
[0005] To achieve the above object, the present invention adopts the following technical solutions:
[0006] A large model performance optimization method based on deep learning, comprising the following steps:
[0007] S1: Select a deep learning model based on the task type, conduct a detailed analysis of the performance of the large model on the specific task, and obtain the key features of the task;
[0008] S2: Based on the gradient descent algorithm, the weights of evaluation indicators are dynamically adjusted according to the key characteristics of the task, ensuring that the system can quickly adapt to task changes. Task complexity (including multimodal, multitasking, and multi-agent application tasks) and data distribution (including vertical domain data, augmented data, plug-in data, multilingual data, and multimodal data) are factored in to map task characteristics to the weight adjustment strategy, ensuring that the weight adjustment accurately reflects the actual importance of the task.
[0009] S3: Collect developer feedback on performance evaluation based on the user interface, including satisfaction with the evaluation results and expectations for system performance, continuously monitor the real-time performance of the large model, collect performance data of the large model in various tasks and environments, and update model parameters online through deep learning algorithms to achieve dynamic feedback learning.
[0010] Furthermore, S1 is specifically:
[0011] Using Convolutional Neural Networks (CNNs), we conduct a detailed analysis of the performance of large models on specific tasks, including:
[0012] Define the specific tasks to be solved, such as general question-answering tasks in images, speech, and text, content generation tasks, text-to-image-to-text tasks, and speech transcription and cloning tasks.
[0013] Select performance evaluation indicators, including accuracy and recall, to conduct model precision analysis, model speed analysis, model memory usage analysis, performance analysis under different data distributions, and interpretability analysis;
[0014] By utilizing the feature maps output by the convolutional neural network (CNN), the activation patterns of convolution kernels or recurrent neurons are used to automatically capture the key characteristics of the task, providing an accurate basis for subsequent weight optimization.
[0015] Furthermore, the weight update in the gradient descent algorithm is as follows:
[0016] Suppose there is an objective function, where θ is the weight parameter to be optimized. The gradient descent method can be used to minimize the objective function. The formula for updating the weight is as follows:
[0017] θ t+1 =θ t -η·▽J(θ t );
[0018] Where η is the learning rate, ▽J(θ t ) is the gradient of the objective function J(θ) with respect to the parameter θ;
[0019] Replace the learning rate η with a dynamic learning rate η t , which can be adjusted according to the task characteristics T:
[0020] η t =η0·f(T)
[0021] Where η0 is the initial learning rate and f(T) is a function of the task characteristics, which is used to adjust the learning rate according to the specific properties of the task.
[0022] Furthermore, after dynamically adjusting the weights of evaluation indicators based on the key characteristics of the task, S2 comprehensively considers model accuracy, response time and memory usage indicators, combines task characteristic analysis with dynamic weight adjustment mechanism, and achieves comprehensive optimization of model performance. It also ensures the practicality of the output through probability threshold adjustment and result regularization in the post-processing stage. Through dynamic task characteristic mapping and weight adjustment strategy, it ensures the adaptability of the model in different task scenarios and the accuracy of the evaluation process.
[0023] Furthermore, S3 is specifically:
[0024] Developers provide annotations, performance expectations, and satisfaction ratings for prediction results on the interface; collect system inference time, memory usage, accuracy, and task characteristic data in real time; and integrate user feedback, real-time performance data, and historical task characteristics to build a feedback dataset.
[0025] Use deep learning algorithms to update online learning algorithms: add current data to the training set through streaming data, optimize the objective function, dynamically adjust the weights of evaluation indicators, and quickly update output weights or post-processing parameters for specific task scenarios;
[0026] Evaluate whether the performance of the adjusted model meets expectations, and continuously iterate and optimize to improve model performance.
[0027] A large model performance optimization system based on deep learning includes a processor, a memory, and a computer program stored in the memory. When the processor executes the computer program, it specifically performs the steps in the large model performance optimization method based on deep learning as described above.
[0028] The present invention has the following beneficial effects:
[0029] The present invention can comprehensively consider task complexity, data distribution characteristics and user feedback, and gradually achieve dynamic performance optimization. It can not only greatly improve the adaptability of large models in various task scenarios, but also greatly reduce the cost of model maintenance and manual intervention, providing efficient and intelligent solutions for complex, changeable and real-time practical applications. BRIEF DESCRIPTION OF THE DRAWINGS
[0030] Figure 1 Schematic diagram of the process of the present invention. DETAILED DESCRIPTION
[0031] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments:
[0032] refer to Figure 1 In this embodiment, a large model performance optimization method based on deep learning is provided, comprising the following steps:
[0033] S1: Select a deep learning model based on the task type, conduct a detailed analysis of the performance of the large model on the specific task, and obtain the key features of the task;
[0034] S2: Based on the gradient descent algorithm, the weights of evaluation indicators are dynamically adjusted according to the key characteristics of the task, ensuring that the system can quickly adapt to task changes. Task complexity (including multimodal, multitasking, and multi-agent application tasks) and data distribution (including vertical domain data, augmented data, plug-in data, multilingual data, and multimodal data) are factored in to map task characteristics to the weight adjustment strategy, ensuring that the weight adjustment accurately reflects the actual importance of the task.
[0035] S3: Collect developer feedback on performance evaluation based on the user interface, including satisfaction with the evaluation results and expectations for system performance, continuously monitor the real-time performance of the large model, collect performance data of the large model in various tasks and environments, and update model parameters online through deep learning algorithms to achieve dynamic feedback learning.
[0036] Furthermore, S1 is specifically:
[0037] Using Convolutional Neural Networks (CNNs), we conduct a detailed analysis of the performance of large models on specific tasks, including:
[0038] Define the specific tasks to be solved, such as general question-answering tasks in images, speech, and text, content generation tasks, text-to-image-to-text tasks, and speech transcription and cloning tasks.
[0039] Select performance evaluation indicators, including accuracy and recall, to conduct model precision analysis, model speed analysis, model memory usage analysis, performance analysis under different data distributions, and interpretability analysis;
[0040] By utilizing the feature maps output by the convolutional neural network (CNN), the activation patterns of convolution kernels or recurrent neurons are used to automatically capture the key characteristics of the task, providing an accurate basis for subsequent weight optimization.
[0041] Furthermore, the weight update in the gradient descent algorithm is as follows:
[0042] Suppose there is an objective function, where θ is the weight parameter to be optimized. The gradient descent method can be used to minimize the objective function. The formula for updating the weight is as follows:
[0043] θt+1 =θ t -η·▽J(θ t );
[0044] Where η is the learning rate, ▽J(θ t ) is the gradient of the objective function J(θ) with respect to the parameter θ;
[0045] Replace the learning rate η with a dynamic learning rate η t , which can be adjusted according to the task characteristics T:
[0046] η t =η0·f(T)
[0047] Where η0 is the initial learning rate and f(T) is a function of the task characteristics, which is used to adjust the learning rate according to the specific properties of the task.
[0048] Furthermore, after dynamically adjusting the weights of evaluation indicators based on the key characteristics of the task, S2 comprehensively considers model accuracy, response time and memory usage indicators, combines task characteristic analysis with dynamic weight adjustment mechanism, and achieves comprehensive optimization of model performance. It also ensures the practicality of the output through probability threshold adjustment and result regularization in the post-processing stage. Through dynamic task characteristic mapping and weight adjustment strategy, it ensures the adaptability of the model in different task scenarios and the accuracy of the evaluation process.
[0049] Furthermore, S3 is specifically:
[0050] Developers provide annotations, performance expectations, and satisfaction ratings for prediction results on the interface; collect system inference time, memory usage, accuracy, and task characteristic data in real time; and integrate user feedback, real-time performance data, and historical task characteristics to build a feedback dataset.
[0051] Use deep learning algorithms to update online learning algorithms: add current data to the training set through streaming data, optimize the objective function, dynamically adjust the weights of evaluation indicators, and quickly update output weights or post-processing parameters for specific task scenarios;
[0052] Evaluate whether the performance of the adjusted model meets expectations, and continuously iterate and optimize to improve model performance.
[0053] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0054] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.
[0055] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.
[0056] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.
[0057] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any other manner. Any person skilled in the art may utilize the above-disclosed technical content to modify or modify the present invention into equivalent embodiments. However, any simple modifications, equivalent variations, and modifications to the above embodiments that do not depart from the technical content of the present invention and are based on the technical essence of the present invention remain within the scope of protection of the present invention.
Claims
1. A large model performance optimization method based on deep learning, characterized in that: The following steps are involved: S1: Select a deep learning model based on the task type, conduct a detailed analysis of the performance of the large model on the specific task, and obtain the key features of the task; S2: Based on the gradient descent algorithm, the weights of evaluation indicators are dynamically adjusted according to the key characteristics of the task, ensuring that the system can quickly adapt to task changes. Task complexity and data distribution factors are introduced to map task characteristics to the weight adjustment strategy to ensure that the weight adjustment can accurately reflect the actual importance of the task. S3: Collect developer feedback on performance evaluation based on the user interface, including satisfaction with the evaluation results and expectations for system performance, continuously monitor the real-time performance of the large model, collect performance data of the large model in various tasks and environments, and update model parameters online through deep learning algorithms to achieve dynamic feedback learning.
2. The large model performance optimization method based on deep learning according to claim 1 is characterized in that: The S1 is specifically: Use convolutional neural networks (CNNs) to conduct a detailed analysis of the performance of large models on specific tasks; include: Define the specific tasks to be solved, such as general question-answering tasks in images, speech, and text, content generation tasks, text-to-image-to-text tasks, and speech transcription and cloning tasks. Select performance evaluation indicators, including accuracy and recall, to conduct model precision analysis, model speed analysis, model memory usage analysis, performance analysis under different data distributions, and interpretability analysis; By utilizing the feature maps output by the convolutional neural network (CNN), the key characteristics of the task can be automatically captured through the activation mode of the convolution kernel or recurrent neurons, providing an accurate basis for subsequent weight optimization.
3. The large model performance optimization method based on deep learning according to claim 1 is characterized in that: The weight update in the gradient descent algorithm is as follows: Suppose there is an objective function, where θ is the weight parameter to be optimized. The gradient descent method can be used to minimize the objective function. The formula for updating the weight is as follows: Where η is the learning rate, ▽J(θ t ) is the gradient of the objective function J(θ) with respect to the parameter θ; Replace the learning rate η with a dynamic learning rate η t , which can be adjusted according to the task characteristics T: or t =η0·f(T) Where η0 is the initial learning rate and f(T) is a function of the task characteristics, which is used to adjust the learning rate according to the specific properties of the task.
4. The large model performance optimization method based on deep learning according to claim 1 is characterized in that: After dynamically adjusting the weights of evaluation indicators based on the key characteristics of the task, S2 comprehensively considers the model accuracy, response time and memory usage indicators, combines task characteristic analysis with dynamic weight adjustment mechanism, and realizes comprehensive optimization of model performance. The probability threshold adjustment and result regularization in the post-processing stage ensure the practicality of the output. The dynamic task characteristic mapping and weight adjustment strategy ensure the adaptability of the model in different task scenarios and the accuracy of the evaluation process.
5. The large model performance optimization method based on deep learning according to claim 1 is characterized in that: The S3 is specifically: Developers provide annotations, performance expectations, and satisfaction ratings for prediction results on the interface; collect system inference time, memory usage, accuracy, and task characteristic data in real time; and integrate user feedback, real-time performance data, and historical task characteristics to build a feedback dataset. Use deep learning algorithms to update online learning algorithms: add current data to the training set through streaming data, optimize the objective function, dynamically adjust the weights of evaluation indicators, and quickly update output weights or post-processing parameters for specific task scenarios; Evaluate whether the performance of the adjusted model meets expectations, and continuously iterate and optimize to improve model performance.
6. A large model performance optimization system based on deep learning, characterized by: It includes a processor, a memory and a computer program stored in the memory. When the processor executes the computer program, it specifically performs the steps in the large model performance optimization method based on deep learning as described in any one of claims 1 to 5.