Operation AI modeling method based on CPU image processing

Abstract

The invention relates to an operational AI modeling method based on CPU image processing, which comprises the following steps: S1, analyzing an image processing task, analyzing a specific image processing task, and understanding the property and demand of the task, the information being helpful for selecting a proper algorithm and an optimization strategy; s2, performing algorithm selection and optimization, wherein optimization comprises parallelization of the algorithm, reduction of unnecessary calculation and cache optimization by utilizing a CPU (Central Processing Unit); s3, model construction, wherein data feature extraction, model selection and training processes need to be considered during model construction; s4, performing parallel processing and optimization; and S5, performing performance evaluation and adjustment, performing performance evaluation after model construction and optimization are completed, and checking whether indexes such as processing speed and accuracy meet requirements or not. The method has the advantages that high-efficiency and high-quality model establishment is carried out on the image by fully utilizing the CPU characteristics.

Description

Technical Field

[0001] The present invention relates to the field of AI modeling technology, and in particular to a computational AI modeling method based on CPU image processing. Background Art

[0002] Modeling is the process of creating a three-dimensional model using computer-aided design (CAD) software or other tools. It is the process of converting real-world objects, scenes or concepts into virtual three-dimensional models. Modeling is to build a model, which is an abstraction of things in order to understand things, and an unambiguous written description of things. The process of building a system model is also called modeling. Modeling is an important means and prerequisite for studying systems. Any process that uses a model to describe the cause-effect relationship or mutual relationship of a system belongs to modeling. Because the relationships described are different, the means and methods to achieve this process are also diverse. Modeling can be done by analyzing the movement laws of the system itself and based on the mechanism of things; it can also be done by processing experiments or statistical data on the system and based on existing knowledge and experience about the system.

[0003] Currently, when using the CPU to perform AI modeling on images and processing operations, it is impossible to fully utilize the CPU characteristics to efficiently build and optimize the image model. Summary of the invention

[0004] 1. Technical Problems Solved

[0005] The technical problem to be solved by the present invention is that the existing AI modeling method cannot fully utilize the CPU characteristics to efficiently build a model for the image.

[0006] 2. Technical Solution

[0007] To solve the above technical problems, a computational AI modeling method based on CPU image processing includes the following steps:

[0008] S1. Analysis of image processing tasks: Analyze specific image processing tasks to understand the nature and requirements of the tasks. This information will help select appropriate algorithms and optimization strategies.

[0009] S2, algorithm selection and optimization, select the appropriate image processing algorithm according to the task requirements, and optimize the selected algorithm for efficient execution on the CPU. The optimization includes parallelization of the algorithm, reducing unnecessary calculations, and utilizing CPU cache optimization;

[0010] S3. Model construction. In image processing, models need to be built for more complex analysis or prediction, which involves the application of machine learning or deep learning. Model construction needs to consider data feature extraction, model selection and training process.

[0011] S4, parallel processing and optimization. Since the CPU has multiple cores, this can be used for parallel processing to accelerate the image processing and model training process;

[0012] S5. Performance evaluation and adjustment. After completing model construction and optimization, perform performance evaluation to check whether indicators such as processing speed and accuracy meet the requirements.

[0013] Furthermore, the nature and requirements of the task in S1 include whether it is real-time processing, the amount of data to be processed, and the processing speed and accuracy.

[0014] Furthermore, the image processing operations in S2 include filtering, enhancement, segmentation, and recognition.

[0015] Furthermore, the image is filtered using one of linear filtering and nonlinear filtering, wherein linear filtering includes box filtering, mean filtering and Gaussian filtering, which reduce noise and loss of details by calculating the weighted average around pixels. Nonlinear filtering includes median filtering and bilateral filtering, wherein median filtering eliminates noise by sorting statistical theory.

[0016] Furthermore, the image enhancement adopts one of the spatial domain method and the frequency domain method. The spatial domain method directly operates on each pixel of the image to improve the contrast and clarity of the image; the frequency domain method regards the image as a two-dimensional signal and enhances the signal in the frequency domain through Fourier transform.

[0017] Furthermore, when constructing the model in S3, attention is paid to data preprocessing, model training strategy and how to effectively utilize CPU resources.

[0018] Furthermore, the S4 uses libraries and frameworks that support parallel processing, so that CPU multi-core parallel processing is possible.

[0019] Furthermore, after S5, the algorithm or model is adjusted according to the evaluation result to achieve the best performance.

[0020] 3. Beneficial Effects

[0021] The advantages of the present invention compared with the prior art are:

[0022] Make full use of the CPU characteristics and use the CPU's multi-core to accelerate the image processing and model training process. At the same time, through reasonable image optimization processing and algorithm selection, reduce unnecessary calculations and use CPU cache optimization to further improve the efficiency and effectiveness of modeling. DETAILED DESCRIPTION

[0023] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] It should be noted that all directional indications such as up, down, left, right, front, back, etc. in the embodiments of the present invention are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture. If the specific posture changes, the directional indication will also change accordingly.

[0025] In addition, in the present invention, descriptions such as "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0026] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the invention.

[0027] The present invention will now be further described in conjunction with the specification.

[0028] The present invention provides a CPU-based AI modeling method, which includes the following steps:

[0029] S1. Analysis of image processing tasks: Analyze specific image processing tasks to understand the nature and requirements of the tasks, including whether they are real-time processing, the amount of data to be processed, and the processing speed and accuracy. This information will help select appropriate algorithms and optimization strategies.

[0030] S2. Algorithm selection and optimization. According to the task requirements, select the appropriate image processing algorithm. Image processing operations include filtering, enhancement, segmentation, and recognition. The selected algorithm needs to be optimized for efficient execution on the CPU. The optimization may include parallelization of the algorithm, reducing unnecessary calculations, and utilizing CPU cache optimization.

[0031] The image is filtered using either linear filtering or nonlinear filtering. Linear filtering includes box filtering, mean filtering, and Gaussian filtering. These methods reduce noise and detail loss by calculating the weighted average around pixels. Nonlinear filtering includes median filtering and bilateral filtering. Median filtering eliminates noise through sorting statistical theory.

[0032] The image enhancement adopts one of the space domain method and the frequency domain method. The space domain method directly operates on each pixel of the image to improve the contrast and clarity of the image; the frequency domain method regards the image as a two-dimensional signal and enhances the signal in the frequency domain through Fourier transform;

[0033] S3, model construction. In image processing, models need to be built for more complex analysis or prediction, which involves the application of machine learning or deep learning. Model construction needs to consider data feature extraction, model selection and training process. In this step, attention should be paid to data preprocessing, model training strategy and how to effectively utilize CPU resources.

[0034] S4, parallel processing and optimization. Since the CPU has multiple cores, this can be used for parallel processing to accelerate the image processing and model training process. In this step, libraries and frameworks that support parallel processing are used to enable CPU multi-core parallel processing.

[0035] S5. Performance evaluation and adjustment. After completing model construction and optimization, conduct performance evaluation to check whether indicators such as processing speed and accuracy meet the requirements.

[0036] S6. Adjust the algorithm or model to achieve optimal performance.

[0037] The invention and its implementation methods are described, but such description is not restrictive, and what is shown is only one of the implementation methods of the invention, and the actual structure is not limited thereto. In short, if ordinary technicians in the field are inspired by it, without departing from the purpose of the invention, they can design a structure and embodiment similar to the technical solution without creativity, which should fall within the protection scope of the invention.

Claims

1. A computational AI modeling method based on CPU image processing, characterized in that: The following steps are involved: S1. Analysis of image processing tasks: Analyze specific image processing tasks to understand the nature and requirements of the tasks. This information will help select appropriate algorithms and optimization strategies. S2, algorithm selection and optimization, select the appropriate image processing algorithm according to the task requirements, and optimize the selected algorithm for efficient execution on the CPU. The optimization includes parallelization of the algorithm, reducing unnecessary calculations, and utilizing CPU cache optimization; S3. Model construction. In image processing, models need to be built for more complex analysis or prediction, which involves the application of machine learning or deep learning. Model construction needs to consider data feature extraction, model selection and training process. S4, parallel processing and optimization. Since the CPU has multiple cores, this can be used for parallel processing to accelerate the image processing and model training process; S5. Performance evaluation and adjustment. After completing model construction and optimization, perform performance evaluation to check whether indicators such as processing speed and accuracy meet the requirements.

2. The method for computing AI modeling based on CPU image processing according to claim 1, characterized in that: The nature and requirements of the task in S1 include whether it is real-time processing, the amount of data to be processed, and the processing speed and accuracy.

3. The method for computing AI modeling based on CPU image processing according to claim 1, characterized in that: The image processing operations in S2 include filtering, enhancement, segmentation, and recognition.

4. The method for computing AI modeling based on CPU image processing according to claim 3, characterized in that: The image filtering adopts one of linear filtering and nonlinear filtering. Linear filtering includes box filtering, mean filtering and Gaussian filtering. These methods reduce noise and loss of details by calculating the weighted average around the pixel. Nonlinear filtering includes median filtering and bilateral filtering. Median filtering eliminates noise through sorting statistical theory.

5. The method for computing AI modeling based on CPU image processing according to claim 3, characterized in that: The image enhancement adopts one of a space domain method and a frequency domain method, and the space domain method directly operates each pixel of the image to improve the contrast and clarity of the image; The frequency domain rule regards the image as a two-dimensional signal and enhances the signal in the frequency domain through Fourier transform.

6. The method of computing AI modeling based on CPU image processing according to claim 1, characterized in that: When constructing the model in S3, attention is paid to data preprocessing, model training strategy, and how to effectively utilize CPU resources.

7. The method of computing AI modeling based on CPU image processing according to claim 1, characterized in that: The S4 uses libraries and frameworks that support parallel processing to enable multi-core CPU parallel processing.

8. The method of computing AI modeling based on CPU image processing according to claim 1, characterized in that: After S5, the algorithm or model is adjusted according to the evaluation result to achieve the best performance.