CN117909813A - System for classifying and storing data by using deep learning technology - Google Patents
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- 238000007781 pre-processing Methods 0.000 claims abstract description 10
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- 238000013527 convolutional neural network Methods 0.000 claims description 6
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Abstract
The invention relates to the technical field of system engineering, and discloses a system for classifying and storing data by using a deep learning technology, which comprises the following steps: 1) Determining a demand and a target; 2) Data collection and preprocessing; 3) Designing a characteristic engineering method; 4) Training a model; 5) Classifying and storing data; 6) Model updating and iteration; 7) Performance optimization and deployment. Compared with the traditional method, the system can automatically learn the characteristic representation of the data, improve the classification accuracy, is suitable for processing various types and forms of data, realizes automation and high efficiency, saves cost and resources, has flexibility and iterative optimization capacity, can rapidly deploy and optimize a model, has wide application value in the aspects of processing the classification and storage of complex data, and provides an innovative solution for realizing accurate, rapid and automatic data classification.
Description
Technical Field
The invention relates to the technical field of system engineering, in particular to a system for classifying and storing data by using a deep learning technology.
Background
The existing deep learning system is found to have the following problems through searching:
1. Pain points of feature extraction: in the traditional method, a manual design feature extraction method is needed to properly represent the data; this process requires field expertise and experience and is easily limited by choice of features and design limitations; under the condition, the deep learning system can automatically learn the characteristic representation of the data without manually carrying out characteristic engineering, so that the pain point of characteristic extraction in the traditional method is solved; 2. challenges of high and unstructured data: conventional methods often have difficulties in the processing of high and unstructured data, which is very common in real-world scenarios, such as images, natural language text, audio; the deep learning system can better adapt to processing the complex data through a deep neural network structure, so that the classification accuracy and the overall performance of the system are improved; 3. real-time and automation requirements: for many application scenarios, real-time and automated data classification and storage is critical; traditional methods may require manual processing of the data and cumbersome sorting and storage operations; the deep learning system can realize real-time data classification and storage, process data in real time and automatically classify the data into corresponding categories, thereby improving the efficiency and accuracy of data processing; 4. flexibility and iterative optimization of the model: the deep learning model has higher flexibility and can be adjusted and optimized according to different tasks and data; the system can automatically update and iteratively optimize the model based on new data distribution and change along with the time, and the accuracy and adaptability of the model are maintained.
In order to solve the above problems, a system for classifying and storing data using deep learning technology is proposed.
Disclosure of Invention
(One) solving the technical problems
Aiming at the defects of the prior art, the invention provides a system for classifying and storing data by using a deep learning technology, which solves the technical problems in the background technology.
(II) technical scheme
In order to achieve the above purpose, the present invention provides the following technical solutions: a system for classifying and storing data using deep learning techniques, comprising the steps of:
1) Determining a demand and a target;
2) Data collection and preprocessing;
3) Designing a characteristic engineering method;
4) Training a model;
5) Classifying and storing data;
6) Model updating and iteration;
7) Performance optimization and deployment.
Preferably, the determining needs and objectives are: firstly, the requirements and targets of the data classification and storage system are defined, wherein the requirements and targets comprise the data types to be classified, the classified category number, the precision requirement and the data quantity estimation.
Preferably, the data collection and preprocessing includes: s1, identifying a data source, namely determining the data source; s2, collecting data, namely designing and developing a data collecting pipeline to ensure that the data can be accurately collected on time; s3, data preprocessing: and (3) carrying out pretreatment operations of cleaning, denoising and normalizing on the data so as to improve the quality and consistency of the data.
Preferably, the design feature engineering method designs a proper feature extraction method, such as statistical features, time sequence features and image features, according to different data types and characteristics, and feature engineering implementation is realized: based on the designed feature engineering method, the corresponding feature extraction flow is realized.
Preferably, the constructing the deep learning model includes: s1, selecting a deep learning model architecture, namely a multi-layer perceptron (MLP), a Convolutional Neural Network (CNN) and a cyclic neural network (RNN); s2, preparing a training data set: dividing the preprocessed data into a training set and a verification set for training and evaluating the model S3 model training and optimizing: model training is performed using the training set, and model superparameters are adjusted by the validation set to minimize prediction errors of the model.
Preferably, the data sorting and storing includes: s1, carrying out classification prediction on new data; classifying and predicting new input data by using a trained model; s2, storing classification results: based on the classification result, the data is stored in a corresponding class, which may be a database, file system, or other storage medium.
Preferably, the monitoring model performance includes: s1, periodically monitoring performance indexes, accuracy and recall rate of a model; s2, updating the model according to the need: if the model performance drops or there is new data distribution, a model update is triggered and retraining or incremental training can be performed.
Preferably, the performance optimization and deployment includes: s1, evaluating and optimizing performance: according to actual requirements, evaluating the performance of the system, optimizing, and specifically adopting an acceleration algorithm, parallel computing and model quantization; s2, system deployment: the system is deployed into a target environment, and is tested and verified, so that the system can work as expected.
(III) beneficial effects
Compared with the prior art, the invention provides a system for classifying and storing data by using a deep learning technology, which has the following beneficial effects:
1. Classification accuracy is improved: the deep learning model has strong expression and generalization capability, and can automatically learn useful features in data, thereby improving classification accuracy; the deep learning system is better able to capture complex relationships and patterns of data than conventional methods, thereby providing more accurate and reliable classification results.
2. Adapting to various data: the deep learning model can process various types and forms of data, including image, text, audio, video unstructured data; the flexibility enables the system to adapt to data classification requirements of different fields and application scenes, so that wider application value is provided.
3. Automation and high efficiency are realized: the deep learning system can realize automatic classification and storage of data, and eliminates the complexity of manual processing and the possibility of human errors; through end-to-end training and operation, the system can automatically complete the whole data classification process, thereby improving the processing efficiency and saving human resources.
4. Quick deployment and iterative optimization: the trainability and the flexibility of the deep learning model enable the system to be rapidly deployed, and iterative optimization is performed according to actual conditions; by continuously collecting new data and feedback, the system can automatically update models and parameters to adapt to new data distribution and changes, thereby continuously improving classification accuracy and performance.
5. Cost and resource are saved: in the data classification and storage process, the deep learning system reduces the dependence on professionals, optimizes the efficiency of data management and processing, and thereby saves cost and resources; the automation and the high efficiency of the system reduce the occurrence of human errors and improve the reliability of the whole operation.
Detailed Description
The technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
A system for classifying and storing data using deep learning techniques, comprising the steps of: 1) Determining a demand and a target; 2) Data collection and preprocessing; 3) Designing a characteristic engineering method; 4) Training a model; 5) Classifying and storing data; 6) Model updating and iteration; 7) Performance optimization and deployment, the determining requirements and objectives: firstly, defining the requirements and targets of a data classification and storage system, including data types to be classified, the classified category number, the precision requirement and the data quantity estimation, wherein the data collection and preprocessing comprises the following steps: s1, identifying a data source, namely determining the data source; s2, collecting data, namely designing and developing a data collecting pipeline to ensure that the data can be accurately collected on time; s3, data preprocessing: the data is subjected to pretreatment operations of cleaning, denoising and normalization so as to improve the quality and consistency of the data, and the characteristic engineering method is designed to design a proper characteristic extraction method, such as statistical characteristics, time sequence characteristics and image characteristics, according to different data types and characteristics, and the characteristic engineering is realized: based on the designed feature engineering method, a corresponding feature extraction flow is realized, and the construction of the deep learning model comprises the following steps: s1, selecting a deep learning model architecture, namely a multi-layer perceptron (MLP), a Convolutional Neural Network (CNN) and a cyclic neural network (RNN); s2, preparing a training data set: dividing the preprocessed data into a training set and a verification set for training and evaluating the model S3 model training and optimizing: model training using a training set and adjusting model super parameters by a validation set to minimize prediction errors of the model, the data classification and storage comprising: s1, carrying out classification prediction on new data; classifying and predicting new input data by using a trained model; s2, storing classification results: based on the classification results, the data is stored in the corresponding class, which may be a database, a file system or other storage medium, and the monitoring model performance includes: s1, periodically monitoring performance indexes, accuracy and recall rate of a model; s2, updating the model according to the need: if the model performance drops or there is new data distribution, triggering the model update, retraining or incremental training can be performed, and the performance optimization and deployment comprises: s1, evaluating and optimizing performance: according to actual requirements, evaluating the performance of the system, optimizing, and specifically adopting an acceleration algorithm, parallel computing and model quantization; s2, system deployment: the system is deployed into a target environment, and is tested and verified, so that the system can work according to expectations, and classification accuracy is improved: the deep learning model has strong expression and generalization capability, and can automatically learn useful features in data, thereby improving classification accuracy; compared with the traditional method, the deep learning system can better capture complex relations and modes of data, thereby providing more accurate and reliable classification results and adapting to various data: the deep learning model can process various types and forms of data, including image, text, audio, video unstructured data; the flexibility enables the system to adapt to data classification requirements of different fields and application scenes, so that wider application value is provided, and automation and high efficiency are realized: the deep learning system can realize automatic classification and storage of data, and eliminates the complexity of manual processing and the possibility of human errors; through end-to-end training and operation, the system can automatically complete the whole flow of data classification, thereby improving the processing efficiency, saving human resources, and rapidly deploying and iteratively optimizing: the trainability and the flexibility of the deep learning model enable the system to be rapidly deployed, and iterative optimization is performed according to actual conditions; by continuously collecting new data and feedback, the system can automatically update the model and parameters to adapt to new data distribution and change, thereby continuously improving classification accuracy and performance and saving cost and resources: in the data classification and storage process, the deep learning system reduces the dependence on professionals, optimizes the efficiency of data management and processing, and thereby saves cost and resources; the automation and high efficiency of the system reduce the occurrence of human errors, improve the reliability of the whole operation, and in conclusion, the application of deep learning in the data classification and storage system can improve the classification accuracy, adapt to various data, realize automation and high efficiency, quick deployment and iterative optimization, and save cost and resources. These benefits and effects make deep learning a powerful tool for processing complex data, bringing more advanced and innovative solutions to the data classification and storage arts.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (8)
1. A system for classifying and storing data using deep learning techniques, comprising the steps of:
1) Determining a demand and a target;
2) Data collection and preprocessing;
3) Designing a characteristic engineering method;
4) Training a model;
5) Classifying and storing data;
6) Model updating and iteration;
7) Performance optimization and deployment.
2. A system for data classification and storage using deep learning techniques as claimed in claim 1 wherein said determining needs and objectives are: firstly, the requirements and targets of the data classification and storage system are defined, wherein the requirements and targets comprise the data types to be classified, the classified category number, the precision requirement and the data quantity estimation.
3. A system for data classification and storage using deep learning techniques as claimed in claim 1 wherein said data collection and preprocessing comprises: s1, identifying a data source, namely determining the data source; s2, collecting data, namely designing and developing a data collecting pipeline to ensure that the data can be accurately collected on time; s3, data preprocessing: and (3) carrying out pretreatment operations of cleaning, denoising and normalizing on the data so as to improve the quality and consistency of the data.
4. The system for classifying and storing data by deep learning technology according to claim 1, wherein the design feature engineering method designs a suitable feature extraction method according to different data types and characteristics, such as statistical features, time series features, image features, and feature engineering implementation: based on the designed feature engineering method, the corresponding feature extraction flow is realized.
5. The system for data classification and storage using deep learning techniques of claim 1, wherein said constructing a deep learning model comprises: s1, selecting a deep learning model architecture, namely a multi-layer perceptron (MLP), a Convolutional Neural Network (CNN) and a cyclic neural network (RNN); s2, preparing a training data set: dividing the preprocessed data into a training set and a verification set for training and evaluating the model S3 model training and optimizing: model training is performed using the training set, and model superparameters are adjusted by the validation set to minimize prediction errors of the model.
6. The system for data classification and storage using deep learning techniques of claim 1, wherein the data classification and storage comprises: s1, carrying out classification prediction on new data; classifying and predicting new input data by using a trained model; s2, storing classification results: based on the classification result, the data is stored in a corresponding class, which may be a database, file system, or other storage medium.
7. The system for data classification and storage using deep learning techniques of claim 1 wherein said monitoring model performance comprises: s1, periodically monitoring performance indexes, accuracy and recall rate of a model; s2, updating the model according to the need: if the model performance drops or there is new data distribution, a model update is triggered and retraining or incremental training can be performed.
8. A system for data classification and storage using deep learning techniques as claimed in claim 1 wherein said performance optimization and deployment comprises: s1, evaluating and optimizing performance: according to actual requirements, evaluating the performance of the system, optimizing, and specifically adopting an acceleration algorithm, parallel computing and model quantization; s2, system deployment: the system is deployed into a target environment, and is tested and verified, so that the system can work as expected.
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