Computer artificial intelligence information screening system

Abstract

The invention relates to the technical field of artificial intelligence, and particularly discloses a computer artificial intelligence information screening system which comprises a data acquisition module, a data preprocessing module, a feature extraction module, an information screening module, a result display module and a result feedback and iteration improvement module. The data acquisition module is responsible for collecting information from various data sources; the data preprocessing module is used for cleaning, denoising and preprocessing the collected data so as to facilitate subsequent analysis and processing; the feature extraction module performs feature extraction on the preprocessed data by using a deep learning model; the information screening module screens and classifies the information by using a machine learning algorithm or a deep learning model according to the extracted features; the result display module displays screening and classification results to the user in a visual mode; and the result feedback and iteration improvement module is used for receiving the feedback of the user on the current display result and optimizing and adjusting the whole system based on the feedback so as to improve the accuracy.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a computer artificial intelligence information screening system. Background Art

[0002] In the modern era of rapid development of informatization and digitalization, massive amounts of data are generated in many industries and fields. If these data can be effectively processed and analyzed, they will greatly promote decision-making, new knowledge discovery and business optimization. However, the efficiency and quality of data utilization are often constrained by the complexity of data processing. Especially in scenarios where deep learning technology is required, the conversion from raw data to valuable information involves multiple stages, and each stage may become a bottleneck of inefficiency.

[0003] Data collection: In current technologies, data collection often relies on multiple different sources, and often requires manual intervention to integrate data from various sources. This process is not only time-consuming, but also easily affects data quality due to inconsistent formats or differences in collection methods.

[0004] Data preprocessing: Data often contains noise, missing values ​​or inconsistencies, and needs to be cleaned and standardized through complex preprocessing processes. However, traditional systems often lack flexibility and are inefficient when processing large-scale or diverse data.

[0005] Feature extraction: Feature extraction is a key step in determining the quality of the final analysis results. In traditional methods, feature extraction relies on manual selection by experts, which is not only time-consuming, but also may lead to suboptimal feature selection due to subjectivity.

[0006] Classification and analysis: Currently, many systems use standard machine learning models to classify data, but these models often require adjustment of multiple parameters to adapt to specific data sets. Once the data characteristics change, the adaptability and accuracy of the model will be greatly reduced.

[0007] Results display: The results of data analysis need to be presented to users in an intuitive way. Current solutions are often too static and lack the ability to interact with users, which limits the interpretation and application of analysis results.

[0008] Based on the above problems, the existing technology requires a comprehensive system that can realize efficient data integration and processing, automatic feature extraction, and provide dynamic interactive display results to meet industry needs. Summary of the invention

[0009] The purpose of the present invention is to provide a fully automated, highly efficient integrated machine learning and deep learning support system, which greatly improves data processing efficiency and analysis accuracy through innovative data processing modules, adaptive feature extraction mechanisms and intelligent classification algorithms, and provides real-time analysis results display through a dynamic interactive interface to address the defects in the prior art.

[0010] To achieve the above object, the present invention provides the following technical solution: a computer artificial intelligence information screening system, comprising:

[0011] Data acquisition module, responsible for collecting information from various data sources;

[0012] Data preprocessing module cleans, denoises and preprocesses the collected data for subsequent analysis and processing;

[0013] Feature extraction module, which uses deep learning model to extract features from preprocessed data;

[0014] Information screening module, which uses machine learning algorithms or deep learning models to screen and classify information based on the extracted features;

[0015] The result display module displays the screening and classification results to the user in an intuitive way;

[0016] The result feedback and iterative improvement module is used to receive user feedback on the current display results, and optimize and adjust the entire system based on the feedback to improve accuracy, efficiency and user satisfaction.

[0017] Preferably, the data collection module can collect raw data from a variety of data sources, including social media APIs, website crawler data, and online databases, and the formula is:

[0018] D raw ={d1, d2, ..., d n}

[0019] D raw represents the collected original data set, d i Represents a data point in a data source.

[0020] Preferably, the data preprocessing module can remove missing values, such as data point d i If there are missing values, remove or fill them;

[0021]

[0022] Here, μ and σ are the mean and standard deviation of the data set, respectively.

[0023] Preferably, the feature extraction module can extract useful features from the preprocessed data; use deep learning, CNN to extract image features;

[0024] F(x)=ReLU(W*x+b)

[0025] F(x) represents the feature extraction function; x is the input data; W and b are the weight and bias of the model respectively; ReLU is the nonlinear activation function.

[0026] Preferably, the feature extraction module can extract useful features from the preprocessed data; use deep learning, CNN to extract image features;

[0027] F(x)=ReLU(W*x+b)

[0028] F(x) represents the feature extraction function; x is the input data; W and b are the weight and bias of the model respectively; ReLU is the nonlinear activation function.

[0029] Preferably, the result display module can display the final screening and classification results to the user in the form of charts, reports, etc., providing intuitive information analysis;

[0030] Scatter plot after data clustering: Use PCA to reduce the dimension to 2 or 3 dimensions to display the clustering results of high-dimensional data: Z = XW, where X is the data matrix, W is the principal component matrix, and Z is the data matrix after dimensionality reduction.

[0031] Preferably, the result feedback and iterative improvement module consists of the following submodules:

[0032] Feedback collection submodule: responsible for receiving and storing user feedback;

[0033] Feedback analysis submodule: classify and analyze the collected feedback;

[0034] Improvement planning submodule: generate specific improvement plans;

[0035] Execution and monitoring submodule: implement improvement measures and monitor their effects;

[0036] Archiving and reporting submodule: Archive feedback and improvement records, and generate analysis reports.

[0037] The computer artificial intelligence information screening system proposed by the present invention has the following beneficial effects:

[0038] 1. Improve data processing efficiency: The system of the present invention integrates the entire process from data acquisition to preprocessing, feature extraction and classification, eliminates the isolation between different processing steps, makes the overall data processing process more efficient, and significantly improves the efficiency of data analysis.

[0039] 2. Improve the quality of data analysis: By applying deep learning algorithms to adaptively extract data features and intelligent classification processing, the present invention can accurately identify and extract features that have an important impact on the results, greatly improving the quality and accuracy of data analysis.

[0040] 3. Enhance the applicability and scalability of the system: Since the diversity and scale of data are taken into consideration when designing the system, the ability of the present invention to collect and process various types of data is stronger than that of traditional methods. At the same time, the present invention is easy to expand and maintain through modular and service-oriented design, and can meet the rapid changes in different application requirements.

[0041] 4. Provide dynamic and interactive user experience: The present invention includes an intuitive and highly interactive user interface design that can display data analysis results in real time, making it easier for users to understand and operate. This greatly improves the user experience, makes the system's results display more vivid, and also facilitates further data exploration.

[0042] 5. Reduce human resource investment: Through automated and intelligent design, the present invention reduces dependence on professionals and reduces the need for manual adjustment and optimization, thereby saving a lot of human resource investment.

[0043] In general, the present invention achieves high efficiency and accuracy in data processing through integrated design and application of deep learning technology, while providing excellent user experience. It is expected to greatly improve the efficiency and quality of data processing and analysis in the industry, and bring significant economic and social benefits. DETAILED DESCRIPTION

[0044] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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.

[0045] The present invention provides a technical solution: a computer artificial intelligence information screening system, characterized in that it includes:

[0046] Data acquisition module, responsible for collecting information from various data sources;

[0047] Data preprocessing module cleans, denoises and preprocesses the collected data for subsequent analysis and processing;

[0048] Feature extraction module, which uses deep learning model to extract features from preprocessed data;

[0049] Information screening module, which uses machine learning algorithms or deep learning models to screen and classify information based on the extracted features;

[0050] The result display module displays the screening and classification results to the user in an intuitive way;

[0051] The result feedback and iterative improvement module is used to receive user feedback on the current display results, and optimize and adjust the entire system based on the feedback to improve accuracy, efficiency and user satisfaction.

[0052] The data collection module can collect raw data from a variety of data sources, including social media APIs, website crawler data, and online databases. The formula is:

[0053] D raw ={d1, d2, ..., d n}

[0054] D raw represents the collected original data set, d i Represents a data point in a data source.

[0055] The data preprocessing module can remove missing values, such as data point d i If there are missing values, remove or fill them;

[0056]

[0057] Here, μ and σ are the mean and standard deviation of the data set, respectively.

[0058] The feature extraction module can extract useful features from the preprocessed data; use deep learning, CNN to extract image features;

[0059] F(x)=ReLU(W*x+b)

[0060] F(x) represents the feature extraction function; x is the input data; W and b are the weight and bias of the model respectively; ReLU is the nonlinear activation function.

[0061] The information screening module can classify and screen information based on the extracted features using a machine learning algorithm, using a support vector machine (SVM);

[0062] Objective function:

[0063] Constraints:

[0064] Where w is the normal vector of the separating hyperplane; b is the bias term; x i is the input data; y iis the category label.

[0065] The result display module can display the final screening and classification results to the user in the form of charts, reports, etc., providing intuitive information analysis;

[0066] Scatter plot after data clustering: Use PCA to reduce the dimension to 2 or 3 dimensions to display the clustering results of high-dimensional data: Z = XW, where X is the data matrix, W is the principal component matrix, and Z is the data matrix after dimensionality reduction.

[0067] The result feedback and iterative improvement module consists of the following sub-modules:

[0068] Feedback collection submodule: responsible for receiving and storing user feedback;

[0069] Feedback analysis submodule: classify and analyze the collected feedback;

[0070] Improvement planning submodule: generate specific improvement plans;

[0071] Execution and monitoring submodule: implement improvement measures and monitor their effects;

[0072] Archiving and reporting submodule: Archive feedback and improvement records, and generate analysis reports.

[0073] 1. Feedback Collection Submodule

[0074] User interface: Provide a simple and easy-to-use interface to allow users to submit feedback. This can be done using text boxes, rating systems, multiple-choice questions, etc.

[0075] Automated feedback: Capture user behavior in the system (such as clicks, dwell time, exit rate) as implicit feedback;

[0076] Storage mechanism: Store user feedback in a database for subsequent analysis.

[0077] 2. Feedback analysis submodule

[0078] Preprocessing function: clean the collected feedback, such as removing noise words, punctuation marks, etc.

[0079] Classification function: Use natural language processing (NLP) technology to classify feedback into different categories, such as functional issues, operational suggestions, accuracy evaluation, etc.

[0080] Sentiment analysis: Evaluate the emotional tendency of user feedback, which can be used to judge user satisfaction.

[0081] 3. Improve the planning submodule

[0082] Prioritization: Assign priorities to various issues / suggestions, taking into account their urgency and scope of impact;

[0083] Generate improvement plan: Automatically generate specific suggestions for improvement measures, which can be submitted to the development team for implementation.

[0084] 4. Execution and monitoring submodules

[0085] Task management: Track the progress of each improvement measure to ensure timely completion;

[0086] Effect monitoring: Set key performance indicators (KPIs), monitor the performance of the improved system, and collect new data for verification.

[0087] 5. Archiving and reporting submodules

[0088] Data archiving: All feedback, analysis results, improvement measures and effect monitoring data are archived for easy historical query;

[0089] Report generation: Generate feedback and improvement reports regularly, including analysis of improvement effectiveness, changes in user satisfaction, etc.

[0090] Example 1: Integrated system deployment for retail data analysis:

[0091] System Configuration:

[0092] Processor: 32 cores, 2.8GHz CPU;

[0093] Memory: 128GB RAM;

[0094] Storage: 10TB SSD storage to support fast access to large-scale retail data;

[0095] Software framework: self-developed deep learning library, supporting Python and Java interfaces.

[0096] Functionality:

[0097] Data collection: real-time sales data from dozens of retail locations, including customer purchase records, inventory information, etc.

[0098] Preprocessing and feature extraction: The system automatically cleans and standardizes the raw data and uses autoencoders to extract key features;

[0099] Feature analysis: Apply neural network algorithms to analyze consumer behavior. The system can automatically adjust the analysis model parameters based on historical data and seasonal changes.

[0100] Interactive interface: Provides dynamic charts to display real-time data and analysis results, allowing users to input feedback to optimize the model instantly.

[0101] The effect achieved:

[0102] Improve data processing efficiency by 30% and accelerate data updating and analysis through automated processes and powerful computing capabilities;

[0103] Accuracy increased by 20%. Deep learning algorithms make predictions of consumer behavior more precise, helping companies develop more effective marketing strategies.

[0104] User feedback shows that the operation is simple and convenient, which improves the work efficiency and satisfaction of service personnel.

[0105] Example 2: Intelligent analysis system for medical image diagnosis:

[0106] System Configuration:

[0107] Processor: 64 cores, 3.2GHz GPU accelerated processing;

[0108] Memory: 256GB RAM;

[0109] Storage: 50TB high-speed NVMe SSD to ensure the access speed of large medical images;

[0110] Software framework: A deep learning platform based on TensorFlow and PyTorch, compatible with medical data formats such as DICOM.

[0111] Functionality:

[0112] Data collection: Directly access the hospital's PACS system to obtain medical images such as CT and MRI in real time;

[0113] Preprocessing and feature extraction: Apply convolutional neural networks to automatically label and extract structured features;

[0114] Lesion recognition and classification: Use transfer learning to enhance the model’s ability to recognize serious lesions such as cancer;

[0115] User interface: Provides a doctor-friendly graphical interface that allows doctors to mark images directly, and the system automatically records and optimizes them.

[0116] The effect achieved:

[0117] Reduce initial diagnosis time by 40% through automatic feature extraction, improving overall hospital efficiency;

[0118] The accuracy of lesion identification has been increased to 95%, significantly improving the accuracy of diagnosis and the quality of medical care;

[0119] The real-time interactive interface greatly improves doctors' user satisfaction, simplifies the operation process, and reduces the risk of misdiagnosis.

[0120] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A computer artificial intelligence information screening system, characterized in that: include: Data acquisition module, responsible for collecting information from various data sources; Data preprocessing module cleans, denoises and preprocesses the collected data for subsequent analysis and processing; Feature extraction module, which uses deep learning model to extract features from preprocessed data; Information screening module, which uses machine learning algorithms or deep learning models to screen and classify information based on the extracted features; The result display module displays the screening and classification results to the user in an intuitive way; The result feedback and iterative improvement module is used to receive user feedback on the current display results, and optimize and adjust the entire system based on the feedback to improve accuracy, efficiency and user satisfaction.

2. A computer artificial intelligence information screening system according to claim 1, characterized in that: The data collection module can collect raw data from a variety of data sources, including social media APIs, website crawler data, and online databases. The formula is: D raw ={d1,d2,...,d n } D raw represents the collected original data set, d i Represents a data point in a data source.

3. A computer artificial intelligence information screening system according to claim 2, characterized in that: The data preprocessing module can remove missing values, such as data point d i If there are missing values, remove or fill them; Here, μ and σ are the mean and standard deviation of the data set, respectively.

4. A computer artificial intelligence information screening system according to claim 3, characterized in that: The feature extraction module can extract useful features from the preprocessed data; Use deep learning and CNN to extract image features; F(x)=ReLU(W*x+b) F(x) represents the feature extraction function; x is the input data; W and b are the weight and bias of the model respectively; ReLU is the nonlinear activation function.

5. A computer artificial intelligence information screening system according to claim 4, characterized in that: The information screening module can classify and screen information based on the extracted features using a machine learning algorithm, using a support vector machine (SVM); Objective function: Constraints: Where w is the normal vector of the separating hyperplane; b is the bias term; x i is the input data; y i is the category label.

6. A computer artificial intelligence information screening system according to claim 5, characterized in that: The result display module can display the final screening and classification results to the user in the form of charts, reports, etc., providing intuitive information analysis; Scatter plot after data clustering: Use PCA to reduce the dimension to 2 or 3 dimensions to display the clustering results of high-dimensional data: Z = XW, where X is the data matrix, W is the principal component matrix, and Z is the data matrix after dimensionality reduction.

7. A computer artificial intelligence information screening system according to claim 6, characterized in that: The result feedback and iterative improvement module consists of the following sub-modules: Feedback collection submodule: responsible for receiving and storing user feedback; Feedback analysis submodule: classify and analyze the collected feedback; Improvement planning submodule: generate specific improvement plans; Execution and monitoring submodule: implement improvement measures and monitor their effects; Archiving and reporting submodule: Archive feedback and improvement records, and generate analysis reports.

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