Data archiving system based on artificial intelligence

Abstract

A data archiving system based on artificial intelligence comprises a data preprocessing module, an intelligent classification module, a data archiving module and a self-adaptive learning module. Through automatic data processing, classification and archiving, manual intervention and human errors are reduced by the system, and the accuracy and consistency of archives are ensured. And meanwhile, the intelligent retrieval and analysis function accelerates the acquisition and utilization of information, so that the user can respond to business requirements and decisions more quickly, and the working efficiency is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of archive management, and in particular to an artificial intelligence-based data archiving system. Background Art

[0002] Traditional archive management methods usually rely on manual classification, filing and retrieval, which is not only time-consuming and labor-intensive, but also prone to misoperation and information loss. Existing technologies often lack intelligent processing capabilities and are difficult to meet the needs of efficient and accurate archive management. With the continuous increase in the number of archives, traditional management methods can no longer meet the needs of modern society for archive management. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide an artificial intelligence-based data archiving system that can effectively solve the problems raised in the above-mentioned background technology.

[0004] To solve the above problems, the technical solution adopted by the present invention is: an artificial intelligence-based data archiving system, including the following modules and processing steps:

[0005] Data preprocessing module:

[0006] S1: receiving raw data;

[0007] S2: Apply data cleaning algorithms to handle missing values;

[0008] S3: Standardize the data;

[0009] Intelligent classification module:

[0010] S4: Use K-means clustering algorithm to classify the standardized data;

[0011] S5: Generate a data classification report based on the classification results;

[0012] Data archiving module:

[0013] S6: storing the classified data according to the archiving rules;

[0014] Adaptive learning modules:

[0015] S7: Update the learning model based on new data input and use the online learning algorithm to update the model

[0016] Type parameters; formula:

[0017]

[0018] Among them, θ t+1 is the updated parameter, θ t is the current parameter, η is the learning rate, is the gradient of the loss function.

[0019] S8: Adjust the learning rate η to optimize model performance.

[0020] As a further preferred embodiment of the present invention, the data preprocessing module includes data cleaning and standardization processing.

[0021] As a further preferred embodiment of the present invention, the algorithm processing formula in step S2 is:

[0022]

[0023] Among them, x i To fill missing data;

[0024] The standardization processing formula in step S3 is:

[0025]

[0026] Among them, x′ is the standardized data, x is the original data, μ is the mean, and σ is the standard deviation.

[0027] As a further preferred embodiment of the present invention, the objective function formula is used in step S4, wherein the formula is:

[0028]

[0029] Where J is the objective function, C i is the i-th category, μ i is the i-th type center.

[0030] As a further preferred embodiment of the present invention, the intelligent classification module determines the category by calculating the Euclidean distance between the data point and the category center.

[0031] As a further preferred embodiment of the present invention, the step S6 uses an archiving formula, wherein the formula is:

[0032] A i，j =archive(D i , F j )

[0033] Among them A i，j For archiving data, D i Data input, F j Archive category.

[0034] As a further preferred solution of the present invention, the data archiving module stores the data in order by category and date.

[0035] As a further preferred embodiment of the present invention, the adaptive learning module adjusts the model parameters after each data input to improve the prediction accuracy.

[0036] Compared with the prior art, the present invention provides an artificial intelligence-based data archiving system, which has the following beneficial effects:

[0037] The system reduces manual intervention and human errors through automated data processing, classification and archiving, ensuring the accuracy and consistency of archives. At the same time, intelligent retrieval and analysis functions accelerate the acquisition and use of information, allowing users to respond to business needs and make decisions more quickly, significantly improving work efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a process framework diagram of the present invention; DETAILED DESCRIPTION

[0039] In addition, the technical solutions between the various embodiments can be combined with each other, but it must be based on the fact that ordinary technicians in the field can implement it. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0040] Reference Figure 1 The present invention provides an artificial intelligence-based data archiving system, comprising:

[0041] 1. Data preprocessing module:

[0042] 1.1 Data reception and organization: Receive archival data from different sources and conduct preliminary organization.

[0043] 1.2 Data cleaning: dealing with missing values ​​and inconsistencies in the data.

[0044] 1.3 Data classification and standardization: Classify the data and standardize it in a unified format.

[0045] 2. Intelligent classification module:

[0046] 2.1 Classification algorithm (SVM / Random Forest can be used): Use machine learning algorithm to automatically classify the archives.

[0047] 2.2 Classification report generation: Generate reports and statistical data on archive classification.

[0048] 3. Data archiving module:

[0049] 3.1 Archival storage: Store the classified data in a database or cloud storage according to predetermined rules.

[0050] 3.2 Archive index creation: Create index files for quick retrieval.

[0051] 4. Adaptive learning module:

[0052] 4.1 Data Analysis: Analyze stored data to identify trends and patterns.

[0053] 4.2 Prediction and suggestion generation: Generate predictions and improvement suggestions based on the analysis results.

[0054] 5. Output and report generation:

[0055] 5.1 Generate analysis report: Automatically generate reports on archive management and analysis.

[0056] 5.2 Provide decision-making recommendations: Provide specific decision-making recommendations based on the analysis results.

[0057] As a specific embodiment 1 of the present invention:

[0058] Application background: In school, managing various student information, such as personal files, academic performance, extracurricular activity records, etc., is a tedious and important task. In order to improve the efficiency and accuracy of student information management, the "intelligent file management system" can be used to achieve automated and intelligent student file management.

[0059] S1: Receive student file data D, which includes the student's basic information (such as name, age, class), academic performance, attendance record, extracurricular activities, etc.

[0060] S2: Apply data cleaning algorithms to handle missing values ​​and use mean imputation algorithms to fill in missing grade data. For example, if a student’s math score is missing, use the average math score of the students in that class to fill in the missing grades.

[0061] S3: Standardize student data, such as grades and attendance, to ensure that the data is within the same range. Formula:

[0062]

[0063] Among them, x is the original score, μ is the mean of the score, and σ is the standard deviation of the score.

[0064] S4: Use K-means clustering algorithm to classify the standardized data.

[0065] Classify student data according to academic performance and interests. For example, use K-means to classify students into categories such as academic excellence, academic mediocre, and academic improvement needs. Objective function formula:

[0066]

[0067] Among them C i is the i-th category, μ i is the i-th type center.

[0068] S5: Generate student classification report

[0069] For example, the report shows that 30% of students are classified as academically excellent, 50% as academically average, and 20% as academically in need of improvement.

[0070] S6: Archive the classified student data

[0071] Store student records in the database by academic performance and semester. For example, file all academically excellent student records in the "Academic Excellence" folder and file them by semester.

[0072] Generate archive index for easy retrieval and management

[0073] Create an index file for quick retrieval of student files, for example, using student ID and semester as index.

[0074] S7: Update learning modules based on new student data

[0075] Use online learning algorithms to adjust classification models to adapt to new academic performance and interests. For example, as new student data enters the system, the algorithm is constantly updated so that the classification model can better reflect the student's latest situation.

[0076] S8: Adjust the learning rate η to optimize model performance. After each model update, the learning rate is automatically adjusted to ensure that the model can steadily improve classification accuracy.

[0077] Practical application effect

[0078] Improve efficiency: Through automated data processing and classification, the time and errors of manual management of student records are greatly reduced.

[0079] Accuracy: Standardization and classification algorithms improve the accuracy and consistency of student data.

[0080] Intelligent retrieval: The archiving and indexing system makes it easier to retrieve and access student files, allowing teachers to quickly find the information they need.

[0081] Adaptability: The adaptive learning module enables the system to continuously optimize based on new data to maintain the efficiency and accuracy of the classification model.

[0082] As a specific embodiment 2 of the present invention:

[0083] Application background: In hospitals, managing the medical records of a large number of patients is a complex and important task. In order to improve the management efficiency and accuracy of medical record information, the "intelligent file management system" can be used to achieve automated and intelligent medical record management.

[0084] S1: Receive original medical record data D, which includes the patient's basic information (such as name, age, gender), medical history records, examination results, treatment plans, etc.

[0085] S2: Apply data cleaning algorithms to handle missing values

[0086] Use mean imputation to fill in missing physical examination data. For example, if a patient's blood pressure record is missing, use the average blood pressure value in the patient's history to fill in.

[0087] S3: Standardize medical record data

[0088] Standardize various measurements (such as body temperature, blood sugar, etc.) to eliminate the effects of different measurement units or ranges. Formula:

[0089]

[0090] Among them, x is the original body temperature, μ is the mean of body temperature, and σ is the standard deviation of body temperature.

[0091] S4: Use K-means clustering algorithm to classify the standardized data.

[0092] Classify medical records according to symptoms and diagnosis results. For example, use K-means to classify medical records into categories such as cardiovascular disease, diabetes, and respiratory diseases. Objective function formula:

[0093]

[0094] Among them C i is the i-th category, μ i is the i-th type center.

[0095] S5: Generate medical record classification report

[0096] For example, the report showed that 60% of patients were classified as having cardiovascular disease, 25% as diabetes, and 15% as respiratory disease.

[0097] S6: Archiving the classified medical records

[0098] Store medical records in the database by disease type and date. For example, file all medical records for cardiovascular diseases in the folder "Cardiovascular Diseases" and file them by month.

[0099] Generate archive index for easy retrieval and management

[0100] Create an index file for quick retrieval of medical records, for example, using patient ID and date as index.

[0101] S7: Update learning modules based on new medical record data

[0102] Use online learning algorithms to adjust model parameters to improve classification accuracy. For example, as new case data enters the system, the algorithm is constantly updated, allowing the classification model to better adapt to emerging disease types.

[0103] S8: Adjust the learning rate η to optimize model performance

[0104] After each model update, the learning rate is automatically adjusted to ensure that the model can steadily improve classification performance.

[0105] Practical application effect

[0106] Improve efficiency: Through automated data processing and classification, the time and errors of manual management of medical records are greatly reduced.

[0107] Accuracy: Standardization and classification algorithms improve the accuracy and consistency of medical record data.

[0108] Intelligent retrieval: The archiving and indexing system makes it easier to retrieve and access medical records, allowing doctors to quickly find the information they need.

[0109] Adaptability: The adaptive learning module enables the system to continuously optimize based on new data to maintain the efficiency and accuracy of the classification model.

[0110] As a specific embodiment 3 of the present invention:

[0111] Application background: The internal file management of an enterprise involves employee personal information, company financial records, project documents, contracts and other important documents. Traditional paper or basic electronic file management methods may be inefficient, error-prone, and difficult to quickly retrieve and update. Using an intelligent file management system can improve management efficiency, ensure information security, and optimize data utilization.

[0112] S1: Receive and organize archival data D, which includes basic employee information (such as name, position, date of employment), company financial records (such as bills, reimbursements), project documents, contracts, etc.

[0113] S2: Apply data cleaning algorithms to handle missing values ​​and inconsistencies, fill in missing fields in employee information (such as contact numbers, emergency contacts), and use average or most frequent values ​​to fill in missing items. Standardize the date format in contracts to ensure consistency.

[0114] S3: Classify and standardize archival data, classify data by category (such as employee, financial, project), and standardize financial data, such as unifying currency units and standardizing accounting subjects.

[0115] S4: Use support vector machine (SVM) or random forest algorithm to intelligently classify files and divide documents into different categories, such as human resources files, financial records, project documents, etc. For example, using SVM algorithm for classification, the training data includes labeled file categories. Objective function:

[0116]

[0117] Among them, w is the weight vector, C is the slack variable, ξ i is the regularization parameter.

[0118] S5. Generate classified reports and statistics. Generate classified reports to show the number and proportion of each type of files. For example, the report shows that the company has 500 human resources files, 300 financial records and 200 project documents.

[0119] S6. Archive the classified files and store them in a database or cloud storage by category and creation date. For example, store all financial records by year in the folder "Finance / 20XX".

[0120] Create and maintain archive indexes for fast retrieval, create index files, and use archive ID, category, date, etc. as indexes. Index files can be implemented using an inverted index or a B-tree structure.

[0121] S7: Update the intelligent analysis model based on the new archive data, and use machine learning algorithms to analyze the trends of archive data, such as financial expenditure trends, project progress, etc. Use regression analysis to predict future trends, regression equation: y = β0 + β1x1 + β2x2 + ... + β n x n +∈

[0122] Where y is the predicted value, x i is the characteristic variable, β i is the regression coefficient, and ∈ is the error term.

[0123] S8: The system automatically generates analytical reports on the company's financial status, project progress, etc., and provides suggestions for improvement. For example, the report can suggest increasing the budget or optimizing project resource allocation.

[0124] Practical application effect

[0125] Improve efficiency: Automated classification, storage and retrieval of archives reduces manual operation time and improves management efficiency.

[0126] Accuracy: Intelligent data cleaning and classification improve the accuracy and consistency of archival data.

[0127] Security: Multi-level permission control and logging ensure the security and traceability of archives.

[0128] Intelligent analysis: Through adaptive analysis and prediction, enterprises can better plan resources and optimize decision-making.

[0129] Through an intelligent internal enterprise archive management system, enterprises can significantly improve the efficiency and accuracy of archive management, enhance data security, and use intelligent analysis to support decision-making.

[0130] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. All equivalent structural changes made by using the contents of the present invention specification and drawings under the inventive concept of the present invention, or directly / indirectly applied in other related technical fields are included in the patent protection scope of the present invention.

Claims

1. An artificial intelligence-based data archiving system, comprising the following modules and processing steps: Data preprocessing module: S1: receiving raw data; S2: Apply data cleaning algorithms to handle missing values; S3: Standardize the data; Intelligent classification module: S4: Use K-means clustering algorithm to classify the standardized data; S5: Generate a data classification report based on the classification results; Data archiving module: S6: storing the classified data according to the archiving rules; Adaptive learning modules: S7: Update the learning model based on the new data input and use the online learning algorithm to update the model parameters; formula: in, θ t+1 is the updated parameter, θ t is the current parameter, η is the learning rate, is the gradient of the loss function. S8: Adjust the learning rate η to optimize model performance.

2. The artificial intelligence-based data archiving system according to claim 1, characterized in that: The data preprocessing module includes data cleaning and standardization processing.

3. The artificial intelligence-based data archiving system according to claim 2, characterized in that: The algorithm processing formula in step S2 is: Among them, x i To fill missing data; The standardization processing formula in step S3 is: Among them, x′ is the standardized data, x is the original data, μ is the mean, and σ is the standard deviation.

4. The artificial intelligence-based data archiving system according to claim 1, characterized in that: The objective function formula is used in step S4, where the formula is: Where J is the objective function, C i is the i-th category, μ i is the i-th type center.

5. The artificial intelligence-based data archiving system according to claim 4, characterized in that: The intelligent classification module determines the category by calculating the Euclidean distance between the data point and the category center.

6. The artificial intelligence-based data archiving system according to claim 1, characterized in that: The step S6 uses the filing formula, where the formula is: A i，j =archive(D i ,F j ) Among them A i，j For archiving data, D i Data input, F j Archive category.

7. The artificial intelligence-based data archiving system according to claim 6, characterized in that: The data archiving module stores the data in order by category and date.

8. The artificial intelligence-based data archiving system according to claim 1, characterized in that: The adaptive learning module adjusts the model parameters after each data input to improve the prediction accuracy.

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