Big data artificial intelligence analysis system and method thereof

Abstract

The invention belongs to the technical field of intelligent analysis systems, and particularly relates to a big data artificial intelligence analysis system and method, and the system comprises a user login module which is used for enabling a user to log in a client; the information input module is used for inputting user information, including age, gender, height, weight and family medical history, into the client; the acquisition module is used for acquiring the medical record data of the previous years of the user according to the Internet and uploading the acquired data to the client; and the lifestyle acquisition module is used for acquiring lifestyle data of the user, including dietary habits, exercise conditions and smoking and drinking histories, and uploading the acquired data to the client. According to the invention, during disease prediction, evaluation can be carried out by fusing the lifestyle of the user on the basis of medical record data in previous years, so that the evaluation accuracy of the user can be improved to a certain extent.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent analysis systems, and specifically to a big data artificial intelligence analysis system and method thereof. Background Art

[0002] In the field of healthcare, disease assessment refers to the process by which medical professionals use various methods and tools to comprehensively analyze and judge multiple aspects of the disease. The purpose is to fully understand the condition of the disease, predict the development trend of the disease, and evaluate the treatment effect, etc., to provide a basis for formulating personalized treatment plans and rehabilitation plans.

[0003] Currently, disease prediction is typically based solely on a person's medical records from previous years, without integrating their lifestyle into the assessment. This can lead to limitations and affect the accuracy of the assessment. Therefore, a big data artificial intelligence analysis system and method are invented. Summary of the Invention

[0004] To solve the above technical problems, according to one aspect of the present invention, the present invention provides the following technical solutions:

[0005] A big data artificial intelligence analysis system, comprising:

[0006] User login module, used to enable users to log in to the client;

[0007] Information entry module, used to enter user information into the client, including age, gender, height, weight, and family medical history;

[0008] The acquisition module is used to obtain the user's medical record data from the Internet and upload the acquired data to the client;

[0009] The lifestyle acquisition module is used to obtain the user's lifestyle data, including eating habits, exercise status, smoking and drinking history, and upload the acquired data to the client;

[0010] A data processing module, used to collect and integrate data obtained by the information entry module, the acquisition module and the lifestyle acquisition module;

[0011] Disease prediction model training module, which is used to train multiple artificial intelligence models based on historical data, including decision trees, neural networks, and support vector machines, to predict specific diseases;

[0012] The real-time prediction and early warning module is used to make real-time disease predictions based on the patient's latest data and issue early warnings when the prediction results reach a certain risk threshold;

[0013] The intelligent health advice module generates personalized health advice for users based on user data, including basic information, medical records, lifestyle data, and disease prediction results, using artificial intelligence algorithms and medical knowledge base;

[0014] The doctor-patient interaction module is used to build a doctor-patient communication platform to facilitate communication between users and doctors. Users can consult doctors about health issues on the platform, and doctors can respond to users' inquiries in a timely manner, view users' health records and disease prediction results, and provide users with professional diagnosis and treatment suggestions;

[0015] The special population care module is used to provide exclusive health management services for special groups, including the elderly, children, and pregnant women. It establishes a fall warning mechanism for the elderly, uses smart devices to monitor their activity status, and immediately issues an alarm if a fall is detected. It provides growth and development monitoring functions for children, recording children's height and weight growth data, comparing them with standard growth curves, and promptly identifying growth and development abnormalities. Pregnant women are provided with pregnancy knowledge popularization and prenatal checkup reminder services;

[0016] The lifestyle acquisition module includes:

[0017] An image analysis module, used to analyze the user's lifestyle based on the image;

[0018] A sound analysis module is used to analyze the user's lifestyle based on the sound;

[0019] a storage module, configured to store the user lifestyle data analyzed by the image analysis module and the sound analysis module;

[0020] The arranging and marking module is used to time-mark and arrange the data stored in the storage module;

[0021] The modification module is used to manually modify the data sorted by the sorting and marking module;

[0022] The upload module is used to upload the data sorted by the sorting and marking module to the client.

[0023] As a preferred solution of the big data artificial intelligence analysis system described in the present invention, the image analysis module includes:

[0024] An image acquisition module is used to acquire the content photographed by the terminal;

[0025] The image storage module is used to store various lifestyle images.

[0026] As a preferred solution of the big data artificial intelligence analysis system described in the present invention, the image analysis module further includes:

[0027] An image comparison module, used to compare the image acquired by the image acquisition module with the image stored by the image storage module;

[0028] The image judgment module is used to judge whether the image obtained by the image acquisition module is similar to the image storage module. If so, the user's current detailed behavior will be obtained.

[0029] As a preferred solution of the big data artificial intelligence analysis system described in the present invention, the sound analysis module includes:

[0030] The sound acquisition module is used to acquire the sound collected by the terminal;

[0031] The sound storage module is used to store various voices.

[0032] As a preferred solution of the big data artificial intelligence analysis system described in the present invention, the sound analysis module further includes:

[0033] A sound comparison module, used to compare the sound acquired by the sound acquisition module with the sound stored by the sound storage module;

[0034] The sound judgment module is used to judge whether the sound obtained by the sound acquisition module is similar to the sound storage module. If so, the user's current detailed behavior will be obtained.

[0035] As a preferred solution of the big data artificial intelligence analysis system described in the present invention, the data processing module includes:

[0036] A data collection module, used to collect data obtained by the information entry module, the acquisition module and the lifestyle acquisition module;

[0037] The data integration module is used to integrate the data obtained by the information input module, the acquisition module and the lifestyle acquisition module.

[0038] As a preferred solution of the big data artificial intelligence analysis system described in the present invention, it also includes:

[0039] The visualization display module is used to present the forecast results, relevant indicators and analysis reports to users in the form of intuitive charts and reports.

[0040] As a preferred solution of the big data artificial intelligence analysis system described in the present invention, it also includes:

[0041] The model evaluation and optimization module is used to regularly evaluate and optimize the disease prediction model to improve the accuracy and reliability of the prediction.

[0042] A big data artificial intelligence analysis method includes the following specific steps:

[0043] Step 1: The user logs in to the client through the user login module. After logging in, the user information will be entered into the client through the information entry module. After entering, the user's medical record data from previous years will be obtained through the Internet through the acquisition module and the obtained data will be uploaded to the client;

[0044] Step 2: The content captured by the terminal is acquired through the image acquisition module. After acquisition, the image acquired by the image acquisition module is compared with the image stored in the image storage module through the image comparison module. After comparison, the image judgment module is used to judge whether the image acquired by the image acquisition module is similar to the image storage module. If so, the user's current detailed behavior will be obtained. At the same time, the sound collected by the terminal is acquired through the sound acquisition module. After acquisition, the sound acquired by the sound acquisition module is compared with the sound stored in the sound storage module through the sound comparison module. After comparison, The sound judgment module determines whether the sound acquired by the sound acquisition module is similar to that in the sound storage module. If so, the user's current detailed behavior will be acquired. Then, the user's lifestyle data analyzed by the image analysis module and the sound analysis module will be stored in the storage module. After storage, the data stored in the storage module will be time-stamped and sorted by the sorting and marking module. If there are defects in the sorted data, the data sorted by the sorting and marking module will be manually modified by the modification module. Then, the data sorted by the sorting and marking module will be uploaded to the client through the upload module.

[0045] Step 3: The data obtained by the information input module, acquisition module and lifestyle acquisition module are collected and integrated through the data processing module. Then, multiple artificial intelligence models will be trained based on historical data through the disease prediction model training module to achieve the prediction of specific diseases. After that, the real-time prediction and early warning module will perform real-time disease prediction based on the patient's latest data, and issue an early warning when the prediction result reaches a certain risk threshold. At the same time, the prediction results, relevant indicators and analysis reports will be presented to the user in the form of intuitive charts and reports through the visualization module. Afterwards, the disease prediction model will be regularly evaluated and optimized through the model evaluation and optimization module to improve the accuracy and reliability of the prediction.

[0046] Compared with existing technologies:

[0047] When predicting diseases, the present invention is able to evaluate the user's lifestyle based on medical record data from previous years, thereby improving the accuracy of the user's evaluation to a certain extent. In addition, through the lifestyle acquisition module set up, the user's lifestyle can be collected in real time, further ensuring the accuracy of the evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0049] To make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0050] The present invention provides a big data artificial intelligence analysis system and method, please refer to Figure 1 ;

[0051] It includes: a user login module for enabling users to log in to the client; an information entry module for entering user information into the client, including age, gender, height, weight, and family medical history; an acquisition module for acquiring the user's medical record data from previous years through the Internet and uploading the acquired data to the client; a lifestyle acquisition module for acquiring the user's lifestyle data, including eating habits, exercise conditions, smoking and drinking history, and uploading the acquired data to the client; a data processing module for collecting and integrating the data acquired by the information entry module, acquisition module, and lifestyle acquisition module; a disease prediction model training module for training a variety of artificial intelligence models based on historical data, including decision trees, neural networks, and support vector machines, to achieve prediction of specific diseases; a real-time prediction and early warning module for real-time disease prediction based on the patient's latest data, and issuing an early warning when the prediction result reaches a certain risk threshold; a visualization display module for presenting the prediction results, related indicators, and analysis reports to the user in the form of intuitive charts and reports; model evaluation and optimization The module is used to regularly evaluate and optimize disease prediction models to improve the accuracy and reliability of predictions; the intelligent health advice module uses artificial intelligence algorithms and medical knowledge bases based on user data, including basic information, medical records, lifestyle data, and disease prediction results, to generate personalized health advice for users; the doctor-patient interaction module is used to build a doctor-patient communication platform to facilitate communication between users and doctors. Users can consult doctors about health issues on the platform, and doctors can respond to user inquiries in a timely manner, view user health records and disease prediction results, and provide users with professional diagnosis and treatment advice; the special population care module is used to provide exclusive health management services for special groups, including the elderly, children, and pregnant women, establish a fall warning mechanism for the elderly, use smart devices to monitor the elderly's activity status, and immediately issue an alarm once a fall is detected; provide children with growth and development monitoring functions, record children's height and weight growth data, compare them with standard growth curves, and promptly detect growth and development abnormalities; provide pregnant women with pregnancy knowledge popularization and prenatal check-up reminder services.

[0052] Among them, by setting up the intelligent health advice module, it can help users actively manage their health and prevent the occurrence of diseases. By providing targeted health guidance, users can change their bad living habits and reduce the risk of disease. At the same time, it can improve users' dependence and satisfaction with the system and let users feel the practical value of the system.

[0053] By setting up a doctor-patient interaction module, the medical service experience can be improved, breaking the time and space limitations, allowing users to obtain medical services more conveniently, strengthening information exchange between doctors and patients, and improving diagnostic accuracy and treatment effects. Doctors can make more scientific medical decisions based on the comprehensive data provided by the system.

[0054] By setting up a special population care module, the specific health needs of special groups can be met, reflecting the humanization and refinement of medical services. By focusing on special groups, health risks can be reduced and the health and safety of special groups can be guaranteed.

[0055] The lifestyle acquisition module includes: an image analysis module for analyzing the user's lifestyle based on images; a sound analysis module for analyzing the user's lifestyle based on sounds; a storage module for storing the user's lifestyle data analyzed by the image analysis module and the sound analysis module; a sorting and marking module for time-stamping and sorting the data stored in the storage module; a change module for manually modifying the data sorted by the sorting and marking module; and an upload module for uploading the data sorted by the sorting and marking module to the client.

[0056] The image analysis module includes: an image acquisition module for acquiring the content photographed by the terminal; an image storage module for storing various lifestyle images; an image comparison module for comparing the image acquired by the image acquisition module with the image stored in the image storage module; and an image judgment module for judging whether the image acquired by the image acquisition module is similar to the image storage module. If so, the user's current detailed behavior will be obtained.

[0057] The sound analysis module includes: a sound acquisition module, which is used to acquire the sound collected by the terminal; a sound storage module, which is used to store various voices; a sound comparison module, which is used to compare the sound acquired by the sound acquisition module with the sound stored in the sound storage module; and a sound judgment module, which is used to judge whether the sound acquired by the sound acquisition module is similar to the sound storage module. If so, the user's current detailed behavior will be obtained.

[0058] The data processing module includes: a data collection module for collecting data obtained by the information entry module, the acquisition module and the lifestyle acquisition module; and a data integration module for integrating the data obtained by the information entry module, the acquisition module and the lifestyle acquisition module.

[0059] A big data artificial intelligence analysis method includes the following specific steps:

[0060] Step 1: The user logs in to the client through the user login module. After logging in, the user information will be entered into the client through the information entry module. After entering, the user's medical record data from previous years will be obtained through the Internet through the acquisition module and the obtained data will be uploaded to the client;

[0061] Step 2: The content captured by the terminal is acquired through the image acquisition module. After acquisition, the image acquired by the image acquisition module is compared with the image stored in the image storage module through the image comparison module. After comparison, the image judgment module is used to judge whether the image acquired by the image acquisition module is similar to the image storage module. If so, the user's current detailed behavior will be obtained. At the same time, the sound collected by the terminal is acquired through the sound acquisition module. After acquisition, the sound acquired by the sound acquisition module is compared with the sound stored in the sound storage module through the sound comparison module. After comparison, The sound judgment module determines whether the sound acquired by the sound acquisition module is similar to that in the sound storage module. If so, the user's current detailed behavior will be acquired. Then, the user's lifestyle data analyzed by the image analysis module and the sound analysis module will be stored in the storage module. After storage, the data stored in the storage module will be time-stamped and sorted by the sorting and marking module. If there are defects in the sorted data, the data sorted by the sorting and marking module will be manually modified by the modification module. Then, the data sorted by the sorting and marking module will be uploaded to the client through the upload module.

[0062] Step 3: The data obtained by the information input module, acquisition module and lifestyle acquisition module are collected and integrated through the data processing module. Then, multiple artificial intelligence models will be trained based on historical data through the disease prediction model training module to achieve the prediction of specific diseases. After that, the real-time prediction and early warning module will perform real-time disease prediction based on the patient's latest data, and issue an early warning when the prediction result reaches a certain risk threshold. At the same time, the prediction results, relevant indicators and analysis reports will be presented to the user in the form of intuitive charts and reports through the visualization module. Afterwards, the disease prediction model will be regularly evaluated and optimized through the model evaluation and optimization module to improve the accuracy and reliability of the prediction.

[0063] Although the present invention has been described above with reference to embodiments, various modifications may be made thereto and equivalent components may be substituted without departing from the scope of the present invention. In particular, as long as there are no structural conflicts, the various features of the embodiments disclosed herein may be combined with each other in any manner, and the omission of an exhaustive description of such combinations in this specification is solely for the sake of space and resource conservation. Therefore, the present invention is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A big data artificial intelligence analysis system, characterized in that: include: User login module, used to enable users to log in to the client; Information entry module, used to enter user information into the client, including age, gender, height, weight, and family medical history; The acquisition module is used to obtain the user's medical record data from the Internet and upload the acquired data to the client; The lifestyle acquisition module is used to obtain the user's lifestyle data, including eating habits, exercise status, smoking and drinking history, and upload the acquired data to the client; A data processing module, used to collect and integrate data obtained by the information entry module, the acquisition module and the lifestyle acquisition module; Disease prediction model training module, which is used to train multiple artificial intelligence models based on historical data, including decision trees, neural networks, and support vector machines, to predict specific diseases; The real-time prediction and early warning module is used to make real-time disease predictions based on the patient's latest data and issue early warnings when the prediction results reach a certain risk threshold; The intelligent health advice module generates personalized health advice for users based on user data, including basic information, medical records, lifestyle data, and disease prediction results, using artificial intelligence algorithms and medical knowledge base; The doctor-patient interaction module is used to build a doctor-patient communication platform to facilitate communication between users and doctors. Users can consult doctors about health issues on the platform, and doctors can respond to users' inquiries in a timely manner, view users' health records and disease prediction results, and provide users with professional diagnosis and treatment suggestions; The special population care module is used to provide exclusive health management services for special groups, including the elderly, children, and pregnant women. It establishes a fall warning mechanism for the elderly, uses smart devices to monitor their activity status, and immediately issues an alarm if a fall is detected. It provides growth and development monitoring functions for children, recording children's height and weight growth data, comparing them with standard growth curves, and promptly identifying growth and development abnormalities. Pregnant women are provided with pregnancy knowledge popularization and prenatal checkup reminder services; The lifestyle acquisition module includes: An image analysis module, used to analyze the user's lifestyle based on the image; A sound analysis module is used to analyze the user's lifestyle based on the sound; a storage module, configured to store the user lifestyle data analyzed by the image analysis module and the sound analysis module; The arranging and marking module is used to time-mark and arrange the data stored in the storage module; The modification module is used to manually modify the data sorted by the sorting and marking module; The upload module is used to upload the data sorted by the sorting and marking module to the client.

2. A big data artificial intelligence analysis system according to claim 1, characterized in that: The image analysis module includes: An image acquisition module is used to acquire the content photographed by the terminal; The image storage module is used to store various lifestyle images.

3. A big data artificial intelligence analysis system according to claim 2, characterized in that: The image analysis module also includes: An image comparison module, used to compare the image acquired by the image acquisition module with the image stored by the image storage module; The image judgment module is used to judge whether the image obtained by the image acquisition module is similar to the image storage module. If so, the user's current detailed behavior will be obtained.

4. A big data artificial intelligence analysis system according to claim 1, characterized in that: The sound analysis module includes: The sound acquisition module is used to acquire the sound collected by the terminal; The sound storage module is used to store various voices.

5. A big data artificial intelligence analysis system according to claim 4, characterized in that: The sound analysis module also includes: A sound comparison module, used to compare the sound acquired by the sound acquisition module with the sound stored by the sound storage module; The sound judgment module is used to judge whether the sound obtained by the sound acquisition module is similar to the sound storage module. If so, the user's current detailed behavior will be obtained.

6. A big data artificial intelligence analysis system according to claim 1, characterized in that: The data processing module includes: A data collection module, used to collect data obtained by the information entry module, the acquisition module and the lifestyle acquisition module; The data integration module is used to integrate the data obtained by the information input module, the acquisition module and the lifestyle acquisition module.

7. The big data artificial intelligence analysis system according to claim 1, characterized in that: Also includes: The visualization display module is used to present the forecast results, relevant indicators and analysis reports to users in the form of intuitive charts and reports.

8. The big data artificial intelligence analysis system according to claim 1, characterized in that: Also includes: The model evaluation and optimization module is used to regularly evaluate and optimize the disease prediction model to improve the accuracy and reliability of the prediction.

9. A big data artificial intelligence analysis method, characterized in that: The specific steps are as follows: Step 1: The user logs in to the client through the user login module. After logging in, the user information will be entered into the client through the information entry module. After entering, the user's medical record data from previous years will be obtained through the Internet through the acquisition module and the obtained data will be uploaded to the client; Step 2: The content captured by the terminal is acquired through the image acquisition module. After acquisition, the image acquired by the image acquisition module is compared with the image stored in the image storage module through the image comparison module. After comparison, the image judgment module is used to judge whether the image acquired by the image acquisition module is similar to the image storage module. If so, the user's current detailed behavior will be obtained. At the same time, the sound collected by the terminal is acquired through the sound acquisition module. After acquisition, the sound acquired by the sound acquisition module is compared with the sound stored in the sound storage module through the sound comparison module. After comparison, The sound judgment module determines whether the sound acquired by the sound acquisition module is similar to that in the sound storage module. If so, the user's current detailed behavior will be acquired. Then, the user's lifestyle data analyzed by the image analysis module and the sound analysis module will be stored in the storage module. After storage, the data stored in the storage module will be time-stamped and sorted by the sorting and marking module. If there are defects in the sorted data, the data sorted by the sorting and marking module will be manually modified by the modification module. Then, the data sorted by the sorting and marking module will be uploaded to the client through the upload module. Step 3: The data obtained by the information input module, acquisition module and lifestyle acquisition module are collected and integrated through the data processing module. Then, multiple artificial intelligence models will be trained based on historical data through the disease prediction model training module to achieve the prediction of specific diseases. After that, the real-time prediction and early warning module will perform real-time disease prediction based on the patient's latest data, and issue an early warning when the prediction result reaches a certain risk threshold. At the same time, the prediction results, relevant indicators and analysis reports will be presented to the user in the form of intuitive charts and reports through the visualization module. Afterwards, the disease prediction model will be regularly evaluated and optimized through the model evaluation and optimization module to improve the accuracy and reliability of the prediction.

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