CN116013547B - Chronic disease management system and method based on big data - Google Patents
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Abstract
The invention provides a chronic disease management system and a method based on big data, wherein the system comprises a first terminal, a first edge server, a first communication module, a cloud server, a second communication module and a second terminal; the first terminal is used for receiving a registration request of a first user, and sending first registration information to the cloud server for registration through the first communication module; the cloud server is used for receiving the first registration information, configuring a unique login account for the first user, and distributing a corresponding first edge server; the first terminal is used for acquiring first physiological sign data of a first user and sending the first physiological sign data to the cloud server through the first communication module; the cloud server is used for processing and analyzing the first physiological sign data to obtain a first diagnosis result of the first user; the cloud server is used for sending the first diagnosis result to the second terminal through the second communication module. By the scheme, monitoring on chronic patients can be enhanced, and the working efficiency and accuracy of chronic disease prevention and treatment are improved.
Description
Technical Field
The invention relates to the technical field of intelligent medical treatment, in particular to a chronic disease management system and method based on big data.
Background
The intelligent medical treatment is a health file regional medical information platform established between a patient and medical staff, medical institutions and medical equipment by utilizing the Internet of things technology, and comprises an intelligent hospital system, a regional health system and a family health system. Under the background of Internet medical treatment and big data, various remote medical monitoring systems or platforms are combined with the intellectualization of the biological sensing devices which are gradually popularized, and a new medical management mode is provided for chronic disease management.
Generally, chronic diseases refer to diseases with long latency and slow onset, and the diseases are not serious at first, so the diseases are often ignored by people, and as the requirements of people on healthy life quality are continuously improved, the attention on the chronic diseases is increased. From the medical point of view, many chronic diseases can be completely cured if being found and treated in time, and the subsequent aggravation and complications cannot be caused, so that the construction of a chronic disease health management system under the background of big data is necessary.
Disclosure of Invention
Based on the problems, the invention provides a chronic disease management system and a chronic disease management method based on big data.
In view of this, an aspect of the present invention proposes a chronic disease management system based on big data, comprising: the cloud server comprises a first terminal, a first edge server, a first communication module, a cloud server, a second communication module and a second terminal; wherein,
the first terminal is used for receiving a registration request of a first user, and sending the first registration information to the cloud server for registration through the first communication module;
the cloud server is used for receiving the first registration information, configuring a unique login account for the first user, and distributing the corresponding first edge server;
the first terminal is used for collecting first physiological sign data of the first user and sending the first physiological sign data to the cloud server through the first communication module;
the cloud server is used for processing and analyzing the first physiological sign data to obtain a first diagnosis result of the first user;
and the cloud server is used for sending the first diagnosis result to the second terminal through the second communication module.
Optionally, in the step of processing and analyzing the first physiological sign data to obtain a first diagnosis result of the first user, the cloud server is specifically configured to:
Acquiring regional chronic disease image data from the first edge server;
cleaning and normalizing the first physiological sign data to obtain first normalized physiological sign data, and extracting first chronic disease index data from the first normalized physiological sign data;
judging whether the first user suffers from regional chronic disease or not according to the first chronic disease index data and the regional chronic disease portrait data;
if the first user suffers from regional chronic diseases, outputting a regional chronic disease diagnosis report as the first diagnosis result;
if the first user does not suffer from regional chronic disease, extracting second chronic disease index data from the first standardized physiological sign data;
judging whether the first user suffers from the universal chronic disease according to the second chronic disease index data and the universal chronic disease portrait data;
outputting a diagnosis report of the universal chronic disease as the first diagnosis result if the first user suffers from the universal chronic disease;
and if the first user does not suffer from the universal chronic disease, inputting the standardized first physiological sign data into a chronic disease risk model to obtain a risk report of the first user suffering from the chronic disease, and taking the risk report as the first diagnosis result.
Optionally, the cloud server is further configured to:
according to the regional chronic disease portrait data and/or the universal chronic disease portrait data and various chronic disease pathology data, respectively establishing a regional chronic disease node portrait model and/or a universal chronic disease node portrait model of important monitoring nodes of the regional chronic disease and/or the universal chronic disease;
when the first user suffers from the regional chronic disease or the universal chronic disease, inputting the first chronic disease index data and/or the second chronic disease index data of the first user into the corresponding node regional chronic disease node portrait model and/or the universal chronic disease node portrait model to obtain chronic disease node information of the first user;
and outputting a second diagnosis result and alarm information according to the chronic disease node information, and sending the second diagnosis result and the alarm information to the second terminal.
Optionally, the cloud server is further configured to:
generating a paired first encryption key, first decryption key, first encryption method and first decryption method, and paired second encryption key, second decryption key, second encryption method and second decryption method, and third encryption key, third decryption key, third encryption method and third decryption method, and first encryption policy;
Transmitting the first encryption key, the first encryption method, the second encryption key, the second encryption method and the first encryption policy to the first edge server;
transmitting the third decryption method and the third decryption method to the second terminal;
generating a corresponding second physiological sign data acquisition instruction according to the second diagnosis result and sending the second physiological sign data acquisition instruction to the first terminal;
the first edge server is further configured to generate a paired fourth encryption key, a fourth decryption key, a fourth encryption method, and a fourth decryption method according to the first encryption policy; transmitting the fourth encryption key and the fourth encryption method to the first terminal;
the first terminal is further used for acquiring second physiological sign data of the first user according to the second physiological sign data acquisition instruction.
Optionally, the first terminal is further configured to encrypt the second physiological sign data by using the fourth encryption key and the fourth encryption method to obtain first encrypted data, and send the first encrypted data to the first edge server;
the first edge server is further configured to:
Decrypting the first encrypted data by using the fourth decryption key and the fourth decryption method to obtain the second physiological sign data;
extracting heartbeat data from the second physiological sign data, and converting the heartbeat data into drum point data;
m positive integers are obtained from the positive integer set by combining the pseudo-random number table to form a first number set;
calculating the value of Ai (ai+1) for each number Ai in the first number set to obtain M product values;
taking the M product values as a second set;
performing matrix operation on the first number set and the second number set to obtain a third number set;
performing matrix operation on the third data set and the bulge point data to obtain supplementary data;
encrypting the supplementary data and the second physiological sign data by using a first encryption key and a first encryption method to obtain second encrypted data, and splitting the second encrypted data to obtain third encrypted data; encrypting the third encrypted data by using the second encryption key and a second encryption method to obtain fourth encrypted data;
transmitting the fourth encrypted data to the cloud server;
the cloud server is further configured to:
After decrypting the fourth encrypted data, obtaining the second physiological sign data;
obtaining first chronic disease prediction data of the first user according to the second physiological sign data;
encrypting the first chronic disease prediction data by using the third encryption key and a third encryption method to obtain second chronic disease prediction data;
transmitting the second chronic disease prediction data to the second terminal;
and the second terminal is further configured to decrypt the second chronic disease prediction data by using the third decryption key and the third decryption method to obtain the first chronic disease prediction data, and push the first chronic disease prediction data.
Another aspect of the present invention provides a big data based chronic disease management method, which is applied to a big data based chronic disease management system, the big data based chronic disease management system includes a first terminal, a first edge server, a first communication module, a cloud server, a second communication module and a second terminal, the big data based chronic disease management method includes:
the first terminal receives a registration request of a first user, and sends the first registration information to the cloud server for registration through the first communication module;
The cloud server receives the first registration information, configures a unique login account for the first user, and distributes a corresponding first edge server;
the first terminal collects first physiological sign data of the first user and sends the first physiological sign data to the cloud server through the first communication module;
the cloud server processes and analyzes the first physiological sign data to obtain a first diagnosis result of the first user;
and the cloud server sends the first diagnosis result to the second terminal through the second communication module.
Optionally, the step of processing and analyzing the first physiological sign data by the cloud server to obtain a first diagnosis result of the first user includes:
the cloud server acquires regional chronic disease image data from the first edge server;
the cloud server cleans and normalizes the first physiological sign data to obtain first normalized physiological sign data, and extracts first chronic disease index data from the first normalized physiological sign data;
judging whether the first user suffers from regional chronic disease or not according to the first chronic disease index data and the regional chronic disease portrait data;
If the first user suffers from regional chronic diseases, outputting a regional chronic disease diagnosis report as the first diagnosis result;
if the first user does not suffer from regional chronic disease, extracting second chronic disease index data from the first standardized physiological sign data;
judging whether the first user suffers from the universal chronic disease according to the second chronic disease index data and the universal chronic disease portrait data;
outputting a diagnosis report of the universal chronic disease as the first diagnosis result if the first user suffers from the universal chronic disease;
and if the first user does not suffer from the universal chronic disease, inputting the standardized first physiological sign data into a chronic disease risk model to obtain a risk report of the first user suffering from the chronic disease, and taking the risk report as the first diagnosis result.
Optionally, the method further comprises:
the cloud server respectively establishes a regional chronic disease node portrait model and/or a universal chronic disease node portrait model of the regional chronic disease and/or the important monitoring nodes of the universal chronic disease according to the regional chronic disease portrait data and/or the universal chronic disease portrait data and various chronic disease pathology data;
When the first user suffers from the regional chronic disease or the universal chronic disease, inputting the first chronic disease index data and/or the second chronic disease index data of the first user into the corresponding node regional chronic disease node portrait model and/or the universal chronic disease node portrait model to obtain chronic disease node information of the first user;
and outputting a second diagnosis result and alarm information according to the chronic disease node information, and sending the second diagnosis result and the alarm information to the second terminal.
Optionally, the method further comprises:
the cloud server generates a paired first encryption key, a first decryption key, a first encryption method and a first decryption method, a paired second encryption key, a paired second decryption key, a paired second encryption method and a paired second decryption method, a paired third encryption key, a paired third decryption key, a paired third encryption method and a paired third decryption method, and a paired first encryption policy;
the cloud server sends the first encryption key, the first encryption method, the second encryption key, the second encryption method and the first encryption policy to the first edge server;
Transmitting the third decryption method and the third decryption method to the second terminal;
the cloud server generates a corresponding second physiological sign data acquisition instruction according to the second diagnosis result and sends the second physiological sign data acquisition instruction to the first terminal;
the first edge server generates a paired fourth encryption key, a fourth decryption key, a fourth encryption method and a fourth decryption method according to the first encryption strategy; transmitting the fourth encryption key and the fourth encryption method to the first terminal;
the first terminal collects second physiological sign data of the first user according to the second physiological sign data collection instruction.
Optionally, the method further comprises:
the first terminal encrypts the second physiological sign data by using the fourth encryption key and the fourth encryption method to obtain first encrypted data, and sends the first encrypted data to the first edge server;
the first edge server decrypts the first encrypted data by using the fourth decryption key and the fourth decryption method to obtain the second physiological sign data;
the first edge server extracts heartbeat data from the second physiological sign data and converts the heartbeat data into drum point data;
M positive integers are obtained from the positive integer set by combining the pseudo-random number table to form a first number set;
calculating the value of Ai (ai+1) for each number Ai in the first number set to obtain M product values;
taking the M product values as a second set;
performing matrix operation on the first number set and the second number set to obtain a third number set;
performing matrix operation on the third data set and the bulge point data to obtain supplementary data;
encrypting the supplementary data and the second physiological sign data by using a first encryption key and a first encryption method to obtain second encrypted data, and splitting the second encrypted data to obtain third encrypted data; encrypting the third encrypted data by using the second encryption key and a second encryption method to obtain fourth encrypted data;
transmitting the fourth encrypted data to the cloud server;
the cloud server decrypts the fourth encrypted data to obtain the second physiological sign data;
obtaining first chronic disease prediction data of the first user according to the second physiological sign data;
encrypting the first chronic disease prediction data by using the third encryption key and a third encryption method to obtain second chronic disease prediction data;
Transmitting the second chronic disease prediction data to the second terminal;
and the second terminal decrypts the second chronic disease prediction data by using the third decryption key and the third decryption method to obtain the first chronic disease prediction data, and pushes the first chronic disease prediction data.
By adopting the technical scheme of the invention, the chronic disease management system based on big data comprises a first terminal, a first edge server, a first communication module, a cloud server, a second communication module and a second terminal; the first terminal is used for receiving a registration request of a first user, and sending the first registration information to the cloud server for registration through the first communication module; the cloud server is used for receiving the first registration information, configuring a unique login account for the first user, and distributing the corresponding first edge server; the first terminal is used for collecting first physiological sign data of the first user and sending the first physiological sign data to the cloud server through the first communication module; the cloud server is used for processing and analyzing the first physiological sign data to obtain a first diagnosis result of the first user; and the cloud server is used for sending the first diagnosis result to the second terminal through the second communication module. By the scheme of the embodiment of the invention, the monitoring of chronic patients can be enhanced, and the efficiency and accuracy of the chronic disease prevention and treatment work can be improved.
Drawings
FIG. 1 is a schematic block diagram of a big data based chronic disease management system provided in one embodiment of the present application;
fig. 2 is a flowchart of a method for chronic disease management based on big data according to an embodiment of the present application.
Detailed Description
In order that the above-recited objects, features and advantages of the present application will be more clearly understood, a more particular description of the application will be rendered by reference to the appended drawings and appended detailed description. It should be noted that, without conflict, the embodiments of the present application and features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application, however, the present application may be practiced otherwise than as described herein, and therefore the scope of the present application is not limited to the specific embodiments disclosed below.
The terms first, second and the like in the description and in the claims and in the above-described figures are used for distinguishing between different objects and not necessarily for describing a sequential or chronological order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed steps or elements but may include other steps or elements not listed or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
A chronic disease management system and method based on big data according to some embodiments of the present application are described below with reference to fig. 1 to 2.
As shown in fig. 1, one embodiment of the present application provides a chronic disease management system based on big data, including: the cloud server comprises a first terminal, a first edge server, a first communication module, a cloud server, a second communication module and a second terminal; wherein,
the first terminal is used for receiving a registration request of a first user, and sending the first registration information to the cloud server for registration through the first communication module;
the cloud server is used for receiving the first registration information, configuring a unique login account for the first user, and distributing the corresponding first edge server;
The first terminal is used for collecting first physiological sign data of the first user and sending the first physiological sign data to the cloud server through the first communication module;
the cloud server is used for processing and analyzing the first physiological sign data to obtain a first diagnosis result of the first user;
and the cloud server is used for sending the first diagnosis result to the second terminal through the second communication module.
It is understood that in the embodiment of the present invention, the first terminal may be a physiological data acquisition terminal, and may acquire data including, but not limited to, age, gender, height, weight, history of drinking, pulse, diastolic pressure, systolic pressure, electrocardiogram, heart color ultrasound, glutamic-oxaloacetic transaminase, uric acid, total cholesterol, triglyceride, joint color ultrasound, nuclear magnetic resonance imaging, myoelectricity, low density lipoprotein cholesterol, high density lipoprotein cholesterol, creatinine, electroencephalogram, eye movement examination, glycosylated hemoglobin measurement, platelet count, white blood cell count, glutamic-pyruvic transaminase skin electricity, respiration, skin temperature, and the like. The first terminal may have a plurality of terminals, each having a different function; or a health detection device integrating multiple functions, such as a health station. The first terminal can also receive a registration request which is input by a first user and contains first registration information, and send the first registration information to the cloud server for registration through the first communication module.
The first communication module/the second communication module is used for sending and receiving data.
The cloud server is a central server for managing and connecting first edge servers in different areas, can receive the first registration information, configures a unique login account for the first user, distributes the corresponding first edge server, and processes and analyzes first physiological sign data acquired by the first terminal to obtain a first diagnosis result of the first user.
And the cloud server is used for sending the first diagnosis result to the second terminal through the second communication module.
The first edge server may be a medical data processing server responsible for managing/connecting a first terminal of a cell or community in which said first user is located.
The second terminal can be an intelligent terminal such as an intelligent mobile phone, an intelligent flat panel, an intelligent bracelet and the like which are provided with a health management application program, a client or an applet; in the embodiment of the invention, the user registration can also be performed through the second terminal.
Processing the first physiological sign data acquired by the first terminal includes, but is not limited to: (1) Filling the missing value, such as filling by using the average value of the same attribute except the data of the missing value; filling with averages of other database-like samples; generating data using an algorithm; samples with missing attributes are discarded. The first scheme is chosen in view of the limited number of raw data samples, using the same attribute mean-filling of other data. Whereas for data lacking tag attributes, the tags cannot be filled because they represent the end result, unlike other attributes, the data is selected for direct discarding in embodiments of the present invention. (2) text markup data conversion: for converting non-numeric data in the original data into numeric data, however, if the non-numeric data is simply coded by 0, 1, 2 and 3, for example, when the gender is converted by text marking data, male 1 is represented by 0 to be female, 0 and 1 are the sizes of two categories instead of two numbers, and if the direct input model of 0 and 1 interferes with the whole learning process, two-bit codes 01 and 10 are used for replacing one-bit codes 0 and 1 to represent male and female respectively. (3) dimension normalization: for data with inconsistent dimensions, a normalization method is adopted for processing, namely, characteristic values of samples are converted into the same dimensions, the data is scaled according to a certain proportion and limited in a specific section, the purpose is to process the characteristic values into pure numbers without units, the weighting processing among different indexes is realized, and the influence on a final prediction result due to the non-uniform dimensions is avoided.
By adopting the technical scheme of the embodiment, the chronic disease management system based on big data comprises a first terminal, a first edge server, a first communication module, a cloud server, a second communication module and a second terminal; the first terminal is used for receiving a registration request of a first user, and sending the first registration information to the cloud server for registration through the first communication module; the cloud server is used for receiving the first registration information, configuring a unique login account for the first user, and distributing the corresponding first edge server; the first terminal is used for collecting first physiological sign data of the first user and sending the first physiological sign data to the cloud server through the first communication module; the cloud server is used for processing and analyzing the first physiological sign data to obtain a first diagnosis result of the first user; and the cloud server is used for sending the first diagnosis result to the second terminal through the second communication module. By the scheme of the embodiment of the invention, the monitoring of chronic patients can be enhanced, and the efficiency and accuracy of the chronic disease prevention and treatment work can be improved.
It should be noted that the block diagram of the big data based chronic disease management system shown in fig. 1 is only schematic, and the number of the illustrated modules does not limit the scope of the present invention.
In some possible embodiments of the present invention, in the step of processing and analyzing the first physiological sign data to obtain a first diagnosis result of the first user, the cloud server is specifically configured to:
acquiring regional chronic disease image data from the first edge server;
cleaning and normalizing the first physiological sign data to obtain first normalized physiological sign data, and extracting first chronic disease index data from the first normalized physiological sign data;
judging whether the first user suffers from regional chronic disease or not according to the first chronic disease index data and the regional chronic disease portrait data;
if the first user suffers from regional chronic diseases, outputting a regional chronic disease diagnosis report as the first diagnosis result;
if the first user does not suffer from regional chronic disease, extracting second chronic disease index data from the first standardized physiological sign data;
judging whether the first user suffers from the universal chronic disease according to the second chronic disease index data and the universal chronic disease portrait data;
Outputting a diagnosis report of the universal chronic disease as the first diagnosis result if the first user suffers from the universal chronic disease;
and if the first user does not suffer from the universal chronic disease, inputting the standardized first physiological sign data into a chronic disease risk model to obtain a risk report of the first user suffering from the chronic disease, and taking the risk report as the first diagnosis result.
It can be understood that in some areas, because the influence of factors such as climate characteristics, living habits, water and soil characteristics and the like can cause the occurrence rate of a certain chronic disease in the area to be far higher than that in other areas, regional chronic disease portrait data are deployed on an edge server of the area to be monitored in a targeted manner, and the regional chronic disease portrait data are obtained based on historical chronic disease data and data such as climate characteristics, living habits, water and soil characteristics and the like of the area by using a big data analysis technology and a neural network modeling technology.
It should be noted that, the first physiological sign data is large and complex, and only specific data is required to be acquired for specific chronic diseases to determine, in order to reduce the data processing workload and data interference, the first physiological sign data is cleaned and standardized to obtain first standardized physiological sign data, and first chronic disease index data/second chronic disease index data are extracted from the first standardized physiological sign data; for example, the first chronic disease index data/second chronic disease index data may be index data of cardiovascular disease such as age, sex, smoking history, height, weight, history of drinking, pulse, diastolic pressure, systolic pressure, glutamic-oxaloacetic transaminase, uric acid, total cholesterol, triglycerides, low density lipoprotein cholesterol, high density lipoprotein cholesterol, creatinine, glycosylated hemoglobin assay, platelet count, white blood cell count, glutamic-pyruvic transaminase, and the like.
In the embodiment of the invention, whether the regional chronic disease is present or not is judged firstly, then whether the universal chronic disease is present or not is judged, if not, whether the risk of the chronic disease is present is further judged, and the treatment and the prevention of the chronic disease can be effectively monitored and guided.
In some possible embodiments of the present invention, the cloud server is further configured to:
according to the regional chronic disease portrait data and/or the universal chronic disease portrait data and various chronic disease pathology data, respectively establishing a regional chronic disease node portrait model and/or a universal chronic disease node portrait model of important monitoring nodes of the regional chronic disease and/or the universal chronic disease;
when the first user suffers from the regional chronic disease or the universal chronic disease, inputting the first chronic disease index data and/or the second chronic disease index data of the first user into the corresponding node regional chronic disease node portrait model and/or the universal chronic disease node portrait model to obtain chronic disease node information of the first user;
and outputting a second diagnosis result and alarm information according to the chronic disease node information, and sending the second diagnosis result and the alarm information to the second terminal.
It can be appreciated that in the prevention and treatment process of chronic diseases, some important diseased nodes exist, if intervention or treatment is timely performed, success rate can be increased and efficiency can be improved for prevention and treatment of chronic diseases, so in the embodiment of the invention, the regional chronic disease node portrait model and/or the general chronic disease node portrait model of the important monitoring nodes of regional chronic diseases and/or general chronic diseases are respectively built according to the regional chronic disease portrait data and/or general chronic disease portrait data and various kinds of chronic disease pathology data by utilizing big data analysis technology and data modeling technology; when the first user suffers from the regional chronic disease or the universal chronic disease, the first chronic disease index data and/or the second chronic disease index data of the first user are input into the corresponding node regional chronic disease node portrait model and/or the universal chronic disease node portrait model to obtain chronic disease node information of the first user, so that reminding and intervention can be timely carried out.
In some possible embodiments of the present invention, the cloud server is further configured to:
Generating a paired first encryption key, first decryption key, first encryption method and first decryption method, and paired second encryption key, second decryption key, second encryption method and second decryption method, and third encryption key, third decryption key, third encryption method and third decryption method, and first encryption policy;
transmitting the first encryption key, the first encryption method, the second encryption key, the second encryption method and the first encryption policy to the first edge server;
transmitting the third decryption method and the third decryption method to the second terminal;
generating a corresponding second physiological sign data acquisition instruction according to the second diagnosis result and sending the second physiological sign data acquisition instruction to the first terminal;
the first edge server is further configured to generate a paired fourth encryption key, a fourth decryption key, a fourth encryption method, and a fourth decryption method according to the first encryption policy; the fourth encryption key and the fourth encryption method are sent to the first terminal so as to ensure the safety of data transmission between the first terminal and the first edge server;
The first terminal is further used for acquiring second physiological sign data of the first user according to the second physiological sign data acquisition instruction.
It can be understood that, because the second diagnosis result relates to an important node of the chronic disease, in order not to miss the optimal treatment time, a corresponding second physiological sign data acquisition instruction is generated according to the second diagnosis result and sent to the first terminal to further acquire targeted physiological sign data for confirmation and diagnosis.
In addition, the physiological sign data and the treatment data of the user are very sensitive and important, and in order to ensure the security of these data, in the embodiment of the present invention, the cloud server generates a paired first encryption key, first decryption key, first encryption method and first decryption method, and a paired second encryption key, second decryption key, second encryption method and second decryption method, and a third encryption key, third decryption key, third encryption method and third decryption method, and a first encryption policy; and distributing the corresponding secret key and encryption and decryption method to the corresponding terminal.
In some possible embodiments of the present invention, the first terminal is further configured to encrypt the second physiological sign data with the fourth encryption key and the fourth encryption method to obtain first encrypted data, and send the first encrypted data to the first edge server;
The first edge server is further configured to:
decrypting the first encrypted data by using the fourth decryption key and the fourth decryption method to obtain the second physiological sign data;
the heartbeat data is extracted from the second physiological sign data, and is converted into the drum point data, so that the data is convenient to select, and the safety of data sources is also ensured;
acquiring a pseudo-random number table from a cloud server, acquiring M positive integers from a positive integer set by combining the pseudo-random number table to form a first number set, wherein M is an integer greater than 10000;
calculating the value of Ai (ai+1) for each number Ai in the first number set to obtain M product values;
taking the M product values as a second set;
performing matrix operation on the first number set and the second number set to obtain a third number set;
performing matrix operation on the third data set and the bulge point data to obtain supplementary data serving as interference data in the data encryption process so as to increase the complexity of the data and improve the difficulty of data cracking;
encrypting the supplementary data and the second physiological sign data by using a first encryption key and a first encryption method to obtain second encrypted data, and splitting the second encrypted data to obtain third encrypted data;
Encrypting the third encrypted data by using the second encryption key and a second encryption method to obtain fourth encrypted data;
transmitting the fourth encrypted data to the cloud server;
the cloud server is further configured to:
after decrypting the fourth encrypted data, obtaining the second physiological sign data;
obtaining first chronic disease prediction data of the first user according to the second physiological sign data;
encrypting the first chronic disease prediction data by using the third encryption key and a third encryption method to obtain second chronic disease prediction data;
transmitting the second chronic disease prediction data to the second terminal;
and the second terminal is further configured to decrypt the second chronic disease prediction data by using the third decryption key and the third decryption method to obtain the first chronic disease prediction data, and push the first chronic disease prediction data.
It can be appreciated that in the embodiment of the invention, the data security is effectively ensured by encrypting the physiological sign data.
Referring to fig. 2, another embodiment of the present invention provides a big data based chronic disease management method, which is applied to a big data based chronic disease management system, wherein the big data based chronic disease management system includes a first terminal, a first edge server, a first communication module, a cloud server, a second communication module and a second terminal, and the big data based chronic disease management method includes:
The first terminal receives a registration request of a first user, and sends the first registration information to the cloud server for registration through the first communication module;
the cloud server receives the first registration information, configures a unique login account for the first user, and distributes a corresponding first edge server;
the first terminal collects first physiological sign data of the first user and sends the first physiological sign data to the cloud server through the first communication module;
the cloud server processes and analyzes the first physiological sign data to obtain a first diagnosis result of the first user;
and the cloud server sends the first diagnosis result to the second terminal through the second communication module.
It is understood that in the embodiment of the present invention, the first terminal may be a physiological data acquisition terminal, and may acquire data including, but not limited to, age, gender, height, weight, history of drinking, pulse, diastolic pressure, systolic pressure, electrocardiogram, heart color ultrasound, glutamic-oxaloacetic transaminase, uric acid, total cholesterol, triglyceride, joint color ultrasound, nuclear magnetic resonance imaging, myoelectricity, low density lipoprotein cholesterol, high density lipoprotein cholesterol, creatinine, electroencephalogram, eye movement examination, glycosylated hemoglobin measurement, platelet count, white blood cell count, glutamic-pyruvic transaminase skin electricity, respiration, skin temperature, and the like. The first terminal may have a plurality of terminals, each having a different function; or a health detection device integrating multiple functions, such as a health station. The first terminal can also receive a registration request which is input by a first user and contains first registration information, and send the first registration information to the cloud server for registration through the first communication module.
The first communication module/the second communication module is used for sending and receiving data.
The cloud server is a central server for managing and connecting first edge servers in different areas, can receive the first registration information, configures a unique login account for the first user, distributes the corresponding first edge server, and processes and analyzes first physiological sign data acquired by the first terminal to obtain a first diagnosis result of the first user.
And the cloud server is used for sending the first diagnosis result to the second terminal through the second communication module.
The first edge server may be a medical data processing server responsible for managing/connecting a first terminal of a cell or community in which said first user is located.
The second terminal can be an intelligent terminal such as an intelligent mobile phone, an intelligent flat panel, an intelligent bracelet and the like which are provided with a health management application program, a client or an applet; in the embodiment of the invention, the user registration can also be performed through the second terminal.
Processing the first physiological sign data acquired by the first terminal includes, but is not limited to: (1) Filling the missing value, such as filling by using the average value of the same attribute except the data of the missing value; filling with averages of other database-like samples; generating data using an algorithm; samples with missing attributes are discarded. The first scheme is chosen in view of the limited number of raw data samples, using the same attribute mean-filling of other data. Whereas for data lacking tag attributes, the tags cannot be filled because they represent the end result, unlike other attributes, the data is selected for direct discarding in embodiments of the present invention. (2) text markup data conversion: for converting non-numeric data in the original data into numeric data, however, if the non-numeric data is simply coded by 0, 1, 2 and 3, for example, when the gender is converted by text marking data, male 1 is represented by 0 to be female, 0 and 1 are the sizes of two categories instead of two numbers, and if the direct input model of 0 and 1 interferes with the whole learning process, two-bit codes 01 and 10 are used for replacing one-bit codes 0 and 1 to represent male and female respectively. (3) dimension normalization: for data with inconsistent dimensions, a normalization method is adopted for processing, namely, characteristic values of samples are converted into the same dimensions, the data is scaled according to a certain proportion and limited in a specific section, the purpose is to process the characteristic values into pure numbers without units, the weighting processing among different indexes is realized, and the influence on a final prediction result due to the non-uniform dimensions is avoided.
By the scheme of the embodiment of the invention, the monitoring of chronic patients can be enhanced, and the efficiency and accuracy of the chronic disease prevention and treatment work can be improved.
In some possible embodiments of the present invention, the step of processing and analyzing the first physiological sign data by the cloud server to obtain a first diagnosis result of the first user includes:
the cloud server acquires regional chronic disease image data from the first edge server;
the cloud server cleans and normalizes the first physiological sign data to obtain first normalized physiological sign data, and extracts first chronic disease index data from the first normalized physiological sign data;
judging whether the first user suffers from regional chronic disease or not according to the first chronic disease index data and the regional chronic disease portrait data;
if the first user suffers from regional chronic diseases, outputting a regional chronic disease diagnosis report as the first diagnosis result;
if the first user does not suffer from regional chronic disease, extracting second chronic disease index data from the first standardized physiological sign data;
judging whether the first user suffers from the universal chronic disease according to the second chronic disease index data and the universal chronic disease portrait data;
Outputting a diagnosis report of the universal chronic disease as the first diagnosis result if the first user suffers from the universal chronic disease;
and if the first user does not suffer from the universal chronic disease, inputting the standardized first physiological sign data into a chronic disease risk model to obtain a risk report of the first user suffering from the chronic disease, and taking the risk report as the first diagnosis result.
It can be understood that in some areas, because the influence of factors such as climate characteristics, living habits, water and soil characteristics and the like can cause the occurrence rate of a certain chronic disease in the area to be far higher than that in other areas, regional chronic disease portrait data are deployed on an edge server of the area to be monitored in a targeted manner, and the regional chronic disease portrait data are obtained based on historical chronic disease data and data such as climate characteristics, living habits, water and soil characteristics and the like of the area by using a big data analysis technology and a neural network modeling technology.
It should be noted that, the first physiological sign data is large and complex, and only specific data is required to be acquired for specific chronic diseases to determine, in order to reduce the data processing workload and data interference, the first physiological sign data is cleaned and standardized to obtain first standardized physiological sign data, and first chronic disease index data/second chronic disease index data are extracted from the first standardized physiological sign data; for example, the first chronic disease index data/second chronic disease index data may be index data of cardiovascular disease such as age, sex, smoking history, height, weight, history of drinking, pulse, diastolic pressure, systolic pressure, glutamic-oxaloacetic transaminase, uric acid, total cholesterol, triglycerides, low density lipoprotein cholesterol, high density lipoprotein cholesterol, creatinine, glycosylated hemoglobin assay, platelet count, white blood cell count, glutamic-pyruvic transaminase, and the like.
In the embodiment of the invention, whether the regional chronic disease is present or not is judged firstly, then whether the universal chronic disease is present or not is judged, if not, whether the risk of the chronic disease is present is further judged, and the treatment and the prevention of the chronic disease can be effectively monitored and guided.
In some possible embodiments of the invention, the method further comprises:
the cloud server respectively establishes a regional chronic disease node portrait model and/or a universal chronic disease node portrait model of the regional chronic disease and/or the important monitoring nodes of the universal chronic disease according to the regional chronic disease portrait data and/or the universal chronic disease portrait data and various chronic disease pathology data;
when the first user suffers from the regional chronic disease or the universal chronic disease, inputting the first chronic disease index data and/or the second chronic disease index data of the first user into the corresponding node regional chronic disease node portrait model and/or the universal chronic disease node portrait model to obtain chronic disease node information of the first user;
and outputting a second diagnosis result and alarm information according to the chronic disease node information, and sending the second diagnosis result and the alarm information to the second terminal.
It can be appreciated that in the prevention and treatment process of chronic diseases, some important diseased nodes exist, if intervention or treatment is timely performed, success rate can be increased and efficiency can be improved for prevention and treatment of chronic diseases, so in the embodiment of the invention, the regional chronic disease node portrait model and/or the general chronic disease node portrait model of the important monitoring nodes of regional chronic diseases and/or general chronic diseases are respectively built according to the regional chronic disease portrait data and/or general chronic disease portrait data and various kinds of chronic disease pathology data by utilizing big data analysis technology and data modeling technology; when the first user suffers from the regional chronic disease or the universal chronic disease, the first chronic disease index data and/or the second chronic disease index data of the first user are input into the corresponding node regional chronic disease node portrait model and/or the universal chronic disease node portrait model to obtain chronic disease node information of the first user, so that reminding and intervention can be timely carried out.
In some possible embodiments of the invention, the method further comprises:
the cloud server generates a paired first encryption key, a first decryption key, a first encryption method and a first decryption method, a paired second encryption key, a paired second decryption key, a paired second encryption method and a paired second decryption method, a paired third encryption key, a paired third decryption key, a paired third encryption method and a paired third decryption method, and a paired first encryption policy;
The cloud server sends the first encryption key, the first encryption method, the second encryption key, the second encryption method and the first encryption policy to the first edge server;
transmitting the third decryption method and the third decryption method to the second terminal;
the cloud server generates a corresponding second physiological sign data acquisition instruction according to the second diagnosis result and sends the second physiological sign data acquisition instruction to the first terminal;
the first edge server generates a paired fourth encryption key, a fourth decryption key, a fourth encryption method and a fourth decryption method according to the first encryption strategy; the fourth encryption key and the fourth encryption method are sent to the first terminal so as to ensure the safety of data transmission between the first terminal and the first edge server;
the first terminal collects second physiological sign data of the first user according to the second physiological sign data collection instruction.
It can be understood that, because the second diagnosis result relates to an important node of the chronic disease, in order not to miss the optimal treatment time, a corresponding second physiological sign data acquisition instruction is generated according to the second diagnosis result and sent to the first terminal to further acquire targeted physiological sign data for confirmation and diagnosis.
In addition, the physiological sign data and the treatment data of the user are very sensitive and important, and in order to ensure the security of these data, in the embodiment of the present invention, the cloud server generates a paired first encryption key, first decryption key, first encryption method and first decryption method, and a paired second encryption key, second decryption key, second encryption method and second decryption method, and a third encryption key, third decryption key, third encryption method and third decryption method, and a first encryption policy; and distributing the corresponding secret key and encryption and decryption method to the corresponding terminal.
In some possible embodiments of the invention, the method further comprises:
the first terminal encrypts the second physiological sign data by using the fourth encryption key and the fourth encryption method to obtain first encrypted data, and sends the first encrypted data to the first edge server;
the first edge server decrypts the first encrypted data by using the fourth decryption key and the fourth decryption method to obtain the second physiological sign data;
the first edge server extracts heartbeat data from the second physiological sign data and converts the heartbeat data into drum point data, so that the data is convenient to select, and the safety of data sources is also ensured;
Acquiring a pseudo-random number table from a cloud server, acquiring M positive integers from a positive integer set by combining the pseudo-random number table to form a first number set, wherein M is an integer greater than 10000;
calculating the value of Ai (ai+1) for each number Ai in the first number set to obtain M product values;
taking the M product values as a second set;
performing matrix operation on the first number set and the second number set to obtain a third number set;
performing matrix operation on the third data set and the bulge point data to obtain supplementary data serving as interference data in the data encryption process so as to increase the complexity of the data and improve the difficulty of data cracking;
encrypting the supplementary data and the second physiological sign data by using a first encryption key and a first encryption method to obtain second encrypted data, and splitting the second encrypted data to obtain third encrypted data; encrypting the third encrypted data by using the second encryption key and a second encryption method to obtain fourth encrypted data;
transmitting the fourth encrypted data to the cloud server;
the cloud server decrypts the fourth encrypted data to obtain the second physiological sign data;
Obtaining first chronic disease prediction data of the first user according to the second physiological sign data;
encrypting the first chronic disease prediction data by using the third encryption key and a third encryption method to obtain second chronic disease prediction data;
transmitting the second chronic disease prediction data to the second terminal;
and the second terminal decrypts the second chronic disease prediction data by using the third decryption key and the third decryption method to obtain the first chronic disease prediction data, and pushes the first chronic disease prediction data.
It can be appreciated that in the embodiment of the application, the data security is effectively ensured by encrypting the physiological sign data.
It should be noted that, for simplicity of description, the foregoing method embodiments are all described as a series of acts, but it should be understood by those skilled in the art that the present application is not limited by the order of acts described, as some steps may be performed in other orders or concurrently in accordance with the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily required for the present application.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to related descriptions of other embodiments.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, such as the above-described division of units, merely a division of logic functions, and there may be additional manners of dividing in actual implementation, such as multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, or may be in electrical or other forms.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units described above, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable memory. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a memory, comprising several instructions for causing a computer device (which may be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the above-mentioned method of the various embodiments of the present application. And the aforementioned memory includes: a U-disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Those of ordinary skill in the art will appreciate that all or a portion of the steps in the various methods of the above embodiments may be implemented by a program that instructs associated hardware, and the program may be stored in a computer readable memory, which may include: flash disk, read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), magnetic disk or optical disk.
The foregoing has outlined rather broadly the more detailed description of embodiments of the application, wherein the principles and embodiments of the application are explained in detail using specific examples, the above examples being provided solely to facilitate the understanding of the method and core concepts of the application; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present application, the present description should not be construed as limiting the present application in view of the above.
Although the present application is disclosed above, the present application is not limited thereto. Variations and modifications, including combinations of the different functions and implementation steps, as well as embodiments of the software and hardware, may be readily apparent to those skilled in the art without departing from the spirit and scope of the application.
Claims (2)
1. A big data based chronic disease management system, comprising: the cloud server comprises a first terminal, a first edge server, a first communication module, a cloud server, a second communication module and a second terminal; wherein,
the first terminal is used for receiving a registration request of a first user, and sending first registration information to the cloud server for registration through the first communication module;
the cloud server is used for receiving the first registration information, configuring a unique login account for the first user, and distributing the corresponding first edge server; the first edge server is a server for managing medical data processing of the first terminal in a cell or community where the first user is located;
the first terminal is used for collecting first physiological sign data of the first user and sending the first physiological sign data to the cloud server through the first communication module;
the cloud server is configured to process and analyze the first physiological sign data to obtain a first diagnosis result of the first user, and specifically includes:
acquiring regional chronic disease image data from the first edge server, wherein the regional chronic disease image data is obtained by utilizing a big data analysis technology and a neural network modeling technology based on historical chronic disease data, climate characteristics, living habits and water and soil characteristics of a region where the first user is located;
Cleaning and normalizing the first physiological sign data to obtain first normalized physiological sign data, and extracting first chronic disease index data from the first normalized physiological sign data;
judging whether the first user suffers from regional chronic disease or not according to the first chronic disease index data and the regional chronic disease portrait data;
if the first user suffers from regional chronic diseases, outputting a regional chronic disease diagnosis report as the first diagnosis result;
if the first user does not suffer from regional chronic disease, extracting second chronic disease index data from the first standardized physiological sign data;
judging whether the first user suffers from the universal chronic disease according to the second chronic disease index data and the universal chronic disease portrait data;
outputting a diagnosis report of the universal chronic disease as the first diagnosis result if the first user suffers from the universal chronic disease;
if the first user does not suffer from the universal chronic disease, inputting the first standardized physiological sign data into a chronic disease risk model to obtain a risk report of the first user suffering from the chronic disease, and taking the risk report as the first diagnosis result;
the cloud server is used for sending the first diagnosis result to the second terminal through the second communication module;
The cloud server is further configured to:
according to the regional chronic disease portrait data and/or the universal chronic disease portrait data and various chronic disease pathology data, respectively establishing a regional chronic disease node portrait model and/or a universal chronic disease node portrait model of important monitoring nodes of the regional chronic disease and/or the universal chronic disease;
when the first user suffers from the regional chronic disease or the universal chronic disease, inputting the first chronic disease index data and/or the second chronic disease index data of the first user into the corresponding regional chronic disease node portrait model and/or the universal chronic disease node portrait model to obtain chronic disease node information of the first user;
outputting a second diagnosis result and alarm information according to the chronic disease node information, and sending the second diagnosis result and the alarm information to the second terminal;
the cloud server is further configured to:
generating a paired first encryption key, first decryption key, first encryption method and first decryption method, and paired second encryption key, second decryption key, second encryption method and second decryption method, and third encryption key, third decryption key, third encryption method and third decryption method, and first encryption policy;
Transmitting the first encryption key, the first encryption method, the second encryption key, the second encryption method and the first encryption policy to the first edge server;
transmitting the third decryption method and the third decryption method to the second terminal;
generating a corresponding second physiological sign data acquisition instruction according to the second diagnosis result and sending the second physiological sign data acquisition instruction to the first terminal;
the first edge server is further configured to generate a paired fourth encryption key, a fourth decryption key, a fourth encryption method, and a fourth decryption method according to the first encryption policy; transmitting the fourth encryption key and the fourth encryption method to the first terminal;
the first terminal is further used for acquiring second physiological sign data of the first user according to the second physiological sign data acquisition instruction;
the first terminal is further configured to encrypt the second physiological sign data by using the fourth encryption key and the fourth encryption method to obtain first encrypted data, and send the first encrypted data to the first edge server;
the first edge server is further configured to:
Decrypting the first encrypted data by using the fourth decryption key and the fourth decryption method to obtain the second physiological sign data;
extracting heartbeat data from the second physiological sign data, and converting the heartbeat data into drum point data;
m positive integers are obtained from the positive integer set by combining the pseudo-random number table to form a first number set;
calculating the value of Ai (ai+1) for each number Ai in the first number set to obtain M product values;
taking the M product values as a second set;
performing matrix operation on the first number set and the second number set to obtain a third number set;
performing matrix operation on the third data set and the bulge point data to obtain supplementary data;
encrypting the supplementary data and the second physiological sign data by using a first encryption key and a first encryption method to obtain second encrypted data, and splitting the second encrypted data to obtain third encrypted data; encrypting the third encrypted data by using the second encryption key and a second encryption method to obtain fourth encrypted data;
transmitting the fourth encrypted data to the cloud server;
the cloud server is further configured to:
After decrypting the fourth encrypted data, obtaining the second physiological sign data;
obtaining first chronic disease prediction data of the first user according to the second physiological sign data;
encrypting the first chronic disease prediction data by using the third encryption key and a third encryption method to obtain second chronic disease prediction data;
transmitting the second chronic disease prediction data to the second terminal;
and the second terminal is further configured to decrypt the second chronic disease prediction data by using the third decryption key and the third decryption method to obtain the first chronic disease prediction data, and push the first chronic disease prediction data.
2. The big data based chronic disease management method according to claim 1, wherein the big data based chronic disease management system includes a first terminal, a first edge server, a first communication module, a cloud server, a second communication module, and a second terminal, and the big data based chronic disease management method includes:
the first terminal receives a registration request of a first user, and sends first registration information to the cloud server for registration through the first communication module;
The cloud server receives the first registration information, configures a unique login account for the first user, and distributes a corresponding first edge server; the first edge server is a server for managing medical data processing of the first terminal in a cell or community where the first user is located;
the first terminal collects first physiological sign data of the first user and sends the first physiological sign data to the cloud server through the first communication module;
the cloud server processes and analyzes the first physiological sign data to obtain a first diagnosis result of the first user, specifically:
acquiring regional chronic disease image data from the first edge server, wherein the regional chronic disease image data is obtained by utilizing a big data analysis technology and a neural network modeling technology based on historical chronic disease data, climate characteristics, living habits and water and soil characteristics of a region where the first user is located;
cleaning and normalizing the first physiological sign data to obtain first normalized physiological sign data, and extracting first chronic disease index data from the first normalized physiological sign data;
judging whether the first user suffers from regional chronic disease or not according to the first chronic disease index data and the regional chronic disease portrait data;
If the first user suffers from regional chronic diseases, outputting a regional chronic disease diagnosis report as the first diagnosis result;
if the first user does not suffer from regional chronic disease, extracting second chronic disease index data from the first standardized physiological sign data;
judging whether the first user suffers from the universal chronic disease according to the second chronic disease index data and the universal chronic disease portrait data;
outputting a diagnosis report of the universal chronic disease as the first diagnosis result if the first user suffers from the universal chronic disease;
if the first user does not suffer from the universal chronic disease, inputting the first standardized physiological sign data into a chronic disease risk model to obtain a risk report of the first user suffering from the chronic disease, and taking the risk report as the first diagnosis result;
the cloud server sends the first diagnosis result to the second terminal through the second communication module;
the method further comprises the steps of:
the cloud server respectively establishes a regional chronic disease node portrait model and/or a universal chronic disease node portrait model of the regional chronic disease and/or the important monitoring nodes of the universal chronic disease according to the regional chronic disease portrait data and/or the universal chronic disease portrait data and various chronic disease pathology data;
When the first user suffers from the regional chronic disease or the universal chronic disease, inputting the first chronic disease index data and/or the second chronic disease index data of the first user into the corresponding regional chronic disease node portrait model and/or the universal chronic disease node portrait model to obtain chronic disease node information of the first user;
outputting a second diagnosis result and alarm information according to the chronic disease node information, and sending the second diagnosis result and the alarm information to the second terminal;
the method further comprises the steps of:
the cloud server generates a paired first encryption key, a first decryption key, a first encryption method and a first decryption method, a paired second encryption key, a paired second decryption key, a paired second encryption method and a paired second decryption method, a paired third encryption key, a paired third decryption key, a paired third encryption method and a paired third decryption method, and a paired first encryption policy;
the cloud server sends the first encryption key, the first encryption method, the second encryption key, the second encryption method and the first encryption policy to the first edge server;
Transmitting the third decryption method and the third decryption method to the second terminal;
the cloud server generates a corresponding second physiological sign data acquisition instruction according to the second diagnosis result and sends the second physiological sign data acquisition instruction to the first terminal;
the first edge server generates a paired fourth encryption key, a fourth decryption key, a fourth encryption method and a fourth decryption method according to the first encryption strategy; transmitting the fourth encryption key and the fourth encryption method to the first terminal;
the first terminal collects second physiological sign data of the first user according to the second physiological sign data collection instruction;
the method further comprises the steps of:
the first terminal encrypts the second physiological sign data by using the fourth encryption key and the fourth encryption method to obtain first encrypted data, and sends the first encrypted data to the first edge server;
the first edge server decrypts the first encrypted data by using the fourth decryption key and the fourth decryption method to obtain the second physiological sign data;
the first edge server extracts heartbeat data from the second physiological sign data and converts the heartbeat data into drum point data;
M positive integers are obtained from the positive integer set by combining the pseudo-random number table to form a first number set;
calculating the value of Ai (ai+1) for each number Ai in the first number set to obtain M product values;
taking the M product values as a second set;
performing matrix operation on the first number set and the second number set to obtain a third number set;
performing matrix operation on the third data set and the bulge point data to obtain supplementary data;
encrypting the supplementary data and the second physiological sign data by using a first encryption key and a first encryption method to obtain second encrypted data, and splitting the second encrypted data to obtain third encrypted data; encrypting the third encrypted data by using the second encryption key and a second encryption method to obtain fourth encrypted data;
transmitting the fourth encrypted data to the cloud server;
the cloud server decrypts the fourth encrypted data to obtain the second physiological sign data;
obtaining first chronic disease prediction data of the first user according to the second physiological sign data;
encrypting the first chronic disease prediction data by using the third encryption key and a third encryption method to obtain second chronic disease prediction data;
Transmitting the second chronic disease prediction data to the second terminal;
and the second terminal decrypts the second chronic disease prediction data by using the third decryption key and the third decryption method to obtain the first chronic disease prediction data, and pushes the first chronic disease prediction data.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211567201.0A CN116013547B (en) | 2022-12-07 | 2022-12-07 | Chronic disease management system and method based on big data |
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| CN105450650A (en) * | 2015-12-03 | 2016-03-30 | 中国人民大学 | Safety mobile electronic health record access control system |
| CN211155815U (en) * | 2019-07-10 | 2020-08-04 | 齐鲁工业大学 | Blood oxygen monitoring and early warning medical system based on cloud database |
| CN115171863A (en) * | 2022-07-22 | 2022-10-11 | 广州医科大学附属第一医院（广州呼吸中心） | Chronic disease management system based on two-dimensional code |
| CN115412903A (en) * | 2022-08-30 | 2022-11-29 | 广东工业大学 | Chain type compressed sensing data stream encoding method, decoding method and device |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN105450650A (en) * | 2015-12-03 | 2016-03-30 | 中国人民大学 | Safety mobile electronic health record access control system |
| CN211155815U (en) * | 2019-07-10 | 2020-08-04 | 齐鲁工业大学 | Blood oxygen monitoring and early warning medical system based on cloud database |
| CN115171863A (en) * | 2022-07-22 | 2022-10-11 | 广州医科大学附属第一医院（广州呼吸中心） | Chronic disease management system based on two-dimensional code |
| CN115412903A (en) * | 2022-08-30 | 2022-11-29 | 广东工业大学 | Chain type compressed sensing data stream encoding method, decoding method and device |
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