Cloud health data management method and system for heart and cerebral vessels

Abstract

The invention provides a cloud health data management method and system for heart and cerebral vessels. The system comprises a cloud health data management platform and a brain and heart health pre-adaptation training instrument, the brain and heart health pre-adaptation training instrument automatically collects human body vital sign health data of a user and sends the human body vital sign health data to the cloud health data management platform for storage; wherein the human body vital sign health data at least comprises two-arm blood pressure IAD values; the cloud health data management platform performs deep analysis on the human body vital sign health data based on cloud server big data mining and a machine learning technology to obtain an analysis result; and the cloud health data management platform generates a health suggestion scheme based on the analysis result. According to the invention, deep analysis is carried out on the human body vital sign health data based on cloud server big data mining and a machine learning technology, so that an accurate health suggestion scheme is provided, and the defect that the collected health data is lack of effective deep analysis at present is overcome.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a cardiovascular and cerebrovascular cloud health data management method and system. Background Art

[0002] With the change of people's lifestyle, the increase of work pressure and the acceleration of the aging process, the incidence of cardiovascular and cerebrovascular diseases has increased year by year. Traditional cardiovascular and cerebrovascular health management methods have many limitations. For example, patients can usually only undergo regular examinations in hospitals or physical examination institutions, and real-time and continuous data monitoring cannot be achieved. Moreover, these examinations are often intermittent, making it difficult to capture early changes and subtle fluctuations in the disease.

[0003] In the past, health data collection equipment had relatively simple functions and was unable to comprehensively and accurately obtain health data on human vital signs, especially the collection and analysis of key indicators such as bilateral blood pressure IAD values ​​was not accurate enough.

[0004] The collected data lacks effective big data mining and in-depth analysis, making it difficult to provide personalized and accurate health advice. At the same time, since health data involves user privacy information, special attention should be paid to preventing privacy leaks when storing health data.

[0005] For example, some portable health monitoring devices can only simply record basic data such as blood pressure and heart rate, but cannot conduct in-depth analysis and integrated utilization of these data; and some health management platforms can store data, but their analysis capabilities are limited and they cannot provide users with targeted and forward-looking health advice. Summary of the invention

[0006] The main purpose of the present invention is to provide a cardiovascular and cerebrovascular cloud health data management method and system, aiming to overcome the current defect of lack of effective in-depth analysis of collected health data.

[0007] To achieve the above-mentioned purpose, the present invention provides a cardiovascular and cerebrovascular cloud health data management system, including a cloud health data management platform and a brain-heart pre-adaptation training device;

[0008] The brain-heart pre-adaptation training device automatically collects the user's human vital characteristics health data and sends it to the cloud health data management platform for storage; wherein the human vital characteristics health data at least includes the IAD value of the blood pressure of both arms;

[0009] The cloud health data management platform performs in-depth analysis on the human vital characteristics health data based on cloud server big data mining and machine learning technology to obtain analysis results;

[0010] The cloud health data management platform generates a health advice plan based on the analysis results.

[0011] Furthermore, the system also includes information collection equipment for collecting ankle-brachial index, pulse wave velocity, electrocardiogram, heart rate, blood sugar, blood lipids, blood oxygen, uric acid, genetic information, and living habit information.

[0012] Furthermore, the cloud health data management platform is integrated with a trained cardiovascular and cerebrovascular risk assessment big data model for performing in-depth analysis on the cloud health data to obtain analysis results.

[0013] Furthermore, the cloud health data management platform is also used for:

[0014] Extracting keywords from the health advice plan;

[0015] Based on the extracted keywords, an identification code is generated; wherein the identification code includes a plurality of characters;

[0016] Adding the human vital characteristics health data and health advice plan to a data table to obtain a health data table, and identifying the health data table based on the identification code;

[0017] The identified health data table is encrypted using the identification code as an encryption password and sent to a user terminal.

[0018] Furthermore, the cloud health data management platform is also used for:

[0019] Obtain the user's user information; the user information includes the user's name, contact information, and identity information;

[0020] Desensitizing and rearranging the characters in the user information, and splicing them in sequence to obtain a spliced ​​sequence;

[0021] Acquire the quantity of the human body vital characteristic health data, and based on the quantity, divide the spliced ​​sequence into a corresponding number of subsequences;

[0022] For each of the human life characteristic health data, a subsequence is selected from the spliced ​​sequence in sequence, and a mapping relationship is established;

[0023] Splicing the human life characteristic health data and subsequences with mapping relationships to obtain spliced ​​health data;

[0024] The individual spliced ​​health data are saved in a database.

[0025] Furthermore, the characters in the user information are desensitized and rearranged, and spliced ​​in sequence to obtain a spliced ​​sequence, including:

[0026] Concatenate each of the user information in a preset order to obtain concatenated user information;

[0027] Generate a data table with multiple rows and columns, and add the characters in the concatenated user information to the data table one by one in order from left to right in each row to obtain a character data table;

[0028] The characters in each column of the character data table are combined in order from top to bottom to obtain the combined characters of each column; the combined characters of each column are spliced ​​in sequence to obtain the spliced ​​sequence.

[0029] Furthermore, the step of storing each spliced ​​health data in a database includes:

[0030] Creating a data packet sequence; wherein the data packet sequence includes a plurality of data packets arranged in sequence;

[0031] Adding each spliced ​​health data to the data packet sequence to obtain a health data packet sequence; wherein only one spliced ​​health data is added to one data packet, and the front and back order of each spliced ​​health data remains unchanged;

[0032] Obtaining the empty data packets in the healthy data packet sequence and the sequence numbers of each empty data packet in the healthy data packet sequence, and adding forged data to each empty data packet; encoding the sequence numbers of each empty data packet in the healthy data packet sequence in sequence and then combining them in sequence to obtain a sequence number encoding combination;

[0033] The sequence number coding combination is added to the last data packet of the healthy data packet sequence, and the healthy data packet sequence after the sequence number coding combination is added is saved.

[0034] The present invention also provides a cardiovascular and cerebrovascular cloud health data management method, which is applied to any of the above-mentioned cardiovascular and cerebrovascular cloud health data management systems, wherein the system includes a cloud health data management platform and a brain-heart pre-adaptation training device;

[0035] The method comprises:

[0036] The brain-heart pre-adaptation training device automatically collects the user's human vital characteristics health data and sends it to the cloud health data management platform for storage; wherein the human vital characteristics health data at least includes the IAD value of the blood pressure of both arms;

[0037] The cloud health data management platform performs in-depth analysis on the human vital characteristics health data based on cloud server big data mining and machine learning technology to obtain analysis results;

[0038] The cloud health data management platform generates a health advice plan based on the analysis results.

[0039] The present invention also provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the above methods when executing the computer program.

[0040] The present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above-mentioned methods are implemented.

[0041] The cardiovascular cloud health data management method and system provided by the present invention include: a cloud health data management platform and a brain-heart pre-adaptation training instrument; the brain-heart pre-adaptation training instrument automatically collects the user's human vital characteristics health data and sends it to the cloud health data management platform for storage; wherein the human vital characteristics health data at least includes the IAD value of the blood pressure of both arms; the cloud health data management platform performs in-depth analysis on the human vital characteristics health data based on cloud server big data mining and machine learning technology to obtain analysis results; the cloud health data management platform generates a health advice plan based on the analysis results. In the present invention, the user's human vital characteristics health data is automatically collected by the brain-heart pre-adaptation training instrument, which facilitates the accurate collection of health data; at the same time, the cloud health data management platform performs in-depth analysis on the human vital characteristics health data based on cloud server big data mining and machine learning technology, and then provides an accurate health advice plan, overcoming the current defect of lack of effective in-depth analysis of the collected health data. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a principle block diagram of a cardiovascular and cerebrovascular cloud health data management system in one embodiment of the present invention;

[0043] Figure 2 This is a schematic diagram of the steps of a method for managing cardiovascular and cerebrovascular cloud health data in one embodiment of the present invention;

[0044] Figure 3 It is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention.

[0045] The implementation, functional features and advantages of the present invention will be further described in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0047] Reference Figure 1 , in one embodiment of the present invention, a cardiovascular and cerebrovascular cloud health data management system is provided, including a cloud health data management platform and a brain-heart pre-adaptation training device;

[0048] The brain-heart pre-adaptation training device automatically collects the user's human vital characteristics health data and sends it to the cloud health data management platform for storage; wherein the human vital characteristics health data at least includes the IAD value of the blood pressure of both arms;

[0049] The cloud health data management platform performs in-depth analysis on the human vital characteristics health data based on cloud server big data mining and machine learning technology to obtain analysis results;

[0050] The cloud health data management platform generates a health advice plan based on the analysis results.

[0051] In this embodiment, an innovative cardiovascular and cerebrovascular cloud health data management system is proposed. Among them, the brain-heart health pre-adaptation training device, as the front-end data acquisition device of the system, bears a vital responsibility. It can automatically and continuously collect the user's human vital characteristics health data. This automated process greatly reduces the user's operating burden. The user does not need to perform complex settings or frequent manual operations, just use the trainer normally to achieve accurate data collection.

[0052] Among the many collected data, the IAD value of blood pressure in both arms is particularly critical. The IAD value of blood pressure in both arms (i.e. the difference in blood pressure between both arms) is an important indicator for assessing cardiovascular and cerebrovascular health. Under normal circumstances, a large difference in blood pressure between both arms may indicate an increased risk of cardiovascular diseases such as atherosclerosis and subclavian artery stenosis. Through the precise collection of this indicator, the system can obtain more subtle and comprehensive cardiovascular and cerebrovascular health information, laying a solid foundation for subsequent accurate analysis and evaluation.

[0053] The rich data collected will be quickly and securely sent to the cloud health data management platform for centralized storage. This platform relies on the powerful computing and storage capabilities of cloud servers to handle massive amounts of data.

[0054] The big data mining technology used by the platform mines massive amounts of data. It can dig out deep connections and potential patterns between data. For example, by analyzing the historical data of a large number of users, it is found that there is a certain correlation between certain combinations of life characteristics and the occurrence of specific cardiovascular and cerebrovascular diseases.

[0055] Machine learning technology can analyze and predict newly collected data more accurately by continuously learning new data and optimizing its own algorithms. For example, by learning a large amount of known health conditions and corresponding vital signs data, the machine learning model can accurately determine the trend and potential risks of the user's cardiovascular health status based on the newly input user data.

[0056] Based on the in-depth analysis results generated by big data mining and machine learning technology, the cloud health data management platform can tailor personalized health advice plans for each user. Assuming that the analysis results show that a user's blood pressure is showing a slow upward trend, combined with factors such as his age and lifestyle, the health advice plan may include the following details:

[0057] Diet: It is recommended to reduce the intake of sodium salt, increase the intake of potassium-rich foods such as bananas, spinach, etc., control the intake of fat and sugar, and increase the intake of whole grains, vegetables and fruits rich in dietary fiber.

[0058] Exercise: Develop a moderate-intensity aerobic exercise plan of at least 150 minutes per week, such as brisk walking, swimming, etc. It is also recommended to intersperse some strength training, such as weightlifting, push-ups, etc., to enhance cardiovascular function and muscle strength.

[0059] In terms of living habits: remind users to maintain a regular schedule, ensure adequate sleep every day, and reduce staying up late; avoid excessive mental stress, and learn to relax through meditation, deep breathing, etc.

[0060] Monitoring: It is recommended that users measure their blood pressure at a fixed time every day and record it so as to detect changes in blood pressure in a timely manner.

[0061] For example, if the analysis results reveal an abnormal fluctuation in a certain blood indicator of the user, the health advice plan will immediately recommend that the user go to a medical institution for further detailed examinations, such as a full set of blood lipid tests, cardiac ultrasound, etc., to identify potential health problems and take appropriate treatment measures in a timely manner.

[0062] In summary, the above technical solutions have built a complete, rigorous and efficient closed-loop system from data collection, efficient transmission and secure storage, to deep intelligent analysis, and then to the generation of personalized health advice, providing a new, accurate and convenient solution for the prevention, monitoring and management of cardiovascular and cerebrovascular diseases. The cloud health data management platform conducts in-depth analysis of the health data of human life characteristics based on cloud server big data mining and machine learning technology, and then provides accurate health advice solutions, overcoming the current lack of effective in-depth analysis of the collected health data.

[0063] In one embodiment, the system further comprises an information collection device for collecting ankle-brachial index, pulse wave velocity, electrocardiogram, heart rate, blood sugar, blood lipids, blood oxygen, uric acid, genetic information, and living habit information.

[0064] In this embodiment, the ankle-brachial index is an important indicator for evaluating the degree of lower extremity arterial stenosis and is of great significance for predicting peripheral vascular disease.

[0065] Pulse wave velocity can reflect the elasticity and hardness of arteries and is a key parameter for early detection of arteriosclerosis.

[0066] The electrocardiogram can intuitively reflect the electrical activity of the heart and help detect heart diseases such as arrhythmia and myocardial ischemia.

[0067] Changes in heart rate can indicate the body's stress state and cardiovascular function.

[0068] Blood sugar, blood lipids, blood oxygen, uric acid and other indicators reflect the body's metabolic state and health status from different aspects. Abnormal blood sugar may indicate the risk of diabetes, abnormal blood lipids are closely related to atherosclerosis, blood oxygen levels reflect the functions of the respiratory and circulatory systems, and high uric acid may cause diseases such as gout.

[0069] Genetic information is crucial for assessing an individual's innate risk of cardiovascular and cerebrovascular diseases. Certain specific genetic mutations or gene combinations may increase susceptibility to cardiovascular and cerebrovascular diseases.

[0070] Information on lifestyle habits, such as dietary preferences, exercise frequency, sleep quality, smoking and drinking habits, etc., is also indispensable for the comprehensive assessment of cardiovascular and cerebrovascular health. Bad lifestyle habits are often an important cause of cardiovascular and cerebrovascular diseases.

[0071] The above rich and diverse data are accurately collected by information collection equipment and transmitted to the cloud health data management platform. With the help of powerful cloud servers, the platform uses big data mining and machine learning technology to deeply integrate and analyze these multi-dimensional data. For example, by comprehensively analyzing genetic information, lifestyle information and various physiological indicators, it can more accurately predict the risk of an individual suffering from cardiovascular and cerebrovascular diseases in the future.

[0072] In one embodiment, the cloud health data management platform is integrated with a trained cardiovascular and cerebrovascular risk assessment big data model for performing in-depth analysis on the cloud health data to obtain analysis results.

[0073] In this embodiment, the cloud health data management platform serves as the core hub and integrates the trained cardiovascular risk assessment big data model, which is the key to the in-depth analysis and accurate assessment of the entire system. The above-mentioned cardiovascular risk assessment big data model is a deep learning model trained with a large amount of data. The training data comes from a wide range of sources, covering information on people of different age groups, genders, regions, living habits, and various cardiovascular and cerebrovascular health conditions. Through the study and analysis of these massive data, the model is able to grasp the various characteristics, patterns, and laws of cardiovascular and cerebrovascular health data.

[0074] In one embodiment, the cloud health data management platform is also used for:

[0075] Extracting keywords from the health advice plan;

[0076] Based on the extracted keywords, an identification code is generated; wherein the identification code includes a plurality of characters;

[0077] Adding the human vital characteristics health data and health advice plan to a data table to obtain a health data table, and identifying the health data table based on the identification code;

[0078] The identified health data table is encrypted using the identification code as an encryption password and sent to a user terminal.

[0079] In this embodiment, the cloud health data management platform is not only responsible for generating health advice plans, but is also used for a series of subsequent important data processing and security protection tasks to avoid leakage of user privacy in health data.

[0080] First, the platform will extract keywords from the generated health advice plan. This process is to extract the most critical words that best represent its core content from a rich text. For example, for a health advice plan about "controlling diet and increasing exercise to lower blood pressure", the keywords that may be extracted are "blood pressure control", "diet adjustment", "increased exercise", etc.

[0081] Based on the extracted keywords, the platform generates an identification code. This identification code consists of multiple characters, is unique and has specific encoding rules. It is like an exclusive identity, giving each health data form a unique "label".

[0082] Next, the platform adds the collected health data of human vital signs and the generated health advice plan to the data table to form a complete health data table. Then, the data table is marked with the identification code generated previously so that it can be clearly identified and distinguished.

[0083] In order to ensure the security and privacy of user data, the platform will use the identification code as the encryption password to encrypt the identified health data table. This encryption method increases the confidentiality of the data, making it difficult to interpret the content during data transmission and storage, even if it is obtained by unauthorized visitors.

[0084] Finally, the encrypted health data form will be sent to the user terminal. After receiving the data, the user can only obtain clear and readable health data and recommended solutions through corresponding decryption methods, usually based on the identification code.

[0085] For example, a user's health data sheet includes the blood pressure IAD values ​​of both arms, blood sugar levels, blood lipid indicators, and the health advice plan of "moderate exercise and low-salt diet help improve cardiovascular function". The platform extracts keywords such as "cardiovascular improvement", "moderate exercise", and "low-salt diet" and generates an identification code "CVIMLSD". The health data sheet is identified and encrypted with this identification code and sent to the user. The user receives the encrypted data on his terminal and enters the identification code "CVIMLSD" for decryption, thereby obtaining detailed and accurate health information and targeted suggestions.

[0086] Through the above technical solution, we can not only achieve effective organization and identification of user health data, but also fully guarantee the security and privacy of data during transmission and storage, providing users with reliable, convenient and secure health management services.

[0087] In one embodiment, the cloud health data management platform is also used for:

[0088] Obtain the user's user information; the user information includes the user's name, contact information, and identity information;

[0089] Desensitizing and rearranging the characters in the user information, and splicing them in sequence to obtain a spliced ​​sequence;

[0090] Acquire the quantity of the human body vital characteristic health data, and based on the quantity, divide the spliced ​​sequence into a corresponding number of subsequences;

[0091] For each of the human life characteristic health data, a subsequence is selected from the spliced ​​sequence in sequence, and a mapping relationship is established;

[0092] Splicing the human life characteristic health data and subsequences with mapping relationships to obtain spliced ​​health data;

[0093] The individual spliced ​​health data are saved in a database.

[0094] In this embodiment, first, the platform obtains the user information of the user, and the user information includes key personal identifiers such as the user's name, contact information, and identity information.

[0095] In order to protect the privacy of users, the platform desensitizes and rearranges the characters in the user information. This means that the original order of the characters is disrupted, making it extremely difficult to directly identify the user's real information from these characters. After rearrangement, these characters are spliced ​​in order to form a spliced ​​sequence.

[0096] Next, the platform obtains the number of human vital signs and health data, and based on this number, divides the spliced ​​sequence into subsequences of the corresponding number.

[0097] Then, for each human vital characteristic health data, the platform selects a subsequence from the spliced ​​sequence according to a preset order and establishes a mapping relationship between the two. Through this mapping relationship, the associated human vital characteristic health data and the corresponding subsequence are spliced ​​to obtain spliced ​​health data.

[0098] Finally, each spliced ​​health data is saved in the database.

[0099] For example, suppose the sequence obtained after desensitization, rearrangement and splicing of the user information is "ABCDEFGH I JKLMNOPQRST", and there are 3 items of human vital characteristics and health data. Then this spliced ​​sequence can be divided into 3 subsequences, such as "ABCDE", "FGH IJ", and "KLMNOPQRST".

[0100] For the first item of human vital characteristics health data, select "ABCDE" to establish a mapping relationship and splice it; for the second item of human vital characteristics health data, select "FGH IJ"; for the third item of human vital characteristics health data, select "KLMNOPQRST".

[0101] The above data processing method can effectively associate and store user information with health data while ensuring the security and privacy of the data, so that data can be accurately called and analyzed when needed, while at the same time avoiding direct exposure of users' personal sensitive information to the greatest extent, providing strong protection for users' data security.

[0102] In one embodiment, desensitizing and rearranging the characters in the user information and sequentially splicing them to obtain a spliced ​​sequence includes:

[0103] Concatenate each of the user information in a preset order to obtain concatenated user information;

[0104] Generate a data table with multiple rows and columns, and add the characters in the concatenated user information to the data table one by one in order from left to right in each row to obtain a character data table;

[0105] The characters in each column of the character data table are combined in order from top to bottom to obtain the combined characters of each column; the combined characters of each column are spliced ​​in sequence to obtain the spliced ​​sequence.

[0106] In this embodiment, first, each user information is concatenated in a preset order, such as user name, contact information, identity information, etc., to form a continuous concatenated user information. This preset order can be determined according to the importance of the information or a specific rule.

[0107] Next, a data table with multiple rows and columns is generated. Then, the characters in the concatenated user information are added to the data table one by one in order from left to right in each row, thereby obtaining a character data table.

[0108] For example, if the concatenated user information is "ABCDEFGH I JKLMNOPQRSTUVWXYZ", assuming that a data table with 5 rows and 5 columns is generated, then the first row is filled with "A", "B", "C", "D", "E" from left to right, and the second row is filled with "F", "G", "H", "I", "J", and so on.

[0109] After completing the filling of characters in the data table, combine the characters in each column of the character data table in order from top to bottom. For example, the combined characters in the first column are "AFKPU" and the second column is "BGQLV".

[0110] Finally, the combined characters of each column are concatenated in order to obtain the final concatenated sequence. Such a sequence is difficult to directly associate with the original user information, which effectively protects the security of the data.

[0111] The above processing method greatly increases the randomness and complexity of characters through multiple conversions and recombination, so that the original user information is effectively desensitized and rearranged, thereby protecting user privacy while providing a unique encoding method for subsequent data processing and storage.

[0112] In one embodiment, the step of storing each spliced ​​health data in a database includes:

[0113] Creating a data packet sequence; wherein the data packet sequence includes a plurality of data packets arranged in sequence;

[0114] Adding each spliced ​​health data to the data packet sequence to obtain a health data packet sequence; wherein only one spliced ​​health data is added to one data packet, and the front and back order of each spliced ​​health data remains unchanged;

[0115] Obtaining the empty data packets in the healthy data packet sequence and the sequence numbers of each empty data packet in the healthy data packet sequence, and adding forged data to each empty data packet; encoding the sequence numbers of each empty data packet in the healthy data packet sequence in sequence and then combining them in sequence to obtain a sequence number encoding combination;

[0116] The sequence number coding combination is added to the last data packet of the healthy data packet sequence, and the healthy data packet sequence after the sequence number coding combination is added is saved.

[0117] In this embodiment, first, a data packet sequence is created. This data packet sequence is an ordered container queue, which contains multiple data packets arranged in sequence, and is prepared for subsequent storage and splicing of health data.

[0118] Then, each spliced ​​health data is added to the data packet sequence in sequence to form a healthy data packet sequence. It should be noted that only one spliced ​​health data is added to each data packet, and the front and back order of these spliced ​​health data remains unchanged to ensure that the original order and logical relationship of the data are not disrupted. It is understandable that the data packets with spliced ​​health data added may not be continuous, that is, they may be separated by one or more empty data packets.

[0119] Next, the system detects empty packets in the healthy packet sequence, i.e., those packets without actual spliced ​​healthy data, and obtains their sequence numbers in the healthy packet sequence. In order to improve data security and confuse potential attackers, forged data is added to these empty packets.

[0120] Afterwards, the sequence numbers of each empty data packet in the healthy data packet sequence are encoded in sequence, and then combined in sequence to obtain a sequence number encoding combination. This encoding combination can be regarded as an encrypted identifier of the position of the empty data packet.

[0121] Finally, this sequence number and coding combination is added to the last data packet of the health data packet sequence. In this way, when saving the entire health data packet sequence with the sequence number and coding combination added, not only can the accurate storage of the spliced ​​health data be ensured, but also the complexity and security of the data are increased by processing the empty data packets and adding the coding combination.

[0122] For example, suppose there is a sequence of 10 data packets, of which the 3rd, 6th, and 8th packets are empty. After adding fake data to these empty packets, the system encodes 3, 6, and 8, assuming that the encoding is "0C" "0F" "0H", and then combines them into "0C0F0H", and adds it to the 10th packet for storage.

[0123] Through the above method, even if someone illegally obtains the data packet sequence, it is difficult to directly distinguish which are the real and valid spliced ​​health data, which are the added forged data, and the location of the empty data packets, thereby effectively protecting the patient's health data security.

[0124] In one embodiment, the sequence numbers of the empty data packets in the healthy data packet sequence are encoded in sequence, including:

[0125] Obtain a preset coding table; the coding table includes a number column and a coded character column, and the number column and the coded character column are mapped one by one;

[0126] Get the total number of empty packets;

[0127] Adding a target number before each character in the coded character column to obtain an updated coding table; wherein the target number is the total number of the empty data packets;

[0128] The sequence numbers of the empty data packets in the healthy data packet sequence are encoded in sequence based on the updated coding table.

[0129] In this embodiment, first, a preset coding table is obtained. This coding table contains a number column and a coded character column, and there is a one-to-one mapping relationship between the two. Each number in the number column has its unique corresponding coded character in the coded character column.

[0130] Then, the total number of empty packets is determined, which will play an important role in the subsequent encoding process.

[0131] Next, in order to enhance the uniqueness, complexity and security of the encoding, a target number is added before each character in the encoding character column. The target number here is the total number of empty data packets previously determined, thereby obtaining an updated encoding table.

[0132] Finally, based on this updated coding table, the serial numbers of each empty data packet in the healthy data packet sequence are encoded in sequence. It can be understood that the characters after encoding include the target number, and this target number can not only confuse the data, but also serve as the audit standard for the above coding table changes, which is convenient for data tracking and verification, and also convenient for subsequent data reverse deduction.

[0133] For example, suppose that in the preset coding table, number 1 corresponds to the coding character "A", number 2 corresponds to the coding character "B", and the total number of empty data packets is 5. Then in the updated coding table, the coding character corresponding to number 1 becomes "5A", and the coding character corresponding to number 2 becomes "5B".

[0134] If the empty data packets in the healthy data packet sequence are numbered 2, 4, and 7, respectively, then based on the updated coding table, the coding results are "5B", "5D", and "5G", respectively.

[0135] Through the above-mentioned unique encoding method, the encoding of the empty data packet sequence number is made more unique and difficult to crack, which increases the confidentiality and security of the data and further protects the integrity and privacy of the data in the healthy data packet sequence.

[0136] Reference Figure 2 In another embodiment of the present invention, a cardiovascular cloud health data management method is provided, which is applied to any of the above-mentioned cardiovascular cloud health data management systems, wherein the system includes a cloud health data management platform and a brain-heart preconditioning training device;

[0137] The method comprises:

[0138] The brain-heart pre-adaptation training device automatically collects the user's human vital characteristics health data and sends it to the cloud health data management platform for storage; wherein the human vital characteristics health data at least includes the IAD value of the blood pressure of both arms;

[0139] The cloud health data management platform performs in-depth analysis on the human vital characteristics health data based on cloud server big data mining and machine learning technology to obtain analysis results;

[0140] The cloud health data management platform generates a health advice plan based on the analysis results.

[0141] In this embodiment, for the specific implementation of each step in the above method embodiment, please refer to the above system embodiment, which will not be repeated here.

[0142] Reference Figure 3 In an embodiment of the present invention, a computer device is also provided. The computer device may be a server, and its internal structure may be as follows: Figure 3As shown. The computer device includes a processor, a memory, a display screen, an input device, a network interface and a database connected through a system bus. Among them, the processor designed by the computer is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the corresponding data in this embodiment. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the above method is implemented.

[0143] Those skilled in the art will understand that Figure 3 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied.

[0144] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the above method is implemented. It can be understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0145] In summary, the cardiovascular cloud health data management method and system provided in the embodiment of the present invention include: a cloud health data management platform and a brain-heart pre-adaptation training instrument; the brain-heart pre-adaptation training instrument automatically collects the user's human vital characteristics health data and sends it to the cloud health data management platform for storage; wherein the human vital characteristics health data at least includes the IAD value of the blood pressure of both arms; the cloud health data management platform performs in-depth analysis on the human vital characteristics health data based on cloud server big data mining and machine learning technology to obtain analysis results; the cloud health data management platform generates a health advice plan based on the analysis results. In the present invention, the user's human vital characteristics health data is automatically collected by the brain-heart pre-adaptation training instrument, which facilitates the accurate collection of health data; at the same time, the cloud health data management platform performs in-depth analysis on the human vital characteristics health data based on cloud server big data mining and machine learning technology, and then provides accurate health advice plans, overcoming the current defect of lack of effective in-depth analysis of the collected health data.

[0146] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media provided by the present invention and used in the embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double-speed data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synch link) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM.

[0147] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0148] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A cardiovascular and cerebrovascular cloud health data management system, characterized in that: Including cloud health data management platform and brain-heart pre-adaptation training device; The brain-heart pre-adaptation training device automatically collects the user's human vital characteristics health data and sends it to the cloud health data management platform for storage; wherein the human vital characteristics health data at least includes the IAD value of the blood pressure of both arms; The cloud health data management platform performs in-depth analysis on the human vital characteristics health data based on cloud server big data mining and machine learning technology to obtain analysis results; The cloud health data management platform generates a health advice plan based on the analysis results.

2. The cardiovascular and cerebrovascular cloud health data management system according to claim 1, characterized in that: The system also includes information collection equipment for collecting ankle-brachial index, pulse wave velocity, electrocardiogram, heart rate, blood sugar, blood lipids, blood oxygen, uric acid, genetic information, and living habit information.

3. The cardiovascular and cerebrovascular cloud health data management system according to claim 1, characterized in that: The cloud health data management platform is integrated with a trained cardiovascular and cerebrovascular risk assessment big data model for performing in-depth analysis on the cloud health data to obtain analysis results.

4. The cardiovascular and cerebrovascular cloud health data management system according to claim 1, characterized in that: The cloud health data management platform is also used for: Extracting keywords from the health advice plan; Based on the extracted keywords, an identification code is generated; wherein the identification code includes a plurality of characters; Adding the human vital characteristics health data and health advice plan to a data table to obtain a health data table, and identifying the health data table based on the identification code; The identified health data table is encrypted using the identification code as an encryption password and sent to a user terminal.

5. The cardiovascular and cerebrovascular cloud health data management system according to claim 1, characterized in that: The cloud health data management platform is also used for: Obtain the user's user information; the user information includes the user's name, contact information, and identity information; Desensitizing and rearranging the characters in the user information, and splicing them in sequence to obtain a spliced ​​sequence; Acquire the quantity of the human body vital characteristic health data, and based on the quantity, divide the spliced ​​sequence into a corresponding number of subsequences; For each of the human life characteristic health data, a subsequence is selected from the spliced ​​sequence in sequence, and a mapping relationship is established; Splicing the human life characteristic health data and subsequences with mapping relationships to obtain spliced ​​health data; The individual spliced ​​health data are saved in a database.

6. The cardiovascular and cerebrovascular cloud health data management system according to claim 5, characterized in that: The desensitizing and rearranging the characters in the user information and splicing them in sequence to obtain a spliced ​​sequence includes: Concatenate each of the user information in a preset order to obtain concatenated user information; Generate a data table with multiple rows and columns, and add the characters in the concatenated user information to the data table one by one in order from left to right in each row to obtain a character data table; The characters in each column of the character data table are combined in order from top to bottom to obtain the combined characters of each column; the combined characters of each column are spliced ​​in sequence to obtain the spliced ​​sequence.

7. The cardiovascular and cerebrovascular cloud health data management system according to claim 5, characterized in that: The step of storing each spliced ​​health data in a database includes: Creating a data packet sequence; wherein the data packet sequence includes a plurality of data packets arranged in sequence; Adding each spliced ​​health data to the data packet sequence to obtain a health data packet sequence; wherein only one spliced ​​health data is added to one data packet, and the front and back order of each spliced ​​health data remains unchanged; Obtaining the empty data packets in the healthy data packet sequence and the sequence numbers of each empty data packet in the healthy data packet sequence, and adding forged data to each empty data packet; encoding the sequence numbers of each empty data packet in the healthy data packet sequence in sequence and then combining them in sequence to obtain a sequence number encoding combination; The sequence number coding combination is added to the last data packet of the healthy data packet sequence, and the healthy data packet sequence after the sequence number coding combination is added is saved.

8. A method for managing cardiovascular and cerebrovascular cloud health data, characterized in that: A cardiovascular cloud health data management system applied to any one of claims 1 to 7, the system comprising a cloud health data management platform and a brain-heart pre-adaptation training device; The method comprises: The brain-heart pre-adaptation training device automatically collects the user's human vital characteristics health data and sends it to the cloud health data management platform for storage; wherein the human vital characteristics health data at least includes the IAD value of the blood pressure of both arms; The cloud health data management platform performs in-depth analysis on the human vital characteristics health data based on cloud server big data mining and machine learning technology to obtain analysis results; The cloud health data management platform generates a health advice plan based on the analysis results.

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