Cloud-based health data management method and system for cardiovascular and cerebrovascular diseases

Abstract

The present invention provides a cloud health data management method and system for cardiovascular and cerebrovascular diseases, including: a cloud health data management platform and a cerebrocardiac preconditioning training instrument; the cerebrocardiac preconditioning training instrument automatically collects the human vital sign health data of the user and sends it to the cloud health data management platform for storage; wherein, the human vital sign health data at least includes the IAD value of bilateral arm blood pressure; the cloud health data management platform performs in-depth analysis on the human vital sign health data based on big data mining and machine learning technologies of the cloud server to obtain an analysis result; the cloud health data management platform generates a health advice plan based on the analysis result. In the present invention, in-depth analysis is performed on the human vital sign health data based on big data mining and machine learning technologies of the cloud server, and then an accurate health advice plan is provided, overcoming the defect that there is a lack of effective in-depth analysis of the collected health data at present.

Description

Technical Field

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

[0002] With the changes in people's lifestyles, the increase in work pressure, and the acceleration of the aging process, the incidence of cardiovascular and cerebrovascular diseases has been rising 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 and it is difficult to capture the early changes and subtle fluctuations of the disease.

[0003] Previous health data collection devices have relatively single functions and cannot comprehensively and accurately obtain human vital sign health data. In particular, the collection and analysis of key indicators such as the IAD value of bilateral arm blood pressure are not accurate enough.

[0004] For the collected data, there is a lack of effective big data mining and in-depth analysis, and it is difficult to provide personalized and accurate health advice solutions. At the same time, since health data involves user privacy information, special attention needs to be paid to preventing privacy leakage 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; there are also some health management platforms that can store data, but their analysis capabilities are limited and they cannot provide targeted and forward-looking health advice for users. Summary of the Invention

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

[0007] To achieve the above object, the present invention provides a cloud health data management system for cardiovascular and cerebrovascular diseases, including a cloud health data management platform and a cerebrocardiovascular preconditioning training instrument;

[0008] The cerebrocardiovascular preconditioning training instrument automatically collects the human vital sign health data of the user and sends it to be stored in the cloud health data management platform; wherein, the human vital sign health data at least includes the IAD value of bilateral arm blood pressure;

[0009] The cloud health data management platform conducts in-depth analysis on the human vital sign health data based on big data mining and machine learning technologies of the cloud server to obtain an analysis result;

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

[0011] Furthermore, the system further includes information collection devices for collecting ankle-brachial index, pulse wave velocity, electrocardiogram, heart rate, blood glucose, blood lipid, blood oxygen, uric acid, genetic information, and lifestyle information.

[0012] Furthermore, a trained cardiovascular risk assessment big data model is integrated on the cloud health data management platform to deeply analyze the cloud health data and obtain analysis results.

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

[0014] Extract keywords from the health advice plan;

[0015] Based on the extracted keywords, generate an identification code; where the identification code includes multiple characters;

[0016] Add the human vital sign health data and the health advice plan to a data table to obtain a health data table, and identify the health data table based on the identification code;

[0017] Use the identification code as an encryption password to encrypt the identified health data table and send it to the user terminal.

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

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

[0020] Desensitize and rearrange the characters in the user information, and sequentially splice them to obtain a splicing sequence;

[0021] Obtain the quantity of the human vital sign health data, and based on the quantity, divide the splicing sequence into corresponding sub-sequences;

[0022] For each human vital sign health data, sequentially select a sub-sequence from the splicing sequence and establish a mapping relationship;

[0023] Splice the human vital sign health data and the sub-sequence with the mapping relationship to obtain spliced health data;

[0024] Save each spliced health data in the database.

[0025] Furthermore, the desensitizing and rearranging the characters in the user information and sequentially splicing them to obtain a splicing sequence includes:

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

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

[0028] Combine the characters in each column of the character data table in the order from top to bottom to obtain the combined characters of each column; splice the combined characters of each column in sequence to obtain the splicing sequence.

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

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

[0031] Add each of the 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-to-back sorting of each of the spliced health data remains unchanged;

[0032] Obtain the empty data packets in the health data packet sequence and the sequence numbers of each of the empty data packets in the health data packet sequence, and add forged data to each of the empty data packets; encode the sequence numbers of each of the empty data packets in the health data packet sequence in sequence and then combine them in sequence to obtain a sequence number encoding combination;

[0033] Add the sequence number encoding combination to the last data packet of the health data packet sequence, and save the health data packet sequence after adding the sequence number encoding combination.

[0034] The present invention also provides a cloud health data management method for cardiovascular and cerebrovascular diseases, which is applied to the cloud health data management system for cardiovascular and cerebrovascular diseases described in any one of the above, and the system includes a cloud health data management platform and a brain-heart preconditioning trainer;

[0035] The method includes:

[0036] The brain-heart preconditioning trainer automatically collects the human vital sign health data of the user and sends it to the cloud health data management platform for storage; wherein, the human vital sign health data at least includes the IAD value of bilateral arm blood pressure;

[0037] The cloud health data management platform performs in-depth analysis on the human vital sign health data based on big data mining and machine learning technologies of the cloud server to obtain an analysis result;

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

[0039] The present invention also provides a computer device, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps of the method described in any one of the above are implemented.

[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 the method described in any one of the above are implemented.

[0041] The cloud health data management method and system for cardiovascular and cerebrovascular diseases provided by the present invention include: a cloud health data management platform and a cerebrocardiovascular preconditioning trainer; the cerebrocardiovascular preconditioning trainer automatically collects the human vital sign health data of the user and sends it to the cloud health data management platform for storage; wherein, the human vital sign health data at least includes the IAD value of bilateral arm blood pressure; the cloud health data management platform deeply analyzes the human vital sign health data based on big data mining and machine learning technologies of the cloud server to obtain an analysis result; the cloud health data management platform generates a health advice plan based on the analysis result. In the present invention, the cerebrocardiovascular preconditioning trainer automatically collects the human vital sign health data of the user, which is convenient for accurately collecting health data; at the same time, the cloud health data management platform deeply analyzes the human vital sign health data based on big data mining and machine learning technologies of the cloud server, and then provides a precise health advice plan, overcoming the defect of the lack of effective in-depth analysis of the collected health data at present. Brief Description of the Drawings

[0042] Figure 1 is a schematic block diagram of the cloud health data management system for cardiovascular and cerebrovascular diseases in an embodiment of the present invention;

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

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

[0045] The implementation, functional features and advantages of the present invention will be further described with reference to the embodiments and the accompanying drawings. Detailed Description of the Embodiments

[0046] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to 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 used to limit the present invention.

[0047] Reference Figure 1 , in an embodiment of the present invention, a cloud health data management system for cardiovascular and cerebrovascular diseases is provided, including a cloud health data management platform and a cerebrocardial preconditioning trainer;

[0048] The cerebrocardial preconditioning trainer automatically collects the health data of the user's human vital signs and sends it to the cloud health data management platform for storage; wherein, the health data of the user's human vital signs at least includes the IAD value of bilateral arm blood pressure;

[0049] The cloud health data management platform deeply analyzes the health data of the user's human vital signs based on big data mining and machine learning technologies of the cloud server to obtain an analysis result;

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

[0051] In this embodiment, an innovative cloud health data management system for cardiovascular and cerebrovascular diseases is proposed. Among them, the cerebrocardial preconditioning trainer, as the front-end data acquisition device of the system, plays a crucial role. It can automatically and continuously collect the health data of the user's human vital signs, and this automated process greatly reduces the operation burden of the user. The user does not need to perform complex settings or frequent manual operations. Just by using the trainer normally, accurate data collection can be achieved.

[0052] Among the numerous collected data, the IAD value of bilateral arm blood pressure is particularly crucial. The IAD value of bilateral arm blood pressure (i.e., the difference in blood pressure between both arms) is an important indicator for evaluating the cardiovascular and cerebrovascular health status. Usually, too large a difference in blood pressure between both arms may imply an increased risk of cardiovascular diseases such as atherosclerosis and subclavian artery stenosis. By accurately collecting this indicator, the system can obtain more subtle and comprehensive cardiovascular and cerebrovascular health information, laying a solid foundation for subsequent precise analysis and evaluation.

[0053] The rich collected data 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 the cloud server and can process a large amount of data.

[0054] The big data mining technology used by the platform mines in the large amount of data. It can discover the deep-seated associations and potential patterns between the data. For example, by analyzing the historical data of a large number of users, it is found that there is a certain correlation law between certain specific combinations of vital signs and the occurrence of specific cardiovascular and cerebrovascular diseases.

[0055] Machine learning technology, by continuously learning new data and optimizing its own algorithms, can analyze and predict newly collected data more accurately. For example, by learning a large amount of data on known health conditions and corresponding vital signs, a machine learning model can accurately judge the trend and potential risks of a user's cardiovascular health 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 customize personalized health advice plans for each user. Suppose the analysis results show that a certain user's blood pressure shows a slow upward trend, and considering factors such as their age and lifestyle, the health advice plan may include the following details:

[0057] Diet: It is recommended to reduce sodium intake, increase the intake of potassium-rich foods such as bananas and spinach, control the intake of oils and sugars, and appropriately 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 for them, such as brisk walking and swimming. At the same time, it is recommended to intersperse some strength training, such as weightlifting and push-ups, to enhance cardiovascular function and muscle strength.

[0059] Lifestyle: Remind the user to maintain a regular schedule, ensure sufficient 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 the user measure their blood pressure at a fixed time every day and record it to promptly detect changes in blood pressure.

[0061] Another example, if the analysis results find abnormal fluctuations in a certain blood index of the user, the health advice plan will immediately recommend that they go to a medical institution for further detailed examinations, such as a complete lipid profile examination and a cardiac ultrasound, to clarify potential health problems and promptly take corresponding treatment measures.

[0062] In summary, the above technical solution constructs a complete, rigorous, and efficient closed-loop system from data collection, high-efficiency transmission and secure storage, to in-depth intelligent analysis, and then to the generation of personalized health advice, providing a new, precise, and convenient solution for the prevention, monitoring, and management of cardiovascular diseases. The cloud health data management platform conducts in-depth analysis on the human vital sign health data based on big data mining and machine learning technology on the cloud server, and then provides accurate health advice plans, overcoming the defect of 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 big data model for cardiovascular and cerebrovascular risk assessment, which is the key for the entire system to achieve in-depth analysis and accurate assessment. The above-mentioned big data model for cardiovascular and cerebrovascular risk assessment is trained from a large amount of data by a deep learning model. The training data sources are extensive and rich, covering population information of different ages, genders, regions, living habits, and various cardiovascular and cerebrovascular health conditions. Through learning and analyzing these massive data, the model has mastered various characteristics, patterns, and rules of cardiovascular and cerebrovascular health data.

[0074] In one embodiment, the cloud health data management platform is further configured to:

[0075] Extract keywords from the health advice plan;

[0076] Generate an identification code based on the extracted keywords; wherein, the identification code includes multiple characters;

[0077] Add the human vital sign health data and the health advice plan to a data table to obtain a health data table, and identify the health data table based on the identification code;

[0078] Use the identification code as an encryption password to encrypt the identified health data table and send it to the user terminal.

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

[0080] First, the platform extracts keywords from the generated health advice plan. This process extracts the most critical and representative vocabulary from a rich text. For example, for a health advice plan on "controlling diet and increasing exercise to lower blood pressure", the possible extracted keywords may be "blood pressure control", "diet adjustment", "exercise increase", etc.

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

[0082] Then, the platform adds the collected human vital sign health data and the generated health advice plan to the data table to form a complete health data table. Then, use the previously generated identification code to identify this data table so that it can be clearly recognized and distinguished.

[0083] To ensure the security and privacy of user data, the platform encrypts the identified health data table using the identification code as an encryption password. This encryption method enhances data confidentiality, making it difficult to interpret the content even if it is obtained by unauthorized accessors during data transmission and storage.

[0084] Finally, the encrypted health data table is sent to the user terminal. After receiving the data, the user can only obtain the legible health data and recommended solutions through corresponding decryption means, usually based on the identification code.

[0085] For example, a certain user's health data table contains the IAD values of their blood pressure in both arms, blood sugar levels, blood lipid indicators, and a health recommendation solution of "moderate exercise and a low-salt diet help improve cardiovascular function". The platform extracts keywords such as "cardiovascular improvement", "moderate exercise", and "low-salt diet" to generate the identification code "CVIMLSD". The health data table is identified and encrypted with this identification code and sent to the user. The user receives the encrypted data on their terminal and enters the identification code "CVIMLSD" for decryption to obtain detailed and accurate health information and targeted recommendations.

[0086] Through the above technical solution, it not only realizes the effective organization and identification of user health data but also fully guarantees the security and privacy of the data during transmission and storage, providing users with a reliable, convenient, and secure health management service.

[0087] In one embodiment, the cloud health data management platform is further configured to:

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

[0089] Desensitize and rearrange the characters in the user information, and sequentially splice them to obtain a splicing sequence;

[0090] Obtain the quantity of the human vital characteristic health data, and based on the quantity, divide the splicing sequence into corresponding sub-sequences;

[0091] For each of the human vital characteristic health data, sequentially select a sub-sequence from the splicing sequence and establish a mapping relationship;

[0092] Splice the human vital characteristic health data with the mapping relationship and the sub-sequence to obtain spliced health data;

[0093] Save each spliced health data in the database.

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

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

[0096] Next, the platform obtains the quantity of the human vital characteristic health data. Based on this quantity, the concatenated sequence is divided into the corresponding number of subsequences.

[0097] Then, for each human vital characteristic health data, the platform selects a subsequence from the concatenated sequence in 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 subsequences are concatenated to obtain the concatenated health data.

[0098] Finally, each piece of concatenated health data is stored in the database.

[0099] For example, assume that the sequence obtained after desensitizing, rearranging, and concatenating the user information of the user is "ABCDEFGH I JKLMNOPQRST", and there are 3 items of human vital characteristic health data. Then this concatenated sequence can be divided into 3 subsequences, such as "ABCDE", "FGH I J", and "KLMNOPQRST".

[0100] For the first item of human vital characteristic health data, select "ABCDE" to establish a mapping relationship with it and concatenate; for the second item of human vital characteristic health data, select "FGH I J"; for the third item of human vital characteristic health data, select "KLMNOPQRST".

[0101] The above data processing method can effectively associate and store the user information and health data while ensuring the security and privacy of the data, so that data can be accurately called and analyzed when needed, and at the same time, the direct exposure of the user's personal sensitive information is maximally avoided, providing a strong guarantee for the data security of the user.

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

[0103] Concatenate each piece of the user information in a preset order to obtain the 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 the order from left to right in each row to obtain a character data table;

[0105] Combine the characters in each column of the character data table in the order from top to bottom to obtain the combined characters of each column; concatenate the combined characters of each column in sequence to obtain the concatenated sequence.

[0106] In this embodiment, first, concatenate each user information in a preset order, such as concatenating user names, 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 specific rules.

[0107] Next, generate a data table with multiple rows and columns. Then, add the characters in the concatenated user information to this data table one by one in the order from left to right in each row to obtain a character data table.

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

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

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

[0111] The above processing method greatly increases the randomness and complexity of the characters through multiple conversions and recombinations, effectively desensitizes and rearranges the original user information, thus providing a unique coding method for subsequent data processing and storage while protecting user privacy.

[0112] In one embodiment, the storing each concatenated health data in a database includes:

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

[0114] Add 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 - back sorting of each spliced health data remains unchanged;

[0115] Obtain the empty data packets in the health data packet sequence and the sequence numbers of each empty data packet in the health data packet sequence, and add forged data to each empty data packet; encode the sequence numbers of each empty data packet in the health data packet sequence in sequence and then combine them in sequence to obtain a sequence number encoding combination;

[0116] Add the sequence number encoding combination to the last data packet of the health data packet sequence, and save the health data packet sequence after adding the sequence number encoding combination.

[0117] In this embodiment, first, create a data packet sequence. This data packet sequence is an ordered container queue, which contains a plurality of data packets arranged in sequence, preparing for storing spliced health data subsequently.

[0118] Then, add each spliced health data to the data packet sequence in sequence to form a health data packet sequence. It should be noted that only one spliced health data is added to each data packet, and the front - back sorting of these spliced health data remains unchanged to ensure that the original order and logical relationship of the data are not disrupted. It can be understood that the data packets with spliced health data added may not be consecutive, that is, one or more empty data packets can be interspersed.

[0119] Next, the system will detect the empty data packets in the health data packet sequence, that is, those data packets without actual spliced health data, and obtain their sequence numbers in the health data packet sequence. To improve the security of the data and confuse potential attackers, forged data is added to these empty data packets.

[0120] After that, encode the sequence numbers of each empty data packet in the health data packet sequence in sequence, and then combine them in sequence to obtain a sequence number encoding combination. This encoding combination can be regarded as an encrypted identifier regarding the positions of the empty data packets.

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

[0122] For example, assume there is a sequence of 10 data packets, where the 3rd, 6th, and 8th data packets are empty. After adding forged data to these empty data packets, the system encodes 3, 6, and 8. Assume the encoding is "0C", "0F", and "0H", and then combines them into "0C0F0H", and adds it to the 10th data 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 positions of the empty data packets, thus effectively protecting the security of the patient's health data.

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

[0125] Obtain a preset encoding table; the encoding table includes a digital column and an encoded character column, and the digital column and the encoded character column are in one-to-one mapping;

[0126] Obtain the total number of empty data packets;

[0127] Add a target number before each character in the encoded character column to obtain an updated encoding table; wherein, the target number is the total number of the empty data packets;

[0128] Based on the updated encoding table, sequentially encode the sequence numbers of the respective empty data packets in the health data packet sequence.

[0129] In this embodiment, first, obtain a preset encoding table. This encoding table contains a digital column and an encoded character column, and there is a one-to-one mapping relationship between them. Each number in the digital column has its uniquely corresponding encoded character in the encoded character column.

[0130] Then, determine the total number of empty data packets. This number will play an important role in the subsequent encoding process.

[0131] Next, in order to enhance the uniqueness, complexity, and security of the encoding, add a target number before each character in the encoded character column. Here, the target number is the total number of the empty data packets determined before, so as to obtain an updated encoding table.

[0132] Finally, based on this updated encoding table, sequentially encode the sequence numbers of the respective empty data packets in the health data packet sequence. It can be understood that the encoded characters include the target number, and this target number can not only confuse the data, but also serve as an inspection standard for the change of the above encoding table, facilitating data tracking and verification, and also facilitating subsequent data reverse inference.

[0133] For example, assume that in the preset coding table, the number 1 corresponds to the coding character "A", the 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 the number 1 becomes "5A", and the coding character corresponding to the number 2 becomes "5B".

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

[0135] Through the above unique coding method, the coding of the sequence numbers of the empty data packets becomes more unique and difficult to crack, increasing the confidentiality and security of the data, and further protecting the integrity and privacy of the data in the healthy data packet sequence.

[0136] Refer to Figure 2 , in another embodiment of the present invention, a cloud health data management method for cardiovascular and cerebrovascular diseases is further provided, which is applied to the cloud health data management system for cardiovascular and cerebrovascular diseases described in any one of the above. The system includes a cloud health data management platform and a brain-heart preconditioning training instrument;

[0137] The method includes:

[0138] The brain-heart preconditioning training instrument automatically collects the human vital sign health data of the user and sends it to the cloud health data management platform for storage; wherein, the human vital sign health data at least includes the IAD value of bilateral blood pressure.

[0139] The cloud health data management platform deeply analyzes the human vital sign health data based on big data mining and machine learning technologies of the cloud server to obtain an analysis result;

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

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

[0142] Refer to Figure 3 , in an embodiment of the present invention, a computer device is further provided. The computer device can be a server, and its internal structure can be as Figure 3As shown in the figure. 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 of the computer design 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. The computer program, when executed by the processor, implements the above method.

[0143] Those skilled in the art can understand that Figure 3 the structure shown in the figure is only a block diagram of some structures 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. 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 cloud health data management method and system for cardiovascular and cerebrovascular diseases provided in the embodiments of the present invention include: a cloud health data management platform and a brain-heart health preconditioning training instrument; the brain-heart health preconditioning training instrument automatically collects the human vital sign health data of the user and sends it to the cloud health data management platform for storage; among them, the human vital sign health data at least includes the IAD value of bilateral blood pressure; the cloud health data management platform performs in-depth analysis on the human vital sign health data based on big data mining and machine learning technologies of the cloud server to obtain an analysis result; the cloud health data management platform generates a health advice plan based on the analysis result. In the present invention, the human vital sign health data of the user is automatically collected by the brain-heart health preconditioning training instrument, which is convenient for accurately collecting health data; at the same time, the cloud health data management platform performs in-depth analysis on the human vital sign health data based on big data mining and machine learning technologies of the cloud server, and then provides a precise health advice plan, overcoming the defect of the lack of effective in-depth analysis of the collected health data at present.

[0146] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. 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 methods. Among them, any reference to a memory, storage, database, or other medium provided by the present invention and used in the embodiments can include non-volatile and / or volatile memories. 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. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM, etc.

[0147] It should be noted that in this document, the term "including", "comprising", or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that includes a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, apparatus, article, or method that includes the element.

[0148] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structural or equivalent process transformation made by using the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall equally be included in the patent protection scope of the present invention.

Claims

1. A cloud health data management system for cardiovascular and cerebrovascular diseases, characterized in that, It includes a cloud health data management platform and a cerebro-cardiovascular preconditioning trainer; The cerebro-cardiovascular preconditioning trainer automatically collects the human life characteristic health data of the user and sends it to be stored in the cloud health data management platform; wherein, the human life characteristic health data at least includes the IAD value of bilateral arm blood pressure; The cloud health data management platform deeply analyzes the human life characteristic health data based on big data mining and machine learning technologies of the cloud server to obtain an analysis result; The cloud health data management platform generates a health advice plan based on the analysis result; The cloud health data management platform is further used for: obtaining the user information of the user; wherein, the user information includes the user name, contact information, and identity information; desensitizing and rearranging the characters in the user information and sequentially splicing them to obtain a splicing sequence; obtaining the quantity of the human life characteristic health data, and based on the quantity, dividing the splicing sequence into corresponding quantities of subsequences; for each human life characteristic health data, sequentially selecting a subsequence from the splicing sequence and establishing a mapping relationship; splicing the human life characteristic health data and the subsequence with the mapping relationship to obtain spliced health data; storing each spliced health data in a database; Storing each spliced health data in the database includes: creating a data packet sequence; wherein, the data packet sequence includes a plurality of sequentially arranged data packets; 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 sorting of each spliced health data remains unchanged; obtaining the empty data packets in the health data packet sequence and the sequence numbers of each empty data packet in the health data packet sequence, and adding forged data to each empty data packet; sequentially encoding the sequence numbers of each empty data packet in the health data packet sequence and then sequentially combining them to obtain a sequence number encoding combination; adding the sequence number encoding combination to the last data packet of the health data packet sequence, and storing the health data packet sequence after adding the sequence number encoding combination; Sequentially encoding the sequence numbers of each empty data packet in the health data packet sequence includes: obtaining a preset encoding table; the encoding table includes a digital column and an encoding character column, and the digital column is in one-to-one mapping with the encoding character column; obtaining the total quantity of the empty data packets; adding a target number before each character in the encoding character column to obtain an updated encoding table; wherein, the target number is the total quantity of the empty data packets; encoding the sequence numbers of each empty data packet in the health data packet sequence based on the updated encoding table.

2. The cloud health data management system for cardiovascular and cerebrovascular diseases according to claim 1, characterized in that, The system further includes an information collection device for collecting ankle-brachial index, pulse wave velocity, electrocardiogram, heart rate, blood glucose, blood lipid, blood oxygen, uric acid, genetic information, and living habit information.

3. The cloud health data management system for cardiovascular and cerebrovascular diseases according to claim 1, wherein The cloud health data management platform is integrated with a big data model for risk assessment of completed cardiovascular and cerebrovascular diseases, which is used to deeply analyze the cloud health data to obtain an analysis result.

4. The cloud health data management system for cardiovascular and cerebrovascular diseases according to claim 1, characterized in that, The cloud health data management platform is further used for: extracting keywords from the health advice plan; Generate an identification code based on the extracted keywords; wherein, the identification code includes multiple characters; Add the human vital sign health data and the health advice plan to a data table to obtain a health data table, and identify the health data table based on the identification code; Use the identification code as an encryption password to encrypt the identified health data table and send it to the user terminal.

5. The cloud health data management system for cardiovascular and cerebrovascular diseases according to claim 1, characterized in that, Desensitize and rearrange the characters in the user information, and concatenate them in sequence to obtain a concatenated sequence, including: Concatenate each piece 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 the order from left to right in each row to obtain a character data table; Combine the characters in each column of the character data table in the order from top to bottom to obtain the combined characters of each column; concatenate the combined characters of each column in sequence to obtain the concatenated sequence.

6. A cloud health data management method for cardiovascular and cerebrovascular diseases, characterized in that, Applied to the cloud health data management system for cardiovascular and cerebrovascular diseases according to any one of claims 1-5, the system includes a cloud health data management platform and a cerebrocardial preconditioning trainer; The method includes: The cerebrocardial preconditioning trainer automatically collects the human vital sign health data of the user and sends it to be stored in the cloud health data management platform; wherein, the human vital sign health data at least includes the IAD value of bilateral arm blood pressure; The cloud health data management platform deeply analyzes the human vital sign health data based on big data mining and machine learning technologies of the cloud server to obtain an analysis result; The cloud health data management platform generates a health advice plan based on the analysis result.

Images