A secure sharing method of health and environmental data based on blockchain technology

Abstract

The present invention relates to the field of data sharing technology, and in particular to a method for securely sharing health and environmental data based on blockchain technology. The present invention analyzes the asthma attack risk of asthma patients by importing the location environment data and physical sign data of asthma patients into an asthma attack risk analysis model; imports the environmental data of nearby hospitals into a hospital environmental condition analysis model to analyze the environmental conditions of hospitals near asthma patients; and recommends the best hospital for asthma patients based on the environmental condition analysis results of hospitals near asthma patients. It can evaluate and predict the patient's attack risk in real time, help patients take measures to prevent attacks as early as possible, and thus reduce the health hazards caused by acute asthma attacks. At the same time, it recommends the hospital that best suits the patient's health needs, improves the efficiency of patient visits, and rationalizes the allocation of medical resources.

Description

Technical Field

[0001] The present invention relates to the technical field of data sharing, and in particular to a method for securely sharing health and environmental data based on blockchain technology. Background Art

[0002] With the development of information technology, the healthcare industry has ushered in an intelligent and digital transformation. In this context, health data and environmental data have become an important foundation for precision medicine, personalized treatment and disease prevention. Especially for environmentally sensitive diseases such as asthma, real-time monitoring and sharing of patients' health and environmental data is the key to improving patient health management and optimizing medical resource allocation.

[0003] In the management of chronic diseases such as asthma, the correlation between health data and environmental data is crucial for the prediction of patients' conditions, prevention of attacks, and adjustment of treatment plans. For example, increased concentrations of pollen and pollutants in the air may increase the risk of asthma attacks. Therefore, the secure sharing of health and environmental data through blockchain technology can improve the efficiency of data sharing while protecting the privacy and security of data, providing more efficient and accurate health management services for patients with environmentally sensitive diseases such as asthma. At the same time, patients' early understanding of these data can also help them better analyze their conditions.

[0004] However, the existing technology does not consider using the health and environmental data of asthma patients to help them predict the risk of asthma attacks, which not only reduces the timeliness of asthma management, but also makes it difficult for patients to obtain personalized and dynamic health management advice. In addition, the existing technology mainly relies on patients' active feedback or periodic hospital testing, and cannot achieve real-time monitoring and analysis of patients' physical conditions and external environment, and thus cannot help them find the best hospital to visit. For asthma patients, environmental factors such as air quality, humidity, temperature, and vital signs data have a direct impact on the condition, and these data are dynamically changing, especially in the patient's daily life, travel or treatment process, environmental conditions may change at any time. Traditional methods are unable to monitor, analyze and predict these multi-source data in real time, resulting in asthma patients being unable to choose the most appropriate medical resources in a timely manner.

[0005] To solve these problems, this application designs a secure sharing method for health and environmental data based on blockchain technology. Summary of the invention

[0006] In order to overcome the defects and deficiencies of the existing technology, the present invention provides a method for secure sharing of health and environmental data based on blockchain technology. By analyzing the risk of asthma attacks of asthma patients and the environmental conditions of hospitals near asthma patients, the optimal hospital for asthma patients can be recommended, which can help improve the hospitalization experience and treatment effect of asthma patients and reduce the risk of asthma attacks caused by environmental factors. And by using a prediction model, the risk of asthma attacks in asthma patients in the future is predicted based on time series data, which helps patients take preventive measures early and reduce the onset of asthma.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] In a first aspect, an embodiment of the present invention provides a method for securely sharing health and environmental data based on blockchain technology, comprising the following steps:

[0009] S1. Obtain location environment data and physical sign data of asthma patients;

[0010] S2, importing the location environment data and physical sign data of the asthma patient into the asthma attack risk analysis model to analyze the asthma attack risk of the asthma patient;

[0011] S3. Based on the asthma attack risk analysis results of asthma patients, obtain environmental data of hospitals near asthma patients;

[0012] S4, importing the environmental data of nearby hospitals into the hospital environmental condition analysis model to analyze the environmental conditions of hospitals near asthma patients;

[0013] S5. Based on the environmental condition analysis results of hospitals near asthma patients, recommend the best hospital for asthma patients to visit.

[0014] In an optional implementation, step S2 includes the following specific steps:

[0015] S21, extracting the location environment data and vital sign data of the asthma patient; the location environment data includes historical location environment data and real-time location environment data; the vital sign data includes historical vital sign data corresponding to the historical location environment data and real-time vital sign data corresponding to the real-time location environment data;

[0016] S22, substituting the location environment data and physical sign data of the asthma patient into the asthma attack risk coefficient calculation formula to calculate the asthma attack risk coefficient of the asthma patient at the real-time location; the asthma attack risk coefficient calculation formula is:

[0017] ;

[0018] Where Fz is the asthma attack risk coefficient of asthma patients at the real-time location, ev represents the environmental condition abnormality coefficient at the real-time location, tz represents the abnormal change coefficient of physical signs of asthma patients at the real-time location, a and b represent the abnormal proportion coefficient of environmental condition and the abnormal change coefficient of physical signs, respectively.

[0019] In an optional implementation manner, the specific calculation formula for the environmental condition abnormality coefficient of the real-time location is:

[0020] ;

[0021] Where A, T and H represent the real-time air quality index, real-time temperature and real-time humidity of the real-time location of the asthma patient in the real-time location environment data, respectively. represents the average air quality index when the asthma patient is located at the i-th historical location in the historical location environmental data, They respectively represent the average temperature and average humidity when the asthma patient is located at the i-th historical location in the historical location environmental data, n represents the number of historical locations in the historical location environmental data, and i is any item from 1 to n.

[0022] In an optional implementation, the specific calculation formula for the abnormal change coefficient of the vital signs of the asthma patient at the real-time location is:

[0023] ;

[0024] Where x, r and bs represent the current heart rate, current respiratory rate and current blood oxygen saturation of the asthma patient in the real-time vital sign data, respectively. Represents the average heart rate, average respiratory rate, and average blood oxygen saturation of the asthma patient at the i-th historical position in the historical vital signs data.

[0025] In an optional implementation, step S2 further includes the following specific steps:

[0026] S23, extracting the calculated asthma attack risk coefficients of the asthma patient at the real-time location at multiple equally spaced moments, constructing an asthma attack risk coefficient time series according to the time sequence of the asthma attack risk coefficients of all equally spaced moments, and training an asthma attack risk prediction model for predicting the asthma attack risk coefficients of the asthma patient at the real-time location at future equally spaced moments based on the asthma attack risk coefficient time series;

[0027] S24. Preset the sliding step size to be L and the sliding window length to be W; use the sliding window method to convert the asthma attack risk coefficients of asthma patients at real-time locations at all equally spaced moments in the asthma attack risk coefficient time series into multiple training samples, wherein each training sample is composed of an asthma attack risk coefficient sequence within the sliding window, use the training samples as the input of the asthma attack risk prediction model, use the asthma attack risk coefficients of asthma patients at real-time locations at future equally spaced moments with a preset sliding step size of L as the output, and train the asthma attack risk prediction model with prediction accuracy as the training target; generate an asthma attack risk prediction model that predicts the asthma attack risk coefficients of asthma patients at real-time locations at future equally spaced moments based on the asthma attack risk coefficients of asthma patients at real-time locations at all equally spaced moments; wherein the asthma attack risk prediction model is a recurrent neural network model.

[0028] In an optional implementation manner, step S2 further includes the following specific contents:

[0029] Obtain predicted asthma attack risk coefficients of asthma patients at real-time locations at multiple groups of equally spaced moments in the future, sort the asthma attack risk coefficients of asthma patients at real-time locations at multiple groups of equally spaced moments in the future in chronological order, preset an asthma attack risk threshold, extract the corresponding future equally spaced moments closest to the current moment from the sorted multiple groups of asthma attack risk coefficients of asthma patients at real-time locations at equally spaced moments in the future whose asthma attack risk coefficient is greater than the asthma attack risk threshold, and obtain the duration between the current moment and the corresponding future equally spaced moment as the asthma attack risk analysis result of the asthma patient.

[0030] In an optional implementation manner, step S3 includes the following specific contents:

[0031] Multiple hospitals corresponding to the shortest time required for an asthma patient to travel from the current real-time location to multiple hospitals are obtained as nearby hospitals, and the environmental data of the nearby hospitals are obtained.

[0032] In an optional implementation, step S4 includes the following specific steps:

[0033] S41. Obtain environmental data of nearby hospitals;

[0034] S42, substituting the environmental data of the nearby hospitals into the hospital environmental condition comparison coefficient calculation formula to calculate the hospital environmental condition comparison coefficient of the nearby hospitals; the hospital environmental condition comparison coefficient calculation formula is:

[0035] ;

[0036] Where hevj represents the hospital environment comparison coefficient of the jth nearby hospital, represents the shortest time required for an asthma patient to travel from the current real-time location to the jth nearby hospital in the environmental data of nearby hospitals, Bj represents the number of vacant wards in the jth nearby hospital in the environmental data of nearby hospitals, and Bmax represents the maximum number of vacant wards in all nearby hospitals in the environmental data of nearby hospitals. It represents the minimum value of the shortest time required for an asthma patient to travel from the current real-time location to all nearby hospitals in the environmental data of nearby hospitals. Aj, Tj, and Hj respectively represent the average air quality index, average temperature, and average humidity of all vacant wards in the jth nearby hospital in the environmental data of nearby hospitals. m represents the number of nearby hospitals, and j is any item from 1 to m.

[0037] In an optional implementation manner, step S5 includes the following specific contents:

[0038] S51, obtaining the hospital environment condition comparison coefficients of all nearby hospitals, and obtaining the asthma cure rates of all nearby hospitals;

[0039] S52. Substituting the hospital environment comparison coefficient and the asthma cure rate into the treatment priority coefficient calculation formula to calculate the treatment priority coefficient of the jth nearby hospital, the treatment priority coefficient calculation formula is:

[0040] ;

[0041] In the formula, Pj represents the priority coefficient of the jth nearby hospital, Qj represents the cure rate of asthma in the jth nearby hospital; They represent the coefficients of hospital environmental conditions and hospital cure rate respectively;

[0042] S53. Obtain the priority coefficients of all nearby hospitals, and extract the corresponding nearby hospital with the largest priority coefficient as the optimal hospital for asthma patients.

[0043] In a second aspect, an embodiment of the present invention provides an electronic device, comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes a method for secure sharing of health and environmental data based on blockchain technology by calling the computer program stored in the memory.

[0044] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0045] The present invention analyzes the asthma attack risk of asthma patients by importing the location environment data and physical sign data of asthma patients into the asthma attack risk analysis model; imports the environmental data of nearby hospitals into the hospital environmental condition analysis model to analyze the environmental conditions of hospitals near asthma patients; and recommends the best hospital for asthma patients based on the environmental condition analysis results of hospitals near asthma patients. It can evaluate and predict the patient's attack risk in real time, help patients take measures to prevent attacks as early as possible, and thus reduce the health hazards caused by acute asthma attacks. At the same time, it recommends the hospital that best suits the patient's health needs, improves the efficiency of patient treatment, and rationalizes the allocation of medical resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0047] Figure 1 This is a schematic diagram of the overall process of the method for securely sharing health and environmental data based on blockchain technology of the present invention;

[0048] Figure 2 The flowchart of the recurrent neural network model in the method for secure sharing of health and environmental data based on blockchain technology of the present invention;

[0049] Figure 3 This is a schematic diagram of the structure of the electronic device in the method for securely sharing health and environmental data based on blockchain technology of the present invention. DETAILED DESCRIPTION

[0050] The technical solution of the present invention is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations on the technical solution of the present invention. The embodiments of the present invention and the technical features in the embodiments may be combined with each other unless there is a conflict.

[0051] like Figure 1 As shown, this embodiment provides a method for securely sharing health and environmental data based on blockchain technology, which specifically includes the following steps:

[0052] S1. Obtain location environment data and physical sign data of asthma patients;

[0053] S2, importing the location environment data and physical sign data of the asthma patient into the asthma attack risk analysis model to analyze the asthma attack risk of the asthma patient;

[0054] S3. Based on the asthma attack risk analysis results of asthma patients, obtain environmental data of hospitals near asthma patients;

[0055] S4, importing the environmental data of nearby hospitals into the hospital environmental condition analysis model to analyze the environmental conditions of hospitals near asthma patients;

[0056] S5. Based on the environmental condition analysis results of hospitals near asthma patients, recommend the best hospital for asthma patients to visit.

[0057] In this embodiment, step S2 includes the following specific steps:

[0058] S21, extracting the location environment data and vital sign data of the asthma patient; the location environment data includes historical location environment data and real-time location environment data; the vital sign data includes historical vital sign data corresponding to the historical location environment data and real-time vital sign data corresponding to the real-time location environment data;

[0059] S22, substituting the location environment data and physical sign data of the asthma patient into the asthma attack risk coefficient calculation formula to calculate the asthma attack risk coefficient of the asthma patient at the real-time location; the asthma attack risk coefficient calculation formula is:

[0060] ;

[0061] Where Fz is the asthma attack risk coefficient of asthma patients at the real-time location, ev represents the environmental condition abnormality coefficient at the real-time location, tz represents the abnormal change coefficient of physical signs of asthma patients at the real-time location, a and b represent the abnormal proportion coefficient of environmental condition and the abnormal change coefficient of physical signs, respectively.

[0062] In this embodiment, the specific calculation formula of the environmental condition abnormality coefficient of the real-time location is:

[0063] ;

[0064] Where A, T and H represent the real-time air quality index, real-time temperature and real-time humidity of the real-time location of the asthma patient in the real-time location environment data, respectively. represents the average air quality index when the asthma patient is located at the i-th historical location in the historical location environmental data, They respectively represent the average temperature and average humidity when the asthma patient is located at the i-th historical location in the historical location environmental data, n represents the number of historical locations in the historical location environmental data, and i is any item from 1 to n.

[0065] In this embodiment, the specific calculation formula of the abnormal change coefficient of the physical signs of the asthma patient at the real-time position is:

[0066] ;

[0067] Where x, r and bs represent the current heart rate, current respiratory rate and current blood oxygen saturation of the asthma patient in the real-time vital sign data, respectively. Represents the average heart rate, average respiratory rate, and average blood oxygen saturation of the asthma patient at the i-th historical position in the historical vital signs data.

[0068] In this embodiment, step S2 also includes the following specific steps:

[0069] S23, extracting the calculated asthma attack risk coefficients of the asthma patient at the real-time location at multiple equally spaced moments, constructing an asthma attack risk coefficient time series according to the time sequence of the asthma attack risk coefficients of all equally spaced moments, and training an asthma attack risk prediction model for predicting the asthma attack risk coefficients of the asthma patient at the real-time location at future equally spaced moments based on the asthma attack risk coefficient time series;

[0070] S24. Preset the sliding step size to be L and the sliding window length to be W; use the sliding window method to convert the asthma attack risk coefficients of asthma patients at real-time locations at all equally spaced moments in the asthma attack risk coefficient time series into multiple training samples, wherein each training sample is composed of an asthma attack risk coefficient sequence within the sliding window, use the training samples as the input of the asthma attack risk prediction model, use the asthma attack risk coefficients of asthma patients at real-time locations at future equally spaced moments with a preset sliding step size of L as the output, and train the asthma attack risk prediction model with prediction accuracy as the training target; generate an asthma attack risk prediction model that predicts the asthma attack risk coefficients of asthma patients at real-time locations at future equally spaced moments based on the asthma attack risk coefficients of asthma patients at real-time locations at all equally spaced moments; wherein the asthma attack risk prediction model is a recurrent neural network model.

[0071] In this embodiment, if Figure 2As shown, the use of the sliding window method combined with the recurrent neural network model to predict the future asthma attack risk factor of asthma patients can effectively capture the temporal dependency and dynamic changes of the asthma attack risk factor. According to the asthma attack risk factor of the patient's real-time location, the asthma attack risk prediction model can continuously learn and adapt to environmental factors and health status changes that change over time, and provide predictions of future risk levels, thereby assisting doctors or patients in early intervention. At the same time, the asthma attack risk prediction model can generate a personalized prediction model based on the data of individual patients, adapt to the special circumstances of each patient, and improve the accuracy and effectiveness of the prediction. In this embodiment, the sliding window method improves the richness of the training data by dividing the time series data into multiple training samples, avoids overfitting, and improves the generalization ability of the model. The adjustable design of the sliding window length and step size enables the asthma attack risk prediction model to more flexibly adapt to the prediction needs of different time granularities.

[0072] In this embodiment, step S2 also includes the following specific contents:

[0073] Obtain predicted asthma attack risk coefficients of asthma patients at real-time locations at multiple groups of equally spaced moments in the future, sort the asthma attack risk coefficients of asthma patients at real-time locations at multiple groups of equally spaced moments in the future in chronological order, preset an asthma attack risk threshold, extract the corresponding future equally spaced moments closest to the current moment from the sorted multiple groups of asthma attack risk coefficients of asthma patients at real-time locations at equally spaced moments in the future whose asthma attack risk coefficient is greater than the asthma attack risk threshold, and obtain the duration between the current moment and the corresponding future equally spaced moment as the asthma attack risk analysis result of the asthma patient.

[0074] It should be noted that the above-mentioned environmental condition abnormality ratio coefficient, physical sign abnormal change ratio coefficient and asthma attack risk threshold are determined in the following manner: obtain the location environment data and physical sign data of 5,000 groups of asthma patients, import them into the asthma attack risk coefficient calculation formula to calculate the asthma attack risk coefficients of 5,000 groups of asthma patients at their real-time locations, and obtain the judgment results of whether the 5,000 groups of asthma patients actually have asthma attacks; import the calculated asthma attack risk coefficients and the judgment results of whether the asthma patients actually have asthma attacks into the fitting software, and output the values ​​of the environmental condition abnormality ratio coefficient, physical sign abnormal change ratio coefficient and asthma attack risk threshold that meet the highest asthma attack judgment accuracy.

[0075] In this embodiment, step S3 includes the following specific contents:

[0076] Multiple hospitals corresponding to the shortest time required for an asthma patient to travel from the current real-time location to multiple hospitals are obtained as nearby hospitals, and the environmental data of the nearby hospitals are obtained.

[0077] In this embodiment, step S4 includes the following specific steps:

[0078] S41. Obtain environmental data of nearby hospitals;

[0079] S42, substituting the environmental data of the nearby hospitals into the hospital environmental condition comparison coefficient calculation formula to calculate the hospital environmental condition comparison coefficient of the nearby hospitals; the hospital environmental condition comparison coefficient calculation formula is:

[0080] ;

[0081] Where hevj represents the hospital environment comparison coefficient of the jth nearby hospital, represents the shortest time required for an asthma patient to travel from the current real-time location to the jth nearby hospital in the environmental data of nearby hospitals, Bj represents the number of vacant wards in the jth nearby hospital in the environmental data of nearby hospitals, and Bmax represents the maximum number of vacant wards in all nearby hospitals in the environmental data of nearby hospitals. It represents the minimum value of the shortest time required for an asthma patient to travel from the current real-time location to all nearby hospitals in the environmental data of nearby hospitals. Aj, Tj, and Hj respectively represent the average air quality index, average temperature, and average humidity of all vacant wards in the jth nearby hospital in the environmental data of nearby hospitals. m represents the number of nearby hospitals, and j is any item from 1 to m.

[0082] In this embodiment, step S5 includes the following specific contents:

[0083] S51, obtaining the hospital environment condition comparison coefficients of all nearby hospitals, and obtaining the asthma cure rates of all nearby hospitals;

[0084] S52. Substituting the hospital environment comparison coefficient and the asthma cure rate into the treatment priority coefficient calculation formula to calculate the treatment priority coefficient of the jth nearby hospital, the treatment priority coefficient calculation formula is:

[0085] ;

[0086] In the formula, Pj represents the priority coefficient of the jth nearby hospital, Qj represents the cure rate of asthma in the jth nearby hospital; They represent the coefficients of hospital environmental conditions and hospital cure rate respectively;

[0087] S53. Obtain the treatment priority coefficients of all nearby hospitals, and extract the corresponding nearby hospital with the largest treatment priority coefficient as the optimal hospital for asthma patients. It should be noted that the values ​​of the hospital environment condition influence coefficient and the hospital cure rate influence coefficient are obtained in the following way: obtain 5,000 groups of hospital environmental data and the hospital asthma cure rate, import them into the treatment priority coefficient calculation formula to calculate the treatment priority coefficients of 5,000 groups of hospitals, and obtain the treatment priority judgment results of 5,000 groups of hospitals; import the treatment priority coefficient and the treatment priority judgment results into the fitting software, and output the values ​​of the hospital environment condition influence coefficient and the hospital cure rate influence coefficient that meet the highest treatment priority judgment accuracy;

[0088] In this embodiment, by calculating the priority coefficient of nearby hospitals, asthma patients can be helped to go to the hospital with the most favorable conditions for their health and recovery, thereby improving the effective use of medical resources. The comfort of the hospital environment and the cure rate are very important for asthma patients. Choosing a suitable hospital helps to improve the treatment effect and hospitalization experience of patients. At the same time, the formula for calculating the environmental condition coefficient of nearby hospitals in this embodiment includes the optimization consideration of the arrival time, which can help asthma patients choose hospitals that are close and have good conditions, shorten the time required to go to the hospital, thereby speeding up the treatment response speed and reducing the risk on the way. According to the specific needs of each asthma patient, such as special requirements for air quality and ward environment, this embodiment selects the most suitable hospital to provide personalized medical services for patients and improve the individualization of the medical process. It should be noted that this embodiment also considers the number of vacant wards in the hospital when analyzing the priority coefficient of hospital visits, which helps to reasonably guide asthma patients, balance the bed utilization rate among hospitals, and avoid overcrowding in some hospitals and idle resources in other hospitals. Moreover, in seasons with frequent asthma attacks or when air pollution is severe, this embodiment can also be used as a response measure to help allocate medical resources quickly and orderly.

[0089] like Figure 3 As shown, an electronic device according to an embodiment of the present invention includes: a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes a method for secure sharing of health and environmental data based on blockchain technology by calling the computer program stored in the memory. It should be noted that all computer programs of the method for secure sharing of health and environmental data based on blockchain technology are implemented in C language.

[0090] Each embodiment of the present invention is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the IoT device and medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0091] The system and medium provided in the embodiments of the present invention correspond one-to-one to the method, and therefore, the system and medium also have similar beneficial technical effects to the corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the system and medium will not be repeated here.

[0092] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0093] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0094] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0095] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0096] Memory may include non-permanent storage in a computer-readable medium, in the form of random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0097] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0098] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0099] The above are only embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A method for securely sharing health and environmental data based on blockchain technology, characterized in that: The steps include: S1. Obtain location environment data and physical sign data of asthma patients; S2, importing the location environment data and physical sign data of the asthma patient into the asthma attack risk analysis model to analyze the asthma attack risk of the asthma patient; S3. Based on the asthma attack risk analysis results of asthma patients, obtain environmental data of hospitals near asthma patients; S4, importing the environmental data of nearby hospitals into the hospital environmental condition analysis model to analyze the environmental conditions of hospitals near asthma patients; S5. Recommend the best hospital for asthma patients based on the environmental status analysis results of hospitals near asthma patients; The step S2 comprises the following specific steps: S21, extracting the location environment data and vital sign data of the asthma patient; the location environment data includes historical location environment data and real-time location environment data; the vital sign data includes historical vital sign data corresponding to the historical location environment data and real-time vital sign data corresponding to the real-time location environment data; S22, substituting the location environment data and physical sign data of the asthma patient into the asthma attack risk coefficient calculation formula to calculate the asthma attack risk coefficient of the asthma patient at the real-time location; The asthma attack risk coefficient calculation formula is: ； Where Fz is the asthma attack risk coefficient of asthma patients at the real-time location, ev represents the environmental condition abnormality coefficient at the real-time location, tz represents the abnormal change coefficient of physical signs of asthma patients at the real-time location, a and b represent the abnormal proportion coefficient of environmental condition and the abnormal change coefficient of physical signs, respectively; The specific calculation formula of the abnormal coefficient of the environmental condition at the real-time location is: ； Where A, T and H represent the real-time air quality index, real-time temperature and real-time humidity of the real-time location of the asthma patient in the real-time location environment data, respectively. represents the average air quality index when the asthma patient is located at the i-th historical location in the historical location environmental data, They respectively represent the average temperature and average humidity when the asthma patient is located at the i-th historical location in the historical location environmental data, n represents the number of historical locations in the historical location environmental data, and i is any one from 1 to n; The specific calculation formula of the abnormal change coefficient of the physical signs of the asthma patient at the real-time position is: ； Where x, r and bs represent the current heart rate, current respiratory rate and current blood oxygen saturation of the asthma patient in the real-time vital sign data, respectively. represents the average heart rate, average respiratory rate and average blood oxygen saturation of the asthma patient at the i-th historical position in the historical physical sign data; The step S4 comprises the following specific steps: S41. Obtain environmental data of nearby hospitals; S42, substituting the environmental data of the nearby hospitals into the hospital environmental condition comparison coefficient calculation formula to calculate the hospital environmental condition comparison coefficient of the nearby hospitals; the hospital environmental condition comparison coefficient calculation formula is: ； Where hevj represents the hospital environment comparison coefficient of the jth nearby hospital, represents the shortest time required for an asthma patient to travel from the current real-time location to the jth nearby hospital in the environmental data of nearby hospitals, Bj represents the number of vacant wards in the jth nearby hospital in the environmental data of nearby hospitals, and Bmax represents the maximum number of vacant wards in all nearby hospitals in the environmental data of nearby hospitals. It represents the minimum value of the shortest time required for an asthma patient to travel from the current real-time location to all nearby hospitals in the environmental data of nearby hospitals. Aj, Tj, and Hj respectively represent the average air quality index, average temperature, and average humidity of all vacant wards in the jth nearby hospital in the environmental data of nearby hospitals. m represents the number of nearby hospitals, and j is any item from 1 to m.

2. The method for secure sharing of health and environmental data based on blockchain technology according to claim 1 is characterized in that: The step S2 also includes the following specific steps: S23, extracting the calculated asthma attack risk coefficients of the asthma patient at the real-time location at multiple equally spaced moments, constructing an asthma attack risk coefficient time series according to the time sequence of the asthma attack risk coefficients of all equally spaced moments, and training an asthma attack risk prediction model for predicting the asthma attack risk coefficients of the asthma patient at the real-time location at future equally spaced moments based on the asthma attack risk coefficient time series; S24, presetting the sliding step length to L and the sliding window length to W; A sliding window method is used to convert the asthma attack risk coefficients of asthma patients at real-time locations at all equally spaced moments in the asthma attack risk coefficient time series into multiple training samples, wherein each training sample is composed of an asthma attack risk coefficient sequence within a sliding window, the training samples are used as inputs of an asthma attack risk prediction model, and the asthma attack risk coefficients of asthma patients at real-time locations at future equally spaced moments with a preset sliding step size of L are used as outputs, and the asthma attack risk prediction model is trained with prediction accuracy as a training target; an asthma attack risk prediction model is generated that predicts the asthma attack risk coefficients of asthma patients at real-time locations at future equally spaced moments based on the asthma attack risk coefficients of asthma patients at real-time locations at all equally spaced moments; wherein the asthma attack risk prediction model is a recurrent neural network model.

3. The method for secure sharing of health and environmental data based on blockchain technology according to claim 2 is characterized in that: The step S2 also includes the following specific contents: Obtain predicted asthma attack risk coefficients of asthma patients at real-time locations at multiple groups of equally spaced moments in the future, sort the asthma attack risk coefficients of asthma patients at real-time locations at multiple groups of equally spaced moments in the future in chronological order, preset an asthma attack risk threshold, extract the corresponding future equally spaced moments closest to the current moment from the sorted multiple groups of asthma attack risk coefficients of asthma patients at real-time locations at equally spaced moments in the future whose asthma attack risk coefficient is greater than the asthma attack risk threshold, and obtain the duration between the current moment and the corresponding future equally spaced moment as the asthma attack risk analysis result of the asthma patient.

4. The method for secure sharing of health and environmental data based on blockchain technology according to claim 3 is characterized in that: The step S3 includes the following specific contents: Obtain multiple hospitals corresponding to the shortest time required for an asthma patient to travel from the current real-time location to multiple hospitals that is shorter than the asthma attack risk analysis result of the asthma patient as nearby hospitals; and obtain environmental data of the nearby hospitals.

5. The method for secure sharing of health and environmental data based on blockchain technology according to claim 4 is characterized in that: The step S5 includes the following specific contents: S51, obtaining the hospital environment condition comparison coefficients of all nearby hospitals, and obtaining the asthma cure rates of all nearby hospitals; S52. Substituting the hospital environment comparison coefficient and the asthma cure rate into the treatment priority coefficient calculation formula to calculate the treatment priority coefficient of the jth nearby hospital, the treatment priority coefficient calculation formula is: ； In the formula, Pj represents the priority coefficient of the jth nearby hospital, Qj represents the cure rate of asthma in the jth nearby hospital; They represent the coefficients of hospital environmental conditions and hospital cure rate respectively; S53. Obtain the priority coefficients of all nearby hospitals, and extract the corresponding nearby hospital with the largest priority coefficient as the optimal hospital for asthma patients.

6. An electronic device comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; characterized in that the processor executes the method for secure sharing of health and environmental data based on blockchain technology as described in any one of claims 1 to 5 by calling the computer program stored in the memory.

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