Remote consultation system and method for chronic disease management

Abstract

The invention relates to the technical field of information aggregation, and particularly discloses a remote consultation system and method for chronic disease management, and the method comprises the steps: comparing the body parameters and examination results of different users, calculating the condition distance of the users, and carrying out the clustering of the users according to the condition distance; when a new check result is generated, querying the class of the user corresponding to the check result, and taking the check result as a virtual check result of the class of user; when a new check result is generated, querying a user corresponding to the check result, querying a virtual check result of the user, verifying the virtual check result, and updating statistical data of the user when the verification is passed; when a consultation request sent by a user is received, feeding back the updated statistical data; according to the method, each user is placed in one type of users, and for each user, multiple times of non-personal check are equivalent, so that the data volume is greatly expanded, and the user can conveniently know the state of the user.

Description

Technical Field

[0001] This invention relates to the field of information aggregation technology, specifically a remote consultation system and method for chronic disease management. Background Technology

[0002] "Chronic disease patients" refers to people suffering from chronic diseases. These diseases are usually characterized by slow onset, long course, and incurability but controllability. Chronic disease patients require long-term medical management, health monitoring, and lifestyle interventions, and are a key focus of health management in modern society.

[0003] For patients with chronic diseases, it is necessary to understand their own status frequently. To achieve this function, a third party will provide a database to store the patient's daily data and provide feedback to the user when needed. However, the existing storage and feedback architecture is entirely based on the user's own data. The user can only obtain their own data during follow-up examinations, resulting in a very narrow data scope. How to provide a data volume expansion scheme based on the existing architecture to provide users with a more comprehensive reference is the technical problem that the present invention aims to solve. Summary of the Invention

[0004] The purpose of this invention is to provide a remote consultation system and method for chronic disease management to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A remote consultation method for chronic disease management, the method comprising:

[0007] The system acquires the user's body parameters in real time using smart wearable devices, reads the user's historical examination results, and statistically analyzes the body parameters and historical examination results on the same timeline.

[0008] By comparing the physical parameters and examination results of different users, the condition distance of each user is calculated, and the users are clustered based on the condition distance.

[0009] Establish a connection channel with the inspection end. When a new inspection result is generated, query the class of the user corresponding to the inspection result and use the inspection result as a virtual inspection result for that user class.

[0010] When a new inspection result is generated, the user corresponding to the inspection result is queried, the user's virtual inspection result is queried, the virtual inspection result is verified, and if the verification is successful, the user's statistical data is updated.

[0011] When a user sends an inquiry request, the system provides updated statistical data.

[0012] As a further aspect of the present invention: the step of acquiring the user's body parameters in real time based on the smart wearable device, reading the user's historical examination results, and statistically analyzing the body parameters and historical examination results according to the same time axis includes:

[0013] Send permission requests to users, receive permissions granted by users, and establish a connection channel with users' smart wearable devices;

[0014] The system acquires users' body parameters using smart wearable devices and creates a daily parameter array.

[0015] Compare parameter arrays from different periods, fill in any missing data, and use the filled-in daily parameter arrays within a preset time range as the user's body parameters.

[0016] Read the user's historical inspection results, digitize the historical inspection results, and obtain an inspection array;

[0017] To retrieve the historical inspection results by date, concatenate the inspection array with the parameter array corresponding to that date.

[0018] As a further aspect of the present invention: the step of comparing the body parameters and examination results of different users, calculating the user's condition distance, and clustering users based on the condition distance includes:

[0019] Users are selected sequentially, and their physical parameters and examination results are read.

[0020] Compare the physical parameters and examination results of different users to calculate the user's condition distance;

[0021] Users are clustered using the situational distance as a clustering metric; the clustering algorithm is an unlimited number of clusters, and the distance condition in the clustering process is a preset value;

[0022] The process of calculating the situation distance is as follows:

[0023] Read the body parameters and examination results of each day in sequence, and determine the first weight based on the difference in the number of days between the corresponding date and the current date;

[0024] The system compares the body parameters and examination results of two users on the same date and calculates the array difference; the process of obtaining the array difference from the body parameters and examination results is adjusted by a preset second weight.

[0025] The sum of the differences in the arrays is calculated, and combined with a preset correction coefficient, the situation distance is obtained.

[0026] As a further aspect of the present invention: the step of establishing a connection channel with the inspection end, and when a new inspection result is generated, querying the class of the user corresponding to the inspection result, and using the inspection result as a virtual inspection result for that user class, includes:

[0027] Establish a connection channel with the inspection terminal; the inspection terminal contains an access control port for obtaining information sharing permissions granted by the user.

[0028] When a new inspection result is detected at the inspection end, query the class of the user corresponding to that inspection result;

[0029] Obtain the inspection date of the inspection results, and use the inspection results containing the inspection date as virtual inspection results for other users of the same type.

[0030] As a further aspect of the present invention: the step of querying the user corresponding to the new inspection result when a new inspection result is generated, querying the user's virtual inspection result, verifying the virtual inspection result, and updating the user's statistical data when the verification is successful includes:

[0031] When a new inspection result is generated, query the user corresponding to that inspection result;

[0032] Query all virtual inspection results for the user, and validate all virtual inspection results based on the new inspection results;

[0033] When the verification passes, the virtual inspection result will be set as the user's historical inspection result;

[0034] If the verification fails, delete the corresponding virtual check result.

[0035] As a further aspect of the present invention: the step of querying all virtual inspection results of a user and verifying all virtual inspection results based on the new inspection results includes:

[0036] Query the user's virtual inspection results, including dates, in chronological order;

[0037] Read the user's new inspection results, randomly select a user record from the user record database, and mark the user when both the new inspection result and the virtual inspection result containing the date are included in the user record;

[0038] Record the number of random selections and the number of tags, and calculate the ratio of the number of tags to the number of random selections;

[0039] When the ratio reaches the preset first threshold, the verification is deemed successful;

[0040] If the ratio is less than the preset second threshold, the verification is deemed unsuccessful.

[0041] The present invention also provides a remote consultation system for chronic disease management, the system comprising:

[0042] The parameter reading module is used to acquire the user's body parameters in real time from the smart wearable device, read the user's historical examination results, and statistically analyze the body parameters and historical examination results on the same time axis.

[0043] The user comparison and clustering module is used to compare the physical parameters and examination results of different users, calculate the status distance of users, and cluster users according to the status distance.

[0044] The inspection result promotion module is used to establish a connection channel with the inspection end. When a new inspection result is generated, it queries the class of the user corresponding to the inspection result and uses the inspection result as a virtual inspection result for that user class.

[0045] The data verification and correction module is used to query the user corresponding to the new inspection result when a new inspection result is generated, query the user's virtual inspection result, verify the virtual inspection result, and update the user's statistical data when the verification is successful.

[0046] The data feedback module is used to provide updated statistical data when it receives a user's inquiry request.

[0047] As a further aspect of the present invention: the parameter reading module includes:

[0048] The first channel establishment unit is used to send permission acquisition requests to users, receive permissions granted by users, and establish a connection channel with the user's smart wearable device.

[0049] The parameter array creation unit is used to obtain the user's body parameters based on the smart wearable device and create a daily parameter array;

[0050] The data completion unit is used to compare parameter arrays of different periods, fill in empty data, and use the completed daily parameter arrays within a preset time range as the user's body parameters.

[0051] The digitization conversion unit is used to read the user's historical inspection results, digitize the historical inspection results, and obtain the inspection array;

[0052] The data insertion unit is used to query the acquisition date of historical inspection results and concatenate the inspection array with the parameter array corresponding to the date.

[0053] As a further aspect of the present invention: the user comparison and clustering module includes:

[0054] The data reading unit is used to select users sequentially and read their physical parameters and examination results.

[0055] The condition distance calculation unit is used to compare the body parameters and examination results of different users to calculate the condition distance of the user.

[0056] The clustering execution unit is used to cluster users using the status distance as a clustering metric; the clustering algorithm is an unlimited number of clusters, and the distance condition in the clustering process is a preset value;

[0057] The process of calculating the situation distance is as follows:

[0058] Read the body parameters and examination results of each day in sequence, and determine the first weight based on the difference in the number of days between the corresponding date and the current date;

[0059] The system compares the body parameters and examination results of two users on the same date and calculates the array difference; the process of obtaining the array difference from the body parameters and examination results is adjusted by a preset second weight.

[0060] The sum of the differences in the arrays is calculated, and combined with a preset correction coefficient, the situation distance is obtained.

[0061] As a further aspect of the present invention: the inspection result promotion module includes:

[0062] The second channel establishment unit is used to establish a connection channel with the inspection end; the inspection end contains an access control port for obtaining information sharing permissions granted by the user.

[0063] The same-class user query unit is used to query the class of the user corresponding to the new inspection result when a new inspection result is detected at the inspection end.

[0064] The application unit is used to obtain the inspection date of the inspection result and use the inspection result containing the inspection date as a virtual inspection result for other users of the same type.

[0065] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention acquires the user's body parameters through a smart wearable device, then obtains the user's examination results, and classifies the user according to the body parameters and examination results, placing each user in a category. When any user in the same category undergoes an examination, their examination results are shared with other users in the same category. For each user, this is equivalent to undergoing multiple "non-self" examinations, greatly expanding the amount of data and making it easier for users to understand their own status. For example, when a user experiences a special situation, users with similar body parameters are very likely to experience the same problem. When they see a special situation, they can take preventive measures as soon as possible, which has great reference value. Attached Figure Description

[0066] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention.

[0067] Figure 1 A flowchart of remote consultation methods for chronic disease management.

[0068] Figure 2 A structural diagram of a remote consultation system for chronic disease management. Detailed Implementation

[0069] To make the technical problems to be solved, the technical solutions, and the beneficial effects 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 merely illustrative of the present invention and are not intended to limit the present invention.

[0070] Figure 1 This is a flowchart of a remote consultation method for chronic disease management. In this embodiment of the invention, a remote consultation method for chronic disease management includes:

[0071] Step S100: Obtain the user's body parameters in real time from the smart wearable device, read the user's historical examination results, and statistically analyze the body parameters and historical examination results on the same time axis;

[0072] Existing smart wearable devices have high performance and are worn by many users, especially those in the technical solution of this invention, who are mostly patients with chronic diseases. Smart wearable devices can acquire many parameters, collectively referred to as body parameters; the specific parameters required are determined in advance by the staff. Specifically, the parameters that can be acquired include, but are not limited to:

[0073] I. Physiological parameters:

[0074] Heart rate, including real-time / resting / maximum heart rate, is generally detected by an optical heart rate sensor (PPG);

[0075] Heart rate variability is used to assess stress, recovery status, and autonomic nervous system activity.

[0076] Blood oxygen saturation reflects the oxygen content in the blood and is often used for monitoring at high altitudes and during sleep.

[0077] Blood pressure: Some devices support estimated blood pressure measurement, but the accuracy is limited.

[0078] Body temperature is often used to detect fever or periodic physiological changes.

[0079] Respiratory rate, the number of breaths per unit time, is often analyzed in conjunction with heart rate.

[0080] II. Motion and Posture Parameters:

[0081] Step count, distance, and calorie consumption are determined using accelerometers and gyroscopes.

[0082] Sports type recognition, such as running, cycling, swimming, yoga, etc., can be automatically identified.

[0083] Exercise intensity / aerobic and anaerobic zones, combined with heart rate and movement analysis.

[0084] Body posture monitoring, such as reminders for prolonged sitting and screen activation upon raising the wrist.

[0085] Fall detection uses gravity sensing and motion to identify rapid fall events.

[0086] III. Health Assessment and Stress Indicators:

[0087] Stress index, assessed based on heart rate variability and skin conductance response, etc.

[0088] Emotional / psychological state analysis (partial equipment), combined with voice tone analysis and heart rate variability and other indicators.

[0089] Recovery status: Whether the body has recovered to a state suitable for the next training session after exercise.

[0090] Of course, there are also some parameters that can be used in the future, such as skin conductance, sweat gland activity, blood glucose monitoring, and lactate threshold; there are many of these parameters, and which ones are needed will be determined in advance by the staff.

[0091] Historical examination results are the results of the user's examination at the service location. These are relatively detailed and comprehensive examination results, which are equivalent to consultation data. In practical applications, patients with chronic diseases need to have regular follow-up examinations, and each follow-up examination yields an examination result. These are collectively referred to as historical examination results.

[0092] Step S200: Compare the body parameters and examination results of different users, calculate the condition distance of the users, and cluster the users according to the condition distance;

[0093] Each user can obtain physical parameters and examination results. By reading each user's physical parameters and examination results, and comparing each user pairwise, the differences between users can be calculated and represented by the status distance parameter. Based on the status distance, a clustering algorithm is introduced to convert all users into multiple user classes, thereby clustering the users.

[0094] Step S300: Establish a connection channel with the inspection end. When a new inspection result is generated, query the class of the user corresponding to the inspection result and use the inspection result as the virtual inspection result for that user class.

[0095] The inspection terminal is the port for inspecting users and obtaining inspection results. It is generally a computer device installed in the re-inspection scenario. A connection channel is established with the inspection terminal. When a new inspection result is generated, the inspection result is read, and the user corresponding to the inspection result is queried. The user's class can be queried to find users of the same class. Since the similarity of users of the same class is very high in the technical solution of this invention, when one user of the same class is re-inspected, the inspection result obtained from the re-inspection is used as the virtual inspection result of the same class of users. That is, it is equivalent to the same class of users also being inspected. The reason why it is called a virtual inspection result is that it is equivalent to using one person's inspection result as another person's inspection result, which will definitely have a certain error. That is, it is virtual.

[0096] Step S400: When a new inspection result is generated, query the user corresponding to the inspection result, query the user's virtual inspection result, verify the virtual inspection result, and update the user's statistical data when the verification is successful.

[0097] After the processing in step S300, each user will have many virtual inspection results over a period of time. As long as one user of the same type performs the inspection process, an inspection result will be obtained. When a new inspection result is obtained, the virtual inspection result is verified based on the new inspection result. If the verification passes, it is retained and regarded as a real inspection result. If the verification fails, the corresponding inspection result is deleted. Of course, regardless of the operation, the virtual inspection result still needs an attribute label to indicate that it is not a real inspection result.

[0098] Step S500: Upon receiving a consultation request from a user, update the statistical data.

[0099] The overall process of the solution provided above is as follows:

[0100] Suppose there is a user, A, who has many similar users. In real life, he might recheck his results every month, assuming he checks on the 1st of each month. During this check, he receives an actual result. In the following month, his similar users will also recheck, and each time they do, user A receives a virtual result. Over the next month, user A accumulates many virtual results. On the 1st of the following month, the virtual results from the previous month are verified against the actual results. If the verification passes, it is taken as the actual result. This effectively increases the frequency of rechecks. Although it's not 100% accurate, the clustering and verification processes greatly improve accuracy, at least providing a useful reference.

[0101] It is worth mentioning that the same user may receive multiple virtual inspection results on a single day. In such cases, one virtual inspection result can be randomly retained, or the virtual inspection result of the user with the smallest distance from the target can be retained.

[0102] Regarding step S100, the step of acquiring the user's body parameters in real time using the smart wearable device, reading the user's historical examination results, and statistically analyzing the body parameters and historical examination results according to the same time axis includes:

[0103] Send permission requests to users, receive permissions granted by users, and establish a connection channel with users' smart wearable devices;

[0104] The system acquires users' body parameters using smart wearable devices and creates a daily parameter array.

[0105] Compare parameter arrays from different periods, fill in any missing data, and use the filled-in daily parameter arrays within a preset time range as the user's body parameters.

[0106] Read the user's historical inspection results, digitize the historical inspection results, and obtain an inspection array;

[0107] To retrieve the historical inspection results by date, concatenate the inspection array with the parameter array corresponding to that date.

[0108] The system sends a permission request to the user, receives the permissions granted by the user, and establishes a connection channel with the user's smart wearable device. Based on the smart wearable device, it acquires the user's body parameters and builds a daily parameter array. Since the body parameters themselves are an array, the resulting parameter array can be a matrix (a sequence of body parameters at different times). It compares the parameter arrays of different periods, fills in any missing data, and uses the filled-in daily parameter array within a preset time range as the user's body parameters. It reads the user's historical examination results, digitizes the historical examination results, and obtains an examination array. Thus, both the body parameters and examination results are converted into numerical form. It queries the acquisition date of the historical examination results and connects the examination array to the parameter array of the corresponding date.

[0109] Specifically, regarding the data completion process, in practical applications, smart wearable devices acquire different types of body data at varying frequencies, and there are also various reasons why it is difficult to acquire data. As a result, there will be more or less missing data in the body parameters (array, each element corresponding to a type of data) at each moment, that is, the empty data mentioned above. The completion methods include: comparing parameter arrays of different periods, querying the data of the most recent moment corresponding to the empty data in the parameter array of each period, and using it as the data to complete the data.

[0110] Regarding step S200, the step of comparing the body parameters and examination results of different users, calculating the condition distance of users, and clustering users based on the condition distance includes:

[0111] Users are selected sequentially, and their physical parameters and examination results are read.

[0112] Compare the physical parameters and examination results of different users to calculate the user's condition distance;

[0113] Users are clustered using the situation distance as a clustering metric; the clustering algorithm is an unlimited number of clusters, and the distance condition in the clustering process is a preset value.

[0114] Users are selected sequentially, and their physical parameters and examination results are read. The physical parameters and examination results of different users are compared to obtain the differences between users, which are represented by the condition distance parameter. The condition distance is used as a clustering index to cluster users. The clustering algorithm is an unlimited number of clusters clustering algorithm. The clustering algorithm can be a density-based clustering algorithm, such as DBSCAN. The distance condition in the clustering process is a preset value used to adjust how many users within a certain distance range are considered to be in the same class. The distance condition is generally a threshold. The smaller the threshold, the more similar the users in the same class are, and the more classes are obtained.

[0115] The process of calculating the situation distance is as follows:

[0116] Read the body parameters and examination results of each day in sequence, and determine the first weight based on the difference in the number of days between the corresponding date and the current date;

[0117] The system compares the body parameters and examination results of two users on the same date and calculates the array difference; the process of obtaining the array difference from the body parameters and examination results is adjusted by a preset second weight.

[0118] The sum of the differences in the arrays is calculated, and combined with a preset correction coefficient, the situation distance is obtained.

[0119] Specifically, for the status distance, the body parameters and examination results of each day are read sequentially. Based on the difference in the number of days between the corresponding date and the current date, a first weight is determined according to the difference in the number of days. The body parameters and examination results of two users on the same date are compared sequentially, and the array difference is calculated. The process of calculating the array difference includes calculating the difference in body parameters and the difference in examination results. The difference is then summed according to the second weight to obtain the array difference. Since the body parameters and examination results are in numerical form, although they are in matrix or array form, the data structure of the same type of data is the same. Therefore, the process of calculating the difference is a conventional mathematical calculation process, which will not be elaborated further in this invention.

[0120] The final status distance is obtained by summing the array differences of each date with the first weight; where the first weight is inversely proportional to the difference in the number of days, indicating that the data of the date closer to the current time is more important.

[0121] Regarding step S300, the step of establishing a connection channel with the inspection end, and when a new inspection result is generated, querying the class of the user corresponding to the inspection result, and using the inspection result as a virtual inspection result for that user class, includes:

[0122] Establish a connection channel with the inspection terminal; the inspection terminal contains an access control port for obtaining information sharing permissions granted by the user.

[0123] When a new inspection result is detected at the inspection end, query the class of the user corresponding to that inspection result;

[0124] Obtain the inspection date of the inspection results, and use the inspection results containing the inspection date as virtual inspection results for other users of the same type.

[0125] The inspection terminal contains an access control port. When inspecting a user, it is necessary to inform the user that their data may be shared with the implementer of the present invention, that is, it is necessary to obtain the information sharing permission granted by the user. A connection channel is established with the inspection terminal. When a new inspection result is detected at the inspection terminal, the user class corresponding to the inspection result is queried, the inspection date of the inspection result is obtained, and the inspection result containing the inspection date is used as the virtual inspection result of other users of the same class.

[0126] Regarding step S400, the step of querying the user corresponding to the new inspection result, querying the user's virtual inspection result, verifying the virtual inspection result, and updating the user's statistical data when the verification is successful includes:

[0127] When a new inspection result is generated, query the user corresponding to that inspection result;

[0128] Query all virtual inspection results for the user, and validate all virtual inspection results based on the new inspection results;

[0129] When the verification passes, the virtual inspection result will be set as the user's historical inspection result;

[0130] If the verification fails, delete the corresponding virtual check result.

[0131] For users who are being checked, while generating new check results, all of the user's virtual check results are queried. All virtual check results are then verified based on the new check results. When the verification passes, the verified virtual check result is set as the user's historical check result. That is, after passing, the virtual check result is marked as true. Conversely, when the verification fails, the corresponding virtual check result is deleted. This means that when the verification fails, the virtual check result is false.

[0132] As a preferred embodiment of the technical solution of the present invention, the step of querying all virtual inspection results of the user and verifying all virtual inspection results based on the new inspection results includes:

[0133] Query the user's virtual inspection results, including dates, in chronological order;

[0134] Read the user's new inspection results, randomly select a user record from the user record database, and mark the user when both the new inspection result and the virtual inspection result containing the date are included in the user record;

[0135] Record the number of random selections and the number of tags, and calculate the ratio of the number of tags to the number of random selections;

[0136] When the ratio reaches the preset first threshold, the verification is deemed successful;

[0137] If the ratio is less than the preset second threshold, the verification is deemed unsuccessful.

[0138] For the current user, query the user's virtual inspection results containing the date in chronological order, read the user's new inspection results, and randomly select a user record from the user record database. When both the new inspection result and the virtual inspection result containing the date are present in the user record, the user is marked. The principle behind this process is that the virtual inspection result is a previous inspection result, and the current inspection result is the latest inspection result. If these two inspection results have appeared for a user in the history, it means that these two results are likely to occur. This process is repeated cyclically, recording the number of random selections and the number of marks, and calculating the ratio of the number of marks to the number of random selections. The larger this ratio, the more normal the two results are (because they have appeared for most users and are within the normal range). When the ratio reaches a preset first threshold, the verification is considered successful; when the ratio is less than a preset second threshold, the verification is considered unsuccessful.

[0139] It is worth mentioning that the process of cyclic execution is generally limited to users of the same type; that is, the user record database is a database composed of the historical records of users of the same type.

[0140] Figure 2The diagram shows the structural composition of a remote consultation system for chronic disease management. As a preferred embodiment of the technical solution of this invention, the invention also provides a remote consultation system for chronic disease management, wherein system 10 includes:

[0141] The parameter reading module 11 is used to acquire the user's body parameters in real time from the smart wearable device, read the user's historical examination results, and statistically analyze the body parameters and historical examination results on the same time axis.

[0142] User comparison and clustering module 12 is used to compare the body parameters and examination results of different users, calculate the status distance of users, and cluster users according to the status distance;

[0143] The inspection result promotion module 13 is used to establish a connection channel with the inspection end. When a new inspection result is generated, it queries the class of the user corresponding to the inspection result and uses the inspection result as a virtual inspection result for that user class.

[0144] The data verification and correction module 14 is used to query the user corresponding to the new inspection result when a new inspection result is generated, query the user's virtual inspection result, verify the virtual inspection result, and update the user's statistical data when the verification is successful.

[0145] The data feedback module 15 is used to provide updated statistical data when it receives a user's inquiry request.

[0146] Furthermore, the parameter reading module 11 includes:

[0147] The first channel establishment unit is used to send permission acquisition requests to users, receive permissions granted by users, and establish a connection channel with the user's smart wearable device.

[0148] The parameter array creation unit is used to obtain the user's body parameters based on the smart wearable device and create a daily parameter array;

[0149] The data completion unit is used to compare parameter arrays of different periods, fill in empty data, and use the completed daily parameter arrays within a preset time range as the user's body parameters.

[0150] The digitization conversion unit is used to read the user's historical inspection results, digitize the historical inspection results, and obtain the inspection array;

[0151] The data insertion unit is used to query the acquisition date of historical inspection results and concatenate the inspection array with the parameter array corresponding to the date.

[0152] Specifically, the user comparison and clustering module 12 includes:

[0153] The data reading unit is used to select users sequentially and read their physical parameters and examination results.

[0154] The condition distance calculation unit is used to compare the body parameters and examination results of different users to calculate the condition distance of the user.

[0155] The clustering execution unit is used to cluster users using the status distance as a clustering metric; the clustering algorithm is an unlimited number of clusters, and the distance condition in the clustering process is a preset value;

[0156] The process of calculating the situation distance is as follows:

[0157] Read the body parameters and examination results of each day in sequence, and determine the first weight based on the difference in the number of days between the corresponding date and the current date;

[0158] The system compares the body parameters and examination results of two users on the same date and calculates the array difference; the process of obtaining the array difference from the body parameters and examination results is adjusted by a preset second weight.

[0159] The sum of the differences in the arrays is calculated, and combined with a preset correction coefficient, the situation distance is obtained.

[0160] Furthermore, the inspection result promotion module 13 includes:

[0161] The second channel establishment unit is used to establish a connection channel with the inspection end; the inspection end contains an access control port for obtaining information sharing permissions granted by the user.

[0162] The same-class user query unit is used to query the class of the user corresponding to the new inspection result when a new inspection result is detected at the inspection end.

[0163] The application unit is used to obtain the inspection date of the inspection result and use the inspection result containing the inspection date as a virtual inspection result for other users of the same type.

[0164] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A remote consultation method for chronic disease management, characterized in that, The method includes: The system acquires the user's body parameters in real time using smart wearable devices, reads the user's historical examination results, and statistically analyzes the body parameters and historical examination results on the same timeline. By comparing the physical parameters and examination results of different users, the condition distance of each user is calculated, and the users are clustered based on the condition distance. Establish a connection channel with the inspection end. When a new inspection result is generated, query the class of the user corresponding to the inspection result and use the inspection result as a virtual inspection result for that user class. When a new inspection result is generated, the user corresponding to the inspection result is queried, the user's virtual inspection result is queried, the virtual inspection result is verified, and if the verification is successful, the user's statistical data is updated. When a user sends an inquiry request, the system provides updated statistical data.

2. The remote consultation method for chronic disease management according to claim 1, characterized in that, The steps of acquiring the user's body parameters in real time using a smart wearable device, reading the user's historical examination results, and statistically analyzing the body parameters and historical examination results according to the same timeline include: Send permission requests to users, receive permissions granted by users, and establish a connection channel with users' smart wearable devices; The system acquires users' body parameters using smart wearable devices and creates a daily parameter array. Compare parameter arrays from different periods, fill in any missing data, and use the filled-in daily parameter arrays within a preset time range as the user's body parameters. Read the user's historical inspection results, digitize the historical inspection results, and obtain an inspection array; To retrieve the historical inspection results by date, concatenate the inspection array with the parameter array corresponding to that date.

3. The remote consultation method for chronic disease management according to claim 1, characterized in that, The step of comparing the physical parameters and examination results of different users, calculating the condition distance of users, and clustering users based on the condition distance includes: Users are selected sequentially, and their physical parameters and examination results are read. Compare the physical parameters and examination results of different users to calculate the user's condition distance; Users are clustered using the situational distance as a clustering metric; the clustering algorithm is an unlimited number of clusters, and the distance condition in the clustering process is a preset value; The process of calculating the situation distance is as follows: Read the body parameters and examination results of each day in sequence, and determine the first weight based on the difference in the number of days between the corresponding date and the current date; The system compares the body parameters and examination results of two users on the same date and calculates the array difference; the process of obtaining the array difference from the body parameters and examination results is adjusted by a preset second weight. The sum of the differences in the arrays is calculated, and combined with a preset correction coefficient, the situation distance is obtained.

4. The remote consultation method for chronic disease management according to claim 1, characterized in that, The step of establishing a connection channel with the inspection end, and querying the class of the user corresponding to the inspection result when a new inspection result is generated, and using the inspection result as a virtual inspection result for that user class, includes: Establish a connection channel with the inspection terminal; the inspection terminal contains an access control port for obtaining information sharing permissions granted by the user. When a new inspection result is detected at the inspection end, query the class of the user corresponding to that inspection result; Obtain the inspection date of the inspection results, and use the inspection results containing the inspection date as virtual inspection results for other users of the same type.

5. The remote consultation method for chronic disease management according to claim 1, characterized in that, The steps of querying the user corresponding to the new inspection result, querying the user's virtual inspection result, verifying the virtual inspection result, and updating the user's statistical data when the verification is successful include: When a new inspection result is generated, query the user corresponding to that inspection result; Query all virtual inspection results for the user, and validate all virtual inspection results based on the new inspection results; When the verification passes, the virtual inspection result will be set as the user's historical inspection result; If the verification fails, delete the corresponding virtual check result.

6. The remote consultation method for chronic disease management according to claim 5, characterized in that, The step of querying all virtual inspection results for the user and verifying all virtual inspection results based on the new inspection results includes: Query the user's virtual inspection results, including dates, in chronological order; Read the user's new inspection results, randomly select a user record from the user record database, and mark the user when both the new inspection result and the virtual inspection result containing the date are included in the user record; Record the number of random selections and the number of tags, and calculate the ratio of the number of tags to the number of random selections; When the ratio reaches the preset first threshold, the verification is deemed successful; If the ratio is less than the preset second threshold, the verification is deemed unsuccessful.

7. A remote consultation system for chronic disease management, characterized in that, The system includes: The parameter reading module is used to acquire the user's body parameters in real time from the smart wearable device, read the user's historical examination results, and statistically analyze the body parameters and historical examination results on the same time axis. The user comparison and clustering module is used to compare the physical parameters and examination results of different users, calculate the status distance of users, and cluster users according to the status distance. The inspection result promotion module is used to establish a connection channel with the inspection end. When a new inspection result is generated, it queries the class of the user corresponding to the inspection result and uses the inspection result as a virtual inspection result for that user class. The data verification and correction module is used to query the user corresponding to the new inspection result when a new inspection result is generated, query the user's virtual inspection result, verify the virtual inspection result, and update the user's statistical data when the verification is successful. The data feedback module is used to provide updated statistical data when it receives a user's inquiry request.

8. The remote consultation system for chronic disease management according to claim 7, characterized in that, The parameter reading module includes: The first channel establishment unit is used to send permission acquisition requests to users, receive permissions granted by users, and establish a connection channel with the user's smart wearable device. The parameter array creation unit is used to obtain the user's body parameters based on the smart wearable device and create a daily parameter array; The data completion unit is used to compare parameter arrays of different periods, fill in empty data, and use the completed daily parameter arrays within a preset time range as the user's body parameters. The digitization conversion unit is used to read the user's historical inspection results, digitize the historical inspection results, and obtain the inspection array; The data insertion unit is used to query the acquisition date of historical inspection results and concatenate the inspection array with the parameter array corresponding to the date.

9. The remote consultation system for chronic disease management according to claim 7, characterized in that, The user comparison and clustering module includes: The data reading unit is used to select users sequentially and read their physical parameters and examination results. The condition distance calculation unit is used to compare the body parameters and examination results of different users to calculate the condition distance of the user. The clustering execution unit is used to cluster users using the status distance as a clustering metric; the clustering algorithm is an unlimited number of clusters, and the distance condition in the clustering process is a preset value; The process of calculating the situation distance is as follows: Read the body parameters and examination results of each day in sequence, and determine the first weight based on the difference in the number of days between the corresponding date and the current date; The system compares the body parameters and examination results of two users on the same date and calculates the array difference; the process of obtaining the array difference from the body parameters and examination results is adjusted by a preset second weight. The sum of the differences in the arrays is calculated, and combined with a preset correction coefficient, the situation distance is obtained.

10. The remote consultation system for chronic disease management according to claim 7, characterized in that, The inspection result promotion module includes: The second channel establishment unit is used to establish a connection channel with the inspection end; the inspection end contains an access control port for obtaining information sharing permissions granted by the user. The same-class user query unit is used to query the class of the user corresponding to the new inspection result when a new inspection result is detected at the inspection end. The application unit is used to obtain the inspection date of the inspection result and use the inspection result containing the inspection date as a virtual inspection result for other users of the same type.

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