Chronic disease health management platform based on enterprise service bus

Abstract

The present invention relates to the technical field of chronic disease health management, specifically to a chronic disease health management platform based on an enterprise service bus, comprising a patient feedback subsystem, a server subsystem, a service bus, a diagnosis subsystem and a management statistics module, wherein the patient feedback subsystem comprises a plurality of patient mobile modules, each of which comprises a mobile monitoring unit, a patient interaction unit and an information recording unit, the server subsystem comprises a plurality of servers, and the diagnosis subsystem comprises a plurality of diagnosis modules; the present invention obtains blood sugar, blood pressure and other vital sign values ​​of chronic disease patients at preset time intervals through a mobile monitoring module based on biosensors and transdermal electrode measurement technology, thereby improving the real-time monitoring of vital sign of chronic disease patients; through the self-check judgment of the server, comprehensively considering the CPU occupancy rate, memory occupancy rate a2 and hard disk occupancy rate, according to the specific value of the occupancy rate, the data is preferentially sent to the server with a smaller occupancy rate, thereby improving the efficiency of data processing.

Description

Technical Field

[0001] The present invention relates to the technical field of chronic disease health management, and specifically to a chronic disease health management platform based on an enterprise service bus. Background Art

[0002] Chronic diseases, also known as chronic non-communicable diseases, are a general term for a class of diseases that have an insidious onset, a long course of illness, and a protracted course of illness, lack definite evidence of infectious biological causes, and have complex etiologies. These diseases are mainly related to factors such as metabolism, environment, and lifestyle in the human body, rather than being directly transmitted through biological factors. Chronic diseases are characterized by a long course of illness, slow progression, complex conditions, and many complications, and often require long-term treatment and management. Chronic disease management aims to control disease progression, reduce complications, improve patients' quality of life, and reduce medical costs by changing bad living habits, conducting health education, regularly monitoring disease indicators, and using medications rationally.

[0003] Chronic disease health management refers to the medical behavior and process of regular testing, continuous monitoring, evaluation and comprehensive intervention management of chronic non-communicable diseases and their risk factors. It includes timely detection of potential risks or early symptoms of chronic diseases through a series of examinations and evaluations, prediction of the possibility of disease development based on screening results, early warning, and taking corresponding comprehensive intervention measures to control the progression of the disease. For patients with confirmed chronic diseases, comprehensive management methods such as drug treatment, lifestyle adjustment, patient education and self-management are used to improve the quality of life of patients and delay the progression of the disease.

[0004] However, existing chronic disease health management technologies have many shortcomings and challenges, including information asymmetry, lack of patient knowledge, insufficient patient participation awareness and self-management ability, and management levels being greatly limited by technology. Summary of the invention

[0005] The purpose of the present invention is to solve the problems existing in the background technology and to propose a chronic disease health management platform based on an enterprise service bus.

[0006] The purpose of the present invention can be achieved through the following technical solutions: a chronic disease health management platform based on an enterprise service bus, including a patient feedback subsystem, a server subsystem, a service bus, a diagnosis subsystem and a management statistics module, wherein the patient feedback subsystem includes several patient mobile modules, each patient mobile module includes a mobile monitoring unit, a patient interaction unit and an information recording unit, the server subsystem includes several servers, and the diagnosis subsystem includes several diagnosis modules.

[0007] The preset chronic disease types include hypertension, coronary heart disease, stroke, diabetes, obesity, asthma, chronic rhinitis, chronic obstructive pulmonary disease, benign tumors, depression, chronic liver disease, chronic kidney disease, etc.

[0008] Each patient mobile module is preset with a set of monitoring items, including blood sugar, diastolic blood pressure, systolic blood pressure, blood oxygen concentration, total cholesterol, alanine aminotransferase and aspartate aminotransferase.

[0009] As a preferred embodiment of the present invention, the mobile monitoring module obtains the blood glucose concentration e1 of chronic disease patients through a portable non-invasive blood glucose meter based on biosensors and transdermal electrode measurement technology at preset intervals, and obtains diastolic blood pressure e2, systolic blood pressure e3 and blood oxygen concentration e4 through a portable vital sign monitoring device at preset intervals; and obtains alanine aminotransferase concentration e5 and aspartate aminotransferase concentration e6 through regular blood tests at preset intervals. The mobile monitoring module uses time as the vertical axis and the concentration values ​​obtained from each monitoring item as the vertical axis to draw images of blood glucose concentration e1, diastolic blood pressure e2, systolic blood pressure e3, blood oxygen concentration e4, alanine aminotransferase concentration e5 and aspartate aminotransferase concentration e6 changing with time t. The image is fitted by the function ei=fi(t), and the first-order derivative of each function is obtained by the formula ei´=dfi(t) / dt. When the value of the monitoring indicator ei is greater than its own preset threshold value eimax and ei´ is greater than its own preset threshold value ei´max, the corresponding alarm signal is generated, where i is the number of the monitoring item, i=1, 2, 3, 4, 5, 6.

[0010] As a preferred embodiment of the present invention, the doctor-patient interaction unit provides a patient-side input touch keyboard and output display screen for follow-up visits and doctor-patient Q&A, and converts the information into a data stream after the patient inputs the follow-up visit and Q&A information.

[0011] The information recording unit obtains the patient's examination report, examination images, physical examination form, medicine prescription, doctor's order and medical history and converts it into data stream.

[0012] The alarm signal, the follow-up information input by the patient, the question and answer information and the data stream of the information recording unit are integrated into the patient-side data stream and imported into the service bus.

[0013] As a preferred embodiment of the present invention, each server obtains its own CPU occupancy rate a1, memory occupancy rate a2 and hard disk occupancy rate a3 at preset intervals, and calculates the occupancy rate parameter A by formula A=k1*a1+k2*a2+k3*a3, wherein k1, k2 and k3 are preset weight factors. When the value of the occupancy rate parameter of the server is less than the preset threshold value one, a first-level heartbeat packet is sent to the service bus, when the value of the occupancy rate of the server is greater than or equal to the preset threshold value one and less than the preset threshold value two, a second-level heartbeat packet is sent to the service bus, and when the value of the occupancy rate of the server is greater than or equal to the preset threshold value two, the heartbeat packet is stopped from being sent to the service bus, and the preset threshold value two is greater than the preset threshold value one.

[0014] As a preferred embodiment of the present invention, the service bus preferentially distributes the patient-side data stream evenly to the server that sends the primary heartbeat packet, and when there is no service that sends the primary heartbeat packet, the data stream is evenly distributed to the server that sends the secondary heartbeat packet, and the server that sends the primary heartbeat packet and the secondary heartbeat packet is used as the output interface of the service bus, and each patient mobile module is used as the input interface, and the server with the smallest occupancy parameter A is used as the optimal server;

[0015] The topological structure diagram of the service bus. Each input structure obtains input data, each output interface outputs data, and each node is a data forwarding point in the service bus. All nodes are self-checked at preset intervals to obtain their own bandwidth occupancy rate μ. Let the state vector of the input data of each input interface be , where a11 is the IP address of the patient mobile module, a12 is the IP address of the optimal server, a13 is the number of bytes in the input data file, a21 is the number of bytes occupied by the text part of the input data, a22 is the number of bytes occupied by the image part of the input data, and a23 is the number of bytes occupied by the voice part of the input data; a31, a32 and a33 are three parameters of the noise vector, and their initial values ​​are 0. Each node is self-checked at a preset time to obtain the node's own IP address r11 and the maximum number of file bytes r13 that the node can accommodate, the maximum number of text data bytes r21 that the node can accommodate, the number of image data bytes r22, the number of voice data bytes r23, the waveform noise disturbance r31 caused by the node's own forwarding data, the synchronization time deviation r32 and the file forwarding packet loss rate r33, and form the node disturbance vector , where r12 is the target node element. Every preset time, each node obtains the node perturbation vector of all subordinate nodes connected to the node, obtains the element value, and uses the formula Calculate its forwarding efficiency index ER, obtain the node with the smallest forwarding efficiency index ER and record it as the optimal node, obtain its IP address, and set the value of the target node element r12 of the node to the IP address. The target node element value of the last level node is the IP address a12 of the optimal server.

[0016] As a preferred method of the present invention, the path formed by all the optimal nodes is recorded as the optimal path, and the disturbance vectors of all the optimal nodes are obtained. Get the transmission vector Q.

[0017] Expressing formulas through data changes Calculate the final state vector XC. By formula Calculate the perturbation coefficients, where p1, p2, and p3 are preset weight factors.

[0018] As a preferred embodiment of the present invention, the service bus counts the disturbance coefficients EA of all input data at preset time intervals and sums them up to obtain a total disturbance index E0. When the total disturbance index E0 is less than a total disturbance index threshold value E0min, nodes whose bandwidth occupancy μ is lower than a preset occupancy threshold value μmin are closed. When the total disturbance index E0 is greater than a total disturbance index threshold value E0max, all nodes are opened, and the value of the total disturbance index threshold value E0min is less than the total disturbance index threshold value E0max.

[0019] When the server in the server subsystem receives the data sent by the service bus, it classifies and saves the data according to the value of the IP address a11 of the patient's mobile module, and generates a structured data table classified by the patient's mobile module number.

[0020] When the server in the server subsystem receives the data sent by the service bus, it classifies and saves it according to the value of the IP address a11 of the patient mobile module.

[0021] When the diagnosis subsystem sends a call request, it obtains the IP address b11 of the diagnosis subsystem and the IP address b12 of the target mobile patient module to be called, and forms an access vector (b11, b12). The access vector is sent to all servers, and each server performs a self-check. When the IP address a11=b12 of the patient mobile module stored in the server disk is retrieved, all data stored in the IP address of the patient mobile module is output and sent to the diagnosis module with IP status b11 in the diagnosis subsystem through the optimal path.

[0022] The server obtains the serial number of each patient's mobile module and the preset chronic disease type, where the chronic disease types include hypertension, coronary heart disease, stroke, diabetes, obesity, asthma, chronic rhinitis, chronic obstructive pulmonary disease, benign tumors, depression, chronic liver disease, chronic kidney disease, etc., and matches each chronic disease type with a preset type parameter Y1 according to the threat level; obtains the monitoring project data sent by each patient's mobile module through the service bus, and obtains the inspection report, inspection image, physical examination form, medical prescription, doctor's order and medical record and other information of the information recording unit in each patient's mobile module. The total number of alarm signals generated each month N is counted in the monitoring project data. Extract the item values ​​in the physical examination form that are higher than the threshold, record them as abnormal values ​​and number them according to the value γ of the part exceeding the threshold and the respective threshold γ´ z=γ / γ´, and record them from high to low as h1, h2,...hj, where j is the number of the abnormal value. Through the formula Calculate the review evaluation index ω, where a1 is the preset impact index. Calculate the review evaluation index ω once every preset time, and divide the review evaluation index ω obtained at the current time by the review evaluation index ω calculated at the previous preset time to obtain the review recovery rate η. Obtain the patient name and number corresponding to the patient mobile module whose review recovery rate η is higher than the preset threshold value ηmax, mark it as a low recovery patient and highlight it in red in the data table of the server. Obtain the patient name and number corresponding to the patient mobile module whose review recovery rate η is lower than the preset threshold value ηmmin, mark it as a high recovery patient and highlight it in green in the data table of the server. Collect the names and numbers of all low recovery patients and high recovery patients to generate a low recovery patient data table. The diagnostic module in the diagnostic subsystem can retrieve the low recovery patient data table from the server through data transmission of the service bus.

[0023] Compared with the prior art, the present invention has the following beneficial effects:

[0024] (1) The present invention uses a mobile monitoring module to obtain blood glucose concentration, diastolic blood pressure, systolic blood pressure and blood oxygen concentration of chronic disease patients at preset intervals based on biosensors and transdermal electrode measurement technology, and determines whether to generate an alarm signal through data analysis and calculation, thereby improving the real-time monitoring of vital signs of chronic disease patients;

[0025] (2) The present invention uses the self-check judgment of the server to comprehensively consider the CPU occupancy rate, memory occupancy rate and hard disk occupancy rate, and preferentially sends data to the server with the smaller occupancy rate according to the specific value of the occupancy rate, thereby improving the efficiency of data processing and storage;

[0026] (3) The present invention obtains the optimal forwarding node through regular self-checking of each node in the service bus, and transmits the data stream through the optimal transmission path composed of each optimal node, thereby improving the efficiency of data transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings;

[0028] Figure 1 It is the overall block diagram of the system of the present invention;

[0029] Figure 2 It is a schematic diagram of the topological structure of the service bus of the present invention. DETAILED DESCRIPTION

[0030] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0031] See also Figure 1 As shown, the chronic disease health management platform based on the enterprise service bus includes a patient feedback subsystem, a server subsystem, a service bus, a diagnosis subsystem and a management statistics module, wherein the patient feedback subsystem includes several patient mobile modules, each patient mobile module includes a mobile monitoring unit, a patient interaction unit and an information recording unit, the server subsystem includes several servers, and the diagnosis subsystem includes several diagnosis modules.

[0032] Preset chronic disease types include hypertension, coronary heart disease, stroke, diabetes, obesity, asthma, chronic rhinitis, chronic obstructive pulmonary disease, benign tumors, depression, chronic liver disease, chronic kidney disease, etc.

[0033] Each patient mobile module is preset with a set of monitoring items, including blood sugar, diastolic blood pressure, systolic blood pressure, blood oxygen concentration, total cholesterol, alanine aminotransferase and aspartate aminotransferase.

[0034] The mobile monitoring module obtains the blood glucose concentration e1 of chronic disease patients through a portable non-invasive blood glucose meter based on biosensor and transdermal electrode measurement technology at preset time intervals, and obtains diastolic blood pressure e2, systolic blood pressure e3 and blood oxygen concentration e4 through a portable vital sign monitoring device at preset time intervals; and obtains alanine aminotransferase concentration e5 and aspartate aminotransferase concentration e6 through regular blood tests at preset time intervals. The mobile monitoring module uses time as the vertical axis and the concentration values ​​obtained from each monitoring item as the vertical axis to draw images of blood glucose concentration e1, diastolic blood pressure e2, systolic blood pressure e3, blood oxygen concentration e4, alanine aminotransferase concentration e5 and aspartate aminotransferase concentration e6 changing with time t. The image is fitted by the function ei=fi(t), and the first-order derivative of each function is obtained by the formula ei´=dfi(t) / dt. When the value of the monitoring indicator ei is greater than its own preset threshold value eimax and ei´ is greater than its own preset threshold value ei´max, the corresponding alarm signal is generated, where i is the number of the monitoring item, i=1, 2, 3, 4, 5, 6.

[0035] The doctor-patient interaction unit provides a patient-side input touch keyboard and output display screen for follow-up visits and doctor-patient Q&A. When the patient enters the follow-up visit and Q&A information, the information is converted into a data stream.

[0036] The information recording unit obtains the patient's examination report, examination images, physical examination form, medicine prescription, doctor's order and medical history and converts it into data stream.

[0037] The alarm signal, the follow-up information input by the patient, the question and answer information and the data stream of the information recording unit are integrated into the patient-side data stream and imported into the service bus.

[0038] Each server obtains its own CPU occupancy rate a1, memory occupancy rate a2 and hard disk occupancy rate a3 at preset intervals, and calculates the occupancy rate parameter A by the formula A=k1*a1+k2*a2+k3*a3, where k1, k2 and k3 are preset weight factors, and their sizes are custom settings. When the value of the server's occupancy rate parameter is less than the preset threshold value one, a first-level heartbeat packet is sent to the service bus. When the value of the server's occupancy rate is greater than or equal to the preset threshold value one and less than the preset threshold value two, a second-level heartbeat packet is sent to the service bus. When the value of the server's occupancy rate is greater than or equal to the preset threshold value two, the server stops sending heartbeat packets to the service bus, and the preset threshold value two is greater than the preset threshold value one.

[0039] The service bus prioritizes evenly distributing the patient-side data flow to the server that has sent the first-level heartbeat packet. When there is no service that sends the first-level heartbeat packet, the data flow is evenly distributed to the server that has sent the second-level heartbeat packet. The server that sends the first-level heartbeat packet and the second-level heartbeat packet is used as the output interface of the service bus, and each patient mobile module is used as the input interface. The server with the smallest occupancy parameter A is used as the optimal server.

[0040] See also Figure 2 As shown in the figure, the topology diagram of the service bus is shown. Each input structure obtains input data, each output interface outputs data, and each node is a data forwarding point in the service bus. All nodes are self-checked at preset intervals to obtain their own bandwidth occupancy rate μ. Let the state vector of the input data of each input interface be , where a11 is the IP address of the patient mobile module, a12 is the IP address of the optimal server, a13 is the number of bytes in the input data file, a21 is the number of bytes occupied by the text part of the input data, a22 is the number of bytes occupied by the image part of the input data, and a23 is the number of bytes occupied by the voice part of the input data; a31, a32 and a33 are three parameters of the noise vector, and their initial values ​​are 0. Each node is self-checked at a preset time to obtain the node's own IP address r11 and the maximum number of file bytes r13 that the node can accommodate, the maximum number of text data bytes r21 that the node can accommodate, the number of image data bytes r22, the number of voice data bytes r23, the waveform noise disturbance r31 caused by the node's own forwarding data, the synchronization time deviation r32 and the file forwarding packet loss rate r33, and form the node disturbance vector , where r12 is the target node element. Every preset time, each node obtains the node perturbation vector of all subordinate nodes connected to the node, obtains the element value, and uses the formula Calculate its forwarding efficiency index ER, obtain the node with the smallest forwarding efficiency index ER and record it as the optimal node, obtain its IP address, and set the value of the target node element r12 of the node to the IP address. The target node element value of the last level node is the IP address a12 of the optimal server.

[0041] Record the path formed by all optimal nodes, record it as the optimal path, obtain the perturbation vector of all optimal nodes, and use the formula Get the transmission vector Q.

[0042] Expressing formulas through data changes Calculate the final state vector XC. By formula Calculate the perturbation coefficient EA, where p1, p2 and p3 are preset weight factors and their sizes are custom settings.

[0043] The service bus counts the disturbance coefficients EA of all input data at preset intervals and sums them up to obtain the total disturbance index E0. When the total disturbance index E0 is less than the total disturbance index threshold value E0min, the nodes whose bandwidth occupancy rate μ is lower than the preset occupancy rate threshold value μmin are closed. When the total disturbance index E0 is greater than the total disturbance index threshold value E0max, all nodes are opened, and the value of the total disturbance index threshold value E0min is less than the total disturbance index threshold value E0max.

[0044] When the server in the server subsystem receives the data sent by the service bus, it classifies and saves the data according to the value of the IP address a11 of the patient's mobile module, and generates a structured data table classified by the patient's mobile module number.

[0045] When the diagnosis subsystem sends a call request, it obtains the IP address b11 of the diagnosis subsystem and the IP address b12 of the target mobile patient module to be called, and forms an access vector (b11, b12). The access vector is sent to all servers, and each server performs a self-check. When the IP address a11=b12 of the patient mobile module stored in the server disk is retrieved, all data stored in the IP address of the patient mobile module is output and sent to the diagnosis module with IP status b11 in the diagnosis subsystem through the optimal path.

[0046] The server obtains the serial number of each patient's mobile module and the preset chronic disease type, where the chronic disease types include hypertension, coronary heart disease, stroke, diabetes, obesity, asthma, chronic rhinitis, chronic obstructive pulmonary disease, benign tumors, depression, chronic liver disease, chronic kidney disease, etc., and matches each chronic disease type with a preset type parameter Y1 according to the threat level; obtains the monitoring project data sent by each patient's mobile module through the service bus, and obtains the inspection report, inspection image, physical examination form, medical prescription, doctor's order and medical record and other information of the information recording unit in each patient's mobile module. The total number of alarm signals generated each month N is counted in the monitoring project data. Extract the value of the item in the physical examination form that is higher than the threshold, record it as abnormal value and number it according to the value γ of the part exceeding the threshold and the respective threshold γ´ z=γ / γ´, and record it from high to low as h1, h2,...hj, where j=3, 4,..., n1, n1 is the total number of abnormal values. By formula Calculate the review evaluation index ω, where a1 is a preset impact index, and its size is a custom value. Calculate the review evaluation index ω once every preset time, and divide the review evaluation index ω obtained at the current time by the review evaluation index ω calculated at the previous preset time to obtain the review recovery rate η. Obtain the patient name and number corresponding to the patient mobile module whose review recovery rate η is higher than the preset threshold value ηmax, mark it as a low recovery patient and highlight it in red in the data table of the server. Obtain the patient name and number corresponding to the patient mobile module whose review recovery rate η is lower than the preset threshold value ηmmin, mark it as a high recovery patient and highlight it in green in the data table of the server. Collect the names and numbers of all low recovery patients and high recovery patients to generate a low recovery patient data table. The diagnostic module in the diagnostic subsystem can retrieve the low recovery patient data table from the server through the data transmission of the service bus. The formulas involved in this application are all empirical formulas in which the parameters are dimensioned and their values ​​are taken for calculation.

[0047] It should be understood that the terms "include" and "comprising" used in the specification and claims of the present disclosure indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0048] It should also be understood that the terms used in this disclosure are only for the purpose of describing specific embodiments and are not intended to limit the disclosure. As used in this disclosure and claims, the singular forms "a", "an", and "the" are intended to include plural forms unless the context clearly indicates otherwise. It should also be further understood that the term "and / or" used in this disclosure and claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations;

[0049] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A chronic disease health management platform based on an enterprise service bus, including a server subsystem and a service bus, characterized by: The server in the server subsystem obtains its own CPU occupancy, memory occupancy and hard disk occupancy at preset intervals, and obtains the occupancy parameter A through calculation; when the value of the server occupancy parameter is less than the preset threshold value one, a first-level heartbeat packet is sent to the service bus; when the value of the server occupancy is greater than or equal to the preset threshold value one and less than the preset threshold value two, a second-level heartbeat packet is sent to the service bus; when the value of the server occupancy is greater than or equal to the preset threshold value two, the heartbeat packet is stopped from being sent to the service bus, and the preset threshold value two is greater than the preset threshold value one; the service bus preferentially distributes the patient-side data stream evenly to the server that has sent the first-level heartbeat packet, and when there is no service that sends the first-level heartbeat packet, the data stream is evenly distributed to the server that has sent the second-level heartbeat packet, and the server that sends the first-level heartbeat packet and the second-level heartbeat packet is used as the output interface of the service bus, and each patient mobile module is used as the input interface, and the server with the smallest occupancy parameter A is used as the optimal server; the server obtains the number of the patient mobile module, the monitoring project data and the data of the information recording unit obtained to perform chronic disease health management; The server obtains the serial number and preset chronic disease type of each patient mobile module, and matches a preset category parameter Y1 to each chronic disease type according to the threat level; obtains the monitoring project data sent by each patient mobile module through the service bus, and obtains the inspection report, inspection image, physical examination form, medicine prescription, doctor's order and medical record information of the information recording unit in each patient mobile module; counts the total number of alarm signals generated each month N in the monitoring project data; extracts the item values ​​in the physical examination form that are higher than the threshold, records them as abnormal values ​​and numbers them according to the value γ of the part exceeding the threshold and the respective threshold γ´ z=γ / γ´, and records them from high to low as h1, h2, ...hj, where j is the total number of abnormal values; through the formula Calculate the review evaluation index ω, where a1 is the preset impact index; Calculate the review evaluation index ω once every preset time, divide the review evaluation index ω obtained at the current time by the review evaluation index ω calculated at the previous preset time to obtain the review recovery rate η; obtain the patient name and number corresponding to the patient mobile module whose review recovery rate η is higher than the preset threshold value ηmax, mark it as a low recovery patient and highlight it in red in the data table of the server; obtain the patient name and number corresponding to the patient mobile module whose review recovery rate η is lower than the preset threshold value ηmmin, mark it as a high recovery patient and highlight it in green in the data table of the server; collect the names and numbers of all low recovery patients and high recovery patients to generate a low recovery patient data table; the diagnosis module in the diagnosis subsystem can retrieve the low recovery patient data table from the server through the data transmission of the service bus; Each input structure in the service bus obtains input data, each output interface outputs data, and each node is a data forwarding point in the service bus, generating a state vector of the input data of each input interface; Let a11 be the IP address of the patient mobile module, a12 be the IP address of the optimal server, a13 be the number of bytes in the input data file, a21 be the number of bytes occupied by the text part of the input data, a22 be the number of bytes occupied by the image part of the input data, and a23 be the number of bytes occupied by the voice part of the input data; a31, a32 and a33 are the three parameters of the noise vector, whose initial values ​​are 0, and the state vector of the generated input data is ; Each node is made to self-check at a preset time interval to generate a node disturbance vector. Get the node's own IP address r11 and the maximum number of file bytes that the node can accommodate r13, get the maximum number of text data bytes that the node can accommodate r21, image data bytes r22, voice data bytes r23, get the waveform noise disturbance r31 caused by the node's own forwarding data, synchronization time deviation r32 and file forwarding packet loss rate r33, and form the node disturbance vector ; At preset intervals, each node obtains the node disturbance vectors of all lower-level nodes connected to the node, obtains the element values ​​therein and calculates its forwarding efficiency index ER, obtains the node with the smallest forwarding efficiency index ER and records it as the optimal node, obtains its IP address, and sets the value of the target node element r12 of the node to the IP address; the target node element value of the last-level node is the IP address a12 of the optimal server; records the path formed by all optimal nodes and records it as the optimal path; obtains the disturbance vectors of all optimal nodes and analyzes them to obtain the transmission vector; calculates the final state vector through the data change expression formula; and then obtains the disturbance coefficient through calculation; optimizes the transmission node based on the value of the disturbance coefficient; At preset intervals, all nodes are made to self-check and obtain their own bandwidth occupancy rate μ; at preset intervals, the service bus counts the disturbance coefficients EA of all input data and sums them up to obtain the total disturbance index E0; when the total disturbance index E0 is less than the total disturbance index threshold value E0min, the nodes whose bandwidth occupancy rate μ is lower than the preset occupancy rate threshold value μmin are closed; when the total disturbance index E0 is greater than the total disturbance index threshold value E0max, all nodes are opened, and the value of the total disturbance index threshold value E0min is less than the total disturbance index threshold value E0max; When the server in the server subsystem receives the data sent by the service bus, it classifies and saves the data according to the value of the IP address a11 of the patient's mobile module, and generates a structured data table classified by the patient's mobile module number; When the server in the server subsystem receives data from the service bus, it classifies and saves it according to the value of the IP address a11 of the patient mobile module; when the diagnosis subsystem sends a call request, it obtains the IP address of the diagnosis subsystem and the IP address of the target mobile patient module to be called, forms an access vector and sends it to all servers, each server performs self-check, outputs all data saved with the IP address of the patient mobile module, and sends it to the diagnosis module with IP status b11 in the diagnosis subsystem through the optimal path.

2. The chronic disease health management platform based on enterprise service bus according to claim 1 is characterized in that: It also includes a mobile monitoring unit, a doctor-patient interaction unit, and an information recording unit: The mobile monitoring unit obtains the blood glucose concentration of chronic disease patients through a portable non-invasive blood glucose meter based on biosensor and transdermal electrode measurement technology at preset intervals, and obtains diastolic blood pressure, systolic blood pressure and blood oxygen concentration through a portable vital sign monitoring device at preset intervals; obtains alanine aminotransferase concentration and aspartate aminotransferase concentration through regular blood tests at preset intervals, and determines whether to generate an alarm signal through calculation; The doctor-patient interaction unit provides a patient-side input touch keyboard and output display screen for return visits and doctor-patient Q&A. When the patient inputs the return visit and Q&A information, the information is converted into a data stream; The information recording unit obtains the patient's examination report, examination image, physical examination form, medicine prescription, doctor's order and medical record information and converts them into data stream; The alarm signal, the follow-up information input by the patient, the question and answer information and the data stream of the information recording unit are integrated into the patient-side data stream and imported into the service bus.

3. The chronic disease health management platform based on enterprise service bus according to claim 2 is characterized in that: The specific process of determining whether an alarm signal is generated through calculation is as follows: With time as the vertical axis and the concentration value obtained from each monitoring item as the vertical axis, images of blood glucose concentration e1, diastolic blood pressure e2, systolic blood pressure e3, blood oxygen concentration e4, alanine aminotransferase concentration e5 and aspartate aminotransferase concentration e6 changing with time t are plotted respectively; the image is fitted by the function ei=fi(t), and the first-order derivative of each function is obtained by the formula ei´=dfi(t) / dt. When the value of the monitoring indicator ei is greater than its own preset threshold value eimax and ei´ is greater than its own preset threshold value ei´max, the corresponding alarm signals are generated, where i is the number of the monitoring item, i=1, 2, 3, 4, 5, 6.

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