An Internet of Things management system and management method for deep vein thrombosis prevention equipment

Abstract

The present invention is applicable to the field of Internet of Things management technology, and in particular, relates to an Internet of Things management system and management method for deep vein thrombosis prevention equipment, the method comprising: constructing a historical data set for deep vein thrombosis treatment, wherein the historical data set comprises: usage time, clinical data and equipment parameters, and using usage time as the horizontal coordinate and equipment parameters as the vertical coordinate to draw a query curve to obtain the patient's deep vein thrombosis diagnosis data, wherein the diagnostic data comprises the patient's unique identification number, medical history and diagnosis results, and using the medical history to correct the query curve to obtain a personal parameter curve. The present invention, by constructing a multi-level control architecture, can classify patients according to their clinical course, use terminal patients to adjust the equipment parameters of early patients, and use patients in the same period to provide adjustment participation, thereby providing patients with personalized treatment, identifying potential problems in advance, and improving the use effect of prevention equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things management, and in particular to an Internet of Things management system and a management method for deep vein thrombosis prevention equipment. Background Art

[0002] Patients who have undergone major orthopedic surgery on their lower limbs are bedridden for a long time, which leads to slow blood circulation in their lower limbs and blood stasis, which in turn causes deep vein thrombosis. Currently, the main methods for preventing deep vein thrombosis in the lower limbs include drug prevention and mechanical prevention. Among them, mechanical prevention mainly includes preventive equipment such as elastic stockings and intermittent pneumatic compression devices.

[0003] The above-mentioned deep vein thrombosis prevention devices are generally integrated with Internet of Things modules to transmit the usage data of the prevention equipment to the cloud or medical management platform in real time, so that doctors and nurses can remotely track the patient's health status and adjust the treatment plan in time. However, due to limited medical resources, the adjustment of the treatment plan has a certain period of time.

[0004] After the patient is diagnosed through the Internet of Things module, he or she will follow the doctor's advice and set the device parameters such as the pressure range, inflation cycle and number of airbags of the preventive device to a constant value, and continue treatment according to this constant parameter. If no diagnosis is performed again, the patient will continue to use the preventive device according to this constant parameter; as a result, the patient will not be able to obtain the best treatment effect; therefore, "how to adaptively adjust the device parameters of the preventive device" is the technical problem that the present invention needs to solve. Summary of the invention

[0005] The purpose of the present invention is to provide an Internet of Things management system and management method for deep vein thrombosis prevention equipment to solve the problem of "how to adaptively adjust the equipment parameters of the prevention equipment" raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] An Internet of Things and management method for deep vein thrombosis prevention equipment, the method comprising:

[0008] Constructing a historical data set for deep vein thrombosis treatment, wherein the historical data set includes: usage time, clinical data and device parameters, and using the usage time as the horizontal axis and the device parameters as the vertical axis to draw a query curve, and obtain the patient's deep vein thrombosis diagnosis data, wherein the diagnostic data includes the patient's unique identification number, medical history and diagnosis results, using the medical history to correct the query curve, obtain a personal parameter curve, and set the fluctuation range of the vertical axis;

[0009] Constructing a management platform composed of a multi-level control architecture, wherein the multi-level control architecture consists of a top layer, a parent layer, and a child layer, integrating definition rules into the top layer, determining registrants in the management platform, creating nodes corresponding to the registrants one by one, and configuring the corresponding relationship between the nodes, unique identification numbers, and prevention devices;

[0010] Using the diagnostic data, determine the health score of each patient, determine whether the health score is greater than a preset threshold, and if so, mount the corresponding node to the parent layer based on the corresponding relationship, and if not, mount it to the child layer, and embed the permission control strategy into the multi-level control architecture;

[0011] The authority control strategy is: issuing the control authority of the prevention equipment in the parent layer to the top layer, and issuing the control authority of the prevention equipment in the child layer to the parent layer;

[0012] Based on the management platform, after identifying the power-on signal of the target device, the usage time of the target device is read, the personal parameter curve is queried, the target parameters are obtained, the permission control strategy is activated, the front layer and the current layer are defined, and a consultation request is sent to the online nodes in the front layer and the current layer, and the consultation result is received;

[0013] The consulting result of the previous layer is used to offset the target parameter within the fluctuation range and adjust the preventive device. The consulting result of the current layer is used to generate an adjustment reference and send it to the patient terminal.

[0014] Furthermore, the step of constructing a historical data set of deep vein thrombosis treatment includes:

[0015] From the existing electronic health records, we defined the DVT survivors, defined the time when the survivors first used the prevention device as the start time, and calculated the duration of use by the survivors;

[0016] Record the equipment parameters of the preventive equipment and establish a mapping between equipment parameters and usage time;

[0017] Extracting clinical data of the patient during the consultation from the electronic health record, recording the consultation time, and converting the consultation time into usage time;

[0018] Integrate the device parameters, usage time and clinical data of all deep vein thrombosis survivors to build a historical data set.

[0019] Further, the step of obtaining the patient's deep vein thrombosis diagnosis data, wherein the diagnosis data includes the patient's unique identification number, medical history and diagnosis result, using the medical history to correct the query curve to obtain a personal parameter curve, and setting the fluctuation range of the vertical coordinate includes:

[0020] Traversing the influencing factors of the diagnostic data, wherein the influencing factors at least include: age and behavioral habits;

[0021] The influencing factors are used to fine-tune the ordinate in the personal parameter curve.

[0022] Furthermore, the definition rule is:

[0023] Querying a preset comparison table to authenticate the identity of the registrant, wherein the comparison table at least includes: doctors and professional scholars;

[0024] If the registrant passes the identity authentication, the node corresponding to the registrant is placed in the top layer.

[0025] Furthermore, the step of constructing a management platform consisting of a multi-level control architecture includes:

[0026] In the management platform, a mutual assistance community is created and the historical data set is released;

[0027] Build communication links between the top layer and the parent layer, and between the top layer and the child layer.

[0028] Furthermore, the method further comprises:

[0029] Opening the top layer to the adjustment authority of the nodes in the parent layer and the child layer;

[0030] Constructing a classification model, and inputting the historical data set into the classification model, and outputting a recovery feature;

[0031] When the management platform detects that the position of a node in the parent layer or child layer has changed, a change item is defined, the corresponding relationship is traced back, and it is determined whether there is a recovery feature in the change item. If so, the position of the change item is adjusted. If not, the change item and diagnostic data are integrated, and the integration result is pushed to the preset terminal.

[0032] Furthermore, the step of obtaining the target parameters includes:

[0033] After the power-on signal is identified, the source of the power-on signal is traced back and the source is defined as the target device;

[0034] defining a position of the target device in a multi-level control architecture as a current level;

[0035] The adjustment reference is integrated into a preset pop-up window, and "yes" and "no" buttons are set to obtain an option pop-up window, and the option pop-up window is pushed to the patient terminal.

[0036] Furthermore, the system comprises:

[0037] A construction module is used to construct a historical data set for deep vein thrombosis treatment, wherein the historical data set includes: usage time, clinical data and device parameters, and uses the usage time as the horizontal axis and the device parameters as the vertical axis to draw a query curve to obtain the patient's deep vein thrombosis diagnosis data, wherein the diagnostic data includes the patient's unique identification number, medical history and diagnosis results, and uses the medical history to correct the query curve to obtain a personal parameter curve, and set the fluctuation range of the vertical axis;

[0038] A configuration module is used to construct a management platform composed of a multi-level control architecture, wherein the multi-level control architecture consists of a top layer, a parent layer, and a child layer, and integrates definition rules into the top layer, determines registrants in the management platform, creates nodes corresponding to the registrants, and configures the corresponding relationship between the nodes, unique identification numbers, and prevention devices;

[0039] An embedding module, used to use the diagnostic data to determine the health score of each patient, determine whether the health score is greater than a preset threshold, and if so, mount the corresponding node to the parent layer based on the corresponding relationship, and if not, mount it to the child layer, and embed the authority control strategy into the multi-level control architecture;

[0040] The authority control strategy is: issuing the control authority of the prevention equipment in the parent layer to the top layer, and issuing the control authority of the prevention equipment in the child layer to the parent layer;

[0041] A receiving module is used to read the usage time of the target device, query the personal parameter curve, obtain the target parameter, activate the authority control strategy, define the front layer and the current layer, send a consultation request to the online nodes in the front layer and the current layer, and receive the consultation result after identifying the power-on signal of the target device based on the management platform;

[0042] The sending module is used to use the consultation results of the previous layer to offset the target parameters within the fluctuation range and adjust the prevention equipment, and use the consultation results of the current layer to generate an adjustment reference and send it to the patient terminal.

[0043] Furthermore, the building blocks include:

[0044] A calculation unit, used for defining a recovered person of deep vein thrombosis from an existing electronic health record, defining the time when the recovered person first uses the prevention device as the start time, and calculating the use time of the recovered person;

[0045] Establish a unit for recording the equipment parameters of the preventive equipment and establishing a mapping between the equipment parameters and the usage time;

[0046] A conversion unit, used to extract clinical data of the patient during the consultation from the electronic health record, record the consultation time, and convert the consultation time into usage time;

[0047] Integration unit, used to integrate the device parameters, usage time and clinical data of all DVT survivors to build a historical data set;

[0048] A traversal unit, used to traverse the influencing factors of the diagnostic data, wherein the influencing factors at least include: age and behavioral habits;

[0049] The fine-tuning unit is used to fine-tune the vertical coordinate in the personal parameter curve by using the influencing factors.

[0050] Furthermore, the configuration module includes:

[0051] A publishing unit, used to create a mutual aid community in the management platform and publish the historical data set;

[0052] The building unit is used to build a communication link between the top layer and the parent layer, and between the top layer and the child layer.

[0053] Compared with the prior art, the present invention has the following beneficial effects:

[0054] By constructing historical data sets, data support can be provided for the adjustment of equipment parameters. By constructing personal parameter curves and fluctuation ranges, deviations in equipment parameters can be avoided, thereby greatly improving the prevention effect while providing personalized treatment to patients. By building a multi-level control architecture, patients can be graded according to their clinical course, and equipment parameters for early patients can be adjusted using terminal patients, and adjustment references can be provided using patients of the same period, thereby providing patients with personalized treatment, identifying potential problems in advance, and improving the effectiveness of preventive equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 A flowchart of an Internet of Things management method for a deep vein thrombosis prevention device provided in an embodiment of the present invention;

[0056] Figure 2 A first sub-flow chart of the method for managing a deep vein thrombosis prevention device using the Internet of Things provided by an embodiment of the present invention;

[0057] Figure 3 A second sub-flow chart of the method for managing a deep vein thrombosis prevention device using the Internet of Things provided by an embodiment of the present invention;

[0058] Figure 4 A third sub-flow chart of the Internet of Things management method for a deep vein thrombosis prevention device provided in an embodiment of the present invention;

[0059] Figure 5 A block diagram of the composition of the Internet of Things management system for the deep vein thrombosis prevention device provided in an embodiment of the present invention;

[0060] Figure 6 A block diagram of the components of the building blocks in the Internet of Things management system for the deep vein thrombosis prevention device provided in an embodiment of the present invention;

[0061] Figure 7 A block diagram of the configuration module in the Internet of Things management system of the deep vein thrombosis prevention device provided in an embodiment of the present invention;

[0062] Figure 8 A block diagram of the composition of a receiving module in an Internet of Things management system for a deep vein thrombosis prevention device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0063] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0064] In Example 1, Figure 1 The implementation process of the Internet of Things management method for deep vein thrombosis prevention equipment provided by the embodiment of the present invention is shown, and is described in detail below, as follows:

[0065] S100: Construct a historical data set for deep vein thrombosis treatment, wherein the historical data set includes: usage time, clinical data and equipment parameters, and draw a query curve with usage time as the horizontal axis and equipment parameters as the vertical axis to obtain the patient's deep vein thrombosis diagnosis data, wherein the diagnostic data includes the patient's unique identification number, medical history and diagnosis results, and use the medical history to correct the query curve to obtain a personal parameter curve, and set the fluctuation range of the vertical axis.

[0066] Based on the electronic health records of each patient, the recovered patients are identified. The recovered patients are those who once suffered from deep vein thrombosis but have recovered through the auxiliary treatment of prevention equipment. The treatment data of the recovered patients throughout the entire course of the disease are obtained to form a historical data set, which at least includes: the usage time of the prevention equipment, clinical data and equipment parameters, etc. The usage time in the historical data set is the first day of use, the second day of use and the third day of use, and so on. The equipment parameters are the parameters when the user uses the prevention equipment.

[0067] With the first day of use, the second day of use, and the third day of use as the horizontal axis and the daily device parameters as the vertical axis, a query curve is drawn, wherein each device parameter corresponds to a query curve, and the query curve is offset to a standard curve using a historical data set; the patient's diagnostic data is read from the patient's electronic health record to determine the patient's past medical history, wherein the medical history includes: a history of thrombosis or a history of tumor, etc.; the query curve is adjusted based on the medical history of each patient to obtain different personal parameter curves, and a fluctuation range is configured for the device parameters in the personal parameter curve; by constructing a personal parameter curve, patients can query device parameters that better suit their own condition based on their number of days of use.

[0068] S200: Construct a management platform composed of a multi-level control architecture, wherein the multi-level control architecture consists of a top layer, a parent layer and a child layer, and integrate definition rules into the top layer, determine the registrants in the management platform, create nodes corresponding to the registrants one by one, and configure the correspondence between the nodes, unique identity numbers and prevention devices.

[0069] A multi-level control architecture consisting of a top layer, a parent layer, and a child layer is created, wherein the parent layer is under the top layer, and the child layer is under the parent layer. Definition rules are embedded in the top layer. Only after verification of the definition rules can the nodes corresponding to the registrants be included in the top layer. Each registrant in the management platform corresponds to a node. In addition to deep vein thrombosis patients, the registrants here can also be doctors or professionals. The nodes are the virtual existence of the registrants in the management platform.

[0070] S300: using the diagnostic data, determining the health score of each patient, judging whether the health score is greater than a preset threshold, if yes, mounting the corresponding node to the parent layer based on the corresponding relationship, if no, mounting it to the child layer, and embedding the authority control strategy into the multi-level control architecture;

[0071] The authority control strategy is: issuing the control authority of the prevention equipment in the parent layer to the top layer, and issuing the control authority of the prevention equipment in the child layer to the parent layer.

[0072] The diagnostic data of each patient is cleaned, invalid or erroneous data is removed, missing values ​​are filled, and standardized. The changed diagnostic data is input into a health scoring model that integrates scoring standards to determine the health score of each patient. Nodes with health scores greater than the preset threshold are mounted in the parent layer, and nodes with health scores less than the preset threshold are mounted in the child layer; nodes in the top layer can control the preventive devices corresponding to the nodes in the parent layer, and nodes in the parent layer can control the preventive devices corresponding to the nodes in the child layer.

[0073] The control in this embodiment mainly involves the adjustment of device parameters.

[0074] S400: Based on the management platform, after identifying the power-on signal of the target device, read the usage time of the target device, query the personal parameter curve, obtain the target parameters, activate the permission control strategy, define the front layer and the current layer, and send a consultation request to the online nodes in the front layer and the current layer, and receive the consultation results.

[0075] Connect all users' prevention devices to the management platform. When the management platform detects the power-on signal, the prevention device that sends the power-on signal is defined as the target device. The patient's usage time of the target device is determined, and the personal parameter curve is queried to determine the device parameters that the patient should set. The device parameters are determined as the target parameters. The position of the target device in the multi-level control architecture is determined as the current layer, the upper layer of the current layer is defined as the front layer, and consultation requests are sent to the front layer and the current layer at the same time to obtain consultation results.

[0076] For example, if a patient's preventive device is located in the parent layer, the current layer is the parent layer, the previous layer is the top layer, and a consultation request is sent to the online nodes in the top layer and the parent layer. After receiving the consultation request, the patient at the corresponding node gives the consultation result based on his or her experience in using the preventive device.

[0077] S500: Using the consultation results of the previous layer, offset the target parameter within the fluctuation range and adjust the preventive device, and using the consultation results of the current layer, generate an adjustment reference and send it to the patient terminal.

[0078] According to the consultation results of the previous layer, the target parameters are offset, and the preventive equipment is adjusted using the offset target parameters; using the consultation results of the current layer, an adjustment reference is generated, and the adjustment reference is sent to the patient for reference.

[0079] In Example 2, Figure 2 The implementation process of the Internet of Things management method for deep vein thrombosis prevention equipment provided by an embodiment of the present invention is shown. The steps of constructing a historical data set for deep vein thrombosis treatment are described in detail as follows:

[0080] S101: From the existing electronic health records, define the patients who have recovered from deep vein thrombosis, define the time when the patients first used the prevention device as the start time, and calculate the duration of use by the patients.

[0081] Based on the patients’ diagnostic data, recoveries from deep vein thrombosis were defined, and the duration of use of preventive devices by the recoveries was recorded.

[0082] S102: Record the device parameters of the prevention device and establish a mapping between the device parameters and the usage time.

[0083] S103: extracting the clinical data of the patient during the consultation from the electronic health record, recording the consultation time, and converting the consultation time into usage time.

[0084] After the consultation, new clinical data will be generated, and the length of time the patient uses the preventive device at this time will be recorded as the usage time.

[0085] S104: Integrate the device parameters, usage time and clinical data of all patients recovering from deep vein thrombosis to construct a historical data set.

[0086] In this embodiment, it is assumed that a patient's condition has improved significantly after using the preventive device for 42 days, and the patient stops using the preventive device. The patient is determined to be a recovered person, and the recovery time of the recovered person is 42 days. The device parameters of the preventive device used by the recovered person every day are recorded. During the usage time, if the recovered person has a medical consultation, the clinical data of the consultation is recorded.

[0087] In Example 3, Figure 2 The implementation process of the Internet of Things management method for the deep vein thrombosis prevention device provided by the embodiment of the present invention is shown. The following is a detailed description of the steps of obtaining the patient's deep vein thrombosis diagnosis data, wherein the diagnosis data includes the patient's unique identification number, medical history and diagnosis result, using the medical history, correcting the query curve, obtaining a personal parameter curve, and setting the fluctuation range of the vertical coordinate, as follows:

[0088] S105: Traverse out the influencing factors of the diagnostic data, where the influencing factors at least include: age and behavioral habits.

[0089] Determine the factors that may affect the patient's diagnostic data and obtain the influencing factors; the influencing factors include age and behavioral habits, etc.; for example, two patients have the same course of disease, but when using preventive equipment, they need to adjust the equipment parameters according to their respective ages and behavioral habits, etc.

[0090] S106: Using the influencing factors, fine-tune the vertical coordinate in the personal parameter curve.

[0091] By fine-tuning the vertical coordinate in the personal parameter curve, patients can obtain more accurate device parameters.

[0092] In Example 4, different from Example 1, in this embodiment of the present invention, the definition rule is:

[0093] Querying a preset comparison table to authenticate the identity of the registrant, wherein the comparison table at least includes: doctors and professional scholars;

[0094] If the registrant passes the identity authentication, the node corresponding to the registrant is placed in the top layer.

[0095] Construct a comparison table, which consists of identity items, where the identity items should be professionals such as doctors and professional scholars; when a patient registers on the management platform, if the patient's identity information is the same as the identity item in the comparison table, the node corresponding to the patient will be classified into the top level.

[0096] In Example 5, Figure 3 The implementation process of the Internet of Things management method for deep vein thrombosis prevention equipment provided by an embodiment of the present invention is shown. The steps of constructing a management platform composed of a multi-level control architecture are described in detail as follows:

[0097] S201: In the management platform, a mutual aid community is created and the historical data set is released.

[0098] Upload historical data sets to the management platform and create an interactive community, allowing patients to upload, share and discuss information and experiences related to deep vein thrombosis prevention in the interactive community.

[0099] S202: Build communication links between the top layer and the parent layer, and between the top layer and the child layer.

[0100] Via the communication link, the positions of nodes within the patient parent and child layers in the top layer are adjusted.

[0101] In Example 6, Figure 4 The implementation process of the Internet of Things management method for deep vein thrombosis prevention equipment provided by an embodiment of the present invention is shown, and the steps of obtaining the target parameters are described in detail as follows:

[0102] S401: After a power-on signal is identified, the source of the power-on signal is traced back and the source is defined as a target device.

[0103] Identify the source of the power-on signal as the target device.

[0104] S402: Define the position of the target device in the multi-level control architecture as the current level.

[0105] S403: Integrate the adjustment reference into a preset pop-up window, set "yes" and "no" buttons, obtain an option pop-up window, and push the option pop-up window to the patient terminal.

[0106] When the patient terminal receives the adjustment reference, if the patient selects the "Yes" button, the preventive device will be adjusted according to the device parameters in the adjustment reference. If the patient selects the "No" button, the preventive device will not be changed.

[0107] In Example 7, different from Example 1, in this embodiment of the present invention, the method further includes:

[0108] Opening the top layer to the adjustment authority of the nodes in the parent layer and the child layer;

[0109] Constructing a classification model, and inputting the historical data set into the classification model, and outputting a recovery feature;

[0110] When the management platform detects that the position of a node in the parent layer or child layer has changed, a change item is defined, the corresponding relationship is traced back, and it is determined whether there is a recovery feature in the change item. If so, the position of the change item is adjusted. If not, the change item and diagnostic data are integrated, and the integration result is pushed to the preset terminal.

[0111] The nodes in the top layer can adjust the positions of the nodes in the parent layer and the child layer. When the management platform detects that the position of the node in the parent layer or the child layer has changed, the node with the changed position is determined as a change item, and it is checked whether the change item contains the recovery feature. If it does, the position of the change item is allowed to be adjusted; if it does not contain the recovery feature, the change item is integrated with the diagnostic data and pushed to the preset terminal, where the preset terminal can be the auditor terminal of the management platform.

[0112] For example, patient A in the sublayer has rich medical knowledge about deep vein thrombosis prevention and a strong sense of volunteer service. After verifying patient A's professional knowledge, the doctor or professional in the top layer can adjust patient A to the parent layer to facilitate patient A to set equipment parameters for other patients in the sublayer; however, before adjusting the position of patient A, it is necessary to determine whether patient A's condition is gradually improving to avoid patient A affecting his own prevention effect while volunteering to serve others.

[0113] Figure 5 The structure block diagram of the Internet of Things management system for deep vein thrombosis prevention equipment provided by an embodiment of the present invention is shown. The Internet of Things management system 1 for deep vein thrombosis prevention equipment includes:

[0114] A construction module 11 is used to construct a historical data set for deep vein thrombosis treatment, wherein the historical data set includes: usage time, clinical data and device parameters, and uses the usage time as the horizontal axis and the device parameters as the vertical axis to draw a query curve to obtain the patient's deep vein thrombosis diagnosis data, wherein the diagnostic data includes the patient's unique identification number, medical history and diagnosis results, and uses the medical history to correct the query curve to obtain a personal parameter curve, and set the fluctuation range of the vertical axis;

[0115] Configuration module 12, used to construct a management platform composed of a multi-level control architecture, wherein the multi-level control architecture consists of a top layer, a parent layer and a child layer, and integrate definition rules into the top layer, determine registrants in the management platform, create nodes corresponding to the registrants one by one, and configure the corresponding relationship between the nodes, unique identity numbers and prevention devices;

[0116] The embedding module 13 is used to determine the health parameters of each patient using the diagnostic data, and judge whether the health parameters are greater than a preset threshold value. If yes, the corresponding node is mounted to the parent layer based on the corresponding relationship, and if no, it is mounted to the child layer, so as to embed the authority control strategy into the multi-level control architecture;

[0117] The authority control strategy is: issuing the control authority of the prevention equipment in the parent layer to the top layer, and issuing the control authority of the prevention equipment in the child layer to the parent layer;

[0118] The receiving module 14 is used to read the usage time of the target device, query the personal parameter curve, obtain the target parameter, activate the authority control strategy, define the front layer and the current layer, send a consultation request to the online nodes in the front layer and the current layer, and receive the consultation result after identifying the power-on signal of the target device based on the management platform;

[0119] The sending module 15 is used to use the consultation results of the previous layer to offset the target parameters within the fluctuation range and adjust the prevention equipment, and use the consultation results of the current layer to generate an adjustment reference and send it to the patient terminal.

[0120] Figure 6 The structure block diagram of the Internet of Things management system for deep vein thrombosis prevention equipment provided by an embodiment of the present invention is shown, and the building module 11 includes:

[0121] The calculation unit 111 is used to define a recovered person of deep vein thrombosis from the existing electronic health records, define the time when the recovered person first uses the prevention device as the start time, and calculate the use time of the recovered person;

[0122] An establishing unit 112 is used to record device parameters of the prevention device and establish a mapping between the device parameters and the usage time;

[0123] The conversion unit 113 is used to extract the clinical data of the patient during the consultation from the electronic health record, record the consultation time, and convert the consultation time into the usage time;

[0124] An integration unit 114 is used to integrate device parameters, usage duration, and clinical data of all patients recovering from deep vein thrombosis to construct a historical data set;

[0125] A traversal unit 115, used to traverse the influencing factors of the diagnostic data, wherein the influencing factors at least include: age and behavioral habits;

[0126] The fine-tuning unit 116 is configured to use the influencing factors to fine-tune the ordinate in the personal parameter curve.

[0127] Figure 7 The structure diagram of the Internet of Things management system for deep vein thrombosis prevention equipment provided by an embodiment of the present invention is shown, and the configuration module 12 includes:

[0128] A publishing unit 121, configured to create a mutual aid community in the management platform and publish the historical data set;

[0129] The building unit 122 is used to build communication links between the top layer and the parent layer, and between the top layer and the child layer.

[0130] Figure 8 The structure block diagram of the Internet of Things management system for the deep vein thrombosis prevention device provided by the embodiment of the present invention is shown, and the receiving module 14 includes:

[0131] The identification unit 141 is used to trace back the source of the power-on signal after identifying the power-on signal, and define the source as a target device;

[0132] A defining unit 142, configured to define a position of the target device in the multi-level control architecture as a current level;

[0133] The integration unit 143 is used to integrate the adjustment reference into a preset pop-up window, set "yes" and "no" buttons, obtain an option pop-up window, and push the option pop-up window to the patient terminal.

[0134] The construction module 11 is mainly used to complete step S100, the configuration module 12 is mainly used to complete step S200, the embedding module 13 is mainly used to complete step S300, the receiving module 14 is mainly used to complete step S400, and the sending module 15 is mainly used to complete step S500;

[0135] The calculation unit 111 is mainly used to complete step S101, the establishment unit 112 is mainly used to complete step S102, the conversion unit 113 is mainly used to complete step S103, the integration unit 114 is mainly used to complete step S104, the traversal unit 115 is mainly used to complete step S105, and the fine-tuning unit 116 is mainly used to complete step S106;

[0136] The publishing unit 121 is mainly used to complete step S201, and the building unit 122 is mainly used to complete step S202;

[0137] The identification unit 141 is mainly used to complete step S401, the definition unit 142 is mainly used to complete step S402, and the integration unit 143 is mainly used to complete step S403.

[0138] In actual use, the nodes in the top layer are doctors or professionals who use preventive devices, the nodes in the parent layer are patients who use preventive devices and are about to recover, and the nodes in the child layer are ordinary patients. For example, when ordinary patient B starts the preventive device, the preventive device will send a consultation request to the online patients in the top layer and the parent layer. The patients in the top layer and the parent layer who receive the consultation request will adjust the equipment parameters of the preventive device of ordinary patient B within the fluctuation range based on their own experience, so that the equipment parameters of ordinary patient B can be adjusted in time (if the preventive device receives multiple adjustment signals, it only responds to the adjustment signal received first). After the preventive device is started, it will also send consultation requests to other ordinary patients in the child layer. The ordinary patients who receive the consultation request will give suggestions based on their own experience and send the suggestions to ordinary patient B. Ordinary patient B will adjust the equipment parameters of the preventive device again based on the suggestions and his own feelings, so as to ensure that ordinary patients can obtain the best treatment effect.

[0139] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0140] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0141] 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 in the protection scope of the present invention.

Claims

1. An Internet of Things management method for deep vein thrombosis prevention equipment, characterized in that: The method comprises: Constructing a historical data set for deep vein thrombosis treatment, wherein the historical data set includes: usage time, clinical data and device parameters, and using the usage time as the horizontal axis and the device parameters as the vertical axis to draw a query curve, and obtain the patient's deep vein thrombosis diagnosis data, wherein the diagnostic data includes the patient's unique identification number, medical history and diagnosis results, using the medical history to correct the query curve, obtain a personal parameter curve, and set the fluctuation range of the vertical axis; Constructing a management platform composed of a multi-level control architecture, wherein the multi-level control architecture consists of a top layer, a parent layer, and a child layer, integrating definition rules into the top layer, determining registrants in the management platform, creating nodes corresponding to the registrants one by one, and configuring the corresponding relationship between the nodes, unique identification numbers, and prevention devices; Using the diagnostic data, determine the health score of each patient, determine whether the health score is greater than a preset threshold, and if so, mount the corresponding node to the parent layer based on the corresponding relationship, and if not, mount it to the child layer, and embed the permission control strategy into the multi-level control architecture; The authority control strategy is: issuing the control authority of the prevention equipment in the parent layer to the top layer, and issuing the control authority of the prevention equipment in the child layer to the parent layer; Based on the management platform, after identifying the power-on signal of the target device, the usage time of the target device is read, the personal parameter curve is queried, the target parameters are obtained, the permission control strategy is activated, the front layer and the current layer are defined, and a consultation request is sent to the online nodes in the front layer and the current layer, and the consultation result is received; Using the consultation result of the previous layer, the target parameter is offset within the fluctuation range, and the preventive device is adjusted, and using the consultation result of the current layer, an adjustment reference is generated and sent to the patient terminal; The step of obtaining the target parameters comprises: After the power-on signal is identified, the source of the power-on signal is traced back and the source is defined as the target device; defining a position of the target device in a multi-level control architecture as a current level; Integrate the adjustment reference into a preset pop-up window, set "yes" and "no" buttons, obtain an option pop-up window, and push the option pop-up window to the patient terminal; The method further comprises: Opening the top layer to the adjustment authority of the nodes in the parent layer and the child layer; Constructing a classification model, and inputting the historical data set into the classification model, and outputting a recovery feature; When the management platform detects that the position of a node in the parent layer or child layer has changed, a change item is defined, the corresponding relationship is traced back, and it is determined whether there is a recovery feature in the change item. If so, the position of the change item is adjusted. If not, the change item and diagnostic data are integrated, and the integration result is pushed to the preset terminal.

2. The Internet of Things management method for deep vein thrombosis prevention equipment according to claim 1, characterized in that: The steps of constructing a historical data set of deep vein thrombosis treatment include: From the existing electronic health records, we defined the DVT survivors, defined the time when the survivors first used the prevention device as the start time, and calculated the duration of use by the survivors; Record the equipment parameters of the preventive equipment and establish a mapping between equipment parameters and usage time; Extracting clinical data of the patient during the consultation from the electronic health record, recording the consultation time, and converting the consultation time into usage time; Integrate the device parameters, usage time and clinical data of all deep vein thrombosis survivors to build a historical data set.

3. The Internet of Things management method for deep vein thrombosis prevention equipment according to claim 1, characterized in that: The step of obtaining the patient's deep vein thrombosis diagnosis data, wherein the diagnosis data includes the patient's unique identification number, medical history and diagnosis result, using the medical history to correct the query curve to obtain a personal parameter curve, and setting the fluctuation range of the vertical coordinate includes: Traversing the influencing factors of the diagnostic data, wherein the influencing factors at least include: age and behavioral habits; The influencing factors are used to fine-tune the ordinate in the personal parameter curve.

4. The Internet of Things management method for deep vein thrombosis prevention equipment according to claim 1, characterized in that: The definition rules are: Querying a preset comparison table to authenticate the identity of the registrant, wherein the comparison table at least includes: doctors and professional scholars; If the registrant passes the identity authentication, the node corresponding to the registrant is placed in the top layer.

5. The Internet of Things management method for deep vein thrombosis prevention equipment according to claim 1, characterized in that: The steps of constructing a management platform consisting of a multi-level control architecture include: In the management platform, a mutual assistance community is created and the historical data set is released; Build communication links between the top layer and the parent layer, and between the top layer and the child layer.

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