Adaptive remote medical monitoring method and system based on Internet of Things

Abstract

The present invention discloses an adaptive remote medical monitoring method and system based on the Internet of Things, which relates to the field of medical monitoring technology, including creating patient files and performing equipment allocation and configuration; collecting patients' real-time health data and transmitting it to a cloud server for data preprocessing; performing abnormality detection on the preprocessed data and performing preliminary analysis on the abnormal data; and evaluating the patient's health status and formulating response measures. The present invention creates personalized patient files, performs intelligent equipment allocation and configuration, constructs an abnormality detection algorithm, and performs preliminary analysis on abnormal data, ensuring that the monitoring equipment can accurately meet the patient's personalized monitoring needs, which not only improves the efficiency and accuracy of remote medical monitoring, but also can timely detect abnormal changes in the patient's health status, providing a scientific decision-making basis for medical staff.

Description

Technical Field

[0001] The present invention relates to the field of medical monitoring technology, and in particular to an adaptive remote medical monitoring method and system based on the Internet of Things. Background Art

[0002] In recent years, with the rapid development of Internet of Things technology and its wide application in various fields, remote medical monitoring as an emerging health management method has received great attention. Through the interconnection of smart devices, Internet of Things technology can collect and transmit patients' health data in real time, and provide medical staff with instant health status information. The application of this technology, especially in chronic disease management, health monitoring of the elderly and remote medical consultation, has shown great potential. Through automated data collection and processing, medical staff can monitor patients' health status in real time and adjust treatment plans in time, thereby improving the efficiency and effectiveness of treatment. However, although Internet of Things technology has made certain progress in the field of remote medical monitoring, existing technologies and methods still face many challenges, especially the lack of an efficient and adaptive method to process and analyze massive health monitoring data, which limits the ability to detect potential health problems at an early stage. Summary of the invention

[0003] In view of the problems existing in the above-mentioned existing adaptive remote medical monitoring method and system based on the Internet of Things, the present invention is proposed.

[0004] Therefore, the problem to be solved by the present invention is the lack of an efficient and adaptive method to process and analyze massive amounts of health monitoring data, which limits the ability to detect potential health problems at an early stage.

[0005] To solve the above technical problems, the present invention provides the following technical solutions: an adaptive remote medical monitoring method based on the Internet of Things, which includes creating patient files and allocating and configuring equipment; collecting patients' real-time health data and transmitting it to a cloud server for data preprocessing; performing anomaly detection on the preprocessed data and performing preliminary analysis on the abnormal data; and evaluating the patient's health status and formulating response measures.

[0006] As a preferred solution of the adaptive remote medical monitoring method based on the Internet of Things described in the present invention, wherein: the creation of patient files and device allocation and configuration refers to collecting the patient's basic information from the medical record system to create an electronic health file record and update the patient's health monitoring data, and allocating devices to the patient based on the patient's medical history and current health status, configuring device parameters according to the patient's personal health needs, associating the selected device with the patient's electronic health file and synchronizing the device data.

[0007] As a preferred solution of the adaptive remote medical monitoring method based on the Internet of Things described in the present invention, wherein: the collection of real-time health data of patients refers to using monitoring equipment to collect physiological data and behavioral data of patients, and integrating the collected data into a data set.

[0008] As a preferred solution of the adaptive remote medical monitoring method based on the Internet of Things described in the present invention, the transmission to the cloud server for data preprocessing refers to transmitting the integrated data set to the cloud server through the HTTPS protocol and denoising and filling missing values ​​for the collected data.

[0009] As a preferred solution of the adaptive remote medical monitoring method based on the Internet of Things described in the present invention, wherein: the abnormality detection of the pre-processed data is to construct an abnormality detection algorithm to perform abnormality detection on the preliminarily pre-processed data:

[0010] Normalize the data, and the normalized data value It is expressed as:

[0011]

[0012] in, and is the adjustment factor, is the actual measured value, and is the historical mean and standard deviation of the i-th category data;

[0013] Evaluate the abnormality of the data It is expressed as:

[0014]

[0015] in, is the normalized data value, is the hyperbolic tangent function, is an exponential decay function;

[0016] Calculate the cumulative abnormal fluctuation It is expressed as:

[0017]

[0018] in, is the weight factor, is the number of measurements within a given time window, represents the jth measurement value in the i-th category data, represents the historical average of the jth measurement value in the i-th category of data, arrive Indicates the time interval for analysis. Indicates at time The actual measured value of the i-th type of data, Indicates at time The historical average value of the data in the i-th category;

[0019] Calculating the composite anomaly score It is expressed as:

[0020]

[0021] in is the comprehensive abnormality score, Indicates the application of fast Fourier transform to the p-th data, P is the number of data categories for FFT analysis, is the weight assigned according to the importance of the p-th type of data in the frequency domain analysis, is the weight of the i-th type of data in the comprehensive health assessment, is the actual measured value, is the historical mean of the i-th category data;

[0022] Comprehensively construct anomaly detection formula It is expressed as:

[0023]

[0024] in, is the comprehensive anomaly index, n is the number of monitored data types, is the weight of the i-th type of data in health assessment, The data is normalized The abnormality of is the cumulative abnormal fluctuation, is the comprehensive abnormality score.

[0025] As a preferred solution of the adaptive remote medical monitoring method based on the Internet of Things of the present invention, the preliminary analysis of abnormal data is to construct a formula to perform preliminary analysis on the detected abnormal data:

[0026]

[0027]

[0028]

[0029] in, is the result of a comprehensive assessment. is the balance coefficient, is the weight of the i-th type of data in health assessment, is the abnormal fluctuation evaluation value of the i-th type of data in the time series, is the abnormal event assessment value, It's at the time The actual measured value of the i-th type of data, It's at the time The historical average value of the i-th category data, arrive is the time interval, is the importance score of the k-th abnormal event, is the urgency of the kth abnormal event, is the difference between the kth abnormal event and the known health risk pattern, Represents the frequency of events similar to the kth abnormal event in historical data.

[0030] As a preferred solution of the adaptive remote medical monitoring method based on the Internet of Things described in the present invention, wherein: the evaluation of the patient's health status and the formulation of response measures refer to setting a judgment threshold through the patient's health data, abnormal detection results and preliminary analysis results and Compare with comprehensive assessment result A to assess the patient's health status:

[0031] like , it is low-risk data, indicating that the patient's vital signs are stable, so regular health assessment and monitoring should be carried out, and the patient should be encouraged to continue or start a healthy lifestyle, including regular physical activity, a balanced diet and adequate rest, and regular telemedicine consultation;

[0032] like , it is medium-risk data, indicating that the patient has moderate fluctuations in vital signs, then the patient's observation and medical intervention will be strengthened, the medical monitoring system will collect data more frequently, notify patients and medical staff of related potential risks, adjust the patient's lifestyle and treatment plan according to medical advice, and perform additional diagnostic tests on the patient, including blood tests or electrocardiograms;

[0033] like , it is high-risk data, indicating that the patient has serious abnormal vital signs. Emergency measures should be taken immediately, including immediately notifying the attending physician and relevant medical team, and automatically initiating the emergency response protocol. The medical team should quickly assess the patient's specific situation, including reviewing abnormal reports, conducting remote video consultations, and arranging emergency referrals to the hospital to monitor the patient's vital signs in real time.

[0034] Another object of the present invention is to provide an adaptive remote medical monitoring system based on the Internet of Things, which includes a file management module, a data acquisition module, an anomaly detection module, and an evaluation module;

[0035] The file management module is used to collect and store basic information of the patient and automatically select and allocate appropriate monitoring equipment based on the patient's health status and monitoring needs;

[0036] The data acquisition module is used to collect the patient's physiological data and behavioral data in real time through the health monitoring equipment, transmit the data to the cloud server and perform pre-processing;

[0037] The anomaly detection module is used to construct an anomaly detection algorithm to perform anomaly detection on the preprocessed data and perform preliminary analysis on the abnormal data;

[0038] The evaluation module is used to comprehensively evaluate the patient's health status and formulate response measures based on the evaluation results.

[0039] A computer device comprises: a memory and a processor; the memory stores a computer program, and the processor implements the steps of an adaptive remote medical monitoring method based on the Internet of Things when executing the computer program.

[0040] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of an adaptive remote medical monitoring method based on the Internet of Things.

[0041] The beneficial effects of the present invention are as follows: the present invention creates personalized patient files, performs intelligent equipment allocation and configuration, builds anomaly detection algorithms, and performs preliminary analysis of abnormal data, thereby ensuring that the monitoring equipment can accurately meet the patient's personalized monitoring needs. This not only improves the efficiency and accuracy of remote medical monitoring, but also can promptly detect abnormal changes in the patient's health status, providing medical staff with a scientific basis for decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0043] Figure 1 Schematic diagram of the process of adaptive remote medical monitoring method based on the Internet of Things.

[0044] Figure 2 Schematic diagram of the implementation of the adaptive remote medical monitoring method based on the Internet of Things.

[0045] Figure 3 Schematic diagram of the structure of the adaptive remote medical monitoring method based on the Internet of Things. DETAILED DESCRIPTION

[0046] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings.

[0047] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0048] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.

[0049] Example 1

[0050] Reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, and this embodiment provides an adaptive remote medical monitoring method based on the Internet of Things. The adaptive remote medical monitoring method based on the Internet of Things includes the following steps:

[0051] S1. Create patient files and perform equipment allocation and configuration;

[0052] Specifically, creating patient records and allocating and configuring equipment means collecting the patient's basic information from the medical record system to create an electronic health record to record and update the patient's health monitoring data, allocating equipment to the patient based on the patient's medical history and current health status, configuring equipment parameters based on the patient's personal health needs, associating the selected device with the patient's electronic health record, and synchronizing the device data.

[0053] By collecting the patient's basic information from the medical record system to create an electronic health record, and assigning monitoring equipment according to the patient's medical history and current health status, a personalized health management plan is implemented. The device parameters are personalized and the selected device is associated with the patient's electronic health record, realizing a highly customized monitoring strategy. This not only improves the accuracy and efficiency of monitoring, but also enhances the patient's convenience and satisfaction, establishes a comprehensive and accurate patient health information database, provides a solid foundation for subsequent monitoring and analysis, improves the quality of personalized medical services, and optimizes resource allocation and reduces unnecessary medical expenses by accurately matching monitoring equipment.

[0054] S2, collects patients’ real-time health data and transmits it to the cloud server for data preprocessing;

[0055] Specifically, collecting real-time health data of patients means using monitoring equipment to collect physiological data and behavioral data of patients, and integrating the collected data into a data set.

[0056] Using monitoring devices to collect patients' physiological and behavioral data and integrate them into data sets ensures real-time and continuous health monitoring. Real-time data collection can promptly detect signs of health problems, allowing medical staff or systems to respond immediately, helping to capture complex health patterns and trends, improving the accuracy and timeliness of early diagnosis, and supporting long-term health management and disease prevention through continuous monitoring.

[0057] Furthermore, transmitting to a cloud server for data preprocessing refers to transmitting the integrated data set to the cloud server via the HTTPS protocol and denoising and filling missing values ​​on the collected data.

[0058] The integrated data set is securely transmitted to the cloud server via the HTTPS protocol, and denoising and filling of missing values ​​are performed in the cloud, which can more accurately reflect the patient's true health status and reduce the risk of misdiagnosis and missed diagnosis. Cloud processing provides powerful computing power and supports complex data analysis tasks, so that even large-scale data sets can be processed quickly and effectively, improving the efficiency and accuracy of data processing, ensuring the quality of data analysis, and improving the scalability and flexibility of telemedicine services.

[0059] S3, perform anomaly detection on the preprocessed data and conduct preliminary analysis on the abnormal data;

[0060] Specifically, anomaly detection is performed on the preprocessed data to construct an anomaly detection algorithm to perform anomaly detection on the preliminarily preprocessed data:

[0061] Normalize the data, and the normalized data value It is expressed as:

[0062]

[0063] in, and is an adjustment factor used to adjust the normalization process according to the characteristics of the data, is the actual measured value, and is the historical mean and standard deviation of the i-th category data, obtained through simple statistical analysis of historical data and used for normalization processing;

[0064] Evaluate the abnormality of the data It is expressed as:

[0065]

[0066] in, is the normalized data value, is the hyperbolic tangent function, is an exponential decay function;

[0067] Calculate the cumulative abnormal fluctuation It is expressed as:

[0068]

[0069] in, is a weighting factor used to balance the impact of short-term fluctuations and long-term changes in the total fluctuation. is the number of measurements within a given time window, represents the jth measurement value in the i-th category data, represents the historical average of the jth measurement value in the i-th category of data, arrive Indicates the time interval for analysis. Indicates at time The actual measured value of the i-th type of data, Indicates at time The historical average value of the data in the i-th category;

[0070] Calculating the composite anomaly score It is expressed as:

[0071]

[0072] in is the comprehensive abnormality score, Indicates that the fast Fourier transform is applied to the p-th data to analyze its frequency domain characteristics. P is the number of data categories for FFT analysis. is the weight assigned according to the importance of the p-th type of data in the frequency domain analysis, is the weight of the i-th type of data in the comprehensive health assessment, is the actual measured value, is the historical mean of the i-th category data;

[0073] Comprehensively construct anomaly detection formula It is expressed as:

[0074]

[0075] in, is the comprehensive anomaly index, n is the number of monitored data types, is the weight of the i-th type of data in health assessment, The data is normalized The abnormality of is the cumulative abnormal fluctuation, is the comprehensive abnormality score.

[0076] By constructing an anomaly detection algorithm to normalize and evaluate the abnormality of the data that has been preliminarily preprocessed, we can achieve refined processing of the data and reflect more obvious health abnormality signals. Normalization processing optimizes different types of health data by adjusting factors to ensure the accuracy and consistency of data processing. By calculating the cumulative abnormal fluctuations and comprehensive abnormality scores, not only the abnormality of a single measurement is considered, but also the long-term change trend of the data is comprehensively considered, thereby achieving accurate monitoring of changes in patients' health status, being able to identify potential health risks in a timely manner, providing a scientific basis for subsequent medical interventions, and greatly improving the early warning capabilities of remote medical monitoring.

[0077] Furthermore, a preliminary analysis of the abnormal data is performed to construct a formula for preliminary analysis of the detected abnormal data:

[0078]

[0079]

[0080]

[0081] in, is the result of a comprehensive assessment. is the balance coefficient, which is used to adjust the relative importance of the abnormal fluctuation assessment of the time series and the abnormal event assessment in the final score. is the weight of the i-th type of data in health assessment, reflecting the importance of different types of health data on the overall health status. is the abnormal fluctuation evaluation value of the i-th type of data in the time series, is the abnormal event assessment value, It's at the time The actual measured value of the i-th type of data, It's at the time The historical average of the i-th category data is usually determined based on historical data. arrive is the time interval, is the importance score of the k-th abnormal event, is the urgency of the kth abnormal event, determined by the speed of the event and the possible health impact, is the difference between the kth abnormal event and the known health risk pattern, Represents the frequency of events similar to the kth abnormal event in historical data.

[0082] The constructed formula is used to conduct a preliminary analysis of the detected abnormal data. The importance of the time series abnormal fluctuation assessment and the abnormal event assessment in the final score is adjusted by the balance coefficient. The nature and potential impact of the abnormal data are analyzed in detail. It not only reflects the severity of the abnormal event, but also takes into account the speed of the abnormal occurrence and the difference from the known health risk pattern, thereby providing medical staff with more detailed and accurate abnormal information, enhancing the understanding of the potential pathological causes behind the abnormal health data, and laying the foundation for formulating personalized medical intervention measures.

[0083] S4. Assess the patient's health status and develop response measures;

[0084] Specifically, assessing the patient's health status and formulating response measures means setting judgment thresholds based on the patient's health data, abnormal test results, and preliminary analysis results. and Compare with comprehensive assessment result A to assess the patient's health status:

[0085] like , it is low-risk data, indicating that the patient's vital signs are stable, so regular health assessment and monitoring should be carried out, and the patient should be encouraged to continue or start a healthy lifestyle, including regular physical activity, a balanced diet and adequate rest, and regular telemedicine consultation;

[0086] like , it is medium-risk data, indicating that the patient has moderate fluctuations in vital signs, then the patient's observation and medical intervention will be strengthened, the medical monitoring system will collect data more frequently, notify patients and medical staff of related potential risks, adjust the patient's lifestyle and treatment plan according to medical advice, and perform additional diagnostic tests on the patient, including blood tests or electrocardiograms;

[0087] like , it is high-risk data, indicating that the patient has serious abnormal vital signs. Emergency measures should be taken immediately, including immediately notifying the attending physician and relevant medical team, and automatically initiating the emergency response protocol. The medical team should quickly assess the patient's specific situation, including reviewing abnormal reports, conducting remote video consultations, and arranging emergency referrals to the hospital to monitor the patient's vital signs in real time.

[0088] The judgment threshold will change according to different application scenarios and the different physical health conditions of different patients. Therefore, the setting of this threshold is based on the experience of medical staff to manually set the judgment threshold for different application scenarios and different patients, and set the judgment threshold according to the comprehensive evaluation results, and compare them with them, so as to evaluate the patient's health status and formulate corresponding response measures. According to the patient's health risk level, low-risk, medium-risk and high-risk response measures are formulated respectively. From routine health guidance to emergency medical intervention, a multi-level and targeted medical response system has been formed, which can provide personalized medical advice and intervention measures according to the patient's specific health status, greatly improving the adaptability and flexibility of the medical monitoring system, while also optimizing the allocation of medical resources to ensure that necessary measures can be taken for high-risk patients in a timely and effective manner.

[0089] Example 2

[0090] Reference Figure 3 , which is the second embodiment of the present invention, which is different from the previous embodiment and provides an adaptive remote medical monitoring system based on the Internet of Things, including a file management module, a data acquisition module, an abnormality detection module, and an evaluation module;

[0091] The file management module is used to collect and store basic information of the patient and automatically select and allocate appropriate monitoring equipment based on the patient's health status and monitoring needs;

[0092] The data acquisition module is used to collect the patient's physiological data and behavioral data in real time through the health monitoring equipment, transmit the data to the cloud server and perform pre-processing;

[0093] The anomaly detection module is used to construct an anomaly detection algorithm to perform anomaly detection on the preprocessed data and perform preliminary analysis on the abnormal data;

[0094] The evaluation module is used to comprehensively evaluate the patient's health status and formulate response measures based on the evaluation results.

[0095] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0096] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0097] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0098] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit with a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit with a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

Claims

1. An adaptive remote medical monitoring method based on the Internet of Things, characterized by: include, Create patient records and perform equipment assignment and configuration; Collect patients’ real-time health data and transmit it to cloud servers for data preprocessing; Perform anomaly detection on the preprocessed data and conduct preliminary analysis on the abnormal data; Assess patient health status and develop response measures; The creation of patient files and allocation and configuration of devices refers to collecting basic information of patients from the medical record system to create electronic health records and update the health monitoring data of patients, allocating devices to patients based on their medical history and current health conditions, configuring device parameters according to the individual health needs of patients, associating the selected devices with the electronic health records of patients and synchronizing the device data; The collecting of the patient's real-time health data refers to using monitoring equipment to collect the patient's physiological data and behavioral data, and integrating the collected data into a data set; The transmission to the cloud server for data preprocessing refers to transmitting the integrated data set to the cloud server via the HTTPS protocol and performing denoising and filling missing values ​​on the collected data; The anomaly detection on the preprocessed data is to construct an anomaly detection algorithm to perform anomaly detection on the preliminarily preprocessed data: Normalize the data, and the normalized data value It is expressed as: in, and is the adjustment factor, is the actual measured value, and is the historical mean and standard deviation of the i-th category data; Evaluate the abnormality of the data It is expressed as: in, is the normalized data value, is the hyperbolic tangent function, is an exponential decay function; Calculate the cumulative abnormal fluctuation It is expressed as: in, is the weight factor, is the number of measurements within a given time window, represents the jth measurement value in the i-th category data, represents the historical average of the jth measurement value in the i-th category of data, arrive Indicates the time interval for analysis. Indicates at time The actual measured value of the i-th type of data, Indicates at time The historical average value of the data in the i-th category; Calculating the composite anomaly score It is expressed as: in is the comprehensive abnormality score, Indicates the application of fast Fourier transform to the p-th data, P is the number of data categories for FFT analysis, is the weight assigned according to the importance of the p-th type of data in the frequency domain analysis, is the weight of the i-th type of data in the comprehensive health assessment, is the actual measured value, is the historical mean of the i-th category data; Comprehensively construct anomaly detection formula It is expressed as: in, is the comprehensive anomaly index, n is the number of monitored data types, is the weight of the i-th type of data in health assessment, The data is normalized The abnormality of is the cumulative abnormal fluctuation, is the comprehensive abnormality score; The preliminary analysis of abnormal data is to construct a formula to perform preliminary analysis on the detected abnormal data: in, is the result of a comprehensive assessment. is the balance coefficient, is the weight of the i-th type of data in health assessment, is the abnormal fluctuation evaluation value of the i-th type of data in the time series, is the abnormal event assessment value, It's at the time The actual measured value of the i-th type of data, It's at the time The historical average value of the i-th category data, arrive is the time interval, is the importance score of the kth abnormal event, is the urgency of the kth abnormal event, is the difference between the kth abnormal event and the known health risk pattern, Represents the frequency of events similar to the kth abnormal event in historical data.

2. The method for adaptive remote medical monitoring based on the Internet of Things as claimed in claim 1, characterized in that: The said assessing the patient's health status and formulating response measures refers to setting judgment thresholds through patient health data, abnormal detection results and preliminary analysis results. and Compare with comprehensive assessment result A to assess the patient's health status: like , it is low-risk data, indicating that the patient's vital signs are stable, so regular health assessment and monitoring should be carried out, and the patient should be encouraged to continue or start a healthy lifestyle, including regular physical activity, a balanced diet and adequate rest, and regular telemedicine consultation; like , it is medium-risk data, indicating that the patient has moderate fluctuations in vital signs, then the patient's observation and medical intervention will be strengthened, the medical monitoring system will collect data more frequently, notify patients and medical staff of related potential risks, adjust the patient's lifestyle and treatment plan according to medical advice, and perform additional diagnostic tests on the patient, including blood tests or electrocardiograms; like , it is high-risk data, indicating that the patient has serious abnormal vital signs. Emergency measures should be taken immediately, including immediately notifying the attending physician and relevant medical team, and automatically initiating the emergency response protocol. The medical team should quickly assess the patient's specific situation, including reviewing abnormal reports, conducting remote video consultations, and arranging emergency referrals to the hospital to monitor the patient's vital signs in real time.

3. An adaptive remote medical monitoring system based on the Internet of Things for executing the adaptive remote medical monitoring method based on the Internet of Things according to any one of claims 1-2, characterized in that: Including, archive management module, data collection module, anomaly detection module, and evaluation module; The file management module is used to collect and store basic information of the patient and automatically select and allocate appropriate monitoring equipment based on the patient's health status and monitoring needs; The data acquisition module is used to collect the patient's physiological data and behavioral data in real time through the health monitoring equipment, transmit the data to the cloud server and perform pre-processing; The anomaly detection module is used to construct an anomaly detection algorithm to perform anomaly detection on the preprocessed data and perform preliminary analysis on the abnormal data; The evaluation module is used to comprehensively evaluate the patient's health status and formulate response measures based on the evaluation results.

4. A computer device comprising: Memory and processor; The memory stores a computer program, characterized in that: when the processor executes the computer program, the steps of the adaptive remote medical monitoring method based on the Internet of Things described in any one of claims 1 to 2 are implemented.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the adaptive remote medical monitoring method based on the Internet of Things described in any one of claims 1 to 2 are implemented.

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