Remote medical system convenient for long-term continuous observation of monitored object

Abstract

The invention discloses a remote medical system facilitating long-term continuous observation of a monitored object, and relates to the technical field of monitoring analysis, and the system comprises a data collection module which is used for collecting basic data and video data corresponding to a target monitored object; the data analysis module is used for carrying out analysis processing on the basic data and the video data corresponding to the target monitoring object, carrying out abnormity analysis on the body state corresponding to the target monitoring object according to an analysis processing result, and formulating an early warning mode according to an abnormity analysis result; the early warning module is used for responding to the early warning mode and outputting an early warning signal to the target monitoring object; and the pre-estimation module is used for pre-estimating the physical state of the target monitored object in the future time period. The method has the effect of reducing certain potential safety hazards caused by the fact that the physical state information of the monitored object cannot be analyzed and processed.

Description

Technical Field

[0001] The present application relates to the field of monitoring and analysis technology, and in particular to a remote medical system that facilitates long-term continuous observation of monitored subjects. Background Art

[0002] With the continuous development of information and communication technologies, telemedicine systems have become an important means to improve the quality and efficiency of medical services. Especially for patients with chronic diseases, the elderly, the disabled, and those living in remote areas, telemedicine systems provide an effective platform for monitoring health status, real-time communication, and emergency assistance.

[0003] Telemedicine is the product of the integration of network technology and medical technology. It generally includes several key components: remote diagnosis, expert consultation, information services, online examinations, and remote communication. Based on computers and network communications, it enables the transmission, storage, query, comparison, display, and sharing of medical data and remote video and audio information. Some difficult and urgent cases require consultation with experts in multiple locations; infectious diseases are not easily accessible to public hospitals. Traditional medical care often encounters these problems. Today, the rapid development of computer, multimedia, and communication technologies has provided opportunities for the development of telemedicine, solving these problems: doctors and patients can communicate securely and quickly via video. Timely online communication between patients and doctors allows doctors to better understand the patient's disease progression and symptoms. It also enables experts from multiple hospitals to conduct remote consultations on a single case.

[0004] In related technologies, traditional telemedicine systems can only observe the condition of the monitored person, but cannot analyze and process the physical condition information of the monitored person, which causes the monitored person to miss treatment time and further creates certain safety hazards. There is room for improvement. Summary of the Invention

[0005] In order to reduce the occurrence of certain safety hazards caused by the inability to analyze and process the physical condition information of the monitored object, the present application provides a remote medical system that facilitates long-term continuous observation of the monitored object.

[0006] In a first aspect, the present application provides a telemedicine system that facilitates long-term continuous observation of a monitored subject, employing the following technical solutions:

[0007] A telemedicine system for facilitating long-term continuous observation of a monitored subject, comprising:

[0008] Data acquisition module, used to collect basic data and video data corresponding to the target monitored object;

[0009] The data analysis module is used to analyze and process the basic data and video data corresponding to the target monitored object, and perform abnormal analysis on the physical condition of the target monitored object according to the results of the analysis and processing, and formulate early warning methods according to the results of the abnormal analysis;

[0010] An early warning module is used to respond to the early warning mode and output an early warning signal to the target monitored object;

[0011] The prediction module is used to predict the physical condition of the target monitored object in the future time period.

[0012] Preferably, the data analysis module includes a data identification unit, a data analysis unit and a data processing unit;

[0013] The data identification unit is used to obtain the vital sign data, identity data and treatment cycle data corresponding to the target monitored object according to the basic data, and is also used to obtain the emotional data corresponding to the target monitored object according to the video data;

[0014] The data analysis unit is used to perform comprehensive analysis on vital sign data, identity data, treatment cycle data and emotional data;

[0015] The data processing unit is used to process the physical condition of the target monitored object according to the result of the comprehensive analysis.

[0016] Preferably, the process of acquiring emotion data specifically includes:

[0017] Confirm the video data corresponding to the target monitored object, and extract the expression data set a corresponding to the target monitored object from the video data i and behavioral dataset b n , where a i It is represented by the feature value corresponding to the i-th facial expression data of the target monitored object, i=1,2,3...j, b n It is represented by the characteristic value corresponding to the nth behavioral data of the target monitored object, where n = 1, 2, 3...m;

[0018] By formula Confirm the emotional evaluation coefficient C corresponding to the target monitored object, where f(a i ) represents the processing function corresponding to the i-th expression data of the target monitored object, w i It is expressed as the weight coefficient corresponding to the i-th facial expression data of the target monitored object, g(b n ) represents the processing function corresponding to the nth behavior data of the target monitored object, v n It is represented as the weight coefficient corresponding to the nth behavior data of the target monitored object;

[0019] The emotion evaluation coefficient of the target monitored object is obtained according to the emotion evaluation coefficient corresponding to the target monitored object over time data C(t).

[0020] Preferably, a comprehensive analysis of the vital sign data, identity data, treatment cycle data, and emotional data is performed, specifically including:

[0021] Analyze the risk of abnormal physical condition of the target monitored person. The analysis process specifically includes:

[0022] The vital signs data, identity data and treatment cycle data corresponding to the target monitoring object are linearly normalized and the formula Calculate the risk factor K of abnormal physical condition of the target monitored object risk ;

[0023] Among them, γ tz It is expressed as the physical sign assessment coefficient corresponding to the target monitoring object, Expressed as the identity assessment coefficient corresponding to the target guardian, β zl It is expressed as the treatment cycle evaluation coefficient corresponding to the target monitoring object, and e is a natural constant;

[0024] The risk factor K of abnormal physical condition of the target monitored object risk Compare with the preset risk threshold K′;

[0025] If K risk ≤K′, there is no need to perform abnormal analysis on the physical condition of the target monitored object;

[0026] If K risk >K′, it is necessary to perform abnormal analysis on the physical condition of the target monitored object.

[0027] Preferably, the abnormality analysis of the physical condition of the target monitored object is performed, specifically including:

[0028] Confirm the target monitoring object's emotion evaluation coefficient over time data C(t), and extract the target monitoring object's corresponding reference emotion evaluation coefficient c from the cloud database 参考 ;

[0029] By formula Confirm the status assessment coefficient Sta corresponding to the target monitoring object;

[0030] Compare the state assessment coefficient Sta corresponding to the target monitored object with the preset state assessment threshold S';

[0031] If the state assessment coefficient Sta ≥ S′ corresponding to the target monitored object, there is no need to formulate an early warning method;

[0032] If the status assessment coefficient Sta corresponding to the target monitored object is less than S′, an early warning method needs to be formulated for the target monitored object.

[0033] Preferably, the process of formulating an early warning method for the target monitored object specifically includes:

[0034] By formula Confirm the comprehensive evaluation coefficient Q corresponding to the target monitoring object com ;

[0035] Among them, δ1 and δ2 represent the preset correlation coefficients;

[0036] The comprehensive evaluation coefficient Q corresponding to the target monitoring object com Compare with the preset comprehensive evaluation threshold interval [Q1, Q2];

[0037] If the comprehensive evaluation coefficient Q corresponding to the target monitoring object com <Q1, a first warning signal needs to be output to the target monitored object, where Q1 represents the preset first comprehensive evaluation threshold;

[0038] If the comprehensive evaluation coefficient Q corresponding to the target monitoring object com If the value is between [Q1, Q2], a second warning signal needs to be output to the target monitored object, where Q2 represents the preset second comprehensive evaluation threshold, and the warning intensity of the second warning signal is greater than the warning intensity of the first warning signal;

[0039] If the comprehensive evaluation coefficient Q corresponding to the target monitoring object com >Q2, it is necessary to output a second warning signal to the target monitored object and output a warning message to the monitoring personnel at the same time.

[0040] Preferably, the process of estimating the physical condition of the target monitored subject in a future time period specifically includes:

[0041] In the selected time window, the real-time comprehensive evaluation coefficient corresponding to the target monitoring object is collected to form a time series, and the real-time comprehensive evaluation coefficient corresponding to the target monitoring object is converted into a time series using the function Q com (t) indicates;

[0042] By formula Calculate and obtain the reference change coefficient h of the comprehensive evaluation coefficient of the target monitored object, where q(t) represents the preset standard comprehensive evaluation coefficient change curve over time;

[0043] Comparing the reference change coefficient h of the comprehensive evaluation coefficient of the target monitored object with the preset reference change threshold h′;

[0044] If the reference change coefficient of the comprehensive evaluation coefficient of the target monitored subject h≤h′, it is determined that the physical condition of the target monitored subject is gradually improving;

[0045] If the reference variation coefficient h of the comprehensive evaluation coefficient of the target monitoring object is greater than h′, it is determined that the target monitoring object still needs long-term monitoring observation.

[0046] In a second aspect, the present application provides a remote medical method for facilitating long-term continuous observation of a monitored subject, using the following technical solutions:

[0047] A telemedicine method for facilitating long-term continuous observation of a monitored subject comprises the following steps:

[0048] Collect basic data and video data corresponding to the target monitored object;

[0049] Analyze and process the basic data and video data corresponding to the target monitored object, and perform abnormal analysis on the physical condition of the target monitored object based on the results of the analysis and processing, and formulate early warning methods based on the results of the abnormal analysis;

[0050] Respond to the early warning mode and output the early warning signal to the target monitored object;

[0051] Estimate the physical condition of the target monitored person in the future time period.

[0052] In a third aspect, the present application provides a computer-readable storage medium storing instructions, which, when executed on a computer, enables the computer to execute any one of the above-mentioned telemedicine systems for facilitating long-term continuous observation of monitored subjects.

[0053] In summary, this application includes at least one of the following beneficial technical effects:

[0054] 1. The present invention provides a telemedicine system that facilitates long-term continuous observation of a monitored subject. By collecting basic data and video data corresponding to the target monitored subject, the system confirms the target monitored subject's corresponding vital sign data, identity data, treatment cycle data, and emotional data. A comprehensive analysis of the target monitored subject's corresponding vital sign data, identity data, treatment cycle data, and emotional data is then performed. Based on the results of the comprehensive analysis, an early warning method is formulated, and an early warning signal is output to the target monitored subject in response to the early warning method. This effectively reduces the occurrence of certain safety hazards caused by the inability to analyze and process the monitored subject's physical condition information.

[0055] 2. By confirming the reference change coefficient of the comprehensive evaluation coefficient of the target monitored object, and comparing the reference change coefficient of the comprehensive evaluation coefficient of the target monitored object with the preset reference change threshold, the physical state of the target monitored object in the future time period is analyzed based on the comparison results, thereby effectively reducing the occurrence of certain safety hazards caused by the inability to analyze and process the physical state information of the monitored object. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0057] Figure 1 This is a schematic diagram of a telemedicine system for facilitating long-term continuous observation of a monitored subject according to an embodiment of the present application.

[0058] Figure 2 This is a flow chart of a telemedicine method for facilitating long-term continuous observation of a monitored subject according to an embodiment of the present application. DETAILED DESCRIPTION

[0059] The following is combined with Figure 1-2 This application is described in further detail.

[0060] Example 1

[0061] The embodiments of the present application disclose a telemedicine system that facilitates long-term continuous observation of a monitored subject.

[0062] Reference Figure 1 A telemedicine system for facilitating long-term continuous observation of a monitored subject, comprising:

[0063] Data acquisition module, used to collect basic data and video data corresponding to the target monitored object;

[0064] The data analysis module is used to analyze and process the basic data and video data corresponding to the target monitored object, and perform abnormal analysis on the physical condition of the target monitored object according to the results of the analysis and processing, and formulate early warning methods according to the results of the abnormal analysis;

[0065] An early warning module is used to respond to the early warning mode and output an early warning signal to the target monitored object;

[0066] The prediction module is used to predict the physical condition of the target monitored object in the future time period.

[0067] Through the above technical solution, by collecting the basic data and video data corresponding to the target monitored object, and analyzing and processing the basic data and video data corresponding to the target monitored object, an abnormal analysis of the physical state corresponding to the target monitored object is performed according to the results of the analysis and processing, and an early warning method is formulated according to the results of the abnormal analysis. The early warning method is responded to and an early warning signal is output to the target monitored object, thereby effectively reducing the occurrence of certain safety hazards caused by the inability to analyze and process the physical state information of the monitored object.

[0068] Furthermore, the data analysis module includes a data identification unit, a data analysis unit and a data processing unit;

[0069] The data identification unit is used to obtain the vital sign data, identity data and treatment cycle data corresponding to the target monitored object according to the basic data, and is also used to obtain the emotional data corresponding to the target monitored object according to the video data;

[0070] Specifically, in the embodiment of the present application, identity data includes but is not limited to age, height, weight, gender and family medical history, and vital sign data can be collected through smart wearable devices worn by the target monitored subject. Vital sign data includes but is not limited to heart rate, blood pressure, body temperature, respiratory parameters or electrocardiogram data, and treatment cycle data includes but is not limited to the time from the start of treatment to the present or the number of treatment cycles.

[0071] The data analysis unit is used to perform comprehensive analysis on vital sign data, identity data, treatment cycle data and emotional data;

[0072] The data processing unit is used to process the physical condition of the target monitored object according to the result of the comprehensive analysis.

[0073] It should be noted that the process of obtaining emotion data specifically includes:

[0074] Confirm the video data corresponding to the target monitored object, and extract the expression data set a corresponding to the target monitored object from the video data i and behavioral dataset b n , where a i It is represented by the feature value corresponding to the i-th facial expression data of the target monitored object, i=1,2,3...j, b n It is represented by the characteristic value corresponding to the nth behavioral data of the target monitored object, where n = 1, 2, 3...m;

[0075] Specifically, the facial image corresponding to the target monitored object is collected and processed. The processing process includes resizing, cropping, rotation correction, brightness and contrast adjustment, etc. to improve the accuracy of the facial image;

[0076] Apply facial images to facial detection algorithms to locate the facial area in the image, identify key facial features (such as eyes, mouth, or eyebrows) through feature extraction technology, analyze the relative position and movement of key facial features, and use expression recognition algorithms to confirm expression data;

[0077] Collecting the body movements of the target monitored person, and correlating the body movements of the target monitored person with the facial expression data, thereby confirming the behavioral data of the target monitored person;

[0078] By formula Confirm the emotional evaluation coefficient C corresponding to the target monitored object, where f(a i ) represents the processing function corresponding to the i-th expression data of the target monitored object, w i It is expressed as the weight coefficient corresponding to the i-th facial expression data of the target monitored object, g(b n ) represents the processing function corresponding to the nth behavior data of the target monitored object, v n It is represented as the weight coefficient corresponding to the nth behavior data of the target monitored object;

[0079] Specifically, the processing function is expressed as the contribution of converting the eigenvalue into the emotion evaluation coefficient;

[0080] The emotion evaluation coefficient of the target monitored object is obtained according to the emotion evaluation coefficient corresponding to the target monitored object over time data C(t).

[0081] It should be noted that the comprehensive analysis of physical sign data, identity data, treatment cycle data and emotional data includes:

[0082] Analyze the risk of abnormal physical condition of the target monitored person. The analysis process specifically includes:

[0083] The vital signs data, identity data and treatment cycle data corresponding to the target monitoring object are linearly normalized and the formula Calculate the risk factor K of abnormal physical condition of the target monitored object risk ;

[0084] Among them, γ tz It is expressed as the physical sign assessment coefficient corresponding to the target monitoring object, Expressed as the identity assessment coefficient corresponding to the target guardian, β zl It is expressed as the treatment cycle evaluation coefficient corresponding to the target monitoring object, and e is a natural constant;

[0085] Specifically, by inputting the physical sign data, identity data and treatment cycle data corresponding to the target monitored object into a preset evaluation system, the physical sign evaluation coefficient, identity evaluation coefficient and treatment cycle evaluation coefficient corresponding to the target monitored object are determined;

[0086] The risk factor K of abnormal physical condition of the target monitored object risk Compare with the preset risk threshold K′;

[0087] If K risk ≤K′, there is no need to perform abnormal analysis on the physical condition of the target monitored object;

[0088] If K risk >K′, it is necessary to perform abnormal analysis on the physical condition of the target monitored object.

[0089] It should be noted that the abnormality analysis of the physical condition of the target monitored object specifically includes:

[0090] Confirm the target monitoring object's emotion evaluation coefficient over time data C(t), and extract the target monitoring object's corresponding reference emotion evaluation coefficient c from the cloud database 参考 ;

[0091] By formula Confirm the status assessment coefficient Sta corresponding to the target monitoring object;

[0092] Compare the state assessment coefficient Sta corresponding to the target monitored object with the preset state assessment threshold S';

[0093] If the state assessment coefficient Sta ≥ S′ corresponding to the target monitored object, there is no need to formulate an early warning method;

[0094] If the status assessment coefficient Sta corresponding to the target monitored object is less than S′, an early warning method needs to be formulated for the target monitored object.

[0095] Furthermore, the process of developing early warning methods for target monitoring objects specifically includes:

[0096] By formula Confirm the comprehensive evaluation coefficient Q corresponding to the target monitoring object com ;

[0097] Among them, δ1 and δ2 represent the preset correlation coefficients;

[0098] Specifically, δ1 and δ2 can be obtained by fitting historical data;

[0099] The comprehensive evaluation coefficient Q corresponding to the target monitoring object com Compare with the preset comprehensive evaluation threshold interval [Q1, Q2];

[0100] If the comprehensive evaluation coefficient Q corresponding to the target monitoring object com <Q1, a first warning signal needs to be output to the target monitored object, where Q1 represents the preset first comprehensive evaluation threshold;

[0101] If the comprehensive evaluation coefficient Q corresponding to the target monitoring object com If the value is between [Q1, Q2], a second warning signal needs to be output to the target monitored object, where Q2 represents the preset second comprehensive evaluation threshold, and the warning intensity of the second warning signal is greater than the warning intensity of the first warning signal;

[0102] If the comprehensive evaluation coefficient Q corresponding to the target monitoring object com >Q2, it is necessary to output a second warning signal to the target monitored object and output a warning message to the monitoring personnel at the same time.

[0103] It should be noted that the process of estimating the physical condition of the target monitored person in the future time period specifically includes:

[0104] In the selected time window, the real-time comprehensive evaluation coefficient corresponding to the target monitoring object is collected to form a time series, and the real-time comprehensive evaluation coefficient corresponding to the target monitoring object is converted into a time series using the function Q com (t) indicates;

[0105] By formula Calculate and obtain the reference change coefficient h of the comprehensive evaluation coefficient of the target monitored object, where q(t) represents the preset standard comprehensive evaluation coefficient change curve over time;

[0106] Comparing the reference change coefficient h of the comprehensive evaluation coefficient of the target monitored object with the preset reference change threshold h′;

[0107] If the reference change coefficient of the comprehensive evaluation coefficient of the target monitored subject h≤h′, it is determined that the physical condition of the target monitored subject is gradually improving;

[0108] If the reference variation coefficient h of the comprehensive evaluation coefficient of the target monitoring object is greater than h′, it is determined that the target monitoring object still needs long-term monitoring observation.

[0109] Through the above technical solution, by confirming the reference change coefficient of the comprehensive evaluation coefficient of the target monitored object and comparing the reference change coefficient of the comprehensive evaluation coefficient of the target monitored object with the preset reference change threshold, the physical state of the target monitored object in the future time period is analyzed based on the comparison results, thereby effectively reducing the occurrence of certain safety hazards caused by the inability to analyze and process the physical state information of the monitored object.

[0110] Example 2

[0111] The embodiments of the present application also disclose a remote medical method that facilitates long-term continuous observation of a monitored subject.

[0112] Reference Figure 2 A remote medical method for facilitating long-term continuous observation of a monitored subject comprises the following steps:

[0113] Collect basic data and video data corresponding to the target monitored object;

[0114] Analyze and process the basic data and video data corresponding to the target monitored object, and perform abnormal analysis on the physical condition of the target monitored object based on the results of the analysis and processing, and formulate early warning methods based on the results of the abnormal analysis;

[0115] Respond to the early warning mode and output the early warning signal to the target monitored object;

[0116] Estimate the physical condition of the target monitored person in the future time period.

[0117] The above content is merely an example and explanation of the concept of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the claims, they should all fall within the scope of protection of the present invention.

[0118] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0119] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A telemedicine system for facilitating long-term continuous observation of a monitored subject, characterized in that: include: Data acquisition module, used to collect basic data and video data corresponding to the target monitored object; The data analysis module is used to analyze and process the basic data and video data corresponding to the target monitored object, and perform abnormal analysis on the physical condition of the target monitored object according to the results of the analysis and processing, and formulate early warning methods according to the results of the abnormal analysis; An early warning module is used to respond to the early warning mode and output an early warning signal to the target monitored object; The prediction module is used to predict the physical condition of the target monitored object in the future time period.

2. A remote medical system for facilitating long-term continuous observation of a monitored subject according to claim 1, characterized in that: The data analysis module includes a data identification unit, a data analysis unit and a data processing unit; The data identification unit is used to obtain the vital sign data, identity data and treatment cycle data corresponding to the target monitored object according to the basic data, and is also used to obtain the emotional data corresponding to the target monitored object according to the video data; The data analysis unit is used to perform comprehensive analysis on vital sign data, identity data, treatment cycle data and emotional data; The data processing unit is used to process the physical condition of the target monitored object according to the result of the comprehensive analysis.

3. A remote medical system for facilitating long-term continuous observation of a monitored subject according to claim 2, characterized in that: The process of acquiring emotion data includes: Confirm the video data corresponding to the target monitored object, and extract the expression data set ai and behavior data set bn corresponding to the target monitored object from the video data, where ai represents the feature value corresponding to the i-th expression data of the target monitored object, i = 1, 2, 3...j, and bn represents the feature value corresponding to the n-th behavior data of the target monitored object, n = 1, 2, 3...m; By formula Determine the emotion evaluation coefficient C corresponding to the target monitored object, where f(ai) represents the processing function corresponding to the i-th expression data of the target monitored object, wi represents the weight coefficient corresponding to the i-th expression data of the target monitored object, g(bn) represents the processing function corresponding to the n-th behavior data of the target monitored object, and vn represents the weight coefficient corresponding to the n-th behavior data of the target monitored object; The emotion evaluation coefficient of the target monitored object is obtained according to the emotion evaluation coefficient corresponding to the target monitored object over time data C(t).

4. A remote medical system for facilitating long-term continuous observation of a monitored subject according to claim 3, characterized in that: Comprehensive analysis of vital sign data, identity data, treatment cycle data, and emotional data, including: Analyze the risk of abnormal physical condition of the target monitored person. The analysis process specifically includes: The vital signs data, identity data and treatment cycle data corresponding to the target monitoring object are linearly normalized and the formula Calculate the risk factor Krisk of abnormal physical condition of the target monitored object; Among them, γtz represents the physical sign assessment coefficient corresponding to the target monitoring object, It is represented as the identity evaluation coefficient corresponding to the target monitoring object, βzl is represented as the treatment cycle evaluation coefficient corresponding to the target monitoring object, and e is a natural constant; Compare the risk factor Krisk of abnormal physical condition of the target monitored object with the preset risk threshold K′; If Krisk≤K′, there is no need to perform abnormal analysis on the corresponding physical state of the target monitored object; If Krisk>K′, it is necessary to perform abnormal analysis on the physical condition corresponding to the target monitored object.

5. A remote medical system for facilitating long-term continuous observation of a monitored subject according to claim 4, characterized in that: Perform abnormal analysis on the physical condition of the target monitored object, including: Confirm the emotional evaluation coefficient of the target monitored object over time data C(t), and extract the reference emotional evaluation coefficient c reference corresponding to the target monitored object from the cloud database; By formula Confirm the status assessment coefficient Sta corresponding to the target monitoring object; Compare the state assessment coefficient Sta corresponding to the target monitored object with the preset state assessment threshold S'; If the state assessment coefficient Sta ≥ S′ corresponding to the target monitored object, there is no need to formulate an early warning method; If the status assessment coefficient Sta corresponding to the target monitored object is less than S′, an early warning method needs to be formulated for the target monitored object.

6. A remote medical system for facilitating long-term continuous observation of a monitored subject according to claim 5, characterized in that: The process of developing early warning methods for target surveillance subjects specifically includes: By formula Determine the comprehensive assessment coefficient Qcom corresponding to the target monitoring object; Among them, δ1 and δ2 represent the preset correlation coefficients; Compare the comprehensive evaluation coefficient Qcom corresponding to the target monitoring object with the preset comprehensive evaluation threshold interval [Q1, Q2]; If the comprehensive evaluation coefficient Qcom corresponding to the target monitored object is less than Q1, a first warning signal needs to be output to the target monitored object, where Q1 represents a preset first comprehensive evaluation threshold; If the comprehensive evaluation coefficient Qcom corresponding to the target monitored object is between [Q1, Q2], a second warning signal needs to be output to the target monitored object, where Q2 represents the preset second comprehensive evaluation threshold, and the warning intensity of the second warning signal is greater than the warning intensity of the first warning signal; If the comprehensive evaluation coefficient Qcom corresponding to the target monitored object is greater than Q2, a second warning signal needs to be output to the target monitored object and a warning message needs to be output to the monitoring personnel at the same time.

7. A remote medical system for facilitating long-term continuous observation of a monitored subject according to claim 6, characterized in that: The process of estimating the target monitored person's physical condition in the future period of time, specifically including: In the selected time window, the real-time comprehensive evaluation coefficient corresponding to the target monitoring object is collected to form a time series, and the real-time comprehensive evaluation coefficient corresponding to the target monitoring object is expressed by the function Qcom(t) according to the time series; By formula Calculate and obtain the reference change coefficient h of the comprehensive evaluation coefficient of the target monitored object, where q(t) represents the preset standard comprehensive evaluation coefficient change curve over time; Comparing the reference change coefficient h of the comprehensive evaluation coefficient of the target monitored object with the preset reference change threshold h′; If the reference change coefficient of the comprehensive evaluation coefficient of the target monitored subject h≤h′, it is determined that the physical condition of the target monitored subject is gradually improving; If the reference variation coefficient h of the comprehensive evaluation coefficient of the target monitoring object is greater than h′, it is determined that the target monitoring object still needs long-term monitoring observation.

8. A telemedicine method for facilitating long-term continuous observation of a monitored subject, applied to a telemedicine system for facilitating long-term continuous observation of a monitored subject according to claims 1-7, characterized in that: The following steps are involved: Collect basic data and video data corresponding to the target monitored object; Analyze and process the basic data and video data corresponding to the target monitored object, and perform abnormal analysis on the physical condition of the target monitored object based on the results of the analysis and processing, and formulate early warning methods based on the results of the abnormal analysis; Respond to the early warning mode and output the early warning signal to the target monitored object; Estimate the physical condition of the target monitored person in the future period of time.

9. A computer-readable storage medium, characterized in that: Instructions are stored, and when the instructions are run on a computer, the computer is caused to execute a remote medical system for facilitating long-term continuous observation of a monitored object as described in any one of claims 1 to 7.

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