CN111128326A - Community patient monitoring method and system based on target tracking - Google Patents
Community patient monitoring method and system based on target tracking Download PDFInfo
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Abstract
The embodiment of the application provides a community patient monitoring method and system based on target tracking. The method comprises the following steps: acquiring basic data of patients in a community to form a target patient set; each target patient in the set has a unique identifier; forming a tutoring scheme of the target patient according to the basic data; taking each patient in the target patient set as a tracking target, and tracking in a community range by using a detector; monitoring the posture characteristic, the action characteristic, the sound characteristic and the expression characteristic of a target patient in real time, generating a characteristic change weighting coefficient and a characteristic sampling quantity by utilizing a deep learning algorithm, and calculating a characteristic change degree to judge whether the target patient is abnormal or not; when abnormality occurs, providing on-site rescue coaching and/or psychological coaching; when no abnormality occurs, on-line dietary counseling and/or medication counseling is provided at a predetermined period. According to the method and the device, the efficiency and the accuracy of monitoring the patients in the community are improved through a target tracking technology.
Description
Technical Field
The application relates to the field of target tracking and patient monitoring, in particular to a community patient monitoring method and system based on target tracking.
Background
The target tracking is a comprehensive application technology which integrates advanced results in various fields such as image processing, mode recognition, artificial intelligence, automatic control, sensors and the like. The tracking of the moving target includes detecting, identifying and tracking the moving target, and usually, a detector acquires parameters such as the position and the speed of the target or characteristics such as the shape, the color and the sound of the target, and then further processes the parameters to realize accurate tracking of the moving target. In traditional community patient monitoring, a certain number of sensors are generally worn on a patient, body data of the patient are collected through the sensors, the collected data are downloaded or transmitted to a medical center, and the medical center judges whether the patient is abnormal or not, so that whether treatment and a specific treatment scheme are provided or not is determined. According to the monitoring method, the patients in the community are required to wear a certain number of sensors at any time, so that the physical burden of the patients is increased, the waste of resources is caused, meanwhile, the acquired data cannot be transmitted back to the medical center in real time, the response time of treatment provided by the medical center is long, and the abnormal patients cannot be actively and accurately positioned.
Disclosure of Invention
In view of this, an object of the present application is to provide a method and a system for monitoring community patients based on target tracking, so as to reduce the physical burden of the patients, reduce the delay of therapy provided by a medical center, and save medical resources.
Based on the above purpose, the present application provides a community patient monitoring method based on target tracking, which includes:
acquiring basic data of the patients in the community according to a preset period to form a target patient set; each target patient in the target patient set has a unique identifier; forming diet, medication, psychology and rescue counseling schemes of the target patient according to the basic data;
taking each patient in the target patient set as a tracking target, and tracking in a community range by using a detector, wherein the tracking content comprises the geographic position, the occurrence time and the moving speed of the target patient;
monitoring the posture characteristic, the action characteristic, the sound characteristic and the expression characteristic of a target patient in real time, generating a characteristic change weighting coefficient and a characteristic sampling quantity by utilizing a deep learning algorithm based on the basic data of the target patient, and calculating the characteristic change degree to judge whether the target patient is abnormal or not;
when the target patient is abnormal, providing on-site rescue counseling and/or psychological counseling for the target patient; and when the target patient is not abnormal, providing online diet guidance and/or medication guidance according to a preset period.
In some embodiments, the method further comprises:
the basic data includes: physical characteristics, pathological characteristics, treatment history, and living characteristics.
In some embodiments, tracking with probes on a community scale includes:
the tracking comprises public area tracking and patient housing tracking;
in the public area tracking, a detector used is a video monitoring device, and tracking contents comprise the geographic position, the occurrence time and the moving speed of a target patient;
in the patient housing tracking, the used detector is a mobile terminal with a preset patient monitoring program, and the tracking content comprises the geographic position and the occurrence time of the target patient.
In some embodiments, in the patient housing tracking, the detector used in the patient housing tracking is a mobile terminal in which a patient monitoring program is preset, and the tracking content includes the geographic location and the occurrence time of the target patient, including:
and when the target patient enters the house of the target patient, pushing information to the mobile terminal, and starting the house tracking of the patient.
In some embodiments, generating a feature change weighting coefficient and a feature sampling number by using a deep learning algorithm based on the basic data of the target patient, and calculating a feature change degree to determine whether the target patient is abnormal includes:
calculating a characteristic change degree according to basic data and monitoring contents, and judging that the target patient is abnormal when the characteristic change degree exceeds a set threshold;
the calculation formula of the characteristic change degree is as follows:
i denotes the degree of change in the characteristic, I0The initial value of the change degree of the characteristics is expressed, i represents the characteristic number of the monitored target patient, 4 characteristics of posture, action, expression and sound are shared, and muiA characteristic variation weighting coefficient f representing the ith characteristic of the target patientiRepresenting the variation degree of the ith characteristic of the target patient;
wherein,
wherein, aijRepresenting the confidence of the jth sampling point of the ith characteristic of the target patient, aij0Representing the initial confidence of the jth sampling point of the ith characteristic of the target patient, niAnd (5) sampling points for the ith characteristic of the target patient.
According to the formula, the characteristic change degree covers 4 characteristics of the target patient, each characteristic is provided with a plurality of sampling points, and the number of the sampling points of each characteristic is dynamically set according to the specific condition of the target patient. Whether the target patient is abnormal or not is judged by integrating the change degrees of the plurality of characteristics, so that the error judgment of the abnormality is effectively avoided, and the accuracy of the abnormality judgment is improved.
In some embodiments, the providing on-site rescue coaching and/or psychological coaching comprises:
the control center sends the position and the coaching scheme of the target patient to a medical center, and the medical center dispatches medical personnel for on-site coaching;
the providing of online dietary and/or medication coaching on a predetermined cycle comprises:
and the control center sends the tutoring scheme to the mobile terminal with the preset patient monitoring program according to the preset period.
Based on the above object, the present application further provides a target tracking community patient monitoring system, which includes:
the system comprises an initial module, a target module and a management module, wherein the initial module is used for collecting basic data of patients in a community according to a preset period to form a target patient set; each target patient in the target patient set has a unique identifier; forming a diet, medication, psychology and rescue coaching scheme of the target patient by using a patient coaching expert system through the basic data;
the tracking module is used for tracking each patient in the target patient set in a community range by using a detector, wherein the tracking content comprises the geographic position, the occurrence time and the moving speed of the target patient;
the scheme module is used for monitoring the posture characteristic, the action characteristic, the sound characteristic and the expression characteristic of a target patient in real time, generating a characteristic change weighting coefficient and a characteristic sampling quantity by utilizing a deep learning algorithm based on the basic data of the target patient, and calculating the characteristic change degree to judge whether the target patient is abnormal or not;
the tutoring module is used for providing on-site rescue tutoring and/or psychological tutoring when the target patient is abnormal; and when the target patient is not abnormal, providing online diet guidance and/or medication guidance according to a preset period.
In some embodiments, the tracking module comprises:
a public area unit for performing public intra-area tracking on the target patient;
and the patient housing unit is used for tracking the target patient in the housing. The system further comprises:
in some embodiments, the protocol module comprises:
the data processing unit is used for receiving, analyzing and storing basic data, monitoring data and coaching data of the target patient;
and the scheme decision unit is used for generating and updating the tutoring scheme of the target patient.
In some embodiments, the system further comprises:
the evaluation feedback module is used for evaluating and feeding back the coaching effect after the coaching of the target patient is finished;
and the fault diagnosis module is used for monitoring the state of the detector in the community, and sending a fault signal and displaying a fault code when the detector fails.
Drawings
In the drawings, like reference numerals refer to the same or similar parts or elements throughout the several views unless otherwise specified. The figures are not necessarily to scale. It is appreciated that these drawings depict only some embodiments in accordance with the disclosure and are therefore not to be considered limiting of its scope.
Fig. 1 shows a flowchart of a community patient monitoring method based on object tracking according to an embodiment of the present invention.
Fig. 2 shows a block diagram of a community patient monitoring system based on object tracking according to an embodiment of the present invention.
Fig. 3 shows a configuration diagram of a tracking module according to an embodiment of the present invention.
Fig. 4 shows a configuration diagram of a scenario module according to an embodiment of the present invention.
Fig. 5 shows a block diagram of a community patient monitoring system based on object tracking according to an embodiment of the present invention.
Detailed Description
The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
Fig. 1 shows a flowchart of a community patient monitoring method based on object tracking according to an embodiment of the present invention. As shown in fig. 1, the method for monitoring community patients based on target tracking includes:
s11, collecting basic data of the patients in the community according to a preset period to form a target patient set; each target patient in the target patient set has a unique identifier; and forming a diet, medication, psychology and rescue guidance scheme of the target patient through the basic data.
Specifically, due to the complex environment and the numerous personnel in the community, in order to improve the monitoring efficiency, a specific monitoring target needs to be accurately determined, so that the monitored target patients are determined periodically (for example, weekly, monthly, quarterly, semi-annually, yearly), and the basic data of the monitored target patients are collected to form a target patient set. Meanwhile, in order to facilitate the system to identify and track the target, a unique identifier is added to each target patient in the target patient set, and the monitoring accuracy is improved. Basic data of the target patient are analyzed to form a diet, medication, psychology and rescue guidance scheme of the target patient, and the basic data can be conveniently called at any time.
In one embodiment, the patient's underlying data includes physical characteristics, pathological characteristics, treatment history, and lifestyle characteristics.
Specifically, different patients have different coaching requirements, and different coaching schemes are formulated according to physical characteristics (such as height, weight, heart rate, blood pressure and the like), pathological characteristics (clinical manifestations of diseases suffered by target patients), treatment history (such as treatment time, medication types, treatment means, recovery conditions and the like of the patients) and living characteristics (such as living habits, dietary structures, exercise habits and the like of the patients), so that the pertinence and the accuracy of patient monitoring and coaching are improved.
Step S12, taking each patient in the target patient set as a tracking target, and tracking in a community range by using a detector, wherein the tracking content comprises the geographic position, the occurrence time and the moving speed of the target patient.
Specifically, when the target patient moves in the community, the detector is used for identifying and tracking, the geographic position, the appearance time and the moving speed of the target patient are mastered at any time, and when the tutoring is required to be provided, the positioning can be carried out at the first time.
In one embodiment, tracking with probes in community domain includes:
the tracking comprises public area tracking and patient housing tracking;
in the public area tracking, a detector used is a video monitoring device, and tracking contents comprise the geographic position, the occurrence time and the moving speed of a target patient;
in the patient housing tracking, the used detector is a mobile terminal with a preset patient monitoring program, and the tracking content comprises the geographic position and the occurrence time of the target patient.
For example, a public area may include a green lane, stadium, parking lot, etc. within a community; the mobile terminal for monitoring the preset patient can comprise a smart phone, a tablet computer, a notebook computer, a smart watch and the like.
In one embodiment, in the patient housing tracking, the detector used in the patient housing tracking is a mobile terminal that presets a patient monitoring program, and the tracking content includes the geographic location and the occurrence time of the target patient, including:
and when the target patient enters the house of the target patient, pushing information to the mobile terminal, and starting the house tracking of the patient.
Particularly, when the target patient enters a house, the detector in the public area cannot position and monitor the target patient, at the moment, information is pushed to the mobile terminal, a patient monitoring program is automatically started, the target patient is repositioned and monitored, and the full-time-domain and full-community seamless monitoring of the target patient is achieved.
Step S13, real-time monitoring the posture characteristic, the action characteristic, the sound characteristic and the expression characteristic of the target patient, generating a characteristic change weighting coefficient and a characteristic sampling quantity by utilizing a deep learning algorithm based on the basic data of the target patient, and calculating the characteristic change degree to judge whether the target patient is abnormal or not.
Specifically, whether the target patient is abnormal or not is judged, and the single characteristic is adopted for judgment, so that misjudgment is easily caused by the influence of environmental change, the difference of the patient and the conversion of a detector, and the accuracy rate is low, so that the multiple characteristics of the target patient need to be comprehensively judged, and the accuracy and the real-time performance of abnormal judgment are improved. When a plurality of characteristics of the target patient are synthesized for judgment, the weighting coefficient of characteristic change and the characteristic sampling quantity are very important. Therefore, basic data collected in each target time interval are stored and analyzed, a deep learning algorithm is applied, learning and optimization are continuously carried out, the weighting coefficient and the feature sampling quantity of feature changes are determined, the comprehensive feature change degree of the target patient is further calculated, and whether abnormality occurs is judged.
In one embodiment, generating a feature change weighting coefficient and a feature sampling number by using a deep learning algorithm based on the basic data of the target patient, and calculating a feature change degree to determine whether the target patient is abnormal includes:
calculating a characteristic change degree according to basic data and monitoring contents, and judging that the target patient is abnormal when the characteristic change degree exceeds a set threshold;
the calculation formula of the characteristic change degree is as follows:
i denotes the degree of change in the characteristic, I0The initial value of the change degree of the characteristics is expressed, i represents the characteristic number of the monitored target patient, 4 characteristics of posture, action, expression and sound are shared, and muiA characteristic variation weighting coefficient f representing the ith characteristic of the target patientiRepresenting the variation degree of the ith characteristic of the target patient;
wherein,
wherein, aijRepresenting the confidence of the jth sampling point of the ith characteristic of the target patient, aij0Initial confidence of j sampling point representing ith characteristic of target patient，niAnd (5) sampling points for the ith characteristic of the target patient.
Step S14, when the target patient is abnormal, providing on-site rescue counseling and/or psychological counseling for the target patient; and when the target patient is not abnormal, providing online diet guidance and/or medication guidance according to a preset period.
In one embodiment, a method of providing on-site rescue coaching and/or psychological coaching includes:
the control center sends the position and the coaching scheme of the target patient to a medical center, and the medical center dispatches medical personnel for on-site coaching;
the providing of online dietary and/or medication coaching on a predetermined cycle comprises: and the control center sends the tutoring scheme to the mobile terminal with the preset patient monitoring program according to the preset period.
Specifically, when the control center finds that the target patient is abnormal through monitoring and calculation, a request is sent to the medical center, meanwhile, the specific position and the coaching scheme of the target patient are sent, and the medical center can arrive at the site as soon as possible to provide rescue coaching and/or psychological coaching; when the target patient is normal, on-site rescue coaching and psychological coaching are not implemented, but only diet coaching and/or medication coaching are implemented, and at the moment, the control center conducts developed diet coaching and/or medication coaching to the mobile terminal (such as a smart phone, a tablet computer, a notebook computer, a smart watch and the like) of the target patient according to a preset period (such as every day, every week, every month and the like).
Fig. 2 shows a block diagram of a community patient monitoring system based on object tracking according to an embodiment of the present invention. As shown in fig. 2, the community patient monitoring system based on target tracking may be divided into:
the initial module 21 is used for collecting basic data of the patients in the community according to a preset period to form a target patient set; each target patient in the target patient set has a unique identifier; forming a diet, medication, psychology and rescue coaching scheme of the target patient by using a patient coaching expert system through the basic data;
the tracking module 22 is configured to track each patient in the target patient set in a community range by using a detector, where the tracking content includes a geographic location, an occurrence time, and a moving speed of the target patient;
the scheme module 23 is configured to monitor a posture characteristic, an action characteristic, a voice characteristic and an expression characteristic of a target patient in real time, generate a characteristic change weighting coefficient and a characteristic sampling number based on basic data of the target patient by using a deep learning algorithm, and calculate a characteristic change degree to determine whether the target patient is abnormal;
a tutoring module 24, configured to provide on-site rescue tutoring and/or psychological tutoring when the target patient is abnormal; and when the target patient is not abnormal, providing online diet guidance and/or medication guidance according to a preset period.
Fig. 3 shows a trace module configuration diagram according to an embodiment of the present invention. As can be seen from fig. 3, the tracking module 22 includes:
a public area unit 221, configured to perform public intra-area tracking on the target patient;
a patient housing unit 222 for performing in-house tracking of the target patient.
Fig. 4 shows a schematic block diagram according to an embodiment of the present invention. As can be seen from fig. 4, the solution module 23 includes:
a data processing unit 231 for receiving, analyzing and storing the basic data, monitoring data and coaching data of the target patient;
a plan decision unit 232, configured to generate and update the tutoring plan of the target patient.
Fig. 5 shows a block diagram of a community patient monitoring system based on object tracking according to an embodiment of the present invention. As shown in fig. 5, the target tracking-based community patient monitoring system further includes:
the evaluation feedback module 25 is used for evaluating and feeding back the coaching effect after the coaching of the target patient is finished;
and the fault diagnosis module 26 is used for monitoring the state of the detector in the community, and when the detector fails, sending a fault signal and displaying a fault code.
The functions of the modules in the systems in the embodiments of the present application may refer to the corresponding descriptions in the above methods, and are not described herein again.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and alternate implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present invention may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a separate product, may also be stored in a computer readable storage medium. The storage medium may be a read-only memory, a magnetic or optical disk, or the like.
The above description is only for the specific embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive various changes or substitutions within the technical scope of the present invention, and these should be covered by the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (10)
1. A community patient monitoring method based on target tracking is characterized by comprising the following steps:
acquiring basic data of the patients in the community according to a preset period to form a target patient set; each target patient in the target patient set has a unique identifier; forming diet, medication, psychology and rescue counseling schemes of the target patient according to the basic data;
taking each patient in the target patient set as a tracking target, and tracking in a community range by using a detector, wherein the tracking content comprises the geographic position, the occurrence time and the moving speed of the target patient;
monitoring the posture characteristic, the action characteristic, the sound characteristic and the expression characteristic of a target patient in real time, generating a characteristic change weighting coefficient and a characteristic sampling quantity by utilizing a deep learning algorithm based on the basic data of the target patient, and calculating the characteristic change degree to judge whether the target patient is abnormal or not;
when the target patient is abnormal, providing on-site rescue counseling and/or psychological counseling for the target patient; and when the target patient is not abnormal, providing online diet guidance and/or medication guidance according to a preset period.
2. The method of claim 1, wherein the base data comprises: physical characteristics, pathological characteristics, treatment history, and living characteristics.
3. The method of claim 1, wherein tracking with a probe in community domain comprises:
the tracking comprises public area tracking and patient housing tracking;
in the public area tracking, a detector used is a video monitoring device, and tracking contents comprise the geographic position, the occurrence time and the moving speed of a target patient;
in the patient housing tracking, the used detector is a mobile terminal with a preset patient monitoring program, and the tracking content comprises the geographic position and the occurrence time of the target patient.
4. The method according to claim 3, wherein in the patient housing tracking, a detector used in the patient housing tracking is a mobile terminal with a preset patient monitoring program, and tracking content comprises the geographic position and the occurrence time of the target patient, and the method comprises the following steps:
and when the target patient enters the house of the target patient, pushing information to the mobile terminal, and starting the house tracking of the patient.
5. The method of claim 1, wherein the step of calculating a characteristic change degree for determining whether the target patient is abnormal by using a deep learning algorithm to generate a characteristic change weighting coefficient and a characteristic sampling number based on the basic data of the target patient comprises:
calculating a characteristic change degree according to basic data and monitoring contents, and judging that the target patient is abnormal when the characteristic change degree exceeds a set threshold;
the calculation formula of the characteristic change degree is as follows:
i denotes the degree of change in the characteristic, I0The initial value of the change degree of the characteristics is expressed, i represents the characteristic number of the monitored target patient, 4 characteristics of posture, action, expression and sound are shared, and muiA characteristic variation weighting coefficient f representing the ith characteristic of the target patientiRepresenting the variation degree of the ith characteristic of the target patient;
wherein,
wherein, aijRepresenting the confidence of the jth sampling point of the ith characteristic of the target patient, aij0Representing the initial confidence of the jth sampling point of the ith characteristic of the target patient, niAnd (5) sampling points for the ith characteristic of the target patient.
6. The method of claim 1, wherein said providing on-site rescue coaching and/or psychological coaching comprises:
the control center sends the position and the coaching scheme of the target patient to a medical center, and the medical center dispatches medical personnel for on-site coaching;
the providing of online dietary and/or medication coaching on a predetermined cycle comprises:
and the control center sends the tutoring scheme to the mobile terminal with the preset patient monitoring program according to the preset period.
7. A patient monitoring system based on target tracking, comprising:
the system comprises an initial module, a target module and a management module, wherein the initial module is used for collecting basic data of patients in a community according to a preset period to form a target patient set; each target patient in the target patient set has a unique identifier; forming a diet, medication, psychology and rescue coaching scheme of the target patient by using a patient coaching expert system through the basic data;
the tracking module is used for tracking each patient in the target patient set in a community range by using a detector, wherein the tracking content comprises the geographic position, the occurrence time and the moving speed of the target patient;
the scheme module is used for monitoring the posture characteristic, the action characteristic, the sound characteristic and the expression characteristic of a target patient in real time, generating a characteristic change weighting coefficient and a characteristic sampling quantity by utilizing a deep learning algorithm based on the basic data of the target patient, and calculating the characteristic change degree to judge whether the target patient is abnormal or not;
the tutoring module is used for providing on-site rescue tutoring and/or psychological tutoring when the target patient is abnormal; and when the target patient is not abnormal, providing online diet guidance and/or medication guidance according to a preset period.
8. The system of claim 7, wherein the tracking module comprises:
a public area unit for performing public intra-area tracking on the target patient;
and the patient housing unit is used for tracking the target patient in the housing.
9. The system of claim 7, wherein the solution module comprises:
the data processing unit is used for receiving, analyzing and storing basic data, monitoring data and coaching data of the target patient;
and the scheme decision unit is used for generating and updating the tutoring scheme of the target patient.
10. The system of claim 7, further comprising:
the evaluation feedback module is used for evaluating and feeding back the coaching effect after the coaching of the target patient is finished;
and the fault diagnosis module is used for monitoring the state of the detector in the community, and sending a fault signal and displaying a fault code when the detector fails.
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