Electrocardiogram data intelligent order sending and matching method and system

Abstract

The invention relates to the technical field of medical auxiliary systems, and discloses an electrocardio data intelligent order sending and matching method and system. The dispatch matching method is applied to a medical auxiliary system and specifically comprises the following steps that S101, basic information of a patient input by a basic medical institution is received, and the basic information of the patient comprises electrocardiogram data of the patient collected by operation equipment; and S102, acquiring the basic information of the patient, inputting the basic information of the patient into a preset identification model, acquiring an identification result output by the identification model based on the basic information of the patient and the acquired data, acquiring most matched processing doctor information in the identification result, and pushing related data to a corresponding doctor workstation.

Description

Technical Field

[0001] The present invention relates to the technical field of medical assistance systems, and in particular to an intelligent dispatching and matching method and system for electrocardiogram data. Background Art

[0002] In each cardiac cycle, the heart is excited successively by the pacemaker, atrium, and ventricle, accompanied by changes in bioelectricity. Electrocardiograms are used to draw various forms of potential change patterns from the body surface. Electrocardiograms are objective indicators of the occurrence, spread, and recovery of cardiac excitement. Electrocardiograms are the earliest, most commonly used, and most basic diagnostic method for coronary heart disease.

[0003] At present, the process of electrocardiogram examination for patients in hospitals is as follows: Figure 1 As shown, the collecting doctor is responsible for collecting and uploading ECG data. After the data is uploaded to the system, it will automatically be displayed in the system's undiagnosed list. The diagnosing doctor selects the application form in the undiagnosed list to open the ECG analysis and issue a report, but the following problems still exist: 1. The number of doctors in the ECG diagnosis center is limited, and their qualifications and experience vary. Faced with a large amount of ECG data, how to push the ECG application form to the most suitable diagnostic doctor and thus improve the overall ECG diagnosis efficiency is a certain challenge; 2. The ECG of high-risk critical patients is assigned to an inappropriate doctor, which may lead to delayed ECG diagnosis and delay the patient's treatment time; 3. Assigning complex electrocardiograms to inexperienced diagnosticians may result in prolonged diagnosis time or misdiagnosis; 4. Assigning simple ECGs to highly qualified diagnosticians may result in a waste of ECG diagnostic resources.

[0004] Based on the above, it is urgent to design an intelligent dispatching and matching system for ECG data to solve the above problems currently encountered by patients undergoing ECG examinations and diagnoses in hospitals. Summary of the invention

[0005] The purpose of the present invention is to provide an intelligent dispatching and matching method and system for ECG data. By intelligently analyzing the collected ECG data of patients and combining it with comprehensive analysis of the online diagnostic doctor information in the system, accurate push of ECG data can be achieved, and the diagnostic efficiency of ECG reports can be improved. This aims to solve the problems of limited number of doctors in the electrocardiographic diagnosis center and different qualifications and experience of doctors in the existing technology.

[0006] The present invention is implemented in this way: the ECG data intelligent dispatch matching method is applied to a medical assistance system, specifically comprising the following steps: S101: receiving basic information of a patient input by a primary medical institution, wherein the basic information of the patient includes electrocardiogram data of the patient collected by an operating device; S102: Obtaining the content text of the patient's basic information, inputting the patient's basic information into a preset recognition model, obtaining a recognition result output by the recognition model based on the patient's basic information, and obtaining the content text of the patient's electrocardiogram data collected by the operating device from the recognition result; S103: Obtain the diagnosis group classification item of the medical end, determine the current examination request is assigned to the targeted medical end group item through the content text of the patient's electrocardiogram data collected by the operating device, and obtain the operation status of the doctor end in the current medical end group item; S104: Obtain the experience index data of different doctor terminals in the current medical terminal group item, match the doctor terminal that is suitable for the current ECG data through weighted calculation of the experience index data, and directly push the current patient ECG data to the suitable doctor.

[0007] Furthermore, in S101, basic patient information entered by the primary medical institution is received, including: receiving a connection request sent by a primary medical institution through a preset network, wherein the connection request is used to request to establish a connection with the medical assistance system; Detecting whether the current account of the primary medical institution is the target account; If the current account of the primary medical institution is the target account, the primary medical institution is connected according to the connection request, and after the connection is completed, the basic information data of the patient entered by the primary medical institution is received.

[0008] Furthermore, the preset network includes one or a combination of a 3G network, a 4G network, a 5G network, and a WIFI network.

[0009] Furthermore, in S102, the content text of the basic information of the patient is obtained, and the basic information of the patient is input into a preset recognition model, including: Acquire multiple recognition models, and sort the multiple recognition models in descending order according to the frequency of the collected patient electrocardiogram data; The content text of the patient's basic information is obtained, and the patient's basic information is input into the recognition model ranked first.

[0010] Further, in S103, the current examination request is assigned to a targeted medical end group item according to the content text of the patient's electrocardiogram data collected by the operating device, including: Single-item tagging is performed on the content text of the current patient ECG data, and then all medical end group items are obtained, and the identification content of all medical end group items is extracted; By comparing the content text of the single-item marked patient ECG data with the identification content extracted from all medical end group items, the content text of the single-item marked patient ECG data with the most identification content is matched to obtain the best matching medical end group item for the current examination request.

[0011] Furthermore, in S104, the experience index data of different doctor terminals in the current medical terminal group item is obtained, including: The quality of past diagnoses by doctors: by conducting quality control statistics on the quality of the reports of doctors; The diagnostic efficiency of doctors in the past: the diagnostic time of different diseases is statistically analyzed; The number of tasks that the current doctor is waiting to process determines how busy the current doctor is with patients; The diagnostician’s years of experience is used to determine the diagnostician’s qualifications.

[0012] Furthermore, through weighted calculation of the empirical index data, the doctor end that is suitable for the current ECG data is matched, and the current patient ECG data is directly pushed to the suitable doctor, including: The weighted calculation formula of empirical indicator data is: In the formula: n=f1+f2+…+fk, x is the experience index data of the doctor side, f is the weight ratio of the experience index data, and the original data is calculated according to a reasonable proportion by averaging the data with different weights to obtain the weighted average, and the doctor side that is suitable for the current examination request is matched.

[0013] Compared with the prior art, the ECG data intelligent dispatch matching method and system provided by the present invention has the following beneficial effects: 1. The system receives basic patient information entered by primary medical institutions, including the patient's ECG data collected by the operating equipment. The system matches the diagnosis group classification items of the medical end through the patient's ECG data, and obtains the best matching doctor end in the medical end group item. The doctor end in the best matching medical end group item is obtained by weighted calculation of the doctor's experience index data to match the doctor suitable for the current examination request. Through intelligent analysis of the collected patient ECG data and comprehensive analysis of the online diagnosis doctor information in the system, the accurate push of ECG data is realized, and the diagnostic efficiency of ECG reports is improved; 2. Through automatic analysis of ECG AI algorithm, normal, positive and critical ECGs can be identified, and the categories can be arranged for priority sorting, reminding doctors to give priority to the diagnosis of critical data, or configure automatic priority sorting of patient data from some sources to achieve accurate push of ECG data and improve the efficiency of ECG reporting.

[0014] An ECG data intelligent dispatch matching system is applied to the above ECG data intelligent dispatch matching method, and the system comprises: A transmission unit, used for signal transmission of a preset network, to complete the communication between the patient end and the medical assistance system; A control unit is used to process data and send signals, and to determine the execution status of each unit; The recognition unit is used to input the patient's current ECG data into a preset recognition model, and obtain a recognition result output by the recognition model based on the patient's current ECG data; A matching unit, used for determining, through the patient's current electrocardiogram data, to allocate the current examination request to a directed medical end group item; A weighting unit, used to match the doctor end suitable for the current examination request through weighted calculation of the experience index data; Specifically, the control unit includes: A receiving unit, used for receiving signal data transmitted by each unit; A determination unit, used to determine the content of the signal data transmitted by each unit; An execution unit, used to send execution commands to each unit through the content of signal data; The output unit is used to output the generated or processed data and send it to each unit to complete the corresponding action.

[0015] Specifically, the judging unit includes: The instruction judgment unit is used to judge whether the current instruction is a signal instruction issued by the patient end, the doctor end or the system end, and obtain the signal and send it to the signal feedback unit for data feedback; The signal feedback unit performs signal feedback if the data feedback is within the system preset threshold, otherwise it executes the current instruction. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a flowchart of the current electrocardiogram examination process for patients in hospitals; Figure 2 This is a schematic flowchart of the process of the ECG data intelligent dispatch matching method proposed by the present invention; Figure 3 It is a schematic diagram of the process of receiving basic patient information entered by a primary medical institution in the ECG data intelligent dispatching and matching method proposed by the present invention; Figure 4 This is a structural diagram of the ECG data intelligent dispatch matching system proposed by the present invention; Figure 5 This is a structural schematic diagram of a control unit in the ECG data intelligent dispatch matching system proposed by the present invention; Figure 6 This is a structural schematic diagram of the judgment unit in the ECG data intelligent dispatching and matching system proposed in the present invention. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0018] The implementation of the present invention is described in detail below in conjunction with specific embodiments.

[0019] The same or similar numbers in the drawings of this embodiment correspond to the same or similar parts; in the description of the present invention, it should be understood that if the terms "upper", "lower", "left", "right" and the like indicate directions or positional relationships based on the directions or positional relationships shown in the drawings, it is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction. Therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes and cannot be understood as limitations on the present invention. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0020] Reference Figure 2 As shown, the ECG data intelligent dispatch matching method is applied to the medical assistance system, and specifically includes the following steps: S101: receiving basic information of patients input by primary medical institutions, where the basic information of patients includes electrocardiogram data of patients collected by operating equipment; Among them, refer to Figure 3 As shown, the basic information of patients entered by primary medical institutions is received, including: receiving a connection request sent by a primary medical institution through a preset network, the connection request being used to request to establish a connection with a medical assistance system; Check whether the current account of the primary medical institution is the target account; If the current account of the primary medical institution is the target account, the primary medical institution is connected according to the connection request, and after the connection is completed, the basic information data of the patient entered by the primary medical institution is received; Specifically: the system is a client-server mode: the grassroots client collects data and sends it to the server, but the account of the layer medical institution needs to be verified before uploading. After confirming as a layer medical institution, it receives data information and realizes the intelligent dispatching method running on the server side. It can be understood as the process of the grassroots medical institution collecting the patient's basic information + ECG data and uploading it to the server.

[0021] S102: Obtaining a content text of the patient's basic information, where the content text of the patient's basic information is the patient's electrocardiogram data, inputting the patient's basic information into a preset recognition model, obtaining a recognition result output by the recognition model based on the patient's basic information, and obtaining the content text of the patient's electrocardiogram data collected by the operating device in the recognition result; The process of obtaining the content text of the patient's basic information and inputting the patient's basic information into a preset recognition model includes: Acquire multiple recognition models, and sort the multiple recognition models in descending order according to the frequency of the collected patient electrocardiogram data; Obtain the content text of the patient's basic information, and input the patient's basic information into the first-ranked recognition model; S103: Obtain the diagnosis group classification item of the medical end, determine the current examination request is assigned to the targeted medical end group item by operating the content text of the patient's electrocardiogram data collected by the device, and obtain the operation status of the doctor end in the current medical end group item; Medical groups are usually divided into groups according to the type of examination or the source of the patient. For example, Group A is responsible for dynamic ECG diagnosis, and Group B is responsible for static ECG diagnosis. For obtaining the diagnosis group classification items of the medical side, the content text of the patient's ECG data collected by the operating device is used to determine the current examination request to be assigned to the targeted medical side group item, which is matched to the corresponding group according to the examination type of the patient's application form; Among them, by operating the content text of the patient's electrocardiogram data collected by the device, determining that the current examination request is assigned to a targeted medical end group item includes: Single-item tagging is performed on the content text of the current patient ECG data, and then all medical end group items are obtained, and the identification content of all medical end group items is extracted; By comparing the content text of the single-item marked patient ECG data with the identification content extracted from all medical end group items, the medical end group item with the most identification content in the content text of the single-item marked patient ECG data is matched, and the medical end group item with the best matching content for the current examination request is obtained; S104: Obtain the experience index data of different doctor terminals in the current medical terminal group item, match the doctor terminal that is compatible with the current ECG data through weighted calculation of the experience index data, and directly push the current patient ECG data to the compatible doctor. The system of this technical solution receives the basic information of patients entered by primary medical institutions. The basic information of patients includes the ECG data of patients collected by operating equipment. The diagnosis group classification items of the medical terminal are matched through the ECG data of patients to obtain the best matching doctor terminal in the medical terminal group item. The best matching doctor terminal in the medical terminal group item is obtained by weighted calculation of the doctor's experience index data to match the doctor that is compatible with the current examination request, thereby achieving pathological feature matching between the doctor terminal and the patient terminal and realizing accurate diagnosis. The present invention combines the ECG analysis results with the system's online doctor information to calculate the most suitable doctor for diagnosing the data.

[0022] In this embodiment, the preset network includes one or a combination of a 3G network, a 4G network, a 5G network, and a WIFI network.

[0023] In S104 of this embodiment, the experience index data of different doctor terminals in the current medical terminal group item is obtained, including: The quality of past diagnoses by doctors: by conducting quality control statistics on the quality of the reports of doctors; The diagnostic efficiency of doctors in the past: the diagnostic time of different diseases is statistically analyzed; The number of tasks that the current doctor is waiting to process determines how busy the current doctor is with patients; The diagnostician’s years of experience is used to determine the diagnostician’s qualifications.

[0024] The specific points are as follows: (1) Quality of diagnosis by previous doctors: The system can obtain the quality of each doctor's report on a certain disease data by conducting quality control statistics on the quality of the diagnosis doctor's report; Specifically, the system collects statistics on the patient reports diagnosed by the diagnostic doctors, including classification, the number of patients diagnosed, and the number of patients who recovered after diagnosis. By classifying different diseases, the system collects statistics on the number of diagnoses made by the diagnostic doctors under different diseases and the number of patients who recovered after diagnosis, thereby determining the diseases that the diagnostic doctors are good at treating, that is, obtaining the quality of each doctor's report on a certain disease data; (2) The diagnostic efficiency of doctors in the past; this can be broken down into diagnostic timeliness for different types of diseases; By counting the diagnosis time, the system can obtain the average reporting time of each doctor for a certain disease data; (3) The number of tasks currently pending for doctors and the tasks currently pending for doctors; (4) The diagnostic physician’s years of experience.

[0025] Specifically, the ECG AI algorithm can automatically analyze and identify normal, positive, and critical ECGs, arrange the classification for priority sorting, and remind doctors to give priority to diagnosing critical data, or configure automatic priority sorting of patient data from some sources to achieve accurate push of ECG data and improve the efficiency of ECG reporting; Specifically, for "automatically analyzing through ECG AI algorithm to identify normal, positive, and critical ECGs", the specific implementation method is S101-S102 of the present technical solution, that is, receiving the basic information of the patient entered by the primary medical institution, the basic information of the patient includes the ECG data of the patient collected by the operating device, obtaining the content text of the basic information of the patient, inputting the basic information of the patient into a preset recognition model, obtaining the recognition result output by the recognition model based on the basic information of the patient, and in the recognition result, obtaining the content text of the ECG data of the patient collected by the operating device, that is, identifying the content in the ECG, and classifying the identified ECG content into three types: normal, positive, and critical.

[0026] The experience indicator data of different doctors can also utilize the system's historical experience indicator data to automatically introduce more factors into the dispatching mechanism, push data to suitable doctors more accurately, and give full play to the advantages of different doctors.

[0027] In this embodiment, the doctor end that is adapted to the current ECG data is matched through weighted calculation of the empirical index data, and the current patient ECG data is directly pushed to the adapted doctor, including: The weighted calculation formula of empirical indicator data is: In the formula: n=f1+f2+…+fk, x is the experience indicator data on the doctor side, f is the weight ratio of the experience indicator data, and the original data is calculated in a reasonable proportion through the average of data with different weights to obtain the weighted average, which matches the doctor side that is suitable for the current examination request. The ECG AI algorithm automatically analyzes and identifies normal, positive, and critical ECGs, arranges classification for priority sorting, reminds doctors to give priority to diagnosing critical data, or configures automatic priority sorting of some source patient data, so as to achieve accurate push of ECG data and improve ECG reporting efficiency.

[0028] Reference Figure 4-6As shown, the ECG data intelligent dispatching and matching system is applied to the above-mentioned ECG data intelligent dispatching and matching method, and the system includes: a transmission unit, which is used for signal transmission of a preset network to complete the communication between the patient end and the medical assistance system; a control unit, which is used to realize data processing and signal transmission, and judge the execution status of each unit; an identification unit, which is used to input the patient's current ECG data into a preset identification model, and obtain the identification result output by the identification model based on the patient's current ECG data; a matching unit, which is used to determine the current examination request to be assigned to a directional medical end group item through the patient's current ECG data; The weighting unit is used to match the doctor end that is suitable for the current examination request through weighted calculation of the experience index data. The system receives the basic information of the patient entered by the primary medical institution, and the basic information of the patient includes the electrocardiogram data of the patient collected by the operating equipment. The diagnosis group classification items of the medical end are matched through the electrocardiogram data of the patient to obtain the best matching doctor end in the medical end group item. The doctor end in the best matching medical end group item is matched by weighted calculation of the doctor's experience index data to match the doctor end that is suitable for the current examination request, thereby realizing the matching of the pathological characteristics of the doctor end and the patient end, and realizing accurate diagnosis; Specifically, the recognition unit is used to input the patient's current ECG data into a preset recognition model, wherein the ECG data includes the patient's basic information (such as age, gender, medical history, chief complaint, clinical symptoms, etc.).

[0029] In this embodiment, the control unit includes: a receiving unit for receiving signal data transmitted by each unit; a judging unit for determining the content of the signal data transmitted by each unit; an executing unit for sending an execution command to each unit through the content of the signal data; an output unit for outputting the generated or processed data and sending it to each unit to complete the corresponding action, automatically analyzing the ECG AI algorithm to identify normal, positive, and critical ECGs, arranging the categories for priority sorting, reminding doctors to give priority to diagnosing critical data, or configuring automatic priority sorting of some source patient data, so as to achieve accurate push of ECG data and improve the efficiency of ECG reporting.

[0030] In this embodiment, the judgment unit includes: an instruction judgment unit, which is used to judge whether the current instruction is a signal instruction issued by the patient end, the doctor end or the system end, and obtain the waiting time for uploading the current examination request and send it to the signal feedback unit for data feedback; The signal feedback unit performs signal feedback if the data feedback is within the system preset threshold, otherwise it executes the current instruction.

[0031] The usage logic of this technical solution is: when the primary medical institution enters the ECG examination data of the patient's basic information, it will be directly uploaded to the medical assistance system. The medical assistance system will perform weighted calculations on the current doctor's experience indicator data, calculate the matching degree of each diagnostic doctor for the task, obtain the diagnostic doctor with the highest matching degree, and automatically push the data to the doctor to accurately push the ECG data, thereby improving the efficiency of ECG reports. In special circumstances, the doctor can also refuse to accept this task or transfer this task to other doctors to ensure that the examination request from the doctor can be fed back in time.

[0032] In this embodiment, the entire operation process can be controlled by a computer to achieve automatic operation control, and in each operation link, sensors can be set up for signal feedback to achieve sequential execution of steps. These are all common knowledge of current automatic control and will not be described in detail in this embodiment.

[0033] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent dispatching and matching method for ECG data, characterized in that: Applied to medical auxiliary systems, specifically including the following steps: S101: receiving basic information of a patient input by a primary medical institution, wherein the basic information of the patient includes electrocardiogram data of the patient collected by an operating device; S102: Obtaining the content text of the patient's basic information, inputting the patient's basic information into a preset recognition model, obtaining a recognition result output by the recognition model based on the patient's basic information, and obtaining the content text of the patient's electrocardiogram data collected by the operating device from the recognition result; S103: Obtain the diagnosis group classification item of the medical end, determine the current examination request is assigned to the targeted medical end group item through the content text of the patient's electrocardiogram data collected by the operating device, and obtain the operation status of the doctor end in the current medical end group item; S104: Obtain the experience index data of different doctor terminals in the current medical terminal group item, match the doctor terminal that is suitable for the current ECG data through weighted calculation of the experience index data, and directly push the current patient ECG data to the suitable doctor.

2. The ECG data intelligent dispatch matching method according to claim 1, characterized in that: In S101, basic patient information entered by the primary medical institution is received, including: receiving a connection request sent by a primary medical institution through a preset network, wherein the connection request is used to request to establish a connection with the medical assistance system; Detecting whether the current account of the primary medical institution is the target account; If the current account of the primary medical institution is the target account, the primary medical institution is connected according to the connection request, and after the connection is completed, the basic information data of the patient entered by the primary medical institution is received.

3. The ECG data intelligent dispatching and matching method according to claim 2, characterized in that: The preset network includes one or a combination of a 3G network, a 4G network, a 5G network, and a WIFI network.

4. The ECG data intelligent dispatching and matching method according to claim 3, characterized in that: In S102, the content text of the basic information of the patient is obtained, and the basic information of the patient is input into a preset recognition model, including: Acquire multiple recognition models, and sort the multiple recognition models in descending order according to the frequency of the collected patient electrocardiogram data; The content text of the patient's basic information is obtained, and the patient's basic information is input into the recognition model ranked first.

5. The ECG data intelligent dispatching and matching method according to claim 4, characterized in that: In S103, the current examination request is assigned to a targeted medical end group item according to the content text of the patient's electrocardiogram data collected by the operating device, including: Single-item marking is performed on the content of the current patient ECG data, and then all medical end group items are obtained, and the identification content of all medical end group items is extracted; By comparing the content of the single-item marked patient ECG data with the identification content extracted from all medical end group items, the medical end group item with the most identification content in the single-item marked patient ECG data is matched to obtain the best matching medical end group item for the current examination request.

6. The ECG data intelligent dispatching and matching method according to claim 5, characterized in that: In S104, the experience index data of different doctor terminals in the current medical terminal group item is obtained, including: The quality of past diagnosing doctors: by conducting quality control statistics on the quality of the reports of diagnosing doctors on different diseases; The diagnostic efficiency of doctors in the past: the diagnostic time of different diseases is statistically analyzed; The number of tasks that the current doctor is waiting to process to determine the current doctor's busyness; The diagnostician’s years of experience is used to determine the diagnostician’s qualifications.

7. The ECG data intelligent dispatching and matching method according to claim 6, characterized in that: Through weighted calculation of empirical index data, the doctor end that matches the current ECG data is matched, and the current patient's ECG data is directly pushed to the matched doctor, including: The weighted calculation formula of empirical indicator data is: In the formula: n=f1+f2+…+fk, x is the experience index data of the doctor side, f is the weight ratio of the experience index data, and the original data is calculated according to a reasonable proportion by averaging the data with different weights to obtain the weighted average, and the doctor side that is suitable for the current examination request is matched.

8. The ECG data intelligent dispatch matching system is characterized by: The method for intelligent dispatching and matching of electrocardiogram data according to any one of claims 1 to 7, wherein the system comprises: A transmission unit, used for signal transmission of a preset network, to complete the communication between the patient end and the medical assistance system; A control unit is used to process data and send signals, and to determine the execution status of each unit; The recognition unit is used to input the patient's current ECG data into a preset recognition model, and obtain a recognition result output by the recognition model based on the patient's current ECG data; A matching unit, used for determining, through the patient's current electrocardiogram data, to allocate the current examination request to a directed medical end group item; The weighting unit is used to match the doctor end suitable for the current examination request through weighted calculation of the experience indicator data.

9. The ECG data intelligent dispatch matching system according to claim 8, characterized in that: The control unit comprises: A receiving unit, used for receiving signal data transmitted by each unit; A determination unit, used to determine the content of the signal data transmitted by each unit; An execution unit, used to send execution commands to each unit through the content of signal data; The output unit is used to output the generated or processed data and send it to each unit to complete the corresponding action.

10. The ECG data intelligent dispatch matching system according to claim 9, characterized in that: The judging unit comprises: The instruction judgment unit is used to judge whether the current instruction is a signal instruction issued by the patient end, the doctor end or the system end, and obtain the signal and send it to the signal feedback unit for data feedback; The signal feedback unit performs signal feedback if the data feedback is within the system preset threshold, otherwise it executes the current instruction.

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