Remote medical equipment control method, device and equipment

Abstract

The invention is suitable for the technical field of remote medical equipment control, and particularly relates to a remote medical equipment control method, device and equipment, and the method comprises the steps: obtaining reservation information; acquiring detection data; analyzing according to the reservation information, and dividing the detection data into first data and second data; transmitting the first data to a second device, and receiving a first auscultation result of the second device; analyzing according to the second data to obtain a difference coefficient; transmitting the difference coefficient and the second data to a second device, and receiving a second auscultation result of the second device; and transmitting the first auscultation result and the second auscultation result to the first equipment. According to the method, the speed of the diagnosis process is accelerated, that is, the data analysis based on the reservation information is beneficial to extracting real useful indexes from a large amount of health monitoring data, the problem of information overload possibly occurring when doctors face too much irrelevant information is avoided, and the accuracy and efficiency of diagnosis are improved.

Description

Technical Field

[0001] The present application belongs to the technical field of remote medical equipment control, and in particular, relates to a remote medical equipment control method, device and equipment. Background Art

[0002] The current consultation mode is usually through offline doctor consultation, which is time-consuming. Many people do not have a good experience when seeking medical treatment. "Waiting in line for 3 hours and seeing a doctor for 3 minutes" is the real feeling of many patients and their family members. With the rapid development of information technology, telemedicine services have gradually become an important part of the medical field. By utilizing modern communication technology and computer networks, it enables patients to receive diagnosis and treatment advice from professional doctors far away from medical institutions, which greatly facilitates those who live in remote areas or have limited mobility. Patients can make an appointment with a professional doctor on a special consultation APP. Under the guidance of the doctor, they monitor the body's indicators through telemedicine devices. The doctor makes a diagnosis and gives reasonable suggestions based on the monitored indicators.

[0003] However, sometimes during the consultation process, patients may seek other help from the doctor in addition to the currently scheduled examination items. For example, when a patient makes an appointment with an ophthalmologist to examine his eyes, he may ask the doctor about the cause of discomfort in other parts of the body such as the heart. In this case, the doctor will ask the patient to collect relevant monitoring data through a telemedicine device. At this time, all the collected data will often be sent to the doctor for analysis. If the doctor is not professional enough in other projects, the multiple data may reduce the doctor's analysis efficiency and may also cause certain interference to the doctor's judgment. Summary of the invention

[0004] The embodiments of the present application provide a remote medical equipment control method, device and equipment, which can solve the problem that when a patient asks a doctor about multiple items, the doctor's analysis efficiency may be reduced and the doctor's judgment may be interfered with to a certain extent.

[0005] In a first aspect, an embodiment of the present application provides a remote medical device control method, comprising: Acquire reservation information; wherein the reservation information is used to reflect the project and doctor that the user has reserved through the first device, and the first device is used to reflect the user's usage terminal; Acquire detection data; wherein the detection data is used to reflect the user's physical characteristic data monitored by the remote medical device; Analyze the test data according to the reservation information, and divide the test data into first data and second data; wherein the first data is used to reflect the test index whose correlation degree with the reserved item in the test data is greater than the preset correlation degree; the second data is used to reflect the test index whose correlation degree with the item outside the reservation in the test data is less than the preset correlation degree; Transmitting the first data to a second device, and receiving a first auscultation result of the second device; wherein the second device is used to reflect the doctor's usage terminal; Analyze the second data to obtain a coefficient of difference; wherein the coefficient of difference is used to reflect the degree of difference between an indicator of the second data and a normal indicator; transmitting the difference coefficient and the second data to the second device, and receiving a second auscultation result of the second device; The first auscultation result and the second auscultation result are transmitted to the first device.

[0006] The above technical solutions in the embodiments of the present application have at least the following technical effects: The remote medical equipment control method provided by the present application obtains the items and doctor's appointment information that are reserved by the user through the first device that reflects the user's terminal; and obtains the detection data that reflects the physical characteristic data of the user monitored by the remote medical device; then analyzes according to the appointment information, and divides the detection data into the first data that reflects the detection index that the degree of association with the reserved items in the detection data is greater than the preset degree of association and the second data that reflects the detection index that the degree of association with the reserved items in the detection data is less than the preset degree of association; then transmits the first data to the second device, and receives the first auscultation result of the second device that reflects the doctor's terminal; then analyzes according to the second data, and obtains the difference coefficient of the difference between the index of the second data and the normal index; then transmits the difference coefficient and the second data to the second device, and receives the second auscultation result of the second device; finally, transmits the first auscultation result and the second auscultation result to the first device. The method can prioritize and transmit the most critical information for the current diagnosis and treatment to the doctor by dividing the detection data into the first data and the second data with the degree of association with the reserved items. This not only reduces the amount of unnecessary data transmission, but also speeds up the diagnosis process. That is, data analysis based on appointment information helps to extract truly useful indicators from a large amount of health monitoring data, avoiding the problem of information overload that may occur when doctors face too much irrelevant information, allowing doctors to focus more on key data that are directly related to the patient's condition, thereby improving the accuracy and efficiency of diagnosis. By taking differentiated treatment methods for different types of test data and generating auscultation results based on doctor feedback, users can be provided with more personalized health management suggestions. In particular, for the second data (i.e., physiological parameters that are less correlated with the appointment items but still important), potential health risks are evaluated by calculating the difference coefficient between them and normal indicators, further enriching the service content. Ultimately, all auscultation results and suggestions provided by doctors will be promptly fed back to users, promoting effective communication between the two parties. This two-way interactive model helps to establish a more trusting and harmonious doctor-patient relationship, and also allows patients to have a clearer understanding of their health status.

[0007] In a possible implementation manner of the first aspect, analyzing according to the reservation information to divide the detection data into first data and second data includes: Analyze the reservation information to obtain a first test item; wherein the first test item is used to reflect the type of physical feature to be tested corresponding to the reserved item; The detection data is analyzed according to the first detection item and divided into first data and second data.

[0008] In a possible implementation manner of the first aspect, analyzing according to the first detection item to divide the detection data into first data and second data includes: Determine first data according to the first detection item; Determine the detection item range according to the detection data; wherein the detection item range is used to reflect all the types of body features detected; The second data is obtained by analyzing the detection item range, the first detection item and the first data.

[0009] In a possible implementation manner of the first aspect, the performing analysis according to the detection item range, the first detection item, and the first data to obtain the second data includes: Analyze the detection item range and the first detection item to obtain a second detection item; wherein the second detection item is used to reflect the types of physical characteristics detected in the detection item range other than the first detection item; Analyze the second detection item to obtain a detection feature; wherein the detection feature is used to reflect the item that the user has not reserved for the second detection item; The second data is obtained by analyzing the detection feature and the first data.

[0010] In a possible implementation manner of the first aspect, the obtaining second data by analyzing the detection feature and the first data includes: Analyze the first data according to the detection feature to obtain third data; wherein the third data is used to reflect the part of the detection data corresponding to the detection feature in the first data; The third data is integrated with the detection data corresponding to the second detection item to obtain second data.

[0011] In a possible implementation manner of the first aspect, performing analysis according to the second data to obtain a coefficient of variation includes: Analyze the second data to obtain first key data and second key data; wherein the first key data is used to reflect the detection data in the second data that directly corresponds to other abnormal physical characteristics of the user, and the second key data is used to reflect other detection data in the second data except the first key data; Calculating respectively a first difference value between the first key data and a standard data set, and a second difference value between the second key data and a standard data set; wherein the standard data set includes normal values ​​of various physical detection indicators; An analysis is performed based on the first difference value and the second difference value to obtain a difference coefficient.

[0012] In a possible implementation manner of the first aspect, performing analysis according to the first difference value and the second difference value to obtain a difference coefficient includes: Get a first weight and a second weight; The first difference value is weighted by the first weight, and the second difference value is weighted by the second weight, and the difference coefficient is obtained by summing the sum.

[0013] In a possible implementation manner of the first aspect, transmitting the difference coefficient and the second data to the second device, and receiving a second auscultation result of the second device includes: When the difference coefficient is greater than a preset threshold, a reminder message is generated and transmitted to the second device; wherein the reminder message is used to indicate that the user's physical characteristic data does not meet the current consultation conditions; receiving the suggestion information from the second device, and determining the suggestion information as the second auscultation result; wherein the suggestion information is used to indicate the items and doctors that the user needs to make an appointment for; And / or, transmitting the difference coefficient and the second data to the second device, and receiving a second auscultation result of the second device, further includes: When the difference coefficient is less than the preset threshold, the difference coefficient and the second data are transmitted to the second device, and a second consultation result of the second device is received.

[0014] In a second aspect, an embodiment of the present application provides a remote medical equipment control device, comprising: A first acquisition module is used to acquire appointment information; wherein the appointment information is used to reflect the project and doctor that the user has booked through the first device, and the first device is used to reflect the user's usage terminal; A second acquisition module is used to acquire detection data; wherein the detection data is used to reflect the user's physical characteristic data monitored by the remote medical device; A first analysis module is used to analyze the test data according to the reservation information and divide the test data into first data and second data; wherein the first data is used to reflect the test index whose correlation degree with the reserved item in the test data is greater than a preset correlation degree; and the second data is used to reflect the test index whose correlation degree with the reserved item in the test data is less than a preset correlation degree; A first transmission module, used to transmit the first data to a second device, and receive a first auscultation result of the second device; wherein the second device is used to reflect the doctor's usage terminal; A second analysis module, used to analyze the second data to obtain a difference coefficient; wherein the difference coefficient is used to reflect the degree of difference between the index of the second data and the normal index; a second transmission module, configured to transmit the difference coefficient and the second data to the second device, and receive a second auscultation result of the second device; A third transmission module is used to transmit the first auscultation result and the second auscultation result to the first device.

[0015] In a third aspect, an embodiment of the present application provides a telemedical device, comprising a telemedical device and a control device, wherein the control device is electrically connected to the telemedical device, the control device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any of the methods described in the first aspect when executing the computer program.

[0016] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described in any one of the above-mentioned first aspects is implemented.

[0017] In a fifth aspect, an embodiment of the present application provides a computer program product, which, when executed on a telemedical device, enables the telemedical device to execute the telemedical device control method described in any one of the above-mentioned first aspects.

[0018] It can be understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0020] Figure 1 is a flow chart of a remote medical device control method provided in an embodiment of the present application; Figure 2 It is a schematic diagram of the implementation flow of the remote medical device control method provided in the embodiment of the present application; Figure 3 is a schematic diagram of the structure of a remote medical equipment control device provided in an embodiment of the present application; Figure 4It is a structural schematic diagram of a control device of a remote medical device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0021] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0022] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0023] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0024] As used in the specification of this application and the appended claims, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting" depending on the context. Similarly, the phrase "if it is determined" or "if the described condition or event is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce the described condition or event is detected" or "in response to detecting the described condition or event" depending on the context.

[0025] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0026] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0027] The current consultation mode is usually through offline doctor consultation, which is time-consuming. Many people do not have a good experience when seeking medical treatment. "Waiting in line for 3 hours and seeing a doctor for 3 minutes" is the real feeling of many patients and their family members. With the rapid development of information technology, telemedicine services have gradually become an important part of the medical field. By utilizing modern communication technology and computer networks, it enables patients to receive diagnosis and treatment advice from professional doctors far away from medical institutions, which greatly facilitates those who live in remote areas or have limited mobility. Patients can make an appointment with a professional doctor on a special consultation APP. Under the guidance of the doctor, they monitor the body's indicators through telemedicine devices. The doctor makes a diagnosis and gives reasonable suggestions based on the monitored indicators.

[0028] However, sometimes during the consultation process, patients may seek other help from the doctor in addition to the currently scheduled examination items. For example, when a patient makes an appointment with an ophthalmologist to examine his eyes, he may ask the doctor about the cause of discomfort in other parts of the body such as the heart. In this case, the doctor will ask the patient to collect relevant monitoring data through a telemedicine device. At this time, all the collected data will often be sent to the doctor for analysis. If the doctor is not professional enough in other projects, the multiple data may reduce the doctor's analysis efficiency and may also cause certain interference to the doctor's judgment.

[0029] To solve the above problems, the embodiment of the present application provides a remote medical equipment control method, device and device. In the method, by obtaining the items and doctor's appointment information that the user has booked through the first device that reflects the user's terminal; and obtaining the detection data that reflects the physical feature data of the user monitored by the remote medical device; then analyzing according to the appointment information, the detection data is divided into the first data for reflecting the detection index that the degree of association with the scheduled item in the detection data is greater than the preset degree of association and the second data for reflecting the detection index that the degree of association with the scheduled item in the detection data is less than the preset degree of association; then the first data is transmitted to the second device, and the first auscultation result of the second device that reflects the doctor's terminal is received; then the second data is analyzed to obtain the difference coefficient of the difference between the index of the second data and the normal index; then the difference coefficient and the second data are transmitted to the second device, and the second auscultation result of the second device is received; finally, the first auscultation result and the second auscultation result are transmitted to the first device. By dividing the detection data into the first data and the second data with the high and low degree of association with the scheduled item, the method can give priority to processing and transmitting the most critical information for the current diagnosis and treatment to the doctor. This not only reduces the amount of unnecessary data transmission, but also speeds up the diagnosis process. That is, data analysis based on appointment information helps to extract truly useful indicators from a large amount of health monitoring data, avoiding the problem of information overload that may occur when doctors face too much irrelevant information, allowing doctors to focus more on key data that are directly related to the patient's condition, thereby improving the accuracy and efficiency of diagnosis. By taking differentiated treatment methods for different types of test data and generating auscultation results based on doctor feedback, users can be provided with more personalized health management suggestions. In particular, for the second data (i.e., physiological parameters that are less correlated with the appointment items but still important), potential health risks are evaluated by calculating the difference coefficient between them and normal indicators, further enriching the service content. Ultimately, all auscultation results and suggestions provided by doctors will be promptly fed back to users, promoting effective communication between the two parties. This two-way interactive model helps to establish a more trusting and harmonious doctor-patient relationship, and also allows patients to have a clearer understanding of their health status.

[0030] The remote medical device control method provided in the embodiment of the present application can be applied to the remote medical device. In this case, the remote medical device is the executor of the remote medical device control method provided in the embodiment of the present application. The embodiment of the present application does not impose any restrictions on the specific type of the remote medical device.

[0031] For example, the telemedicine equipment may include a telemedicine device and a control device, and the control device is electrically connected to the telemedicine device. The telemedicine device is used to detect multiple physical characteristic indicators of the user, and the telemedicine device may include a head-mounted monitor, a stethoscope, a blood pressure monitor, and a thermometer. The head-mounted monitor is used to detect relevant data of the user's eyes, mouth, ears and other organs. The head-mounted monitor includes a camera and an iris recognition device. The camera is used to obtain the appearance characteristics of the user's body parts, and the iris recognition device is used to obtain the user's eye data. The stethoscope is used to monitor the sounds of various parts of the user's body, such as the collection of pulse, heartbeat, lung sounds and other data. The blood pressure monitor is used to monitor the user's blood pressure and other data. The thermometer is used to monitor the user's body temperature. The control device monitors and controls the monitoring process.

[0032] For example, the control device can be a mobile phone, a tablet computer, a wearable device, an augmented reality (AR) / virtual reality (VR) device, a laptop computer, an ultra-mobile personal computer (UMPC), a netbook, a personal digital assistant (PDA), a desktop computer, a smart screen, a smart TV and other terminal devices, a handheld device with wireless communication function, a computing device or other processing device connected to a wireless modem, an Internet of Things terminal, a computer, a laptop computer, a handheld communication device, a handheld computing device, a satellite wireless device, a user premises equipment (customer premises equipment, CPE) and / or other devices for communicating on a wireless system and a next-generation communication system, for example, a mobile terminal in a 5G network or a mobile terminal in a future evolved public land mobile network (Public Land Mobile Network, PLMN), etc.

[0033] In order to better understand the remote medical device control method provided in the embodiment of the present application, the specific implementation process of the remote medical device control method provided in the embodiment of the present application is exemplarily introduced below.

[0034] Figure 1 A schematic flow chart of a remote medical device control method provided in an embodiment of the present application is shown, and the remote medical device control method includes: S100, obtaining appointment information; wherein the appointment information is used to reflect the items and doctors that the user has made an appointment for through the first device, and the first device is used to reflect the user's usage terminal.

[0035] It is understandable that the first device may be a mobile terminal such as a mobile phone, a tablet computer, etc. The user may make a consultation appointment through an APP installed on the first device, and in the APP, the user may select the appointment item and the doctor corresponding to the preferred hospital.

[0036] S200, obtaining detection data; wherein the detection data is used to reflect the physical characteristic data of the user monitored by the remote medical device.

[0037] It can be understood that the test data is data measured by the user under the guidance of a doctor, and includes data corresponding to the department the user has made an appointment for and data on additional physical characteristics that require additional consultation.

[0038] S300, analyzing according to the reservation information, dividing the detection data into first data and second data; wherein the first data is used to reflect the detection index that the degree of association with the reserved item in the detection data is greater than the preset degree of association; the second data is used to reflect the detection index that the degree of association with the item outside the reservation in the detection data is less than the preset degree of association.

[0039] It can be understood that the preset correlation degree refers to the degree to which the detection index is directly related to the scheduled project, which is a pre-set value. It can be manually input by humans, or obtained in the analysis database, etc., but is not limited to this. The analysis database refers to a database containing the preset correlation degree. These data can be obtained by laboratory experiments, on-site measurements and monitoring, and previous experience. After obtaining, the collected data will be sorted, classified and archived, useful information and rules will be extracted, and the relevant data will be saved in the database to form an analysis database. For example, after the patient makes an appointment and consults the ophthalmology department, the retina, eyeball and other data and blood pressure are tested. At the same time, there are symptoms of chest tightness. The doctor requires the patient to monitor the pulse and heart again, where the detection data includes retina, eyeball data, blood pressure, pulse and heart rate. The degree of correlation between the retina, eyeball data, blood pressure, pulse and heart rate and the ophthalmology department and the degree of correlation with the chest department are calculated respectively, and the corresponding detection data are divided into the first data and the second data, and so on.

[0040] Exemplarily, the detection name corresponding to the detection data can be matched with the analysis database to obtain the degree of association between each detection data and the appointment item and other items. The appointment information and detection data can also be input into the learning model, and the learning model outputs the corresponding first data and second data, etc., but not limited to this. The analysis database also includes the detection items corresponding to different departments, and the degree of association between different detection items and each department. The learning model is trained with multiple sets of training data, and each set of training data in the multiple sets of training data includes appointment information, detection data, first data and second data.

[0041] In a possible implementation, in step S300, analyzing the reservation information to divide the detection data into first data and second data includes: S310, analyzing the reservation information to obtain a first detection item; wherein the first detection item is used to reflect the type of physical feature to be detected corresponding to the reserved item.

[0042] It can be understood that the appointment information corresponds to a first test item. Assuming that the appointment item is ophthalmology, the first test item is the item that the doctor tests when checking the cause of the disease. For example, the patient makes an appointment with an ophthalmologist, and the doctor finds that the patient has retinal lesions during the analysis process and needs to measure blood pressure for further confirmation. Then the first test item includes eyes and blood pressure, and so on. Exemplarily, the appointment information can be input into the analysis database for matching to obtain the corresponding first test item; the appointment information can also be input into the learning model, and the learning model outputs the corresponding first test item, and so on, but it is not limited to this.

[0043] S320, analyzing according to the first detection item, dividing the detection data into first data and second data.

[0044] Exemplarily, the first data can be directly obtained through the first detection item, and then the types of physical characteristics of all detections can be determined based on the detection data, so as to obtain the types of physical characteristics of other detections except the first detection item, and the conditions of the patients who have not made an appointment but have been examined can be determined through the types of physical characteristics of other detections, so as to further determine the second data; the first detection item and the detection data can also be input into the learning model, and the learning model outputs the corresponding first data and second data, and so on, but is not limited to this.

[0045] In a possible implementation, in step S320, analyzing according to the first detection item to divide the detection data into first data and second data includes: S321, determining first data according to the first detection item.

[0046] It can be understood that the first data is the detection data corresponding to the first detection item.

[0047] S322, determining a detection item range according to the detection data; wherein the detection item range is used to reflect all types of body features detected.

[0048] It can be understood that by analyzing the test data, the test items corresponding to each data, that is, the type of physical feature corresponding to each data, can be determined through the size and unit of the numerical value of each test item, and all the test items can be summarized to obtain the test item range; the test data can also be input into a learning model, and the learning model outputs the corresponding test item range, and so on, but is not limited to this.

[0049] S323, analyzing according to the detection item range, the first detection item and the first data to obtain second data.

[0050] It can be understood that the detection of other types of physical characteristics of the patient can be obtained through the detection item range and the first detection item, and then other detection items except the first detection item can be obtained through the detection of other physical characteristics, and then the corresponding detection data can be packaged and summarized to obtain the second data; the detection item range, the first detection item and the first data can also be input into the learning model, and the learning model outputs the corresponding second data, and so on, but not limited to this.

[0051] With this setting, through intelligent analysis and classification of detection data, the first data that is highly relevant to the appointment item will be processed and transmitted first, while the second data with a lower correlation will be analyzed for the coefficient of difference, thereby improving data processing efficiency and accuracy and enhancing the accuracy and personalization of diagnosis.

[0052] In a possible implementation, in step S323, analyzing the detection item range, the first detection item, and the first data to obtain the second data includes: S3231, analyzing the detection item range and the first detection item to obtain a second detection item; wherein the second detection item is used to reflect the types of physical features detected in the detection item range other than the first detection item.

[0053] It can be understood that the detection item range can be directly screened through the first detection item, and other detection items other than the first detection item can be screened out to obtain the second detection item.

[0054] S3232, analyzing the second detection item to obtain a detection feature; wherein the detection feature is used to reflect the item corresponding to the second detection item that the user has not reserved.

[0055] It can be understood that the detection feature represents other conditions that require a doctor's diagnosis outside of the items scheduled by the patient. The second detection item reflects the monitoring content required for the condition, and the second detection item can be input into the analysis database for matching to obtain the detection feature corresponding to the second detection item; the second detection item can also be input into the learning model, and the learning model outputs the corresponding detection feature, and so on, but not limited to this. Exemplarily, assuming that the scheduled item is ophthalmology, the corresponding detection items include pupil size, retinal blood vessels, optic disc, macular area and blood pressure. At the same time, the patient communicates with the doctor about discomfort in other parts of the body and asks the doctor to diagnose the cause. Assuming that the doctor asks the patient to measure the heart rate, gums and tonsils again, the second detection items are heart rate, pulse and tonsils, and the corresponding detection feature may be oral cavity, and so on.

[0056] S3233, analyzing the detection feature and the first data to obtain second data.

[0057] It is understandable that the required detection data corresponding to the detection feature in the first data can be obtained according to the detection feature, and then the detection data corresponding to the second detection item and the required detection data corresponding to the detection feature in the first data are aggregated to obtain the second data. In other words, for the detection item of the discomfort in the second part of the patient, a partial detection may be performed when the discomfort in the first part is detected, that is, the detection items required for the diagnosis of the first part and the detection items required for the diagnosis of the second part may overlap, the first part refers to the body part of the patient's discomfort corresponding to the scheduled item, and the second part refers to the body part of the patient's discomfort that is not scheduled for additional diagnosis by the doctor.

[0058] In this way, by analyzing the scope of the test items and the first test items, the second test items are determined, and then other types of physical characteristics that the user has not reserved are extracted, and these characteristics are compared and analyzed with the first data to obtain the second data. This method not only improves the efficiency and accuracy of data processing, but also ensures that doctors can fully understand the patient's health status, including information that is not explicitly mentioned in the appointment but may be helpful for diagnosis.

[0059] In a possible implementation, in step S3233, analyzing the detection feature and the first data to obtain the second data includes: S32331, analyzing the first data according to the detection feature to obtain third data; wherein the third data is used to reflect part of the detection data corresponding to the detection feature in the first data.

[0060] It can be understood that the third data is the same data as the detection data reflected by the detection feature and the first data. For example, the first data includes pupil size, retinal blood vessels, optic disc, macular area and blood pressure, the detection feature is the oral cavity, and the corresponding all detection items (different from the items that the doctor requires the patient to have additional tests, that is, different from the second detection items) are heart rate, pulse, gums, tonsils and blood pressure, then the third data is blood pressure data, and so on.

[0061] S32332, integrating the third data with the detection data corresponding to the second detection item to obtain second data.

[0062] It can be understood that the third data and the detection data corresponding to the second detection item are packaged and aggregated to form the second data.

[0063] With such a setting, the required second data can be obtained accurately, the efficiency and accuracy of data processing can be improved, the utilization of medical resources can be optimized, the comprehensiveness and personalization of diagnosis can be enhanced, and more efficient and accurate medical services can be supported. At the same time, the data management and transmission processes can be simplified, and the overall quality and response speed of medical services can be improved.

[0064] S400, transmitting the first data to the second device, and receiving the first auscultation result of the second device; wherein the second device is used to reflect the doctor's usage terminal.

[0065] It can be understood that the second device can be a mobile terminal such as a mobile phone or a tablet computer. The user can obtain the first data through the APP installed on the first device, input the auscultation result into the APP through the second device, and then receive the auscultation result.

[0066] S500, analyzing the second data to obtain a difference coefficient; wherein the difference coefficient is used to reflect the degree of difference between an indicator of the second data and a normal indicator.

[0067] It can be understood that normal indicators refer to indicator data reflected by the user's physical characteristics under normal circumstances. It can collect and summarize the indicators of various physical characteristics collected by a large number of users under normal circumstances through big data, and analyze to obtain indicator data, that is, the indicator data can include the normal indicator range of various physical characteristics, or it can be the average value of all indicator data collected for various physical characteristics. Exemplarily, each indicator data in the second data analysis can be compared with the normal indicator to obtain a difference value, and the corresponding difference coefficient can be obtained through the difference value; the second data can also be input into a learning model, and the learning model outputs the corresponding difference coefficient, etc., but it is not limited to this.

[0068] In a possible implementation, in step S500, analyzing the second data to obtain a difference coefficient includes: S510, analyze the second data to obtain first key data and second key data; wherein the first key data is used to reflect the detection data in the second data directly corresponding to other abnormal physical characteristics of the user, and the second key data is used to reflect other detection data in the second data except the first key data.

[0069] It is understood that other abnormal physical characteristics refer to the affected parts of the patient's condition that require additional diagnosis by the doctor. For example, when the second data includes heart rate, gum and tonsil data, since the gum and tonsil correspond to the oral cavity, and the detection of heart rate may involve the detection of multiple abnormal physical characteristics, that is, the specificity is low, the first key data is the gum and tonsil data, the second key data is the heart rate data, and so on.

[0070] S520, respectively calculating a first difference value between the first key data and the standard data set, and a second difference value between the second key data and the standard data set; wherein the standard data set includes normal values ​​of various physical detection indicators.

[0071] It can be understood that there is a difference value corresponding to the data difference between the first key data and the standard data set, and there is a difference value corresponding to the data difference between the second key data and the standard data set. The difference between each data in the first key data and the corresponding data in the standard data set can be calculated, and the difference can be matched with the difference value in the analysis database to obtain the corresponding first difference value; the first key data can also be input into the learning model through the learning model, and the learning model outputs the corresponding first difference value, etc., but not limited to this. The analysis database contains the standard data set and the difference values ​​corresponding to the difference between different data combinations and the standard data set. The second difference value can be obtained by using a similar analysis method to the first difference value.

[0072] S530: Analyze the first difference value and the second difference value to obtain a difference coefficient.

[0073] It is understandable that the final difference coefficient can be obtained by weighting the first difference value and the second difference value, or the first difference value and the second difference value can be input into a learning model to output the corresponding difference coefficient, etc., but not limited to this.

[0074] This setting improves the accuracy and personalization of diagnosis, while simplifying the processing of complex data and improving the overall efficiency and quality of medical services.

[0075] In a possible implementation, in step S530, analyzing the first difference value and the second difference value to obtain a difference coefficient includes: S531, obtaining a first weight and a second weight.

[0076] It can be understood that the first weight is used to reflect the influence of the first key data on the condition analysis, and the second weight is used to reflect the influence of the second key data on the condition analysis. The first weight and the second weight are pre-set weight values, which can be manually input by humans, or obtained from the analysis database, etc., but are not limited thereto.

[0077] S532: Weight the first difference value with the first weight, weight the second difference value with the second weight, and obtain a difference coefficient after summing the sums.

[0078] It can be understood that the difference coefficient=first weight×first difference value+second weight×second difference value.

[0079] S600, transmitting the difference coefficient and the second data to a second device, and receiving a second auscultation result from the second device.

[0080] It can be understood that the coefficient of difference can be used by doctors to make preliminary judgments. Doctors can choose to diagnose the second data based on their own understanding and expertise of the second condition. They can also provide patients with recommendations and preliminary suggestions from relevant professional doctors based on the severity of the second data.

[0081] In a possible implementation, in step S600, transmitting the difference coefficient and the second data to the second device, and receiving the second auscultation result of the second device includes: S610a, when the difference coefficient is greater than a preset threshold, a reminder message is generated and transmitted to the second device; wherein the reminder message is used to indicate that the user's physical characteristic data does not meet the current consultation conditions.

[0082] It can be understood that the preset threshold and the content of the reminder information are preset in advance, and can be manually input or obtained from a database, etc., but not limited to this. When the difference coefficient is greater than the preset threshold, it means that the physical characteristics reflected by the second data of the patient are more serious. At this time, the reminder information is automatically obtained and transmitted to the doctor's second device.

[0083] S620a, receiving the suggestion information from the second device, and determining the suggestion information as the second auscultation result; wherein the suggestion information is used to indicate the items and doctors that the user needs to make an appointment for.

[0084] It is understandable that after receiving the reminder information, the doctor can make a preliminary judgment on the patient's second condition in combination with the second data, and input relevant suggestions to prevent the condition from worsening. Alternatively, the doctor may directly recommend the department and doctor that are suitable for the condition without analyzing the second data.

[0085] In a possible implementation, in step S600, the difference coefficient and the second data are transmitted to the second device, and the second auscultation result of the second device is received, further comprising: S610b, when the difference coefficient is less than a preset threshold, the difference coefficient and the second data are transmitted to the second device, and a second consultation result of the second device is received.

[0086] It can be understood that when the coefficient of difference is less than the preset threshold, it means that the physical characteristics reflected by the patient's second data are relatively mild. The doctor can judge the patient's condition based on the second data and obtain the second consultation result.

[0087] Such a setting not only improves the timeliness and accuracy of disease assessment, but also enhances the effectiveness of doctor-patient communication, supports doctors to make preliminary judgments quickly and provide targeted advice or referral recommendations, thereby optimizing the utilization of medical resources and improving the overall efficiency and quality of medical services.

[0088] S700, transmitting the first auscultation result and the second auscultation result to the first device.

[0089] It can be understood that the first auscultation result and the second auscultation result can be displayed on the first device through the APP.

[0090] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0091] Corresponding to the remote medical equipment control method described in the above embodiment, the embodiment of the present application also provides a remote medical equipment control device, and each module of the device can implement each step of the remote medical equipment control method. Figure 3 A structural block diagram of a remote medical equipment control device provided in an embodiment of the present application is shown. For ease of explanation, only the parts related to the embodiment of the present application are shown.

[0092] Reference Figure 3 , the remote medical equipment control device comprises: The first acquisition module is used to acquire the reservation information; wherein the reservation information is used to reflect the items and doctors reserved by the user through the first device, and the first device is used to reflect the user's usage terminal.

[0093] The second acquisition module is used to acquire detection data; wherein the detection data is used to reflect the physical characteristic data of the user monitored by the remote medical device.

[0094] The first analysis module is used to analyze according to the reservation information and divide the detection data into first data and second data; wherein the first data is used to reflect the detection index whose correlation degree with the reserved item in the detection data is greater than the preset correlation degree; the second data is used to reflect the detection index whose correlation degree with the reserved item in the detection data is less than the preset correlation degree.

[0095] The first transmission module is used to transmit the first data to the second device and receive the first auscultation result of the second device; wherein the second device is used to reflect the doctor's usage terminal.

[0096] The second analysis module is used to analyze the second data to obtain a difference coefficient; wherein the difference coefficient is used to reflect the degree of difference between the index of the second data and the normal index.

[0097] The second transmission module is used to transmit the difference coefficient and the second data to the second device, and receive the second auscultation result of the second device.

[0098] The third transmission module is used to transmit the first auscultation result and the second auscultation result to the first device.

[0099] It should be noted that the information interaction, execution process and other contents between the above-mentioned modules are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0100] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above modules is used as an example for illustration. In actual applications, the above functions can be assigned to different modules as needed, that is, the internal structure of the device can be divided into different modules to complete all or part of the functions described above. The modules in the embodiment can be integrated into a processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the modules in the above device can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0101] An embodiment of the present application also provides a telemedicine device, including a telemedicine device and a control device, wherein the control device is electrically connected to the telemedicine device. Figure 4 This is a schematic diagram of the structure of a control device 6 provided in an embodiment of the present application. Figure 4 As shown, the control device 6 of this embodiment includes: at least one processor 60 ( Figure 4 Only one is shown), at least one memory 61 ( Figure 4 Only one is shown in the figure) and a computer program 62 stored in the at least one memory 61 and executable on the at least one processor 60. When the processor 60 executes the computer program 62, the control device 6 implements the steps in any of the above-mentioned embodiments of the remote medical device control method, or implements the functions of the modules in the above-mentioned device embodiments.

[0102] Exemplarily, the computer program 62 may be divided into one or more modules / units, which are stored in the memory 61 and executed by the processor 60 to complete the present application. The one or more modules / units may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program 62 in the control device 6.

[0103] The control device 6 can be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The telemedicine device may include, but is not limited to, a processor 60 and a memory 61. Those skilled in the art will appreciate that Figure 4 It is only an example of the control device 6 and does not constitute a limitation on the control device 6. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, it may also include input and output devices, network access devices, buses, etc.

[0104] The processor 60 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0105] In some embodiments, the memory 61 may be an internal storage unit of the control device 6, such as a hard disk or memory of the control device 6. In other embodiments, the memory 61 may also be an external storage device of the control device 6, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the control device 6. Further, the memory 61 may also include both an internal storage unit and an external storage device of the control device 6. The memory 61 is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program. The memory 61 may also be used to temporarily store data that has been output or is to be output.

[0106] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0107] An embodiment of the present application provides a computer program product. When the computer program product is executed on a remote medical device, the remote medical device implements the steps in any of the above method embodiments.

[0108] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device that can carry the computer program code to the telemedicine device, a recording medium, a computer memory, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), an electrical carrier signal, a telecommunication signal, and a software distribution medium. For example, a USB flash drive, a mobile hard disk, a disk or an optical disk.

[0109] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0110] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0111] In the embodiments provided in the present application, it should be understood that the disclosed telemedical equipment and devices can be implemented in other ways. For example, the telemedical equipment control device embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or modules, which can be electrical, mechanical or other forms.

[0112] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0113] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A remote medical device control method, characterized in that: include: Acquire reservation information; wherein the reservation information is used to reflect the project and doctor that the user has reserved through the first device, and the first device is used to reflect the user's usage terminal; Acquire detection data; wherein the detection data is used to reflect the user's physical characteristic data monitored by the remote medical device; Analyze the test data according to the reservation information, and divide the test data into first data and second data; wherein the first data is used to reflect the test index whose correlation degree with the reserved item in the test data is greater than the preset correlation degree; the second data is used to reflect the test index whose correlation degree with the item outside the reservation in the test data is less than the preset correlation degree; Transmitting the first data to a second device, and receiving a first auscultation result of the second device; wherein the second device is used to reflect the doctor's usage terminal; Analyze the second data to obtain a coefficient of difference; wherein the coefficient of difference is used to reflect the degree of difference between an indicator of the second data and a normal indicator; transmitting the difference coefficient and the second data to the second device, and receiving a second auscultation result of the second device; The first auscultation result and the second auscultation result are transmitted to the first device.

2. The remote medical device control method according to claim 1, characterized in that: The analyzing according to the reservation information to divide the detection data into first data and second data comprises: Analyze the reservation information to obtain a first test item; wherein the first test item is used to reflect the type of physical feature to be tested corresponding to the reserved item; The detection data is analyzed according to the first detection item and divided into first data and second data.

3. The remote medical device control method according to claim 2, characterized in that: The analyzing according to the first detection item to divide the detection data into first data and second data includes: Determine first data according to the first detection item; Determine the detection item range according to the detection data; wherein the detection item range is used to reflect all the types of body features detected; The second data is obtained by analyzing the detection item range, the first detection item and the first data.

4. The remote medical device control method according to claim 3, characterized in that: The step of analyzing the detection item range, the first detection item, and the first data to obtain the second data includes: Analyze the detection item range and the first detection item to obtain a second detection item; wherein the second detection item is used to reflect the types of physical characteristics detected in the detection item range other than the first detection item; Analyze the second detection item to obtain a detection feature; wherein the detection feature is used to reflect the item that the user has not reserved for the second detection item; The second data is obtained by analyzing the detection feature and the first data.

5. The remote medical device control method according to claim 4, characterized in that: The step of analyzing the detection feature and the first data to obtain second data includes: Analyze the first data according to the detection feature to obtain third data; wherein the third data is used to reflect the part of the detection data corresponding to the detection feature in the first data; The third data is integrated with the detection data corresponding to the second detection item to obtain second data.

6. The remote medical device control method according to claim 4, characterized in that: The performing analysis according to the second data to obtain the coefficient of difference comprises: Analyze the second data to obtain first key data and second key data; wherein the first key data is used to reflect the detection data in the second data that directly corresponds to other abnormal physical characteristics of the user, and the second key data is used to reflect other detection data in the second data except the first key data; Calculating respectively a first difference value between the first key data and a standard data set, and a second difference value between the second key data and a standard data set; wherein the standard data set includes normal values ​​of various physical detection indicators; An analysis is performed based on the first difference value and the second difference value to obtain a difference coefficient.

7. The remote medical device control method according to claim 6, characterized in that: The step of analyzing the first difference value and the second difference value to obtain a difference coefficient includes: Get a first weight and a second weight; The first difference value is weighted by the first weight, and the second difference value is weighted by the second weight, and the difference coefficient is obtained by summing the sum.

8. The remote medical device control method according to claim 1, characterized in that: The step of transmitting the difference coefficient and the second data to the second device, and receiving a second auscultation result of the second device, comprises: When the difference coefficient is greater than a preset threshold, a reminder message is generated and transmitted to the second device; wherein the reminder message is used to indicate that the user's physical characteristic data does not meet the current consultation conditions; receiving the suggestion information from the second device, and determining the suggestion information as the second auscultation result; wherein the suggestion information is used to indicate the items and doctors that the user needs to make an appointment for; And / or, transmitting the difference coefficient and the second data to the second device, and receiving a second auscultation result of the second device, further includes: When the difference coefficient is less than the preset threshold, the difference coefficient and the second data are transmitted to the second device, and a second consultation result of the second device is received.

9. A remote medical equipment control device, characterized in that: include: A first acquisition module is used to acquire appointment information; wherein the appointment information is used to reflect the project and doctor that the user has booked through the first device, and the first device is used to reflect the user's usage terminal; A second acquisition module is used to acquire detection data; wherein the detection data is used to reflect the user's physical characteristic data monitored by the remote medical device; A first analysis module is used to analyze the test data according to the reservation information and divide the test data into first data and second data; wherein the first data is used to reflect the test index whose correlation degree with the reserved item in the test data is greater than a preset correlation degree; and the second data is used to reflect the test index whose correlation degree with the reserved item in the test data is less than a preset correlation degree; A first transmission module, used to transmit the first data to a second device, and receive a first auscultation result of the second device; wherein the second device is used to reflect the doctor's usage terminal; A second analysis module, used to analyze the second data to obtain a difference coefficient; wherein the difference coefficient is used to reflect the degree of difference between the index of the second data and the normal index; a second transmission module, configured to transmit the difference coefficient and the second data to the second device, and receive a second auscultation result of the second device; A third transmission module is used to transmit the first auscultation result and the second auscultation result to the first device.

10. A telemedicine device, characterized in that: The method comprises a telemedical device and a control device, wherein the control device is electrically connected to the telemedical device, the control device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the method according to any one of claims 1 to 8 when executing the computer program.

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