Data monitoring and data acquisition system for laryngoscope equipment

Abstract

The invention discloses a data monitoring and data acquisition system for laryngoscope equipment, and relates to the technical field of medical equipment. The system comprises a throat data front-end acquisition module, a data comprehensive processing and analysis module, a personalized diagnosis model construction module, a throat three-dimensional dynamic model construction module and a throat data abnormity early warning and recording module. By integrating various sensors and cameras at the front end of the laryngoscope, comprehensive acquisition of throat temperature, humidity, gas components and image data is realized, and after the data is comprehensively processed and analyzed, not only can the throat gas exchange function be evaluated, but also the physiological state comprehensive index and the disease possibility can be calculated; according to the method, detailed laryngeal health information is provided for doctors, in addition, a personalized laryngeal disease diagnosis model is constructed in combination with the individual characteristics of patients, and continuous updating is performed according to the difference between the actual diagnosis result and the predicted diagnosis result, so that the diagnosis accuracy and the personalized level are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of medical equipment, in particular to a data monitoring and data acquisition system for laryngoscope equipment. Background Art

[0002] In the field of medical diagnosis, early detection and precise treatment of laryngeal diseases are crucial to the recovery of patients. With the continuous advancement of medical technology, laryngoscope equipment, as an important means of laryngeal examination, has been widely used in clinical practice. However, traditional laryngoscope equipment mainly relies on the doctor's naked eye observation and experience judgment, and it is difficult to achieve a comprehensive and objective evaluation of the physiological state of the laryngeal. In order to overcome this limitation, in recent years, data monitoring and data acquisition technology has developed rapidly in the medical field, providing new ideas and methods for the diagnosis of laryngeal diseases. The present invention came into being under this technical background, and aims to achieve accurate evaluation of laryngeal health through an integrated data monitoring and data acquisition system.

[0003] Although traditional laryngoscope equipment plays an important role in the diagnosis of laryngeal diseases, it still has many shortcomings. First, traditional laryngoscope equipment mainly relies on the doctor's naked eye observation and is easily affected by subjective factors, which makes it difficult to ensure the accuracy and consistency of the diagnostic results. Secondly, traditional equipment lacks sufficient consideration of the individual characteristics of patients, cannot provide personalized diagnostic solutions, and is difficult to meet the needs of different patients. In addition, traditional laryngoscope equipment has limitations in data collection and processing, making it difficult to achieve real-time monitoring and dynamic analysis of the physiological state of the laryngeal region, thereby limiting its application in the early detection and treatment of laryngeal diseases.

[0004] Therefore, a data monitoring and data acquisition system for laryngoscope equipment is developed to improve the accuracy and efficiency of diagnosis and enhance the personalization and real-time nature of diagnosis. Summary of the invention

[0005] The purpose of the present invention is to make up for the shortcomings of the prior art and to provide a data monitoring and data acquisition system for laryngoscope equipment. By integrating a sensor group and camera technology, the system can realize the comprehensive collection of laryngeal temperature, humidity, gas composition and image data. The system can construct a three-dimensional dynamic model of the larynx in real time, and provide a personalized diagnostic model through a comprehensive data processing and analysis module, thereby accurately evaluating the health status of the larynx.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a data monitoring and data acquisition system for laryngoscope equipment, the system comprising a laryngeal data front-end acquisition module, a data comprehensive processing and analysis module, a personalized diagnosis model construction module, a laryngeal three-dimensional dynamic model construction module and a laryngeal data abnormality warning and recording module; The laryngeal data front-end acquisition module: uses micro-electromechanical system technology to integrate a sensor group and a camera at the front end of a laryngoscope, collects data on the temperature, humidity, gas composition and exchange data of the larynx through the sensor group, collects image data through the camera, and the collected raw data is amplified, filtered and analog-to-digital converted by a signal conditioning circuit, converted into a digital signal, and transmitted to the data comprehensive processing and analysis module; The data comprehensive processing and analysis module receives the digital signal from the laryngeal data front-end acquisition module, pre-processes and standardizes the data, and at the same time, evaluates the laryngeal gas exchange function by calculating the dynamic index of gas exchange efficiency, calculates the degree of deviation of laryngeal health status by using the comprehensive index of physiological status, calculates the probability of common laryngeal diseases by using the disease possibility assessment model, and classifies and stores the original and analysis results in a relational database; The personalized diagnosis model building module collects individual characteristic data of the patient's age, gender, medical history, living habits, and occupational environment, and builds a personalized laryngeal disease diagnosis model based on the results of the data comprehensive processing and analysis module, and continuously updates the model according to the difference between the actual and predicted diagnosis results through the laryngeal diagnosis model optimization update formula; The laryngeal three-dimensional dynamic model construction module extracts relevant image data, performs image enhancement, denoising and segmentation preprocessing on the two-dimensional laryngeal image data in sequence, constructs a three-dimensional dynamic model of the larynx by voxel reconstruction, adds real textures by texture mapping according to the analysis results of the data comprehensive processing and analysis module, and presents the three-dimensional image in real time on a high-resolution display screen. When new data is received, it can be updated in real time according to the actual situation of the larynx and the diagnosis results; The laryngeal data abnormality warning and recording module receives data from each module in real time and presets abnormal thresholds based on population characteristics, allowing doctors to make personalized adjustments and record adjustment information in real time. At the same time, it measures the degree of data abnormality through a dynamic abnormal deviation index, triggers multiple alarm methods when the warning start value is exceeded, and records relevant warning information in detail.

[0007] Furthermore, the sensor group in the laryngeal data front-end acquisition module consists of: a temperature sensor, a humidity sensor, an oxygen sensor, a carbon dioxide sensor and a nitric oxide sensor.

[0008] Furthermore, the data comprehensive processing and analysis module calculates the dynamic index of gas exchange efficiency To assess the gas exchange function of the larynx, the formula is: ,in is a time interval that is dynamically adjusted according to the respiratory rate, and are the molar volumes of oxygen and carbon dioxide, respectively, It is a correction factor based on the human respiratory physiological model and the law of gas diffusion.

[0009] Furthermore, the data comprehensive processing and analysis module calculates the degree to which the health status of the larynx deviates from the normal state through the comprehensive physiological status index, and the formula is: ,in, , , , , is the weight coefficient and satisfies .

[0010] Furthermore, the data comprehensive processing and analysis module calculates the probability of occurrence of various common laryngeal diseases through a disease probability assessment model. Common throat diseases, the probability of each disease is , , the calculation formula is: ,in, and are the model parameters corresponding to each disease.

[0011] Furthermore, the construction of the personalized laryngeal disease diagnosis model in the personalized diagnosis model construction module is formulated as follows: ,in represents the comprehensive diagnostic index of the personalized laryngeal disease diagnosis model, It is a comprehensive index of physiological status. and are the minimum and maximum values ​​of its normal range, respectively. , , is its weight coefficient, It is a dynamic indicator of gas exchange efficiency. and are the minimum and maximum values ​​of its normal range, respectively. Represents the age of the patients ,gender , medical history , lifestyle habits , professional environment Quantified values ​​of individual characteristics, is the weight coefficient corresponding to the individual characteristics.

[0012] Furthermore, the personalized diagnosis model building module continuously updates the personalized diagnosis model by optimizing the throat diagnosis model update formula, and the formula is: ,in are the updated model parameters, are the current model parameters, is the step size of the learning rate control update, For the actual diagnosis result, is the diagnostic result predicted by the model, is the input data vector related to individual patient characteristics.

[0013] Furthermore, the steps of constructing a three-dimensional dynamic model of the larynx by the voxel reconstruction method are as follows: determining the size and resolution of the voxels, converting the two-dimensional image data of the larynx after enhancement, denoising and segmentation preprocessing into three-dimensional voxel data according to the established voxel size and spatial position relationship, using the laryngeal voxel surface construction formula to extract isosurfaces from the voxel data to construct a three-dimensional surface model of the larynx, optimizing the three-dimensional surface model, and adding real texture using texture mapping according to the analysis results of the data comprehensive processing and analysis module.

[0014] Furthermore, the laryngeal three-dimensional dynamic model construction module uses the laryngeal voxel surface construction formula to extract isosurfaces from the voxel data to construct the laryngeal three-dimensional surface model, and the formula is: ,in represents the constructed 3D surface model of the throat, It means traversing all voxels. It is Individual elements, is a judgment function, voxel The gray value changes of the adjacent voxels conform to the characteristic pattern of the laryngeal tissue boundary. ,otherwise , is the weight coefficient.

[0015] Furthermore, the laryngeal data abnormality warning and recording module uses a dynamic abnormal deviation index To measure the degree of abnormality of the data, the formula is: ,in is the number of dimensions of the monitoring data, It's time No. The actual value of the monitoring data. It is the normal range mean value dynamically adjusted based on the current individual characteristics of the patient and the real-time physiological state. is the corresponding standard deviation, is the weight coefficient determined according to the importance of this data item in the overall laryngeal health assessment. Exceeds the preset dynamic warning start value When an alarm occurs, the early warning signal is triggered immediately and the early warning process begins.

[0016] Compared with the prior art, the data monitoring and data acquisition system for laryngoscope equipment has the following beneficial effects: 1. The present invention integrates multiple sensors and cameras at the front end of the laryngoscope to achieve comprehensive collection of laryngeal temperature, humidity, gas composition and image data. After comprehensive processing and analysis, these data can not only evaluate the laryngeal gas exchange function, but also calculate the comprehensive index of physiological status and the possibility of disease, providing doctors with detailed laryngeal health information. In addition, combined with the individual characteristics of the patient, a personalized laryngeal disease diagnosis model is constructed, and it is continuously updated according to the difference between the actual and predicted diagnostic results, thereby improving the accuracy and personalization level of diagnosis.

[0017] 2. The present invention utilizes image processing technology to construct and present a three-dimensional dynamic model of the larynx in real time, allowing doctors to intuitively observe the structure and dynamic changes of the larynx, which not only helps doctors to judge the condition of laryngeal lesions more accurately, but also improves the efficiency and accuracy of diagnosis. At the same time, the present invention also provides a laryngeal data abnormality warning and recording module, which measures the degree of abnormality of the data through a dynamic abnormal deviation index, and triggers multiple alarm modes when the warning start value is exceeded, ensuring that doctors can obtain abnormal information in a timely manner and take corresponding treatment measures, thereby effectively protecting the patient's laryngeal health.

[0018] Other advantages, objectives and features of the present invention will be set forth in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0020] Figure 1 is a flow chart of a data monitoring and data acquisition system for a laryngoscope device; Figure 2 The present invention is a process framework diagram of a data monitoring and data acquisition system for laryngoscope equipment. DETAILED DESCRIPTION

[0021] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation mode, structure, characteristics and effects of the present invention are described in detail below in combination with the accompanying drawings and preferred embodiments.

[0022] Embodiment 1 General Outpatient Throat Examination A patient goes to a hospital outpatient clinic for throat discomfort. The doctor first asks the patient in detail about his age (e.g., 30 years old), gender (male), medical history (no history of major throat diseases, but the throat has not fully recovered after a recent cold), lifestyle (occasionally smoking, about 3-5 cigarettes per week; no drinking; likes spicy food), and occupational environment (office worker, relatively comfortable working environment, no obvious exposure to harmful gases or dust), and enters this information into the system of the present invention.

[0023] The doctor uses a laryngoscope with an integrated laryngeal data front-end acquisition module to examine the patient. The sensor group (including temperature sensor, humidity sensor, oxygen sensor, carbon dioxide sensor and nitric oxide sensor) starts working to collect raw data of laryngeal temperature (such as 37.3°C), humidity (such as 78%), gas composition and exchange data (oxygen content is about 19.2%, carbon dioxide content is about 4.8%, nitric oxide content is about 0.6ppm). At the same time, the camera collects image data of the larynx (such as slightly red and swollen laryngeal mucosa, smooth surface of the vocal cords but slightly reduced mobility).

[0024] The collected raw data is amplified, filtered, and converted into digital signals by the signal conditioning circuit, and then sent to the data processing and analysis platform via wireless transmission.

[0025] The data comprehensive processing and analysis module receives digital signals, pre-processes and standardizes the data, and calculates the dynamic indicators of gas exchange efficiency. To assess the gas exchange function of the larynx, the formula is: ,in is a time interval that is dynamically adjusted according to the respiratory rate, and are the molar volumes of oxygen and carbon dioxide, respectively, is a correction coefficient based on the human respiratory physiological model and the law of gas diffusion. The degree of deviation of laryngeal health status is calculated through the comprehensive index of physiological status. The formula is: ,in, , , , , As the weight coefficient, the disease possibility assessment model is used to calculate the probability of common laryngeal diseases. Assuming that Common throat diseases, the probability of each disease is , , the calculation formula is: ,in, and For the model parameters corresponding to each disease, the original and processed data are classified and stored in a relational database to facilitate subsequent query and comparative analysis.

[0026] The personalized diagnosis model construction module combines the individual characteristics of the patient and the results of the data comprehensive processing and analysis module to construct a personalized laryngeal disease diagnosis model. According to the comprehensive index of physiological state, dynamic index of gas exchange efficiency, quantitative value of individual characteristics of the patient and corresponding weight coefficient calculated above, the comprehensive diagnostic index of the personalized laryngeal disease diagnosis model is calculated. The formula is: ,in represents the comprehensive diagnostic index of the personalized laryngeal disease diagnosis model, It is a comprehensive index of physiological status. and are the minimum and maximum values ​​of its normal range, respectively. , , is its weight coefficient, It is a dynamic indicator of gas exchange efficiency. and are the minimum and maximum values ​​of its normal range, respectively. Represents the age of the patients ,gender , medical history , lifestyle habits , professional environment Quantified values ​​of individual characteristics, is the weight coefficient corresponding to the individual characteristics. The personalized diagnosis model is continuously updated through the optimization and update formula of the laryngeal diagnosis model. The formula is: ,in are the updated model parameters, are the current model parameters, is the step size of the learning rate control update, For the actual diagnosis result, is the diagnostic result predicted by the model, The input data vector is related to the individual characteristics of the patient. The system assists the doctor in making a diagnosis based on the personalized diagnosis model and the examination data. The system preliminarily determines that the patient's laryngeal inflammation has not completely subsided. The patient is advised to drink more water, talk less, avoid eating spicy food, and is prescribed some medication to relieve laryngeal inflammation.

[0027] The laryngeal 3D dynamic model construction module performs image enhancement, denoising and segmentation preprocessing on the 2D laryngeal image data in sequence, determines the voxel size (such as 0.08mm×0.08mm×0.08mm) and resolution, converts the preprocessed 2D laryngeal image data into 3D voxel data, and uses the laryngeal voxel surface construction formula to extract isosurfaces from the voxel data to construct the 3D laryngeal surface model. The formula is: ,in represents the constructed 3D surface model of the throat, It means traversing all voxels. It is Individual elements, is a judgment function, voxel The gray value changes of the adjacent voxels conform to the characteristic pattern of the laryngeal tissue boundary. ,otherwise , It is the weight coefficient, which optimizes the three-dimensional surface model and uses texture mapping to add real texture to the model based on actual data. For example, the actual color and texture of the laryngeal mucosa is mapped to the three-dimensional model, and the three-dimensional image is presented in real time on a high-resolution display. Doctors can observe the laryngeal structure from different angles and have a clearer understanding of the laryngeal condition, such as checking the specific range of laryngeal redness and swelling, and subtle changes in the vocal cords, to further assist in diagnosis and assessment of the condition.

[0028] The laryngeal data abnormality warning and recording module receives data in real time and presets abnormal thresholds based on population characteristics. For example, the normal range of laryngeal temperature is preset to 36.5°C-37.5°C, and the normal range of humidity is preset to 60%-85%. At the same time, doctors are allowed to make personalized adjustments based on the patient's specific situation, and the system records the adjustment information.

[0029] Dynamic Anomaly Deviation Index To measure the degree of data anomaly, assuming the number of monitored data dimensions (temperature, humidity, oxygen content, carbon dioxide content, nitric oxide content), the formula is: ,in is the number of dimensions of the monitoring data, It's time No. The actual value of the monitoring data. It is the normal range mean value dynamically adjusted based on the current individual characteristics of the patient and the real-time physiological state. is the corresponding standard deviation, is a weight coefficient determined based on the importance of this data item in the overall laryngeal health assessment. In this examination, it is assumed that the calculated (Not exceeding the warning value ), the system records the relevant data but does not trigger an early warning. The doctor informs the patient that if the throat symptoms worsen or other abnormal conditions occur, timely return for consultation is required.

[0030] In summary, in ordinary outpatient laryngeal examinations, the present invention inquires about the patient's information in detail and enters it into the system, uses a laryngoscope with an integrated sensor to collect laryngeal data, calculates various indicators through a processing and analysis module, builds a personalized diagnostic model to assist doctors in diagnosis, uses a three-dimensional dynamic model to assist in observing the laryngeal structure, and monitors data with the help of an abnormal warning and recording module. Ultimately, the system provides doctors with comprehensive information, assists in diagnosis and gives reasonable treatment suggestions, thereby improving the accuracy and efficiency of outpatient laryngeal examinations.

[0031] Embodiment 2 Postoperative monitoring of laryngeal diseases The patient has just completed laryngeal surgery (such as vocal cord polypectomy). The doctor enters the patient's surgery-related information (surgery time, surgical method, size and location of the removed polyp) and the patient's basic individual characteristics (age 50, female, history of chronic pharyngitis, non-smoker, seldom drinks, housewife) into the system. At the same time, the laryngoscope device of the present invention is used to collect the initial data of the patient's postoperative larynx, including laryngeal temperature (37.1°C), humidity (70%), gas composition and exchange data (oxygen 19%, carbon dioxide 5%, nitric oxide 0.7ppm) and laryngeal image data (there is slight bleeding at the laryngeal surgery site and mild edema of the surrounding tissues).

[0032] During the patient's postoperative recovery period, laryngeal data were collected at time intervals set by the doctor (such as the 1st, 3rd, 7th, and 14th days after surgery). Each collected data went through the processing flow of the above system, including data front-end collection, transmission, comprehensive processing, and analysis steps. For example, on the 3rd day after surgery, the collected laryngeal temperature was 37.4°C, the humidity was 72%, the oxygen content was 18.8%, the carbon dioxide content was 5.2%, and the nitric oxide content was 0.8ppm. Image data showed that bleeding at the surgical site was reduced and edema was slightly relieved.

[0033] The data comprehensive processing and analysis module calculated various indicators and found that the dynamic indicators of gas exchange efficiency were improved compared with the early postoperative period. The evaluation formula for laryngeal gas exchange function was: The comprehensive physiological index shows that the health of the larynx is gradually recovering to normal. The calculation formula of the comprehensive physiological index is: The disease probability assessment model calculates that the probability of postoperative infectious complications gradually decreases (for example, the probability of infection is 5% on the first day after surgery and decreases to 3% on the third day after surgery). The calculation formula is: .

[0034] The personalized diagnosis model building module builds a personalized laryngeal disease diagnosis model based on the data collected each time and the actual diagnosis results (such as good postoperative recovery and no obvious complications) combined with the results of the data comprehensive processing and analysis module. The formula is: The personalized diagnosis model is continuously updated through the optimization and update formula of the laryngeal diagnosis model. The formula is: In order to more accurately predict the patient's recovery process, doctors provide patients with personalized rehabilitation guidance based on the comprehensive information provided by the system, including the results of the personalized diagnostic model and the changes in laryngeal structure shown by the three-dimensional dynamic model. For example, patients are advised to start simple voice exercises on the 7th day after surgery, but be careful to avoid excessive use of the voice and gradually transition from liquid food to semi-liquid food.

[0035] After extracting relevant data, the two-dimensional image data of the larynx are sequentially subjected to image enhancement, denoising and segmentation preprocessing. The three-dimensional dynamic model of the larynx is constructed by the voxel reconstruction method. The size and resolution of the voxels are determined. The two-dimensional image data of the larynx after enhancement, denoising and segmentation preprocessing are converted into three-dimensional voxel data according to the established voxel size and spatial position relationship. The laryngeal voxel surface construction formula is used to extract the isosurface from the voxel data to construct the three-dimensional surface model of the larynx. The formula is: , optimize the three-dimensional surface model, use texture mapping to add real texture to the model based on actual data, and the laryngeal three-dimensional dynamic model construction module will update the laryngeal three-dimensional dynamic model in time after each data collection. Doctors and patients can intuitively see the recovery of the laryngeal surgical site, such as the disappearance of edema and the repair of laryngeal tissue. This helps doctors to detect abnormalities in time, and also allows patients to better understand their own recovery progress and enhance their confidence in recovery.

[0036] During the postoperative recovery process, if any abnormal situation occurs, the system can issue a timely warning. For example, on the 10th day after the operation, the patient coughed loudly and the throat pain and hoarseness worsened. When the data was collected again, the throat temperature rose to 38.0°C, the humidity became 75%, the oxygen content was 18.5%, the carbon dioxide content was 5.5%, and the nitric oxide content was 0.9ppm. The dynamic abnormal deviation index To measure the abnormality of the data, the formula is: , Dynamic Abnormal Deviation Index (Exceeding the preset warning value ), triggering an early warning.

[0037] Based on the warning information, combined with the detailed data and three-dimensional model changes provided by the system, the doctor determined that the laryngeal tissue might be irritated, causing local inflammation to worsen. The doctor immediately adjusted the treatment plan for the patient, increased the dosage of anti-inflammatory drugs, instructed the patient to strictly keep quiet and rest, and closely observed changes in the patient's condition. Through subsequent data monitoring, the doctor observed the treatment effects and ensured the patient's smooth recovery.

[0038] In summary, in terms of postoperative monitoring of laryngeal diseases, the present invention enters relevant information and collects initial data after the patient's operation, regularly collects and analyzes data during the recovery period, continuously updates the personalized diagnosis model to guide rehabilitation, and intuitively presents the recovery process through a three-dimensional dynamic model. Once an abnormality occurs, the system will issue a timely warning to help doctors adjust treatment plans, effectively ensuring the smooth recovery of the patient's throat after surgery, reflecting the important value of the system in postoperative monitoring.

[0039] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technical personnel in this field can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A data monitoring and data acquisition system for laryngoscope equipment, characterized in that: The system includes a laryngeal data front-end acquisition module, a data comprehensive processing and analysis module, a personalized diagnosis model construction module, a laryngeal three-dimensional dynamic model construction module, and a laryngeal data abnormality warning and recording module; The laryngeal data front-end acquisition module: uses micro-electromechanical system technology to integrate a sensor group and a camera at the front end of a laryngoscope, collects data on the temperature, humidity, gas composition and exchange data of the larynx through the sensor group, collects image data through the camera, and the collected raw data is amplified, filtered and analog-to-digital converted by a signal conditioning circuit, converted into a digital signal, and transmitted to the data comprehensive processing and analysis module; The data comprehensive processing and analysis module receives the digital signal from the laryngeal data front-end acquisition module, pre-processes and standardizes the data, and at the same time, evaluates the laryngeal gas exchange function by calculating the dynamic index of gas exchange efficiency, calculates the degree of deviation of laryngeal health status by using the comprehensive index of physiological status, calculates the probability of common laryngeal diseases by using the disease possibility assessment model, and classifies and stores the original and analysis results in a relational database; The personalized diagnosis model building module collects individual characteristic data of the patient's age, gender, medical history, living habits, and occupational environment, and builds a personalized laryngeal disease diagnosis model based on the results of the data comprehensive processing and analysis module, and continuously updates the model according to the difference between the actual and predicted diagnosis results through the laryngeal diagnosis model optimization update formula; The laryngeal three-dimensional dynamic model construction module extracts relevant image data, performs image enhancement, denoising and segmentation preprocessing on the two-dimensional laryngeal image data in sequence, constructs a three-dimensional dynamic model of the larynx by voxel reconstruction, adds real textures by texture mapping according to the analysis results of the data comprehensive processing and analysis module, and presents the three-dimensional image in real time on a high-resolution display screen. When new data is received, it can be updated in real time according to the actual situation of the larynx and the diagnosis results; The laryngeal data abnormality warning and recording module receives data from each module in real time and presets abnormal thresholds based on population characteristics, allowing doctors to make personalized adjustments and record adjustment information in real time. At the same time, it measures the degree of data abnormality through a dynamic abnormal deviation index, triggers multiple alarm methods when the warning start value is exceeded, and records relevant warning information in detail.

2. A data monitoring and data acquisition system for laryngoscope equipment according to claim 1, characterized in that: The sensor group in the laryngeal data front-end acquisition module is composed of: a temperature sensor, a humidity sensor, an oxygen sensor, a carbon dioxide sensor and a nitric oxide sensor.

3. A data monitoring and data acquisition system for laryngoscope equipment according to claim 1, characterized in that: The data comprehensive processing and analysis module calculates the dynamic index of gas exchange efficiency To assess the gas exchange function of the larynx, the formula is: ,in is a time interval that is dynamically adjusted according to the respiratory rate, and are the molar volumes of oxygen and carbon dioxide, respectively, It is a correction factor based on the human respiratory physiological model and the law of gas diffusion.

4. A data monitoring and data acquisition system for laryngoscope equipment according to claim 1, characterized in that: The data comprehensive processing and analysis module calculates the degree to which the health status of the larynx deviates from the normal state through the comprehensive physiological status index, and the formula is: ,in, , , , , is the weight coefficient and satisfies .

5. A data monitoring and data acquisition system for laryngoscope equipment according to claim 1, characterized in that: In the data comprehensive processing and analysis module, the probability of occurrence of various common laryngeal diseases is calculated by a disease probability assessment model. Common throat diseases, the probability of each disease is , , the calculation formula is: ,in, and are the model parameters corresponding to each disease.

6. A data monitoring and data acquisition system for laryngoscope equipment according to claim 1, characterized in that: The construction of the personalized laryngeal disease diagnosis model in the personalized diagnosis model construction module is formulated as follows: ,in represents the comprehensive diagnostic index of the personalized laryngeal disease diagnosis model, It is a comprehensive index of physiological status. and are the minimum and maximum values ​​of its normal range, respectively. , , is its weight coefficient, It is a dynamic indicator of gas exchange efficiency. and are the minimum and maximum values ​​of its normal range, respectively. Represents the age of the patients ,gender , medical history , lifestyle habits , professional environment Quantified values ​​of individual characteristics, is the weight coefficient corresponding to the individual characteristics.

7. A data monitoring and data acquisition system for laryngoscope equipment according to claim 1, characterized in that: The personalized diagnosis model building module continuously updates the personalized diagnosis model by optimizing the update formula of the laryngeal diagnosis model, and the formula is: ,in are the updated model parameters, are the current model parameters, is the step size of the learning rate control update, For the actual diagnosis result, is the diagnostic result predicted by the model, is the input data vector related to individual patient characteristics.

8. A data monitoring and data acquisition system for laryngoscope equipment according to claim 1, characterized in that: The steps of constructing a three-dimensional dynamic model of the larynx by the voxel reconstruction method are as follows: determining the size and resolution of the voxels, converting the two-dimensional image data of the larynx that has been preprocessed by enhancement, denoising and segmentation into three-dimensional voxel data according to the established voxel size and spatial position relationship, using the laryngeal voxel surface construction formula to extract isosurfaces from the voxel data to construct a three-dimensional surface model of the larynx, optimizing the three-dimensional surface model, and adding real texture using texture mapping according to the analysis results of the data comprehensive processing and analysis module.

9. A data monitoring and data acquisition system for laryngoscope equipment according to claim 8, characterized in that: The laryngeal three-dimensional dynamic model construction module uses the laryngeal voxel surface construction formula to extract isosurfaces from voxel data to construct a laryngeal three-dimensional surface model. The formula is: ,in represents the constructed 3D surface model of the throat, It means traversing all voxels. It is Individual elements, is a judgment function, voxel The gray value changes of the adjacent voxels conform to the characteristic pattern of the laryngeal tissue boundary. ,otherwise , is the weight coefficient.

10. A data monitoring and data acquisition system for laryngoscope equipment according to claim 1, characterized in that: The laryngeal data abnormal warning and recording module uses a dynamic abnormal deviation index To measure the degree of abnormality of the data, the formula is: ,in is the number of dimensions of the monitoring data, It's time No. The actual value of the monitoring data. It is the normal range mean value dynamically adjusted based on the current individual characteristics of the patient and the real-time physiological state. is the corresponding standard deviation, is the weight coefficient determined according to the importance of this data item in the overall laryngeal health assessment. Exceeds the preset dynamic warning start value When an alarm occurs, the early warning signal is triggered immediately and the early warning process begins.

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