An online monitoring system and method for oxygen concentration in a patient undergoing gynecological surgery

Abstract

The application provides an online monitoring system and method for oxygen concentration in a gynecological surgery patient, and belongs to the technical field of patient oxygen concentration detection. The system comprises a multi-data acquisition module, a data preprocessing module, a data analysis module and an optimization module. The multi-data acquisition module is used to construct a first data set, a second data set and a third data set. The data preprocessing module is used to perform preprocessing operations on various data based on the first data set, the second data set and the third data set. The data analysis module is used to construct an oxygen delivery channel transmission efficiency coefficient, a target patient exhalation intensity interference coefficient and a breathing mask structure interference coefficient S. The optimization module is used to construct a comprehensive influence coefficient and perform evaluation and optimization. The system can realize comprehensive optimization of intraoperative oxygen delivery efficiency and oxygen concentration by collecting data in multiple aspects.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of patient oxygen concentration detection, in particular to an online monitoring system and method for oxygen concentration in a gynecological surgery patient. BACKGROUND

[0002] In clinical surgery, patients usually wear a breathing mask to inhale oxygen during the induction, maintenance and recovery periods of surgery to maintain good oxygenation. A large single-center retrospective database study found that the risk of respiratory complications increased at high FiO2 compared to low FiO2. In order to avoid excessive oxygen concentration in patients, causing oxygen poisoning, leading to complications such as lung parenchyma damage and retinal damage, it is particularly important to control the oxygen concentration during surgery to no more than 50%.

[0003] In gynecological surgery, especially in gynecological malignant tumors such as comprehensive staging surgery for ovarian cancer and radical surgery for cervical cancer, the operation time is > 2 hours. Continuous and stable provision of high-concentration oxygen to patients during surgery is a key factor in ensuring stable vital signs, smooth anesthesia process and good postoperative recovery.

[0004] Gynecological surgery patients wear a breathing mask that covers their mouth and nose. Oxygen is delivered to the inside of the breathing mask for the patient to inhale. In the prior art, although the oxygen delivery equipment is equipped with an oxygen flow meter or a pressure gauge to detect the flow and flow rate of oxygen delivery, this equipment is usually installed at the oxygen source position, so it can only detect the oxygen concentration at the oxygen source position, ignoring the oxygen concentration inside the breathing mask.

[0005] However, due to the limited oxygen absorption area covered by the breathing mask, oxygen mixed with exhaled gas, especially carbon dioxide, after entering the mask, and as the concentration of carbon dioxide exhaled by the patient gradually accumulates inside the mask, its concentration will increase, causing a dilution effect on the oxygen inside the mask, directly leading to a decrease in oxygen concentration, thereby affecting the actual inhaled oxygen concentration. In addition, during the delivery of oxygen from the gas source to the mask, the flow stability and pressure level inside the oxygen delivery pipeline will significantly affect the efficiency of oxygen reaching the mask. If there are problems such as bending, gas leakage or insufficient pressure in the oxygen delivery pipeline, it will also cause a decrease in oxygen delivery efficiency, thereby affecting the oxygen absorption effect.

[0006] Moreover, the structure design of the breathing mask itself is also a key factor. For example, if the mask cannot be tightly sealed to the patient's face, external air may seep in or oxygen may leak, thereby further reducing the oxygen concentration in the mask. In addition, the design of the number of exhaust holes of the mask also affects the efficiency of carbon dioxide exhaust. If there are not enough exhaust holes, carbon dioxide will be difficult to exhaust in time, which will further dilute the oxygen. Therefore, during the actual operation process, it is difficult to achieve comprehensive control of the oxygen absorption quality by relying on a single monitoring or adjusting means. SUMMARY

[0007] In order to make up for the above shortcomings, the present application provides an online monitoring system and method for the oxygen concentration in the body of a gynecological surgery patient.

[0008] The present application is implemented as follows:

[0009] The present application provides an online monitoring system for the oxygen concentration in the body of a gynecological surgery patient, comprising a multi-data acquisition module, a data preprocessing module, a data analysis module and an optimization module.

[0010] The multi-data acquisition module is used to monitor the pipeline pressure and oxygen flow rate in real time when oxygen is being supplied to the target patient during gynecological surgery, and to collect the number of exhaust holes on the breathing mask and the oxygen dilution degree inside the breathing mask by wearing the breathing mask on the mouth and nose of the target patient, so as to construct a first data set, a second data set and a third data set.

[0011] The data preprocessing module is used to perform preprocessing operations on each item of data based on the first data set, the second data set and the third data set.

[0012] The data analysis module is used to execute preprocessing based on each item of data in the first data set, the second data set and the third data set, to construct an oxygen supply channel transmission efficiency coefficient by the first data set, to construct a target patient exhalation intensity interference coefficient by the second data set, and to construct a breathing mask structure interference coefficient S by the third data set.

[0013] The optimization module is used to associate the oxygen supply channel transmission efficiency coefficient , the target patient exhalation intensity interference coefficient and the breathing mask structure interference coefficient S, to construct a comprehensive influence coefficient , and to evaluate and optimize.

[0014] In a preferred scheme, the multi-data acquisition module comprises an oxygen supply pipeline feature acquisition unit, a target patient exhalation interference intensity acquisition unit and a breathing mask interference feature acquisition unit.

[0015] The oxygen pipeline feature acquisition unit is used for installing a pressure sensor at a position 5-10 cm away from the breathing mask interface inside the oxygen pipeline, so as to obtain the pipeline pressure value at the position in the induction period, the maintenance period and the awakening period in real time ; based on the installation of a thermal gas flow sensor at a position 5-10 cm away from the breathing mask interface, the flow rate information of oxygen in the oxygen pipeline in the induction period, the maintenance period and the awakening period is detected in real time, and the flow of the oxygen pipeline in the induction period, the maintenance period and the awakening period is obtained , to construct a first data set;

[0016] The target patient exhalation interference intensity acquisition unit is used for setting an integrated detection module inside the breathing mask based on the induction period, the maintenance period and the awakening period, the integrated detection module comprising a carbon dioxide concentration sensor, a humidity sensor and a temperature sensor, the carbon dioxide concentration inside the breathing mask is obtained in real time based on the carbon dioxide concentration sensor , the temperature inside the breathing mask is monitored in real time based on the temperature sensor and the humidity sensor , and the humidity , and the exhalation flow rate v of the gynecological surgery patient in the operation is obtained by setting a flow rate sensor inside the breathing mask, and the pressure peak-valley signal formed by the breathing of the gynecological surgery patient is obtained by setting a pressure sensor inside the breathing mask, the breathing frequency f and the flow rate feature are extracted by using a signal processing algorithm, and a second data set is constructed

[0017] In a preferred scheme, the breathing mask interference feature acquisition unit is used for obtaining the number of exhaust holes of the breathing mask directly from the product specification of the breathing mask based on the induction period, the maintenance period and the awakening period , and a strain gauge sensor is bonded to the outer surface of the elastic band of the breathing mask, and the tensile strain generated by the elastic band is detected by using the strain gauge sensor , the stress is obtained through a calculation formula , the stress obtained by calculation , the width and the thickness of the two key geometric parameters in the cross section are extracted by shooting an image of the cross section of the elastic band, the image pixel unit is converted into actual physical size unit through a pixel calibration factor, and then the cross-sectional area of the elastic band is obtained through an area calculation formula , the cross-sectional area of the elastic band is combined with the stress , the tension Z of the elastic band is obtained through a calculation formula, and a third data set is constructed.

[0018] In a preferred scheme, the data preprocessing module comprises a pipeline characteristic data preprocessing unit, a target patient exhalation data preprocessing unit, and a breathing mask interference data preprocessing unit;

[0019] The pipeline characteristic data preprocessing unit is configured to set ranges of various data according to clinical experience, perform data verification on the oxygen administration pressure value and the flow of the oxygen pipeline in the first data set, and remove data beyond the data ranges according to a CPU controller in the system. The pipeline characteristic data preprocessing unit is configured to perform filtering operation on the oxygen administration pressure value and the flow of the oxygen pipeline in the first data set by using a low-pass filter, while suppressing high-frequency interference in the data acquisition process.

[0020] The target patient exhalation data preprocessing unit is configured to set ranges of various data according to clinical experience, perform data verification on the carbon dioxide concentration , the temperature , and the humidity inside the breathing mask in the second data set, and remove data beyond the data ranges according to a CPU controller in the system. The target patient exhalation data preprocessing unit is configured to perform filtering operation on the carbon dioxide concentration , the temperature , and the humidity inside the breathing mask in the second data set by using a low-pass filter, while suppressing high-frequency interference in the data acquisition process.

[0021] The breathing mask interference data preprocessing unit is configured to set ranges of various data according to clinical experience, perform data verification on the number of exhaust holes and the tension of the elastic band Z in the third data set, and remove data beyond the data ranges according to a CPU controller in the system.

[0022] In a preferred scheme, the data preprocessing module comprises a first extraction unit, a first evaluation unit, a second extraction unit, a second evaluation unit, a third extraction unit, and a third evaluation unit.

[0023] The first extraction unit is configured to extract the pipeline pressure value and the flow of the oxygen pipeline in the first data set, and calculate the oxygen pipeline transmission efficiency coefficient .

[0024] The first evaluation unit is configured to preset a transmission efficiency threshold W, compare the transmission efficiency threshold W with the oxygen pipeline transmission efficiency coefficient , and generate a first evaluation instruction, including: ​​​

[0025] When ≥ W, it means that the oxygen delivery efficiency inside the oxygen delivery channel is normal, the current oxygen delivery efficiency is maintained, and real-time monitoring is continued;

[0026] When W, it means that the oxygen delivery efficiency inside the oxygen delivery channel is abnormal, and the first execution strategy is generated, including: detecting the sealing performance of the oxygen delivery pipeline and the oxygen delivery terminal, replacing the damaged oxygen delivery pipeline, and adjusting the oxygen delivery pipeline to avoid bending, crossing and winding, increasing the internal oxygen delivery pressure of the oxygen delivery pipeline by 5%-15%, and increasing the oxygen flow rate of the oxygen delivery terminal by 10%-25% based on the current oxygen delivery pipeline flow .

[0027] In a preferred scheme, the second extraction unit is configured to extract the carbon dioxide concentration inside the breathing mask in the second data set , the temperature and humidity inside the breathing mask, and obtain the target patient exhalation intensity interference coefficient by calculation;

[0028] The second evaluation unit is configured to preset an interference threshold E, compare the interference threshold E with the target patient exhalation intensity interference coefficient , and generate a second evaluation instruction, including;

[0029] When ≥ E, it means that the exhaled gas of the target patient interferes with the oxygen concentration inside the breathing mask abnormally, and the second execution strategy is generated, including: replacing the breathing mask, increasing the number of exhaust holes by 2-4 compared with the original breathing mask, improving the exhaust efficiency by 20%-35%, and improving the heat dissipation efficiency by 6%-8%, and setting a small dehumidification filter in the breathing mask to improve the dehumidification efficiency by 24%-37%;

[0030] When E, it means that the exhaled gas of the patient interferes with the oxygen concentration inside the breathing mask normally, and continuous monitoring is implemented.

[0031] In a preferred scheme, the third extraction unit is configured to extract the number of exhaust holes of the breathing mask and the tension Z of the elastic band in the third data set .

[0032] The tension Z of the elastic band is obtained by calculation;

[0033] According to the tensile strain variable generated by the strain gauge sensor , the stress is obtained by the calculation formula based on the tensile dependent variable .

[0034] based on the stress obtained by calculation , the cross-sectional area of the elastic band , the tension of the elastic band Z is obtained by calculation;

[0035] based on the tension of the elastic band Z obtained by calculation and the number of exhaust holes of the respiratory mask , the structure interference coefficient S of the respiratory mask is obtained by calculation.

[0036] In a preferred scheme, the third evaluation unit is configured to preset a respiratory mask interference threshold R, and compare the respiratory mask interference threshold R with the structure interference coefficient S of the respiratory mask to generate a third evaluation instruction, which includes:

[0037] When S≥R, it indicates that the structure of the respiratory mask abnormally interferes with the oxygen concentration inside the respiratory mask, and a third execution strategy is generated, which includes replacing the respiratory mask, increasing the number of exhaust holes by 3-6 compared with the original respiratory mask, increasing the tension of the elastic band by 40%-45%, and adding a sealing pad at the position of the contact surface between the respiratory mask and the patient to improve the sealing performance by 14%-17%, the gas delivery rate by 36%-55%, and the oxygen delivery pressure by 3%-12%.

[0038] When S<R, it indicates that the structure of the respiratory mask normally interferes with the oxygen concentration inside the respiratory mask, and the respiratory mask continues to be used and is continuously monitored.

[0039] In a preferred scheme, the optimization module includes an association unit and a fourth evaluation unit.

[0040] The association unit is configured to associate the oxygen delivery channel transmission efficiency coefficient , the target patient exhalation intensity interference coefficient , and the structure interference coefficient S of the respiratory mask to obtain a comprehensive influence coefficient by calculation.

[0041] The fourth evaluation unit is configured to preset a comprehensive influence threshold X, and compare the comprehensive influence threshold X with the comprehensive influence coefficient to generate a fourth evaluation instruction, which includes:

[0042] When When X, it indicates that the current patient oxygen concentration is abnormal, the fourth execution strategy is generated, including: increasing the oxygen pressure in the oxygen pipeline by 19% to 28%, increasing the oxygen flow in the oxygen pipeline by 21% to 27%, replacing the breathing mask, increasing the number of exhaust holes in the breathing mask by 1 to 5 compared with the original breathing mask, reducing the carbon dioxide concentration in the breathing mask by 11% to 33%, reducing the temperature in the breathing mask by 13% to 43%, and adding a small dehumidification filter in the breathing mask, increasing the humidity in the breathing mask by 16% to 34%;

[0043] When X, it indicates that the current patient oxygen concentration is normal, continue to monitor. When X, it indicates that the current patient oxygen concentration is normal, continue to monitor.

[0044] An online monitoring method for oxygen concentration in a gynecological surgery patient, comprising:

[0045] S1: First, the oxygen management pressure and oxygen flow rate of the target patient during oxygen inhalation in gynecological surgery are collected, and the number of exhaust holes on the breathing mask worn on the face of the target patient and the internal dilution of the mask are also collected, and the first data set, the second data set and the third data set are constructed;

[0046] S2: Secondly, the data in the first data set, the second data set and the third data set are data verified, the abnormal data is removed, and the remaining data is filtered and denoised;

[0047] S3: Then, the first data set, the second data set and the third data set after preprocessing are constructed respectively oxygen channel transmission efficiency coefficient , target patient exhalation intensity interference coefficient And breathing mask structure interference coefficient S;

[0048] S4: Finally, the oxygen channel transmission efficiency coefficient , oxygen channel transmission efficiency coefficient And breathing mask structure interference coefficient S are associated, and the comprehensive influence coefficient is constructed, and is evaluated, which is used for judging the oxygen concentration of the target patient.

[0049] The online monitoring system and method for oxygen concentration in a gynecological surgery patient provided by the application have the following beneficial effects:

[0050] 1、The system can quantize the actual transmission efficiency of oxygen in the oxygen transmission path by setting pressure sensors and thermal gas flow sensors at a position 5-10 cm away from the breathing mask interface of the oxygen transmission pipeline and pre-processing the collected first data set, constructing the oxygen transmission channel transmission efficiency coefficient, and can quantitatively reflect the transmission performance of oxygen from the oxygen source to the breathing mask, effectively reflecting the influence of pipeline blockage, air leakage or bending on the transmission efficiency, providing decision support for precise control of oxygen intensity in gynecological surgery, and ensuring that gynecological surgery patients inhale high-concentration oxygen.

[0051] 2、By collecting the carbon dioxide concentration, temperature and humidity of the patient's exhaled gas, the target patient's exhalation intensity interference coefficient is constructed, which can effectively identify the dilution interference caused by changes in the patient's breathing state on the oxygen concentration. This index can also dynamically reflect the influence of physiological state fluctuations such as changes in the patient's respiratory rate and tidal volume during gynecological surgery on the inhaled oxygen concentration. Once the system detects an abnormal increase in the interference coefficient, it can generate an exhaust optimization strategy (such as increasing the exhaust hole, improving the heat dissipation and dehumidification capacity, etc.), effectively eliminating the dilution interference of exhaled gas retention in the mask on the oxygen quality, and maintaining a high oxygen concentration environment in the mask.

[0052] 3、By collecting the number of exhaust holes and the tension of the elastic band of the breathing mask, the breathing mask structure interference coefficient is constructed, which is used to evaluate the comprehensive state of wearing comfort, exhaust smoothness and sealing effect. If the interference coefficient is too high, the system can prompt to increase the exhaust hole, adjust the wearing tension or add a sealing gasket, so as to optimize the wearing fit and internal airflow organization, reduce the risk of CO2 residue and heat accumulation, and improve the overall oxygen transmission efficiency and gas quality in the mask. This can reduce intraoperative discomfort and improve the oxygen concentration of gynecological surgery patients. BRIEF DESCRIPTION OF DRAWINGS

[0053] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can also be obtained without creative labor.

[0054] Figure 1 is the system block diagram in the present application;

[0055] Figure 2 is the method flowchart in the present application. DETAILED DESCRIPTION

[0056] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0057] Embodiment 1, refer to Figure 1 The present application provides a technical solution: an online monitoring system for oxygen concentration in a gynecological surgery patient, comprising a multi-data acquisition module, a data preprocessing module, a data analysis module and an optimization module.

[0058] The multi-data acquisition module is used for monitoring the pipeline pressure and oxygen flow rate in real time when oxygen is being supplied to the target patient in a gynecological surgery, and simultaneously collecting the number of exhaust holes on the breathing mask and the oxygen dilution degree inside the breathing mask by wearing the breathing mask on the mouth and nose of the target patient, so as to construct a first data set, a second data set and a third data set.

[0059] The data preprocessing module is used for performing preprocessing operations on each item of data based on the first data set, the second data set and the third data set, wherein the preprocessing operations include data verification of the data, elimination of abnormal data and filtering and noise reduction processing on the retained data.

[0060] The data analysis module is used for constructing an oxygen supply channel transmission efficiency coefficient by the first data set, constructing a target patient exhalation intensity interference coefficient by the second data set and constructing a breathing mask structure interference coefficient S by the third data set after performing preprocessing on each item of data based on the first data set, the second data set and the third data set.

[0061] The optimization module is used for associating the oxygen supply channel transmission efficiency coefficient , the target patient exhalation intensity interference coefficient and the breathing mask structure interference coefficient S, constructing a comprehensive influence coefficient , and performing evaluation and optimization.

[0062] In this embodiment, by constructing the oxygen supply channel transmission efficiency coefficient, the actual transmission efficiency of oxygen in the oxygen supply path can be quantified, which provides decision support for precise control of oxygen intensity during the surgery. By collecting the carbon dioxide concentration, temperature and humidity of the exhaled gas of the patient, the target patient exhalation intensity interference coefficient is constructed, which effectively identifies the dilution interference caused by the change of the breathing state of the patient on the oxygen concentration. By collecting the number of exhaust holes and the tension of the elastic band of the breathing mask, the breathing mask structure interference coefficient is constructed.

[0063] The system models the above three key influencing factors, constructs a unified comprehensive influence coefficient, and performs real-time evaluation based on a preset threshold. According to the evaluation result, optimization measures such as adjusting the oxygen flow rate, adjusting the pipeline structure, or improving the fit of the mask can be automatically recommended, so as to realize the comprehensive optimization of intraoperative oxygen delivery efficiency and oxygen concentration.

[0064] Embodiment 2, this embodiment is explained in embodiment 1, please refer to Figure 1 , Specifically, the plurality of data acquisition modules include oxygen pipeline feature acquisition unit, target patient exhalation interference intensity acquisition unit and breathing mask interference feature acquisition unit;

[0065] The oxygen pipeline feature acquisition unit is used to install a pressure sensor inside the oxygen pipeline, 5-10 cm away from the breathing mask interface, for real-time acquisition of the pipeline pressure value at this position ; Based on the installation of a thermal gas flow sensor 5-10 cm away from the breathing mask interface, the flow rate information of the oxygen in the oxygen pipeline is detected in real time, and the flow of the oxygen pipeline is acquired , to construct the first data set;

[0066] The target patient exhalation interference intensity acquisition unit is used to install a pressure sensor inside the oxygen pipeline, 5-10 cm away from the breathing mask interface, for real-time acquisition of the pipeline pressure value at this position ; Based on the installation of a thermal gas flow sensor 5-10 cm away from the breathing mask interface, the flow rate information of the oxygen in the oxygen pipeline is detected in real time, and the flow of the oxygen pipeline is acquired , to construct the first data set;

[0067] The target patient exhalation interference intensity acquisition unit is used to set an integrated detection module inside the breathing mask based on the induction period, the maintenance period and the recovery period, the integrated detection module includes a carbon dioxide concentration sensor, a humidity sensor and a temperature sensor, based on the carbon dioxide concentration sensor, the carbon dioxide concentration inside the breathing mask is acquired in real time , based on the temperature sensor and the humidity sensor, the temperature and humidity inside the breathing mask are monitored in real time, and by setting a flow rate sensor inside the breathing mask, the exhalation flow rate of the gynecological surgery patient during the operation is acquired, and by setting a pressure sensor inside the breathing mask, the air pressure peak-valley signal formed by the breathing of the gynecological surgery patient is obtained, the respiratory frequency and flow rate characteristics are extracted by using a signal processing algorithm, and a second data set is constructed;

[0068] The algorithm is based on the combination method of time domain signal peak value detection and sliding window frequency extraction, learns from the traditional physiological signal processing method (such as R-peak detection method, zero-crossing frequency method), and is optimized for the instability, noise interference and multi-stage difference characteristics existing in the respiratory signal. It is suitable for dynamic monitoring of the patient's respiratory frequency during the operation.

[0069] In this embodiment, pressure sensors and thermal gas flow sensors are synchronously arranged at the key positions of the pipeline 5-10 cm away from the breathing mask interface, which can accurately collect the pressure value and instantaneous flow rate of oxygen near the terminal, thereby constructing complete oxygen delivery channel dynamic data and providing a reliable basis for subsequent transmission efficiency evaluation. By integrating CO2 concentration, temperature and humidity sensors inside the breathing mask, the key parameters of the patient's exhaled gas can be monitored in real time, reflecting the respiratory frequency, intensity and gas dilution, which helps to accurately evaluate the interference degree of breathing on oxygen concentration.

[0070] The first data set records the dynamic characteristics of the oxygen delivery path, and the second data set collects the interference influence generated by the patient's own breathing. The structured raw data basis is established to improve the data integrity and analysis feasibility of the overall monitoring system. Based on the continuous collection of the above two types of key data, the system can realize early identification of oxygen transmission abnormalities or respiratory interference trends, provide real-time basis for intraoperative oxygen delivery control strategies (such as increasing flow rate, adjusting pressure, etc.), and enhance the system intervention ability and intelligent level.

[0071] Embodiment 3, this embodiment is an explanation and description in embodiment 1, please refer to Figure 1 , specifically, the breathing mask interference feature acquisition unit is used to obtain the number of exhaust holes of the breathing mask based on the induction period, the maintenance period and the awakening period , and the strain gauge sensor is bonded on the outer surface of the elastic band of the breathing mask , the tensile strain of the elastic band is detected by the strain gauge sensor , the stress is obtained by the calculation formula , the stress obtained by calculation , the image of the cross section of the elastic band is shot, the cross section of the elastic band in the image is identified, the image pixel unit is converted into actual physical size unit through the pixel calibration factor, and then the width and thickness of the cross section are extracted. The cross-sectional area of the elastic band is obtained by the area calculation formula , the cross-sectional area of the elastic band is combined with the stress , the tension Z of the elastic band is obtained by the calculation formula, and the third data set is constructed.

[0072] In this embodiment, the number of exhaust holes is directly read from the product specification of the respiratory mask, without image recognition or manual statistics, improving the efficiency and accuracy of data collection, reducing system complexity and energy consumption. The strain gauge sensor is attached to the outer surface of the elastic band of the respiratory mask to collect the tensile strain in real time. Based on the strain-stress conversion model, the corresponding stress value is obtained, effectively realizing dynamic perception of the tightness of the respiratory mask. By shooting the cross-sectional image of the elastic band and converting the image unit to the actual physical size unit by combining the pixel calibration factor, the cross-sectional width and thickness are accurately extracted, and the cross-sectional area is quickly calculated based on the rectangular area formula, avoiding the traditional manual measurement method and improving the data acquisition accuracy.

[0073] The cross-sectional area obtained by image recognition is combined with the stress value to calculate the tension value Z according to the physical formula, which accurately reflects the actual influence of the wearing state on the fit degree and ventilation efficiency of the respiratory mask.

[0074] Embodiment 4, this embodiment is the explanation and description in embodiment 1, please refer to Figure 1 , specifically, the data preprocessing module includes pipeline feature data preprocessing unit, target patient exhalation data preprocessing unit and respiratory mask interference data preprocessing unit;

[0075] The pipeline feature data preprocessing unit is used to set the range of each data according to clinical experience, perform data verification on the oxygen management pressure value in the first data set and the flow of the oxygen pipeline , and remove the data beyond the data range according to the CPU controller in the system, based on the oxygen management pressure value in the first data set and the flow of the oxygen pipeline , a low-pass filter is used for filtering operation, while suppressing high-frequency interference in the data acquisition process;

[0076] The target patient exhalation data preprocessing unit is used to set the range of each data according to clinical experience, perform data verification on the carbon dioxide concentration in the second data set , the temperature and humidity inside the respiratory mask, and remove the data beyond the data range according to the CPU controller in the system, based on the carbon dioxide concentration in the second data set , the temperature and humidity inside the respiratory mask, a low-pass filter is used for filtering operation, while suppressing high-frequency interference in the data acquisition process;

[0077] The respiratory mask interference data preprocessing unit is used to set the range of each data according to clinical experience, perform data verification on the number of exhaust holes in the third data set and the elastic band tension Z, data verification is performed, and data exceeding the data range is rejected according to the CPU controller in the system.

[0078] In this embodiment, by setting a reasonable data range according to clinical experience, the preprocessing module can perform effective data verification on the input raw data, reject abnormal data exceeding the reasonable range, ensure the accuracy and reliability of the data used for subsequent analysis, filter the key parameters such as pressure, flow, carbon dioxide concentration, temperature and humidity using a low-pass filter, significantly reduce the high-frequency noise generated in the sensor collection process, ensure the smoothness and stability of the data, improve the anti-interference ability of the overall monitoring system, based on the real-time calculation ability of the CPU controller in the system, the abnormal data can be dynamically monitored and rejected, avoiding human intervention, ensuring the automation and real-time response of the data preprocessing process, and meeting the high requirements of clinical intraoperative oxygen monitoring on data timeliness.

[0079] For different data types of oxygen pipeline pressure and flow, patient exhalation parameters and breathing mask structure characteristics, special data preprocessing units are designed respectively to ensure the optimization of preprocessing methods and strategies for various data and improve the data processing effect and analysis accuracy of the overall system.

[0080] Embodiment 5, this embodiment is an explanation and description in embodiment 1, please refer to Figure 1 , specifically, the data preprocessing module includes a first extraction unit, a first evaluation unit, a second extraction unit, a second evaluation unit, a third extraction unit and a third evaluation unit;

[0081] The first extraction unit is configured to extract the pipeline pressure value in the first data set and the flow of the oxygen pipeline After dimensionless processing, the oxygen channel transmission efficiency coefficient is calculated by the following formula ;

[0082]

[0083] In the formula, and are weight coefficients, satisfying , represents the maximum rated pressure of the oxygen pipeline, represents the maximum rated flow of the oxygen pipeline;

[0084] The preset and , based on large sample oxygen data, the values of and are derived, , L / min, the maximum output pressure and the maximum flow of the oxygen pipeline will be specified in the medical-grade oxygen delivery device specification, which can be directly obtained from the oxygen delivery device specification;

[0085] The following oxygen channel transmission efficiency coefficient Data example table, see Table 1 below;

[0086]

[0087] The first evaluation unit is used to preset a transmission efficiency threshold W,

[0088] The transmission efficiency threshold W is determined according to the empirical value statistics of the oxygen pipeline pressure and flow rate in the clinical oxygen delivery process, combined with historical case data analysis, and its value is set to 0.6, which is used as the critical judgment standard of the oxygen channel transmission efficiency coefficient;

[0089] The transmission efficiency threshold W is compared with the oxygen channel transmission efficiency coefficient to generate a first evaluation instruction, including;

[0090] When ≥W, it indicates that the delivery efficiency of oxygen in the oxygen channel is normal, the current oxygen delivery efficiency is maintained, and real-time monitoring is continued;

[0091] When <W, it indicates that the delivery efficiency of oxygen in the oxygen channel is abnormal, a first execution strategy is generated, including: detecting the sealing property of the oxygen pipeline and the oxygen terminal connection, replacing the damaged oxygen pipeline, and adjusting the oxygen pipeline to eliminate bending, crossing and winding, improving the internal oxygen pressure of the oxygen pipeline by 5%-15%, and based on the flow of the current oxygen pipeline to improve the oxygen flow rate of the oxygen terminal by 10%-25%, and ensure that the oxygen concentration does not exceed 50%.

[0092] Based on the data sequence in Table 1, the following is a comparison table of the transmission efficiency threshold W and the oxygen channel transmission efficiency coefficient , see Table 2 below;

[0093]

[0094] In this embodiment, by real-time acquisition and dimensionless processing of the pipeline pressure value and flow, combined with reasonable setting of the weight coefficient, the oxygen channel transmission efficiency coefficient is calculated, which quantitatively reflects the delivery state of oxygen in the oxygen pipeline, and provides reliable data support for scientific evaluation of oxygen delivery efficiency. By setting the transmission efficiency threshold W and comparing it with the calculated transmission efficiency coefficient, the delivery efficiency of the oxygen channel can be effectively judged, potential risks can be identified in time, and the safety and stability of the oxygen delivery process can be ensured.

[0095] When the oxygen delivery channel delivery efficiency is lower than the threshold value, the system automatically executes the strategy to guide the detection of the oxygen delivery pipeline sealing, replace the damaged pipeline, adjust the pipeline layout, eliminate the adverse state of bending and winding, etc. to ensure the smoothness of the oxygen delivery pipeline, improve the oxygen pressure by 5% to 15%, and increase the oxygen flow rate by 10% to 25%, effectively improve the oxygen delivery efficiency, and ensure the quality of intraoperative oxygen inhalation of patients.

[0096] Embodiment 6, this embodiment is an explanation in embodiment 1, please refer to Figure 1 , Specifically, the second extraction unit is configured to extract the carbon dioxide concentration in the breathing mask in the second data set , the respiratory flow rate v and the respiratory frequency f of the gynecological surgery patient in the induction period, the maintenance period and the recovery period are dimensionless, and the target patient exhalation intensity interference coefficient K is obtained by calculation.

[0097]

[0098] In the formula, , and are weight coefficients, represents the maximum value of the carbon dioxide concentration in the breathing mask, represents the maximum value of the intraoperative respiration of the gynecological surgery patient, represents the maximum value of the intraoperative respiratory frequency of the gynecological surgery patient;

[0099] The preset , and , based on a large amount of historical data and combined with experience, the weight values of , and can be derived from the historical data, According to the health person's resting breathing state, the carbon dioxide concentration of the exhaled gas is generally 4% to 5%, but after wearing the breathing mask, because of the re-breathing effect and the exhaust efficiency limit, according to the past historical clinical experience, when the carbon dioxide concentration in the breathing mask is greater than 1%, it will cause danger to the patient, so based on the historical data combined with the clinical experience, the maximum value of the carbon dioxide concentration in the breathing mask is =10L / min, =22 times / minute, according to the past detection data of the patient's intraoperative respiration, and statistical analysis of the data, the maximum value of the respiratory flow rate v and the maximum value of the respiratory frequency f can be directly obtained from the past historical data;

[0100] The following is a reference example of the gynecological surgery patient during the operation;

[0101] Surgical stage Respiratory rate (breaths / min) Expiratory flow rate (L / min)

[0102] Induction period 8-11 (not including 11) 3-5 (not including 5)

[0103] Maintenance period 11-15 (not including 15) 5-8 (not including 8)

[0104] Recovery period 15-22 8-10

[0105] Based on the data sequence in Table 1, the following is an example table of target patient exhalation intensity interference coefficients , as shown in Table 3 below:

[0106]

[0107] Based on the influence of changes in the temperature and humidity inside the breathing mask on the oxygen concentration of the patient, the temperature is generally controlled between 22°C and 28°C, and the humidity is preferably maintained between 30% RH and 50% RH. If the temperature exceeds this range, such as reaching above 30°C, it will cause the diffusion speed of oxygen molecules to increase, and the carbon dioxide concentration inside the breathing mask may increase due to limited gas flow, thereby reducing the oxygen concentration by 5% to 10%. Similarly, if the humidity exceeds 60% RH, it may cause the gas mixing effect inside the breathing mask to increase, increasing the oxygen dilution phenomenon, resulting in a decrease in the oxygen concentration by 3% to 8%.

[0108] Conversely, too low a temperature (such as below 20°C) may affect the patient's respiratory comfort, and too low humidity (below 20% RH) may dry the respiratory mucosa, indirectly affecting the oxygen exchange efficiency. In summary, abnormal temperature and humidity directly result in a decrease in the oxygen concentration inside the breathing mask by 3% to 12%, thereby affecting the patient's intraoperative oxygen absorption effect. Therefore, real-time monitoring and adjustment of the temperature and humidity inside the breathing mask are of great significance for ensuring the oxygen concentration and safety of gynecological surgery patients.

[0109] The second evaluation unit is configured to preset an interference threshold E,

[0110] The interference threshold E, combined with clinical experience and respiratory oxygen-related literature, uses the understanding of doctors and respiratory therapists of the influence of exhalation interference on oxygen concentration, and through experimental measurement and a large amount of data collection, statistical analysis of the relationship between patient exhalation parameters and oxygen concentration changes, and scientific analysis to determine the optimal critical value to distinguish normal and abnormal interference;

[0111] And compare the interference threshold E with the target patient exhalation intensity interference coefficient To generate the second evaluation instruction, including;

[0112] When ≥E, it means that the exhaled gas of the target patient interferes with the oxygen concentration inside the breathing mask abnormally, and the second execution strategy is generated, including: replacing the breathing mask, increasing the number of exhaust holes by 2-4 compared to the original breathing mask, improving the exhaust efficiency by 20%-35%, and improving the heat dissipation efficiency by 6%-8%, and setting a small dehumidification filter in the breathing mask, such as a silica gel bag, to improve the dehumidification efficiency by 24%-37%, and ensure that the oxygen concentration does not exceed 50%;

[0113] When <E, it means that the exhaled gas of the patient interferes with the oxygen concentration inside the breathing mask normally, and continuous monitoring is implemented.

[0114] Based on the data sequence in Table 1, the interference threshold E and the target patient exhalation intensity interference coefficient are compared, as shown in the following Table 4;

[0115]

[0116] In this embodiment, by setting reasonable weight coefficients and standard temperature, combining the maximum reference values of carbon dioxide concentration, temperature and humidity inside the breathing mask, scientifically calculating the exhalation intensity interference coefficient, quantifying the interference intensity of the exhaled gas of the patient on the oxygen concentration inside the breathing mask, realizing dynamic monitoring, setting the interference threshold E, comparing the interference coefficient with the threshold in real time, effectively judging whether the exhalation interference is abnormal, timely discovering and feeding back the negative influence of exhalation on oxygen concentration, and guaranteeing the accuracy and stability of intraoperative oxygen delivery, when the exhalation interference exceeds the threshold, an optimization strategy is automatically generated: increasing 2-4 exhaust holes at the corresponding position of the breathing mask and the nasal cavity, improving the exhaust efficiency by 20%-35%; At the same time, improve the heat dissipation efficiency by 5%-8%; And configure a small dehumidification filter (such as a silica gel bag) in the mask, improve the dehumidification efficiency by 24%-37%, effectively reduce the oxygen dilution and humidity interference caused by exhalation, and guarantee the concentration and comfort of the inhaled gas of the patient.

[0117] Through the real-time monitoring and dynamic adjustment, the internal environment of the breathing mask is optimized, the influence of exhalation on the oxygen concentration is reduced, the oxygen absorption effect and the stability of the patient's vital signs during the operation are improved, and the safety of the operation process is enhanced.

[0118] Embodiment 7, this embodiment is an explanation in embodiment 1, please refer to Figure 1 , specifically, the third extraction unit is used to extract the number of exhaust holes of the breathing mask in the third data set and the tension Z of the elastic band;

[0119] Wherein the tension Z of the elastic band is calculated by the following method;

[0120] According to the tensile strain of the elastic band detected by the strain sensor , based on the tensile dependent variable Stress is obtained by calculation formula , the following calculation formula is obtained;

[0121]

[0122] In the formula, represents the initial length of the elastic band, which is directly obtained from the production specification of the breathing mask;

[0123] Based on the stress obtained by calculation , combined with the cross-sectional area of the elastic band , after dimensionless processing, the tension Z of the elastic band is calculated by the following formula;

[0124]

[0125] Based on the calculated tension Z of the elastic band and the number of exhaust holes of the breathing mask , after dimensionless processing, the structure interference coefficient S of the breathing mask is calculated by the following formula;

[0126]

[0127] In the formula, and are weight coefficients, which satisfy , represents the maximum reference value of the number of exhaust holes of the breathing mask, which is directly obtained according to the specification and structure design of the breathing mask, for example, a certain type of breathing mask is designed with a maximum of 10 exhaust holes, then , represents the maximum reference value of the tension of the elastic band, which can be directly read by the maximum tension test of the elastic band on the breathing mask through the standard tension meter.

[0128] preset and During the use of the breathing mask, it is observed that the number of exhaust holes plays a key role in the discharge of CO2 and heat dissipation, and the tension of the elastic band on the breathing mask affects the fit of the mask to the patient's face. Through clinical feedback, the contribution of the two to the interference of the mask result is comparable, so the is set to 0.5 and is set to 0.5;

[0129] , 15N;

[0130] Based on the data sequence in Table 1, further monitoring is carried out, and a breathing mask structure interference coefficient S example table is obtained, as shown in Table 5 below;

[0131]

[0132] In this embodiment, the tensile strain of the elastic band is monitored in real time by using a strain gauge sensor, and the stress and tension of the elastic band are accurately calculated by combining the initial length and cross-sectional area of the elastic band through mechanical formulas. The influence of the elastic band on the wearing state of the mask is effectively reflected. By introducing the key structural parameter of the number of exhaust holes, combining the calculated elastic band tension, and through dimensionless processing, the breathing mask structure interference coefficient is scientifically constructed, the overall structure state of the mask is comprehensively quantitatively analyzed, the maximum reference value of the number of exhaust holes and the maximum reference value of the elastic band tension are set, and based on the breathing mask specification manual and standard tension meter test data, the scientificity and standardization of the interference coefficient calculation are ensured. It is suitable for different specifications and models of breathing masks.

[0133] Through dynamic monitoring of the structure interference coefficient, the abnormal tension of the elastic band or the number of exhaust holes that do not meet the design requirements can be found in time, and the tightness of the elastic band and the mask structure can be adjusted to improve the comfort and sealing of the wearing, and to ensure the stability and effect of oxygen delivery.

[0134] Embodiment 8, this embodiment is an explanation and description in embodiment 1, please refer to Figure 1 , specifically, the third evaluation unit is used to preset a breathing mask interference threshold R,

[0135] When testing different models of breathing masks, engineers and clinical personnel compared a plurality of parameter combinations, such as the number of exhaust holes and the tension of the elastic band, on the influence of oxygen concentration. By collecting actual patient wearing data and analyzing, it is found that when the breathing mask structure interference coefficient S is greater than 0.5, the oxygen concentration decreases significantly, so the breathing mask interference threshold R is set to 0.5;

[0136] And the respiratory mask interference threshold R is compared with the respiratory mask structure interference coefficient S to generate a third evaluation instruction, including:

[0137] When S≥R, it indicates that the respiratory mask structure interferes with the oxygen concentration in the respiratory mask abnormally, and a third execution strategy is generated, including replacing the respiratory mask, increasing the number of exhaust holes by 3-6 compared with the original respiratory mask, and increasing the tension of the elastic band by 40%-45%, and additionally adding a sealing pad at the position of the contact surface between the respiratory mask and the patient to improve the sealing performance by 14%-17%, improve the gas delivery rate by 36%-55%, improve the oxygen delivery pressure by 3%-12%, and ensure that the oxygen concentration does not exceed 50%;

[0138] When S<R, it indicates that the respiratory mask structure interferes with the oxygen concentration in the respiratory mask normally, and the respiratory mask continues to be used and continuously monitored.

[0139] Based on the data sequence in Table 1, a comparison example table of the respiratory mask interference threshold R and the respiratory mask structure interference coefficient S is obtained, as shown in Table 6 below;

[0140]

[0141] In this embodiment, by comparing the structure interference coefficient with the preset threshold, it is effectively determined whether the respiratory mask causes abnormal interference to the internal oxygen concentration during use, and the oxygen delivery quality is guaranteed. When the interference is detected to be abnormal, an optimization instruction is automatically generated, the number of exhaust holes is reasonably increased, the tension of the elastic band is adjusted, and a sealing pad is additionally added at the patient contact surface to comprehensively improve the sealing performance and exhaust efficiency of the mask from multiple angles.

[0142] By optimizing the structure of the respiratory mask, the gas delivery rate and the oxygen delivery pressure are improved, the concentration and flow rate of the gas inhaled by the patient are effectively guaranteed, the respiratory experience and intraoperative oxygenation state of the patient are improved, the continuous monitoring and dynamic adjustment of the state of the respiratory mask structure are supported, the abnormalities occurring during use are responded in a timely manner, the service life of the mask is prolonged, and the clinical maintenance cost is reduced.

[0143] Embodiment 9, this embodiment is an explanation and description in Embodiment 1, please refer to Figure 1 Specifically, the optimization module comprises an association unit and a fourth evaluation unit;

[0144] The association unit is configured to associate the oxygen delivery channel transmission efficiency coefficient , the target patient exhalation intensity interference coefficient , and the respiratory mask structure interference coefficient S, and obtain a comprehensive influence coefficient through dimensionless processing and the following formula.

[0145]

[0146] wherein, , and are weight coefficients, satisfying ;

[0147] The preset , and , through the statistics and analysis of historical data, in order to ensure the rationality of the weight, usually also reference the experience of clinical experts, the weight obtained is appropriately adjusted and optimized, so as to obtain the weight coefficient;

[0148] Based on the data sequence in Table 1, and on this basis, continue to monitor and obtain the comprehensive influence coefficient The example table is shown in Table 7 below;

[0149]

[0150] The fourth evaluation unit is used to preset a comprehensive influence threshold X,

[0151] The comprehensive influence threshold X is obtained based on in-depth analysis of a large amount of clinical monitoring data and actual application effect, by collecting and organizing the comprehensive influence coefficient data of different patients under various oxygen supply conditions, including oxygen supply channel transmission efficiency coefficient, target patient exhalation intensity interference coefficient and breathing mask structure interference coefficient, forming a historical database, using statistical methods to analyze the distribution of these data, especially paying attention to the comprehensive influence coefficient range corresponding to the abnormal and normal state of the patient's oxygen concentration, through comparative analysis, a reasonable critical value interval can be determined, so that when the comprehensive influence coefficient exceeds the threshold, the system can accurately judge the risk state of abnormal oxygen concentration;

[0152] And compare the comprehensive influence threshold X with the comprehensive influence coefficient Generate the fourth evaluation instruction, including:

[0153] When ≥X, it indicates that the patient's oxygen concentration is abnormal at present, generate the fourth execution strategy, including: increase the oxygen pressure in the 19%-28% oxygen pipeline, increase the oxygen flow in the 21%-27% oxygen pipeline, replace the breathing mask, increase the number of exhaust holes by 1-5 compared with the original breathing mask, reduce the carbon dioxide concentration in the breathing mask by 11%-33%, reduce the temperature in the breathing mask by 13%-43%, add a small dehumidification filter in the breathing mask, increase the humidity in the breathing mask by 16%-34%, and ensure that the oxygen concentration does not exceed 50%;

[0154] When X, indicates that the current patient oxygen concentration is normal, continue to monitor.

[0155] Based on the data sequence in Table 1, and on this basis, continue to monitor and obtain the comprehensive influence threshold X and the comprehensive influence coefficient The comparative example table is shown in Table 8 below;

[0156]

[0157] In this embodiment, by dimensionless processing and weighted association of multiple key parameters, a comprehensive influence coefficient is constructed to comprehensively reflect the overall performance of the oxygen delivery system and the patient's breathing state, and to realize accurate and real-time monitoring of the oxygen concentration. When the comprehensive influence coefficient exceeds the preset threshold, the optimization strategy is automatically triggered to increase the oxygen pressure and flow of the oxygen delivery pipeline, reasonably increase the number of exhaust holes of the breathing mask, effectively reduce the carbon dioxide concentration and temperature in the breathing mask, and adjust the humidity through the dehumidification filter. Comprehensive improvement of the oxygen delivery environment.

[0158] The optimization measures comprehensively regulate multiple influencing factors, reduce the respiratory burden of the patient, improve the inhaled oxygen concentration and comfort, and ensure the life safety and treatment effect of the patient during and after the operation. The fourth evaluation unit compares and judges the comprehensive influence coefficient in real time to realize continuous monitoring and dynamic adjustment of the oxygenation state, and improve the intelligent management level of the system.

[0159] Embodiment 10, please refer to Figure 2 , specifically, an online monitoring method for the oxygen concentration in the body of a gynecological surgery patient, comprising:

[0160] S1: First, collect the oxygen management pressure and oxygen flow rate of the target patient during oxygen inhalation in gynecological surgery, and also collect the number of exhaust holes on the breathing mask worn on the face of the target patient and the internal dilution of the mask, and construct a first data set, a second data set and a third data set;

[0161] S2: Secondly, data verification is performed on each item of data in the first data set, the second data set and the third data set, abnormal data is removed, and the remaining data is subjected to filtering and noise reduction processing;

[0162] S3: Then, the first data set, the second data set and the third data set after preprocessing are respectively constructed to construct the oxygen delivery channel transmission efficiency coefficient , the target patient exhalation intensity interference coefficient and the breathing mask structure interference coefficient S;

[0163] S4: Finally, the oxygen delivery channel transmission efficiency coefficient , the oxygen delivery channel transmission efficiency coefficient and the breathing mask structure interference coefficient S are associated to construct a comprehensive influence coefficient and evaluate the oxygen concentration of the target patient.

[0164] In this embodiment, by simultaneously collecting oxygen management pressure, oxygen flow rate, number of exhaust holes of the breathing mask and internal dilution of the mask, a multi-level data set is constructed, which comprehensively reflects the performance of the oxygen delivery system and the breathing state of the patient, improves the comprehensiveness and accuracy of the monitoring data, effectively filters the noise and abnormalities in the collection process through data verification, abnormal data rejection and filtering noise processing, ensures that the subsequent analysis is based on high-quality data information, and improves the credibility of the judgment result.

[0165] Respectively constructing oxygen channel transmission efficiency coefficient, target patient exhalation intensity interference coefficient and breathing mask structure interference coefficient helps to quantitatively analyze the influence of each factor on the oxygen concentration, supports scientific clinical decision-making, and through the association of each influence coefficient, forms a comprehensive influence coefficient, provides overall evaluation of the oxygen concentration of the patient, effectively identifies abnormal conditions, and realizes dynamic monitoring and timely warning.

[0166] The size of the threshold is set for easy comparison, and the size of the threshold depends on the amount of sample data and the base number set by the person skilled in the art for each group of sample data; as long as it does not affect the proportional relationship between the parameters and the quantized values.

[0167] The above formulas are obtained by collecting a large amount of data for software simulation and selecting a formula close to the true value, and the coefficients in the formula are set by the person skilled in the art according to the actual situation. The above is only a preferred specific embodiment of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical range disclosed by the present application, according to the technical scheme and inventive concept of the present application, equivalent replacement or change, should be covered within the protection scope of the present application.

Claims

1. An on-line monitoring system for oxygen concentration in a patient undergoing gynecological surgery, characterized by, The system comprises a multi-data acquisition module, a data preprocessing module, a data analysis module and an optimization module. The multi-data acquisition module is configured to monitor the pipeline pressure and oxygen flow rate in real time when oxygen is supplied to a target patient in a gynecological surgery, and to collect the number of exhaust holes on a breathing mask worn on the nose and mouth of the target patient and the oxygen dilution inside the breathing mask to construct a first data set, a second data set and a third data set. The data preprocessing module is configured to perform preprocessing operations on the data based on the first data set, the second data set and the third data set. The data analysis module is configured to, after performing preprocessing on each item of data in the first data set, the second data set, and the third data set, construct an oxygen delivery channel transmission efficiency coefficient through the first data set , construct an exhalation intensity interference coefficient of the target patient through the second data set , and construct a breathing mask structure interference coefficient S through the third data set. The optimization module is configured to associate the oxygen delivery channel transmission efficiency coefficient , the target patient exhalation intensity interference coefficient , and the breathing mask structure interference coefficient S to construct a comprehensive influence coefficient , and obtain the comprehensive influence coefficient by the following formula: In the formula, , and are weight coefficients, satisfying and a fourth evaluation unit; The data preprocessing module further comprises a first extraction unit, a first evaluation unit, a second extraction unit, a second evaluation unit, a third extraction unit and a third evaluation unit. The third extraction unit is configured to extract the number of exhaust holes of the respiratory mask in the third data set and the tension Z of the elastic band. The tension Z of the elastic band is obtained by calculation. The amount of tensile strain generated by the elastic belt is detected by a strain gauge sensor , based on the tensile strain variable The stress is obtained by a calculation formula ; based on the stress obtained by calculation , the cross-sectional area of the elastic band , the tension of the elastic band is obtained by calculation based on the calculated tension Z of the elastic band and the number of exhaust holes of the breathing mask in combination, a breathing mask structure disturbance coefficient S is obtained by calculation; The third evaluation unit is configured to preset a breathing mask interference threshold R and compare the breathing mask interference threshold R with a breathing mask structure interference coefficient S to generate a third evaluation instruction, including: When S≥R, it indicates that the breathing mask structure interferes abnormally with the oxygen concentration inside the breathing mask, and a third execution strategy is generated, including replacing the breathing mask, increasing the number of exhaust holes by 3-6 compared to the original breathing mask, increasing the tension of the elastic band by 40%-45%, and adding a sealing pad at the position where the breathing mask contacts the patient to improve the sealing performance by 14%-17%, the gas delivery rate by 36%-55%, and the oxygen delivery pressure by 3%-12%. When S<R, it indicates that the breathing mask structure interferes normally with the oxygen concentration inside the breathing mask, and the breathing mask continues to be used and continuously monitored.

2. The system for on-line monitoring of oxygen concentration in a patient undergoing gynecological surgery as claimed in claim 1 wherein, The multi-data acquisition module comprises an oxygen delivery pipeline feature acquisition unit, a target patient exhalation interference intensity acquisition unit and a breathing mask interference feature acquisition unit. The oxygen supply pipeline feature acquisition unit is used for installing a pressure sensor at a position 5-10 cm away from the breathing mask interface in the oxygen supply pipeline, so as to obtain the pipeline pressure values at the induction period, the maintenance period and the awakening period at the position in real time ; based on the hot gas flow sensor installed at the position 5-10 cm away from the breathing mask interface, the flow rate information of oxygen in the oxygen supply pipeline at the induction period, the maintenance period and the awakening period is detected in real time, and the flow of the oxygen supply pipeline at the induction period, the maintenance period and the awakening period is obtained , and a first data set is constructed The target patient exhalation interference intensity acquisition unit is arranged inside the breathing mask and is provided with an integrated detection module, which includes a carbon dioxide concentration sensor, a humidity sensor and a temperature sensor. The carbon dioxide concentration sensor is used to acquire the carbon dioxide concentration inside the breathing mask in real time The temperature sensor and the humidity sensor are used to monitor the temperature and humidity inside the breathing mask in real time. The flow rate sensor arranged inside the breathing mask is used to acquire the exhalation flow rate v of the gynecological surgery patient during the surgery. The pressure sensor arranged inside the breathing mask is used to acquire the air pressure peak-valley signal formed by the breathing of the gynecological surgery patient. The signal processing algorithm is used to extract the breathing frequency f and the flow rate characteristics, and a second data set is constructed.

3. The system for on-line monitoring of oxygen concentration in a patient undergoing gynecological surgery as claimed in claim 2 wherein, The breathing mask interference feature acquisition unit is used for obtaining the number of exhaust holes of the breathing mask directly from the product manual of the breathing mask based on the induction period, the maintenance period and the awakening period And the strain gauge sensor is adhered to the outer surface of the elastic band on the breathing mask, and the strain gauge sensor is used to detect the tensile strain of the elastic band Based on the dependent variable of the tensile strain The stress is obtained through a calculation formula Based on the calculated stress An image of the cross section of the elastic band is captured, the cross section of the elastic band in the image is identified, the image pixel unit is converted into actual physical size unit through a pixel calibration factor, and then two key geometric parameters of width and thickness in the cross section are extracted, and the cross section area of the elastic band is obtained through an area calculation formula The cross section area of the elastic band Is combined with the stress The tension Z of the elastic band is obtained through a calculation formula, and a third data set is constructed.

4. The system for on-line monitoring of oxygen concentration in a patient undergoing gynecological surgery according to claim 3, wherein, The data preprocessing module comprises a pipeline feature data preprocessing unit, a target patient exhalation data preprocessing unit and a breathing mask interference data preprocessing unit. The pipeline characteristic data preprocessing unit is used for setting the range of each data according to clinical experience, and the oxygen administration pressure value in the first data set and the flow of the oxygen pipeline performs data checking, and removes data beyond the data range according to the CPU controller in the system, and the oxygen administration pressure value in the first data set and the flow of the oxygen pipeline uses a low-pass filter to perform filtering operation while suppressing high-frequency interference in the data acquisition process; The target patient expiration data preprocessing unit is used to set the range of each data according to clinical experience, to perform data verification on the carbon dioxide concentration , the temperature and humidity inside the breathing mask in the second data set, and to remove the data exceeding the data range according to the CPU controller in the system. Based on the carbon dioxide concentration , the temperature and humidity inside the breathing mask in the second data set, a low-pass filter is used for filtering operation while suppressing high-frequency interference in the data acquisition process. The breathing mask interference data preprocessing unit is used to set the range of each data according to clinical experience, and the number of exhaust holes of the breathing mask in the third data set and the elastic band tension Z, perform data verification, and according to the CPU controller in the system, the data exceeding the data range is rejected.

5. The system for on-line monitoring of oxygen concentration in a patient's body for gynecological surgery as claimed in claim 4, The second evaluation unit is configured to preset an interference threshold E and compare the interference threshold E with a target patient exhalation intensity interference coefficient to generate a second evaluation instruction, including: The first extraction unit is configured to extract the pipeline pressure value in the first data set And the flow of the oxygen supply pipeline The oxygen supply channel transmission efficiency coefficient is calculated ; The first evaluation unit is configured to preset a transmission efficiency threshold W, and compare the transmission efficiency threshold W with the oxygen transmission channel transmission efficiency coefficient The comparison is performed to generate a first evaluation instruction, including; When ≥ W, it indicates that the oxygen delivery efficiency inside the oxygen delivery channel is normal, the current oxygen delivery efficiency is maintained, and real-time monitoring is continued; When When <W, it indicates that the delivery efficiency of oxygen inside the oxygen delivery channel is abnormal, and the first execution strategy is generated, including: detecting the sealing property of the oxygen delivery pipeline and the oxygen delivery terminal connection, replacing the damaged oxygen delivery pipeline, and adjusting the oxygen delivery pipeline to make it free of bending, crossing and winding, increasing the oxygen delivery pressure inside the oxygen delivery pipeline by 5% to 15%, and increasing the oxygen flow rate of the oxygen delivery terminal by 10% to 25% based on the current oxygen delivery pipeline flow rate. .

6. The system for on-line monitoring of oxygen concentration in a patient undergoing gynecological surgery according to claim 5, wherein, The second extraction unit is configured to extract the carbon dioxide concentration inside the respiratory mask in the second data set , the temperature , and the humidity inside the respiratory mask ; The second evaluation unit is configured to preset an interference threshold E and compare the interference threshold E with a target patient exhalation intensity interference coefficient to generate a second evaluation instruction, including: When When E, it indicates that the target patient exhaled gas interferes with the oxygen concentration inside the breathing mask abnormally, and the second execution strategy is generated, including: replacing the breathing mask, increasing the number of exhaust holes by 2-4 compared with the original breathing mask, improving the exhaust efficiency by 20%-35%, and improving the heat dissipation efficiency by 6%-8%, and setting a small dehumidification filter in the breathing mask to improve the dehumidification efficiency by 24%-37%. When When the value of E is < 0.95, it indicates that the patient's exhaled gas interferes with the normal oxygen concentration inside the breathing mask, and continuous monitoring is implemented.

7. A method for on-line monitoring of oxygen concentration in a patient's body during gynecological surgery, applied to the system for on-line monitoring of oxygen concentration in a patient's body during gynecological surgery according to any one of claims 1-6, characterized in that, S1: First, collect the oxygen delivery management pressure and oxygen flow rate when the target patient inhales oxygen during a gynecological surgery, and also collect the number of exhaust holes on the breathing mask worn on the face of the target patient and the dilution inside the mask to construct a first data set, a second data set and a third data set; S2: Second, perform data verification, eliminate abnormal data and perform filtering and noise reduction processing on the retained data on each item of data in the first data set, the second data set and the third data set. ​ S3: Then, the oxygen delivery channel transmission efficiency coefficient is constructed for the pre-processed first data set, second data set and third data set respectively , target patient exhalation intensity interference coefficient and breathing mask structure interference coefficient S; S4: Finally, the oxygen delivery channel transmission efficiency coefficient , oxygen delivery channel transmission efficiency coefficient and the breathing mask structure interference coefficient S are associated to build a comprehensive influence coefficient , and evaluation is carried out to determine the target patient oxygen concentration.

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