Nursing machine safety control method and system for thoracic surgery and nursing machine

Abstract

The invention discloses a nursing machine safety control method and system for the thoracic surgery department and a nursing machine, and relates to the technical field of nursing machine control. The safety control method of the nursing machine for the thoracic surgery department comprises the following steps: S1, supply control judgment; s2, connection detection judgment; and S3, aging detection judgment. According to the method, the supply control evaluation value is obtained by evaluating the stability degree of the respirator gas flow supply control, whether the respirator connection leakage detection is executed or not is judged based on the supply control evaluation value, and then the result of the respirator connection leakage detection is evaluated to judge whether the sensor aging detection is executed or not. Finally, the conformity degree of sensor aging detection is evaluated, whether sensor monitoring optimization is executed or not is judged, the defect detection control precision of the breathing machine for the thoracic surgery department is improved, and the problem that in the prior art, the defect detection control precision of the breathing machine for the thoracic surgery department is not high is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of nursing machine control, and in particular to a nursing machine safety control method, system and nursing machine for thoracic surgery. Background Art

[0002] During thoracic surgery and treatment, the safety of nursing equipment is fundamental to ensuring patient safety. With advances in medical technology, the scope of thoracic surgery has continuously expanded to include complex chest surgeries involving the lungs, heart, and major blood vessels. Thoracic surgery patients often face breathing difficulties and delayed recovery from anesthesia after surgery. Nursing equipment plays a crucial role during this period, especially ventilators and ECG monitors, which provide real-time patient health data and ensure immediate action in the event of anomalies. Therefore, ensuring the safe operation of nursing equipment in thoracic surgery is crucial to ensuring patient safety and treatment effectiveness.

[0003] Existing safety control methods for nursing equipment used in thoracic surgery utilize multiple technical means to safeguard patient safety, encompassing alarm systems, automatic adjustments, device self-tests, and calibration. For example, a common thoracic surgery nursing equipment—a mechanical ventilator, for example—is equipped with multiple sensors to monitor key parameters such as the patient's respiratory rate, tidal volume, airway pressure, and oxygen concentration in real time. It also features multiple alarm functions, such as for low oxygen concentration, excessive air pressure, and apnea. Furthermore, ventilators require regular technical calibration to ensure accurate parameters such as airflow, pressure, and oxygen concentration. By employing multiple safety control methods, patient treatment safety can be effectively improved and surgical risks reduced.

[0004] For example, the patent application with publication number CN116736704A discloses a ventilator control method and system, which includes: S1, establishing a ventilator ventilation control sequence, which includes a patient monitoring end, a central control end, and a ventilation end, which are electrically connected in a tree-like structure; S2, the patient monitoring end collects data, including blood pressure, blood oxygen, heart rate, respiratory rate and inspiratory pressure, and sends it to the control center end every unit time; S3, the control center end is installed with a computing control unit to pre-process the data, and send it to the model together with historical data for training and reasoning; S4, obtaining the ventilation control results under PID mode, and combining them with the model reasoning results; S5, the ventilation end adjusts the ventilation valve position according to the instructions to control the size of the air output and the frequency of air pressure changes.

[0005] For example, the invention patent publication number CN116500903B discloses an AI-based ventilator heating and humidification control method and system, as well as a ventilator. The method includes: arranging a system; constructing a neural network model that uses the actual output temperature of the heating plate, ambient temperature, and nasal airflow temperature as input, and controller control actions as output; and deploying the trained, verified, tested, and evaluated neural network model into the ventilator for use. The system includes a heating plate, a temperature sensor, a fan, a nasal temperature and humidity sensor, an ambient temperature and humidity sensor, and a controller.

[0006] However, in the process of implementing the technical solutions of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems: Ventilators used in thoracic surgery are typically equipped with multiple sensors for data collection. These sensors can be affected by environmental factors, sensor aging, or inaccurate placement of the endotracheal tube, leading to reduced data accuracy. For example, oxygen concentration sensors can drift, resulting in inaccurate real-time oxygen concentration monitoring, which can affect patient treatment outcomes.

[0007] Another thing to consider is that gas supply is a key component of the ventilator. For example, too high an oxygen flow rate may cause oxygen poisoning, while too low a flow rate will prevent the patient from getting enough oxygen. At the same time, if there is a leak in the connecting pipe, the flow rate of oxygen and air may be reduced and the required oxygen support cannot be obtained. There is a problem of low accuracy in defect detection and control of thoracic surgery ventilators. Summary of the Invention

[0008] The embodiments of the present application solve the problem of low accuracy of defect detection and control of ventilators for thoracic surgery in the prior art by providing a safety control method, system and nursing machine for thoracic surgery, thereby improving the accuracy of defect detection and control of ventilators for thoracic surgery.

[0009] An embodiment of the present application provides a safety control method for a nursing machine for thoracic surgery, comprising the following steps: S1, evaluating the stability of the ventilator gas flow supply control according to the gas flow control data to obtain a supply control evaluation value, and judging whether to perform a ventilator connection leakage detection based on the supply control evaluation value, wherein the supply control evaluation value is used to quantitatively evaluate the stability of the ventilator gas flow supply control; S2, if the ventilator connection leakage detection is performed, evaluating the result of the ventilator connection leakage detection, and judging whether to perform the sensor aging detection based on the evaluation result; S3, if the sensor aging detection is performed, evaluating the compliance degree of the sensor aging detection, and judging whether to perform the sensor monitoring optimization.

[0010] Furthermore, the specific steps of evaluating the stability of the ventilator gas flow supply control based on the gas flow control data to obtain a supply control evaluation value are as follows: performing statistical analysis on the oxygen flow measurement values obtained within a preset time period to obtain an oxygen flow average value, and obtaining an oxygen fluctuation evaluation value based on the oxygen flow measurement value and the oxygen flow average value; obtaining the airway pressure deviation within the preset time period, the airway pressure deviation including the maximum airway pressure deviation and the minimum airway pressure deviation, and thereby obtaining a pressure fluctuation amplitude, the pressure fluctuation amplitude being the difference between the maximum airway pressure deviation and the minimum airway pressure deviation; judging whether the obtained pressure fluctuation amplitude is within a pressure fluctuation amplitude reference range obtained from a preset database, and if so, combining the supply control related data with the supply reference data obtained from the preset database to obtain a supply control evaluation value, otherwise, recording the supply control evaluation value as 0; the supply control related data including the oxygen flow measurement value, the oxygen flow average value, the oxygen flow maximum deviation and the pressure fluctuation amplitude; the supply reference data including the flow fluctuation evaluation weight, the flow deviation evaluation weight, the pressure control evaluation weight and the reference maximum pressure fluctuation amplitude.

[0011] Furthermore, the specific process of judging whether to perform ventilator connection leakage detection based on the supply control evaluation value is as follows: comparing the obtained supply control evaluation value with the preset supply control threshold range obtained from the preset database; if the supply control evaluation value is within the preset supply control threshold range obtained from the preset database, the ventilator connection leakage detection is not performed, and the supply control evaluation value is continuously monitored to see whether it is within the preset supply control threshold range; if the supply control evaluation value is not within the preset supply control threshold range obtained from the preset database, the ventilator connection leakage detection is performed, and the result of the ventilator connection leakage detection is evaluated to obtain a connection detection evaluation value; the connection detection evaluation value is used to quantitatively evaluate the probability of a connection leakage in the ventilator.

[0012] Furthermore, the specific steps of evaluating the results of the ventilator connection leakage detection to obtain a connection detection evaluation value are as follows: obtaining connection detection related evaluation data after performing the ventilator connection leakage detection; the connection detection related evaluation data includes the input end oxygen flow, the output end oxygen flow, the input end pressure, the output end pressure and the supply control evaluation value; judging whether the obtained input and output difference values are not greater than the corresponding reference input and output difference values, if so, obtaining the connection detection evaluation value based on the connection detection related data and the connection detection reference data obtained from the preset database, otherwise, recording the connection detection evaluation value as 1; the input and output difference value includes the input and output oxygen flow difference and the input and output pressure difference, the input and output oxygen flow difference is the difference between the input end oxygen flow and the output end oxygen flow, and the input and output pressure difference is the difference between the input end pressure and the output end pressure; the reference input and output difference value includes a reference maximum oxygen flow difference value and a reference maximum pressure difference value; the connection detection reference data includes a flow detection evaluation weight, a pressure detection evaluation weight and a detection airflow supply evaluation weight.

[0013] Furthermore, the specific process of determining whether to perform sensor aging detection based on the evaluation results is as follows: comparing the obtained connection detection evaluation value with the preset detection compliance threshold range obtained from the preset database; if the connection detection effect evaluation value is within the preset detection compliance threshold range obtained from the preset database, the sensor aging detection is not performed, and the preset personnel are reminded to optimize the ventilator connection leakage; if the connection detection effect evaluation value is not within the preset detection compliance threshold range obtained from the preset database, the sensor aging detection is performed, and the compliance degree of the sensor aging detection is evaluated to obtain the aging detection evaluation value.

[0014] Furthermore, the specific steps of evaluating the compliance degree of the sensor aging detection are as follows: obtaining aging detection related data after performing the sensor aging detection; the aging detection related data includes oxygen concentration measurement value, sensor measurement response delay, sensor operating average voltage, sensor operating average current, total sensor operating time and supply control evaluation value; combining the aging detection related data with aging detection reference data obtained from a preset database to obtain an aging detection evaluation value; the aging detection reference data includes an aging environment correction factor, an oxygen concentration reference value, an oxygen concentration drift threshold, a reference maximum response delay, a reference maximum operating energy consumption, a sensor measurement evaluation weight, a sensor response evaluation weight, a sensor energy consumption evaluation weight and an aging detection airflow supply impact weight; the aging detection evaluation value is used to quantitatively evaluate the compliance degree of the sensor aging detection.

[0015] Furthermore, the specific process of determining whether to perform sensor monitoring optimization is as follows: comparing the obtained aging detection evaluation value with the preset aging detection threshold range obtained from the preset database; if the aging detection evaluation value is within the preset aging detection threshold range obtained from the preset database, performing sensor monitoring optimization; the sensor monitoring optimization includes sensor adaptive calibration and sensor acquisition optimization; if the aging detection evaluation value is not within the preset aging detection threshold range obtained from the preset database, not performing sensor monitoring optimization, and reminding the preset personnel to perform ventilator aging detection.

[0016] Furthermore, the sensor monitoring optimization includes sensor adaptive calibration and sensor acquisition optimization; the sensor adaptive calibration is used to achieve sensor output calibration through a sensor adaptive calibration algorithm; and the sensor acquisition optimization is used to optimize the sensor data acquisition process.

[0017] An embodiment of the present application provides a nursing machine safety control system for thoracic surgery, including: a supply control judgment module, a connection detection judgment module and an aging detection judgment module; the supply control judgment module is used to evaluate the stability of the ventilator gas flow supply control according to the gas flow control data to obtain a supply control evaluation value, and judge whether to perform ventilator connection leakage detection based on the supply control evaluation value, and the supply control evaluation value is used to quantitatively evaluate the stability of the ventilator gas flow supply control; the connection detection judgment module is used to evaluate the result of the ventilator connection leakage detection if the ventilator connection leakage detection is performed, and judge whether to perform sensor aging detection based on the evaluation result; the aging detection judgment module is used to evaluate the compliance degree of the sensor aging detection if the sensor aging detection is performed, and judge whether to perform sensor monitoring optimization.

[0018] An embodiment of the present application provides a nursing machine, which executes the nursing machine safety control method for thoracic surgery, including a ventilator, a data acquisition device, a storage device and a processing device; the data acquisition device is used to collect gas flow control data, and the data acquisition device includes an oxygen flow sensor and a pressure sensor; the storage device is used to store gas flow control data; the processing device is used to analyze the stability of the ventilator gas flow supply control according to the data stored in the storage device and determine whether to perform ventilator connection leakage detection and sensor aging detection.

[0019] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: 1. By evaluating the stability of the ventilator gas flow supply control to determine whether to perform ventilator connection leakage detection, then evaluating the results of the ventilator connection leakage detection, and based on the evaluation results, determining whether to perform sensor aging detection, and finally evaluating the compliance of the sensor aging detection to determine whether to perform sensor monitoring optimization, the stability of the ventilator gas flow supply control is analyzed and determined, thereby achieving an improvement in the defect detection control accuracy of thoracic surgery ventilators, and effectively solving the problem of low defect detection control accuracy of thoracic surgery ventilators in the existing technology.

[0020] 2. By obtaining the connection detection related data after performing the ventilator connection leakage detection, it is determined whether the obtained input and output difference values are not greater than the corresponding reference input and output difference values. If so, the connection detection evaluation value is obtained based on the connection detection related data and the connection detection reference data obtained from the preset database. Otherwise, the connection detection evaluation value is recorded as 1, thereby realizing a numerical evaluation of the probability of connection leakage of the ventilator, and further realizing a more accurate evaluation of the probability of connection leakage of the ventilator.

[0021] 3. By obtaining aging detection related data after performing sensor aging detection, and then combining the aging detection related data with aging detection reference data obtained from a preset database to obtain an aging detection evaluation value, a numerical evaluation of the sensor aging detection compliance degree is achieved, thereby achieving a more accurate evaluation of the sensor aging detection compliance degree. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A flowchart of a safety control method for a nursing machine for thoracic surgery provided in an embodiment of the present application; Figure 2 A schematic diagram showing changes in the aging detection evaluation value along with the supply control evaluation value provided in an embodiment of the present application; Figure 3 A schematic diagram showing how the aging detection evaluation value provided in an embodiment of the present application changes with the sensor measurement response delay; Figure 4 A schematic structural diagram of a safety control system for a nursing machine for thoracic surgery provided in an embodiment of the present application. DETAILED DESCRIPTION

[0023] The embodiments of the present application solve the problem of low accuracy in defect detection control of ventilators for thoracic surgery in the prior art by providing a nursing machine safety control method, system and nursing machine for thoracic surgery. The stability of the ventilator gas flow supply control is evaluated to obtain a supply control evaluation value, and based on the supply control evaluation value, it is determined whether to perform ventilator connection leakage detection. If the ventilator connection leakage detection is performed, the result of the ventilator connection leakage detection is evaluated, and at the same time, based on the evaluation result, it is determined whether to perform sensor aging detection. Finally, if the sensor aging detection is performed, the compliance degree of the sensor aging detection is evaluated to determine whether to perform sensor monitoring optimization, thereby improving the accuracy of defect detection control of ventilators for thoracic surgery.

[0024] The technical solution in the embodiments of the present application is to solve the problem of low accuracy in defect detection and control of ventilators for thoracic surgery. The overall idea is as follows: By evaluating the stability of the ventilator gas flow supply control to determine whether to perform ventilator connection leakage detection, then evaluating the results of the ventilator connection leakage detection, and based on the evaluation results, determining whether to perform sensor aging detection, and finally determining whether to perform sensor monitoring optimization, the effect of improving the control accuracy of ventilator defect detection in thoracic surgery is achieved.

[0025] In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0026] like Figure 1 As shown, it is a flow chart of a nursing machine safety control method for thoracic surgery provided by an embodiment of the present application, the method comprising the following steps: S1, evaluating the stability of the ventilator gas flow supply control according to the gas flow control data to obtain a supply control evaluation value, and judging whether to perform a ventilator connection leakage detection based on the supply control evaluation value, the supply control evaluation value is used to quantitatively evaluate the stability of the ventilator gas flow supply control; S2, if the ventilator connection leakage detection is performed, evaluating the result of the ventilator connection leakage detection, and judging whether to perform the sensor aging detection based on the evaluation result; S3, if the sensor aging detection is performed, evaluating the compliance degree of the sensor aging detection, and judging whether to perform the sensor monitoring optimization.

[0027] In this embodiment, specifically, the gas flow control data includes oxygen flow data and airway pressure data, wherein the oxygen flow data is obtained through a built-in oxygen flow sensor of the ventilator, and the airway pressure data is obtained through a built-in pressure sensor of the ventilator.

[0028] By obtaining gas flow control data, the stability of the ventilator's gas flow supply control can be evaluated. Based on the supply control evaluation value, it is determined whether to perform ventilator leakage detection and sensor aging detection, thereby improving the data reliability during defect detection of thoracic surgery ventilators.

[0029] Furthermore, the specific steps of evaluating the stability of the ventilator gas flow supply control according to the gas flow control data to obtain the supply control evaluation value are as follows: statistically analyzing the oxygen flow measurement values obtained within the preset time period to obtain the oxygen flow average value, and obtaining the oxygen fluctuation evaluation value (i.e., the oxygen fluctuation in the supply control evaluation value) according to the oxygen flow measurement values and the oxygen flow average value. ); obtain the airway pressure deviation within the preset time period, the airway pressure deviation includes the maximum airway pressure deviation and the minimum airway pressure deviation, and thus obtain the pressure fluctuation amplitude (i.e., the pressure in the supply control evaluation value) ), the pressure fluctuation amplitude is the difference between the maximum airway pressure deviation and the minimum airway pressure deviation; it is determined whether the obtained pressure fluctuation amplitude is within the pressure fluctuation amplitude reference range obtained from the preset database; if so, the supply control related data is combined with the supply reference data obtained from the preset database to obtain a supply control evaluation value; otherwise, the supply control evaluation value is recorded as 0; the supply control related data includes the oxygen flow measurement value, the oxygen flow average value, the maximum oxygen flow deviation and the pressure fluctuation amplitude; the supply reference data includes the flow fluctuation evaluation weight, the flow deviation evaluation weight, the pressure control evaluation weight and the reference maximum pressure fluctuation amplitude.

[0030] The method to obtain the supply control evaluation value is as follows: ; ; ; Where, Indicates the supply control evaluation value within a preset time period, Indicates the traffic fluctuation assessment weight, Indicates the flow deviation evaluation weight, represents the pressure control evaluation weight, Indicates the number of the preset time in the preset time period, , Indicates the total number of preset moments in the preset time period. Indicates the oxygen fluctuation assessment value within the preset time period. Indicates the oxygen flow measurement value at the yth preset time within the preset time period, Indicates the average oxygen flow rate within the preset time period. Indicates the maximum deviation of oxygen flow rate within the preset time period. Indicates the reference maximum pressure fluctuation amplitude, Indicates the pressure fluctuation amplitude within the preset time period. Indicates the reference range of pressure fluctuation amplitude, Indicates the maximum airway pressure deviation within a preset time period. Indicates the minimum airway pressure deviation within a preset time period.

[0031] In this embodiment, the oxygen flow measurement value, i.e., the oxygen flow data at a preset time within a preset time period, is statistically analyzed using the np.mean function of the numpy library in Python to obtain an average oxygen flow value. The maximum oxygen flow deviation, i.e., the maximum value of the oxygen flow deviation, is statistically analyzed using the MAX function of Google Sheets to obtain the maximum oxygen flow deviation. The oxygen flow deviation, i.e., the difference between the oxygen flow measurement value and the reference oxygen flow value, is represented by the sum and average of the collected historical oxygen flow data to represent the reference oxygen flow value.

[0032] The airway pressure deviation is the difference between the airway pressure measurement value and the reference airway pressure value. The airway pressure measurement value is the airway pressure data at a preset time within a preset time period. The reference airway pressure value is represented by summing and averaging the collected historical airway pressure data.

[0033] The maximum airway pressure deviation is the maximum value of the airway pressure deviation, and the minimum airway pressure deviation is the minimum value of the airway pressure deviation. The airway pressure deviation is statistically analyzed using the MAX function and MIN function of Google Sheets to obtain the corresponding maximum airway pressure deviation and minimum airway pressure deviation.

[0034] The pressure fluctuation amplitude reference range is the range corresponding to the reference minimum pressure fluctuation amplitude and the reference maximum pressure fluctuation amplitude, wherein the reference minimum pressure fluctuation amplitude is represented by the minimum value of the collected historical pressure fluctuation amplitude, and the reference maximum pressure fluctuation amplitude is represented by the maximum value of the collected historical pressure fluctuation amplitude.

[0035] The flow fluctuation assessment weight, flow deviation assessment weight and pressure control assessment weight are respectively used to describe the degree of influence of the oxygen fluctuation assessment value, the maximum deviation of oxygen flow and the pressure fluctuation amplitude on the supply control assessment value. They can be directly obtained from the preset database when used. Specifically, the oxygen fluctuation assessment value, the maximum deviation of oxygen flow and the pressure fluctuation amplitude are respectively matched with the flow fluctuation assessment weight, flow deviation assessment weight and pressure control assessment weight preset in the preset database to form a mapping set. The real-time oxygen fluctuation assessment value, the maximum deviation of oxygen flow and the pressure fluctuation amplitude are input into the mapping set to obtain the corresponding weights. The mapping relationship is one-to-one correspondence. In this example, the value range is [0, 1], and the sum of the flow fluctuation assessment weight, flow deviation assessment weight and pressure control assessment weight in this example is 1.

[0036] The supply control evaluation value is used to quantitatively evaluate the stability of the ventilator gas flow supply control. Among them, the oxygen fluctuation evaluation value, the maximum deviation of the oxygen flow and the pressure fluctuation amplitude all have an impact on the supply control evaluation value. Specifically, when the pressure fluctuation amplitude is not within the corresponding pressure fluctuation amplitude reference range, the supply control evaluation value is 0; when the pressure fluctuation amplitude is within the corresponding pressure fluctuation amplitude reference range, as the oxygen fluctuation evaluation value, the maximum deviation of the oxygen flow and the pressure fluctuation amplitude increase, the supply control evaluation value decreases accordingly.

[0037] In addition, the supply control evaluation value includes multiple parameters, and each parameter is related to each other and does not exist independently. For example, as the maximum deviation of oxygen flow within a preset time period increases, it indicates that the deviation between the oxygen flow measurement value and the average oxygen flow value increases, which leads to an increase in the oxygen fluctuation evaluation value. Usually, due to a leak in the ventilator connection or aging of the sensor, the maximum deviation of oxygen flow increases, which in turn leads to a decrease in the supply control evaluation value. In addition, the fluctuation of oxygen flow is also related to the fluctuation of airway pressure. The sharp fluctuation of oxygen flow leads to an increase in the amplitude of airway pressure fluctuation. Conversely, the sharp fluctuation of pressure also affects the stability of oxygen flow. Specifically, as the oxygen fluctuation evaluation value increases, the amplitude of pressure fluctuation increases, which in turn leads to a decrease in the supply control evaluation value. At the same time, in the case of a leak in the ventilator connection, the oxygen flow at the output end decreases, which causes the average oxygen flow to decrease, and the oxygen fluctuation evaluation value increases, which in turn leads to a decrease in the supply control evaluation value.

[0038] Therefore, through quantitative means, the correlation and mutual influence between various factors are taken into account, and the supply control evaluation value is obtained through comprehensive analysis, which realizes the numerical evaluation of the stability of the ventilator gas flow supply control. The stability of the ventilator gas flow supply control is judged through the numerical evaluation, thereby achieving the improvement of the accuracy of the evaluation of the stability of the ventilator gas flow supply control.

[0039] Furthermore, the specific process of determining whether to perform ventilator connection leakage detection based on the supply control evaluation value is as follows: comparing the obtained supply control evaluation value with the preset supply control threshold range obtained from the preset database; if the supply control evaluation value is within the preset supply control threshold range obtained from the preset database, then the ventilator connection leakage detection is not performed, and the supply control evaluation value is continuously monitored to see whether it is within the preset supply control threshold range; if the supply control evaluation value is not within the preset supply control threshold range obtained from the preset database, then the ventilator connection leakage detection is performed, and at the same time, the result of the ventilator connection leakage detection is evaluated to obtain a connection detection evaluation value; the connection detection evaluation value is used to quantitatively evaluate the probability of a ventilator connection leakage.

[0040] In this embodiment, specifically, the preset supply control threshold range is set by professionals according to standards in the field, for example, the preset supply control threshold range is set to 0.80 to 0.90; wherein, the ventilator connection leakage detection is to obtain input end data and output end data by sensors deployed at the input end and output end of the ventilator and evaluate the results of the ventilator connection leakage detection, for example, the input end oxygen flow and the output end oxygen flow are obtained by oxygen flow sensors deployed at the input end and output end of the ventilator; by combining the preset supply control threshold range for judgment and performing ventilator connection leakage detection, a more accurate judgment of the stability of the ventilator gas flow supply control is achieved, and the reliability of defect detection is improved when the stability of the ventilator gas flow supply control is abnormal.

[0041] Furthermore, the specific steps for evaluating the results of the ventilator connection leakage detection to obtain a connection detection evaluation value are as follows: obtaining connection detection related evaluation data after performing the ventilator connection leakage detection; the connection detection related evaluation data includes the input end oxygen flow, the output end oxygen flow, the input end pressure, the output end pressure and the supply control evaluation value; judging whether the obtained input and output difference values are not greater than the corresponding reference input and output difference values, if so, obtaining the connection detection evaluation value based on the connection detection related data and the connection detection reference data obtained from the preset database, otherwise, recording the connection detection evaluation value as 1; the input and output difference value includes the input and output oxygen flow difference and the input and output pressure difference, the input and output oxygen flow difference is the difference between the input end oxygen flow and the output end oxygen flow, and the input and output pressure difference is the difference between the input end pressure and the output end pressure; the reference input and output difference value includes the reference maximum oxygen flow difference value and the reference maximum pressure difference value; the connection detection reference data includes the flow detection evaluation weight, the pressure detection evaluation weight and the detection airflow supply evaluation weight.

[0042] The method for obtaining the connection detection evaluation value is as follows: ; Where, Indicates the connection detection evaluation value, Indicates the traffic detection evaluation weight, Indicates the stress detection assessment weight, Indicates the number of the preset time to be detected within the preset time period to be detected, , Indicates the total number of preset moments to be detected within the preset time period to be detected. represents the oxygen flow rate at the input end at the jth preset time to be detected within the preset time period to be detected, represents the oxygen flow rate at the output end at the jth preset time to be detected within the preset time period to be detected, Indicates the reference maximum oxygen flow difference value, represents the input pressure at the jth preset time to be detected within the preset time period to be detected, Indicates the output pressure at the jth preset time to be detected within the preset time period to be detected, Indicates the reference maximum pressure difference value, Indicates the evaluation weight of the air flow supply detection, Indicates the supply control evaluation value after performing a ventilator connection leak test.

[0043] In this embodiment, the input end oxygen flow rate and the output end oxygen flow rate are obtained by deploying oxygen flow sensors at the input end and the output end of the ventilator, and the input end pressure and the output end pressure are obtained by deploying pressure sensors at the input end and the output end of the ventilator.

[0044] The maximum value of the historical input and output oxygen flow rate differences collected is used as the reference maximum oxygen flow rate difference value, and the maximum value of the historical input and output pressure differences collected is used as the reference maximum pressure difference value.

[0045] The flow detection evaluation weight, pressure detection evaluation weight, and air flow supply detection evaluation weight are respectively used to describe the degree of influence of the input and output oxygen flow difference, the input and output pressure difference, and the supply control evaluation value on the connection detection evaluation value. The value range is [0, 1] and the sum is 1. For example, the real-time input and output oxygen flow difference, the input and output pressure difference, and the supply control evaluation value are input into the preset mapping set in the database to obtain the corresponding weights, and the mapping relationship can be one-to-one or many-to-one.

[0046] To simplify the analysis, we define ,in The difference in oxygen flow rate between input and output at the jth preset time within the preset time period to be detected is defined as ,in is the input and output pressure difference at the jth preset time to be detected within the preset time period to be detected. Specifically, it is assumed that the flow detection evaluation weight is 0.3, the pressure detection evaluation weight is 0.3, the air flow supply detection evaluation weight is 0.4, the reference maximum oxygen flow difference value is 20L / min, the reference maximum pressure difference value is 100kPa, the input end oxygen flow rate is 50L / min, and the input end pressure is 300kPa. The connection detection evaluation value can be calculated by the above method. The change statistics of the connection detection evaluation value are shown in Table 1: Table 1 Statistics of changes in connection detection evaluation values ; It can be seen from the second and third groups of data in Table 1 that as the output end oxygen flow rate and the output end pressure increase, the input and output oxygen flow rate difference and the input and output pressure difference decrease, and thus the connection detection evaluation value also decreases. In addition, as in the first group of data, when the input and output oxygen flow rate difference exceeds the reference maximum oxygen flow rate difference value, the connection detection evaluation value is 1. At the same time, as can be seen from the third and fourth groups of data, as the supply control evaluation value increases, the connection detection evaluation value decreases.

[0047] The connection detection evaluation value is used to quantitatively evaluate the probability of connection leakage in the ventilator. The connection detection evaluation value includes multiple parameters. Specifically, the input and output oxygen flow difference (including the input end oxygen flow and the output end oxygen flow), the input and output pressure difference (including the input end pressure and the output end pressure) and the supply control evaluation value all have an impact on the connection detection evaluation value. For example, as the supply control evaluation value increases, the connection detection evaluation value decreases; as the input and output oxygen flow difference and the input and output pressure difference decrease, the connection detection evaluation value also decreases.

[0048] It should be added that the various parameters in the connection detection evaluation value are related and do not exist independently. For example, as the supply control evaluation value increases, the difference between the input and output oxygen flow rates and the difference between the input and output pressures decrease, and the connection detection evaluation value decreases accordingly; as the input oxygen flow rate increases, the resistance increases, and a higher pressure is required to push the airflow, that is, the input pressure increases. At the same time, the output pressure usually decreases with the progress of exhalation, resulting in a decrease in the output oxygen flow rate; in addition, as the output oxygen flow rate decreases, the output pressure also decreases, that is, as the degree of deviation between the output oxygen flow rate and the input oxygen flow rate increases, the degree of deviation between the output pressure and the input pressure also increases, and the connection detection evaluation value increases accordingly.

[0049] Therefore, this algorithm takes into account the correlation and mutual influence between various factors, obtains the connection detection evaluation value in a quantitative way, realizes the quantitative evaluation of the ventilator connection leakage probability, judges the ventilator connection leakage probability through numerical evaluation, and thus realizes a more accurate evaluation of the ventilator connection leakage probability.

[0050] Furthermore, the specific process of determining whether to perform sensor aging detection based on the evaluation results is as follows: comparing the obtained connection detection evaluation value with the preset detection compliance threshold range obtained from the preset database; if the connection detection effect evaluation value is within the preset detection compliance threshold range obtained from the preset database, the sensor aging detection is not performed, and the preset personnel are reminded to optimize the ventilator connection leakage; if the connection detection effect evaluation value is not within the preset detection compliance threshold range obtained from the preset database, the sensor aging detection is performed, and the compliance degree of the sensor aging detection is evaluated to obtain the aging detection evaluation value.

[0051] In this embodiment, specifically, the preset detection compliance threshold range is set by professionals according to standards in the field, for example, the preset detection compliance threshold range is set to 0.682 to 1; wherein, the ventilator connection leakage optimization is achieved through a leakage compensation algorithm, specifically, the PID (Proportional Integral Derivative Control) controller adjusts the output by calculating the error between the set value (reference airway pressure) and the actual value (real-time detected airway pressure) using proportional, integral and differential algorithms.

[0052] Specifically, sensor aging detection is achieved through the reference signal monitoring method. The selected reference signal is input into the ventilator sensor, such as the standard oxygen concentration (generally 30L / min), and the sensor response is monitored in real time. By combining the preset detection threshold range to make judgments and perform ventilator connection leakage optimization and sensor aging detection, a more accurate judgment of the ventilator connection leakage probability is achieved, and the control reliability is improved when the ventilator connection leakage probability is abnormal.

[0053] Furthermore, the specific steps for evaluating the compliance degree of sensor aging detection are as follows: obtaining aging detection related data after performing sensor aging detection; the aging detection related data includes oxygen concentration measurement value, sensor measurement response delay, sensor operating average voltage, sensor operating average current, total sensor operating time and supply control evaluation value; combining the aging detection related data with aging detection reference data obtained from a preset database to obtain an aging detection evaluation value; the aging detection reference data includes an aging environment correction factor, an oxygen concentration reference value, an oxygen concentration drift threshold, a reference maximum response delay, a reference maximum operating energy consumption, a sensor measurement evaluation weight, a sensor response evaluation weight, a sensor energy consumption evaluation weight and an aging detection airflow supply impact weight; the aging detection evaluation value is used to quantitatively evaluate the compliance degree of sensor aging detection.

[0054] The method for obtaining the aging detection evaluation value is as follows: ; Where, Indicates the aging detection evaluation value, represents the aging environment correction factor, Indicates the number of the preset time within the preset time period of aging detection. , Indicates the total number of preset moments within the preset time period of aging detection. Indicates the oxygen concentration measurement value at the hth preset moment within the preset time period of aging detection, Indicates the reference value of oxygen concentration, Indicates the oxygen concentration drift threshold, Indicates the sensor measurement response delay, Indicates the reference maximum response delay, Indicates the average working voltage of the sensor within the preset time period of aging detection. Indicates the average working current of the sensor within the preset time period of aging detection. Indicates the total working time of the sensor within the preset time period of aging detection. Indicates the reference maximum working energy consumption, represents the sensor measurement evaluation weight, represents the sensor response evaluation weight, represents the sensor energy consumption evaluation weight, Indicates the weight of the airflow supply impact of aging detection, Indicates the supply control evaluation value after performing sensor aging detection.

[0055] In this embodiment, the oxygen concentration measurement value is obtained by deploying an oxygen concentration sensor at the output end of the ventilator airflow path. The sensor measurement response delay is the time difference between the sensor receiving the input change (i.e., the input standard oxygen concentration) and its output change. The total working time of the sensor is the length of the preset aging detection time period (for performing sensor aging detection).

[0056] The average working voltage of the sensor is the average value of the working voltage of the sensor at the preset time within the preset time period of the aging detection. The average working current of the sensor is the average value of the working current of the sensor at the preset time within the preset time period of the aging detection. The sensor working current and the sensor working voltage are obtained through the built-in current sensor and voltage sensor of the ventilator. The sensor working current and the sensor working voltage are statistically analyzed using the AVERAGE function of Google Sheets to obtain the sensor working average voltage and the sensor working current.

[0057] The aging environment correction factor is obtained from a preset database and is used to reflect the degree of influence of the sensor aging detection environment influencing factors (such as the environmental electromagnetic interference intensity) on the aging detection evaluation value. The real-time sensor aging detection environment influencing factors (such as environmental electromagnetic interference) are input into the preset mapping set in the database to obtain the corresponding aging environment correction factor.

[0058] The oxygen concentration reference value is represented by summing and averaging the collected historical oxygen concentration measurement values. The oxygen concentration drift threshold is the absolute value of the difference between the oxygen concentration reference value and the input standard oxygen concentration. The maximum value of the historical sensor measurement response delay collected represents the reference maximum response delay. The maximum value of the historical sensor working energy consumption collected represents the reference maximum working energy consumption.

[0059] In this example, the sensor measurement evaluation weight, sensor response evaluation weight, sensor energy consumption evaluation weight, and aging detection airflow supply impact weight all have a value range of [0, 1], and their sum is 1. Specifically, they can be directly obtained from a preset database during use. For example, the real-time sensor measurement compliance (i.e., the absolute value of the difference between the oxygen concentration measurement value and the oxygen concentration reference value), the sensor measurement response compliance (i.e., the sensor measurement response delay), the sensor energy consumption compliance (i.e., the product of the sensor operating average voltage, the sensor operating average current, and the total sensor operating time), and the supply control evaluation value are input into a preset mapping set in the database to obtain corresponding weights. The mapping relationship can be a one-to-one correspondence or a many-to-one relationship. The sensor measurement evaluation weight, sensor response evaluation weight, sensor energy consumption evaluation weight, and aging detection airflow supply impact weight are used to reflect the degree of influence of the sensor measurement compliance, sensor measurement response compliance, sensor energy consumption compliance, and supply control evaluation value on the aging detection evaluation value.

[0060] The settings include: aging environment correction factor is 0.3, the total number of preset moments in the preset time period of aging detection is 1, oxygen concentration measurement value is 20 (%), oxygen concentration reference value is 21 (%), oxygen concentration drift threshold is 1 (%), reference maximum response delay is 10 seconds, sensor working average voltage is 5V, sensor working average current is 1A, sensor working time is 300 seconds, reference maximum working energy consumption is 10W / min, sensor measurement evaluation weight is 0.25, sensor response evaluation weight is 0.25, sensor energy consumption evaluation weight is 0.25, aging detection airflow supply impact weight is 0.25, based on the settings, Figure 2 and Figure 3 .

[0061] like Figure 2 As shown in FIG, it is a schematic diagram of the change of the aging detection evaluation value with the supply control evaluation value provided in an embodiment of the present application. Specifically, the sensor measurement response delay is set to 6 seconds. As the supply control evaluation value increases, the aging detection evaluation value decreases accordingly; Figure 3 As shown, it is a schematic diagram of the change of the aging detection evaluation value provided in an embodiment of the present application with the sensor measurement response delay. Specifically, the supply control evaluation value is set to 0.5. As the sensor measurement response delay increases, the aging detection evaluation value also increases.

[0062] The aging detection evaluation value is used to quantitatively evaluate the compliance degree of the sensor aging detection. The aging detection evaluation value includes parameters from multiple aspects, and the various parameters are related to each other and do not exist independently. For example, as the supply control evaluation value increases, the deviation degree between the oxygen concentration measurement value and the oxygen concentration reference value decreases, and the aging detection evaluation value also decreases. In addition, as the sensor measurement response delay increases, the sensor energy consumption compliance (i.e., the product of the sensor operating average voltage, the sensor operating average current and the total sensor operating time) increases, and the aging detection evaluation value increases. At the same time, the increase in the sensor measurement response delay also increases the deviation degree between the oxygen concentration measurement value and the oxygen concentration reference value, which leads to an increase in the aging detection evaluation value.

[0063] In summary, this algorithm takes into account the correlation and mutual influence between various factors, obtains the aging detection evaluation value through comprehensive analysis, realizes the numerical evaluation of the sensor aging detection compliance, judges the sensor aging detection compliance through numerical evaluation, and thus realizes a more accurate evaluation of the sensor aging detection compliance.

[0064] Furthermore, the specific process for determining whether to perform sensor monitoring optimization is as follows: comparing the obtained aging detection evaluation value with the preset aging detection threshold range obtained from the preset database; if the aging detection evaluation value is within the preset aging detection threshold range obtained from the preset database, performing sensor monitoring optimization; sensor monitoring optimization includes sensor adaptive calibration and sensor acquisition optimization; if the aging detection evaluation value is not within the preset aging detection threshold range obtained from the preset database, not performing sensor monitoring optimization, and reminding the preset personnel to perform ventilator aging detection.

[0065] In this embodiment, specifically, the preset aging detection threshold range is set by professionals according to standards in the field. For example, the preset aging detection threshold range is set to 0.3 to 1.

[0066] Among them, sensor adaptive calibration is achieved through a sensor adaptive calibration algorithm. For example, an adaptive filter such as a least mean square filter iteratively updates the weights by minimizing the sum of squares of errors; sensor acquisition optimization is achieved through a data compression algorithm (such as Huffman coding). For example, the original data value is replaced with the corresponding Huffman code and the data of each block is compressed by Huffman coding and then sent; specifically, ventilator aging detection is achieved by detecting the operating status of the ventilator through a sensor. For example, the ventilator heat dissipation function is detected by a temperature sensor deployed on the ventilator heat sink, and whether the operating temperature of the ventilator in the working state exceeds the set safety temperature range (generally 70 degrees Celsius to 75 degrees Celsius) is continuously monitored; by combining the preset aging detection threshold range for judgment, the accuracy of the sensor aging detection compliance judgment is improved.

[0067] like Figure 4 As shown, it is a structural diagram of a nursing machine safety control system for thoracic surgery provided by an embodiment of the present application. The nursing machine safety control system for thoracic surgery provided by an embodiment of the present application includes: a supply control judgment module, a connection detection judgment module and an aging detection judgment module; the supply control judgment module is used to evaluate the stability of the ventilator gas flow supply control according to the gas flow control data to obtain a supply control evaluation value, and judge whether to perform ventilator connection leakage detection based on the supply control evaluation value. The supply control evaluation value is used to quantitatively evaluate the stability of the ventilator gas flow supply control; the connection detection judgment module is used to evaluate the result of the ventilator connection leakage detection if the ventilator connection leakage detection is performed, and judge whether to perform sensor aging detection based on the evaluation result; the aging detection judgment module is used to evaluate the compliance degree of the sensor aging detection if the sensor aging detection is performed, and judge whether to perform sensor monitoring optimization.

[0068] In this embodiment, by acquiring gas flow data, the stability of the ventilator gas flow supply control is evaluated, and whether to perform ventilator connection leakage detection is determined by judging the stability of the ventilator gas flow supply control. Based on the evaluation results, it is determined whether to perform sensor aging detection and monitoring optimization, thereby achieving improvement in the accuracy of ventilator gas flow supply control and defect detection control.

[0069] Among them, an embodiment of the present application also provides a nursing machine, which executes a nursing machine safety control method for thoracic surgery, including a ventilator, a data acquisition device, a storage device and a processing device; the data acquisition device is used to collect gas flow control data, and the data acquisition device includes an oxygen flow sensor and a pressure sensor; the storage device is used to store gas flow control data; the processing device is used to analyze the stability of the ventilator gas flow supply control according to the data stored in the storage device and determine whether to perform ventilator connection leakage detection and sensor aging detection.

[0070] In summary, the embodiment of the present application evaluates the stability of the ventilator gas flow supply control to determine whether to perform ventilator connection leakage detection, then evaluates the results of the ventilator connection leakage detection, and determines whether to perform sensor aging detection based on the evaluation results, and finally evaluates the compliance of the sensor aging detection to determine whether to perform sensor monitoring optimization, thereby realizing the analysis and judgment of the stability of the ventilator gas flow supply control, and further realizing the improvement of the defect detection control accuracy of the ventilator for thoracic surgery, effectively solving the problem of low defect detection control accuracy of the ventilator for thoracic surgery in the prior art.

[0071] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0072] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0073] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0074] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0075] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0076] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A safety control method for a nursing machine used in thoracic surgery, characterized in that: The following steps are involved: S1, evaluating the stability of the ventilator gas flow supply control according to the gas flow control data to obtain a supply control evaluation value, and determining whether to perform a ventilator connection leak test based on the supply control evaluation value, wherein the supply control evaluation value is used to quantitatively evaluate the stability of the ventilator gas flow supply control; S2, if a ventilator connection leak test is performed, evaluating the result of the ventilator connection leak test, and determining whether to perform a sensor aging test based on the evaluation result; S3: If the sensor aging test is performed, the compliance degree of the sensor aging test is evaluated to determine whether to perform sensor monitoring optimization.

2. A safety control method for a nursing machine for thoracic surgery according to claim 1, characterized in that: The specific steps of evaluating the stability of the ventilator gas flow supply control according to the gas flow control data to obtain the supply control evaluation value are as follows: Performing statistical analysis on the oxygen flow measurement values obtained within a preset time period to obtain an average oxygen flow rate, and obtaining an oxygen fluctuation assessment value based on the oxygen flow measurement values and the average oxygen flow rate; Obtaining an airway pressure deviation within a preset time period, the airway pressure deviation including a maximum airway pressure deviation and a minimum airway pressure deviation, and obtaining a pressure fluctuation amplitude based on the airway pressure deviation, the pressure fluctuation amplitude being the difference between the maximum airway pressure deviation and the minimum airway pressure deviation; Determine whether the obtained pressure fluctuation amplitude is within the pressure fluctuation amplitude reference range obtained from the preset database. If so, combine the supply control related data with the supply reference data obtained from the preset database to obtain a supply control evaluation value. Otherwise, record the supply control evaluation value as 0. The supply control related data includes oxygen flow measurement value, oxygen flow average value, oxygen flow maximum deviation and pressure fluctuation amplitude; The supplied reference data includes a flow fluctuation assessment weight, a flow deviation assessment weight, a pressure control assessment weight, and a reference maximum pressure fluctuation amplitude.

3. A safety control method for a nursing machine for thoracic surgery according to claim 1, characterized in that: The specific process of determining whether to perform ventilator connection leakage detection based on the supply control evaluation value is as follows: comparing the obtained supply control evaluation value with a preset supply control threshold range obtained from a preset database; If the supply control evaluation value is within the preset supply control threshold range obtained from the preset database, then the ventilator connection leak detection is not performed, and the supply control evaluation value is continuously monitored to see if it is within the preset supply control threshold range; If the supply control evaluation value is not within a preset supply control threshold range obtained from a preset database, performing a ventilator connection leak test and simultaneously evaluating the result of the ventilator connection leak test to obtain a connection test evaluation value; The connection detection evaluation value is used to quantitatively evaluate the probability of a connection leak in the ventilator.

4. A safety control method for a nursing machine for thoracic surgery as claimed in claim 3, characterized in that: The specific steps of evaluating the result of the ventilator connection leakage detection to obtain the connection detection evaluation value are as follows: Obtain connection test related evaluation data after performing ventilator connection leak detection; The connection detection related evaluation data includes the input end oxygen flow rate, the output end oxygen flow rate, the input end pressure, the output end pressure and the supply control evaluation value; Determine whether the obtained input-output difference values are not greater than the corresponding reference input-output difference values. If so, obtain a connection detection evaluation value based on the connection detection related data and the connection detection reference data obtained from the preset database. Otherwise, record the connection detection evaluation value as 1. The input-output difference value includes the input-output oxygen flow difference and the input-output pressure difference. The input-output oxygen flow difference is the difference between the input end oxygen flow and the output end oxygen flow. The input-output pressure difference is the difference between the input end pressure and the output end pressure. The reference input-output difference value includes a reference maximum oxygen flow difference value and a reference maximum pressure difference value; The connection detection reference data includes a flow detection evaluation weight, a pressure detection evaluation weight, and a detection airflow supply evaluation weight.

5. A safety control method for a nursing machine for thoracic surgery as claimed in claim 3, characterized in that: The specific process of determining whether to perform sensor aging detection based on the evaluation results is as follows: Comparing the obtained connection detection evaluation value with a preset detection compliance threshold range obtained from a preset database; If the connection detection effect evaluation value is within the preset detection compliance threshold range obtained from the preset database, the sensor aging test will not be performed, and the preset personnel will be reminded to optimize the ventilator connection leakage; If the connection detection effect evaluation value is not within the preset detection compliance threshold range obtained from the preset database, the sensor aging detection is performed, and the compliance degree of the sensor aging detection is evaluated to obtain an aging detection evaluation value.

6. A safety control method for a nursing machine for thoracic surgery as claimed in claim 5, characterized in that: The specific steps for evaluating the compliance level of the sensor aging test are as follows: Obtaining aging detection related data after performing sensor aging detection; The aging detection related data includes oxygen concentration measurement value, sensor measurement response delay, sensor working average voltage, sensor working average current, total sensor working time and supply control evaluation value; Combining the aging detection related data with the aging detection reference data obtained from a preset database to obtain an aging detection evaluation value; The aging detection reference data includes an aging environment correction factor, an oxygen concentration reference value, an oxygen concentration drift threshold, a reference maximum response delay, a reference maximum operating energy consumption, a sensor measurement evaluation weight, a sensor response evaluation weight, a sensor energy consumption evaluation weight, and an aging detection airflow supply impact weight; The aging detection evaluation value is used to quantitatively evaluate the compliance degree of the sensor aging detection.

7. A safety control method for a nursing machine for thoracic surgery according to claim 6, characterized in that: The specific process of determining whether to perform sensor monitoring optimization is as follows: comparing the obtained aging detection evaluation value with a preset aging detection threshold range obtained from a preset database; If the aging detection evaluation value is within a preset aging detection threshold range obtained from a preset database, sensor monitoring optimization is performed; If the aging detection evaluation value is not within the preset aging detection threshold range obtained from the preset database, the sensor monitoring optimization is not performed, and the preset personnel are reminded to perform ventilator aging detection.

8. A safety control method for a nursing machine for thoracic surgery as claimed in claim 7, characterized in that: The sensor monitoring optimization includes sensor adaptive calibration and sensor acquisition optimization; The sensor adaptive calibration is used to achieve sensor output calibration through a sensor adaptive calibration algorithm; The sensor acquisition optimization is used to optimize the sensor data acquisition process.

9. A safety control system for a nursing machine used in thoracic surgery, characterized in that: include: Supply control judgment module, connection detection judgment module and aging detection judgment module; The supply control judgment module is used to evaluate the stability of the ventilator gas flow supply control according to the gas flow control data to obtain a supply control evaluation value, and determine whether to perform ventilator connection leakage detection based on the supply control evaluation value, wherein the supply control evaluation value is used to quantitatively evaluate the stability of the ventilator gas flow supply control; The connection detection judgment module is used to evaluate the result of the ventilator connection leakage detection if the ventilator connection leakage detection is performed, and determine whether to perform the sensor aging detection based on the evaluation result; The aging detection judgment module is used to evaluate the compliance degree of the sensor aging detection if the sensor aging detection is performed, and determine whether to perform sensor monitoring optimization.

10. A nursing machine, the nursing machine executing the nursing machine safety control method for thoracic surgery according to any one of claims 1 to 8, comprising a ventilator, a data acquisition device, a storage device, and a processing device; The data acquisition device is used to collect gas flow control data, and the data acquisition device includes an oxygen flow sensor and a pressure sensor; The storage device is used to store gas flow control data; The processing device is used to analyze the stability of the ventilator gas flow supply control according to the data stored in the storage device and determine whether to perform ventilator connection leakage detection and sensor aging detection.

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