Turbine anesthetic machine flow rate calibration method and circuit

Abstract

The application relates to a turbine anesthesia machine flow rate calibration method and circuit, the calibration method comprising the following steps: short-circuiting an inhalation branch and an exhalation branch to form a corresponding closed-loop calibration gas path; collecting a plurality of zero-flow AD sampling values in a zero-flow state; outputting corresponding target calibration flow rates according to a preset flow rate sequence; continuously detecting based on an oxygen therapy flow rate sensor to obtain a flow rate parameter sequence corresponding to the target calibration flow rates; performing stability determination on the current output flow rate parameter sequence; determining whether all the target calibration flow rates have been output; performing error analysis and processing on the inhalation flow rate sensor and the exhalation flow rate sensor according to the flow rate sampling data to generate corresponding correction parameters; and constructing a corresponding AD-FLOW calibration curve according to the flow rate sampling data and the correction parameters, storing the AD-FLOW calibration curve, reducing maintenance complexity and manual calibration cost, and improving the on-site maintenance efficiency of the anesthesia machine and the clinical use reliability.

Description

Technical Field

[0001] This application relates to the technical field of flow rate calibration for a turbine anesthesia machine, and in particular to a flow rate calibration method and circuit for a turbine anesthesia machine. Background Technology

[0002] Currently, in existing anesthesia machine systems, flow sensors are typically installed inside the breathing circuit to achieve real-time monitoring of the patient's inspiratory and expiratory flow rates. Since the breathing circuit is a component that directly contacts the patient's respiratory gases, it requires frequent high-temperature sterilization to meet medical and health requirements. However, under prolonged high-temperature sterilization, flow sensors are prone to plastic deformation of their internal structure due to thermal stress, material aging, or improper assembly / disassembly. This leads to decreased flow detection accuracy and drift in flow output characteristics, ultimately causing deviations in the anesthesia machine's flow detection results. In existing technologies, when flow sensors drift or err, external professional calibration equipment is usually required for recalibration. This calibration process is complex, requires specialized maintenance personnel, and is costly, failing to meet the needs of rapid maintenance and on-site self-calibration for anesthesia machines. Summary of the Invention

[0003] To address the problem that existing anesthesia machine flow sensors are prone to flow drift after high-temperature sterilization and require cumbersome calibration using external specialized equipment, this application provides a flow rate calibration method and circuit for a turbine anesthesia machine.

[0004] A flow rate calibration method for a turbine anesthesia machine is applied to an oxygen therapy system of a turbine anesthesia machine. The oxygen therapy system includes an inspiratory branch and an expiratory branch, and oxygen therapy branches respectively connected to the inspiratory and expiratory branches. Each oxygen therapy branch is equipped with an oxygen therapy valve and an oxygen therapy flow sensor. The inspiratory branch is equipped with an inspiratory flow sensor, and the expiratory branch is equipped with an expiratory flow sensor. The calibration method includes: The inspiratory branch and the expiratory branch are short-circuited to form a corresponding closed-loop calibration airway. Close the oxygen therapy valve, collect multiple zero-flow AD sampling values ​​under zero-flow conditions, determine the corresponding zero-flow AD value based on the multiple zero-flow AD sampling values, determine whether there is zero-point drift based on the zero-flow AD value, and if there is zero-point drift, generate the corresponding zero-point compensation parameter based on the multiple zero-flow AD sampling values ​​and the zero-flow AD value. The oxygen therapy valve is opened and the target calibration flow is output according to the preset flow sequence. The oxygen therapy flow sensor is continuously detected to obtain the flow parameter sequence corresponding to the target calibration flow. The stability of the current output flow parameter sequence is determined. When the determination result corresponding to the stability determination is stable, the corresponding flow sampling data is collected. The flow sampling data includes at least the inspiratory AD value and the expiratory AD value. If there is zero drift, the flow sampling data is compensated and corrected based on the zero compensation parameter. Determine whether all target calibration flow rates have been output. If not, control the oxygen therapy valve to re-output the next target calibration flow rate in the preset flow rate sequence to obtain the corresponding flow rate sampling data. Based on the various flow sampling data, error analysis is performed on the inspiratory flow sensor and the expiratory flow sensor to generate corresponding correction parameters; Based on the various flow sampling data and correction parameters, the corresponding AD-FLOW calibration curves are constructed and stored as flow calibration parameters for the inspiratory flow sensor and expiratory flow sensor.

[0005] By adopting the above technical solution, a closed-loop calibration airway is formed by the inspiratory and expiratory branches, and the target calibration flow rate is output through the oxygen therapy branch. This enables the self-calibration of the inspiratory and expiratory flow sensors without the need for external professional calibration equipment. At the same time, by combining zero-point drift detection, stability assessment, error analysis, and AD-FLOW calibration curve construction, the accuracy of flow detection and calibration consistency are improved, and the impact of sensor drift caused by high-temperature sterilization on the flow detection results of the anesthesia machine is reduced.

[0006] Preferably, the step of generating the corresponding zero-point compensation parameter based on the zero-flow AD value includes: Based on the zero-flow AD value, determine the corresponding current zero-point offset; Based on the variation range between each zero-flow AD sampling value, the corresponding zero-point fluctuation parameter is generated; Obtain the historical zero-point offset within the historical calibration period, and generate the corresponding zero-point drift trend parameters based on the historical zero-point offset and the current zero-point offset; Based on the zero-point offset, zero-point fluctuation parameters, and zero-point drift trend parameters, a corresponding comprehensive zero-point characterization value is generated. The zero-point comprehensive characterization value is matched with the preset drift level range to determine the corresponding zero-point drift level; Based on the zero-point drift level, obtain the corresponding zero-point compensation weight parameters; The zero-point offset, zero-point fluctuation parameters, zero-point drift trend parameters, and zero-point compensation weight parameters are fused and calculated to generate the corresponding zero-point compensation parameters.

[0007] By adopting the above technical solution, a comprehensive zero-point characterization value is generated based on the zero-flow AD value, zero-point fluctuation parameters, and zero-point drift trend parameters. Combined with the zero-point drift level and zero-point compensation weight parameters, corresponding zero-point compensation parameters are generated. This can simultaneously characterize the degree of zero-point offset, the dynamic fluctuation of zero-point, and the long-term drift trend, thereby improving the accuracy of zero-point drift analysis and avoiding the problem of increased compensation error caused by single zero-point compensation.

[0008] Preferably, the step of determining the stability of the current output flow parameter sequence includes: Based on the flow parameter sequence, the corresponding real-time flow deviation value is obtained. The real-time flow deviation value is used to characterize the difference between the current output flow and the target calibration flow. Based on the real-time traffic deviation value, obtain the corresponding traffic deviation change sequence; Based on the flow deviation change sequence, obtain the corresponding flow convergence trend parameters; Based on the flow convergence trend parameter, determine whether the current output flow has entered the target flow convergence state; When the current output flow enters the target flow convergence state, a corresponding stability judgment result is generated.

[0009] By adopting the above technical solution, by generating a flow deviation change sequence based on the real-time flow deviation value and further obtaining the flow convergence trend parameter, it is possible to analyze the approximation state of the current output flow towards the target calibration flow, thereby avoiding flow sampling before the target flow has stabilized and improving the reliability and stability of subsequent flow sampling data.

[0010] Preferably, the step of compensating and correcting the flow sampling data based on the zero-point compensation parameter includes: Based on the flow convergence trend parameter, the convergence direction and convergence remainder of the current output flow relative to the target calibration flow are obtained. The convergence remainder is used to characterize the flow deviation that has not yet been eliminated between the current output flow and the target calibration flow. Based on the convergence direction and the remaining convergence amount, the dynamic deviation component in the flow sampling data is determined. The dynamic deviation component is used to characterize the sampling deviation introduced into the inspiratory AD value and expiratory AD value because the current output flow has not yet fully converged to the target calibration flow. Based on the zero-point compensation parameters and dynamic deviation components, the corresponding effective zero-point compensation amount is generated; Based on the effective zero-point compensation, the inspiratory AD value and expiratory AD value in the flow sampling data are subtracted and corrected to generate compensated and corrected flow sampling data.

[0011] By adopting the above technical solution, the convergence direction and remaining amount of convergence are determined based on the flow convergence trend parameter, and an effective zero-point compensation amount is generated by combining the zero-point compensation parameter. This allows for the differentiation and processing of transient deviations introduced during the dynamic convergence of the target flow rate from sensor zero-point drift, thereby improving the compensation accuracy of inspiratory AD values ​​and expiratory AD values ​​and reducing the impact of dynamic airflow fluctuations on calibration results.

[0012] Preferably, the steps for generating the preset traffic sequence include: Determine the preset candidate calibration flow ranges and obtain the historical flow deviation data and historical zero drift data corresponding to each candidate calibration flow range; Based on historical flow deviation data and historical zero-point drift data, the corresponding candidate calibration flow ranges are identified as the key calibration flow ranges; Based on the key calibration flow range, determine the corresponding target flow sampling density; Based on the target flow sampling density, multiple target calibration flow points are generated within the key calibration flow range; Based on each target calibration flow point and the target calibration flow points corresponding to the remaining candidate target calibration flow intervals, a corresponding preset flow sequence is constructed.

[0013] By adopting the above technical solution, the key calibration flow range is determined by combining historical flow deviation data and historical zero drift data, and the corresponding target flow sampling density and target calibration flow point are generated based on the key calibration flow range. This can improve the calibration accuracy of the key flow range, reduce redundant sampling in non-key flow ranges, and improve the overall calibration efficiency.

[0014] Preferably, the step of performing error analysis processing on the inspiratory flow sensor and expiratory flow sensor based on each flow sampling data to generate corresponding correction parameters includes: Based on each flow sampling data, obtain the corresponding flow deviation change sequence; Based on the flow deviation change sequence, the corresponding error mode type is determined. The error mode type includes at least the proportional drift mode and the local nonlinear drift mode. Based on the error mode type, segmented deviation correction processing is performed on each flow sampling data to generate corresponding correction parameters.

[0015] By adopting the above technical solution, the error mode type is determined based on the flow deviation change sequence, and segmented deviation correction processing is performed for the proportional drift mode and the local nonlinear drift mode. This enables targeted correction of the error characteristics under different flow ranges, thereby improving the calibration consistency and curve fitting accuracy of the flow sensor in different flow ranges.

[0016] Preferably, the step of constructing the corresponding AD-FLOW calibration curve based on each flow sampling data and correction parameters includes: Based on the traffic sampling data, multiple target traffic ranges are determined. Based on the correction parameters corresponding to each target flow interval, the flow sampling data in each target flow interval is processed to construct a segmented curve to generate the corresponding segmented AD-FLOW curve. Based on the segmented AD-FLOW curves, the corresponding target AD-FLOW calibration curves are constructed.

[0017] By adopting the above technical solution, and by constructing piecewise curves based on correction parameters corresponding to different target flow ranges, and generating piecewise AD-FLOW curves, the accuracy of the correspondence between AD values ​​and flow values ​​under different flow ranges can be improved, and the fitting error of local flow ranges caused by the single overall curve construction method can be reduced.

[0018] A flow rate calibration circuit for a turbine anesthesia machine, using a flow rate calibration method for a turbine anesthesia machine, the calibration circuit includes a control module, an oxygen therapy output control module, an oxygen therapy flow acquisition module, an inspiratory flow acquisition module, an expiratory flow acquisition module, and a calibration storage module; The signal output terminal of the oxygen therapy output control module is connected to the oxygen therapy valve and is used to control the oxygen therapy valve to output the corresponding target calibration flow rate; The signal input terminal of the oxygen therapy flow acquisition module is connected to the oxygen therapy flow sensor, and the signal output terminal of the oxygen therapy flow acquisition module is connected to the flow detection input terminal of the control module. The signal input terminal of the inspiratory flow acquisition module is connected to the inspiratory flow sensor, and the signal output terminal of the inspiratory flow acquisition module is connected to the inspiratory sampling input terminal of the control module. The signal input terminal of the expiratory flow acquisition module is connected to the expiratory flow sensor, and the signal output terminal of the expiratory flow acquisition module is connected to the expiratory sampling input terminal of the control module. The calibration storage module is connected to the control module, the inspiratory flow acquisition module, and the expiratory flow acquisition module, respectively. The control module determines the stability based on the flow parameter sequence output by the oxygen therapy flow acquisition module, and constructs the corresponding AD-FLOW calibration curve based on the flow sampling data output by the inspiratory flow acquisition module and the expiratory flow acquisition module, so as to store the AD-FLOW calibration curve and the corresponding correction parameters in the calibration storage module.

[0019] By adopting the above technical solution and setting up an oxygen therapy output control module, an oxygen therapy flow acquisition module, an inspiratory flow acquisition module, an expiratory flow acquisition module, and a calibration storage module, a complete flow calibration control link can be formed. This enables coordinated control among functions such as target calibration flow output, flow sampling, stability determination, and AD-FLOW calibration curve storage, thereby improving the automation level of the overall calibration process.

[0020] Preferably, the oxygen therapy output control module includes a first input filter unit, a driver chip U9, MOSFETs Q13, Q22, and Q23, a diode D12, a sampling resistor group, and an operational amplifier U8B. The power input terminal of the first input filter unit is connected to an external 12V power supply terminal, and the power output terminal of the first input filter unit is connected to the power input terminal of the driver chip U9. The common node between the power output terminal of the first input filter unit and the power input terminal of the driver chip U9 is connected to the first conducting terminal of MOSFET Q13. The second conducting terminal of MOSFET Q13 is grounded, the controlled terminal of MOSFET Q13 is connected to the drive control signal output terminal of the control module, and the drive signal output terminal of the driver chip U9 is connected to the control module. The positive input terminal of the oxygen therapy valve and the negative terminal of diode D12 are connected respectively. MOSFETs Q22 and Q23 form a parallel conduction network. The anode of diode D12 is connected to the first end of the parallel conduction network. The second end of the parallel conduction network is connected to the first end of the sampling resistor group. The second end of the sampling resistor group is grounded. The common node between the anode of diode D12 and the first end of the parallel conduction network is connected to the negative input terminal of the oxygen therapy valve. The common node between the second end of the parallel conduction network and the first end of the sampling resistor group is connected to the non-inverting input terminal of operational amplifier U8B. The inverting input terminal of operational amplifier U8B is grounded. The output terminal of operational amplifier U8B is connected to the current detection signal input terminal of the control module.

[0021] By adopting the above technical solution, and by using the driver chip U9, MOSFET Q13, MOSFET Q22, MOSFET Q23, sampling resistor group and operational amplifier U8B to construct an oxygen therapy output control module, the target calibration flow output state of the oxygen therapy valve can be stably controlled, and the corresponding drive current of the oxygen therapy valve can be detected in real time, thereby improving the stability of oxygen therapy flow output and the accuracy of target calibration flow control.

[0022] Preferably, the oxygen therapy flow acquisition module includes an interface J10, a second input filter unit, an operational amplifier U36A, and an operational amplifier U36B. The signal input terminal of interface J10 is connected to the oxygen therapy flow sensor, and the signal output terminal of interface J10 is connected to the signal input terminal of the second input filter unit. The signal output terminal of the second input filter unit is connected to the non-inverting input terminal of operational amplifier U36A. The output terminal of operational amplifier U36A is connected to the inverting input terminal of operational amplifier U36A and the flow detection input terminal of the control module, respectively. The common node between the signal output terminal of the second input filter unit and the non-inverting input terminal of operational amplifier U36A is connected to the output terminal and the non-inverting input terminal of operational amplifier U36B, respectively. The inverting input terminal of operational amplifier U36B is connected to the 3.3V reference bias voltage port.

[0023] By adopting the above technical solution, and by constructing an oxygen therapy flow acquisition module using interface J10, the second input filtering unit, operational amplifier U36A, and operational amplifier U36B, the flow detection signal output by the oxygen therapy flow sensor can be filtered, buffered, and biased, thereby improving the stability and anti-interference capability of the flow acquisition signal and improving the detection accuracy of the oxygen therapy flow parameters by the control module.

[0024] In summary, this application includes at least one of the following beneficial technical effects: This application constructs a closed-loop calibration air path based on the existing oxygen therapy system of a turbine anesthesia machine. It utilizes the target calibration flow rate output from the oxygen therapy branch as an internal standard flow source, allowing the oxygen therapy airflow to sequentially pass through the flow sensors in the inspiratory and expiratory branches, thereby achieving online self-calibration of the inspiratory and expiratory flow sensors. During calibration, the system first analyzes the current zero-point drift state of the flow sensor using multiple zero-flow AD sampling values ​​at zero flow conditions. It then generates corresponding zero-point compensation parameters based on zero-point fluctuations and historical drift trends to preemptively eliminate the basic zero-point drift error caused by long-term high-temperature sterilization, thermal expansion and contraction, and material aging. Subsequently, the system controls the oxygen therapy valve to output multiple target calibration flow rates sequentially according to a preset flow sequence. The system continuously monitors the actual output flow rate using the oxygen therapy flow sensor, determining whether the current output flow rate has truly entered the target flow convergence state through real-time flow deviation values ​​and flow convergence trend parameters. This avoids direct flow sampling before the airflow stabilizes, effectively reducing airflow inertia. The system addresses the impact of valve response delay and pipeline pressure fluctuations on sampling results. After stability confirmation, the system further collects inspiratory and expiratory AD values ​​and compensates for the sampling data using dynamic deviation components and zero-point compensation parameters. This eliminates the static zero drift effect and suppresses transient dynamic errors introduced during target flow convergence. Subsequently, based on the flow deviation variation patterns in different flow ranges, the system analyzes error modes such as proportional drift and local nonlinear drift, generating corresponding correction parameters for different error modes. A segmented AD-FLOW calibration curve is constructed to improve curve fitting accuracy and flow detection consistency across different flow ranges. Through these techniques, this application enables rapid self-calibration of the flow sensor without external professional calibration equipment. It also effectively improves the accuracy and long-term stability of flow detection in low, medium, and high flow ranges for anesthesia machines, reducing maintenance complexity and manual calibration costs, and enhancing on-site maintenance efficiency and clinical reliability. Attached Figure Description

[0025] Figure 1 This is a flowchart of a flow rate calibration method for a turbine anesthesia machine according to one embodiment of this application; Figure 2 This is a schematic diagram of the interface of the AD-FLOW calibration curve in a flow rate calibration method for a turbine anesthesia machine according to an embodiment of this application; Figure 3 This is a schematic diagram of the closed-loop calibration gas path in a flow rate calibration method for a turbine anesthesia machine according to an embodiment of this application. Figure 4 This is a simplified flowchart illustrating a flow rate calibration method for a turbine anesthesia machine according to one embodiment of this application; Figure 5 This is a partial circuit diagram of the oxygen therapy output control module in a flow rate calibration circuit of a turbine anesthesia machine according to one embodiment of this application; Figure 6 This is a partial circuit diagram of the oxygen therapy flow acquisition module in a flow rate calibration circuit of a turbine anesthesia machine according to one embodiment of this application. Detailed Implementation

[0026] The present application will be further described in detail below with reference to the accompanying drawings.

[0027] In one embodiment, such as Figure 1-3 As shown, this application discloses a flow rate calibration method for a turbine anesthesia machine, applied to an oxygen therapy system for a turbine anesthesia machine. The turbine anesthesia machine oxygen therapy system includes an inspiratory branch and an expiratory branch, and oxygen therapy branches respectively connected to the inspiratory and expiratory branches. The oxygen therapy branches are equipped with an oxygen therapy valve and an oxygen therapy flow sensor. The inspiratory branch is equipped with an inspiratory flow sensor, and the expiratory branch is equipped with an expiratory flow sensor. The calibration method includes: S10. Short-circuit the inspiratory branch and the expiratory branch to form a corresponding closed-loop calibration airway. The inspiratory branch delivers the target respiratory gas corresponding to the patient's inspiratory phase. An inspiratory flow sensor is installed inside the inspiratory branch to detect the gas flow rate during the inspiratory phase. The expiratory branch delivers the exhaled gas corresponding to the patient's expiratory phase. An expiratory flow sensor is installed inside the expiratory branch to detect the gas flow rate during the expiratory phase. The oxygen therapy branch outputs oxygen therapy gas to the patient's breathing circuit. The oxygen therapy valve in the oxygen therapy branch is used to adjust the output flow rate of the oxygen therapy gas, and the oxygen therapy flow sensor is used to detect the actual output flow rate in the oxygen therapy branch. The closed-loop calibration airway refers to connecting the inspiratory branch and the expiratory branch so that the airflow output from the oxygen therapy branch can flow sequentially through the inspiratory flow sensor and the expiratory flow sensor before returning to the corresponding circulating flow airway structure of the oxygen therapy branch, thereby forming an internal closed-loop calibration environment without the need for external calibration equipment.

[0028] S20. Close the oxygen therapy valve and collect multiple zero-flow AD sampling values ​​under zero-flow conditions. Determine the corresponding zero-flow AD value based on the multiple zero-flow AD sampling values. Determine if zero-point drift exists based on the zero-flow AD value. If zero-point drift exists, generate the corresponding zero-point compensation parameter based on the multiple zero-flow AD sampling values ​​and the zero-flow AD value. Zero-flow state refers to the static state where there is no airflow inside the airway after the oxygen therapy valve is closed. Zero-flow AD sampling values ​​refer to the digital sampling values ​​obtained by the inspiratory flow sensor and expiratory flow sensor through the analog-to-digital conversion circuit under zero-flow conditions. Multiple zero-flow AD sampling values ​​are used to reflect the sensor output under zero-flow conditions. The dynamic changes of the output signal; the zero-flow AD value is a reference zero-flow digital quantity obtained by averaging or filtering multiple zero-flow AD sample values, used to characterize the output reference of the current flow sensor in the absence of airflow; zero-point drift is used to characterize the deviation of the output value of the flow sensor in the zero-flow state relative to the initial calibration value. This deviation is usually caused by high-temperature sterilization, material aging, or thermal stress deformation; the zero-point compensation parameter is compensation data generated based on the fluctuation of multiple zero-flow AD sample values ​​and the degree of deviation of the zero-flow AD value, used to offset the detection error caused by zero-point drift in subsequent flow sampling processes.

[0029] S30. Open the oxygen therapy valve and output the corresponding target calibration flow according to the preset flow sequence. Continuously detect the flow using the oxygen therapy flow sensor to acquire the flow parameter sequence corresponding to the target calibration flow. Determine the stability of the current output flow parameter sequence. When the stability determination result indicates stability, collect the corresponding flow sampling data. The flow sampling data includes at least the inspiratory AD value and the expiratory AD value. If zero-point drift exists, compensate and correct the flow sampling data based on the zero-point compensation parameter. The preset flow sequence is the flow output order composed of multiple target calibration flow points generated according to the preset calibration strategy, used to make the oxygen therapy valve output calibration airflow of different flow rates sequentially. The target calibration flow refers to the target airflow value that the oxygen therapy valve needs to output during the current calibration phase. Flow parameter sequence. It is a data set composed of multiple flow detection values ​​continuously collected by the oxygen therapy flow sensor during the target calibration flow output process, used to characterize the dynamic change process of the current output airflow; stability determination is to analyze and process the flow change trend in the flow parameter sequence to determine whether the current output airflow has entered a stable convergence state; flow sampling data is the digital flow detection data collected by the inspiratory flow sensor and expiratory flow sensor after the target calibration flow has stabilized; inspiratory AD value is the analog-to-digital conversion output value corresponding to the inspiratory flow sensor, and expiratory AD value is the analog-to-digital conversion output value corresponding to the expiratory flow sensor; compensation correction refers to the use of zero-point compensation parameters to subtract the zero-point offset component in the inspiratory AD value and expiratory AD value, thereby improving the data accuracy of subsequent curve construction.

[0030] S40. Determine whether all target calibration flows have been output. If not, control the oxygen therapy valve to re-output the next target calibration flow in the preset flow sequence to obtain another flow sampling data. The next target calibration flow is another calibration flow point in the preset flow sequence that is located after the current target calibration flow. It is used to continue to obtain the corresponding flow sampling data under different flow ranges. The other flow sampling data refers to the inspiratory AD value and expiratory AD value data that are re-collected after the new target calibration flow stabilizes. It is used to expand the flow range corresponding to the subsequent AD-FLOW calibration curve.

[0031] S50. Based on the various flow sampling data, perform error analysis processing on the inspiratory flow sensor and expiratory flow sensor to generate corresponding correction parameters. The error analysis processing involves analyzing the deviation changes of the flow sampling data under different target calibration flow rates to identify the error variation patterns of the flow sensor in different flow ranges. The correction parameters are compensation data generated based on the error analysis results, used to correct proportional drift error and local nonlinear drift error. The proportional drift error refers to the error pattern in which the flow error increases synchronously with the increase of flow rate, and the local nonlinear drift error refers to the error pattern in which a significant abnormal deviation occurs in a certain local flow range.

[0032] S60. Based on the various flow sampling data and correction parameters, construct the corresponding AD-FLOW calibration curve and store the AD-FLOW calibration curve as the flow calibration parameters for the inspiratory flow sensor and expiratory flow sensor. The AD-FLOW calibration curve is a calibration mapping curve used to characterize the correspondence between the AD value output by the flow sensor and the actual flow value. AD represents the digital quantity after analog-to-digital conversion, and FLOW represents the corresponding actual airflow flow value. The construction of the AD-FLOW calibration curve is based on the flow sampling data and correction parameters corresponding to different target calibration flow rates. The correspondence between AD values ​​and flow values ​​in different flow ranges is constructed to form a complete flow calibration mapping relationship. The flow calibration parameters are a set of parameters used for flow conversion and error compensation during the subsequent normal operation of the anesthesia machine.

[0033] The working principle is as follows: First, the system forms a closed-loop calibration airway by integrating the inspiratory and expiratory branches, allowing the calibration airflow from the oxygen therapy branch to flow sequentially through the inspiratory and expiratory flow sensors, thus creating a self-circulating calibration environment within the anesthesia machine without the need for external equipment. Then, with the oxygen therapy valve closed, the system acquires multiple zero-flow AD sampling values. By analyzing the output offset and fluctuations under zero-flow conditions, it determines whether the current flow sensor has zero-point drift and generates corresponding zero-point compensation parameters. Next, the system controls the oxygen therapy valve to output target calibration flow rates of different magnitudes in a preset flow sequence, and continuously monitors the current output airflow through the oxygen therapy flow sensor. After confirming that the current airflow has entered a stable convergence state through stability determination, the system samples the inspiratory and expiratory flow sensors to avoid the impact of dynamic airflow fluctuations on the sampling results. During the sampling process, the system further compensates and corrects the zero drift error in the sampling data using zero-point compensation parameters. Then, it generates corresponding correction parameters based on the error variation patterns under different flow ranges and constructs an AD-FLOW calibration curve based on the corrected flow sampling data. Finally, it forms internal flow calibration parameters suitable for the inspiratory and expiratory flow sensors to improve the flow detection accuracy and long-term stability of the anesthesia machine under long-term high-temperature sterilization environments.

[0034] Furthermore, the step of generating the corresponding zero-point compensation parameters based on the zero-flow AD value includes: S201. Based on the zero-flow AD value, determine the corresponding current zero-point offset; S202. Generate corresponding zero-point fluctuation parameters based on the variation amplitude between each zero-flow AD sampling value; S203. Obtain the historical zero-point offset within the historical calibration period, and generate the corresponding zero-point drift trend parameters based on the historical zero-point offset and the current zero-point offset. S204. Based on the zero-point offset, zero-point fluctuation parameters, and zero-point drift trend parameters, generate the corresponding comprehensive zero-point characterization value; S205. Match the zero-point comprehensive characterization value with the preset drift level range to determine the corresponding zero-point drift level; S206. Based on the zero-point drift level, obtain the corresponding zero-point compensation weight parameters; S207. Based on the zero-point offset, zero-point fluctuation parameters, zero-point drift trend parameters, and zero-point compensation weight parameters, perform fusion calculation to generate the corresponding zero-point compensation parameters.

[0035] In this embodiment, the current zero-point offset is used to characterize the static offset of the current flow sensor relative to the initial calibration reference in the zero-flow state. After the oxygen therapy valve is closed, the system periodically reads the analog-to-digital conversion output values ​​corresponding to the inspiratory flow sensor and the expiratory flow sensor through the control module, and continuously acquires multiple zero-flow AD sampling values ​​based on a preset sampling period. Subsequently, the system performs extreme value filtering on the multiple zero-flow AD sampling values ​​to eliminate abnormal fluctuations caused by transient electromagnetic interference or ADC sampling jitter. Then, it calculates the corresponding average reference value based on the filtered multiple zero-flow AD sampling values ​​and uses this average reference value as the current zero-flow AD value. After that, the system compares the current zero-flow AD value with the standard zero-point AD reference value corresponding to the equipment at the factory stage to determine the current zero-point offset. The current zero-point offset can be used to reflect the overall output offset of the flow sensor after long-term high-temperature sterilization, gas path disassembly and assembly, and material thermal aging.

[0036] In this embodiment, the zero-point fluctuation parameter is used to characterize the dynamic stability of the flow sensor output signal under zero flow conditions. After acquiring multiple zero-flow AD sampling values, the system further analyzes the variation amplitude between each zero-flow AD sampling value. Specifically, the system can acquire the maximum value, minimum value, and variation difference between adjacent sampling values ​​among multiple zero-flow AD sampling values, and generate the corresponding zero-point fluctuation parameter based on the deviation range between the maximum and minimum values. When the zero-point fluctuation parameter is small, it indicates that the current flow sensor output is relatively stable under zero flow conditions. However, when the zero-point fluctuation parameter is large, it indicates that the flow sensor may have thermal drift, increased sampling noise, or decreased stability of internal sensitive elements. Therefore, the zero-point fluctuation parameter can not only reflect the zero-point offset but also the stability of the current zero-point output.

[0037] In this embodiment, the historical zero-point offset is used to characterize the zero-point change of the flow sensor over multiple historical calibration cycles. After each calibration, the system stores the corresponding current zero-point offset in the calibration storage area to form a historical zero-point offset record sequence. Subsequently, in a new calibration cycle, the system reads the historical zero-point offsets corresponding to multiple historical calibration cycles and performs trend analysis on the current zero-point offset and the historical zero-point offset to generate the corresponding zero-point drift trend parameter. The zero-point drift trend parameter is used to characterize the cumulative trend of zero-point drift over time. When the zero-point drift trend parameter continues to increase, it indicates that the flow sensor may have a long-term aging trend or a structural deformation accumulation. When the zero-point drift trend parameter remains stable, it indicates that the overall stability of the current flow sensor is high.

[0038] In this embodiment, the comprehensive zero-point characterization value is used to comprehensively reflect the overall zero-drift state of the current flow sensor. After acquiring the zero-point offset, zero-point fluctuation parameters, and zero-point drift trend parameters, the system performs unified dimensionalization processing on multiple parameters to eliminate dimensional differences between different parameters. Subsequently, the system performs fusion calculation on the zero-point offset, zero-point fluctuation parameters, and zero-point drift trend parameters based on a preset weight ratio to generate the corresponding comprehensive zero-point characterization value. The comprehensive zero-point characterization value can simultaneously reflect the static offset degree, dynamic fluctuation degree, and long-term drift trend. Therefore, compared with the single zero-point offset analysis method, it can more accurately characterize the comprehensive zero-drift state of the current flow sensor.

[0039] In this embodiment, preset drift level ranges are used to classify and manage zero drift states of different degrees. The system pre-establishes multiple different drift level ranges, each corresponding to a different range of zero-point comprehensive characterization values. Then, the system matches the current zero-point comprehensive characterization value with each preset drift level range to determine the zero-point drift level corresponding to the current flow sensor. The zero-point drift level can be used to distinguish between different states such as slight zero drift, moderate zero drift, and severe zero drift, so that the system can adopt different compensation strategies according to different degrees of zero drift.

[0040] In this embodiment, the zero-point compensation weight parameter is used to adjust the compensation ratio corresponding to various drift factors during the zero-point compensation process. After determining the zero-point drift level, the system will obtain the corresponding zero-point compensation weight parameter based on the preset level mapping relationship. When the zero-point drift level is high, the system will increase the compensation ratio corresponding to the zero-point compensation weight parameter to enhance the ability to correct zero-drift errors. When the zero-point drift level is low, the system will decrease the compensation ratio corresponding to the zero-point compensation weight parameter to avoid flow sampling deviation caused by over-compensation.

[0041] In this embodiment, the zero-point compensation parameter is used for zero drift correction processing in the subsequent flow sampling process. After obtaining the zero-point offset, zero-point fluctuation parameter, zero-point drift trend parameter, and zero-point compensation weight parameter, the system performs fusion calculation processing on multiple parameters to generate the final corresponding zero-point compensation parameter. The fusion calculation processing can be implemented by weighted fusion, proportional mapping, or interval correction, so that the zero-point compensation parameter can not only correct the current static zero drift, but also take into account dynamic fluctuation and long-term drift trend, thereby improving the compensation accuracy of the subsequent inspiratory AD value and expiratory AD value.

[0042] Furthermore, the step of determining the stability of the current output flow parameter sequence includes: S3011. Based on the flow parameter sequence, obtain the corresponding real-time flow deviation value. The real-time flow deviation value is used to characterize the difference between the current output flow and the target calibration flow. S3012. Based on the real-time flow deviation value, obtain the corresponding flow deviation change sequence; S3013. Based on the flow deviation change sequence, obtain the corresponding flow convergence trend parameters; S3014. Based on the flow convergence trend parameter, determine whether the current output flow has entered the target flow convergence state; S3015. When the current output flow enters the target flow convergence state, generate the corresponding stability judgment result.

[0043] In this embodiment, the real-time flow deviation value is used to characterize the transient deviation between the current output flow rate and the target calibration flow rate. After the oxygen therapy valve outputs the target calibration flow rate, the system continuously detects the current actual output flow rate through the oxygen therapy flow sensor, and generates the corresponding real-time flow deviation value based on the difference between the current actual output flow rate and the target calibration flow rate. Here, the current actual output flow rate is the actual airflow value detected by the oxygen therapy flow sensor at the current sampling time, while the target calibration flow rate is the target output flow rate value corresponding to the oxygen therapy valve in the current calibration phase. The real-time flow deviation value can reflect the degree of deviation between the current airflow and the target airflow. When the real-time flow deviation value is large, it indicates that the current output airflow has not yet stably converged to the target calibration flow rate.

[0044] In this embodiment, the flow deviation change sequence is used to characterize the dynamic change process of the real-time flow deviation value in the continuous sampling period. After acquiring multiple real-time flow deviation values, the system will arrange the multiple real-time flow deviation values ​​according to the sampling time order to generate the corresponding flow deviation change sequence. The flow deviation change sequence can reflect the changing trend of airflow deviation during the target calibration flow output process. When the flow deviation change sequence shows a continuous decreasing trend, it indicates that the current output flow is gradually approaching the target calibration flow. When the flow deviation change sequence fluctuates continuously or suddenly increases, it indicates that there is still obvious dynamic disturbance in the current airflow.

[0045] In this embodiment, the flow convergence trend parameter is used to characterize the degree of convergence trend of the current output flow towards the target calibration flow. After acquiring the flow deviation change sequence, the system analyzes and processes the change direction, change rate, and continuous change amplitude in the flow deviation change sequence to generate the corresponding flow convergence trend parameter. Among them, the change direction is used to characterize whether the real-time flow deviation value is decreasing or increasing overall, the change rate is used to characterize the speed at which the real-time flow deviation value converges towards the target calibration flow, and the continuous change amplitude is used to characterize the stability of fluctuations during the flow deviation change process. By comprehensively analyzing multiple change characteristics, it can more accurately reflect whether the current output flow has gradually entered a stable convergence state.

[0046] In this embodiment, the target flow convergence state is used to characterize the working state where the current output flow has stably approached the target calibration flow. After acquiring the flow convergence trend parameters, the system compares and analyzes the flow convergence trend parameters with preset convergence judgment conditions. The preset convergence judgment conditions may include conditions such as the real-time flow deviation value being lower than a preset deviation range, the flow deviation change rate being lower than a preset change threshold, and the flow deviation change direction remaining continuously stable. When multiple convergence conditions are met simultaneously, the system determines that the current output flow has entered the target flow convergence state. At this time, it indicates that the target calibration flow output by the oxygen therapy valve has basically stabilized and can meet the accuracy requirements corresponding to the subsequent flow sampling process.

[0047] In this embodiment, the stability determination result is used to characterize whether the current target calibration flow rate meets the stable sampling conditions. When the system determines that the current output flow rate has entered the target flow rate convergence state, it will generate the corresponding stability determination result and use the stability determination result as the triggering basis for subsequent inspiratory AD value and expiratory AD value sampling operations. If the current output flow rate has not entered the target flow rate convergence state, the system will continue to maintain the continuous detection state corresponding to the oxygen therapy flow sensor and continuously update the real-time flow deviation value, flow deviation change sequence and flow convergence trend parameters until the target flow rate convergence conditions are met before performing subsequent flow sampling operations.

[0048] The working principle is as follows: After the oxygen therapy valve outputs the target calibration flow rate, the system uses an oxygen therapy flow sensor to detect the actual output flow rate in real time and generates a corresponding real-time flow deviation value based on the difference between the actual output flow rate and the target calibration flow rate. Subsequently, the system performs time series analysis on multiple real-time flow deviation values ​​to generate a corresponding flow deviation change sequence, reflecting the dynamic changes of the current output flow rate in a continuous sampling period. Then, the system further generates corresponding flow convergence trend parameters based on the direction, rate, and stability of change in the flow deviation change sequence, thereby determining whether the current output flow rate has gradually stabilized and approached the target calibration flow rate. When the system confirms that the current output flow rate has entered the target flow convergence state, it generates a corresponding stability judgment result and triggers subsequent flow sampling operations of the inspiratory and expiratory flow sensors, thereby avoiding flow detection errors caused by sampling before the airflow has stabilized and improving the data stability and calibration accuracy in the overall AD-FLOW calibration curve construction process.

[0049] Furthermore, the step of compensating and correcting the flow sampling data based on the zero-point compensation parameter includes: S3021. Based on the flow convergence trend parameter, obtain the convergence direction and convergence remainder of the current output flow relative to the target calibration flow. The convergence remainder is used to characterize the flow deviation that has not yet been eliminated between the current output flow and the target calibration flow. S3022. Based on the convergence direction and the remaining amount of convergence, determine the dynamic deviation component in the flow sampling data. The dynamic deviation component is used to characterize the sampling deviation introduced into the inspiratory AD value and expiratory AD value because the current output flow has not yet fully converged to the target calibration flow. S3023. Based on the zero-point compensation parameters and dynamic deviation components, generate the corresponding effective zero-point compensation amount; S3024. Based on the effective zero-point compensation, the inspiratory AD value and expiratory AD value in the flow sampling data are subtracted and corrected to generate compensated and corrected flow sampling data.

[0050] In this embodiment, the convergence direction is used to characterize the approach direction of the current output flow relative to the target calibration flow. After acquiring the flow convergence trend parameter, the system further analyzes the change trend of the current output flow in the continuous sampling period. When the current output flow continues to increase towards the target calibration flow, it is determined that the current output flow is in a positive convergence state, while when the current output flow continues to decrease towards the target calibration flow, it is determined that the current output flow is in a negative convergence state. The convergence remainder is used to characterize the remaining flow deviation between the current output flow and the target calibration flow that has not yet been eliminated. The system dynamically estimates the remainder based on the real-time deviation value between the current actual output flow and the target calibration flow, combined with the rate of change of the flow deviation, to determine the remaining degree of convergence between the current output flow and the completely stable state. The smaller the convergence remainder, the closer the current output flow is to the stable output state corresponding to the target calibration flow.

[0051] In this embodiment, the dynamic deviation component is used to characterize the dynamic sampling error introduced into the inspiratory AD value and expiratory AD value due to the current output flow not being fully stable. After obtaining the convergence direction and the remaining convergence amount, the system further analyzes the dynamic change trend of the current output flow during the convergence process. Due to factors such as valve response delay, airflow inertia, and pipeline pressure buffering when the oxygen therapy valve adjusts the target calibration flow, even if the current output flow is basically close to the target calibration flow, the output AD values ​​corresponding to the inspiratory flow sensor and expiratory flow sensor may still contain some dynamic disturbance components. The system will separate the dynamic change components in the current flow sampling data based on the convergence direction and the remaining convergence amount to generate the corresponding dynamic deviation component. The dynamic deviation component is mainly used to reflect the transient sampling error when the current airflow is not fully stable.

[0052] In this embodiment, the effective zero-point compensation amount is used to characterize the target compensation amount that truly participates in zero-drift compensation under the current dynamic airflow state. After acquiring the zero-point compensation parameters and dynamic deviation components, the system performs a combined analysis on the static zero-drift compensation part of the zero-point compensation parameters and the dynamic disturbance part of the dynamic deviation components. Since the zero-point compensation parameters are mainly used to correct the basic zero drift generated after long-term high-temperature sterilization of the flow sensor, and the dynamic deviation components are mainly used to reflect the transient sampling deviation introduced by the current airflow not yet being stable, the system will dynamically correct the zero-point compensation parameters based on the dynamic deviation components to generate the corresponding effective zero-point compensation amount. When the dynamic deviation component is large, the system will reduce the proportion of the zero-point compensation parameters directly participating in compensation to avoid over-compensation when the airflow is not yet fully stable. When the dynamic deviation component is small, it indicates that the current output flow is close to a stable state, and the system will increase the compensation ratio corresponding to the zero-point compensation parameters.

[0053] In this embodiment, the compensated and corrected flow sampling data is used as the target sampling data in subsequent error analysis and AD-FLOW calibration curve construction. After obtaining the effective zero-point compensation amount, the system performs subtraction correction processing on the inspiratory AD value and expiratory AD value in the flow sampling data. The subtraction correction processing refers to synchronously canceling the zero drift component and dynamic disturbance component in the inspiratory AD value and expiratory AD value based on the effective zero-point compensation amount, thereby generating the corresponding compensated and corrected flow sampling data. After the above processing, the compensated and corrected flow sampling data can not only reduce the static deviation caused by zero-point drift, but also reduce the dynamic sampling error generated during the convergence of the target calibration flow, thereby improving the data accuracy in the subsequent flow calibration curve construction process.

[0054] For example, after a turbine anesthesia machine completes high-temperature sterilization, the system begins the flow rate calibration process. At this time, the oxygen therapy valve outputs the target calibration flow rate of 20 L / min according to the preset flow rate sequence, and the oxygen therapy flow sensor continuously detects the current actual output flow rate. In the initial stage, because the oxygen therapy valve has just completed its opening adjustment, the current actual output flow rate does not immediately stabilize at 20 L / min, but instead exhibits a continuous change of 18.2 L / min, 18.9 L / min, 19.4 L / min, 19.7 L / min, and 19.9 L / min. At this time, the system will generate the corresponding value based on the difference between the current actual output flow rate and the target calibration flow rate of 20 L / min. The system calculates real-time flow deviation values, for example, an initial deviation of 1.8 L / min, which gradually decreases to 0.1 L / min. Then, based on multiple real-time flow deviation values, the system generates a corresponding flow deviation change sequence and further analyzes the convergence direction and remaining convergence amount of the current output flow. Since the actual output flow continuously approaches 20 L / min, the system determines that the current output flow is in a positive convergence state, and the current remaining deviation of 0.1 L / min is taken as the remaining convergence amount. Subsequently, the system further analyzes the dynamic disturbances in the current flow sampling data; for example, the inspiratory flow sensor outputs an inspiratory AD value of 2048, and the expiratory flow sensor... The exhaled AD value output by the device is 2060, but since the current output flow rate is not yet fully stable, the system determines that the current inhaled and expiratory AD values ​​still contain dynamic deviation components introduced by airflow inertia and valve response delay. Assume the system obtains the corresponding dynamic deviation component as ±6AD based on the convergence residual analysis. Simultaneously, the system detected zero-point drift in the current flow sensor during the preceding zero-point detection process and has generated corresponding zero-point compensation parameters, for example, a compensation value of 12AD. Subsequently, the system further dynamically corrects the zero-point compensation parameters based on the dynamic deviation component. Since the current dynamic deviation component is still relatively large, the system will not... Instead of directly using 12AD as the final compensation amount, the system suppresses some of the compensation amounts in the zero-point compensation parameters, for example, ultimately generating an effective zero-point compensation amount of 8AD. Subsequently, the system uses this effective zero-point compensation amount to subtract and correct the inspiratory AD value and the expiratory AD value respectively, for example, correcting the inspiratory AD value of 2048 to 2040 and the expiratory AD value of 2060 to 2052, thereby generating the corresponding compensated and corrected flow sampling data. Finally, the system uses the compensated and corrected flow sampling data to participate in subsequent error analysis and AD-FLOW calibration curve construction to improve the sampling stability and flow calibration accuracy throughout the flow rate calibration process.

[0055] Furthermore, the steps for generating the preset traffic sequence include: S01. Determine the preset candidate calibration flow range and obtain the historical flow deviation data and historical zero drift data corresponding to each candidate calibration flow range; S02. Based on historical flow deviation data and historical zero-point drift data, determine the corresponding candidate calibration flow range as the key calibration flow range; S03. Based on the key calibration flow range, determine the corresponding target flow sampling density; S04. Based on the target flow sampling density, generate multiple target calibration flow points within the key calibration flow range; S05. Based on each target calibration flow point and the target calibration flow points corresponding to the remaining candidate target calibration flow intervals, construct the corresponding preset flow sequence.

[0056] In this embodiment, the candidate calibration flow range is used to pre-divide the entire oxygen therapy output flow range. The system divides the overall flow range into multiple different candidate calibration flow ranges based on the actual oxygen therapy output range corresponding to the anesthesia machine. For example, the overall flow range from 0 L / min to 150 L / min can be divided into multiple candidate calibration flow ranges such as 0 L / min to 20 L / min, 20 L / min to 60 L / min, 60 L / min to 100 L / min, and 100 L / min to 150 L / min. Different candidate calibration flow ranges correspond to different airflow output characteristics and flow detection accuracy requirements. Historical flow deviation data is used to characterize the actual detection deviation of the flow sensor in the corresponding flow range during multiple historical calibration cycles. After each calibration, the system records the flow deviation corresponding to different flow ranges to form corresponding historical flow deviation data. Historical zero-point drift data is used to characterize the zero-point drift change of the flow sensor after long-term use and multiple high-temperature sterilizations. The system stores the zero-point drift results obtained from each calibration in the historical database for subsequent analysis of key calibration flow ranges.

[0057] In this embodiment, the key calibration flow range is used to characterize the target flow range that requires key flow calibration processing. After acquiring historical flow deviation data and historical zero-point drift data, the system further analyzes the long-term error changes corresponding to different candidate calibration flow ranges. When the historical flow deviation corresponding to a certain candidate calibration flow range is consistently large, or the historical zero-point drift change frequency corresponding to that flow range is high, the system will determine that the candidate calibration flow range has a high flow drift risk and further identify the candidate calibration flow range as the key calibration flow range. Among them, the low flow range is usually more susceptible to thermal drift, sensor nonlinearity, and small airflow fluctuations, so the low flow range is more likely to be identified as the key calibration flow range.

[0058] In this embodiment, the target flow sampling density is used to characterize the density of the distribution of target calibration flow points within the key calibration flow range. After determining the key calibration flow range, the system will further generate the corresponding target flow sampling density based on the historical error change amplitude of the key calibration flow range. When the historical error fluctuation of a certain key calibration flow range is large, the system will increase the target flow sampling density of the flow range to increase the number of target calibration flow points within the flow range. When the historical error change of a certain key calibration flow range is small and the overall stability is high, the system will decrease the corresponding target flow sampling density to reduce redundant flow sampling processes.

[0059] In this embodiment, the target calibration flow point is used to characterize the specific calibration flow value in the preset flow sequence. After determining the target flow sampling density, the system generates multiple target calibration flow points within the key calibration flow range according to the corresponding sampling density. For example, within the key calibration flow range of 0 L / min to 20 L / min, the system can generate multiple target calibration flow points at intervals of 2 L / min, 4 L / min, 6 L / min, 8 L / min, and 10 L / min. In the non-key flow range of 100 L / min to 150 L / min, the corresponding target calibration flow points can be generated at intervals of 10 L / min. Through the above method, the system can establish a denser distribution of flow calibration points within the key error flow range to improve the calibration accuracy corresponding to the key flow range.

[0060] In this embodiment, the preset flow sequence is used to characterize the flow output order corresponding to the target calibration flow rate of the subsequent oxygen therapy valve. After generating multiple target calibration flow points corresponding to the key calibration flow range, the system will also perform unified sorting processing on the target calibration flow points corresponding to the other candidate calibration flow ranges to generate a complete preset flow sequence. The preset flow sequence not only includes the high-density target calibration flow points corresponding to the key calibration flow range, but also includes the basic calibration flow points corresponding to the other flow ranges to ensure that a complete AD-FLOW calibration relationship can be established throughout the entire oxygen therapy output flow range.

[0061] Furthermore, the step of performing error analysis on the inspiratory and expiratory flow sensors based on the various flow sampling data to generate corresponding correction parameters includes: S501. Based on each flow sampling data, obtain the corresponding flow deviation change sequence; S502. Based on the flow deviation change sequence, determine the corresponding error mode type. The error mode type includes at least the proportional drift mode and the local nonlinear drift mode. S503. Based on the error mode type, perform segmented deviation correction processing on each flow sampling data to generate corresponding correction parameters.

[0062] Furthermore, the step of constructing the corresponding AD-FLOW calibration curve based on each flow sampling data and correction parameters includes: S601. Based on each traffic sampling data, determine the corresponding multiple target traffic intervals; S602. Based on the correction parameters corresponding to each target flow interval, the flow sampling data in each target flow interval is processed to construct a segmented curve to generate the corresponding segmented AD-FLOW curve. S603. Based on the segmented AD-FLOW curves, construct the corresponding target AD-FLOW calibration curve.

[0063] In this embodiment, the flow deviation change sequence is used to characterize the change of flow detection error under different target calibration flow rates. After acquiring each flow sampling data, the system calculates the flow detection deviation at different flow points based on the correspondence between the target calibration flow rate and the inspiratory AD value and the expiratory AD value, and generates the corresponding flow deviation change sequence according to the change order of the target calibration flow rate. When the flow deviation increases synchronously with the increase of the target calibration flow rate, it indicates that the current flow sensor may have a proportional drift. When the flow deviation increases suddenly only in a certain local flow range, it indicates that the current flow sensor may have a local nonlinear drift.

[0064] In this embodiment, the error mode type is used to characterize the error variation characteristics of the flow sensor under different flow ranges. After acquiring the flow deviation change sequence, the system further analyzes the error change trend under different flow ranges and determines the corresponding error mode type based on the error change law. Among them, the proportional drift mode is used to characterize the error situation where the error changes synchronously with the flow in the overall flow range, while the local nonlinear drift mode is used to characterize the error situation where there is a local abnormal deviation in some flow ranges. Then, the system performs segmented deviation correction processing on the flow sampling data in the corresponding flow range based on different error mode types, thereby generating corresponding correction parameters to improve the flow detection accuracy in different flow ranges.

[0065] In this embodiment, the target flow range is used to segment the flow sampling data under different flow ranges. After acquiring each flow sampling data and correction parameters, the system divides the overall flow range into intervals according to the size of the target calibration flow, thereby forming multiple target flow ranges. Then, based on the correction parameters corresponding to each target flow range, the system performs segmented curve construction processing on the flow sampling data in the corresponding flow range to generate corresponding segmented AD-FLOW curves. Each segmented AD-FLOW curve corresponds to the mapping relationship between the AD value and the actual flow value in different flow ranges. Finally, the system splices the segmented AD-FLOW curves to form a complete target AD-FLOW calibration curve, which serves as the target calibration curve in the subsequent flow conversion and flow compensation process.

[0066] like Figure 4-6 As shown, a flow rate calibration circuit for a turbine anesthesia machine is provided, and a flow rate calibration method for a turbine anesthesia machine is used. The calibration circuit includes a control module, an oxygen therapy output control module, an oxygen therapy flow rate acquisition module, an inspiratory flow rate acquisition module, an expiratory flow rate acquisition module, and a calibration storage module. The signal output terminal of the oxygen therapy output control module is connected to the oxygen therapy valve and is used to control the oxygen therapy valve to output the corresponding target calibration flow rate; The signal input terminal of the oxygen therapy flow acquisition module is connected to the oxygen therapy flow sensor, and the signal output terminal of the oxygen therapy flow acquisition module is connected to the flow detection input terminal of the control module. The signal input terminal of the inspiratory flow acquisition module is connected to the inspiratory flow sensor, and the signal output terminal of the inspiratory flow acquisition module is connected to the inspiratory sampling input terminal of the control module. The signal input terminal of the expiratory flow acquisition module is connected to the expiratory flow sensor, and the signal output terminal of the expiratory flow acquisition module is connected to the expiratory sampling input terminal of the control module. The calibration storage module is connected to the control module, the inspiratory flow acquisition module, and the expiratory flow acquisition module, respectively. The control module determines the stability based on the flow parameter sequence output by the oxygen therapy flow acquisition module, and constructs the corresponding AD-FLOW calibration curve based on the flow sampling data output by the inspiratory flow acquisition module and the expiratory flow acquisition module, so as to store the AD-FLOW calibration curve and the corresponding correction parameters in the calibration storage module.

[0067] In this embodiment, the control module serves as the core control unit of the entire flow rate calibration circuit. Internally, the control module may include a microcontroller, an analog-to-digital converter, and a data processing unit. It controls the oxygen therapy output control module to output the corresponding target calibration flow rate and analyzes and processes the sampling data from the oxygen therapy flow rate acquisition module, the inspiratory flow rate acquisition module, and the expiratory flow rate acquisition module. The oxygen therapy output control module controls the opening state of the oxygen therapy valve to adjust the actual output flow rate in the oxygen therapy branch, thereby forming calibration airflows corresponding to different target calibration flow rates. The oxygen therapy flow rate acquisition module acquires and processes the flow detection signal output by the oxygen therapy flow sensor to generate a corresponding flow parameter sequence. The inspiratory flow rate acquisition module acquires and processes the inspiratory AD value output by the inspiratory flow sensor, and the expiratory flow rate acquisition module acquires and processes the expiratory AD value output by the expiratory flow sensor. The calibration storage module stores the AD-FLOW calibration curve and correction parameters as flow calibration parameters during subsequent normal operation of the anesthesia machine.

[0068] Furthermore, the oxygen therapy output control module includes a first input filter unit, a driver chip U9, MOSFETs Q13, Q22, and Q23, a diode D12, a sampling resistor group, and an operational amplifier U8B. The power input terminal of the first input filter unit is connected to an external 12V power supply terminal, and the power output terminal of the first input filter unit is connected to the power input terminal of the driver chip U9. The common node between the power output terminal of the first input filter unit and the power input terminal of the driver chip U9 is connected to the first conducting terminal of MOSFET Q13. The second conducting terminal of MOSFET Q13 is grounded, and the controlled terminal of MOSFET Q13 is connected to the drive control signal output terminal of the control module. The drive signal output of the driver chip U9... The terminals are respectively connected to the positive input terminal of the oxygen therapy valve and the cathode terminal of diode D12. MOSFETs Q22 and Q23 form a parallel conduction network. The anode terminal of diode D12 is connected to the first terminal of the parallel conduction network. The second terminal of the parallel conduction network is connected to the first terminal of the sampling resistor group. The second terminal of the sampling resistor group is grounded. The common node between the anode terminal of diode D12 and the first terminal of the parallel conduction network is connected to the negative input terminal of the oxygen therapy valve. The common node between the second terminal of the parallel conduction network and the first terminal of the sampling resistor group is connected to the non-inverting input terminal of operational amplifier U8B. The inverting input terminal of operational amplifier U8B is grounded. The output terminal of operational amplifier U8B is connected to the current detection signal input terminal of the control module.

[0069] In this embodiment, the first input filtering unit is used to filter the power signal input from the external 12V power supply terminal to the oxygen therapy output control module, so as to reduce the impact of external power supply ripple, switching noise, and valve drive transient impact on the control stability of the subsequent driver chip U9 and the oxygen therapy valve. The driver chip U9 is used to receive the drive control signal output by the control module and convert the drive control signal into a drive signal suitable for driving the oxygen therapy valve and the subsequent MOS conduction network. MOS transistor Q13 is used as a front-end enable controller. Its controlled terminal receives the control level output by the drive control signal output terminal of the control module. When the control module needs to start the oxygen therapy valve output, it controls the conduction or cutoff state of MOS transistor Q13 to enable the driver chip U9 to enter the corresponding drive working state. MOS transistors Q22 and Q23 form a parallel conduction network to jointly bear the conduction of the negative side of the oxygen therapy valve. The current is reduced by parallel connection to decrease the conduction loss and heat generation of individual MOSFETs, thereby improving the current carrying capacity and operational reliability of the oxygen therapy valve drive circuit. Diode D12 is connected in parallel across the oxygen therapy valve to release the reverse induced voltage generated by the oxygen therapy valve coil when the oxygen therapy valve is de-energized or the drive circuit is turned off, preventing the induced voltage from impacting MOSFETs Q22 and Q23 and the driver chip U9. The sampling resistor group is connected in series between the parallel conduction network and ground to convert the oxygen therapy valve drive current into a corresponding sampling voltage signal. Operational amplifier U8B is used to amplify or buffer the sampling voltage signal generated by the sampling resistor group and output the processed current detection signal to the current detection signal input terminal of the control module. This allows the control module to determine whether the oxygen therapy valve is in a normal driving state based on the change in the oxygen therapy valve drive current, thus providing a hardware detection basis for the stable output of the target calibration flow rate.

[0070] The working principle is as follows: When the system needs to output the target calibration flow rate, the external 12V power supply first forms a relatively stable driving power supply through the first input filter unit and supplies it to the driving chip U9 and the oxygen therapy valve driving circuit. The control module controls the MOS transistor Q13 and the driving chip U9 to enter the corresponding driving state through the driving control signal output terminal. The driving chip U9 further controls the circuit where the oxygen therapy valve is located to be turned on, so that the oxygen therapy valve generates the corresponding opening degree or working state according to the driving signal. The working current of the oxygen therapy valve passes through the parallel conducting network composed of the oxygen therapy valve, MOS transistors Q22 and Q23 and the sampling resistor group in sequence and returns to the ground terminal. The sampling resistor group converts the working current into a sampling voltage, and the operational amplifier U8B processes the sampling voltage and feeds it back to the control module. The control module can thus monitor the driving current of the oxygen therapy valve while controlling the output of the target calibration flow rate of the oxygen therapy valve, thereby judging whether there is a driving abnormality, insufficient conduction or unstable output of the oxygen therapy valve. In conjunction with the flow detection results of the subsequent oxygen therapy flow sensor, the stability and controllability of the target calibration flow rate output process are improved.

[0071] Furthermore, the oxygen therapy flow acquisition module includes interface J10, a second input filter unit, operational amplifier U36A, and operational amplifier U36B. The signal input terminal of interface J10 is connected to the oxygen therapy flow sensor, and the signal output terminal of interface J10 is connected to the signal input terminal of the second input filter unit. The signal output terminal of the second input filter unit is connected to the non-inverting input terminal of operational amplifier U36A. The output terminal of operational amplifier U36A is connected to the inverting input terminal of operational amplifier U36A and the flow detection input terminal of the control module, respectively. The common node between the signal output terminal of the second input filter unit and the non-inverting input terminal of operational amplifier U36A is connected to the output terminal and the non-inverting input terminal of operational amplifier U36B, respectively. The inverting input terminal of operational amplifier U36B is connected to the 3.3V reference bias voltage port.

[0072] In this embodiment, interface J10 serves as the electrical connection interface between the oxygen therapy flow sensor and the oxygen therapy flow acquisition module, enabling the analog flow detection signal output by the oxygen therapy flow sensor to be input to the subsequent acquisition link. The second input filtering unit performs pre-stage filtering on the analog flow detection signal output from interface J10 to suppress high-frequency interference caused by oxygen therapy valve operation, airflow disturbance, and power supply noise. Operational amplifier U36A forms a signal buffer follower structure; its non-inverting input receives the flow detection signal processed by the second input filtering unit, and its output is connected back to the inverting input, thereby reducing the frequency of interference in the subsequent control module. The block sampling end affects the load on the output signal of the pre-stage oxygen therapy flow sensor and outputs the stabilized flow acquisition signal to the flow detection input of the control module. Operational amplifier U36B is used to form a reference bias follower structure. Its inverting input receives the reference voltage provided by the 3.3V reference bias voltage port, and its output and non-inverting input are connected to the common node between the second input filter unit and operational amplifier U36A, so that the output signal of the oxygen therapy flow sensor obtains a stable bias reference before entering operational amplifier U36A, thereby improving the sampling stability and anti-interference capability of the control module for the oxygen therapy flow detection signal.

[0073] The working principle is as follows: The oxygen therapy flow sensor detects the current actual output flow in the oxygen therapy branch and inputs the corresponding analog flow detection signal to the oxygen therapy flow acquisition module through interface J10. This analog flow detection signal first undergoes noise suppression by the second input filtering unit to reduce the impact of airflow pulsation and circuit interference on the sampling results. Subsequently, the filtered flow detection signal is input to the non-inverting input of operational amplifier U36A. Operational amplifier U36A forms a voltage follower output through the feedback connection between its output and inverting input, enabling the control module to obtain a stable flow detection signal after impedance isolation. At the same time, operational amplifier U36B provides a stable bias to the upstream sampling node based on the 3.3V reference bias voltage port, keeping the flow detection signal within the voltage range that the control module can sample. Finally, the control module obtains the current output flow of the oxygen therapy branch based on the flow detection signal output by operational amplifier U36A, providing a reliable hardware signal basis for continuous detection, stability determination, and subsequent flow sampling of the target calibration flow.

[0074] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for calibrating the flow rate of a turbine anesthesia machine, characterized in that, An oxygen therapy system for a turbine anesthesia machine is provided, the system comprising an inspiratory branch and an expiratory branch, and an oxygen therapy branch connected to the inspiratory and expiratory branches respectively. Each oxygen therapy branch is equipped with an oxygen therapy valve and an oxygen therapy flow sensor. The inspiratory branch is equipped with an inspiratory flow sensor, and the expiratory branch is equipped with an expiratory flow sensor. The calibration method includes: The inspiratory branch and the expiratory branch are short-circuited to form a corresponding closed-loop calibration airway. Close the oxygen therapy valve, collect multiple zero-flow AD sampling values ​​under zero-flow conditions, determine the corresponding zero-flow AD value based on the multiple zero-flow AD sampling values, determine whether there is zero-point drift based on the zero-flow AD value, and if there is zero-point drift, generate the corresponding zero-point compensation parameter based on the multiple zero-flow AD sampling values ​​and the zero-flow AD value. The oxygen therapy valve is opened and the corresponding target calibration flow is output according to the preset flow sequence. The oxygen therapy flow sensor is continuously detected to obtain the flow parameter sequence corresponding to the target calibration flow. The stability of the current output flow parameter sequence is determined. When the determination result corresponding to the stability determination is stable, the corresponding flow sampling data is collected. The flow sampling data includes at least the inspiratory AD value and the expiratory AD value. If there is zero drift, the flow sampling data is compensated and corrected based on the zero compensation parameter. Determine whether all target calibration flow rates have been output. If not, control the oxygen therapy valve to re-output the next target calibration flow rate in the preset flow rate sequence to obtain the corresponding flow rate sampling data. Based on the flow sampling data, error analysis is performed on the inspiratory flow sensor and the expiratory flow sensor to generate corresponding correction parameters; Based on the flow sampling data and the correction parameters, a corresponding AD-FLOW calibration curve is constructed and stored as the flow calibration parameters for the inspiratory flow sensor and the expiratory flow sensor.

2. The method for calibrating the flow rate of a turbine anesthesia machine according to claim 1, characterized in that, The step of generating the corresponding zero-point compensation parameter based on the zero-flow AD value includes: Based on the zero-flow AD value, determine the corresponding current zero-point offset; Based on the variation amplitude between each of the zero-flow AD sampling values, a corresponding zero-point fluctuation parameter is generated; Obtain the historical zero-point offset within the historical calibration period, and generate the corresponding zero-point drift trend parameter based on the historical zero-point offset and the current zero-point offset; Based on the zero-point offset, the zero-point fluctuation parameter, and the zero-point drift trend parameter, a corresponding comprehensive zero-point characterization value is generated. The zero-point comprehensive characterization value is matched with the preset drift level range to determine the corresponding zero-point drift level; Based on the zero-point drift level, obtain the corresponding zero-point compensation weight parameters; The zero-point offset, the zero-point fluctuation parameter, the zero-point drift trend parameter, and the zero-point compensation weight parameter are fused and calculated to generate the corresponding zero-point compensation parameter.

3. The method for calibrating the flow rate of a turbine anesthesia machine according to claim 1, characterized in that, The step of determining the stability of the current output flow parameter sequence includes: Based on the flow parameter sequence, the corresponding real-time flow deviation value is obtained. The real-time flow deviation value is used to characterize the difference between the current output flow and the target calibration flow. Based on the real-time traffic deviation value, obtain the corresponding traffic deviation change sequence; Based on the flow deviation change sequence, the corresponding flow convergence trend parameters are obtained; Based on the flow convergence trend parameters, determine whether the current output flow has entered the target flow convergence state; When the current output traffic enters the target traffic convergence state, a corresponding stability determination result is generated.

4. The method for calibrating the flow rate of a turbine anesthesia machine according to claim 3, characterized in that, The step of compensating and correcting the flow sampling data based on the zero-point compensation parameter includes: Based on the flow convergence trend parameter, the convergence direction and convergence remainder of the current output flow relative to the target calibration flow are obtained. The convergence remainder is used to characterize the flow deviation between the current output flow and the target calibration flow that has not yet been eliminated. Based on the convergence direction and the remaining convergence amount, the dynamic deviation component in the flow sampling data is determined. The dynamic deviation component is used to characterize the sampling deviation introduced into the inspiratory AD value and expiratory AD value because the current output flow has not yet fully converged to the target calibration flow. Based on the zero-point compensation parameters and the dynamic deviation components, the corresponding effective zero-point compensation amount is generated; Based on the effective zero-point compensation, the inspiratory AD value and expiratory AD value in the flow sampling data are subtracted and corrected to generate compensated and corrected flow sampling data.

5. The method for calibrating the flow rate of a turbine anesthesia machine according to claim 1, characterized in that, The steps for generating the preset traffic sequence include: Determine the preset candidate calibration flow range, and obtain the historical flow deviation data and historical zero drift data corresponding to each candidate calibration flow range; Based on historical flow deviation data and historical zero-point drift data, the corresponding candidate calibration flow ranges are identified as the key calibration flow ranges; Based on the key calibration flow range, determine the corresponding target flow sampling density; Based on the target flow sampling density, multiple target calibration flow points are generated within the key calibration flow range. Based on each of the target calibration flow points and the target calibration flow points corresponding to the remaining candidate target calibration flow intervals, a corresponding preset flow sequence is constructed.

6. The method for calibrating the flow rate of a turbine anesthesia machine according to claim 1, characterized in that, The step of performing error analysis processing on the inspiratory flow sensor and the expiratory flow sensor based on each of the flow sampling data to generate corresponding correction parameters includes: Based on each of the aforementioned traffic sampling data, the corresponding traffic deviation change sequence is obtained; Based on the flow deviation change sequence, the corresponding error mode type is determined, and the error mode type includes at least the proportional drift mode and the local nonlinear drift mode; Based on the error mode type, segmented deviation correction processing is performed on each of the traffic sampling data to generate corresponding correction parameters.

7. The method for calibrating the flow rate of a turbine anesthesia machine according to claim 1, characterized in that, The step of constructing the corresponding AD-FLOW calibration curve based on each of the flow sampling data and the correction parameters includes: Based on the traffic sampling data, multiple target traffic intervals are determined accordingly; Based on the correction parameters corresponding to each of the target flow intervals, the flow sampling data in each of the target flow intervals are processed to construct a segmented curve to generate the corresponding segmented AD-FLOW curve; Based on each of the segmented AD-FLOW curves, a corresponding target AD-FLOW calibration curve is constructed.

8. A flow rate calibration circuit for a turbine anesthesia machine, characterized in that, The flow rate calibration method for a turbine anesthesia machine as described in any one of claims 1-7, wherein the calibration circuit includes a control module, an oxygen therapy output control module, an oxygen therapy flow acquisition module, an inspiratory flow acquisition module, an expiratory flow acquisition module, and a calibration storage module; The signal output terminal of the oxygen therapy output control module is connected to the oxygen therapy valve and is used to control the oxygen therapy valve to output the corresponding target calibration flow rate. The signal input terminal of the oxygen therapy flow acquisition module is connected to the oxygen therapy flow sensor, and the signal output terminal of the oxygen therapy flow acquisition module is connected to the flow detection input terminal of the control module. The signal input terminal of the inhalation flow acquisition module is connected to the inhalation flow sensor, and the signal output terminal of the inhalation flow acquisition module is connected to the inhalation sampling input terminal of the control module. The signal input terminal of the expiratory flow acquisition module is connected to the expiratory flow sensor, and the signal output terminal of the expiratory flow acquisition module is connected to the expiratory sampling input terminal of the control module. The calibration storage module is connected to the control module, the inspiratory flow acquisition module, and the expiratory flow acquisition module, respectively. The control module performs stability determination based on the flow parameter sequence output by the oxygen therapy flow acquisition module, and constructs a corresponding AD-FLOW calibration curve based on the flow sampling data output by the inspiratory flow acquisition module and the expiratory flow acquisition module, so as to store the AD-FLOW calibration curve and the corresponding correction parameters in the calibration storage module.

9. The flow rate calibration circuit for a turbine anesthesia machine according to claim 8, characterized in that, The oxygen therapy output control module includes a first input filter unit, a driver chip U9, MOSFETs Q13, Q22, and Q23, a diode D12, a sampling resistor group, and an operational amplifier U8B. The power input terminal of the first input filter unit is connected to an external 12V power supply. The power output terminal of the first input filter unit is connected to the power input terminal of the driver chip U9. The common node between the power output terminal of the first input filter unit and the power input terminal of the driver chip U9 is connected to the first conducting terminal of the MOSFET Q13. The second conducting terminal of the MOSFET Q13 is grounded. The controlled terminal of the MOSFET Q13 is connected to the drive control signal output terminal of the control module. The drive signal output terminal of the driver chip U9 is connected to the oxygen therapy valve. The positive input terminal of the device is connected to the cathode terminal of the diode D12. The MOSFETs Q22 and Q23 form a parallel conduction network. The anode terminal of the diode D12 is connected to the first end of the parallel conduction network. The second end of the parallel conduction network is connected to the first end of the sampling resistor group. The second end of the sampling resistor group is grounded. The common node between the anode terminal of the diode D12 and the first end of the parallel conduction network is connected to the negative input terminal of the oxygen therapy valve. The common node between the second end of the parallel conduction network and the first end of the sampling resistor group is connected to the non-inverting input terminal of the operational amplifier U8B. The inverting input terminal of the operational amplifier U8B is grounded. The output terminal of the operational amplifier U8B is connected to the current detection signal input terminal of the control module.

10. The flow rate calibration circuit for a turbine anesthesia machine according to claim 8, characterized in that, The oxygen therapy flow acquisition module includes an interface J10, a second input filter unit, an operational amplifier U36A, and an operational amplifier U36B. The signal input terminal of the interface J10 is connected to the oxygen therapy flow sensor, and the signal output terminal of the interface J10 is connected to the signal input terminal of the second input filter unit. The signal output terminal of the second input filter unit is connected to the non-inverting input terminal of the operational amplifier U36A. The output terminal of the operational amplifier U36A is connected to both the inverting input terminal of the operational amplifier U36A and the flow detection input terminal of the control module. The common node between the signal output terminal of the second input filter unit and the non-inverting input terminal of the operational amplifier U36A is connected to both the output terminal and the non-inverting input terminal of the operational amplifier U36B. The inverting input terminal of the operational amplifier U36B is connected to a 3.3V reference bias voltage port.

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