Abnormality monitoring system based on operating parameters of breathing machine

Abstract

The invention discloses an abnormity monitoring system based on breathing machine operation parameters. The abnormity monitoring system comprises a data acquisition module, a breathing machine operation monitoring module and an abnormity monitoring module. The invention belongs to the field of data processing, and particularly relates to an anomaly monitoring system based on breathing machine operating parameters, according to the scheme, static delay optimization is constructed by introducing an environment compensation item, dynamic delay compensation is carried out by utilizing fuzzy logic prediction, and the response speed is increased during sudden breathing fluctuation; real-time data compression and expansion are carried out based on the system load weight; the accuracy of abnormity monitoring is improved; abnormal ranges of different operation parameters are dynamically adjusted based on environment compensation factors, input quantization errors and actuator faults are gradually compensated in combination with a dynamic environment compensation design control law, the fault-tolerant capability of external interference is improved, input quantization is jointly processed based on dynamic compensation actuator gain changes, random interference and quantization errors, and the fault tolerance of the actuator is improved. Accurate response to sudden breathing fluctuation is ensured; and the abnormity monitoring effect is improved.

Description

Technical Field

[0001] The invention relates to the field of data processing, and in particular to an abnormality monitoring system based on operating parameters of a ventilator. Background Art

[0002] The abnormal monitoring system is a system that collects and analyzes equipment and environmental data in real time, identifies abnormal conditions in the operating state, and takes corresponding measures to ensure the safety, stability, and efficiency of equipment operation. However, the general abnormal monitoring system has the problem of ignoring delay errors or insufficient delay compensation, which makes the system unable to respond quickly to dynamic changes, resulting in monitoring lag and poor monitoring accuracy; the general abnormal monitoring system has the problem of fixed abnormal range, insufficient handling of actuator failures and quantization errors, and lack of environmental adaptability, which leads to poor abnormal monitoring effect. Summary of the invention

[0003] In view of the above situation, in order to overcome the defects of the prior art, the present invention provides an abnormal monitoring system based on ventilator operating parameters. In view of the problem that the general abnormal monitoring system ignores delay errors or insufficient delay compensation, resulting in the system being unable to respond quickly to dynamic changes, thereby causing monitoring lag and poor monitoring accuracy, this scheme introduces environmental compensation items to construct static delay optimization, uses fuzzy logic prediction to perform dynamic delay compensation, and improves the response speed in the event of sudden respiratory fluctuations; performs real-time data compression and expansion based on system load weights; thereby improving the accuracy of abnormal monitoring; in view of the problem that the general abnormal monitoring system has a fixed abnormal range, insufficient processing of actuator failures and quantization errors, and lacks environmental adaptability, thereby resulting in poor abnormal monitoring effects, this scheme dynamically adjusts the abnormal range of different operating parameters based on environmental compensation factors, combines dynamic environmental compensation to design control laws to gradually compensate for input quantization errors and actuator failures, improves the fault tolerance to external interference, and combines input quantization based on dynamic compensation actuator gain changes, random interference and quantization errors to ensure accurate response to sudden respiratory fluctuations; thereby improving the abnormal monitoring effect.

[0004] The technical solution adopted by the present invention is as follows: the abnormality monitoring system based on the operating parameters of the ventilator provided by the present invention includes a data acquisition module, a ventilator operation monitoring module and an abnormality monitoring module;

[0005] The data acquisition module collects a comprehensive data set of ventilator operation and environment;

[0006] The ventilator operation monitoring module processes mechanical response, signal transmission, environment and operation parameter data, combines delay compensation, dynamic adjustment of abnormal range and fault compensation, monitors and responds to abnormal conditions;

[0007] The abnormal monitoring module performs an early warning process on the management personnel when the ventilator operation monitoring module determines that the ventilator operation is in an abnormal state.

[0008] Furthermore, in the data acquisition module, the ventilator operation and environment comprehensive data set includes mechanical response data, signal transmission data, environmental data and operation parameter data; the collected data is cleaned, converted and standardized.

[0009] Furthermore, the ventilator operation monitoring module specifically includes the following contents:

[0010] Static delay building unit; static delay includes mechanical response delay and signal transmission delay; static delay express: ; ; Wherein, L is the stroke length of the air flow control valve; is the movement speed of the air flow regulating valve; is the flow resistance of the pipeline gas; and are airflow driving force and gas resistance respectively; d is the signal transmission distance; is the signal transmission speed; is additional delay compensation; and is the environmental compensation coefficient; and They are temperature and pressure changes respectively;

[0011] Dynamic time delay compensation building unit; predict delay through fuzzy logic system, input operating parameters and change rate, calculate dynamic delay compensation value, and combine static delay to compensate delay in real time, dynamic delay compensation value It is expressed as: ;in, and are the central value and membership degree of the Ith fuzzy rule in the fuzzy logic system respectively;

[0012] Real-time data compression and expansion unit; compressing data sequence when delay is reduced, expressed as: ; When the delay increases, the data is expanded by linear interpolation; where, and is the parameter value of two adjacent sampling points; and is the timestamp of two adjacent sampling points; t is the compressed time point; p(·) is the value of the operating parameter; is the system load weight;

[0013] Asymmetric performance constraint unit; dynamic estimation of abnormal trends based on fuzzy logic, fuzzy logic system estimates the uncertainty of operating parameters express: ; ; Define the asymmetric abnormal range, introduce the dynamic adjustment factor of environmental compensation, and dynamically adjust the upper and lower bounds to express: ; ; Center value And the range is expressed as: ; ; The error is mapped to an unbounded domain, expressed as: ; Parameters are adaptively updated, indicating: ; Fuzzy parameter adaptive update law, expressed as: ;in, is the fuzzy basis function vector; is the parameter matrix of the fuzzy logic system; T is the matrix transpose; and They are the dynamic lower and upper bounds of the anomaly range, respectively; is the operating parameter at time t ; is the half amplitude of the abnormal range; is the normalized deviation value; is the adaptive parameter of the fuzzy logic system The rate of change of and are control parameters, which are used to adjust the adaptive update speed and accuracy respectively; V is the current actual value of the operating parameter; is the target value of the operating parameter; i is the operating parameter index; is the dynamic adjustment factor of environmental compensation; is the change in ambient humidity; , and is the environmental weight coefficient; is the parameter uncertainty compensation term; and is the compensation weight coefficient; is the real-time deviation of oxygen concentration; It is the fluctuation of oxygen concentration within a fixed range;

[0014] Backstepping method adaptive abnormal control unit; design the control law by backstepping method to gradually compensate for input quantization error and actuator failure; control input design, the i2-step control law is expressed as: ; Design the filter, expressed as: ;in, is the control input used to compensate for abnormal disturbances; and is the control gain, which is used to adjust the control strength; is the filter output The rate of change of is the filter gain, which is used to adjust the degree of smoothing; is the control adjustment factor;

[0015] Input quantization joint processing unit; model the actuator fault, the fault model includes gain change, deviation and interference, expressed as: ;The quantization error satisfies: ;Introduce quantization error dynamic compensation term , the actual control signal It is expressed as: ; ;in, is the actuator output signal; is the actuator gain; is the gain variation coefficient; and are additive faults, representing random interference and deviation respectively; M is and They are control gain and dynamic adjustment coefficient respectively; and are the error state variables of the current and previous step respectively; is the filter output of the previous step; n is the step index; is the scaling factor of the quantization error; and is the gain of the nth step; is the fuzzy basis function vector of the nth step; is the control update speed factor for step n; is the precision control factor of the nth step;

[0016] Abnormality determination unit; combines asymmetric performance constraints and fuzzy logic prediction to identify abnormalities; if or , it is judged as abnormal state; otherwise it is judged as normal state; among them, and are the minimum and maximum values ​​of the operating parameter uncertainties, respectively.

[0017] The beneficial effects achieved by the present invention using the above scheme are as follows:

[0018] (1) In view of the problem that general abnormality monitoring systems ignore delay errors or have insufficient delay compensation, resulting in the system's inability to respond quickly to dynamic changes, which in turn causes monitoring lags and poor monitoring accuracy, this scheme introduces environmental compensation items to construct static delay optimization, uses fuzzy logic prediction for dynamic delay compensation, and improves the response speed in the event of sudden respiratory fluctuations; performs real-time data compression and expansion based on system load weights, thereby improving the accuracy of abnormality monitoring.

[0019] (2) In view of the problem that the general abnormal monitoring system has a fixed abnormal range, insufficient processing of actuator failures and quantization errors, and lack of environmental adaptability, which leads to poor abnormal monitoring effect, this scheme dynamically adjusts the abnormal range of different operating parameters based on the environmental compensation factor, and combines the dynamic environmental compensation design control law to gradually compensate for the input quantization error and actuator failure, thereby improving the fault tolerance to external interference. Based on the dynamic compensation actuator gain change, random interference and quantization error, the input quantization is jointly processed to ensure accurate response to sudden respiratory fluctuations, thereby improving the abnormal monitoring effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A schematic diagram of a flow chart of an abnormality monitoring system based on ventilator operating parameters provided by the present invention;

[0021] Figure 2 Schematic diagram of the process of running the monitoring module for a ventilator.

[0022] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention. DETAILED DESCRIPTION

[0023] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0024] In the description of the present invention, it should be understood that terms such as “upper”, “lower”, “front”, “back”, “left”, “right”, “top”, “bottom”, “inside” and “outside” indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore should not be understood as limiting the present invention.

[0025] Example 1, see Figure 1 , the abnormality monitoring system based on ventilator operation parameters provided by the present invention includes a data acquisition module, a ventilator operation monitoring module and an abnormality monitoring module;

[0026] The data acquisition module collects the ventilator operation and environment comprehensive data set; and sends the data to the ventilator operation monitoring module;

[0027] The ventilator operation monitoring module processes mechanical response, signal transmission, environment and operation parameter data, combines delay compensation, dynamic adjustment of abnormal range and fault compensation, monitors and responds to abnormal conditions; and sends the data to the abnormal monitoring module;

[0028] The abnormal monitoring module performs an early warning process on the management personnel when the ventilator operation monitoring module determines that the ventilator operation is in an abnormal state.

[0029] Example 2, see Figure 1 This embodiment is based on the above embodiment. In the data acquisition module, the comprehensive data set of ventilator operation and environment includes mechanical response data, signal transmission data, environmental data and operation parameter data; the collected data is cleaned, converted and standardized; the mechanical response data includes the stroke length of the airflow regulating valve, the movement speed of the airflow regulating valve, the pipeline gas flow resistance, the airflow driving force and the gas resistance; the signal transmission data includes the signal transmission distance and the signal transmission speed; the environmental data includes temperature, pressure and humidity; the operation parameter data includes oxygen concentration, respiratory flow rate, operation pressure and flow status data.

[0030] Example 3, see Figure 1 and Figure 2 , this embodiment is based on the above embodiment, and the ventilator operation monitoring module includes the following contents:

[0031] Static delay building unit; static delay includes mechanical response delay and signal transmission delay. The mechanical response delay of the ventilator comes from the dynamic flow of gas in the airflow control valve and the pipeline; the signal transmission delay comes from the data transmission from the sensor to the processor; static delay express: ; ; Wherein, L is the stroke length of the air flow control valve; is the movement speed of the air flow regulating valve; is the flow resistance of the pipeline gas; and are airflow driving force and gas resistance respectively; d is the signal transmission distance; is the signal transmission speed; is additional delay compensation; and is the environmental compensation coefficient; and They are temperature and pressure changes respectively;

[0032] Dynamic time delay compensation construction unit; there is a collection delay in the process of ventilator collecting operating parameters, and the collection delay fluctuates dynamically due to system load and external interference; it will cause abnormal monitoring lag, especially when the patient has sudden respiratory fluctuations, affecting the monitoring response, so dynamic time delay compensation is performed; the delay is predicted by the fuzzy logic system, the operating parameters and the rate of change are input, the dynamic delay compensation value is calculated, and the static delay is combined to compensate the delay in real time, and the dynamic delay compensation value It is expressed as: ;in, and are the central value and membership degree of the Ith fuzzy rule in the fuzzy logic system respectively;

[0033] Real-time data compression and expansion unit; The sampling period of the ventilator is fixed, and dynamic delay compensation will disrupt the sampling sequence, resulting in data loss and duplication. Inconsistent data sequence will affect the accuracy of the abnormal monitoring model, so real-time data compression and expansion are performed; when the delay is reduced, the compressed data sequence is expressed as: ; When the delay increases, the data is expanded by linear interpolation; where, and is the parameter value of two adjacent sampling points; and is the timestamp of two adjacent sampling points; t is the compressed time point; p(·) is the value of the operating parameter; is the system load weight.

[0034] By performing the above operations, the general abnormal monitoring system has the problem of ignoring delay errors or insufficient delay compensation, which makes the system unable to respond quickly to dynamic changes, and then causes monitoring lag and poor monitoring accuracy. This solution introduces environmental compensation items to construct static delay optimization, uses fuzzy logic prediction for dynamic delay compensation, and improves the response speed in the event of sudden respiratory fluctuations; performs real-time data compression and expansion based on the system load weight, thereby improving the accuracy of abnormal monitoring.

[0035] Example 4, see Figure 1 and Figure 2 , this embodiment is based on the above embodiment, and the ventilator operation monitoring module also includes the following contents:

[0036] Asymmetric performance constraint unit; the tolerance of operating parameter deviations, including oxygen concentration, to abnormalities is different, so asymmetric constraints are imposed on the abnormal range of ventilator operating parameters; based on fuzzy logic dynamic estimation of abnormal trends, the fuzzy logic system estimates the uncertainty of operating parameters express: ; ; Define the asymmetric abnormal range, introduce the dynamic adjustment factor of environmental compensation, and dynamically adjust the upper and lower bounds to express: ; ; Center value And the range is expressed as: ; ; The error is mapped to an unbounded domain, expressed as: ; Parameters are adaptively updated, indicating: ; Fuzzy parameter adaptive update law, expressed as: ;in, is the fuzzy basis function vector; is the parameter matrix of the fuzzy logic system; T is the matrix transpose; and They are the dynamic lower and upper bounds of the anomaly range, respectively; is the operating parameter at time t ; is the half amplitude of the abnormal range; is the normalized deviation value; is the adaptive parameter of the fuzzy logic system The rate of change of and are control parameters, which are used to adjust the adaptive update speed and accuracy respectively; V is the current actual value of the operating parameter; is the target value of the operating parameter; i is the operating parameter index; is the dynamic adjustment factor of environmental compensation; is the change in ambient humidity; , and is the environmental weight coefficient; is the parameter uncertainty compensation term; and is the compensation weight coefficient; is the real-time deviation of oxygen concentration; It is the fluctuation of oxygen concentration within a fixed range;

[0037] Backstepping method adaptive abnormal control unit; design the control law by backstepping method to gradually compensate for input quantization error and actuator failure; control input design, the i2-step control law is expressed as: ; Design the filter, expressed as: ;in, is the control input used to compensate for abnormal disturbances; and is the control gain, which is used to adjust the control strength; is the filter output The rate of change of is the filter gain, which is used to adjust the degree of smoothing; is the control adjustment factor;

[0038] Input quantization joint processing unit; The actuator of the ventilator will have gain changes and additive faults. The quantization error of the input signal will cause small abnormalities to be ignored, so input quantization joint processing is performed; The actuator fault model includes gain change, deviation and interference, which is expressed as: ;The quantization error satisfies: ;Introduce quantization error dynamic compensation term , the actual control signal It is expressed as: ; ;in, is the actuator output signal; is the actuator gain; is the gain variation coefficient; and are additive faults, representing random interference and deviation respectively; M is and They are control gain and dynamic adjustment coefficient respectively; and are the error state variables of the current and previous step respectively; is the filter output of the previous step; n is the step index; is the scaling factor of the quantization error; and is the gain of the nth step; is the fuzzy basis function vector of the nth step; is the control update speed factor for step n; is the precision control factor of the nth step;

[0039] Abnormality determination unit; combines asymmetric performance constraints and fuzzy logic prediction to identify abnormalities; if or , it is judged as abnormal state; otherwise it is judged as normal state; among them, and are the minimum and maximum values ​​of the operating parameter uncertainties, respectively.

[0040] By performing the above operations, in order to solve the problem that the general abnormal monitoring system has a fixed abnormal range, insufficient processing of actuator failures and quantization errors, lacks environmental adaptability, and thus leads to poor abnormal monitoring effect, this scheme dynamically adjusts the abnormal range of different operating parameters based on the environmental compensation factor, and combines the dynamic environmental compensation to design the control law to gradually compensate for the input quantization error and actuator failure, improve the fault tolerance to external interference, and jointly process the input quantization based on the dynamic compensation actuator gain change, random interference and quantization error to ensure accurate response to sudden respiratory fluctuations, thereby improving the abnormal monitoring effect.

[0041] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0042] While the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that many changes, modifications, substitutions and variations can be made to the embodiments without departing from the principles and spirit of the invention.

[0043] The present invention and its embodiments are described above, and such description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if ordinary technicians in the field are inspired by it, without departing from the purpose of the invention, they can design a structure and embodiment similar to the technical solution without creativity, which should belong to the protection scope of the present invention.

Claims

1. An abnormal monitoring system based on ventilator operating parameters, characterized in that: The system includes a data acquisition module, a ventilator operation monitoring module and an abnormality monitoring module; The data acquisition module collects a comprehensive data set of ventilator operation and environment; The ventilator operation monitoring module processes mechanical response, signal transmission, environment and operation parameter data, combines delay compensation, dynamic adjustment of abnormal range and fault compensation, monitors and responds to abnormal conditions; The abnormal monitoring module performs early warning processing on the management personnel when the ventilator operation monitoring module determines that the ventilator operation is in an abnormal state; The ventilator operation monitoring module includes: a static delay construction unit; the static delay includes a mechanical response delay and a signal transmission delay; a static delay express: ; ; Wherein, L is the stroke length of the air flow control valve; is the movement speed of the air flow regulating valve; is the flow resistance of the pipeline gas; and are airflow driving force and gas resistance respectively; d is the signal transmission distance; is the signal transmission speed; is additional delay compensation; and is the environmental compensation coefficient; and are temperature and pressure changes respectively.

2. The abnormality monitoring system based on ventilator operating parameters according to claim 1, characterized in that: The ventilator operation monitoring module specifically includes the following contents: Static delay building unit; Dynamic time delay compensation building unit; predict delay through fuzzy logic system, input operating parameters and change rate, calculate dynamic delay compensation value, and combine static delay to compensate delay in real time, dynamic delay compensation value It is expressed as: ;in, and are the central value and membership degree of the Ith fuzzy rule in the fuzzy logic system respectively; Real-time data compression and expansion unit; compressing data sequence when delay is reduced, expressed as: ; When the delay increases, the data is expanded by linear interpolation; where, and is the parameter value of two adjacent sampling points; and is the timestamp of two adjacent sampling points; t is the compressed time point; p(·) is the value of the operating parameter; is the system load weight; Asymmetric performance constraint unit; Backstepping adaptive abnormal control unit; Input quantization joint processing unit; Abnormality determination unit.

3. The abnormality monitoring system based on ventilator operating parameters according to claim 2 is characterized in that: The asymmetric performance constraint unit is based on fuzzy logic to dynamically estimate abnormal trends. The fuzzy logic system estimates the uncertainty of operating parameters. express: ; ; Define the asymmetric abnormal range, introduce the dynamic adjustment factor of environmental compensation, and dynamically adjust the upper and lower bounds to express: ; ; Center value And the range is expressed as: ; ; The error is mapped to an unbounded domain, expressed as: ; Parameters are adaptively updated, indicating: ; The fuzzy parameter adaptive update law is expressed as: ;in, is the fuzzy basis function vector; is the parameter matrix of the fuzzy logic system; T is the matrix transpose; and They are the dynamic lower and upper bounds of the anomaly range, respectively; is the operating parameter at time t ; is the half amplitude of the abnormal range; is the normalized deviation value; is the adaptive parameter of the fuzzy logic system The rate of change of and are control parameters, which are used to adjust the adaptive update speed and accuracy respectively; V is the current actual value of the operating parameter; is the target value of the operating parameter; i is the operating parameter index; is the dynamic adjustment factor of environmental compensation; is the change in ambient humidity; , and is the environmental weight coefficient; is the parameter uncertainty compensation term; and is the compensation weight coefficient; is the real-time deviation of oxygen concentration; It is the fluctuation of oxygen concentration within a fixed range.

4. The abnormality monitoring system based on ventilator operating parameters according to claim 3 is characterized in that: The backstepping method adaptive abnormal control unit is to design the control law by backstepping method, gradually compensate the input quantization error and actuator fault; control input design, the i2-step control law is expressed as: ; Design the filter, expressed as: ;in, is the control input used to compensate for abnormal disturbances; and is the control gain, used to adjust the control intensity; is the filter output The rate of change of is the filter gain, which is used to adjust the degree of smoothing; is the control adjustment factor.

5. The abnormality monitoring system based on ventilator operating parameters according to claim 4 is characterized in that: The input quantization joint processing unit is used to model the actuator fault, and the fault model includes gain change, deviation and interference, which is expressed as: ;The quantization error satisfies: ;Introduce quantization error dynamic compensation term , the actual control signal It is expressed as: ; ;in, is the actuator output signal; is the actuator gain; is the gain variation coefficient; and are additive faults, representing random interference and deviation respectively; M is and They are control gain and dynamic adjustment coefficient respectively; and are the error state variables of the current and previous step respectively; is the filter output of the previous step; n is the step index; is the scaling factor of the quantization error; and is the gain of the nth step; is the fuzzy basis function vector of the nth step; is the control update speed factor for step n; is the precision control factor of the nth step.

6. The abnormality monitoring system based on ventilator operating parameters according to claim 5 is characterized in that: The abnormality determination unit combines asymmetric performance constraints and fuzzy logic prediction to identify abnormalities; if or , determined as an abnormal state; Otherwise it is judged as normal state; and are the minimum and maximum values ​​of the operating parameter uncertainties, respectively.

7. The abnormality monitoring system based on ventilator operating parameters according to claim 6 is characterized in that: In the data acquisition module, the ventilator operation and environment comprehensive data set includes mechanical response data, signal transmission data, environmental data and operation parameter data; the collected data is cleaned, converted and standardized.

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