Intelligent hysteroscopic fluid management system and fluctuation suppression method thereof

Abstract

The present invention discloses an intelligent hysteroscopic liquid management system and a fluctuation suppression method thereof, and relates to the technical field of liquid management of medical equipment. The intelligent hysteroscopic liquid management system and the fluctuation suppression method thereof collect the liquid pressure and flow data inside the uterine cavity and the infusion pipeline by setting a liquid fluctuation detection module, a dynamic fluctuation compensation control module, and a fluctuation suppression module, and extract the liquid fluctuation characteristics and establish a dynamic characteristic model by combining frequency domain and time domain analysis technology. Based on the fluctuation characteristic model, the liquid fluctuation index is calculated in real time, and the flow rate and the opening of the reflux valve of the infusion pump are dynamically adjusted through an adaptive control algorithm, so as to achieve effective compensation and suppression of liquid fluctuations. At the same time, through a flexible shock-absorbing component, high-frequency fluctuations in the liquid transmission process are absorbed at the physical level, and the fluctuation suppression effect is further optimized. The liquid management accuracy in hysteroscopic surgery can be significantly improved, the interference of surgical operations caused by liquid fluctuations can be reduced, and the stability of the surgery can be ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of liquid management of medical equipment, and in particular to an intelligent hysteroscope liquid management system and a fluctuation suppression method thereof. Background Art

[0002] With the continuous improvement of women's health awareness, hysteroscopy has been widely used as a minimally invasive gynecological diagnosis and treatment method. Hysteroscopic surgery diagnoses and treats intrauterine lesions under direct vision, with the advantages of less trauma, quick recovery, and shorter hospitalization time. However, during the operation, fluid needs to be injected to maintain visualization and intracavitary pressure of the uterine cavity, which requires precise control of fluid flow and pressure. With the development of intelligent medical technology, the automation and intelligence of fluid management have become key factors in improving the safety and efficiency of hysteroscopic surgery.

[0003] Existing hysteroscopic fluid management systems usually rely on manual or semi-automatic control methods to adjust fluid flow and pressure. The accuracy of fluid flow and pressure regulation is low, which can easily cause unstable intrauterine pressure, affect the surgical field of view, and even increase the risk of intraoperative complications. Fluid fluctuations during surgery cannot be effectively suppressed in time, resulting in unstable fluid control during surgery, affecting operational safety. Traditional systems fail to fully consider individual differences in patients, and are unable to dynamically adjust fluid management parameters according to the real-time progress of the surgery or the patient's physiological changes, and are not adaptable. Therefore, there is an urgent need for an intelligent fluid management system that can provide precise control and real-time feedback to improve the stability and safety of hysteroscopic surgery. Summary of the invention

[0004] In view of the deficiencies of the prior art, the present invention provides an intelligent hysteroscopic fluid management system and a fluctuation suppression method thereof, which solve the problems of the above-mentioned background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: an intelligent hysteroscopic liquid management system, comprising the following modules: a liquid fluctuation detection module, a dynamic fluctuation compensation control module, and a fluctuation suppression module; the liquid fluctuation detection module is used to collect the liquid pressure and flow data inside the uterine cavity and the infusion pipeline in real time, perform frequency domain and time domain analysis on the liquid data, extract the liquid fluctuation characteristics and establish a dynamic characteristic model; the dynamic fluctuation compensation control module is used to analyze the liquid flow characteristics based on the liquid fluctuation characteristic model, obtain the liquid fluctuation index, and adjust the infusion pump output flow rate and the reflux valve opening in real time through an adaptive control algorithm to dynamically compensate for liquid fluctuations; the fluctuation suppression module is used to absorb high-frequency fluctuations in the liquid transmission process through a flexible shock-absorbing component at the physical level, and dynamically optimize the response of the flexible shock-absorbing component according to the liquid fluctuation index.

[0006] Furthermore, the specific process of frequency domain and time domain analysis of liquid data is as follows: obtain the real-time collected liquid pressure and flow data inside the uterine cavity and the infusion pipeline, and store them in the form of time series; analyze the instantaneous change characteristics of liquid flow by calculating the pressure and flow changes at each moment, including the pressure fluctuation amplitude, change speed and fluctuation duration, evaluate the stability of liquid flow based on the statistical characteristics of instantaneous changes, and extract the periodic characteristics of fluctuations; perform discrete Fourier transform on the time domain data, convert the time domain signals of liquid pressure and flow into frequency domain signals, calculate the energy distribution of the signal in different frequency ranges, identify the main frequency components in the liquid fluctuations, and analyze the high-frequency and low-frequency fluctuations that may occur in the liquid flow; according to the frequency domain analysis results, extract the resonant frequency and interference frequency in the liquid flow, and evaluate the influence of different frequency bands on hysteroscopic operation.

[0007] Furthermore, the specific process of extracting liquid fluctuation characteristics and establishing a dynamic characteristic model is as follows: obtain the time domain and frequency domain data of liquid pressure and flow, calculate the amplitude, frequency, and periodicity of liquid fluctuations, and identify the main characteristics of liquid fluctuations, including fluctuation amplitude, frequency components, and periodicity; according to the extracted fluctuation characteristics, establish a dynamic characteristic model of liquid flow through an autoregressive integral moving average model; optimize model parameters through the least squares method to fit the nonlinear and time-varying characteristics of liquid flow; calculate the transfer function between flow and pressure to describe the dynamic response characteristics of liquid fluctuations; compare the prediction results of the dynamic characteristic model with the actual real-time collected liquid pressure and flow data to verify the fit of the model.

[0008] Furthermore, the specific process of analyzing the liquid flow characteristics and obtaining the liquid fluctuation index is as follows: denoising the liquid pressure and flow data collected in real time, using wavelet transform to remove high-frequency noise, and smoothing through a low-pass filter; calculating the local change rate, fluctuation amplitude and fluctuation period of the liquid pressure and flow based on the frequency domain and time domain analysis of the liquid data, and evaluating the instantaneous change characteristics of the fluctuation through local autoregressive analysis; combining the time domain and frequency domain analysis results, determining the weight of each feature to the fluctuation index through comprehensive calculation of the amplitude, frequency component and periodic change of the liquid fluctuation, and obtaining the liquid fluctuation index.

[0009] Furthermore, the specific process of adjusting the output flow rate of the infusion pump and the opening of the reflux valve in real time through the adaptive control algorithm is as follows: using the liquid fluctuation index as the input signal of the adaptive control algorithm for feedback input; combining the liquid fluctuation characteristic model, defining the relationship between the liquid fluctuation index and the system stability, and evaluating the influence of the fluctuation index on the liquid flow stability within the set range; initializing the adaptive control algorithm, by setting the control threshold and dynamically adjusting the parameters, continuously monitoring the liquid fluctuation index, and comparing it with the preset threshold in real time. When the fluctuation index exceeds the set stability threshold, the control signal is triggered, the adaptive control algorithm is started, and the output flow rate and the opening of the reflux valve of the infusion pump are adjusted in real time.

[0010] Furthermore, the specific process of real-time adjustment of the output flow rate and reflux valve opening of the infusion pump is as follows: based on the liquid fluctuation index, the stability and abnormal fluctuation of the liquid flow are monitored in real time, and the nonlinear relationship between the fluctuation index and the flow rate and the reflux valve opening is used for prediction; in combination with the liquid fluctuation characteristic model, based on historical data and real-time feedback, the adjustment curves of the infusion pump flow rate and the reflux valve opening are dynamically adjusted to form an adaptive adjustment strategy for different fluctuation modes; multiple dynamic threshold mechanisms are set to trigger different adjustment strategies in real time according to the liquid flow characteristics to ensure that when the fluctuation exceeds the preset stable range, the flow rate or reflux valve opening adjustment is responded and started.

[0011] Furthermore, according to the liquid fluctuation index, the specific process of dynamically optimizing the response of the flexible shock-absorbing component is as follows: calculate the liquid fluctuation index, monitor the stability and abnormal fluctuation of the liquid flow, and identify the intensity and frequency characteristics of the fluctuation; according to the relationship between the liquid fluctuation index and the response of the flexible shock-absorbing component, establish a nonlinear model, define the influence of the liquid fluctuation index on the elasticity and damping characteristics of the shock-absorbing component, evaluate the demand for the shock-absorbing effect due to the fluctuation intensity, and adjust the elastic modulus and damping coefficient of the shock-absorbing component by optimizing and adjusting the flexibility and stiffness.

[0012] An intelligent hysteroscopic liquid fluctuation suppression method comprises the following steps: S1. Real-time collection of liquid pressure and flow data inside the uterine cavity and the infusion pipeline, frequency domain and time domain analysis of the liquid data, extraction of liquid fluctuation characteristics and establishment of a dynamic characteristic model; S2. Based on the liquid fluctuation characteristic model, analysis of the liquid flow characteristics, acquisition of the liquid fluctuation index, real-time adjustment of the infusion pump output flow rate and the reflux valve opening through an adaptive control algorithm, and dynamic compensation of liquid fluctuations; S3. Physically absorbing high-frequency fluctuations in the liquid transmission process through a flexible shock-absorbing component, and dynamically optimizing the response of the flexible shock-absorbing component according to the liquid fluctuation index.

[0013] The present invention has the following beneficial effects:

[0014] (1) The intelligent hysteroscopic fluid management system collects and analyzes the pressure and flow data of the fluid in real time, combines time domain and frequency domain analysis, fluid fluctuation index evaluation, adaptive control algorithm, and dynamic adjustment strategy to achieve high-precision real-time monitoring and regulation of fluid flow, improve the fluid flow stability during hysteroscopic operation, reduce operational errors caused by fluid fluctuations, and improve surgical safety and efficiency.

[0015] (2) The intelligent hysteroscopic fluid fluctuation suppression method is based on the fluid fluctuation index and dynamic characteristic model, designs multiple dynamic threshold mechanisms, and combines flexible shock-absorbing components to optimize response and adaptive adjustment, significantly reducing the frequency and amplitude of abnormal fluid fluctuations, effectively maintaining the stability of fluid flow, and improving the intelligence and adaptability of fluid management in complex environments.

[0016] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a flow chart of the intelligent hysteroscopic fluid management system of the present invention.

[0018] Figure 2 This is a flow chart of the intelligent hysteroscopic liquid fluctuation suppression method of the present invention. DETAILED DESCRIPTION

[0019] The embodiments of the present application solve the problems of insufficient operational stability and difficulty in timely regulation of abnormal liquid fluctuations caused by liquid flow fluctuations during hysteroscopic surgery through an intelligent hysteroscopic liquid management system and its fluctuation suppression method, thereby improving the real-time monitoring accuracy and automatic adjustment capability of liquid flow, thereby significantly improving the safety and efficiency of the surgery.

[0020] The overall idea of ​​the problem in the embodiment of this application is as follows:

[0021] The liquid pressure and flow data inside the uterine cavity and the infusion pipeline are collected in real time, and the liquid data is analyzed in the frequency domain and time domain to extract the liquid fluctuation characteristics and establish a dynamic characteristic model.

[0022] Based on the liquid fluctuation characteristic model, the liquid flow characteristics are analyzed and the liquid fluctuation index is obtained. The output flow rate of the infusion pump and the opening of the reflux valve are adjusted in real time through the adaptive control algorithm to dynamically compensate for the liquid fluctuation.

[0023] On a physical level, high-frequency fluctuations in liquid transmission are absorbed by flexible shock-absorbing components, and the response of the flexible shock-absorbing components is dynamically optimized based on the liquid fluctuation index.

[0024] See also Figure 1The embodiment of the present invention provides a technical solution: an intelligent hysteroscopic liquid management system, comprising the following modules: a liquid fluctuation detection module, a dynamic fluctuation compensation control module, and a fluctuation suppression module; the liquid fluctuation detection module is used to collect the liquid pressure and flow data inside the uterine cavity and the infusion pipeline in real time, perform frequency domain and time domain analysis on the liquid data, extract the liquid fluctuation characteristics and establish a dynamic characteristic model; the dynamic fluctuation compensation control module is used to analyze the liquid flow characteristics based on the liquid fluctuation characteristic model, obtain the liquid fluctuation index, and adjust the infusion pump output flow rate and the reflux valve opening in real time through an adaptive control algorithm to dynamically compensate for liquid fluctuations; the fluctuation suppression module is used to absorb high-frequency fluctuations in the liquid transmission process through a flexible shock-absorbing component at the physical level, and dynamically optimize the response of the flexible shock-absorbing component according to the liquid fluctuation index.

[0025] In this implementation scheme, the liquid fluctuation detection module is responsible for real-time acquisition of liquid pressure and flow data inside the uterine cavity and infusion pipeline, and extracts the fluctuation characteristics of the liquid through frequency domain analysis (analyzing the frequency components of the signal, such as periodic fluctuations and frequency distribution) and time domain analysis (analyzing the trend of signal changes over time, such as fluctuation amplitude). By modeling these characteristic data, a dynamic characteristic model of liquid fluctuations is established to provide a scientific basis for subsequent control and suppression. The dynamic fluctuation compensation control module is based on the liquid fluctuation characteristic model and calculates the liquid fluctuation index by analyzing the flow characteristics of the liquid. The fluctuation index is an important parameter for measuring the stability of liquid flow, which is used to quantify the amplitude, frequency and change trend of fluctuations. The module uses an adaptive control algorithm (an algorithm that dynamically adjusts control parameters according to real-time input) to use the liquid fluctuation index as a feedback signal to adjust the output flow rate of the infusion pump and the opening of the reflux valve in real time to achieve dynamic compensation of liquid fluctuations. This adjustment can quickly respond to changes in flow state and ensure the smoothness of liquid flow. The fluctuation suppression module absorbs high-frequency fluctuations generated during liquid transmission through a flexible shock-absorbing component at the physical level. The flexible shock-absorbing component is an elastic structure that absorbs and weakens vibrations and shocks in the fluid through deformation. The flexible shock-absorbing component is made of silicone and polyurethane elastomer. Its exterior is a hollow bellows structure that can absorb high-frequency fluctuations through deformation and quickly restore to its original state. It integrates pressure sensors and acceleration sensors inside to monitor liquid fluctuations in real time and transmit data to the system control module. The component is installed at the outlet of the infusion pump and connected to the infusion pipeline by a quick buckle method to ensure sealing and stability. At the same time, an anti-vibration pad is set on the contact surface between the component and the pipeline to reduce vibration transmission. Through a closed-loop control system, the flexible shock-absorbing component dynamically adjusts its elastic modulus and damping coefficient according to the real-time calculated liquid fluctuation index, so that the component increases stiffness to offset high-frequency impact when the fluctuation intensity increases, and restores flexibility when the fluctuation decreases to reduce the impact on liquid flow, thereby ensuring the stability and shock absorption effect of the liquid transmission process. The module dynamically optimizes the response characteristics of the shock-absorbing component according to the real-time calculated liquid fluctuation index, so that it can automatically adjust according to different fluctuation conditions to maximize the absorption and suppression of high-frequency fluctuations.

[0026] Specifically, the specific process of frequency domain and time domain analysis of liquid data is as follows: obtain the real-time collected liquid pressure and flow data inside the uterine cavity and the infusion pipeline, and store them in the form of time series; analyze the instantaneous change characteristics of liquid flow by calculating the pressure and flow changes at each moment, including the pressure fluctuation amplitude, change speed and fluctuation duration, and evaluate the stability of liquid flow based on the statistical characteristics of instantaneous changes, and extract the periodic characteristics of fluctuations; perform discrete Fourier transform on the time domain data, convert the time domain signals of liquid pressure and flow into frequency domain signals, calculate the energy distribution of the signal in different frequency ranges, identify the main frequency components in the liquid fluctuations, and analyze the high-frequency and low-frequency fluctuations that may occur in the liquid flow; according to the frequency domain analysis results, extract the resonant frequency and interference frequency in the liquid flow, and evaluate the influence of different frequency bands on hysteroscopic operation.

[0027] In this implementation scheme, time domain analysis: Instantaneous change feature analysis: By calculating the pressure and flow changes at each moment, the instantaneous change characteristics of the liquid flow are analyzed, such as the pressure fluctuation amplitude (the gap between the peak and the trough), the change speed (the rate of pressure change), and the fluctuation duration (the length of time the fluctuation is maintained). Stability evaluation: By statistically analyzing these instantaneous change characteristics, the stability of the liquid flow is evaluated. This evaluation can help identify whether there are abnormal fluctuations or irregular changes, thereby providing a basis for subsequent control decisions. Periodic feature extraction: Based on the statistical characteristics of instantaneous changes, the periodic characteristics of the liquid flow, that is, the repetitive period of liquid fluctuations, are extracted to understand the periodic law of the flow. Frequency domain analysis: Fourier transform: By performing discrete Fourier transform (DFT) on the time domain data, the time domain signals of the liquid pressure and flow are converted into frequency domain signals. This conversion helps to reveal the frequency components in the liquid flow. Frequency energy distribution: The energy distribution of the signal in different frequency ranges is calculated to identify the main frequency components in the liquid fluctuations. High-frequency components may be related to rapid fluctuations or disturbances of the liquid, while low-frequency components may indicate the stability or slow fluctuations of the liquid flow. Resonance frequency and interference frequency analysis: Based on the results of frequency domain analysis, the resonance frequency and interference frequency in liquid flow are extracted. Resonance frequency refers to the frequency at which liquid flow in certain frequency bands may cause system resonance, while interference frequency refers to the fluctuating interference frequency that may be generated by external factors. These frequencies are critical for hysteroscopic operation, because the resonance frequency may affect the operation accuracy, while the interference frequency may affect the stability of the equipment. Assess the impact on hysteroscopic operation: Through the results of frequency domain analysis, the degree of impact of liquid fluctuations in different frequency bands on hysteroscopic surgical operations is evaluated. For example, high-frequency fluctuations may cause greater interference to the accuracy of the equipment, while low-frequency fluctuations may affect the stability of liquid flow during surgery, thereby affecting the accuracy of the operation.

[0028] Specifically, the specific process of extracting liquid fluctuation characteristics and establishing a dynamic characteristic model is as follows: obtain the time domain and frequency domain data of liquid pressure and flow, calculate the amplitude, frequency, and periodicity of liquid fluctuations, and identify the main characteristics of liquid fluctuations, including fluctuation amplitude, frequency components, and periodicity; according to the extracted fluctuation characteristics, establish a dynamic characteristic model of liquid flow through an autoregressive integral moving average model; optimize model parameters through the least squares method to fit the nonlinear and time-varying characteristics of liquid flow; calculate the transfer function between flow and pressure to describe the dynamic response characteristics of liquid fluctuations; compare the predicted results of the dynamic characteristic model with the actual real-time collected liquid pressure and flow data to verify the fit of the model.

[0029] In this implementation scheme, the time domain and frequency domain data of liquid pressure and flow are obtained: the time domain data reflects the trend of liquid flow over time, such as the instantaneous change of flow velocity and pressure; the frequency domain data analyzes the frequency components of liquid flow through Fourier transform, revealing the characteristics of periodic fluctuations and high-frequency noise. This lays the foundation for subsequent feature extraction and modeling. Calculate the amplitude, frequency, and periodicity of liquid fluctuations, and identify the main features: extract the fluctuation amplitude (the difference between the peak and valley values ​​of liquid pressure or flow), frequency component (frequency distribution of fluctuations), and periodicity (the time interval of repeated fluctuations) through data analysis tools. These features are used to quantitatively describe the state of liquid flow and are important inputs for model building. The dynamic characteristics model of liquid flow is established through the autoregressive integrated moving average model (ARIMA): The ARIMA model is a time series prediction model that combines autoregression (using historical values ​​to predict current values), integration (eliminating trend changes), and moving average (smoothing random fluctuations). It is suitable for describing complex dynamic characteristics in liquid flow, including non-stationarity and short-term dependence. Optimize model parameters by least squares method: Least squares method is a mathematical optimization method used to minimize the sum of squared errors between predicted values ​​and actual values ​​during model fitting. This method is used to determine the parameters in the ARIMA model so that the model can more accurately describe the nonlinearity (the complex relationship between pressure and flow) and time-varying characteristics (dynamic behavior that changes over time) of liquid flow. Calculate the transfer function between flow and pressure: The transfer function is a mathematical tool used to describe the dynamic response relationship between input (flow) and output (pressure). Through this function, the response characteristics of the liquid system at different frequencies can be analyzed, and the dynamic characteristics of liquid flow, such as delay and gain, can be quantified. Verify the fit of the dynamic characteristics model: Compare the liquid pressure and flow data predicted by the model with the actual data collected in real time to verify the accuracy and reliability of the model. The degree of fit reflects the model's ability to describe the actual liquid flow characteristics.

[0030] Specifically, the specific process of analyzing the liquid flow characteristics and obtaining the liquid fluctuation index is as follows: denoising the liquid pressure and flow data collected in real time, using wavelet transform to remove high-frequency noise, and smoothing through a low-pass filter; calculating the local change rate, fluctuation amplitude and fluctuation period of the liquid pressure and flow based on the frequency domain and time domain analysis of the liquid data, and evaluating the instantaneous change characteristics of the fluctuation through local autoregressive analysis; combining the time domain and frequency domain analysis results, determining the weight of each feature to the fluctuation index through comprehensive calculation of the amplitude, frequency component and periodic change of the liquid fluctuation, and obtaining the liquid fluctuation index.

[0031] In this embodiment, the pressure of the collected liquid and flow Data denoising: Wavelet transform removes high-frequency noise: Discrete wavelet transform (DWT) is used to decompose the data at multiple scales, and the threshold is set. Remove high-frequency noise components and retain the low-frequency part. The formula is: ;in: is the original signal, is the low frequency wavelet coefficient, is the high frequency detail coefficient, , They are scale function and detail function respectively. Low-pass filter smoothing: Use low-pass filter to smooth the data to eliminate mutation noise. The filter expression is: ;in: is the filtered signal, is the length of the smoothing time window. The calculation process of the local change rate, fluctuation amplitude and period of liquid flow is as follows. The liquid fluctuation characteristics are calculated by time domain and frequency domain analysis: Local change rate: ;in: is the local rate of change, , It is the instantaneous value of liquid pressure and flow. Fluctuation amplitude: ;in: is the fluctuation range, is the number of samples, , is the mean value of pressure and flow. Fluctuation period: ;in: is the fluctuation period, The main frequency is calculated by fast Fourier transform (FFT). The liquid fluctuation index is calculated by combining the analysis results of time domain and frequency domain. ; ;in: is the liquid volatility index, is the weight, reflecting the importance of each feature to the volatility index, is the local rate of change, is the fluctuation range, is the main frequency. The weight is determined by the normalization method: in: Features The standard deviation of .

[0032] Specifically, the specific process of adjusting the output flow rate of the infusion pump and the opening of the reflux valve in real time through the adaptive control algorithm is as follows: according to the liquid fluctuation index as the input signal of the feedback input adaptive control algorithm; combined with the liquid fluctuation characteristic model, define the relationship between the liquid fluctuation index and the system stability, and evaluate the impact of the fluctuation index on the stability of the liquid flow within the set range; initialize the adaptive control algorithm, and continuously monitor the liquid fluctuation index by setting the control threshold and dynamically adjusting the parameters, and compare it with the preset threshold in real time. When the fluctuation index exceeds the set stability threshold, the control signal is triggered, the adaptive control algorithm is started, and the output flow rate and the opening of the reflux valve of the infusion pump are adjusted in real time.

[0033] In this embodiment, the liquid fluctuation index is a comprehensive indicator used to characterize the stability and fluctuation amplitude of the current liquid flow. The adaptive control algorithm uses this index as an input signal and dynamically evaluates the state of the liquid flow according to its real-time change trend. According to the liquid fluctuation characteristic model, the corresponding relationship between the liquid fluctuation index and the system stability is defined. This relationship is used to determine whether the current liquid flow is in an ideal state: when the fluctuation index is within the set stable range, it indicates that the liquid flows smoothly; if the fluctuation index exceeds the preset stable range, it indicates that the liquid has abnormal fluctuations, which may have an adverse effect on the operation process or system function. During the startup phase of the system, the control algorithm will be initialized: set the threshold: define the upper and lower limits for the liquid fluctuation index as a basis for judging whether the liquid flow is stable; adjust the parameters: according to the system requirements, set the initial flow rate and valve opening change rules to ensure that the algorithm can respond quickly to fluctuations. The control system continuously monitors the changes in the liquid fluctuation index and evaluates in real time whether it is within the stable range: if the fluctuation index is within the set range, the system maintains the current working state; if the fluctuation index exceeds the range, the system will trigger the corresponding control operation according to the degree of deviation.

[0034] When the fluctuation index exceeds the stability threshold, the control system will start the adaptive control algorithm to adjust the flow rate of the infusion pump and the opening of the reflux valve in real time according to the fluctuation situation: Adjust the flow rate: According to the liquid flow state, appropriately increase or decrease the output flow rate of the infusion pump to compensate for the impact of fluctuations; Adjust the valve opening: Optimize the opening setting of the reflux valve, adjust the resistance of the liquid flow path, and alleviate flow anomalies. The entire control process has dynamic adjustment capabilities, and can continuously optimize the flow rate and valve opening based on real-time feedback to ensure that liquid fluctuations quickly return to the stable range. In this way, the adaptive control algorithm can efficiently handle complex liquid flow problems and effectively improve the stability and accuracy of the system.

[0035] Specifically, the specific process of real-time adjustment of the output flow rate and reflux valve opening of the infusion pump is as follows: based on the liquid fluctuation index, the stability and abnormal fluctuation of the liquid flow are monitored in real time, and the nonlinear relationship between the fluctuation index and the flow rate and the reflux valve opening is used for prediction; combined with the liquid fluctuation characteristic model, based on historical data and real-time feedback, the adjustment curves of the infusion pump flow rate and the reflux valve opening are dynamically adjusted to form an adaptive adjustment strategy for different fluctuation modes; multiple dynamic threshold mechanisms are set to trigger different adjustment strategies in real time according to the liquid flow characteristics to ensure that when the fluctuation exceeds the preset stable range, the flow rate or reflux valve opening adjustment is responded and started.

[0036] In this embodiment, based on the liquid fluctuation index, the stability and abnormal fluctuation of liquid flow are monitored in real time: first, the stability of liquid flow is evaluated in real time by monitoring the liquid fluctuation index. The liquid fluctuation index reflects the flow fluctuation of the liquid in the pipeline, and it can effectively reveal the instability and abnormal fluctuation in the liquid flow. For example, when the flow fluctuation is too large, it may indicate that the system has pressure fluctuations or flow changes. By continuously monitoring the fluctuation index, the system can promptly detect abnormal fluctuations or instability in the flow as a basis for subsequent adjustments. The nonlinear relationship between the fluctuation index and the flow rate and the reflux valve opening is used for prediction: there is a complex nonlinear relationship between the stability of liquid flow and the fluctuation index. In this process, the system can predict the adjustment requirements of the flow rate and the reflux valve opening by analyzing the relationship model between the fluctuation index and the flow rate and the reflux valve opening. According to the changes in the fluctuation index, the system can predict whether the current flow rate or the reflux valve opening needs to be changed, so as to respond in advance to ensure the stability of liquid flow. Combined with the liquid fluctuation characteristic model, the adjustment curve of the infusion pump flow rate and the reflux valve opening is dynamically adjusted based on historical data and real-time feedback: In order to control the liquid flow more accurately, the system combines the liquid fluctuation characteristic model and predicts the changes in liquid flow based on historical data and real-time feedback. The liquid fluctuation characteristic model reflects the fluctuation patterns that may occur in the flow. By analyzing historical data and real-time feedback, the system can adjust the flow rate of the infusion pump and the opening of the reflux valve in real time. This adjustment mechanism can cope with different fluctuation patterns and form an adaptive adjustment strategy to ensure that the adjustment of the flow rate and the reflux valve can accurately match the requirements of different fluctuations. Set a multiple dynamic threshold mechanism to trigger different adjustment strategies in real time according to the liquid flow characteristics: The multiple dynamic threshold mechanism is to cope with the changes in liquid flow in different situations and ensure that the system can respond accurately when facing various fluctuation patterns. Set different thresholds to define different degrees of fluctuation. When the liquid flow exceeds the preset stable range, the system will automatically trigger different adjustment strategies according to the current fluctuation intensity. For example, when the liquid fluctuation is small and still within the stable range, the system may maintain the current flow rate and reflux valve opening; when the fluctuation is too large, the system will start adjusting the flow rate or reflux valve opening to avoid the impact of excessive fluctuations on the system. Ensure that when the fluctuation exceeds the preset stable range, respond and start the flow rate or reflux valve opening adjustment: Finally, when the liquid fluctuation is detected to exceed the set stable range, the system will automatically start the adjustment mechanism. Specifically, this may involve adjusting the infusion pump flow rate or reflux valve opening to ensure that the flow returns to a stable state. Through this adaptive adjustment, the system can dynamically respond to flow fluctuations in different situations to ensure that the liquid flow is always maintained in the optimal state.

[0037] Specifically, according to the liquid fluctuation index, the specific process of dynamically optimizing the response of the flexible shock-absorbing component is as follows: calculate the liquid fluctuation index, monitor the stability and abnormal fluctuation of the liquid flow, and identify the intensity and frequency characteristics of the fluctuation; according to the relationship between the liquid fluctuation index and the response of the flexible shock-absorbing component, establish a nonlinear model, define the influence of the liquid fluctuation index on the elasticity and damping characteristics of the shock-absorbing component, evaluate the demand for the shock-absorbing effect due to the fluctuation intensity, and adjust the elastic modulus and damping coefficient of the shock-absorbing component by optimizing and adjusting the flexibility and stiffness.

[0038] In this embodiment, the intensity and frequency characteristics of the fluctuations are identified: by analyzing the liquid fluctuations, the system can extract the intensity and frequency characteristics of the fluctuations. The intensity describes the amplitude of the fluctuations, while the frequency characteristics reflect the periodicity of the fluctuations. For example, high-intensity, low-frequency fluctuations in liquid flow may require stronger damping, while low-intensity, high-frequency fluctuations may only require moderate adjustments. These characteristics help to further understand the dynamics of liquid flow, thereby providing a basis for the adjustment of the flexible damping component. Based on the relationship between the liquid fluctuation index and the response of the flexible damping component, a nonlinear model is established: Based on the relationship between the liquid fluctuation index and the flexible damping component, a nonlinear model is established to dynamically predict the impact of liquid fluctuations on the response of the damping component. The model takes into account the nonlinear characteristics of liquid flow, such as the unsteadiness of the flow and the complex fluctuation pattern. Through this model, the system can more accurately simulate the impact of liquid fluctuations on the performance of the damping component. Define the impact of the liquid fluctuation index on the elasticity and damping characteristics of the damping component: The liquid fluctuation index not only affects the elasticity (reflecting the softness of the component) and the damping characteristics (reflecting the energy dissipation capacity of the component) of the damping component, but also this impact is nonlinear. By defining the specific impact of the fluctuation index on these characteristics, the system can quantify how to adjust the response of the shock absorber component according to the intensity and frequency of the fluctuation. For example, when the fluctuation is strong, the damping coefficient may need to be increased to better absorb energy; while when the fluctuation is weak, the elasticity may need to be increased to maintain the stability of the system. Evaluate the demand for shock absorption effect due to the intensity of fluctuation: According to the intensity of fluctuation, the system evaluates the demand for shock absorption effect. In the case of strong fluctuation, the demand for shock absorption effect is higher, so a larger damping force and appropriate stiffness may be required; while in weak fluctuation, the shock absorption requirement is relatively low. By evaluating the intensity of fluctuation and the demand for shock absorption effect in real time, the system can accurately adjust the performance of the shock absorber component to adapt to the current fluctuation conditions. Optimize the flexibility and stiffness of the shock absorber component, adjust its elastic modulus and damping coefficient: Finally, based on the above analysis results, the system optimizes the flexibility and stiffness of the shock absorber component to achieve the best shock absorption effect. This includes adjusting the elastic modulus (reflecting its ability to expand and contract) and the damping coefficient (reflecting its ability to dissipate energy) of the component. By dynamically adjusting these parameters, the system can provide appropriate shock absorption response under different fluctuation modes to ensure that the fluctuation of liquid flow does not have an adverse effect on the overall system.

[0039] See also Figure 2 The intelligent hysteroscopic liquid fluctuation suppression method includes the following steps: S1. Real-time collection of liquid pressure and flow data inside the uterine cavity and the infusion pipeline, frequency domain and time domain analysis of the liquid data, extraction of liquid fluctuation characteristics and establishment of a dynamic characteristic model; S2. Based on the liquid fluctuation characteristic model, the liquid flow characteristics are analyzed, the liquid fluctuation index is obtained, and the output flow rate of the infusion pump and the opening of the reflux valve are adjusted in real time through an adaptive control algorithm to dynamically compensate for liquid fluctuations; S3. At the physical level, high-frequency fluctuations in the liquid transmission process are absorbed by a flexible shock-absorbing component, and the response of the flexible shock-absorbing component is dynamically optimized according to the liquid fluctuation index.

[0040] In this implementation scheme, step S1: real-time collection of liquid pressure and flow data inside the uterine cavity and the infusion pipeline, and frequency domain and time domain analysis of these data, extraction of liquid fluctuation characteristics, and then establishment of a dynamic characteristic model. This model helps to understand the laws and characteristics of liquid fluctuations. Step S2: Based on the liquid fluctuation characteristic model, analyze the flow characteristics of the liquid and calculate the liquid fluctuation index. Through the adaptive control algorithm, adjust the flow rate of the infusion pump and the opening of the reflux valve according to the real-time liquid fluctuation situation, dynamically compensate for the fluctuation of the liquid, and ensure stable flow. Step S3: At the physical level, use a flexible shock-absorbing component to absorb high-frequency fluctuations in the liquid transmission process, and dynamically optimize the response of the shock-absorbing component according to the liquid fluctuation index to further reduce the impact of liquid fluctuations on the system.

[0041] In summary, this application has at least the following effects:

[0042] The intelligent hysteroscopic liquid management system and its fluctuation suppression method can accurately monitor the liquid fluctuation and establish a dynamic characteristic model by collecting liquid pressure and flow data in real time, performing frequency domain and time domain analysis, and extracting liquid fluctuation characteristics, thereby providing a precise basis for the stable control of liquid flow. Based on the liquid fluctuation characteristic model, the output flow rate and the opening of the reflux valve of the infusion pump are adjusted in real time by using an adaptive control algorithm to effectively compensate for liquid fluctuations, ensure that the flow rate and liquid pressure change within a predetermined range, and reduce the impact of liquid fluctuations on hysteroscopic surgical operations. The high-frequency fluctuations in liquid transmission are absorbed by a flexible shock-absorbing component, and the response of the shock-absorbing component is dynamically optimized according to the liquid fluctuation index, so as to further reduce the unstable factors caused by the fluctuations and ensure that the liquid flow during the operation is smoother and more accurate. The application of this method can effectively suppress liquid fluctuations, improve the stability of liquid flow in hysteroscopic surgery, and thus improve the accuracy and safety of surgical operations, and reduce errors or operational difficulties caused by liquid fluctuations.

[0043] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0044] The present invention is described with reference to flowcharts and / or block diagrams of systems, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0045] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0046] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0047] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0048] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. Intelligent hysteroscopic fluid management system, characterized in that: It includes the following modules: liquid fluctuation detection module, dynamic fluctuation compensation control module, fluctuation suppression module; The liquid fluctuation detection module is used to collect the liquid pressure and flow data inside the uterine cavity and the infusion pipeline in real time, perform frequency domain and time domain analysis on the liquid data, extract the liquid fluctuation characteristics and establish a dynamic characteristic model; The dynamic fluctuation compensation control module is used to analyze the liquid flow characteristics based on the liquid fluctuation characteristic model, obtain the liquid fluctuation index, and adjust the output flow rate of the infusion pump and the opening of the reflux valve in real time through the adaptive control algorithm to dynamically compensate for the liquid fluctuation; The fluctuation suppression module is used to absorb high-frequency fluctuations in the liquid transmission process through the flexible shock-absorbing component at the physical level, and dynamically optimize the response of the flexible shock-absorbing component according to the liquid fluctuation index; The specific process of extracting liquid wave characteristics and establishing a dynamic characteristic model is as follows: Obtain time domain and frequency domain data of liquid pressure and flow, calculate the amplitude, frequency, and periodicity of liquid fluctuations, and identify the main characteristics of liquid fluctuations, including fluctuation amplitude, frequency components, and periodicity; According to the extracted fluctuation characteristics, the dynamic characteristic model of liquid flow is established through the autoregressive integrated moving average model; the model parameters are optimized through the least squares method to fit the nonlinear and time-varying characteristics of liquid flow; Calculate the transfer function between flow and pressure to describe the dynamic response characteristics of liquid fluctuations; Compare the prediction results of the dynamic characteristic model with the actual real-time collected liquid pressure and flow data to verify the fit of the model; The process of obtaining the liquid wave characteristic model is as follows: ;in: is the liquid volatility index, is the weight, reflecting the importance of each feature to the volatility index, is the local rate of change, is the fluctuation range, is the main frequency, and the weight is determined by the normalization method: in: Features The standard deviation of The specific process of adjusting the output flow rate of the infusion pump and the opening of the reflux valve in real time by the adaptive control algorithm is as follows: The liquid fluctuation index is used as an input signal for the adaptive control algorithm as feedback input; Combined with the liquid fluctuation characteristic model, the relationship between the liquid fluctuation index and system stability is defined, and the impact of the fluctuation index on the liquid flow stability within the set range is evaluated; Initialize the adaptive control algorithm, continuously monitor the liquid fluctuation index by setting the control threshold and dynamically adjusting the parameters, and compare it with the preset threshold in real time. When the fluctuation index exceeds the set stable threshold, trigger the control signal, start the adaptive control algorithm, and adjust the output flow rate of the infusion pump and the opening of the reflux valve in real time.

2. The intelligent hysteroscopic fluid management system according to claim 1, characterized in that: The specific process of frequency domain and time domain analysis of liquid data is as follows: Obtain the real-time collected liquid pressure and flow data inside the uterine cavity and the infusion pipeline, and store them in the form of time series; By calculating the pressure and flow changes at each moment, the instantaneous change characteristics of liquid flow are analyzed, including the pressure fluctuation amplitude, change speed and fluctuation duration. Based on the statistical characteristics of instantaneous changes, the stability of liquid flow is evaluated and the periodic characteristics of fluctuations are extracted. Perform discrete Fourier transform on time domain data, convert time domain signals of liquid pressure and flow into frequency domain signals, calculate the energy distribution of signals in different frequency ranges, identify the main frequency components in liquid fluctuations, and analyze high-frequency and low-frequency fluctuations that may occur in liquid flow; According to the results of frequency domain analysis, the resonance frequency and interference frequency in the liquid flow were extracted, and the influence of different frequency bands on hysteroscopic operation was evaluated.

3. The intelligent hysteroscopic fluid management system according to claim 2, characterized in that: The specific process of analyzing the liquid flow characteristics and obtaining the liquid fluctuation index is as follows: De-noising the real-time collected liquid pressure and flow data, using wavelet transform to remove high-frequency noise, and smoothing through a low-pass filter; Based on the frequency domain and time domain analysis of liquid data, the local change rate, fluctuation amplitude and fluctuation period of liquid pressure and flow are calculated, and the instantaneous change characteristics of fluctuations are evaluated through local autoregressive analysis; Combining the analysis results of time domain and frequency domain, the liquid fluctuation index is obtained by comprehensively calculating the amplitude, frequency component and periodic change of liquid fluctuation, determining the weight of each feature to the fluctuation index.

4. The intelligent hysteroscopic fluid management system according to claim 3, characterized in that: The specific process of real-time adjustment of the output flow rate of the infusion pump and the opening of the reflux valve is as follows: Based on the liquid fluctuation index, the stability and abnormal fluctuation of liquid flow are monitored in real time, and the nonlinear relationship between the fluctuation index and the flow rate and the opening of the reflux valve is used for prediction; Combined with the liquid fluctuation characteristic model, based on historical data and real-time feedback, the regulation curves of the infusion pump flow rate and the reflux valve opening are dynamically adjusted to form an adaptive regulation strategy for different fluctuation modes; Multiple dynamic threshold mechanisms are set to trigger different adjustment strategies in real time according to the liquid flow characteristics, ensuring that when the fluctuation exceeds the preset stable range, the flow rate or reflux valve opening adjustment is responded and started.

5. The intelligent hysteroscopic fluid management system according to claim 4, characterized in that: The specific process of dynamically optimizing the response of the flexible shock absorbing component according to the liquid fluctuation index is as follows: Calculate the liquid fluctuation index, monitor the stability and abnormal fluctuation of liquid flow, and identify the intensity and frequency characteristics of fluctuations; According to the relationship between the liquid fluctuation index and the response of the flexible shock-absorbing component, a nonlinear model is established to define the influence of the liquid fluctuation index on the elasticity and damping characteristics of the shock-absorbing component, evaluate the demand of the fluctuation intensity on the shock-absorbing effect, and adjust the elastic modulus and damping coefficient of the shock-absorbing component by optimizing and adjusting the flexibility and stiffness.

6. An intelligent hysteroscopic liquid fluctuation suppression method, using the intelligent hysteroscopic liquid management system according to any one of claims 1 to 5, characterized in that: The following steps are involved: S1. Collect the liquid pressure and flow data inside the uterine cavity and the infusion pipeline in real time, perform frequency domain and time domain analysis on the liquid data, extract the liquid fluctuation characteristics and establish a dynamic characteristic model; S2. Based on the liquid fluctuation characteristic model, analyze the liquid flow characteristics, obtain the liquid fluctuation index, and adjust the infusion pump output flow rate and reflux valve opening in real time through the adaptive control algorithm to dynamically compensate for liquid fluctuations; S3. On a physical level, high-frequency fluctuations in liquid transmission are absorbed by flexible shock-absorbing components, and the response of the flexible shock-absorbing components is dynamically optimized according to the liquid fluctuation index.

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