A surgical instrument sterilization method and system

Abstract

This application provides a surgical instrument sterilization method and system, relating to the field of medical device sterilization, comprising: continuously collecting pressure data within a sterilization chamber, and calculating the pressure change rate and pressure fluctuation amplitude based on the pressure data; presetting an ideal pressure change path; comparing the pressure change rate with the corresponding ideal pressure change rate in the ideal pressure change path to obtain an evaluation result; performing frequency characteristic analysis on the collected pressure data, and identifying transient fluctuation patterns in the pressure data to identify fluctuations related to microscopic fatigue damage of the instrument materials; determining whether a microscopic abnormal pattern exists based on the frequency characteristic analysis result and the transient fluctuation pattern identification result; and adjusting the operating parameters of the pressure regulation actuator of the sterilization chamber based on the evaluation result and the microscopic abnormal pattern, thereby ensuring the sterilization effect while effectively avoiding instrument damage and improving sterilization efficiency and safety.

Description

Technical Field

[0001] This application relates to the field of medical device disinfection technology, and more specifically, to a method and system for disinfecting surgical instruments. Background Technology

[0002] Sterilization of surgical instruments is a crucial step in the healthcare field, directly impacting patient safety and the quality of medical care. In hospital instrument processing centers, a large number of used surgical instruments undergo rigorous cleaning and sterilization daily to eliminate microbial contamination. Pressure control within the sterilization chamber is key to ensuring sterilization effectiveness and protecting the structural integrity of the instruments. However, traditional pressure control methods often struggle to adapt to various complex situations, requiring trade-offs between efficiency and safety; simply focusing on the pressure value itself is insufficient to guarantee the structural integrity of the instruments.

[0003] In practice, even after initial cleaning, instruments may still retain cleaning fluid or air in their internal cavities or complex structures before entering the sterilization chamber. These residues can have unexpected effects during sterilization, especially when pressure changes rapidly. For example, during vacuuming, air bubbles remaining in the tiny spaces inside the instruments can expand rapidly, applying localized stress to the surrounding instrument walls; conversely, during pressurization, these bubbles are compressed. If these bubbles cannot be expelled in time, they can form localized "air cushions," hindering the full penetration of the sterilization medium or repeatedly applying mechanical stress to the internal structure of the instruments under pressure fluctuations. This localized pressure response caused by residues limits the effectiveness of overall chamber pressure control. Summary of the Invention

[0004] The purpose of this application is to provide a surgical instrument disinfection method and system, which has the advantages of being able to adaptively adjust the pressure change rate according to the dynamic changes in pressure inside the disinfection chamber and the microscopic state of the instruments, thereby ensuring the disinfection effect while effectively avoiding instrument damage and improving disinfection efficiency and safety.

[0005] This application provides a method for sterilizing surgical instruments, which involves adjusting the pressure of a sterilization chamber to ensure the sterilization effect of the chamber. The method includes the following steps:

[0006] Continuously collect pressure data inside the disinfection chamber, and calculate the rate of pressure change and the magnitude of pressure fluctuation based on the pressure data;

[0007] Pre-set the ideal pressure change path;

[0008] The pressure change rate is compared with the corresponding ideal pressure change rate in the ideal pressure change path, and the pressure fluctuation amplitude is evaluated to obtain the evaluation result.

[0009] Frequency characteristic analysis was performed on the collected pressure data to identify energy changes related to internal residues or microstructures of the instrument.

[0010] Transient fluctuation patterns in pressure data are identified to pinpoint fluctuations associated with microscopic fatigue damage in instrument materials.

[0011] Based on the frequency characteristic analysis results and transient fluctuation pattern identification results, determine whether there are microscopic abnormal patterns;

[0012] Based on the assessment results and micro-anomaly patterns, the operating parameters of the pressure regulating actuator in the disinfection chamber are adjusted to change the rate of pressure change. Specifically, if the assessment results show that the pressure change is stable and the deviation from the ideal pressure change path is within an acceptable range, the rate of pressure change is increased; if the assessment results show that the pressure fluctuates violently or deviates significantly from the ideal pressure change path, the rate of pressure change is decreased or the pressure change process is suspended.

[0013] The above-mentioned scheme can adaptively adjust the rate of pressure change based on the dynamic changes in pressure within the disinfection chamber and the microscopic state of the instruments, thereby ensuring the disinfection effect while effectively avoiding instrument damage and improving disinfection efficiency and safety.

[0014] Furthermore, this application also proposes a surgical instrument sterilization system for regulating the pressure of the sterilization chamber to ensure the sterilization effect of the chamber. The system includes:

[0015] The pressure data processing module is used to continuously collect pressure data inside the disinfection chamber and calculate the rate of pressure change and the magnitude of pressure fluctuation based on the pressure data.

[0016] Ideal path storage module, used to store ideal pressure change paths;

[0017] The evaluation result generation module is used to compare the pressure change rate with the corresponding ideal pressure change rate in the ideal pressure change path, evaluate whether the pressure fluctuation amplitude exceeds the preset threshold, and generate evaluation results.

[0018] The frequency characteristic analysis module is used to perform frequency characteristic analysis on the collected pressure data in order to identify energy changes related to internal residues or microstructures of the instrument.

[0019] The transient fluctuation pattern recognition module is used to identify transient fluctuation patterns in pressure data in order to identify fluctuations related to micro-fatigue damage of instrument materials.

[0020] The judgment module is used to determine whether there are microscopic abnormal patterns based on the frequency feature analysis results and transient fluctuation pattern identification results.

[0021] The pressure regulation control module is used to adjust the operating parameters of the pressure regulation actuator of the disinfection chamber according to the evaluation results and micro-anomaly patterns, so as to change the pressure change rate. Specifically, if the evaluation results show that the pressure change is stable and the deviation from the ideal pressure change path is within an acceptable range, the pressure change rate is increased; if the evaluation results show that the pressure fluctuates violently or deviates significantly from the ideal pressure change path, the pressure change rate is decreased or the pressure change process is paused.

[0022] In summary, the surgical instrument disinfection method and system provided in this application, through multi-dimensional analysis of pressure data within the disinfection chamber, including pressure change rate, fluctuation amplitude, frequency characteristics, and transient fluctuation patterns, combined with the judgment of preset ideal paths and microscopic anomaly patterns, achieves adaptive adjustment of the operating parameters of the pressure regulating actuator, thereby ensuring disinfection effectiveness while effectively avoiding instrument damage and improving disinfection efficiency and safety. Attached Figure Description

[0023] Figure 1 This is a schematic diagram of a surgical instrument disinfection method provided in this application.

[0024] Figure 2 This is a schematic diagram of a surgical instrument sterilization system provided in this application. Detailed Implementation

[0025] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0026] This application initially considered optimizing the sterilization process by pre-setting a fixed pressure change curve. However, this method struggles to adapt to the differences in the characteristics of various instruments and the dynamic changes within the sterilization chamber, failing to achieve effective protection of the instruments and the effective function of the sterilization medium. Therefore, this application further considers introducing a real-time monitoring and feedback mechanism to dynamically adjust pressure changes based on actual pressure data. However, relying solely on macroscopic pressure data is insufficient to reveal the microscopic state and potential damage within the instruments. Therefore, this application further explores combining frequency characteristic analysis and transient fluctuation pattern recognition of pressure data to obtain deeper information about the instrument's state, thereby enabling adjustments to the sterilization chamber pressure based on actual conditions, ensuring sterilization effectiveness and preventing instrument damage.

[0027] Reference Figure 1 The diagram illustrates an embodiment of a surgical instrument disinfection method according to the present invention, which may specifically include the following steps:

[0028] S101 continuously collects pressure data inside the disinfection chamber and calculates the rate of pressure change and the amplitude of pressure fluctuation based on the pressure data;

[0029] S102, preset ideal pressure change path;

[0030] S103, compare the rate of pressure change with the corresponding rate of pressure change in the ideal pressure change path, and evaluate whether the pressure fluctuation amplitude exceeds the preset threshold to obtain the evaluation result;

[0031] S104, perform frequency characteristic analysis on the collected pressure data to identify energy changes related to internal residues or microstructures of the instrument;

[0032] S105, perform transient fluctuation pattern identification on pressure data to identify fluctuations related to micro-fatigue damage of instrument materials;

[0033] S106. Based on the frequency characteristic analysis results and transient fluctuation pattern identification results, determine whether there is a microscopic anomaly pattern.

[0034] S107. Based on the assessment results and micro-anomaly patterns, adjust the operating parameters of the pressure regulating actuator of the disinfection chamber to change the pressure change rate. If the assessment results show that the pressure change is stable and the deviation from the ideal pressure change path is within an acceptable range, then increase the pressure change rate. If the assessment results show that the pressure fluctuates violently or deviates significantly from the ideal pressure change path, then decrease the pressure change rate or suspend the pressure change process.

[0035] The rate of pressure change refers to the rate at which the pressure inside the sterilization chamber changes over time, such as the change in Pascals per second. This can be calculated using differential or regression analysis of continuously collected pressure data. Its purpose is to quantify the speed of pressure change, serving as a key indicator for assessing the stability of pressure control. Meanwhile, the pressure fluctuation amplitude refers to the difference between the maximum and minimum pressure values ​​within the sterilization chamber over a certain period, or its standard deviation. This can be calculated using statistical analysis methods on the pressure data, such as calculating the peak-to-trough difference or root mean square value over a period. Its purpose is to measure the severity or stability of pressure changes, identifying any potential instantaneous fluctuations that could impact or damage the instruments. Furthermore, the ideal pressure change path refers to a pre-defined target pressure change curve that should be followed during sterilization. This can be determined based on the characteristics of different types of surgical instruments, the requirements of the sterilization medium, and best practices in sterilization processes. For example, it could be a smooth S-shaped curve or a piecewise linear curve. Its purpose is to provide a benchmark to guide the pressure regulation process, ensuring sterilization effectiveness and protecting the instruments.

[0036] Furthermore, frequency characteristic analysis refers to the spectral decomposition of the acquired pressure data to reveal specific frequency components and their energy distribution. This can be achieved using signal processing techniques such as Fast Fourier Transform, Wavelet Transform, or Short-Time Fourier Transform. The aim is to identify specific vibration or resonance modes associated with internal residues or microstructures of the instrument, thereby indirectly determining the cleanliness or structural integrity of the instrument. Relatedly, transient fluctuation pattern recognition refers to the detection and classification of transient, non-periodic pressure changes with specific shapes in the pressure data. This can be achieved using pattern recognition algorithms, machine learning models, or threshold-based event detection methods. The goal is to identify unique pressure fluctuation characteristics associated with micro-fatigue damage to the instrument material, such as acoustic emission signals caused by microcrack propagation or material stress release, thereby assessing potential damage to the instrument. Through the two analyses described above, it is possible to determine whether a microscopic anomaly pattern exists. This pattern refers to the comprehensive signal characteristics revealed by frequency characteristic analysis and transient fluctuation pattern identification, indicating the presence of residues, structural abnormalities, or material fatigue damage within the device. It can manifest as an abnormal increase in energy within a specific frequency range, the appearance of a specific waveform pattern, or a combination of both. Its purpose is to provide a comprehensive and in-depth assessment of the device's condition, going beyond simple macroscopic pressure monitoring. Finally, the pressure regulation actuator refers to the physical device responsible for actually changing the pressure inside the sterilization chamber. It can include a vacuum pump, air pump, valve, flow controller, or a combination thereof. Its purpose is to precisely adjust the pressure inside the sterilization chamber according to control commands to achieve the expected rate and path of pressure change.

[0037] The core innovation of this application lies in combining macroscopic dynamic evaluation of pressure data within the sterilization chamber (including comparison of pressure change rate with ideal path and evaluation of pressure fluctuation amplitude) with microscopic anomaly pattern recognition (including frequency feature analysis and transient fluctuation pattern recognition). This achieves refined and adaptive control of the sterilization chamber pressure, effectively identifying and avoiding potential damage to delicate surgical instruments while ensuring sterilization effectiveness.

[0038] This application's solution achieves refined management of the internal pressure of the sterilization chamber by constructing an intelligent closed-loop system for pressure control and device status monitoring. First, the system continuously acquires real-time pressure data from inside the sterilization chamber and dynamically calculates the rate of pressure change and fluctuation amplitude based on this raw data. These calculations form the basis for assessing the current pressure state. Simultaneously, the system pre-stores ideal pressure change paths set for different sterilization stages and device types. These paths represent the optimal pressure change trajectory while ensuring sterilization effectiveness and device safety. Next, the system compares the real-time calculated rate of pressure change with the ideal rate of pressure change at the corresponding moment in the ideal path and simultaneously assesses whether the pressure fluctuation amplitude exceeds a preset safety threshold. This comparison and assessment process generates a comprehensive evaluation result to determine the stability of the current pressure control and the degree of deviation from the ideal state. More importantly, to gain a deeper understanding of the device's microscopic state, the system performs two types of advanced analysis on the collected pressure data: frequency characteristic analysis, which aims to reveal the presence of residues or microstructural anomalies within the device by identifying energy changes within a specific frequency range; and transient fluctuation pattern recognition, which aims to determine whether there is microscopic fatigue damage to the device materials by analyzing brief pressure fluctuation waveforms. Subsequently, the system integrates frequency characteristic analysis results and transient fluctuation pattern identification results to determine whether any microscopic anomalies exist. This determination provides in-depth information about the cleanliness and structural integrity of the instruments. Finally, based on the macroscopic assessment results and the identification of microscopic anomalies, the system intelligently adjusts the operating parameters of the pressure regulating actuator. Specifically, if the macroscopic assessment shows that the pressure change is stable and the deviation from the ideal path is within an acceptable range, the system will moderately increase the pressure change rate to improve disinfection efficiency. Conversely, if the assessment shows that the pressure fluctuations are drastic or significantly deviate from the ideal path, the system will reduce the pressure change rate or even suspend the pressure change process to avoid damaging the instruments or affecting the disinfection effect. This dynamic and adaptive adjustment mechanism allows the disinfection process to balance efficiency and safety, ensuring the effective penetration of the disinfection medium into the tiny cavities of the instruments while avoiding damage to delicate instruments.

[0039] In some preferred embodiments, this application is implemented as follows. Multiple high-precision pressure sensors, such as piezoresistive or capacitive pressure sensors, can be configured inside the sterilization chamber, continuously collecting pressure data within the chamber at a high sampling rate. This data is transmitted to a data processing unit, which can be an embedded controller or an industrial PC. The data processing unit uses digital signal processing algorithms, such as moving average and Kalman filtering, to calculate the rate of pressure change and the amplitude of pressure fluctuations in real time. An ideal pressure change path can be pre-stored in the memory of the data processing unit or loaded from an external database; this path can be selected based on the type of surgical instrument and sterilization mode. The data processing unit compares the calculated rate of pressure change with the target rate on the current ideal path and simultaneously checks whether the pressure fluctuation amplitude exceeds a preset threshold; for example, a maximum permissible standard deviation of pressure fluctuation can be set. The evaluation results are then used to guide pressure regulation. Furthermore, the data processing unit performs advanced analysis on the collected pressure data. For example, for frequency characteristic analysis, a fast Fourier transform can be used to convert the time-domain pressure signal into a frequency-domain signal, and then the energy intensity of a specific frequency band can be analyzed. For transient fluctuation pattern identification, wavelet analysis or machine learning classifier-based methods can be used to identify instantaneous pressure pulses with specific waveform characteristics. These pulses may indicate microcrack propagation or material fatigue within the instrument. These analytical results are then synthesized to determine the presence of microscopic anomalies. For example, an abnormal increase in energy at a specific frequency, accompanied by the appearance of a specific transient fluctuation waveform, may be identified as a microscopic anomaly. Finally, based on the evaluation results, the data processing unit generates control commands and sends them to the pressure regulating actuator. This actuator may consist of a variable-frequency driven vacuum pump and a proportional control valve. If the evaluation results show that the pressure is stable and meets expectations, the data processing unit will instruct the vacuum pump or air pump to operate at a faster speed, or adjust the valve opening to accelerate pressure changes. Conversely, if drastic fluctuations or significant deviations are detected, the actuator will be instructed to slow down the rate of pressure change, or even temporarily stop, to protect the instrument and ensure sterilization quality.

[0040] Through the above technical solution, this application achieves precise and adaptive control of the pressure within the surgical instrument sterilization chamber, effectively solving the problem that traditional methods struggle to balance sterilization efficiency and instrument safety. This solution, by real-time monitoring of macroscopic pressure dynamics combined with microscopic frequency characteristics and transient fluctuation morphology analysis, can comprehensively assess potential risks during the sterilization process, including internal instrument residues, microstructural anomalies, and material fatigue damage. This multi-dimensional, in-depth assessment mechanism allows pressure regulation to move beyond relying solely on preset fixed parameters, enabling dynamic adjustments based on the actual state of the instruments and real-time feedback from the sterilization process. Specifically, when pressure changes are stable and follow an ideal path, the system can moderately accelerate the pressure change rate, thereby shortening the sterilization cycle and improving instrument turnover efficiency. Conversely, when drastic pressure fluctuations or microscopic anomalies are detected, the system can promptly reduce the pressure change rate or pause the process, effectively preventing damage to delicate instruments, extending instrument lifespan, and ensuring sufficient penetration and uniform action of the sterilization medium within the instrument's micro-cavities, ultimately guaranteeing thorough sterilization.

[0041] In some embodiments described above in this application, frequency characteristic analysis of the collected pressure data is proposed to identify energy changes related to internal residues or microstructures of the instrument. Specifically, this frequency characteristic analysis of the collected pressure data can be performed by performing a Fourier transform on the pressure data to convert it from the time domain to the frequency domain, and then analyzing the energy distribution within a specific frequency range. For example, by observing whether there are frequency peaks or energy attenuation, it can be determined whether there are abnormalities inside the instrument. This can identify energy changes related to internal residues or microstructures of the instrument. However, in its implementation, relying solely on the analysis of overall pressure data may not be able to identify local energy changes caused by internal residues or microstructures of the instrument, as well as fluctuations related to micro-fatigue damage of the instrument material, thereby affecting the optimization of the sterilization process and the assessment of instrument life.

[0042] In this regard, this application further proposes the following steps for performing frequency characteristic analysis on the collected pressure data to identify energy changes related to internal residues or microstructures of the device:

[0043] Time-frequency analysis was performed on the collected pressure data to obtain the energy spectrum of the pressure data in different time-frequency bands;

[0044] Based on the energy spectrum, the energy intensity or energy distribution pattern within the preset natural frequency range related to the microstructural characteristics of the instrument itself is analyzed to obtain the natural frequency response characteristics.

[0045] Based on the energy spectrum, analyze the energy intensity or energy distribution pattern within the preset abnormal frequency range related to internal residues or micro-damage of the instrument to obtain the abnormal energy change characteristics.

[0046] Based on inherent frequency response characteristics and abnormal energy change characteristics, identify energy changes related to internal residues or microstructures of the instrument.

[0047] Time-frequency analysis (TF-F) is a signal processing technique used to analyze how the frequency components of a signal change over time. It can be implemented using methods such as short-time Fourier transform, wavelet transform, or Hilbert-Huang transform. Its purpose is to reveal the frequency characteristics of the pressure signal at different time points, providing a foundation for subsequent feature extraction. Energy spectrum, obtained through TF-F, is a graph or dataset representing the energy distribution of the signal at different time points and frequencies. It can be a power spectral density map or a spectrum diagram, aiming to show the energy distribution of the pressure signal along the frequency dimension, thus facilitating the identification of energy changes at specific frequencies. Natural frequency range refers to specific frequency intervals related to the instrument's materials, geometry, and structure. These frequencies exhibit vibrational responses when the instrument is in normal operation. They can be predetermined based on the instrument's design parameters, material properties, or through experimental modal analysis. Their purpose is to assess the structural integrity of the instrument by monitoring the responses at these frequencies. The inherent frequency response characteristics refer to the energy intensity, peak position, bandwidth, or attenuation mode of the pressure data energy spectrum within the inherent frequency range. It can be the energy peak at a specific inherent frequency or its trend over time, and its purpose is to reflect the health status of the device's microstructure. The abnormal frequency range refers to the frequency intervals related to abnormal vibrations or acoustic responses that may be caused by residues or micro-damage inside the device. These frequencies are usually not obvious or missing during normal device operation. They can be preset based on known damage patterns, residue types, or by training and identifying through machine learning models, and their purpose is to serve as an indicator of abnormal conditions inside the device. The abnormal energy change characteristics refer to the energy intensity, frequency shift, or sudden fluctuations of the pressure data energy spectrum within the abnormal frequency range. It can be a sudden increase in energy or a continuous energy fluctuation at a specific abnormal frequency, and its purpose is to indicate possible residues or micro-damage inside the device.

[0048] This application's solution involves performing time-frequency analysis on the collected pressure data to obtain the energy spectrum of the pressure data in different time-frequency bands, which forms the basis for detailed analysis of the pressure data. Time-frequency analysis can reveal the changes in pressure signal components at different frequencies over time, providing information for subsequent feature extraction and identification. Time-frequency analysis is necessary because residues or microstructures inside the device may generate energy responses within specific frequency ranges, which are not easily observed directly in the time domain signal. Based on this, the energy intensity or energy distribution pattern within a preset natural frequency range related to the device's own microstructural characteristics is analyzed according to the energy spectrum to obtain natural frequency response characteristics. Different devices have different natural frequencies due to differences in their materials, geometry, and structure. By analyzing the energy responses of these natural frequencies, the structural integrity and damage status of the device can be understood. Analyzing natural frequencies is necessary because changes in the device's microstructure affect its natural frequency response, thus providing a basis for assessing the device's health status. Simultaneously, based on the energy spectrum, the energy intensity or energy distribution pattern within a preset abnormal frequency range related to residues or micro-damage inside the device is analyzed to obtain abnormal energy change characteristics. Residues or microscopic damage within medical devices may produce energy responses within specific anomalous frequency ranges. Analyzing the energy changes at these anomalous frequencies can detect the presence of residues or damage within the device. Analyzing anomalous frequencies is crucial because their energy responses typically differ from the device's normal state, serving as indicators of residues or damage. Ultimately, based on the inherent frequency response characteristics and anomalous energy change characteristics, energy changes associated with residues or microstructures within the device are identified. Comprehensive analysis of the energy responses at both inherent and anomalous frequencies improves identification accuracy and avoids misjudgments that might arise from analyzing a single feature.

[0049] This method, combining frequency characteristic analysis with continuous acquisition of pressure data within the sterilization chamber, enables the system to monitor not only macroscopic pressure changes but also delve into the microscopic level to identify anomalies within the instruments. In this way, the system obtains detailed information about the internal state of the instruments, such as the presence of difficult-to-remove residues or fatigue damage to the instrument materials. This microscopic information serves as a crucial basis for determining the existence of microscopic anomaly patterns, providing precise input for subsequent adjustments to the operating parameters of the pressure regulating actuators within the sterilization chamber. For example, if frequency analysis results indicate energy changes related to residues, the system can adjust pressure change strategies to promote residue removal or optimize the penetration of the sterilization medium; if it identifies inherent frequency response changes related to microscopic damage, it can prompt inspection of the instrument or limit its use, thereby extending the instrument's lifespan and improving safety while ensuring sterilization effectiveness. This ability to perform multi-dimensional, in-depth analysis of pressure data elevates the control of the sterilization process from simple macroscopic regulation to management based on the internal state of the instruments, thus ensuring both sterilization effectiveness and instrument safety.

[0050] In some preferred embodiments, frequency characteristic analysis of the acquired pressure data to identify energy changes related to internal residues or microstructures of the device can be specifically implemented as follows. First, time-frequency analysis can be performed on the continuously acquired pressure data using short-time Fourier transform. Specifically, the pressure data is divided into a series of overlapping time windows, and a Fourier transform is performed on the data within each time window to obtain the frequency components of the pressure signal at different time points. The energy distribution of these frequency components can be visualized as an energy spectrum, such as a two-dimensional spectrum plot, where one axis represents time and the other represents frequency, and color or brightness represents the energy intensity at the corresponding time-frequency point.

[0051] Next, based on this energy spectrum, the energy intensity or energy distribution pattern within a preset natural frequency range related to the instrument's own microstructural characteristics can be analyzed. For example, for a specific type of surgical instrument, it may have one or more inherent vibration frequencies between 100Hz and 200Hz under normal conditions. The system can pre-store these natural frequency ranges and monitor the energy peaks, peak frequency shifts, or energy attenuation within these ranges in the energy spectrum, thereby obtaining the natural frequency response characteristics. These characteristics can indicate changes in the fatigue level of the instrument material or the structural integrity.

[0052] Simultaneously, based on the energy spectrum, the energy intensity or energy distribution pattern within a preset abnormal frequency range related to internal residues or microscopic damage of the device can be analyzed. For example, the rupture of tiny bubbles or friction of residues inside the device may generate instantaneous or continuous energy fluctuations in the frequency range of 500Hz to 1000Hz. The system can preset these abnormal frequency ranges and detect energy spikes, broadband noise, or energy distribution patterns within these ranges in the energy spectrum, thereby obtaining the characteristics of abnormal energy changes.

[0053] Finally, the system can integrate natural frequency response characteristics and abnormal energy change characteristics to identify energy changes related to internal residues or microstructures of the device. For example, if the natural frequency response characteristics show a decrease in the energy peak at a specific natural frequency, while the abnormal energy change characteristics show continuous energy fluctuations within a certain abnormal frequency range, the system can determine that the device may have microscopic fatigue damage accompanied by internal residues. This comprehensive judgment can improve the accuracy of identification and avoid misjudgments that may occur from single feature analysis.

[0054] By employing the aforementioned technical solution, time-frequency analysis is performed on the collected pressure data, and further analysis of the inherent frequency response characteristics and abnormal energy change characteristics can identify local energy changes caused by internal residues or microstructures of the instrument. This allows the system to transcend the limitations of macroscopic pressure monitoring, gain insight into the internal state of the instrument, and thus determine whether there are difficult-to-remove residues or microscopic damage to the instrument materials.

[0055] In some embodiments described above, transient fluctuation pattern identification of pressure data is proposed to identify fluctuations related to microscopic fatigue damage of instrument materials. Specifically, this transient fluctuation pattern identification can be achieved by analyzing instantaneous changes in pressure data to extract potential fluctuation signals related to fatigue damage. This can provide a preliminary identification of microscopic fatigue damage in the instrument. However, in its implementation, relying solely on transient fluctuation characteristics is insufficient to accurately determine microscopic fatigue damage. For example, changes in the operating status of the sterilization chamber (such as the start and stop of the pressure regulating mechanism) may cause similar transient fluctuations, leading to misjudgment. Furthermore, individual pressure sensors may be subject to localized interference, resulting in inconsistent spatial distribution of transient fluctuation characteristics, further affecting the accuracy of the judgment.

[0056] In this regard, this application further proposes steps for identifying transient fluctuation patterns in pressure data to identify fluctuations related to microscopic fatigue damage in instrument materials, including:

[0057] Transient fluctuation patterns are identified in the collected pressure data to obtain transient fluctuation characteristics;

[0058] The temporal correlation between transient fluctuation characteristics and the operating status of the disinfection chamber is determined, and the temporal correlation determination result is obtained.

[0059] The spatial consistency of transient fluctuation characteristics across multiple pressure sensors within the disinfection chamber is determined to obtain spatial consistency assessment results.

[0060] Based on the results of time correlation judgment, spatial consistency judgment, and transient fluctuation characteristics, fluctuations related to micro-fatigue damage of instrument materials are identified.

[0061] The temporal correlation between transient fluctuation characteristics and the operating status of the sterilization chamber involves analyzing the correspondence between the time of the transient fluctuation event and the timestamps of the start-up, shutdown, switching, or specific operating phases of the pressure regulation mechanisms inside the sterilization chamber (e.g., vacuum pump, inlet valve, exhaust valve, etc.) to determine whether the fluctuation is caused by the normal operation of the sterilization chamber itself. Its purpose is to distinguish between genuine fluctuations caused by instrument fatigue damage and background noise or interference caused by equipment operation. The spatial consistency of transient fluctuation characteristics across multiple pressure sensors within the sterilization chamber involves deploying multiple pressure sensors within the chamber. When transient fluctuation characteristics are identified, the pressure data collected by different sensors within the same time period are compared to assess whether these fluctuation characteristics exhibit similar morphology, amplitude, or temporal synchronicity in spatial distribution. Specifically, this can be quantified by calculating the correlation coefficient, Euclidean distance, or performing pattern matching between different sensor data. Its purpose is to eliminate isolated fluctuations caused by single sensor failure, local environmental interference, or non-instrument fatigue damage, thereby improving the reliability of identification. The identification of fluctuations related to micro-fatigue damage of instrument materials, based on temporal correlation judgment results, spatial consistency judgment results, and transient fluctuation characteristics, involves making a comprehensive logical judgment or decision based on the transient fluctuation characteristics, combined with the temporal correlation judgment results of the sterilization chamber's operating status and the spatial consistency judgment results across multiple pressure sensors. Specifically, this can be achieved by setting a series of rules; for example, only when the transient fluctuation characteristics have no temporal correlation with the sterilization chamber's operating status and exhibit high spatial consistency across multiple sensors are they identified as fluctuations related to micro-fatigue damage of instrument materials. The aim is to improve the accuracy and robustness of identifying micro-fatigue damage of instruments through multi-dimensional verification, and to avoid false alarms.

[0062] This application's solution introduces a multi-dimensional verification mechanism to identify transient fluctuation patterns in pressure data, thereby more accurately identifying fluctuations related to microscopic fatigue damage of instrument materials. First, transient fluctuation pattern identification is performed on the collected pressure data, which forms the basis for subsequent analysis and allows for the preliminary extraction of potential damage signals. However, relying solely on this preliminary identification may lead to false positives. Therefore, the solution further introduces time correlation judgment. By comparing the identified transient fluctuation characteristics with the time information of the sterilization chamber's operating status, transient pressure changes caused by the sterilization chamber's own operations (e.g., valve opening and closing, pump start and stop) can be effectively filtered out. Although these operational fluctuations may resemble fatigue damage fluctuations in morphology, their occurrence time is highly synchronized with equipment operation events; time correlation judgment can exclude them, avoiding false alarms. Furthermore, the solution also introduces spatial consistency judgment. Considering that microscopic fatigue damage of instrument materials typically generates propagable fluctuations in the surrounding medium, these fluctuations should be simultaneously captured by multiple pressure sensors within the sterilization chamber and exhibit a certain degree of spatial consistency.

[0063] In some preferred embodiments, transient fluctuation pattern identification of pressure data can be performed using a high-pass filtering method combined with threshold detection to highlight instantaneous peaks or sudden drops in pressure data, thereby obtaining preliminary transient fluctuation characteristics. Specifically, when determining the temporal correlation between transient fluctuation characteristics and the operating status of the sterilization chamber, the precise operation timestamps of all key actuators (e.g., vacuum pump, intake valve, exhaust valve) within the sterilization chamber can be pre-recorded. When the system identifies a transient fluctuation characteristic, it can obtain the timestamp of the fluctuation occurrence and compare it with the preset actuator operation timestamps. If the timestamp of the transient fluctuation falls within a preset time window of an actuator operation timestamp (e.g., 50 milliseconds before or after the operation), it can be determined that the fluctuation is temporally correlated with the operating status of the sterilization chamber and marked as a non-fatigue damage related fluctuation. Conversely, if the fluctuation timestamp is not significantly correlated with the operation timestamps of all actuators, it can be preliminarily considered that it is unrelated to the operating status. Furthermore, when determining the spatial consistency of transient fluctuation characteristics across multiple pressure sensors within the sterilization chamber, it can be assumed that at least three pressure sensors, such as sensor A, sensor B, and sensor C, are deployed in different locations within the sterilization chamber. When sensor A detects a transient fluctuation characteristic, the system can immediately check whether sensors B and sensor C also detect similar transient fluctuation characteristics within the same time window (e.g., 10 milliseconds before and after). Similarity can be quantified based on the shape, amplitude, or duration of the waveform. For example, by calculating the correlation coefficient between waveforms, spatial consistency is considered achieved if the correlation coefficient is higher than a preset threshold (e.g., 0.8). Only when transient fluctuation characteristics exhibit high consistency across multiple sensors is it considered a valid fluctuation with spatial consistency. Finally, based on the temporal correlation judgment result, the spatial consistency judgment result, and the transient fluctuation characteristics, fluctuations related to microscopic fatigue damage of the instrument material are identified. For example, the system only confirms a transient fluctuation related to microscopic fatigue damage of the instrument material when it is determined to have no temporal correlation with the sterilization chamber's operating state and exhibits spatial consistency across at least two or all pressure sensors. This comprehensive judgment mechanism can effectively eliminate false positives caused by equipment operation or local interference, thereby improving the accuracy of identification.

[0064] The above technical solution effectively eliminates the impact of changes in the disinfection chamber's operating status and localized interference from individual sensors on transient fluctuation identification. By introducing time correlation analysis, it is possible to distinguish between fluctuations caused by normal equipment operation and fluctuations truly caused by instrument fatigue damage, thus avoiding misjudgments.

[0065] In some embodiments described above, transient fluctuation pattern identification of pressure data is proposed. Specifically, this identification can be achieved by analyzing abnormal peaks or abrupt changes in the pressure data and combining their temporal continuity and spatial consistency across multiple sensors to determine whether fluctuations related to micro-fatigue damage of the instrument material exist. For example, when a short-duration, high-amplitude spike appears in the pressure data, and this spike exhibits a similar pattern at adjacent time points and on different sensors, it can be preliminarily identified as a transient fluctuation event, thus initially identifying potential signs of micro-fatigue damage. However, in its implementation, the acquired pressure data often contains various noise interferences, which may mask the true transient fluctuation signal, making it difficult to accurately identify fluctuations related to micro-fatigue damage of the instrument material.

[0066] In response, this application further proposes a step for identifying transient fluctuation patterns in the collected pressure data to obtain transient fluctuation characteristics, including:

[0067] Real-time noise characteristics are estimated from the collected pressure data to obtain a dynamic noise baseline;

[0068] Based on the dynamic noise baseline, the pressure data is adaptively filtered to enhance the transient fluctuation signal;

[0069] In the filtered pressure data, instantaneous pressure changes that exceed the preset deviation range of the dynamic noise baseline are identified to obtain preliminary transient fluctuation events;

[0070] Morphological features are extracted from the initial transient fluctuation events to obtain transient fluctuation features;

[0071] Pattern matching was performed on the transient fluctuation characteristics to confirm their correlation with microscopic fatigue damage of the instrument material, thereby obtaining the transient fluctuation characteristics.

[0072] Real-time noise characteristic estimation refers to the dynamic analysis of continuously acquired pressure data streams to obtain the statistical attributes of the current noise, such as the noise mean, variance, power spectral density, or probability distribution. This can be achieved using sliding window averaging, Kalman filtering, or adaptive noise cancellation algorithms, with the aim of providing accurate noise background information for subsequent filtering. The dynamic noise baseline is a time-varying noise level reference line determined based on the real-time noise characteristic estimation results. It can be determined based on the statistical mean or a certain percentile of the noise, aiming to provide a dynamic threshold to distinguish normal noise fluctuations from potential transient fluctuation signals. Adaptive filtering refers to the process of automatically adjusting filter parameters to optimize signal-noise separation based on the real-time estimated noise characteristics. This can be achieved using the Least Mean Square (LMS) algorithm, Recursive Least Squares (RLS) algorithm, or Kalman filtering-based adaptive filtering methods, with the aim of effectively suppressing noise while preserving the integrity of transient fluctuation signals to the maximum extent. The preset deviation range refers to an allowable fluctuation range set based on the dynamic noise baseline to identify instantaneous pressure changes. It can be obtained based on empirical values, statistical principles, or through machine learning model training. Its purpose is to effectively distinguish normal noise fluctuations from significant pressure changes that may represent transient events. Preliminary transient events refer to instantaneous pressure changes in the filtered pressure data whose amplitude exceeds the preset deviation range of the dynamic noise baseline. These can manifest as short-duration pulses, spikes, or step signals, and their purpose is to initially screen out potential transient signals that require further analysis. Morphological feature extraction refers to the process of quantifying the waveform characteristics of preliminary transient events, such as extracting amplitude, duration, rise time, fall time, energy, frequency components, or waveform shape parameters. This can be achieved using signal processing techniques such as peak detection, waveform integration, wavelet transform, or Fourier transform.

[0073] This application's solution achieves accurate identification of transient fluctuation characteristics in pressure data, especially in environments with noise interference, through a series of collaborative steps. First, real-time noise characteristic estimation is performed on the acquired pressure data. This allows the system to dynamically sense and quantify the noise level in the current environment, thus obtaining a dynamic noise baseline. Since the noise level is not fixed but dynamically changes with time and the environment, real-time noise characteristic estimation provides a more accurate understanding of the noise level in the current environment, providing a basis for subsequent filtering. Based on this, adaptive filtering is applied to the pressure data using the dynamic noise baseline to enhance transient fluctuation signals. The adaptive filter automatically adjusts its filtering parameters according to the real-time estimated noise characteristics, thereby filtering out noise while preserving as much useful transient fluctuation signal as possible. Compared to traditional fixed-parameter filters, the adaptive filter can better adapt to different noise environments and improve the signal-to-noise ratio. Subsequently, instantaneous pressure changes exceeding the preset deviation range of the dynamic noise baseline are identified in the filtered pressure data, thus obtaining preliminary transient fluctuation events. Real transient fluctuation signals typically manifest as instantaneous pressure changes exceeding the normal noise range. By setting an appropriate deviation range, potential transient fluctuation events can be effectively screened, reducing the amount of data required for subsequent processing. Next, morphological features are extracted from the initial transient fluctuation events to obtain transient fluctuation characteristics. Different fluctuation patterns may correspond to different physical phenomena or damage types. By extracting various morphological features of the fluctuation events, such as amplitude, duration, and frequency, a basis for subsequent pattern matching can be provided. Finally, pattern matching is performed on the transient fluctuation characteristics to confirm their correlation with microscopic fatigue damage of the instrument material, thus obtaining the final transient fluctuation characteristics.

[0074] In some preferred embodiments, real-time noise characteristic estimation of the acquired pressure data can be performed using a sliding window root mean square algorithm. Specifically, the system can continuously acquire pressure data and maintain a sliding window of fixed length, for example, containing the most recent 1000 sampling points. At each new sampling point, the root mean square value of the pressure data within the current window is calculated as an estimate of the real-time noise characteristics, thus obtaining a dynamic noise baseline. Based on this dynamic noise baseline, adaptive filtering of the pressure data can be performed using an adaptive filter based on the least mean square algorithm. This filter can dynamically adjust its filtering coefficients according to the real-time estimated noise characteristics to suppress noise to the greatest extent and enhance transient fluctuation signals. For example, when the noise baseline rises, the filter can automatically adjust its gain or cutoff frequency to more effectively filter out noise. In the filtered pressure data, instantaneous pressure changes exceeding a preset deviation range of the dynamic noise baseline are identified. A deviation range can be set; for example, when the value of a filtered pressure data point exceeds the dynamic noise baseline plus or minus three times the real-time noise standard deviation, a preliminary transient fluctuation event is considered detected. The system can record the start time, end time, and maximum or minimum pressure value of these events. Morphological feature extraction of preliminary transient fluctuation events allows for the extraction of waveform features. For example, for each preliminary transient fluctuation event, its peak amplitude, duration, rise slope, fall slope, and total energy can be calculated. Furthermore, wavelet transform can be applied to the local waveform of the event to extract energy distribution at different frequency scales as its frequency features. Finally, pattern matching of the transient fluctuation features can be performed using a support vector machine (SVM) classifier. A pre-trained SVM model can store fluctuation templates representing micro-fatigue damage in different types of instruments. When a new transient fluctuation feature is extracted, it is input into the SVM classifier. The classifier calculates the similarity between the feature and each fluctuation template and outputs the most matching damage type, thus confirming the fluctuation as related to micro-fatigue damage in the instrument material.

[0075] The above technical solution can effectively and accurately identify transient fluctuation characteristics related to micro-fatigue damage of instrument materials from pressure data containing noise interference. Through real-time noise characteristic estimation and adaptive filtering, the signal-to-noise ratio of transient fluctuation signals can be significantly improved, ensuring that even weak damage signals can be captured in complex noise environments.

[0076] Furthermore, through morphological feature extraction and pattern matching, identified transient fluctuation events can be finely classified, thereby accurately determining whether they are related to microscopic fatigue damage of specific instrument materials and identifying the specific damage type. This makes early and accurate detection of microscopic fatigue damage to instrument materials possible, helping to take timely maintenance measures, extend the service life of instruments, and ensure the reliability of the sterilization process.

[0077] This application further proposes a method that includes: acquiring the waveform or its transform domain representation of transient fluctuation characteristics; and pre-setting fluctuation templates representing microscopic fatigue damage of different types of instruments.

[0078] Among these, acquiring the waveform or its transform domain representation of transient fluctuation characteristics refers to the conversion or extraction of data form from the identified transient fluctuation characteristics. Specifically, this can involve directly preserving the original signal form of the transient fluctuation on the time axis, i.e., its waveform, or converting it into a representation in the frequency domain, time-frequency domain, or other mathematical spaces, such as through Fourier transform, wavelet transform, or Hilbert-Huang transform. The aim is to comprehensively capture the intrinsic information of the transient fluctuation signal from different dimensions, providing a rich data foundation for subsequent refined analysis and pattern matching. Simultaneously, pre-setting fluctuation templates representing microscopic fatigue damage of different types of instruments refers to establishing and storing a series of standardized data patterns based on a large amount of experimental data, simulation results, or theoretical models before actual testing. These data patterns correspond to the typical transient fluctuation characteristics generated by different types of surgical instruments when specific microscopic fatigue damage occurs. Specifically, this can be achieved by conducting controlled tests on instruments with known damage states, collecting their pressure response data, and extracting representative waveforms or transform domain features as templates. The purpose is to provide a comparable reference system for the actually detected transient fluctuation characteristics, thereby enabling the identification of the damage type and degree.

[0079] This application's solution obtains the waveform or its transform domain representation of transient fluctuation characteristics and pre-sets fluctuation templates representing micro-fatigue damage in different types of instruments, thus providing a more refined and reliable basis for identifying micro-fatigue damage in instruments. Specifically, after performing real-time noise characteristic estimation, adaptive filtering, instantaneous pressure change identification, and morphological feature extraction on the collected pressure data to obtain preliminary transient fluctuation events, a more comprehensive description of these preliminary transient fluctuation events is needed to gain a deeper understanding of their nature and determine whether they are related to micro-fatigue damage in instrument materials. By obtaining the waveform of transient fluctuation characteristics, the changing trend, amplitude, duration, and rising and falling edges of the fluctuation in the time dimension can be intuitively presented. This time-domain information is crucial for understanding the physical process of the fluctuation. Simultaneously, by obtaining its transform domain representation, such as through Fourier transform or wavelet transform, the time-domain signal can be converted to the frequency domain or time-frequency domain, thereby revealing the hidden frequency components, energy distribution, and local features at different time scales within the fluctuation. This multi-dimensional data representation allows for a more complete and detailed description of transient fluctuation characteristics, capturing subtle differences that are difficult to detect with time-domain waveforms alone. These differences are often key to distinguishing different damage types or degrees. Based on this, a standardized reference system is established by pre-setting fluctuation templates representing microscopic fatigue damage of different types of instruments. Due to differences in materials, structures, and usage environments among different types of surgical instruments, the transient fluctuation characteristics generated by their microscopic fatigue damage also exhibit diversity. By pre-constructing a fluctuation template library containing various known damage modes, the waveforms or their transform domain representations of the actually acquired transient fluctuation characteristics can be compared with these pre-set templates.

[0080] In some preferred embodiments, the acquisition of the waveform or its transform domain representation of transient fluctuation characteristics can be specifically implemented as follows: When the system identifies an initial transient fluctuation event, such as a pressure spike lasting several milliseconds, the original pressure signal within a certain time window before and after the spike can be captured as its waveform representation. To obtain its transform domain representation, continuous wavelet transform can be performed on the captured waveform data, and a suitable wavelet basis function can be selected to obtain the energy distribution map of the transient fluctuation at different frequencies and time scales, i.e., the time-frequency diagram. This time-frequency diagram can clearly show the concentration of fluctuation energy within a specific frequency range and its trend over time. Meanwhile, the preset of fluctuation templates representing microscopic fatigue damage of different types of instruments can be specifically implemented as follows: First, a large number of surgical instrument samples with known damage types (e.g., metal fatigue cracks, polymer material degradation, coating peeling, etc.) and undamaged states are collected. Then, under controlled experimental conditions, these instrument samples undergo simulated sterilization processes or specific stress tests, and high-precision pressure sensors are used to continuously collect the microscopic fluctuation data generated during pressure changes. For each damage type, representative transient fluctuation events are extracted from the acquired data and processed in the same way as those described above for obtaining transient fluctuation characteristics, i.e., their waveforms and / or transform domain representations are obtained. For example, for a specific type of fatigue crack, a specific frequency-time energy cluster exhibited in the wavelet transform domain can be extracted as a template. These processed waveforms and / or transform domain representations with typical characteristics can be stored in a template database and associated with corresponding instrument types and damage labels. These templates can be periodically updated and improved to adapt to new instrument materials or damage patterns.

[0081] This application further proposes steps for pattern matching of transient fluctuation characteristics to confirm their correlation with microscopic fatigue damage of instrument materials, including:

[0082] Calculate the similarity between the waveform or its transform domain representation of transient fluctuation characteristics and the fluctuation template;

[0083] Based on similarity, the transient fluctuation characteristics were identified as fluctuations related to micro-fatigue damage of instrument materials, and the micro-fatigue damage type of instrument materials corresponding to the transient fluctuation characteristics was identified.

[0084] The transient fluctuation feature waveform or its transform domain representation refers to a formalized description of the instantaneous pressure change signal extracted from pressure data. This can be the original time-domain waveform data or a frequency-domain or time-frequency-domain representation processed by Fourier transform, wavelet transform, etc., aiming to provide standardized input for subsequent pattern matching. The fluctuation template refers to a pre-established set of typical fluctuation patterns or features representing microscopic fatigue damage in different types of instruments. It can be a standard waveform, feature vector, or statistical model obtained through experiments or simulations on known damaged instruments, serving as a comparison benchmark to identify actually detected transient fluctuation features. Similarity is a quantitative indicator measuring the degree of matching between the transient fluctuation feature waveform or its transform domain representation and the fluctuation template. It can be calculated using correlation coefficients, Euclidean distance, dynamic time warping (DTW) algorithms, or matching scores output by machine learning models, aiming to provide an objective numerical basis for judging the correlation between the two. Pattern matching refers to the process of comparing transient fluctuation features to be identified with preset fluctuation templates to determine their category or correlation. This can be achieved by calculating similarity and making threshold judgments through algorithms, or by classifying through classifiers. Its purpose is to achieve automatic identification and classification of transient fluctuation features to confirm whether they are related to micro-fatigue damage of instrument materials and to identify specific damage types.

[0085] In some preferred embodiments, this application is implemented as follows. When performing pattern matching on transient fluctuation characteristics to confirm their correlation with micro-fatigue damage of instrument materials, waveform data of the transient fluctuation characteristics can be acquired first, for example, a sequence of pressure changes over time containing hundreds of sampling points. Simultaneously, the preset fluctuation template can be a set of standard waveforms stored in a database, each standard waveform corresponding to a known type of micro-fatigue damage, such as an acoustic emission signal waveform template representing the propagation of fatigue cracks in metal, or a pressure transient waveform template representing the formation of internal voids in materials. When calculating the similarity between the waveform of the transient fluctuation characteristics or its transform domain representation and the fluctuation template, the Pearson correlation coefficient can be used to measure the linear correlation between the two waveforms. For example, the waveform data of the transient fluctuation characteristics and the waveform data of each preset fluctuation template are calculated one by one to obtain a series of correlation coefficient values. The closer the correlation coefficient value is to 1, the more similar the two waveforms are. Subsequently, based on the calculated similarity, the fluctuations related to micro-fatigue damage of instrument materials by the transient fluctuation characteristics are confirmed, and the type of micro-fatigue damage of instrument materials corresponding to the transient fluctuation characteristics is identified. For example, a similarity threshold can be set, such as 0.8. If the similarity score between a transient fluctuation feature and the "microcrack" fluctuation template is 0.92, exceeding the threshold of 0.8, while the similarity score with the "corrosion" fluctuation template is only 0.65, then the system can confirm that the transient fluctuation feature is related to micro-fatigue damage in the instrument material and identify it as a "microcrack" damage type. In this way, the classification and identification of micro-fatigue damage in instrument materials can be achieved, providing clear guidance for subsequent maintenance decisions.

[0086] Through the above technical solution, this application can overcome the problem that merely identifying transient fluctuation characteristics cannot accurately confirm their correlation with the micro-fatigue damage of the instrument material or identify the specific damage type. By calculating the similarity between the transient fluctuation characteristics and the preset fluctuation template, and confirming and identifying the type based on the similarity, interference caused by non-damage factors can be effectively eliminated, ensuring that the identified fluctuations do indeed originate from the micro-fatigue damage of the instrument material.

[0087] This application further proposes steps for adjusting the operating parameters of the pressure regulating actuator of the disinfection chamber to change the rate of pressure change based on the evaluation results and micro-anomaly patterns, including:

[0088] Construct a closed-loop feedback control system;

[0089] Based on the evaluation results, the control signal of the pressure regulating actuator is calculated;

[0090] Output control signals to drive the pressure regulating actuator to adjust its operating state in order to change the rate of pressure change;

[0091] Continuously monitor the rate of pressure change and compare it with the target rate of pressure change;

[0092] Based on the comparison results, the control signal of the pressure regulating actuator is recalculated to make the pressure change rate approach the target pressure change rate.

[0093] To better understand the above scheme, some key technical features are described in detail below. The closed-loop feedback control system refers to a control structure that can monitor the system output in real time and feed the output information back to the input. It adjusts the system behavior by comparing the deviation between the output and the desired value. It can be implemented using a proportional-integral-derivative (PID) controller, a fuzzy logic controller, or an adaptive controller. Its purpose is to achieve precise and stable control of the pressure change rate. The control signal for the pressure regulating actuator refers to the instructions or data used to drive the pressure regulating actuator to perform its actions. It can be implemented using analog voltage signals, digital pulse width modulation (PWM) signals, or serial communication instructions. Its purpose is to translate the control system's decisions into the actual actions of the actuator. The target pressure change rate refers to the preset, desired pressure change rate during the disinfection process, based on the characteristics of the instrument or the requirements of the disinfection stage. It can be a fixed value, a range of variation, or a dynamically adjusted curve. Its purpose is to provide a clear control target for pressure regulation.

[0094] Based on the above description of key features, the solution of this application lays the foundation for achieving precise pressure control by constructing a closed-loop feedback control system. Compared with traditional open-loop control, closed-loop control can monitor the system output, i.e., the rate of pressure change, in real time and feed it back to the control system, thereby achieving automatic correction of system deviations. Specifically, the system first calculates the control signal for the pressure regulating actuator based on the evaluation results obtained from the preceding steps. These evaluation results reflect the stability of the current pressure change and the deviation from the ideal pressure change path. This control signal is then output to drive the pressure regulating actuator to adjust its operating state, such as changing its power or valve opening, thereby actually changing the rate of pressure change within the sterilization chamber.

[0095] In some preferred embodiments, this application is implemented as follows to further clarify the operation of the above scheme. The closed-loop feedback control system can be constructed using a digital controller, such as an embedded microcontroller or a programmable logic controller (PLC), which runs a PID control algorithm internally. This controller receives real-time pressure change rate data from a pressure sensor as feedback input and receives evaluation results provided by a preceding evaluation module as the basis for adjusting the control strategy or as a reference for setting targets. The pressure regulating actuator can be an electric proportional valve for precisely controlling the flow rate of gas entering and exiting the sterilization chamber, or a variable frequency drive vacuum pump for adjusting the vacuuming speed. When the controller calculates a control signal based on the evaluation results and the deviation between the current pressure change rate and the target value, such as a 0-10V analog voltage signal or a 4-20mA current signal, this signal is directly output to the electric proportional valve or the variable frequency vacuum pump. The electric proportional valve adjusts its opening according to the received signal, thereby changing the gas flow rate and thus affecting the pressure change rate; the variable frequency vacuum pump adjusts its rotation speed according to the signal to change the pumping rate. The system continuously monitors the rate of pressure change within the sterilization chamber using high-precision pressure sensors and transmits this data back to the digital controller. The digital controller compares the monitored rate of pressure change with a preset target rate of pressure change. For example, if the target is a decrease of 10 kPa per second, and the actual monitored decrease is 12 kPa per second, the controller will calculate a new control signal based on a PID algorithm to reduce the rate of pressure decrease, bringing it closer to the target value. This process is continuous and iterative, ensuring that the rate of pressure change remains stable within the desired range.

[0096] Through the above technical solution, this application can achieve precise and stable control of the rate of pressure change within the disinfection chamber. The introduction of a closed-loop feedback control system makes pressure regulation no longer a simple unidirectional adjustment, but rather enables real-time monitoring, comparison, and automatic correction of deviations, effectively avoiding overshoot or oscillation phenomena that may occur in traditional methods.

[0097] This application further proposes steps for calculating the control signal of the pressure regulating actuator, including:

[0098] Based on the assessment results, determine the target adjustment direction and magnitude for the rate of pressure change;

[0099] Based on the target adjustment direction and magnitude, and combined with the comparison between the pressure change rate and the target pressure change rate, the control signal of the pressure regulating actuator is calculated.

[0100] The target adjustment direction and magnitude of the pressure change rate refer to determining whether the current pressure change rate needs to be increased or decreased, and by how much, based on the assessment of the pressure state within the disinfection chamber. This ensures that subsequent control signals guide the pressure regulating actuator to adjust in the correct direction and to the correct degree, so that the actual pressure change rate approaches the ideal state. The comparison result between the pressure change rate and the target pressure change rate involves comparing the currently measured pressure change rate with the preset, desired target pressure change rate to obtain information on the deviation between the two. This comparison result reflects the accuracy of the current pressure change rate, providing a basis for refined calculation of control signals and avoiding over-adjustment or under-adjustment.

[0101] After elaborating on the aforementioned technical features, the solution of this application achieves effective regulation of the pressure change rate in the disinfection chamber through a refined control signal calculation process. Specifically, in the closed-loop feedback control system, the direction and specific adjustment range of the current pressure change rate needing adjustment are first determined based on the evaluation results of the pressure state within the disinfection chamber. The evaluation results directly reflect the deviation between the actual pressure state and the ideal state; therefore, based on the evaluation results, a preliminary macro-level strategy for pressure regulation can be determined, such as whether acceleration or deceleration is needed, and the approximate adjustment magnitude. It is precisely because of this preliminary judgment that the control system can respond to the actual needs of the disinfection chamber and adjust in the correct direction. On this basis, to achieve more precise control, the solution further combines the comparison results of the actual pressure change rate and the target pressure change rate to calculate the control signal of the pressure regulation actuator. This means that the generation of the control signal no longer relies solely on the macro-level judgment of the evaluation results, but introduces consideration of the micro-level difference between the current pressure change rate and the desired rate. By integrating these two aspects of information, overshoot or undershoot caused by a single factor judgment can be avoided. For example, if the assessment indicates a need to increase the rate of pressure change, but the actual rate of pressure change is already very close to the target value, the system can reduce the amplification of the control signal accordingly by combining the comparison results. This prevents the rate of pressure change from being too rapid and ensures a smooth transition. Conversely, if the deviation is large, the adjustment can be increased. This dual consideration makes the calculation of the control signal more accurate and adaptive, effectively handling complex pressure changes within the sterilization chamber, thereby ensuring the stability of the sterilization process and the safety of the instruments.

[0102] In some preferred embodiments, this application is implemented as follows: During the pressure regulation process of the disinfection chamber, the system first receives the evaluation results output by the evaluation module. For example, if the evaluation results show that the current pressure change rate is lower than the ideal path requirement and the pressure fluctuation is stable, the system can determine that the target adjustment direction of the pressure change rate is "increase". The target adjustment magnitude can be set according to the severity of the evaluation results. For example, if the evaluation results indicate a large deviation, the magnitude is set to "large increase"; if the deviation is small, the magnitude is set to "small increase". Subsequently, after determining the target adjustment direction and magnitude, the system will obtain the current actual pressure change rate and compare it with the preset target pressure change rate in real time. For example, if the target adjustment direction is "increase" and the target adjustment magnitude is "large increase", but the actual pressure change rate is already very close to the target pressure change rate, the system will correct the initially determined control signal based on this comparison result. Specifically, a proportional-integral-derivative (PID) controller can be used to calculate the control signal. The proportional term provides the basic control input based on the target adjustment direction and magnitude determined by the evaluation results; the integral term eliminates long-standing steady-state errors, ensuring the target pressure change rate is ultimately achieved; and the derivative term makes predictive adjustments based on the instantaneous trend of the pressure change rate, avoiding overshoot. In this way, even if the evaluation results indicate a need for significant adjustment, if the actual speed is already close to the target, the PID controller will output a small control signal, achieving smooth and precise regulation. The pressure regulating actuator, such as a variable frequency pump or a proportional valve, will adjust its operating state based on this calculated control signal, thereby precisely changing the pressure change rate within the sterilization chamber.

[0103] Through the above technical solution, this application can accurately convert the evaluation results into control signals for the pressure regulating actuator, thereby achieving effective control over the rate of pressure change in the disinfection chamber. Specifically, by first determining the target adjustment direction and magnitude of the pressure change rate based on the evaluation results, it can be ensured that the control system can respond to the actual pressure state inside the disinfection chamber and adjust in the correct direction.

[0104] In some embodiments described above, this application proposes pattern matching of transient fluctuation characteristics to confirm their association with micro-fatigue damage to instrument materials and to identify the type of micro-fatigue damage to the instrument materials corresponding to the transient fluctuation characteristics. Specifically, this pattern matching can be achieved by comparing the collected transient fluctuation characteristics with a pre-defined general fluctuation pattern library. For example, similarity calculations can be performed between the waveform, frequency components, or energy distribution of the transient fluctuation signal and typical features of general fatigue damage signals to preliminarily determine whether fatigue damage exists and attempt to identify its approximate type. This allows for preliminary screening of potential damage to the instrument. However, in its implementation, the lack of fluctuation templates representing different types of micro-fatigue damage to instruments makes it difficult to guarantee the accuracy and reliability of pattern matching, thus affecting the effective identification of micro-fatigue damage to instruments.

[0105] In this regard, this application further proposes a step of pre-setting a fluctuation template representing the microscopic fatigue damage of different types of instruments, including:

[0106] Controlled pressure variation tests were conducted on surgical instruments with known microscopic fatigue damage types, and the resulting pressure data were collected.

[0107] Feature extraction is performed on the collected pressure data to obtain a feature set related to different damage types;

[0108] Based on the feature set, construct and store the fluctuation templates representing micro-fatigue damage of different types of instruments.

[0109] Feature extraction refers to identifying and quantifying key information that can characterize specific attributes or patterns from raw pressure data. This can be achieved using techniques such as time-domain analysis, frequency-domain analysis, wavelet analysis, or machine learning algorithms. A wave template, after preprocessing and feature extraction, refers to a standardized data model or pattern that can represent a specific type of microscopic fatigue damage. It can be a typical waveform, a set of feature vectors, or a statistical model.

[0110] This application's solution improves the accuracy and reliability of pattern matching by constructing and storing fluctuation templates representing microscopic fatigue damage of different types of surgical instruments. Specifically, firstly, controlled pressure change tests are performed on surgical instruments with known microscopic fatigue damage types, and the resulting pressure data is collected. This step is fundamental to template construction; by simulating pressure changes in a real sterilization environment, pressure response data closely related to specific damage types can be obtained, providing realistic and representative samples for subsequent analysis. Subsequently, feature extraction is performed on the collected pressure data to obtain feature sets related to different damage types. Feature extraction is a crucial step; it transforms the raw, complex pressure signals into quantitative features that can clearly distinguish different damage types. For example, the energy distribution, frequency components, or time-domain waveform features of the signal can be extracted. It is through these refined features that the essential characteristics of microscopic fatigue damage can be effectively captured. Finally, based on these feature sets, fluctuation templates representing microscopic fatigue damage of different types of surgical instruments are constructed and stored. These templates are trained and validated, accurately reflecting the pressure fluctuation patterns of specific damage types.

[0111] In some preferred embodiments, the preset fluctuation template representing different types of instrument micro-fatigue damage can be implemented as follows. First, a batch of surgical scissors, forceps, or endoscopes with known micro-fatigue damage types such as microcracks, material fatigue, or corrosion points can be selected as samples. On a test bench in a simulated sterilization chamber environment, controlled pressure changes are applied to these sample instruments; for example, step pressure change tests, linear pressure increase / decrease tests, or periodic pressure pulse tests can be performed. During this process, high-precision pressure sensors and acoustic emission sensors are used to simultaneously acquire pressure fluctuation signals generated by the instruments at different pressure change stages. Subsequently, feature extraction is performed on the acquired pressure data. Specifically, time-domain analysis can be performed on the pressure data to extract features such as peaks, troughs, rising or falling edge times, and pulse width. Simultaneously, frequency-domain analysis can be performed, using fast Fourier transform or wavelet transform to extract features such as energy distribution, dominant frequency, and harmonic components within a specific frequency range. Furthermore, machine learning methods, such as support vector machines or deep learning networks, can be used to automatically learn and extract high-dimensional features from the raw data, thereby forming feature vectors representing different damage types. Finally, based on these extracted feature sets, fluctuation templates representing micro-fatigue damage of different types of instruments are constructed and stored. Cluster analysis can be performed on the extracted feature sets to generate one or more typical feature templates for each known type of micro-fatigue damage, such as microcracks, material fatigue, or corrosion points. These templates can be stored in a database in the form of multidimensional vectors, feature matrices, or probability distribution models. For example, they can be stored as the average feature vector and its variance corresponding to each damage type, or as Gaussian mixture model parameters, for subsequent similarity calculations during pattern matching.

[0112] The above technical solution solves the problem of difficulty in ensuring accuracy and reliability when performing pattern matching on transient fluctuation characteristics due to the lack of fluctuation templates representing the microscopic fatigue damage of different types of instruments. By conducting controlled testing on instruments with known damage types, extracting features, and constructing and storing representative fluctuation templates, the accuracy and reliability of pattern matching are significantly improved. This enables more effective identification of the microscopic fatigue damage type of instruments, providing a reliable basis for precise pressure adjustment during the sterilization process, and ensuring the structural integrity of the instruments and the sterilization effect.

[0113] Secondly, referring to Figure 2 This application further proposes a surgical instrument sterilization system for regulating the pressure of the sterilization chamber to ensure the sterilization effect of the chamber. The system includes:

[0114] The pressure data processing module 201 is used to continuously collect pressure data inside the disinfection chamber and calculate the pressure change rate and pressure fluctuation amplitude based on the pressure data.

[0115] Ideal path storage module 202 is used to store ideal pressure change paths;

[0116] The evaluation result generation module 203 is used to compare the pressure change rate with the corresponding ideal pressure change rate in the ideal pressure change path, evaluate whether the pressure fluctuation amplitude exceeds a preset threshold, and generate an evaluation result.

[0117] The frequency characteristic analysis module 204 is used to perform frequency characteristic analysis on the collected pressure data to identify energy changes related to internal residues or microstructures of the instrument.

[0118] The transient fluctuation pattern recognition module 205 is used to perform transient fluctuation pattern recognition on the pressure data in order to identify fluctuations related to micro-fatigue damage of the instrument material.

[0119] The judgment module 206 is used to determine whether there is a microscopic abnormal pattern based on the frequency feature analysis results and the transient fluctuation pattern identification results.

[0120] The pressure regulation control module 207 is used to adjust the operating parameters of the pressure regulation actuator of the disinfection chamber according to the evaluation results and micro-anomaly patterns, so as to change the pressure change rate. If the evaluation results show that the pressure change is stable and the deviation from the ideal pressure change path is within an acceptable range, the pressure change rate is increased; if the evaluation results show that the pressure fluctuates violently or deviates significantly from the ideal pressure change path, the pressure change rate is decreased or the pressure change process is paused.

[0121] The above technical solution provides a surgical instrument disinfection system, which serves as a specific implementation carrier for the surgical instrument disinfection method, enabling the method to be effectively executed and applied.

[0122] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for disinfecting surgical instruments, characterized in that, The method for regulating the pressure of the disinfection chamber to ensure its disinfection effect includes the following steps: Continuously collect pressure data inside the disinfection chamber, and calculate the rate of pressure change and the amplitude of pressure fluctuation based on the pressure data; Pre-set the ideal pressure change path; The pressure change rate is compared with the ideal pressure change rate in the ideal pressure change path, and the pressure fluctuation amplitude is evaluated to obtain the evaluation result. Frequency characteristic analysis was performed on the collected pressure data to identify energy changes related to internal residues or microstructures of the instrument. Transient fluctuation pattern identification is performed on the pressure data to identify fluctuations related to micro-fatigue damage of the instrument material; Based on the frequency characteristic analysis results and transient fluctuation pattern identification results, determine whether there are microscopic abnormal patterns; Based on the evaluation results and micro-anomaly patterns, the operating parameters of the pressure regulating actuator in the disinfection chamber are adjusted to change the rate of pressure change. Specifically, if the evaluation results show that the pressure change is stable and the deviation from the ideal pressure change path is within an acceptable range, the rate of pressure change is increased; if the evaluation results show that the pressure fluctuates violently or deviates significantly from the ideal pressure change path, the rate of pressure change is decreased or the pressure change process is paused. The step of identifying transient fluctuation patterns in the pressure data to identify fluctuations related to microscopic fatigue damage of the instrument material includes: Transient fluctuation patterns are identified in the collected pressure data to obtain transient fluctuation characteristics; The temporal correlation between the transient fluctuation characteristics and the operating status of the disinfection chamber is determined to obtain the temporal correlation determination result. The spatial consistency of the transient fluctuation characteristics across multiple pressure sensors within the disinfection chamber is determined to obtain a spatial consistency determination result. Based on the time correlation judgment result, the spatial consistency judgment result, and the transient fluctuation characteristics, fluctuations related to micro-fatigue damage of instrument materials are identified.

2. The method for disinfecting surgical instruments according to claim 1, characterized in that, The step of performing frequency characteristic analysis on the collected pressure data to identify energy changes related to internal residues or microstructures of the device includes: Time-frequency analysis was performed on the collected pressure data to obtain the energy spectrum of the pressure data in different time-frequency bands; Based on the energy spectrum, the energy intensity or energy distribution pattern within the preset natural frequency range related to the microstructural characteristics of the device itself is analyzed to obtain the natural frequency response characteristics. Based on the energy spectrum, analyze the energy intensity or energy distribution pattern within the preset abnormal frequency range related to internal residues or micro-damage of the instrument to obtain abnormal energy change characteristics. Based on the inherent frequency response characteristics and the abnormal energy change characteristics, energy changes related to internal residues or microstructures of the device are identified.

3. The method for disinfecting surgical instruments according to claim 1, characterized in that, The step of identifying transient fluctuation patterns in the collected pressure data to obtain transient fluctuation characteristics includes: Real-time noise characteristics are estimated from the collected pressure data to obtain a dynamic noise baseline; Based on the dynamic noise baseline, the pressure data is adaptively filtered to enhance transient fluctuation signals; In the filtered pressure data, instantaneous pressure changes that exceed the preset deviation range of the dynamic noise baseline are identified to obtain preliminary transient fluctuation events; The transient fluctuation features are obtained by extracting morphological features from the initial transient fluctuation event; Pattern matching is performed on the transient fluctuation characteristics to confirm their correlation with microscopic fatigue damage of the instrument material, thereby obtaining the transient fluctuation characteristics.

4. The method for sterilizing surgical instruments according to claim 3, characterized in that, The method further includes: Obtain the waveform or its transform domain representation of the transient fluctuation characteristics; Preset fluctuation templates to represent microscopic fatigue damage of different types of instruments.

5. The method for sterilizing surgical instruments according to claim 4, characterized in that, The step of performing pattern matching on the transient fluctuation characteristics to confirm their correlation with microscopic fatigue damage of the instrument material includes: Calculate the similarity between the waveform or its transform domain representation of the transient fluctuation feature and the fluctuation template; Based on the similarity, the transient fluctuation characteristics are confirmed to be related to the micro-fatigue damage of the instrument material, and the type of micro-fatigue damage of the instrument material corresponding to the transient fluctuation characteristics is identified.

6. The method for sterilizing surgical instruments according to claim 1, characterized in that, The step of adjusting the operating parameters of the pressure regulating actuator of the disinfection chamber according to the evaluation results and micro-anomaly patterns to change the rate of pressure change includes: Construct a closed-loop feedback control system; Based on the evaluation results, the control signal of the pressure regulating actuator is calculated; The control signal is output to drive the pressure regulating actuator to adjust its operating state in order to change the rate of pressure change; The rate of pressure change is continuously monitored and compared with the target rate of pressure change. Based on the comparison results, the control signal of the pressure regulating actuator is recalculated to make the pressure change rate approach the target pressure change rate.

7. The method for sterilizing surgical instruments according to claim 6, characterized in that, The step of calculating the control signal of the pressure regulating actuator includes: Based on the evaluation results, determine the target adjustment direction and magnitude of the pressure change rate; Based on the target adjustment direction and magnitude, and combined with the comparison result of the pressure change rate and the target pressure change rate, the control signal of the pressure regulating actuator is calculated.

8. A method for disinfecting surgical instruments according to claim 5, characterized in that, The step of presetting a fluctuation template representing microscopic fatigue damage of different types of instruments includes: Controlled pressure variation tests were conducted on surgical instruments with known microscopic fatigue damage types, and the resulting pressure data were collected. Feature extraction is performed on the collected pressure data to obtain a feature set related to different damage types; Based on the set of features, a wave template representing microscopic fatigue damage of different types of instruments is constructed and stored.

9. A surgical instrument sterilization system, characterized in that, This system, used to regulate the pressure of the disinfection chamber to ensure its disinfection effectiveness, includes: The pressure data processing module is used to continuously collect pressure data inside the disinfection chamber and calculate the pressure change rate and pressure fluctuation amplitude based on the pressure data. Ideal path storage module, used to store ideal pressure change paths; The evaluation result generation module is used to compare the pressure change rate with the corresponding ideal pressure change rate in the ideal pressure change path, evaluate whether the pressure fluctuation amplitude exceeds a preset threshold, and generate an evaluation result. The frequency characteristic analysis module is used to perform frequency characteristic analysis on the collected pressure data in order to identify energy changes related to internal residues or microstructures of the instrument. The transient fluctuation pattern recognition module is used to perform transient fluctuation pattern recognition on the pressure data in order to identify fluctuations related to micro-fatigue damage of the instrument material. The judgment module is used to determine whether there is a microscopic abnormal pattern based on the frequency feature analysis results and the transient fluctuation pattern recognition results. The pressure regulation control module is used to adjust the operating parameters of the pressure regulation actuator of the disinfection chamber according to the evaluation results and micro-anomaly patterns, so as to change the pressure change rate. If the evaluation results show that the pressure change is stable and the deviation from the ideal pressure change path is within an acceptable range, the pressure change rate is increased; if the evaluation results show that the pressure fluctuates violently or deviates significantly from the ideal pressure change path, the pressure change rate is decreased or the pressure change process is paused. The transient fluctuation pattern recognition module is also used to identify transient fluctuation patterns in the collected pressure data to obtain transient fluctuation characteristics; The temporal correlation between the transient fluctuation characteristics and the operating status of the disinfection chamber is determined to obtain the temporal correlation determination result. The spatial consistency of the transient fluctuation characteristics across multiple pressure sensors within the disinfection chamber is determined to obtain a spatial consistency determination result. Based on the time correlation judgment result, the spatial consistency judgment result, and the transient fluctuation characteristics, fluctuations related to micro-fatigue damage of instrument materials are identified.

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