Steam sterilization method and system for reusable medical instruments

Abstract

The present invention relates to a steam sterilization method and system for reusable medical instruments. The method comprises: collecting at least two temperature values in a sterilization chamber of a sterilization device and at least two pressure values of steam pressure in the sterilization chamber; calculating the at least two temperature values and the at least two pressure values on the basis of a preset risk prediction model, and determining a temperature uniformity safety margin and a temperature fluctuation margin; and determining an alarm threshold on the basis of a temperature uniformity reference threshold of the temperature uniformity safety margin and / or a sterilization temperature fluctuation threshold of the temperature fluctuation margin, the alarm threshold being close to the temperature uniformity reference threshold or the sterilization temperature fluctuation threshold, such that the sterilization device is adjusted before a fault occurs. The present invention is beneficial to notifying maintenance personnel to adjust the sterilization device in time before a fault occurs, thereby reducing the influence of temperature on the hardware of the sterilization device and prolonging the service life, and maintaining the stability of the sterilization effect by means of in-time adjustment.

Description

A steam sterilization method and system for reusable medical devices Technical Field

[0001] This invention relates to the field of medical sterilization technology, and in particular to a sterilization method and system for reusable medical devices. Background Technology

[0002] Traditional sterilization equipment generally employs real-time monitoring mechanisms for key parameters such as temperature and pressure, triggering alarms for abnormalities by setting fixed thresholds. However, existing technologies have significant limitations: First, in terms of equipment health status assessment, current technologies mostly adopt a passive fault response mode, lacking continuous trend analysis of equipment operating parameters. This makes it difficult to identify potential fault risks in the early stages of equipment performance degradation, often triggering alarms only when obvious functional abnormalities occur, by which time unplanned downtime has already occurred, severely impacting the continuity of sterilization operations.

[0003] Secondly, the current system has significant shortcomings in processing multi-sensor data, particularly in its alarm logic construction. Existing alarm mechanisms typically perform isolated threshold checks on four independent sensors (temperature sensor A, temperature sensor B, pressure sensor C, and pressure sensor D), failing to consider the dynamic error coupling effects between these sensors. Since sensor measurement errors accumulate non-linearly over time, the superposition of drift errors from multiple sensors during operation can easily trigger false alarms. Actual application data shows that this isolated judgment mechanism results in a false alarm rate as high as 32%-45%, severely disrupting normal sterilization procedures.

[0004] Furthermore, in terms of fault location, existing technologies lack sufficient fault diagnosis capabilities for redundant sensors. When the system detects abnormal temperature parameters, the lack of an effective sensor cross-validation mechanism makes it impossible to distinguish the specific fault state of temperature sensor A or B; similarly, when pressure parameters are abnormal, the fault source of sensor C or D cannot be accurately located. This deficiency directly forces maintenance personnel to adopt a complete replacement strategy. Statistics show that approximately 78% of repair cases require the replacement of all four sensors, which not only significantly increases spare parts costs (increasing the cost of a single maintenance by 300%-400%), but also extends the equipment recalibration time by 2-3 times, severely impacting equipment efficiency.

[0005] The aforementioned technical deficiencies have become a key bottleneck restricting the intelligent development of sterilization equipment, and technological breakthroughs are urgently needed to achieve equipment status prediction, false alarm suppression, and accurate fault location through technological innovation.

[0006] The present invention aims to develop a system that can identify potential problems before warnings are issued, thereby avoiding substandard sterilization results and ensuring medical safety.

[0007] CN118609777A discloses a traceability and early warning method and control system for oral medical devices, comprising: sterilizing an instrument pack using sterilization equipment; detecting the instrument pack using a detection system; outputting first data and sending it to a first receiving end, converting it into an identification code, and affixing it to the instrument pack; a second receiving end scanning the identification code for traceability, determining whether the first data is qualified, and issuing an early warning if it is unqualified; if qualified, acquiring the operating data of the sterilization equipment as second data; scanning and identifying the identification code of the instrument pack through a mobile port, reading the first and second data, and determining whether it is qualified; if not, issuing an early warning again; realizing remote monitoring and early warning, enabling patients, medical institutions, and health regulatory departments to scan the code for traceability, tracing the operating status of the sterilization equipment, the sterilization result of the instrument pack, and the expiration date of the instrument pack, achieving a combination of sterilization results and instrument pack detection, making the traceability results more reliable. However, this technical solution cannot effectively identify potential faults in the sterilization process.

[0008] Therefore, how to identify the risk of failure before sterilization equipment malfunctions, so as to adjust the sterilization equipment in advance and avoid failure during sterilization operation, which would lead to sterilization failure and damage to items, is also a problem that the current early warning system for medical sterilization equipment has not yet solved. Summary of the Invention

[0009] To address the shortcomings of existing technologies, this invention provides a sterilization device for reusable medical devices. This device is designed for the effective sterilization of reusable medical devices. The system includes not only core equipment for performing sterilization operations but also a processor. The processor contains an analysis module. This analysis module is connected to the control module in the sterilization equipment via a communication interface, enabling it to receive and process data from the control module in real time. The analysis module utilizes a preset risk prediction model to analyze the changing trends and alarm thresholds of the operating data received from the control module. Its main function is to assess potential failure risks related to temperature or pressure. When the detected failure risk reaches a preset threshold, the system automatically generates an early warning command.

[0010] In existing sterilizers, without a fault detection mechanism, the sterilization process can only be judged as normal after a significant temperature anomaly occurs. However, this reactive approach can lead to decreased sterilization quality and increased costs associated with reprocessing medical supplies. In contrast, this invention achieves timely detection of abnormal temperature changes through trend analysis of operational data. This means that once a temperature deviation from the normal range is detected, the system can immediately notify relevant personnel to perform necessary adjustments, without waiting for the equipment to actually malfunction.

[0011] The sterilization apparatus is equipped with at least two temperature sensors for monitoring the temperature inside the sterilization chamber and at least two pressure sensors for detecting steam pressure. To assess temperature uniformity and safety, one of the following two methods can be used:

[0012] Option 1: At each sampling point during the constant pressure phase, the risk prediction algorithm uses a pressure-temperature conversion formula to convert at least two recorded pressure readings into corresponding equivalent temperature values. Next, the system compares the temperature difference directly obtained from the temperature sensor (first temperature difference) with the pressure-equivalent temperature difference obtained through conversion (second temperature difference). Then, the larger of these two temperature differences is selected and compared with a set temperature uniformity benchmark to determine the first alarm threshold, which serves as the primary warning line.

[0013] Option 2: Similarly, at each sampling time point during the constant pressure phase, the risk prediction model first calculates the equivalent temperature values ​​after converting at least two temperature sensor readings and at least two pressure readings. Then, it identifies the maximum temperature difference among these data and compares this maximum temperature difference with a pre-set temperature uniformity standard to determine the first alarm threshold, i.e., the safety margin for temperature uniformity.

[0014] By combining the strengths of both approaches to calculate the safety margin for temperature uniformity, it becomes easier to accurately identify any abnormal temperature variations. This method not only enhances early warning capabilities for potential problems but also improves the safety and reliability of the entire sterilization process.

[0015] The setting of alarm limits depends on the safety margin for temperature uniformity and the margin for temperature variation; specifically, during the sterilization operation, for each time point t... i The system compares the maximum difference between the equivalent temperature values ​​converted from the temperature and pressure sensors and compares this maximum difference with a preset temperature uniformity standard limit. If the maximum difference approaches or exceeds this standard limit, an alarm mechanism will be triggered. In addition, if the difference between the maximum and minimum values ​​of the pressure equivalent temperature obtained from the temperature and pressure sensors approaches or exceeds the sterilization temperature fluctuation limit, an alarm will also be triggered.

[0016] The determination of the failure risk boundary is based on the stability and variation of temperature and pressure during the sterilization process. This includes calculating the time step t at each moment. iThe system establishes a temperature uniformity safety margin and records the maximum temperature difference throughout the process. Simultaneously, during the sterilization maintenance phase, it calculates the difference between the highest and lowest temperature values ​​provided by the temperature and pressure sensors. Combining the standard limits for temperature uniformity and the limits for sterilization temperature fluctuations, the overall stability of temperature control is assessed. Based on accumulated data and practical experience, failure risk limits are established for the risk prediction model. This configuration helps prevent damage to the sterilization equipment from high temperatures while identifying abnormal temperature fluctuations, thereby ensuring timely adjustments and maintenance before equipment failure occurs.

[0017] The risk prediction model calculates the temperature fluctuation margin by recording the time t at each moment during the entire sterilization holding phase of the sterilization equipment. i Temperature value T1(t) of the temperature sensor i ) and T2(t i ), and the pressure equivalent temperature value T obtained by conversion through the pressure sensor. P1 (t i ) and T P2 (t i ), choose these four at time t i The maximum and minimum values ​​are calculated, and the difference between the maximum and minimum values ​​is compared with the sterilization temperature fluctuation threshold to obtain the percentage of temperature fluctuation margin as the second alarm threshold.

[0018] Monitoring temperature fluctuation margins ensures that the temperature difference at any point within the system does not exceed a specified threshold within the prescribed sterilization time (e.g., 3 minutes). The sterilization temperature fluctuation threshold is used to monitor and limit spatial and temporal temperature fluctuations, ensuring the uniformity and effectiveness of sterilization. Exceeding this temperature difference threshold may affect the thoroughness of sterilization, causing the sterilization process to fail to meet the required microbial kill standards.

[0019] The analysis module determines the malfunction of temperature and / or pressure sensors based on changes and anomalies in temperature uniformity safety margins and temperature fluctuation margins. This monitoring allows for timely notification of maintenance personnel for adjustments before a true malfunction occurs in the sterilization equipment. This reduces the impact of temperature on the hardware of the sterilization equipment, extends its service life, and enables timely adjustments to maintain the stability of the sterilization effect.

[0020] This invention provides a sterilization method for reusable medical devices from a second aspect. The method includes: using a predetermined risk assessment model to analyze the changing trends and / or alarm limits of operational data transmitted by a control module, thereby identifying potential equipment failure risks related to temperature or pressure. Once the detected risk level reaches a preset risk limit, the system issues a warning signal to allow necessary adjustments to be made before an actual equipment failure occurs. The advantage of this method is its low demand for computing resources and its ability to quickly detect anomalies, enabling effective detection of abnormal temperature fluctuations before a warning is triggered. Addressing the limitation of previous methods that relied solely on warnings and could not accurately determine whether temperature changes were normal, this invention achieves real-time judgment of abnormal temperature changes by analyzing the changing trends of operational data. This allows maintenance personnel to be immediately notified and take corrective measures when abnormal temperatures occur, avoiding the situation of waiting for the equipment to actually fail before repairs are carried out. Attached Figure Description

[0021] Figure 1 is a schematic diagram of the steam sterilization system provided by the present invention;

[0022] Figure 2 is a simplified schematic diagram of the module connection relationship of the steam sterilization system provided by the present invention;

[0023] Figure 3 is a schematic diagram of the start-up control process of the sterilization equipment by the control module provided by the present invention.

[0024] Figure 4 is a schematic diagram showing the connection between the temperature sensor and the pressure sensor of the sterilization equipment provided by the present invention.

[0025] Figure 5 is a line graph showing the pressure changes in the sterilization chamber of the sterilization equipment;

[0026] Figure 6 is a graph showing the temperature and time changes of the sterilization equipment during the sterilization process;

[0027] Figure 7 is a data calculation table of temperature and pressure in the sterilization process provided by the present invention;

[0028] Figure 8 is an enlarged schematic diagram of the calculation formulas for schemes A and B of temperature uniformity safety margin and the calculation formula for temperature fluctuation margin provided by the present invention.

[0029] List of reference numerals in the attached figures: 100: Sterilization equipment; 101: First temperature sensor; 102: First pressure sensor; 103: Second temperature sensor; 104: Second pressure sensor; 105: Jacket indicating pressure sensor; 106: Steam generator indicating pressure sensor; 107: Display device; 108: Steam generator; 110: Control module; 200: Analysis module; 210: Data receiving port; 220: Data processing model; 230: Risk prediction model; 300: Terminal. Detailed Implementation

[0030] The following is a detailed explanation with reference to the accompanying drawings.

[0031] In practice, the alarm system of sterilization equipment 100 typically only detects and reports these problems after a malfunction or abnormal parameter occurs. This reactive error detection mechanism can lead to sterilization failures, resulting in some medical supplies that should have been sterilized not reaching the required sterility level, thereby increasing the possibility of infection and posing a potential danger to the quality of medical services and patient safety. Furthermore, if sterilization equipment 100 malfunctions, the relevant sterilization process data may not be able to be exported correctly. This not only hinders the rapid determination of the cause of the malfunction but also creates difficulties for subsequent equipment repair and historical tracing of the sterilization process. In medical environments requiring strict recording and monitoring, the loss or inaccessibility of data is a significant problem that cannot be ignored.

[0032] This invention provides a steam sterilization system and method. When the analysis module 200 is installed on the sterilization device 100 (as shown in Figure 1), or when the analysis module 200 is integrated with the control module 110, this invention provides a sterilization device 100 and its terminal 300. This invention also provides a terminal 300 for steam sterilization risk prediction.

[0033] Example 1

[0034] To address the shortcomings of existing technologies, this invention provides a steam sterilization system, as shown in Figure 4, which includes a sterilization device 100 for sterilizing reusable medical devices. The sterilization device 100 includes a sterilization chamber. The sterilization device 100 has or is equipped with monitoring modules for verifying and / or monitoring the sterilization effect, such as physical monitoring modules, biological indicators, and / or chemical indicators.

[0035] Existing medical sterilization equipment does not include the analysis module 200. In Figures 2 and 4, a first temperature sensor 101 is installed in the sterilization chamber and connected to the control module 110 to indicate the temperature. A second temperature sensor 103 is installed in the sterilization chamber and connected to the control module 110 to record the operating data and temperature data of the first temperature sensor 101.

[0036] In Figures 2 and 4, a first pressure sensor 102 is installed in the sterilization chamber to indicate absolute pressure. A second pressure sensor 104 is installed in the sterilization chamber and connected to the control module 110 to record the operating data and pressure data of the first pressure sensor 102. A jacket indicating pressure sensor 105 is also installed in the sterilization chamber to measure the pressure of the jacket and its changes. A steam generator indicating pressure sensor 106 is connected to the steam generator 108 to indicate the pressure of the steam in the steam generator 108. The control module 110 is also equipped with a display device 107 to centrally display the values ​​of all measurement systems on a single display device 107. When the sterilization equipment 100 is running, various operating parameters of the sterilization equipment 100 are also input to the control module 110 as input information.

[0037] Figure 4 illustrates the steam sterilization system of the present invention. The steam sterilization system includes an analysis module 200. As shown in Figure 2, the control module 110 sends the operating data change trend of the sterilization equipment 100 to the analysis module 200. Further, the control module 110 sends the operating data of the sterilization equipment 100 to the analysis module 200, which calculates the operating data change trend of the sterilization equipment 100. When only the risk prediction model 230 is available, the risk prediction model 230 is connected to the data receiving port 210 of the analysis module 200. The risk prediction model 230 analyzes the operating data and obtains the fault risk related to temperature or pressure. When the fault risk threshold is reached, an early warning command is generated. The risk prediction model 230 sends the fault risk information to the terminal 300 and displays it on the terminal 300. The risk prediction model 230 also sends an early warning command to the terminal 300. The terminal 300 generates early warning information based on the early warning command and issues an early warning using sound, light, electricity, or a combination thereof. Terminal 300 can be installed on sterilization equipment 100, or it can be carried or placed in a mobile manner by the operator, or it can be a non-portable device. Terminal 300 includes one or more of the following components: display component, sound playback component, light emission component, vibration component, etc., which can realize a variety of early warning components.

[0038] The analysis module 200 and its internal models are configured to execute the steam sterilization method of the present invention. The analysis module 200 may be a server, processor, dedicated integrated chip, cloud server, or other hardware device or combination thereof capable of running the coding program of the steam sterilization method of the present invention.

[0039] As shown in Figure 2, the preset risk prediction model 230 analyzes the changing trends and / or alarm thresholds of the operating data sent by the control module 110 to determine the temperature- or pressure-related failure risks of the sterilization equipment 100. When the failure risk threshold is reached, an early warning command is issued so that the sterilization equipment 100 can be debugged before a failure occurs. The terminal 300 is used to receive the early warning command from the analysis module 200 and issue an early warning.

[0040] This invention utilizes a steam sterilization system to monitor the real-time trends and alarm thresholds of the sterilization equipment 100's operational data, and assesses temperature- or pressure-related fault risks based on a preset risk prediction model 230. When a fault risk threshold is reached, the system issues an early warning command, allowing the sterilization equipment 100 to be adjusted before a fault occurs, thus avoiding sterilization failures and substandard sterility of medical supplies due to malfunctions. Simultaneously, the system ensures the integrity and traceability of sterilization process data, improves the speed of fault diagnosis, and reduces equipment repair time and costs. Therefore, this technical solution can improve the quality of medical services and patient safety, and reduce the risk of infection.

[0041] Different models and manufacturers of sterilization equipment 100 result in different alarm timings. For example, when vacuum sterilizing the same medical item, some sterilization equipment 100s are set to alarm when the control module 110 determines it is abnormal and issues a warning after 15 minutes of vacuuming; others are set to alarm when the control module 110 determines it is abnormal and issues a warning after 20 minutes of vacuuming. Therefore, when receiving or collecting alarm thresholds from sterilization equipment 100, the fault risk threshold used for warnings in the analysis module 200 can be manually adjusted by operators with management authority to better match the fault risk threshold with the current sterilization status of sterilization equipment 100.

[0042] In this invention, the sterilization temperature range margin is a temperature range set for the temperature fluctuation range during a single sterilization process, and this range is less than or equal to 3°C. The temperature uniformity safety margin is a temperature range set for the temperature fluctuation range at various simultaneous moments during the sterilization process, and this range is less than or equal to 2°C. The sterilization time safety margin is the duration of a single sterilization process, and this duration is greater than or equal to 180 seconds for the first sterilization temperature of 134°C and greater than or equal to 900 seconds for the second sterilization temperature of 121°C.

[0043] The sterilization equipment 100 has such precise temperature settings because the core objective of sterilization is to eliminate all microorganisms (including bacteria, viruses, fungi, spores, etc.), and these microorganisms are highly dependent on temperature and time tolerance. Different types of microorganisms require different temperatures and times to be completely killed. For example, in autoclaving, 121°C or 134°C is typically used as the sterilization temperature, and these temperatures must be maintained for a sufficiently long time (e.g., 15 minutes at 121°C or 3 minutes at 134°C) to ensure complete killing of microorganisms. If the temperature is below the standard range, even by just a few degrees, some heat-resistant microorganisms or spores may not be completely killed, leading to sterilization failure and increasing the risk of infection. However, while excessively high temperatures can kill microorganisms more quickly, they may damage the structure and performance of equipment and medical supplies, especially heat-sensitive materials such as plastics and rubber. Excessively high temperatures can also cause the sterilization equipment 100 itself to malfunction or even lead to safety accidents (such as overheating or steam explosions), affecting the normal operation of medical work.

[0044] Figure 6 shows a schematic diagram of the permissible temperature and time ranges during the sterilization process of sterilized items. As shown in Figure 6, vertical line A indicates the start of sterilization time. Vertical line B indicates the end of sterilization time. T s Indicates the sterilization temperature. T B Indicates the sterilization temperature range. t x1 Indicates sterilization time. t x2 Indicates the equilibrium time. t x3 t represents 60 seconds. x4 Indicates the duration of sterilization. S1 represents the temperature profile at the reference measurement point. S2 represents the temperature profile at the center point of the sterilized item. S3 represents the temperature profile at a point 50 mm above the sterilized item. T γ1 This represents the maximum temperature difference between the reference measurement point and all points within the test package during the duration of the measurement. T γ2 This represents the maximum temperature difference between the reference measurement point and the measurement point above the item to be sterilized within the first 60 seconds of sterilization. T γ3 Indicates at t x3 (After holding for 60 seconds), the maximum temperature difference between the reference measurement point and the measurement point above the sterilized item.

[0045] As shown in Figure 5, when the sterilization chamber starts operating, from time... Initially, the pressure undergoes repeated changes between negative and positive pressure until, over time... The sterilization time begins when the specified pressure is reached. The sterilization time ends when the specified pressure is reached. At that time, the pressure begins to drop and enters a negative pressure state. When the sterilization time reaches... This marks the start of the drying period. Pressure is maintained from the beginning until [time]. This marks the end of the drying period. There is a correlation between the steam pressure and temperature within the sterilization chamber. Pressure can be converted into ideal temperature, thus obtaining the effective pressure-temperature corresponding to the current pressure. Calculating temperature using pressure allows for a further determination of the actual temperature within the sterilization chamber. Furthermore, the difference between the estimated ideal temperature and the temperature data from any given temperature measurement system can be used to determine if the system is malfunctioning.

[0046] The risk prediction model 230 of this invention is set in a manner related to the safety margin for temperature uniformity, the safety margin for temperature fluctuation, and the sterilization time. The safety margin for temperature uniformity is calculated in the following two ways.

[0047] The sterilization equipment 100 is equipped with at least two temperature sensors for collecting the temperature of the sterilization chamber and at least two pressure sensors for collecting the steam pressure of the sterilization chamber.

[0048] Option A: At each sampling moment during the constant pressure phase, the risk prediction model 230 calculates at least two equivalent temperature values ​​corresponding to at least two pressure values ​​based on the pressure-temperature conversion formula. It then compares the first temperature difference between the at least two temperature values ​​with the second temperature difference between the at least two equivalent temperature values. Finally, it compares the maximum temperature difference between the first and second temperature differences with a uniformity temperature benchmark to determine the temperature uniformity safety margin, which serves as the first alarm threshold. (See Figure 8.)

[0049] Alternatively, Option B: At each sampling moment during the constant pressure phase, the risk prediction model 230 compares the temperature values ​​from at least two temperature sensors with the corresponding equivalent temperature values ​​calculated from at least two pressure values ​​using the pressure-temperature conversion formula. It calculates the maximum temperature difference among the four values ​​and compares this maximum temperature difference with a uniformity temperature benchmark to determine the temperature uniformity safety margin, which serves as the first alarm threshold. As shown in Figure 8, the formula for the percentage of the temperature uniformity safety margin is:

[0050] In schemes A and B, T1(t) i T2(t) represents the first temperature value collected by the first temperature sensor 101. i () represents the second temperature value collected by the second temperature sensor 103, T P1 (t i The first pressure value represents the equivalent temperature difference obtained by converting the first pressure value, T. P2 (t i ) represents the equivalent temperature difference of the second pressure obtained by converting the second pressure value, t iThe time is represented by S, which indicates the end time of the steam pressure reaching steady state; TUBT represents the temperature uniformity reference threshold. t i ∈[0,S] represents the time period during which the steam pressure in the sterilization chamber remains in a stable state (steady state), as shown in Figure 5.

[0051] The Temperature Uniformity Benchmark Threshold (TUBT) of this invention is defined as the maximum permissible limit of temperature distribution deviation at each sampling moment within a sterilization chamber of constant volume. In an embodiment, when the sterilization chamber temperature is 134°C, the Temperature Uniformity Benchmark Threshold TUBT is, for example, 2°C, to ensure that the temperature difference at different locations does not exceed this value throughout the entire sterilization time, thus guaranteeing the effectiveness and consistency of the entire process. The Temperature Uniformity Benchmark Threshold TUBT reflects the spatial consistency requirement of the temperature field; temperature fluctuations exceeding the Temperature Uniformity Benchmark Threshold TUBT may lead to significant differences in sterilization effects. The Temperature Uniformity Benchmark Threshold TUBT accurately assesses the temperature distribution uniformity at any moment during the sterilization process, ensuring that the equipment operates within the specified temperature range and preventing localized overheating or overcooling.

[0052] The formula for calculating the temperature uniformity safety margin is used under the following conditions: During the constant pressure holding phase (taking 134℃ for 300 seconds as an example), the temperature uniformity safety margin is calculated separately for each time point. The temperature difference at each time point is calculated (including the temperature difference through pressure-equivalent temperature values). The maximum value of the temperature uniformity safety margin at each time point is compared with the temperature uniformity reference threshold TUBT, and the final temperature uniformity safety margin for this sterilization is calculated as a percentage. This formula is used to assess whether the temperature difference recorded by the temperature sensor and pressure sensor at each time point (within 0–300 seconds of the entire sterilization process) exceeds the limit of the temperature uniformity reference threshold TUBT, ensuring that the temperature difference between each point is controlled within the specified range.

[0053] This calculation method improves the sensitivity to detecting various sensor faults by comparing multiple temperature values ​​(two temperature values ​​and two pressure equivalent temperature values). If a sensor malfunctions, its reading may deviate significantly from the values ​​of other sensors, resulting in changes in the temperature fluctuation margin. The calculated temperature fluctuation margin provides real-time monitoring of temperature uniformity. A significant decrease in the temperature fluctuation margin indicates a possible temperature sensor malfunction or other anomalies, facilitating timely operator response. Operators do not need to manually check the readings of each sensor; they only need to focus on changes in the temperature fluctuation margin, simplifying the monitoring process and improving operational convenience. Furthermore, this calculation method can promptly identify sensor faults and effectively prevent sterilization failures due to inaccurate temperature monitoring, enhancing the overall safety of the sterilization process. The formula for converting pressure to pressure equivalent temperature is shown below.

[0054] (A) Assuming that the approximate formula of the ideal gas law is used for pressure and temperature conversion:

[0055] Since the volume V and the mass n are constant, this can be simplified to: T P = k·P (4).

[0056] Where k is a conversion factor based on the equipment's calibration data. P is the measured absolute pressure of the saturated steam (in megapascals, MPa), which is the input variable. T P This is another pressure equivalent temperature. This other pressure equivalent temperature is used for critical shutdown early warning. By setting a critical shutdown early warning mechanism, the timing of shutdown can be determined more accurately, thus avoiding situations where sterilization equipment is only discovered when a sterilization failure occurs.

[0057] (B) Assuming the theoretical temperature of saturated steam is used for pressure and temperature conversion calculations, the formula used to calculate the temperature of saturated steam under known pressure is: T=A+B·(lnP+C) -1 (5).

[0058] Where T represents the theoretical temperature of saturated steam (unit: Kelvin, K); A = 42.6776 K is a constant used to correct for the temperature value. B = -3892.70 K is another constant used to correct for the effect of pressure on temperature. P is the measured absolute pressure of saturated steam (unit: megapascal, MPa), which is an input variable. C = 9.48654 is also a constant used to offset the result after taking the logarithm of the pressure. Calculations using a nonlinear formula can better reflect the true physical properties of saturated steam because the relationship between saturated steam pressure and temperature is inherently nonlinear. By taking the logarithm of the pressure and using constant corrections, the pressure can be mapped more accurately to the corresponding temperature value.

[0059] The two formulas for converting pressure into temperature have different advantages and different application scenarios.

[0060] The pressure equivalent temperature value of Option A is beneficial for the safe and stable operation of the sterilization equipment 100. Another pressure equivalent temperature value can provide a simplified way to assess the effect of pressure on temperature in the sterilization equipment 100. This is very helpful for real-time monitoring and control of the uniformity and stability of the sterilization process. Furthermore, in terms of safety, since the sterilization process needs to be carried out within a specific temperature and pressure range, using the pressure equivalent temperature value allows for more accurate detection and adjustment of these parameters, ensuring sterilization effectiveness and equipment safety.

[0061] The special significance of the pressure equivalent temperature value in Scheme A lies in its ability to simplify calculations, as demonstrated by the linear formula T. P =k·P is relatively simple, facilitating rapid calculations and real-time applications. This simplifies the calculation process and improves response speed in some real-time control systems. In calibration and verification, it can serve as a verification tool to cross-check the accuracy of the saturated vapor pressure-temperature conversion, ensuring the correctness of the conversion formula. The formula for calculating the pressure equivalent temperature value in Scheme A can correct small errors in the conversion formula to some extent, especially in approximate calculations within a specific pressure range. However, for applications with a larger range or high precision requirements, a more accurate nonlinear formula is still needed.

[0062] The formula for calculating the pressure equivalent temperature of Scheme B better reflects the true physical properties of saturated steam because the relationship between saturated steam pressure and temperature is inherently non-linear. By taking the logarithm of the pressure and applying a constant correction, the pressure can be mapped to the corresponding temperature value more accurately. The practicality of the linear formula for the pressure equivalent temperature of Scheme B is reflected in: the linear formula T... P =k·P is more suitable for rapid calculation and real-time monitoring, reducing computational complexity. The coefficient k can be adjusted according to specific equipment and operating conditions, so that the formula has a good approximation effect within a certain range. This invention suggests using the calculation formulas for the pressure equivalent temperature values ​​of scheme A and scheme B in combination.

[0063] Combining these two formulas offers complementary advantages: the nonlinear formula is used for scenarios requiring high precision (such as calibration and verification), while the linear formula is used for real-time monitoring and simple correction. By continuously calibrating the coefficient k in the linear formula, small errors in the saturated vapor pressure to temperature conversion formula can be corrected to a certain extent, ensuring the overall stability and accuracy of the system.

[0064] Therefore, based on the linear formula T P=k·P allows setting a specified coefficient k to obtain a specified pressure equivalent temperature value as the shutdown critical warning threshold corresponding to the shutdown critical warning. The shutdown critical warning threshold is greater than the maximum pressure equivalent temperature value that the nonlinear formula of scheme B may reach under normal operating conditions. When any temperature value or pressure equivalent temperature value in the sterilization chamber reaches the shutdown critical warning threshold, the control module 110 triggers and issues a shutdown command to stop the sterilization equipment 100.

[0065] As mentioned above, the pressure equivalent temperature value of Scheme A is beneficial for safe and stable operation, especially in simplifying calculations and real-time monitoring, playing a crucial role in ensuring the safety and uniformity of the sterilization process. Scheme A simplifies calculations, improves real-time response speed, and serves as a calibration and validation tool to cross-check the accuracy of the formula. Under certain conditions, the linear formula of Scheme A can correct small errors in the conversion formula to some extent, but it cannot completely replace the nonlinear formula of Scheme B. Combining the use of both formulas can improve the efficiency of real-time monitoring and control while ensuring high accuracy.

[0066] The calculation steps for the temperature uniformity safety margin of this invention are as follows.

[0067] Convert pressure to temperature: for each time t i The converted temperature, T, is calculated using the pressure sensor readings. P1 (t i )=k·P1(t i ), T P2 (t i )=k·P2(t i (6).

[0068] Wherein, P1(t) i ) and P2(t i The first pressure sensor 102 and the second pressure sensor 104 are respectively located at time t. i The reading.

[0069] The steps for calculating the temperature difference at each moment are as follows. For each moment t... i Temperature difference ΔT 均匀 (t i ) is: ΔT 均匀 (t i )=max(|T1(t i )-T2(t i )|,|T P1 (t i )-T P2 (t i )|) (7); or ΔT 均匀 (t i)=max(T1(t i )-T2(t i ), T P1 (t i ), T P2 (t i ))-min(T1(t i )-T2(t i ), T P1 (t i ), T P2 (t i (8).

[0070] Using one of the two formulas above, calculate the temperature difference between the two sets of sensors (temperature sensor and temperature converted from pressure), and take the maximum value as the temperature difference.

[0071] The steps for calculating the temperature uniformity safety margin at each time point are as follows. For each time point t... i The safety margin for temperature uniformity is calculated as follows:

[0072] The steps for calculating the final temperature uniformity safety margin for this sterilization process are as follows.

[0073] Find the maximum temperature difference at each moment. It was then compared with the temperature uniformity benchmark threshold of 2℃.

[0074] As shown in Figure 8, the percentage formula for temperature fluctuation margin is calculated as follows.

[0075] For all moments during the constant pressure holding phase, the difference between the maximum and minimum temperatures is calculated, and then the temperature fluctuation margin is calculated. Here, STFT is the sterilization temperature fluctuation threshold, which is 3°C. This formula is used to calculate whether the difference between the maximum and minimum temperatures during the entire sterilization process exceeds the allowable fluctuation range, i.e., the sterilization temperature fluctuation threshold, thereby assessing the temperature fluctuation margin.

[0076] The sterilization temperature fluctuation threshold (STFT) of this invention refers to the maximum permissible difference in temperature fluctuation at various locations within the sterilization chamber during the entire sterilization process. Taking a sterilization process at 134°C as an example, the sterilization temperature fluctuation threshold STFT is set to 3°C to ensure that the temperature difference at any point within the system does not exceed the sterilization temperature fluctuation threshold STFT within the specified sterilization time (e.g., 3 minutes). The sterilization temperature fluctuation threshold STFT is used to monitor and limit spatial and temporal temperature fluctuations, ensuring the uniformity and effectiveness of sterilization. Exceeding the sterilization temperature fluctuation threshold STFT may affect the thoroughness of sterilization, causing the sterilization process to fail to meet the required microbial kill standards. For example:

[0077] The steps for calculating the temperature fluctuation margin are as follows.

[0078] For each time t i Record the following temperature data: T1(t i ),T2(t i ),T P1 (t i ),T P2 (t i (13).

[0079] Throughout the constant pressure holding phase, from all times t i Find the maximum temperature T in the middle. max and minimum value T min T max =max(T1(t) i ),T2(t i ),T P1 (t i ),T P2 (t i (14). T min =min(T1(t) i ),T2(t i ),T P1 (t i ),T P2 (t i (15).

[0080] Calculate the temperature fluctuation throughout the process: ΔT 波动 =T max -T min (16).

[0081] Compare the temperature fluctuation with the baseline value of 3°C to calculate the temperature fluctuation margin:

[0082] This formula is used to calculate whether the difference between the maximum and minimum temperature values ​​during the entire sterilization process exceeds the sterilization temperature fluctuation threshold STFT, thereby assessing the temperature fluctuation margin.

[0083] The sterilization time is set based on the time interval within the constant pressure holding phase, with a minimum holding time of 180 seconds at 134℃, and is typically set to 300 seconds. During this period, the time interval t needs to be calculated separately for each moment. i The temperature uniformity safety margin is determined, and the maximum temperature difference throughout the process is statistically analyzed to ultimately determine the temperature stability and uniformity of the sterilization process.

[0084] Risk prediction model 230 assesses the stability and reliability of temperature control during sterilization based on parameter changes such as temperature uniformity safety margin, temperature fluctuation margin, and sterilization time, in order to ensure the effectiveness and safety of the sterilization process.

[0085] Figure 7 records six sets of data obtained from the monitoring of the steam sterilization system. As shown in Figure 7, the data types include the first temperature value, the second temperature value, the first pressure value, the second pressure value, and the theoretical steam temperature T corresponding to P1. P1 (t i ), and the theoretical steam temperature T corresponding to P2 P2 (t i The data includes the first / second temperature value at the same moment and the minimum of the two theoretical steam temperature values, the maximum of the first / second temperature value at the same moment and the two theoretical steam temperature values, the temperature fluctuation range (temperature difference at the same moment), the actual sterilization time (s), the sterilization temperature fluctuation margin (0-3℃), the temperature uniformity safety margin (0-2℃), and the sterilization time safety margin (180s). As shown in Figure 7, all types of data are dynamically changing. The fluctuation data for the sterilization temperature fluctuation margin are 80%, 77%, and 83%; the minimum historical data value displayed by the steam sterilization system is 77%. The fluctuation data for the temperature uniformity safety margin are 70%, 65%, and 75%; the minimum historical data value displayed by the steam sterilization system is 65%. The fluctuation data for the sterilization time safety margin (180s) are 166% and 171%. The dynamic monitoring of various data by the steam sterilization system not only improves the operating efficiency of the equipment but also significantly improves the sterilization effect. The following is a detailed explanation of the advantages of monitoring various data and its contribution to the sterilization equipment and its effect.

[0086] By monitoring the first and second temperature values, better advantages can be brought to the equipment and sterilization effect: (1) Real-time monitoring of temperature changes ensures that the sterilization equipment 100 is always in the best working state, avoiding equipment damage or efficiency reduction caused by excessively high or low temperatures; (2) Temperature is a key factor in sterilization effect. Accurate temperature monitoring ensures that each sterilization cycle can achieve appropriate heat transfer, thereby effectively killing bacteria, viruses and other microorganisms.

[0087] By monitoring the first and second pressure values, better advantages can be brought to the equipment and sterilization effect: (1) By monitoring pressure changes in real time, equipment failures or leaks can be detected early, reducing equipment maintenance costs and downtime. (2) Maintaining an appropriate pressure level helps to ensure the saturation of steam, thereby ensuring uniform heat distribution, which is crucial for the comprehensiveness of sterilization.

[0088] By monitoring the theoretical steam temperatures corresponding to the two pressure values, better advantages can be brought to the equipment and sterilization effect: (1) The comparative monitoring of theoretical steam temperatures can help operators adjust sterilization parameters in real time and avoid potential operational errors. (2) By comparing the actual temperature with the theoretical temperature, the heat transfer efficiency during the sterilization process can be ensured, and each area can reach the required sterilization temperature.

[0089] By monitoring the minimum and maximum values ​​of the temperature and theoretical steam temperature at the same time, as well as the range of temperature fluctuations, better advantages can be brought to the equipment and sterilization effect: (1) Ensure that the equipment maintains temperature consistency during operation, effectively preventing the occurrence of cold or hot spots. (2) Such monitoring can ensure temperature uniformity during sterilization, avoiding sterilization failure due to insufficient local temperature, thereby ensuring the safety of the entire batch of products. (3) Monitoring temperature fluctuations can react in a timely manner, ensuring that the equipment will not affect the sterilization effect due to temperature instability. (4) Maintaining a moderate range of temperature fluctuations can effectively reduce the risk during sterilization and ensure that microorganisms are completely eradicated.

[0090] Furthermore, this invention's real-time tracking of sterilization time helps optimize production processes and avoid resource waste caused by improper time control. Ensuring sterilization time meets standards effectively improves sterilization effectiveness and prevents microbial survival due to insufficient time. The invention's dynamic monitoring of temperature margin provides operational flexibility, allowing equipment to operate effectively under changing conditions. Maintaining a certain temperature margin ensures reliable sterilization even under uncertain environmental changes, further guaranteeing product safety. By monitoring temperature uniformity margin, this invention optimizes equipment design, improving overall performance and stability. Moreover, it ensures uniform temperature distribution during sterilization, reducing the risk of incomplete sterilization due to insufficient temperature in individual areas, thus guaranteeing overall product safety. This invention's monitoring of sterilization time safety margin improves equipment adaptability to a certain extent, preventing changes in operating conditions from affecting sterilization efficiency. It also ensures sterilization time exceeds minimum requirements, effectively reducing the risk of incomplete sterilization due to insufficient time and improving the reliability of the sterilization system. As described above, the steam sterilization system of the present invention, through dynamic monitoring of these key data, not only improves the operating efficiency and reliability of the equipment but also significantly enhances the stability and consistency of the sterilization effect. This real-time monitoring ensures the scientific nature of the entire sterilization process, enabling each batch of products to be sterilized under optimal conditions, ultimately guaranteeing the safety and effectiveness of the products. Through such a steam sterilization system, the sterilization equipment 100 can maintain highly efficient sterilization capabilities in complex and variable environments, providing strong support for fields such as medical, pharmaceutical, and food processing.

[0091] Compared to existing technologies that rely solely on alarm detection to determine abnormalities, and which cannot determine whether the sterilization equipment 100 is malfunctioning during periods without alarms, the technical solution of this invention ensures stable operation by monitoring two safety margins. This significantly reduces the number of equipment maintenance operations, improves equipment utilization, maintains stable sterilization effects, and greatly reduces equipment upgrades and maintenance costs.

[0092] The process of calculating the alarm threshold in this invention is as follows.

[0093] Alarm thresholds are set based on the safety margin for temperature uniformity and the margin for temperature fluctuation.

[0094] Specifically, the alarm threshold can be calculated as follows: During the sterilization process, at each moment, compare the maximum difference between the temperature value from the temperature sensor and the pressure value from the pressure sensor (converted to a pressure-equivalent temperature value), and compare this maximum difference with a temperature uniformity reference threshold of 2°C. If the maximum difference is close to or exceeds this reference (e.g., above 1.8°C), an alarm is triggered.

[0095] Throughout the sterilization holding phase, the difference between the maximum and minimum values ​​of four data points—the temperature value from the temperature sensor and the pressure equivalent temperature value from the pressure sensor—is recorded. If this difference approaches or exceeds a reference temperature of 3°C (e.g., above 2.7°C), an alarm is triggered.

[0096] The calculation process for the fault risk threshold in this invention is as follows.

[0097] The failure risk threshold is set based on the stability and fluctuations of temperature and pressure during the sterilization process. Specifically, the calculation of the failure risk threshold includes: calculating t at each time point during the sterilization process. i The temperature uniformity safety margin is calculated, and the maximum temperature difference throughout the process is recorded. The difference between the maximum and minimum temperature values ​​obtained from the temperature sensor and pressure sensor during the sterilization holding phase is calculated. The overall temperature control stability is evaluated by combining the temperature uniformity baseline threshold (TUBT) and the sterilization temperature fluctuation threshold (STFT). Based on historical data and experience, a failure risk threshold is set for the risk prediction model 230. For example, it is set that if the temperature uniformity safety margin exceeds a certain percentage, or the temperature fluctuation margin exceeds a certain percentage, the failure risk will significantly increase. The risk prediction model 230 compares the above calculation results with the preset temperature uniformity safety margin, temperature fluctuation margin, and sterilization time. When the risk prediction model 230 assesses that the failure risk reaches the set risk threshold, an early warning is triggered.

[0098] As shown in Figure 3, when the sterilization equipment 100 is started, the risk prediction model 230 retrieves historical and early warning information on fault risks that occur during the sterilization process. If the fault risk approaches the fault risk threshold and has not been repaired, the risk prediction model 230 sends a stop command to the control module 110 of the sterilization equipment 100 to interrupt the start-up procedure of the sterilization equipment 100 and prevent the sterilization equipment 100 from malfunctioning during operation.

[0099] Before the sterilization equipment 100 is started, the risk prediction model 230 retrieves historical data, including fault risks and warning records. This proactive assessment mechanism allows the system to predict potential fault risks before the equipment starts. When the risk approaches the fault risk threshold and no adjustments have been made, the risk prediction model 230 can directly send a stop command to the control module 110, interrupting the equipment startup process. This mechanism effectively avoids equipment failures during operation, reduces unplanned downtime caused by equipment failures, improves equipment operating efficiency, further ensures the smooth progress of the sterilization process, and provides additional protection for the continuity of medical services and patient safety.

[0100] The analysis module 200 can determine the specific location of the faulty temperature / pressure sensor based on anomalies in the temperature uniformity safety margin and temperature fluctuation margin. For example, if the temperature uniformity margin is significantly lower than 70%, the temperature sensor may be faulty. If the temperature fluctuation margin is significantly lower than 70%, the pressure sensor may be faulty.

[0101] If the temperature values ​​from the temperature sensors differ significantly (e.g., more than 1°C), while the difference between the pressure equivalent temperature values ​​is within the normal range (e.g., less than 1°C), it indicates that one of the temperature sensors may be faulty and requires further verification.

[0102] Check the historical data of the first and second temperature values ​​to confirm whether there are any continuous or intermittent anomalies. If the first temperature value deviates significantly from other values, the first temperature sensor 101 is considered faulty; otherwise, the second temperature sensor 103 is considered faulty. If the difference between the pressure equivalent temperature sensors (e.g., exceeding 1°C) and the difference between the temperature sensors is within the normal range (e.g., less than 1°C), it indicates that one of the pressure sensors may be faulty and further verification is required. Check the historical data of the first pressure equivalent temperature value to confirm whether there are any continuous or intermittent anomalies. If the first pressure equivalent temperature value deviates significantly from other values, the first pressure sensor 102 is considered faulty; otherwise, the second pressure sensor 104 is considered faulty. Based on the above steps, the operator can identify the specific faulty sensor and perform necessary maintenance or replacement to ensure the normal operation of the sterilization equipment 100.

[0103] Thus, this invention can accurately and efficiently pinpoint the location of faulty sensors, significantly improving the convenience and accuracy of fault location. The analysis module 200 utilizes real-time monitoring and analysis of temperature uniformity safety margin and temperature fluctuation margin to form a systematic fault diagnosis process.

[0104] Specifically, when the system detects that the temperature uniformity safety margin is significantly lower than 70%, the analysis module 200 immediately indicates a possible temperature sensor malfunction. This low indicator means that the temperature values ​​between temperature sensors differ excessively, potentially reflecting a sensor failure or deviation. The analysis module 200 then verifies this by comparing real-time data from the temperature sensors. If the difference between temperature values ​​exceeds 1°C, while the difference in pressure sensor values ​​remains within the normal range (less than 1°C), the fault can be clearly located in a single temperature sensor. Furthermore, the analysis module 200 backtracks and analyzes historical data from the first temperature sensor 101 and the second temperature sensor 103 to confirm whether there are persistent or intermittent temperature anomalies. This process ensures the reliability of the fault diagnosis: if the first temperature value deviates significantly from other values, the first sensor is confirmed to be faulty; otherwise, the second sensor will be identified as the source of the fault.

[0105] The same logic applies to fault location of pressure sensors. When the difference between pressure equivalent temperature sensors exceeds 1°C while the difference between temperature sensors is within the normal range, the system can quickly identify a potentially faulty pressure sensor. At this point, the module will thoroughly examine historical data of the first pressure equivalent temperature value to confirm whether any anomalies exist. If the first pressure value deviates significantly from other values, the first pressure sensor 102 is determined to be faulty; otherwise, the second pressure sensor 104 is confirmed to be faulty. Through this refined analysis and inspection process, the analysis module 200 allows operators to quickly and accurately determine the location of the faulty sensor, reducing the ambiguity and uncertainty that may exist in traditional methods. This enables timely maintenance or replacement measures to ensure the normal operation of the sterilization equipment 100. This fault confirmation mechanism not only improves the efficiency and safety of equipment operation but also saves operators significant time and costs.

[0106] Example 2

[0107] This embodiment is a further explanation of the calculation in Embodiment 1, and repeated content will not be repeated.

[0108] The calculation steps for the temperature uniformity safety margin variation trend in this invention are as follows.

[0109] During the isobaric holding phase of each sterilization cycle, temperature values ​​T1(t) were periodically collected. i ),T2(t i ), and the pressure equivalent temperature value T obtained by conversion through the pressure sensor. P1 (t i ), T P2 (t i ), and pressure value P1(t) i ) and P2(t i Record each time point t. i Temperature ΔT 均匀性安全裕量值 Calculate the temperature uniformity safety margin values ​​according to both Scheme A and Scheme B. Smooth the data. Smooth the collected temperature uniformity safety margin values, for example, using a moving average method to reduce the impact of short-term fluctuations. Perform time series analysis on the smoothed temperature uniformity safety margin data, using methods including but not limited to the Autoregressive Integrated Moving Average (ARIMA) model or other suitable time series forecasting methods. Calculate the mean and standard deviation of the temperature uniformity safety margin for each sterilization cycle. Detect anomalies based on the trend of temperature uniformity safety margin changes.

[0110] Specifically, an alarm threshold is set for the temperature uniformity safety margin (e.g., when the margin drops below 10%, it is considered abnormal). If the time series analysis results show that the percentage of the temperature uniformity safety margin gradually approaches or falls below the alarm threshold, an early warning is triggered.

[0111] The trend of operating data changes (e.g., the trend of temperature uniformity safety margin changes) is compared with the preset trend of operating data changes (e.g., the preset trend of temperature uniformity safety margin changes) to assess the failure risk of sterilization equipment 100. If the trend analysis shows that the temperature uniformity safety margin has a significant downward trend and is below the preset threshold, the equipment is judged to have a high failure risk, and an early warning command is issued for debugging.

[0112] The trend of temperature uniformity safety margin during sterilization cycle n can be expressed as:

[0113] When S = 300

[0114] or:

[0115] When S = 300

[0116] By calculating the above formula, the changing trend of the temperature uniformity safety margin can be obtained, and combined with historical data, abnormal situations in the current sterilization cycle can be judged. The above steps and methods can effectively analyze and predict the changing trend of the temperature uniformity safety margin of the sterilization equipment 100 in each sterilization cycle, thereby timely identifying potential failure risks and taking corresponding measures.

[0117] Scheme A and Scheme B differ in their physical advantages based on the equivalent temperature of temperature and pressure. From a physical perspective, Scheme A and Scheme B each have their applicable scenarios and advantages and disadvantages, depending on the characteristics of the temperature and pressure sensors and the actual application of the measurement data.

[0118] Scheme A primarily focuses on the differences between the first temperature sensor 101 and the second temperature sensor 103, as well as the differences between the first pressure sensor 102 and the second pressure sensor 104 when converted to pressure equivalent temperatures. Since the pressure sensor measurements are averages over a past period, despite time alignment, the actual measured pressure equivalent temperature values ​​still do not perfectly coincide with the instantaneous measurements of the temperature sensors. Therefore, Scheme A evaluates the uniformity of temperature and pressure by comparing these differences.

[0119] The advantages of Scheme A include: (1) It takes into account time non-overlap: Scheme A can better reflect the time deviation of the system in the actual measurement process by calculating the instantaneous temperature difference and the pressure equivalent temperature difference after time averaging. (2) It is suitable for dynamic changing environments: In environments where temperature and pressure change rapidly, the time averaging characteristics of the pressure sensor cause its response to rapid changes to be relatively lagging. Scheme A can better capture the instantaneous non-uniformity of temperature and pressure.

[0120] Advantages of Scheme A under specific conditions: (1) In rapidly changing environments: If the temperature and pressure change rapidly during the sterilization process (e.g., during rapid heating or cooling phases), Scheme A can better reflect instantaneous non-uniformity because it takes into account the differences in instantaneous measurements. (2) In unsteady conditions: Under unsteady conditions (e.g., when the system is just starting up or stopping), the rates of change of temperature and pressure are different, and Scheme A can better capture these changes.

[0121] Option B focuses on the difference between the maximum and minimum values ​​of all temperature sensors (including temperature converted from pressure) over a certain time range. Option B does not explicitly distinguish between the difference between instantaneous measurements and time-averaged values ​​in its calculations, assuming that all temperature and pressure equivalent temperature values ​​coincide perfectly over time.

[0122] The advantages of Scheme B include: (1) Simplified calculation process: It does not need to consider the problem of time non-coincidence, and only the difference between the maximum and minimum values ​​needs to be calculated, making the algorithm relatively simple. (2) Applicable to steady-state conditions: When the system reaches steady-state conditions (such as the constant temperature and pressure stage), Scheme B can well reflect the overall uniformity of the system.

[0123] Advantages of Scheme B under specific conditions: (1) Steady-state environment: If the temperature and pressure change slowly during the sterilization process and the system has reached a steady state, Scheme B can more accurately reflect uniformity because all measurements are more consistent over time. (2) Long-term uniformity assessment: Suitable for assessing long-term uniformity throughout the sterilization process, rather than focusing on instantaneous differences.

[0124] As mentioned above, the advantages of Scheme A include: more accurately reflecting instantaneous non-uniformity in rapidly changing environments, such as when temperature and pressure change rates are high; suitability for non-steady-state conditions, such as system startup, shutdown, or rapid adjustment phases; and dynamic responsiveness, better capturing instantaneous temperature and pressure differences, suitable for dynamic adjustment and monitoring. The advantages of Scheme B include: more accurately reflecting long-term uniformity in steady-state environments, such as when temperature and pressure change rates are low; suitable for evaluating long-term uniformity under stable operating conditions (i.e., the system has reached steady state); and simplified algorithms, suitable for scenarios requiring simplified calculation processes.

Claims

1. A steam sterilization method for reusable medical devices, characterized in that, The method includes: Collect at least two temperature values ​​and at least two pressure values ​​of the steam pressure of the sterilization chamber of the sterilization equipment (100); Based on the preset risk prediction model (230), the at least two temperature values ​​and at least two pressure values ​​are calculated to determine the safety margin for temperature uniformity and the margin for temperature fluctuation. The alarm threshold is determined based on the temperature uniformity baseline threshold with a safety margin for temperature uniformity and / or the sterilization temperature fluctuation threshold with a temperature fluctuation margin. The alarm threshold is close to the temperature uniformity reference threshold or the sterilization temperature fluctuation threshold, so that the sterilization equipment (100) can be debugged before a failure occurs.

2. The method according to claim 1, characterized in that, The method further includes: Calculate the first temperature difference between at least two temperature values ​​of the sterilization equipment (100) collected by the temperature sensor; At each sampling moment during the constant pressure phase, at least two pressure values ​​collected by the pressure sensor are used to calculate at least two corresponding pressure equivalent temperature values ​​based on the pressure-temperature conversion formula, and the second temperature difference between the at least two pressure equivalent temperature values ​​is calculated. An alarm is triggered if the maximum temperature difference approaches or exceeds the temperature uniformity reference threshold of the temperature uniformity safety margin.

3. The method according to claim 2, characterized in that, The method further includes: Throughout the sterilization holding phase, the difference between the maximum and minimum values ​​of the temperature sensor, the pressure sensor, and the pressure-equivalent temperature value at the same moment was recorded. An alarm is triggered if the difference between the maximum and minimum values ​​is close to or exceeds the sterilization temperature fluctuation threshold.

4. The method according to claim 1 or 2, characterized in that, The method further includes: The fault risk threshold is determined based on the aforementioned temperature uniformity safety margin and temperature fluctuation margin. Among them, the safety margin for temperature uniformity at each moment during the sterilization process is calculated, and the maximum temperature difference throughout the process is statistically analyzed; Calculate the difference between the maximum and minimum temperature values ​​obtained from the conversion of temperature and pressure values ​​from the temperature sensor during the sterilization holding phase; The overall stability of temperature control is statistically analyzed, and a fault risk threshold is set for the preset risk prediction model (230) based on historical temperature data. Compare the temperature uniformity safety margin with the preset temperature uniformity safety margin, or compare the temperature fluctuation margin with the preset temperature fluctuation margin. An early warning is triggered when the percentage of temperature uniformity safety margin exceeding the preset temperature uniformity safety margin reaches the fault risk threshold, or when the percentage of temperature fluctuation margin exceeding the preset temperature fluctuation margin reaches the fault risk threshold.

5. The method according to any one of claims 1 to 4, characterized in that, The steps for determining the safety margin for temperature uniformity include: At each sampling moment during the constant pressure phase, the temperature values ​​collected by at least two temperature sensors and the corresponding at least two pressure equivalent temperature values ​​calculated based on the pressure-temperature conversion formula from at least two pressure values ​​are compared. The maximum temperature difference among the four values ​​is calculated, and the maximum temperature difference is compared with the temperature uniformity benchmark threshold to determine the temperature uniformity safety margin.

6. The method according to any one of claims 1 to 5, characterized in that, Scheme A for determining the safety margin for temperature uniformity is: The formula for calculating the percentage of the temperature uniformity safety margin at each sampling moment during the constant pressure phase is as follows: Wherein, T1(t) i T2(t) represents the first temperature value collected by the first temperature sensor (101). i ) represents the second temperature value collected by the second temperature sensor (103), T P1 (t i The first pressure value represents the equivalent temperature difference obtained by converting the first pressure value, T. P2 (t i ) represents the equivalent temperature difference of the second pressure obtained by converting the second pressure value, t i The time represents the time it takes for the steam pressure to reach a steady state; TUBT represents the temperature uniformity reference threshold; t i ∈[0,S] represents the time period during which the steam pressure in the sterilization chamber remains stable.

7. The method according to any one of claims 1 to 5, characterized in that, Scheme B for determining the safety margin for temperature uniformity is: The formula for the percentage of temperature uniformity safety margin at each sampling time during the constant pressure phase is: Wherein, T1(t) i T2(t) represents the first temperature value collected by the first temperature sensor (101). i ) represents the second temperature value collected by the second temperature sensor (103), t i The time represents the time it takes for the steam pressure to reach a steady state; TUBT represents the temperature uniformity reference threshold; t i ∈[0,S] represents the time period during which the steam pressure in the sterilization chamber remains stable.

8. The method according to any one of claims 1 to 7, characterized in that, The method further includes: The pressure-equivalent temperature value is determined using the conversion formula between saturated steam pressure and temperature: T=A+B·(lnP+C) -1 ; Wherein, the pressure equivalent temperature value T represents the converted temperature of the saturated steam pressure value; constant A = 42.6776K is used to correct the temperature value; constant B = -3892.70K is used to correct the influence of pressure on temperature; P represents the absolute pressure of the saturated steam measured by the pressure sensor; and constant C = 9.48654 is used to offset the result after taking the logarithm of the pressure value.

9. The method according to any one of claims 1 to 8, characterized in that, The method further includes: The following formula is used to set another pressure equivalent temperature value corresponding to the steam temperature measured by at least two pressure sensors: T P =k·P; Where k represents the conversion coefficient; the other pressure equivalent temperature value is used for critical shutdown warning to avoid serious equipment failure.

10. The method according to any one of claims 1 to 9, characterized in that, The steps for calculating the temperature fluctuation margin are as follows: Throughout the entire sterilization holding phase of the sterilization equipment (100), the temperature value T1(t) of the temperature sensor at each moment is recorded. i ) and T2(t i ), and record the pressure equivalent temperature value T obtained by converting it through the pressure sensor. P1 (t i ) and T P2 (t i Select the maximum and minimum values ​​of these four values ​​at the same time point, and calculate the difference between the maximum and minimum values: max(T1(t i ),T2(t i ),T P1 (t i ),T P2 (t i ))-min(T1(t i ),T2(t i ),T P1 (t i ),T P2 (t i )), The difference between the maximum and minimum values ​​is compared with the sterilization temperature fluctuation threshold to obtain the percentage of temperature fluctuation margin.

11. The method according to any one of claims 1 to 10, characterized in that, The formula for calculating the percentage of the temperature fluctuation margin is as follows: Wherein, T1(t) i T2(t) represents the first temperature value collected by the first temperature sensor (101). i P1(t) represents the second temperature value collected by the second temperature sensor (103). i P2(t) represents the first pressure value collected by the first pressure sensor (102). i ) represents the second pressure value collected by the second pressure sensor (104); t i denoted by time; k represents the conversion coefficient; S represents the end time when the steam pressure reaches a steady state; and STFT represents the sterilization temperature fluctuation threshold.

12. The method according to any one of claims 1 to 11, characterized in that, The method further includes: If the failure risk approaches the failure risk threshold and is not repaired, the risk prediction model (230) sends a stop command to the control module (110) of the sterilization equipment (100) to interrupt the start-up procedure of the sterilization equipment (100).

13. The method according to any one of claims 1 to 12, characterized in that, The method further includes: The sterilization equipment (100) has or is equipped with monitoring modules for verifying and / or monitoring the sterilization effect, such as physical monitoring modules, biological indicators and / or chemical indicators.

14. A steam sterilization system for reusable medical devices, comprising sterilization equipment (100) for sterilizing reusable medical devices, characterized in that, The steam sterilization system also includes: The analysis module (200) is communicatively connected to the control module (110) of the sterilization equipment (100). Based on a preset risk prediction model (230), it analyzes the trend of changes in the operating data and / or alarm thresholds sent by the control module (110) to determine the temperature or pressure-related failure risk of the sterilization equipment (100). When the failure risk reaches the failure risk threshold, an early warning command is issued so that the sterilization equipment (100) can be debugged before a failure occurs.

15. The system according to claim 14, characterized in that, The sterilization equipment (100) is equipped with at least two temperature sensors for collecting the temperature of the sterilization chamber and at least two pressure sensors for collecting the steam pressure of the sterilization chamber. The temperature sensor collects at least two temperature values ​​of the sterilization chamber of the sterilization equipment (100), and the pressure sensor collects at least two pressure values ​​of the steam pressure of the sterilization chamber. Based on the preset risk prediction model (230), the at least two temperature values ​​and at least two pressure values ​​are calculated to determine the safety margin for temperature uniformity and the margin for temperature fluctuation. The alarm threshold is determined based on the temperature uniformity baseline threshold with a safety margin for temperature uniformity and / or the sterilization temperature fluctuation threshold with a temperature fluctuation margin. The alarm threshold is close to the temperature uniformity reference threshold or the sterilization temperature fluctuation threshold.

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