Steam sterilization system and method

Abstract

The present invention relates to a steam sterilization system, and relates to the field of medical sterilization technology. The steam sterilization system includes a sterilization device for sterilizing reusable medical devices. The steam sterilization system also includes an analysis module, which is communicatively connected to a control module of the sterilization device. The analysis module analyzes the change trend and / or alarm threshold of the operating data sent by the control module based on a preset risk prediction model, and judges the failure risk of the sterilization device related to temperature or pressure, wherein an early warning instruction is issued when the failure risk reaches the failure risk threshold, so that the sterilization device can be debugged before the failure occurs. Thereby, the problem of sterilization failure caused by failure and the failure of medical supplies to meet the sterilization standard is avoided. At the same time, the system can also ensure the integrity and traceability of the sterilization process data, improve the speed of judging the cause of the failure, and reduce the time and cost of equipment repair.

Description

Technical Field

[0001] The present invention relates to the technical field of medical sterilization, and in particular to a steam sterilization system and method. Background Art

[0002] Sterilization equipment is a key device used to sterilize instruments and supplies in medical environments. It eliminates microorganisms such as bacteria, viruses, fungi and spores through physical or chemical methods to ensure that medical supplies do not spread infection and protect medical safety and patient health. These devices must maintain the sterilization temperature within a precise range to ensure sterilization effect, safety, equipment protection, and compliance with international standards.

[0003] Temperature sensors in sterilization equipment, especially those in high-pressure steam sterilization equipment, usually have a measurement error between ±0.1°C and ±0.5°C. For sterilization processes with high precision requirements, such as critical surgical instruments, the error needs to be controlled within ±0.1°C to meet standards such as EN285. Sensor accuracy for industrial or laboratory sterilizers is usually ±0.5°C or ±1.0°C. Thermocouples and thermistors are commonly used temperature sensors. Thermocouples are suitable for high temperature measurements, while thermistors are suitable for low temperature measurements. Calibration is critical to ensure sensor accuracy. Uncalibrated sensors may lead to inaccurate sterilization control.

[0004] Sterilization equipment usually needs to comply with international standards, such as EN285 or ISO17665, which have clear requirements for the accuracy of temperature sensors. For example, EN285 stipulates that the accuracy of the temperature sensor of the high-pressure steam sterilizer must be within ±0.5°C. During the medical sterilization process, multi-point temperature sensors are used to monitor to ensure the temperature consistency in the sterilization room. Methods for controlling deviations include regular calibration, redundant monitoring, and reasonable arrangement of sensors. Manufacturers or users should calibrate temperature sensors every year. Critical operations may use multiple redundant systems, with multiple sensors working simultaneously and recording data for comparison. High-pressure steam sterilization relies on the combined effects of temperature and pressure. Temperature fluctuations may cause pressure changes and affect the sterilization effect. Steam needs to penetrate every corner of medical items. Excessive temperature fluctuations will affect heat penetration and lead to incomplete sterilization. Therefore, temperature and pressure fluctuations must be strictly controlled during each sterilization process.

[0005] The early warning system of the sterilization equipment monitors key parameters in real time and issues an early warning if they exceed the safety range. However, faults or parameter abnormalities are often not identified until after the problem occurs. This passive response may lead to sterilization failure. Maintenance personnel may only repair faulty sensors and lack attention to overall performance.

[0006] At present, there are deficiencies in monitoring the temperature stability, pressure stability and temperature uniformity of medical sterilization equipment. During the operation of the equipment, if the early warning module does not alarm, it is assumed that the sterilization process is qualified, which is unreasonable. The present invention hopes to find problems in time before the early warning to avoid unqualified sterilization results.

[0007] CN112534511A discloses a sterilization system including a communication hub configured to transmit information between the devices of the system (such as a sterilization cabinet, a biological indicator analyzer, and a server) to allow a user to track the medical device throughout the sterilization process. The server can provide data to the sterilization cabinet during the sterilization process, which can be used to verify that the medical device is compatible with the sterilization cabinet and automatically select a compatible sterilization cycle to be performed on the medical device. Although the automatic selection of the sterilization cycle and the confirmation of compatibility reduce the possibility of user error, the sterilization system has no improvement in early warning. The early warning is still issued when the sterilization device cannot complete the sterilization operation, resulting in the failure of the sterilization operation of the sterilized items at this time.

[0008] Therefore, how to assess the failure risk of sterilization equipment before it fails, so as to debug the sterilization equipment in advance and avoid failure during the sterilization operation that leads to sterilization failure and damage to items, is also a problem that the current early warning system of medical sterilization equipment has not yet solved. Summary of the invention

[0009] In actual operation, if the sterilization equipment fails, the relevant sterilization process data may not be exported normally, which not only hinders the rapid judgment of the cause of the failure, but also brings difficulties to the subsequent equipment repair and historical tracing of the sterilization process. In a medical environment that requires strict record keeping and monitoring, the loss or inaccessibility of data is an important issue that cannot be ignored.

[0010] In view of the deficiencies of the prior art, the present invention provides a steam sterilization system from a first aspect, which includes a sterilization device for sterilizing reusable medical devices, and the steam sterilization system also includes an analysis module. The analysis module is communicatively connected with the control module of the sterilization device, and analyzes the change trend and / or alarm threshold of the operating data sent by the control module based on a preset risk prediction model to determine the temperature or pressure-related failure risk of the sterilization device, wherein an early warning instruction is issued when the failure risk reaches the failure risk threshold, so that the sterilization device can be debugged before the failure occurs. In view of the defect that it is impossible to determine whether the temperature change of sterilization is abnormal when no early warning is issued, the present invention achieves the effect of timely judging the abnormal temperature change through the analysis of the change trend of the operating data, and can promptly notify the maintenance personnel to debug according to the abnormal temperature change, without the need for maintenance after the sterilization device actually fails.

[0011] The sterilization equipment is provided 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. Scheme A: At each sampling moment in the constant pressure stage, the risk prediction model calculates at least two pressure values ​​based on the pressure-temperature conversion formula to obtain corresponding at least two pressure equivalent temperature values, compares the first temperature difference of the at least two temperature values ​​with the second temperature difference of the at least two pressure equivalent temperature values, and compares the maximum temperature difference between the first temperature difference and the second temperature difference with the uniformity temperature benchmark to determine the temperature uniformity safety margin as the first alarm threshold, wherein,

[0012] .

[0013] Alternatively, Solution B: At each sampling moment in the constant pressure stage, the risk prediction model compares the temperature values ​​of at least two temperature sensors and at least two pressure values ​​based on the pressure-temperature conversion formula to obtain the corresponding at least two pressure equivalent temperature values, calculates the maximum temperature difference among the four, and compares the maximum temperature difference with the uniformity temperature reference to determine the temperature uniformity safety margin as the first alarm threshold. The formula for the percentage of the temperature uniformity safety margin is:

[0014] .

[0015] Among them, in scheme A and scheme B, represents the first temperature value collected by the first temperature sensor, represents the second temperature value collected by the second temperature sensor, Indicates the first pressure equivalent temperature value obtained by converting the first pressure value, represents the second pressure equivalent temperature value obtained by converting the second pressure value, represents time, S represents the end time of the steam pressure being in a steady state; TUBT represents the temperature uniformity benchmark threshold.

[0016] By calculating the temperature uniformity safety margin, the advantages of Scheme A and Scheme B are used in combination to more accurately judge the abnormality of temperature change.

[0017] The pressure equivalent temperature value is determined by the following saturated steam pressure and temperature conversion formula: ; Wherein: the pressure equivalent temperature value T represents the conversion temperature of the pressure value of saturated steam; constant A=42.6776K, used to correct the temperature value; constant B=-3892.70K, used to correct the influence of pressure on temperature; P represents the absolute pressure of saturated steam measured by the pressure sensor; constant C=9.48654, used to perform offset correction on the result after taking the logarithm of the pressure value. Through the calculation of nonlinear formula, the real physical properties of saturated steam can be better reflected, because the relationship between saturated steam pressure and temperature is essentially nonlinear. By taking the logarithm of pressure and correcting the constant, the pressure can be more accurately mapped to the corresponding temperature value.

[0018] For the pressure equivalent temperature value, the analysis module sets another pressure equivalent temperature value corresponding to the steam temperature measured by at least two pressure sensors based on the following formula: ; k represents the conversion coefficient; the other pressure equivalent temperature value is used for shutdown critical warning to avoid serious equipment failure. By setting up a shutdown critical warning mechanism, the shutdown timing can be judged more accurately, thus avoiding the situation where the sterilization equipment is found to be abnormal only when a sterilization failure occurs.

[0019] The alarm threshold is set based on the temperature uniformity safety margin and the temperature fluctuation margin; in the sterilization process, for each moment t i , compare the maximum difference of the pressure equivalent temperature values ​​converted by the temperature sensor and the pressure sensor, and compare the maximum difference with the temperature uniformity reference threshold; if the maximum difference is close to or exceeds the temperature uniformity reference threshold, an alarm is triggered; or, if the difference between the maximum and minimum values ​​of the pressure equivalent temperature values ​​of the temperature sensor and the pressure sensor is close to or exceeds the sterilization temperature fluctuation threshold, an alarm is triggered; the fault risk threshold is set based on the stability and fluctuation of temperature and pressure during the sterilization process; wherein, the fault risk threshold is calculated at each moment t during the sterilization process i The temperature uniformity safety margin is calculated, and the maximum temperature difference in the whole process is counted; the difference between the maximum and minimum temperature converted by the temperature sensor and the pressure sensor during the sterilization holding stage is calculated; the stability of the overall temperature control is evaluated by combining the temperature uniformity reference threshold and the sterilization temperature fluctuation threshold; based on historical data and experience, the failure risk threshold for the risk prediction model is set. With this setting, the temperature fluctuation abnormality can be judged while avoiding the sterilization equipment from being damaged by high temperature, so that the sterilization equipment can be debugged before failure occurs.

[0020] The risk prediction model calculates the temperature fluctuation margin in the following way: During the entire sterilization maintenance phase of the sterilization equipment, record the temperature fluctuation margin at each moment. The temperature value of the temperature sensor and , and the pressure equivalent temperature value converted by the pressure sensor and , choose these four at time The maximum and minimum values ​​in , calculate the difference between the maximum and minimum values:

[0021] , compare the difference between the maximum value and the minimum value with the sterilization temperature fluctuation threshold, and obtain the percentage of the temperature fluctuation margin as the second alarm threshold.

[0022] The formula used by the risk prediction model to calculate the percentage of temperature fluctuation margin is:

[0023] ;

[0024] in, represents the first temperature value collected by the first temperature sensor, represents the second temperature value collected by the second temperature sensor, represents a first pressure value collected by the first pressure sensor (102), represents a second pressure value collected by a second pressure sensor (104); Indicates time; represents the conversion coefficient, S represents the end time of the steam pressure in steady state, and STFT represents the sterilization temperature fluctuation threshold. Monitoring the temperature fluctuation margin can ensure that the temperature difference at any point in the system does not exceed the threshold within the specified sterilization time (such as 3 minutes). The sterilization temperature fluctuation threshold is used to monitor and limit the spatial and temporal fluctuations of temperature to ensure the uniformity and effectiveness of sterilization. Exceeding this temperature difference threshold may affect the thoroughness of sterilization, resulting in the sterilization process failing to meet the required microbial killing standards.

[0025] The sterilization equipment has or is additionally provided with a monitoring module for verifying and / or monitoring the sterilization effect, such as a physical monitoring module, a biological indicator and / or a chemical indicator. The physical monitoring module can effectively verify whether the bacteria are effectively eliminated.

[0026] The analysis module determines the fault of the temperature sensor and / or pressure sensor based on the changes and anomalies of the temperature uniformity safety margin and the temperature fluctuation margin. Such monitoring is conducive to timely notifying maintenance personnel for debugging before the sterilization equipment actually fails. While ensuring that the hardware equipment of the sterilization equipment is less affected by temperature and prolongs its service life, it can also maintain the stability of the sterilization effect through timely debugging.

[0027] The present invention provides a steam sterilization method from a second aspect, the method comprising: analyzing the change trend and / or alarm threshold of the operating data sent by the control module based on a preset risk prediction model, and judging the failure risk of the sterilization equipment related to temperature or pressure, wherein an early warning instruction is issued when the failure risk reaches the failure risk threshold, so that the sterilization equipment can be debugged before the failure occurs. The method of the present invention calculates less data, finds abnormalities quickly, and can effectively find abnormal temperature fluctuations before an early warning occurs. In view of the defect that it is impossible to judge whether the temperature change of sterilization is abnormal when no early warning is issued, the present invention achieves the effect of timely judging the abnormal temperature change through the analysis of the change trend of the operating data, and can promptly notify the maintenance personnel to debug according to the abnormal temperature change, without the need for maintenance after the sterilization equipment actually fails. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a structural schematic diagram of the steam sterilization system provided by the present invention;

[0029] Figure 2 It is a simplified schematic diagram of the module connection relationship of the steam sterilization system provided by the present invention;

[0030] Figure 3 It is a logical schematic diagram of the risk prediction model provided by the present invention;

[0031] Figure 4 It is a logical schematic diagram of the steam sterilization system provided by the present invention;

[0032] Figure 5 is a schematic diagram of the analysis process of the risk prediction model provided by the present invention according to the second operating parameter;

[0033] Figure 6 is a schematic diagram of the analysis process of the maintenance analysis model provided by the present invention;

[0034] Figure 7 It is a schematic diagram of the process of the control module provided by the present invention controlling the start-up of the sterilization equipment;

[0035] Figure 8 It is a schematic diagram of the connection between the temperature sensor and the pressure sensor of the sterilization equipment provided by the present invention;

[0036] Fig. 9 It is a line graph of the pressure change in the sterilization chamber of the sterilization equipment;

[0037] Fig.10 It is a graph showing the temperature and time changes of the sterilization equipment during the sterilization process;

[0038] Fig.11 It is a data calculation table of temperature and pressure of the sterilization process provided by the present invention;

[0039] Fig.12 It is an enlarged schematic diagram of the calculation formulas of Scheme A and Scheme B of the temperature uniformity safety margin and the calculation formula of the temperature fluctuation margin provided by the present invention.

[0040] Reference numerals list

[0041] 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; 240: maintenance analysis model; 250: storage module; 260: data output port; 300: terminal. DETAILED DESCRIPTION

[0042] The following is a detailed description with reference to the accompanying drawings.

[0043] In actual operation, the alarm system of the sterilization device 100 usually detects and reports these problems only after a fault or parameter abnormality occurs. This reactive error detection mechanism may cause sterilization failure, 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 risk to the quality of medical services and patient safety. At the same time, if the sterilization device 100 fails, the relevant sterilization process data may not be exported normally, which not only hinders the rapid judgment of the cause of the failure, but also brings difficulties to the subsequent equipment repair and historical tracing of the sterilization process. In a medical environment that requires strict recording and monitoring, the loss or inaccessibility of data is an important issue that cannot be ignored.

[0044] The present invention provides a steam sterilization system and method. When the analysis module 200 is installed on the sterilization device 100 (such as Figure 1 As shown in FIG. 1 ), or when the analysis module 200 is integrated with the control module 110, the present invention provides a sterilization device 100 and a terminal 300 thereof. The present invention also provides a terminal 300 for steam sterilization risk prediction.

[0045] Example 1

[0046] In view of the shortcomings of the prior art, the present invention provides a steam sterilization system, such as Figure 8 As shown, it includes a sterilization device 100 for sterilizing reusable medical devices. A sterilization chamber is provided in the sterilization device 100. The sterilization device 100 has or is additionally provided with a monitoring module for verifying and / or monitoring the sterilization effect, such as a physical monitoring module, a biological indicator and / or a chemical indicator.

[0047] The medical sterilization equipment in the prior art does not include the analysis module 200. Figure 8 In the embodiment, the first temperature sensor 101 is arranged in the sterilization chamber and connected to the control module 110 for indicating the temperature. The second temperature sensor 103 is arranged in the sterilization chamber and connected to the control module 110 for recording the operation data and temperature data of the first temperature sensor 101 .

[0048] exist Figure 8 In the embodiment, a first pressure sensor 102 is arranged in the sterilization chamber for indicating the absolute pressure. A second pressure sensor 104 is arranged in the sterilization chamber and is connected to a control module 110 for recording the operation data and pressure data of the first pressure sensor 102. A jacket indicating pressure sensor 105 is also arranged in the sterilization chamber for measuring the pressure of the jacket and its changes. A steam generator indicating pressure sensor 106 is connected to a steam generator 108 for indicating the pressure of the steam of the steam generator 108. The control module 110 is also provided with a display device 107 for centrally displaying the values ​​of all measurement systems on a display device 107. When the sterilization device 100 is in operation, various operation parameters of the sterilization device 100 are also input into the control module 110 as input information.

[0049] Figure 8 The steam sterilization system of the present invention is shown. The steam sterilization system includes an analysis module 200. Figure 2 As shown, the analysis module 200 is connected to the control module 110 of the sterilization device 100 in communication. The analysis module 200 is connected to the terminal 300 in a wired or wireless communication manner. The analysis module 200 includes a data processing model 220, a risk prediction model 230 and a maintenance analysis model 240. The analysis module 200 also includes a storage module 250. The data processing model 220, the risk prediction model 230 and the maintenance analysis model 240 are connected in communication via a line. Alternatively, the data processing model 220, the risk prediction model 230 and the maintenance analysis model 240 are integrated in the same processor in the form of a coding program and run.

[0050] like Figure 2As shown, the control module 110 sends the operation data change trend of the sterilization equipment 100 to the analysis module 200. Further, the control module 110 sends the operation data of the sterilization equipment 100 to the analysis module 200, and the analysis module 200 calculates the operation data change trend of the sterilization equipment 100. When there is only a risk prediction model 230, the risk prediction model 230 is connected to the data receiving port 210 of the analysis module 200, and the risk prediction model 230 analyzes the operation data and obtains the failure risk related to temperature or pressure. When the failure risk threshold is reached, an early warning instruction is generated. The risk prediction model 230 sends the failure risk information to the terminal 300, and displays it through the terminal 300, and the risk prediction model 230 sends the early warning instruction to the terminal 300. The terminal 300 generates early warning information according to the early warning instruction and warns in the form of sound, light, electricity and a combination thereof. The terminal 300 can be installed on the sterilization equipment 100; it can also be a mobile terminal, carried by an operator or placed in a mobile manner; it can also be a non-portable and mobile device. The terminal 300 includes a display component, a sound playing component, a light emitting component, a vibration component, etc., which can implement one or more of a variety of early warning components.

[0051] The analysis module 200 and its internal models are configured to execute the steam sterilization method of the present invention. The analysis module 200 can be a server, processor, dedicated integrated chip, cloud server and other hardware devices and combinations thereof that can run the coding program of the steam sterilization method of the present invention.

[0052] like Figure 2 As shown, the preset risk prediction model 230 analyzes the change trend of the operating data and / or the alarm threshold sent by the control module 110, determines the failure risk related to temperature or pressure of the sterilization device 100, and issues a warning instruction when the failure risk threshold is reached, so that the sterilization device 100 can be debugged before the failure occurs. The terminal 300 is used to receive the warning instruction of the analysis module 200 and issue a warning.

[0053] The present invention uses a steam sterilization system to monitor the operating data change trend and alarm threshold of the sterilization equipment 100 in real time, and make a fault risk judgment related to temperature or pressure based on the preset risk prediction model 230. When the fault risk threshold is reached, the system will issue a warning instruction so that the sterilization equipment 100 can be debugged before a fault occurs, thereby avoiding the problem of sterilization failure caused by a fault and medical supplies not meeting the sterility standard. At the same time, the system can also ensure the integrity and traceability of the sterilization process data, improve the speed of judging the cause of the fault, and reduce the time and cost of equipment repair. Therefore, this technical solution can improve the quality of medical services and the safety of patients, and reduce the risk of infection.

[0054] The risk prediction model 230 is formed by training an artificial intelligence model using operation data samples and fault data samples of the sterilization device 100. The method for constructing the risk prediction model 230 is as follows.

[0055] Collect various sensor data during the operation of the sterilization equipment 100. Sensor data includes temperature, pressure, vibration, rotation speed, current, etc. Collect historical fault records of the sterilization equipment 100, including the time point and fault type of the fault. Collect maintenance records of the sterilization equipment 100, including maintenance time, maintenance content, etc. Extract time series features, such as sliding average, sliding standard deviation, trend, periodicity, etc. Calculate statistical features of sensor data, such as mean, variance, kurtosis, skewness, etc. If there is specific domain knowledge, specific features can be extracted according to the working principle of the sterilization equipment 100. Define the prediction target, that is, whether the sterilization equipment 100 will fail in the future. The data in a time window before the failure can be marked as "1" (that is, the failure is about to occur), and the data in other time periods can be marked as "0" (that is, normal operation). Determine a reasonable time window, which can capture the signs before the failure occurs without issuing an alarm too early. The artificial intelligence model can preferably be: a supervised learning model or a deep learning model. Divide the data into a training set, a validation set, and a test set, perform model training, and obtain a risk prediction model 230. Continuously optimize the model based on actual operating conditions, regularly update training data, and retrain the model.

[0056] Different models and manufacturers of sterilization equipment 100 result in different alarm timings of sterilization equipment 100. For example, when performing vacuum sterilization on the same medical item, the alarm timing of some sterilization equipment 100 is set to: when the vacuum process lasts for 15 minutes, the control module 110 determines it to be abnormal and issues an early warning; the alarm timing of some sterilization equipment 100 is set to: when the vacuum process lasts for 20 minutes, the control module 110 determines it to be abnormal and issues an early warning. Therefore, when the alarm threshold of the sterilization equipment 100 is received or collected, the fault risk threshold used for early warning of the analysis module 200 can allow operators with management authority to manually adjust it so that the fault risk threshold is more consistent with the sterilization status of the current sterilization equipment 100.

[0057] like Figure 3 As shown, the risk prediction model 230 calculates and determines the time window related to the failure risk based on the historical maintenance record information, fault information and historical operating data change trend before the failure of the sterilization equipment 100, and determines the failure risk and main influencing factors based on the operating data change trend within the time window.

[0058] Based on the historical maintenance records, fault information, and operating data change trends before the failure of the sterilization equipment 100, the risk prediction model 230 can effectively calculate the time window related to the failure risk. This model can accurately assess the failure risk and its main influencing factors by analyzing the changing trend of the equipment's operating data within a specific time window. This forward-looking analysis not only improves the accuracy of fault prediction, but also enables the maintenance team to take preventive measures in a timely manner, thereby avoiding or reducing the occurrence of unexpected downtime. This helps to ensure the continuity and reliability of the sterilization process, ensure the aseptic processing of medical supplies, thereby reducing the risk of infection and improving the overall quality of medical services and patient safety. In this way, maintenance costs can be effectively saved and the service life of the equipment can be extended, providing medical institutions with more efficient, economical and safe sterilization solutions.

[0059] like Figure 2 As shown, the analysis module 200 further includes a data processing model 220 . The data processing model 220 is connected to the data receiving port 210 of the analysis module 200 , receives the operation data, and sends the first operation parameter to the risk prediction model 230 .

[0060] like Figure 2 and Figure 4 As shown, the data processing model 220 retrieves the first operating parameter related to the sterilization efficiency within the time window based on the type of sterilized items and / or the type of bacteria sent by the terminal 300. The risk prediction model 230 predicts the sterilization operation prediction parameter change trend and operation prediction time required to complete the sterilization procedure of the sterilized items based on the first operating parameter. When there is a difference between the operation data change trend within the time window of the sterilization equipment 100 and the sterilization operation prediction parameter change trend, or when there is a difference between the preset operation time and the operation prediction time when the sterilization procedure of the sterilization equipment 100 is completed, the risk prediction model 230 issues an early warning message through the terminal 300.

[0061] The first operating parameter is related to the sterilization efficiency and is called within the time window to predict the parameter change trend and operating time required for the sterilization procedure to be completed. These parameters may include temperature, pressure, time, humidity, and sterilization gas concentration, etc. They are variables in the sterilization process and are used to ensure that the sterilization device 100 operates effectively and achieves the expected sterilization effect.

[0062] For example, during a sterilization cycle, the temperature sensor of the sterilization device 100 shows that the temperature fluctuates at 121°C. The risk prediction model 230 detects this fluctuation and compares it with the predicted change curve. Since the fluctuation exceeds the predicted tolerance range, the risk prediction model 230 immediately records the fluctuation and issues a first warning through the terminal 300.

[0063] Early warning at the completion of sterilization: After the sterilization cycle is completed, the risk prediction model 230 re-evaluates the data of the entire sterilization process, including temperature fluctuations and other factors that may affect the sterilization effect.

[0064] The risk prediction model 230 finds that although the temperature is finally stable, the sterilization effect may not reach the expected sterility assurance level (SAL) due to previous fluctuations. Therefore, when the sterilization is completed, the risk prediction model 230 again issues an early warning through the terminal 300, prompting the operator to perform additional evaluation or re-sterilization of the sterilized items.

[0065] like Figure 5 As shown, the risk prediction model 230 retrieves the predicted change curve of the second operating parameter corresponding to the sterilization process based on the type of sterilized article and / or the type of bacteria received. The risk prediction model 230 compares the real-time change curve of the second operating parameter of the sterilization device 100 with the predicted change curve and calculates the difference value, and issues warning information and main influencing factors when the difference value is greater than the fault risk threshold.

[0066] The second operating parameters refer to a series of parameters closely related to the sterilization process, and the real-time change curves of these parameters can be used by the risk prediction model 230 to compare with the predicted change curve. The second operating parameters may include, but are not limited to, the temperature change rate, the pressure change rate, the humidity change rate, the change in the concentration of the sterilizing gas, the airflow pattern inside the sterilizing device 100, the temperature uniformity inside the sterilizing device 100, the duration of the sterilization cycle, etc.

[0067] The temperature change rate refers to the speed at which the temperature rises or falls during the sterilization process, for example, the temperature change rate of a pre-vacuum pressure steam sterilizer during the heating stage. The pressure change rate refers to the real-time change speed of the pressure inside the sterilization device 100, which may affect the penetration of the steam and the sterilization effect. The airflow pattern inside the sterilization device 100 refers to the flow pattern of steam or gas inside the sterilization device 100, which may affect the distribution and uniformity of the sterilization medium. The temperature uniformity inside the sterilization device 100 refers to the temperature difference at different locations inside the sterilization device 100, ensuring that all sterilized items can reach the predetermined sterilization temperature. In addition to the total sterilization time, the duration of the sterilization cycle includes the duration of each stage in the sterilization process, such as the heating time, the insulation time, and the cooling time.

[0068] For example, suppose that an enterprise uses a pre-vacuum pressure steam sterilization device 100 for sterilization. During a sterilization cycle, the risk prediction model 230 receives the following second operating parameters:

[0069] Temperature change rate: It is predicted that during the heating stage, the temperature should rise at a rate of 0.7°C / second.

[0070] Pressure change rate: It is predicted that during the sterilization stage, the pressure should be maintained at 205.8 kPa and the change rate should not exceed 0.5 kPa / second.

[0071] Humidity change rate: It is predicted that during the sterilization phase, the humidity should be maintained at 80% and the change rate should not exceed 5% / minute.

[0072] During the sterilization process, the temperature change rate monitored in real time by the risk prediction model 230 is 0.9°C / second, which exceeds the predicted 0.7°C / second, while the pressure change rate and humidity change rate are within the predicted range. Since the difference in the temperature change rate exceeds the preset risk threshold, the risk prediction model 230 detects this deviation and compares it with the predicted change curve. Since the deviation exceeds the preset risk threshold, the risk prediction model 230 immediately issues an early warning message through the terminal 300, indicating that there may be an abnormality in the temperature change rate. This early warning is used to indicate that the sterilization process may be abnormal, and does not mean that the sterilization process is actually faulty. This allows the operator to make a preliminary assessment of the early warning information and decide whether further inspection or adjustment is needed. The operator first checks the heating system of the sterilization equipment 100 to confirm that there is no fault. At the same time, check the loading method of the sterilized items to ensure that there is no obstruction to the uniform distribution of steam. The operator decides to continue monitoring the sterilization process instead of stopping the cycle immediately because they believe that a slight deviation in the temperature change rate may not affect the overall sterilization effect.

[0073] In the present invention, the sterilization temperature range margin is a temperature range set for the temperature fluctuation range in a single sterilization process, which is less than or equal to 3°C. The temperature uniformity safety margin is a temperature range set for the temperature fluctuation range at each same moment in the sterilization process, which is less than or equal to 2°C. The sterilization time safety margin is the duration of a single sterilization process, which is greater than or equal to 180s for the first sterilization temperature of 134°C and greater than or equal to 900s for the second sterilization temperature of 121°C.

[0074] The reason why the sterilization device 100 has such precise temperature setting requirements is that the core goal of sterilization is to eliminate all microorganisms (including bacteria, viruses, fungi, spores, etc.), and these microorganisms have a strong dependence on temperature and time tolerance. Different types of microorganisms require different temperatures and times to be completely killed. For example, when high-pressure steam sterilization is performed, 121°C or 134°C is usually used as the sterilization temperature, and these temperatures must be maintained for a sufficiently long time (for example, 15 minutes of sterilization at 121°C or 3 minutes of sterilization at 134°C) to ensure that the microorganisms are completely killed. If the temperature is lower than the standard range, even by only a few degrees, some heat-resistant microorganisms or spores may not be completely killed, resulting in sterilization failure, thereby increasing the risk of infection. However, although excessively high temperatures can kill microorganisms faster, they may damage the structure and performance of equipment and medical supplies, especially for heat-sensitive materials such as plastics and rubber; excessively high temperatures may also cause the sterilization device 100 itself to operate abnormally, or even cause safety accidents (such as overheating, steam explosion, etc.), affecting the normal development of medical work.

[0075] Fig.10 The schematic diagram shows the temperature and time range allowed during the sterilization process of sterilized items. Fig.10 As shown, the vertical line A indicates the start of the sterilization time. The vertical line B indicates the end of the sterilization time. s Indicates the sterilization temperature. B Indicates the sterilization temperature range. x1 Indicates the sterilization time. x2 represents the equilibrium time. x3 Indicates 60S. x4 Indicates the holding time. S1 indicates the temperature curve of the reference measurement point. S2 indicates the temperature curve of the center of the sterilized object. S3 indicates the temperature curve 50 mm above the sterilized object. γ1 Indicates the maximum temperature difference between the reference measurement point and each point in the test package during the maintenance time. γ2 It indicates the maximum temperature difference between the reference measuring point and the measuring point above the sterilized object within the first 60 seconds of the sterilization time. γ3 Indicates the maximum temperature difference between the reference measuring point and the measuring point above the sterilized article after tx3 (maintained for 60s).

[0076] like Fig. 9 As shown, when the sterilization chamber starts running, from time φ iAt the beginning, the pressure begins to change from negative pressure to positive pressure repeatedly until it reaches the specified pressure at time φ2, which is the start time of the sterilization time. When the sterilization time reaches the end time φ3, the pressure begins to drop and enters a negative pressure state. When the sterilization time reaches φ4, it is the start time of the drying period. The pressure begins to be maintained until time φ5, which is the end time of the drying period. There is a certain correlation between the pressure of the steam in the sterilization chamber and the temperature of the sterilization chamber. The pressure can be converted into an ideal temperature to obtain the effective pressure temperature corresponding to the current pressure. By calculating the temperature through the pressure, the actual temperature in the sterilization chamber can be further determined, and the temperature measurement system can also be judged whether it is damaged by the difference between the inferred ideal temperature and the temperature data of any temperature measurement system.

[0077] The risk prediction model 230 of the present invention is set in a manner related to the temperature uniformity safety margin, the temperature fluctuation margin and the sterilization duration. The temperature uniformity safety margin is calculated in the following two ways.

[0078] The sterilization device 100 is provided 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.

[0079] Solution A: At each sampling moment in the constant pressure stage, the risk prediction model 230 calculates at least two corresponding pressure equivalent temperature values ​​based on the pressure-temperature conversion formula from at least two pressure values, compares the first temperature difference of the at least two temperature values ​​with the second temperature difference of the at least two pressure equivalent temperature values, and compares the maximum temperature difference between the first temperature difference and the second temperature difference with the uniformity temperature reference to determine the temperature uniformity safety margin as the first alarm threshold. Fig.12 As shown:

[0080] (1).

[0081] Alternatively, Solution B: At each sampling moment in the constant pressure stage, the risk prediction model 230 compares the temperature values ​​of at least two temperature sensors with the corresponding at least two pressure equivalent temperature values ​​calculated based on the pressure-temperature conversion formula from at least two pressure values, calculates the maximum temperature difference among the four, and compares the maximum temperature difference with the uniformity temperature reference to determine the temperature uniformity safety margin as the first alarm threshold. Fig.12 As shown, the formula for the percentage of temperature uniformity safety margin is:

[0082] (2).

[0083] In both Scheme A and Scheme B, represents the first temperature value collected by the first temperature sensor 101, represents the second temperature value collected by the second temperature sensor 103, Indicates the first pressure equivalent temperature value obtained by converting the first pressure value, represents the second pressure equivalent temperature value obtained by converting the second pressure value, represents time, S represents the end time of the steam pressure being in a steady state; TUBT represents the temperature uniformity benchmark threshold. Indicates the time period when the steam pressure in the sterilization chamber is in a stable state (steady state), see Fig. 9 The time period φ2~φ3 is shown.

[0084] The temperature uniformity benchmark threshold (TUBT) of the present invention is defined as: the maximum allowable limit of the allowable temperature distribution deviation at each sampling moment in a sterilization chamber of a constant volume. In an embodiment, when the temperature of the sterilization chamber is 134°C, the temperature uniformity benchmark threshold TUBT is, for example, 2°C, which is used to ensure that the temperature difference at different locations does not exceed this value during the entire sterilization time, thereby ensuring the effectiveness and consistency of the entire process. The temperature uniformity benchmark threshold TUBT reflects the spatial consistency requirements of the temperature field. Temperature fluctuations that exceed the temperature uniformity benchmark threshold TUBT may lead to significant differences in the sterilization effect. The temperature uniformity benchmark threshold TUBT can be used to accurately evaluate the uniformity of temperature distribution at any time during the sterilization process, ensure that the equipment operates within the specified temperature range, and prevent local overheating or overcooling.

[0085] The conditions for using the calculation formula of the temperature uniformity safety margin are as follows: During the constant pressure holding stage (taking 134°C, 300 seconds as an example), the temperature uniformity safety margin is calculated for each moment. The temperature difference at each moment is calculated (including the temperature difference of the pressure equivalent temperature value converted by pressure). The maximum value of the temperature uniformity safety margin at each moment is compared with the temperature uniformity reference threshold TUBT threshold, and the final temperature uniformity safety margin of the sterilization is calculated as a percentage. This formula is used to evaluate whether the temperature difference recorded by the temperature sensor and the pressure sensor at each moment (within 0 to 300 seconds of the entire sterilization process) exceeds the limit of the temperature uniformity reference threshold TUBT to ensure that the temperature difference between each point is controlled within the specified range.

[0086] This calculation method improves the detection sensitivity of various sensor failures by comparing multiple temperature values ​​(two temperature values ​​and two pressure equivalent temperature values). If a sensor fails, its reading may deviate significantly from the values ​​of other sensors, resulting in a change in the temperature fluctuation margin. The calculated temperature fluctuation margin provides real-time temperature consistency monitoring. If the temperature fluctuation margin drops significantly, it indicates that there may be a temperature sensor failure or other abnormal conditions, which facilitates the operator to respond in time. Operators do not need to manually check the readings of each sensor, but only need to pay attention to the changes in the temperature fluctuation margin, which simplifies the monitoring process and improves the convenience of operation. In addition, this calculation method can identify sensor failures in a timely manner and effectively avoid sterilization failures caused by inaccurate temperature monitoring, enhancing the safety of the overall sterilization process. The formula for converting pressure to pressure equivalent temperature is shown below.

[0087] (A) Assume the ideal gas law approximation is used for pressure and temperature conversions: (3).

[0088] Due to volume and quantity of matter constant, this can be simplified to: (4).

[0089] in is a conversion factor based on the calibration data of the device. P is the absolute pressure of the saturated steam measured (in megapascals, MPa), which is the input variable. Another pressure equivalent temperature. Another pressure equivalent temperature is used for shutdown critical warning.

[0090] (B) Assuming that the theoretical temperature of saturated steam is used to calculate the pressure and temperature conversion, the formula used to calculate the temperature of saturated steam when the pressure is known is: (5). Where T represents the theoretical temperature of saturated steam (unit: Kelvin, K); A = 42.6776K is a constant used to correct the temperature value. B = -3892.70K is another constant used to correct 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 pressure.

[0091] There are two schemes for converting pressure into temperature, each with different advantages and application scenarios.

[0092] The pressure equivalent temperature value of scheme A is conducive to the safe and stable operation of the sterilization device 100: another pressure equivalent temperature value can provide a simplified way for the sterilization device 100 to evaluate the effect of pressure on temperature. This is very helpful for real-time monitoring and control of the uniformity and stability of the sterilization process. In addition, in terms of safety: since the sterilization process needs to be carried out within a specific temperature and pressure range, the use of pressure equivalent temperature values ​​can more accurately detect and adjust these parameters to ensure the sterilization effect and equipment safety.

[0093] The special significance of the pressure equivalent temperature value of scheme A is: in terms of simplified calculation: linear formula It is relatively simple and helps with fast calculation and real-time application. This can simplify the calculation process and improve the response speed in some real-time control systems. In terms of calibration and verification: it can be used as a verification tool to cross-check the accuracy of the conversion between saturated steam pressure and temperature to ensure the correctness of the conversion formula. The calculation formula of the pressure equivalent temperature value of Scheme A can correct the small error of the conversion formula to a certain extent, especially the approximate calculation within a specific pressure range. However, for applications with a larger range or high precision requirements, it is still necessary to rely on more accurate nonlinear formulas.

[0094] The calculation formula of the pressure equivalent temperature value of Scheme B can better reflect the real physical characteristics of saturated steam, because the relationship between saturated steam pressure and temperature is essentially nonlinear. By taking the logarithm of the pressure and the constant correction, the pressure can be mapped to the corresponding temperature value more accurately. The practicality of the linear formula of the pressure equivalent temperature value of Scheme B: Linear formula It is more suitable for fast calculation and real-time monitoring, and reduces the calculation complexity. The coefficient k can be adjusted according to the specific equipment and operating conditions, so that the formula has a good approximation effect within a specific range. The present invention recommends that the calculation formulas for the pressure equivalent temperature value of scheme A and scheme B be used in combination.

[0095] The combination of these two formulas can complement each other's advantages: the nonlinear formula is used in scenarios with high precision requirements (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, the small error of the saturated steam pressure and temperature conversion formula can be corrected to a certain extent, ensuring the overall stability and accuracy of the system.

[0096] Therefore, based on the linear formula A specified coefficient k can be set to obtain a specified pressure equivalent temperature value as a 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 can be reached by the nonlinear formula of scheme B under normal operation. When any temperature value or pressure equivalent temperature value of the sterilization chamber reaches the shutdown critical warning threshold, the control module 110 triggers and issues a shutdown command according to the situation to shut down the sterilization equipment 100.

[0097] As mentioned above, the pressure equivalent temperature value of Scheme A is beneficial for safe and stable operation, especially in terms of simplified calculation and real-time monitoring, which plays an important role in ensuring the safety and uniformity of the sterilization process. Scheme A can simplify calculations, improve real-time response speed, and serve as a calibration and verification 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 a certain extent, but it cannot completely replace the nonlinear formula of Scheme B. The combination of the two formulas can improve the efficiency of real-time monitoring and control while ensuring high accuracy.

[0098] The temperature uniformity safety margin calculation steps of the present invention are as follows.

[0099] Convert pressure to temperature: For each moment , calculate the converted temperature using the pressure sensor reading:

[0100] (6).

[0101] in and The first pressure sensor 102 and the second pressure sensor 104 are respectively The readings.

[0102] The calculation steps of the temperature difference at each moment are as follows. , temperature difference for:

[0103] (7); or

[0104] (8).

[0105] Use one of the above two formulas to calculate the temperature difference between the two sets of sensors (temperature sensor and temperature after pressure conversion), and take the maximum value as the temperature difference.

[0106] The steps to calculate the temperature uniformity safety margin at each moment are as follows. , calculate the temperature uniformity safety margin as: (9).

[0107] The steps to calculate the final temperature uniformity safety margin for this sterilization are as follows.

[0108] Find the maximum temperature difference at each moment and compared with the temperature uniformity benchmark threshold of 2°C.

[0109] (10).

[0110] like Fig.12 As shown, the temperature fluctuation margin is calculated as follows.

[0111] (11).

[0112] For all moments in the entire constant pressure holding stage, the difference between the maximum and minimum values ​​in temperature 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 values ​​of temperature during the entire sterilization process exceeds the allowable fluctuation range, that is, the sterilization temperature fluctuation threshold, so as to evaluate the temperature fluctuation margin.

[0113] The sterilization temperature fluctuation threshold (Sterilization Temperature Fluctuation Threshold, STFT) of the present invention refers to the maximum allowable difference in temperature fluctuations at various locations in the sterilization chamber during the entire sterilization process. Taking the sterilization process at 134°C as an example, the value of the sterilization temperature fluctuation threshold STFT is set to 3°C to ensure that within the specified sterilization time (such as 3 minutes), the temperature difference at any point in the system shall not exceed the sterilization temperature fluctuation threshold STFT. The sterilization temperature fluctuation threshold STFT is used to monitor and limit spatial and temporal fluctuations of temperature to ensure the uniformity and effectiveness of sterilization. Exceeding the sterilization temperature fluctuation threshold STFT may affect the thoroughness of sterilization, resulting in the sterilization process failing to meet the required microbial killing standards. For example:

[0114] (12).

[0115] The temperature fluctuation margin calculation steps are as follows.

[0116] For every moment , record the following temperature data: (13).

[0117] During the entire constant pressure holding phase, from all moments Find the maximum temperature and minimum value .

[0118] (14).

[0119] (15).

[0120] Calculate the temperature fluctuation throughout the process: (16).

[0121] Compare the temperature fluctuation with the reference value of 3°C and calculate the temperature fluctuation margin: (17).

[0122] This formula is used to calculate the difference between the maximum and minimum temperature during the entire sterilization process and whether it exceeds the sterilization temperature fluctuation threshold STFT, thereby evaluating the temperature fluctuation margin.

[0123] The sterilization time is set according to the time interval during the constant pressure holding stage. It is maintained for a minimum of 180 seconds at a temperature of 134°C and is usually set to 300 seconds. During this period, it is necessary to calculate each moment separately. The temperature uniformity safety margin is calculated and the maximum temperature difference in the whole process is counted to ultimately determine the temperature stability and uniformity of the sterilization process.

[0124] The risk prediction model 230 evaluates the stability of temperature control and process reliability during the sterilization process based on the temperature uniformity safety margin, temperature fluctuation margin and parameter changes of the sterilization time to ensure the effectiveness and safety of the sterilization process.

[0125] Fig.11 Six sets of data obtained from monitoring the steam sterilization system were recorded. Fig.11 As shown, 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 corresponding to P1. , Theoretical steam temperature corresponding to P2 , the minimum value of the first / second temperature value and the two theoretical steam temperature values ​​at the same time, the maximum value of the first / second temperature value and the two theoretical steam temperature values ​​at the same time, the temperature fluctuation range (temperature difference at the same time), the actual sterilization time (S), the margin of the sterilization temperature range (0~3℃), the temperature uniformity safety margin (0~2℃) and the sterilization time safety margin (180S). Fig.11As shown, all kinds of data are in dynamic change. The fluctuation data of the margin of the sterilization temperature range are 80%, 77% and 83%; the steam sterilization system shows the minimum value of its historical data is 77%. The fluctuation data of the temperature uniformity safety margin are 70%, 65% and 75%; the steam sterilization system shows the minimum value of its historical data is 65%. The fluctuation data of the sterilization time safety margin (180S) are 166% and 171%. The steam sterilization system's dynamic change monitoring of various data not only improves the operating efficiency of the equipment, but also significantly improves the sterilization effect. The following is a detailed description of the advantages of various data monitoring and its contribution to the sterilization equipment 100 and effect.

[0126] By monitoring the first temperature value and the second temperature value, better advantages can be brought to the equipment and sterilization effect: (1) Real-time monitoring of temperature changes can ensure that the sterilization equipment 100 is always in the best working state, avoiding equipment damage or reduced efficiency caused by excessively high or low temperatures; (2) Temperature is a key factor in the sterilization effect. Accurate temperature monitoring ensures that each sterilization cycle can achieve appropriate heat transfer, thereby effectively killing bacteria, viruses and other microorganisms.

[0127] By monitoring the first pressure value and the second pressure value, better advantages can be brought to the equipment and sterilization effect: (1) By monitoring the pressure changes in real time, equipment failure or leakage problems can be discovered early, reducing equipment maintenance costs and downtime. (2) Maintaining the appropriate pressure level helps ensure the saturation of steam, thereby ensuring uniform heat distribution, which is crucial to the comprehensiveness of sterilization.

[0128] By monitoring the theoretical steam temperature corresponding to the two pressure values, better advantages can be brought to the equipment and sterilization effect: (1) Comparative monitoring of the theoretical steam temperature can help operators adjust the sterilization parameters in real time to avoid potential operating errors. (2) By comparing the actual temperature with the theoretical temperature, the heat transfer efficiency during the sterilization process can be ensured, ensuring that each area can reach the required sterilization temperature.

[0129] By monitoring the minimum and maximum values ​​of the temperature value and the theoretical steam temperature value at the same time, as well as the temperature fluctuation range, better advantages can be brought to the equipment and sterilization effect: (1) Ensure that the equipment maintains temperature consistency during operation and effectively prevent the occurrence of cold spots or hot spots. (2) Such monitoring can ensure temperature uniformity during the sterilization process and avoid sterilization failure caused by insufficient local temperature, thereby ensuring the safety of the entire batch of products. (3) Monitoring of temperature fluctuations can respond in a timely manner to ensure that the equipment will not affect the sterilization effect due to unstable temperature. (4) Maintaining a moderate temperature fluctuation range can effectively reduce the risk of the sterilization process and ensure that microorganisms are completely exterminated.

[0130] In addition, the present invention tracks the sterilization time in real time, which helps to optimize the production process and avoid waste of resources caused by improper time control. In this way, ensuring that the sterilization time meets the standard can effectively improve the effectiveness of sterilization and prevent the survival of microorganisms caused by insufficient time. The dynamic monitoring of the temperature margin of the present invention can provide operational flexibility for the equipment, so that it can still operate effectively under changing conditions. At the same time, maintaining a certain temperature margin in this way ensures that the sterilization effect is still reliable even under uncertain environmental changes, further ensuring the safety of the product. The present invention can optimize the equipment design and improve the overall performance and stability of the equipment by monitoring the temperature uniformity margin. Not only that, the present invention can ensure that the temperature is evenly distributed during the sterilization process, reduce the risk of incomplete sterilization caused by insufficient temperature in individual areas, and ensure the overall safety of the product. The present invention monitors the safety margin of the sterilization time, which can improve the adaptability of the equipment to a certain extent, avoid affecting the sterilization efficiency due to changes in operating conditions, and ensure that the sterilization time exceeds the minimum requirement, effectively reducing the risk of incomplete sterilization caused by insufficient time, and improving the reliability of the sterilization system. As described above, the steam sterilization system of the present invention not only improves the operating efficiency and reliability of the equipment, but also significantly enhances the stability and consistency of the sterilization effect through dynamic monitoring of these key data. This real-time monitoring ensures the scientific nature of the entire sterilization process, so that each batch of products can be sterilized under optimal conditions, ultimately ensuring the safety and effectiveness of the products. Through such a steam sterilization system, the sterilization equipment 100 can maintain efficient sterilization capabilities in complex and changeable environments, providing strong support for the fields of medical care, pharmaceuticals, and food processing.

[0131] Compared with the prior art which only determines abnormality by whether an alarm is triggered, and cannot determine whether the sterilization equipment 100 is abnormal during the period when no alarm is triggered, the technical solution of the present invention can ensure stable operation by monitoring two types of safety margins, which can significantly reduce the number of equipment maintenance times, improve equipment utilization, maintain a stable sterilization effect, and significantly reduce equipment renewal and maintenance costs.

[0132] The process of calculating the alarm threshold in the present invention is as follows.

[0133] The alarm threshold is based on the setting of the temperature uniformity safety margin and the temperature fluctuation margin. Specifically, the alarm threshold can be calculated in the following way: During the sterilization process, for each moment, compare the maximum difference in the pressure equivalent temperature values ​​converted by the temperature sensor and the pressure sensor, and compare the difference with the temperature uniformity reference threshold of 2°C. If the difference approaches or exceeds this reference (for example, above 1.8°C), an alarm is triggered. During the entire sterilization hold phase, record the difference between the maximum and minimum values ​​of the pressure equivalent temperature values ​​of the temperature sensor and the pressure sensor. If the difference approaches or exceeds the 3°C reference (for example, above 2.7°C), an alarm is triggered.

[0134] The calculation process of the fault risk threshold of the present invention is as follows.

[0135] The failure risk threshold is set based on the stability and fluctuation of temperature and pressure during the sterilization process. Specifically, the calculation of the failure risk threshold includes: calculating the failure risk threshold at each time t during the sterilization process i The temperature uniformity safety margin is calculated, and the maximum temperature difference in the whole process is counted. The difference between the maximum and minimum temperature converted by the temperature sensor and the pressure sensor during the sterilization holding stage is calculated. Combined with the temperature uniformity reference threshold TUBT and the sterilization temperature fluctuation threshold STFT, the stability of the overall temperature control is evaluated. According to historical data and experience, the failure risk threshold for the risk prediction model 230 is set. For example, it is set that when the temperature uniformity safety margin exceeds a certain percentage during the entire sterilization process, or the temperature fluctuation margin exceeds a certain percentage, the failure risk will increase significantly. 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.

[0136] like Figure 2 As shown, the analysis module 200 also includes a maintenance analysis model 240. The maintenance analysis model 240 is connected to the risk prediction model 230 for communication and receives data sent by the risk prediction model 230. The risk prediction model 230 and the maintenance analysis model 240 are also connected to the data output port 260 of the analysis module 200 respectively. The data output port 260 is connected to the port of the terminal 300 in a wired or wireless communication manner. Figure 6 As shown, the maintenance analysis model 240 retrieves the hardware fault table related to the abnormal operation data change trend of the sterilization device 100. The maintenance analysis model 240 compares the operation data change trend of all parameter types of the current operation of the sterilization device 100 with the hardware fault table, retrieves the hardware fault information and / or maintenance plan corresponding to the abnormal operation data change trend, and sends it to the terminal 300.

[0137] Specifically, the maintenance analysis model 240 found in the fault table that there were three similar cases in the past, all of which were caused by damage to the heating element. In these cases, the resistance measurement values ​​were all lower than the normal range. The maintenance analysis model 240 sends an analysis instruction to the risk prediction model 230, so that the risk prediction model 230 further analyzes the resistance measurement value of the current sterilization equipment 100 and feeds back to the maintenance analysis model 240. The maintenance analysis model 240 determines that the resistance value of the heating element is abnormally low, which is consistent with the data in the historical case. The maintenance analysis model 240 determines that the heating element is damaged and needs to be replaced. Once the possible cause of the fault is determined, the maintenance analysis model 240 will retrieve the corresponding maintenance plan from the fault table. If it is inferred that the heating element is damaged, the maintenance analysis model 240 will provide the steps for replacing the heating element and the model of the heating element. The maintenance analysis model 240 sends the diagnosis results and maintenance plan to the terminal 300 for reference by maintenance personnel.

[0138] When the analysis module 200 detects abnormal operating data, the maintenance analysis model 240 immediately retrieves the hardware fault table related to the sterilization device 100 and compares and analyzes it with the current operating data of all parameter types. This intelligent comparative analysis can quickly and accurately diagnose the specific information of the hardware fault and even provide corresponding maintenance solutions. This instant fault analysis and maintenance suggestions significantly shorten the downtime of the equipment, improve maintenance efficiency, ensure the continuous and stable operation of the sterilization device 100, and thus ensure the continuity and safety of medical supply. Figure 2 As shown, the analysis module 200 further includes a storage module 250. The storage module 250 is respectively connected in communication with the data processing model 220, the risk prediction model 230 and the maintenance analysis model 240 to receive and store the operation data of each model.

[0139] like Figure 2 As shown, the storage module 250 also stores a hardware failure table and / or a maintenance plan, a first operating parameter within a time window related to the sterilization efficiency, a second operating parameter corresponding to the sterilization process and its predicted change curve. The storage module 250 also stores the received operating data of the sterilization equipment 100 and its change trend. The storage module 250 provides a solid data foundation for the entire steam sterilization system. It not only saves the hardware failure table and the maintenance plan, but also records the first operating parameter within the time window that is closely related to the sterilization efficiency, and the second operating parameter corresponding to the sterilization process and its predicted change curve. This centralized data management ensures the integrity and traceability of the data, and provides accurate and reliable data support for risk prediction and maintenance decisions. In particular, when the sterilization equipment 100 fails and its operating data cannot be exported, the storage module 250 can provide a backup of the operating data of the sterilization equipment 100.

[0140] like Figure 7As shown, when the sterilization device 100 is started, the risk prediction model 230 retrieves the historical record information and warning record information of the failure risk that occurs during the sterilization process. When 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 device 100 to interrupt the startup procedure of the sterilization device 100 to avoid failure of the sterilization device 100 during operation.

[0141] Before the sterilization device 100 is started, the risk prediction model 230 will first retrieve historical record information, including failure risks and early warning records. This forward-looking evaluation mechanism enables the system to predict potential failure risks before the device is started. When the risk approaches the failure risk threshold and no debugging is performed, the risk prediction model 230 can directly send a stop command to the control module 110 to interrupt the startup program of the device. This mechanism effectively avoids failures of the device during operation, reduces unplanned downtime caused by equipment failure, improves the operating efficiency of the equipment, further ensures the smooth progress of the sterilization process, and provides additional protection for the continuity of medical services and the safety of patients. The analysis module 200 can determine the specific location of the failed temperature / pressure sensor based on the abnormalities of the temperature uniformity safety margin and the temperature fluctuation margin. For example, if the temperature uniformity margin is significantly lower than 70%, it may be a temperature sensor failure. If the temperature fluctuation margin is significantly lower than 70%, it may be a pressure sensor failure.

[0142] If the difference between the temperature values ​​of the temperature sensors is large (for example, more than 1°C), and the difference between the pressure equivalent temperature values ​​is within the normal range (for example, less than 1°C), it indicates that one of the temperature sensors may be faulty and further verification is required.

[0143] Check the historical data of the first temperature value and the second temperature value to confirm whether there are continuous or intermittent abnormalities. If the first temperature value deviates significantly from other values, the first temperature sensor 101 is judged to be faulty; otherwise, the second temperature sensor 103 is judged to be faulty. If the difference between the pressure equivalent temperature sensors (for example, more than 1°C) and the difference between the temperature sensors is within the normal range (for example, 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 continuous or intermittent abnormalities. If the first pressure equivalent temperature value deviates significantly from other values, the first pressure sensor 102 is judged to be faulty; otherwise, the second pressure sensor 104 is judged to be faulty. According to the above steps, the operator can confirm the specific faulty sensor and perform necessary maintenance or replacement to ensure the normal operation of the sterilization equipment 100.

[0144] In this way, the present invention can accurately and efficiently identify the specific location of the faulty sensor, significantly improving the convenience and accuracy of fault location. The analysis module 200 utilizes real-time monitoring and analysis of the temperature uniformity safety margin and the temperature fluctuation margin to form a systematic fault diagnosis process.

[0145] Specifically, when the system detects that the temperature uniformity safety margin is significantly lower than 70%, the analysis module 200 immediately indicates that there may be a temperature sensor failure. The low indicator means that the temperature values ​​between the temperature sensors are too different, potentially reflecting the failure or deviation of a certain sensor. The analysis module 200 then verifies this nature by comparing the real-time data of the temperature sensor. If it is found that the difference between the temperature values ​​exceeds 1°C, while the difference of the pressure sensor is still within the normal range (less than 1°C), the fault can be clearly located to a temperature sensor. Further, the analysis module 200 will trace back and analyze the historical data of the first temperature sensor 101 and the second temperature sensor 103 to confirm whether there is a continuous or intermittent temperature anomaly. This process ensures the reliability of fault judgment: if the first temperature value deviates significantly from other values, the first sensor is confirmed to be faulty; otherwise, the second sensor will be judged as the source of the fault.

[0146] The same logic applies to the fault location of the pressure sensor. When the difference between the pressure equivalent temperature sensors exceeds 1°C and the difference between the temperature sensors is within the normal range, the system can quickly identify the pressure sensor that may have failed. At this time, the module will conduct an in-depth check on the historical data of the first pressure equivalent temperature value to confirm whether there is an abnormality. If the first pressure value deviates significantly from other values, the first pressure sensor 102 is judged to be faulty, otherwise the second pressure sensor 104 is confirmed to be faulty. Through the above refined analysis and inspection process of the analysis module 200, the operator can quickly and accurately determine the location of the faulty sensor, reducing the ambiguity and uncertainty that may exist in traditional methods, so that maintenance or replacement measures can be taken in time 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 a lot of time and cost for operators.

[0147] Example 2

[0148] This embodiment is a further explanation of the calculation in Embodiment 1, and the repeated contents will not be repeated here.

[0149] The calculation steps of the temperature uniformity safety margin change trend of the present invention are as follows.

[0150] During the constant pressure holding phase of each sterilization cycle, temperature values ​​are collected regularly. , and the pressure equivalent temperature value converted by the pressure sensor , and pressure values and . Record every moment Temperature Calculate the temperature uniformity safety margin values ​​according to Scheme A and Scheme B respectively. Smooth the data. Smooth the collected temperature uniformity safety margin values, such as 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, including but not limited to an autoregressive integrated moving average model (ARIMA model) or other appropriate time series prediction methods. Calculate the mean and standard deviation of the temperature uniformity safety margin in each sterilization cycle. Perform anomaly detection based on the trend of temperature uniformity safety margin changes.

[0151] Specifically, an alarm threshold of the temperature uniformity safety margin is set (for example, when the margin drops below 10%, it is determined to be 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.

[0152] The change trend of the operating data, such as the change trend of the temperature uniformity safety margin, is compared with the preset change trend of the temperature uniformity safety margin to evaluate the failure risk of the sterilization equipment 100. If the trend analysis shows that the temperature uniformity safety margin has a significant downward trend and is lower than the preset threshold, it is judged that the equipment has a high failure risk and an early warning instruction is issued for debugging.

[0153] In sterilization cycle n, the change trend of temperature uniformity safety margin can be expressed as:

[0154] (18);

[0155] or:

[0156] (19).

[0157] By calculating the above formula, the change trend of the temperature uniformity safety margin can be obtained, and combined with historical data to judge the abnormal situation of the current sterilization cycle. The above steps and methods can effectively analyze and predict the change trend of the temperature uniformity safety margin of the sterilization equipment 100 in each sterilization cycle, so as to timely discover potential failure risks and take corresponding measures.

[0158] Solution A and Solution B have different advantages in terms of temperature and pressure equivalent temperature at the physical level. From a physical perspective, Solution A and Solution B have their own applicable scenarios and advantages and disadvantages, which depend on the characteristics of the temperature and pressure sensors and the actual application of the measurement data.

[0159] Scheme A mainly focuses on the difference between the first temperature sensor 101 and the second temperature sensor 103, and the difference between the first pressure sensor 102 and the second pressure sensor 104 converted to pressure equivalent temperature. Since the measurement value of the pressure sensor is the average value of the past period of time, despite the time alignment, the actual measured pressure equivalent temperature value still has incomplete time overlap with the instantaneous measurement value of the temperature sensor. Therefore, Scheme A evaluates the uniformity of temperature and pressure by comparing the differences.

[0160] The advantages of Scheme A include: (1) Taking into account time non-coincidence: 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) Applicable to dynamically changing environments: In an environment where temperature and pressure change rapidly, the time averaging characteristics of the pressure sensor cause its response to rapid changes to be relatively delayed. Scheme A can better capture the instantaneous non-uniformity of temperature and pressure.

[0161] Advantages of Scheme A under certain conditions: (1) Rapidly changing environments: If the temperature and pressure change rapidly during the sterilization process (such as during rapid heating or cooling), Scheme A can better reflect the instantaneous inhomogeneity because it takes into account the differences in instantaneous measurements. (2) Non-steady-state conditions: Under non-steady-state conditions (such as when the system is just started or stopped), the temperature and pressure change at different rates, and Scheme A can better capture these changes.

[0162] Solution B focuses on the difference between the maximum and minimum values ​​of all temperature sensors (including temperature converted by pressure) within a certain time range. Solution B does not clearly distinguish the difference between instantaneous measurement values ​​and time average values ​​in calculation, assuming that all temperature and pressure equivalent temperature values ​​are completely coincident in time.

[0163] The advantages of Scheme B include: (1) Simplified calculation process: There is no need to consider the problem of time non-coincidence, only the difference between the maximum and minimum values ​​needs to be calculated, and the algorithm is relatively simple. (2) Applicable to steady-state conditions: When the system reaches steady-state conditions (such as constant temperature and pressure stage), Scheme B can well reflect the overall uniformity of the system.

[0164] 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 consistent over time. (2) Long-term uniformity assessment: Applicable to assessing long-term uniformity throughout the sterilization process, rather than focusing on instantaneous differences.

[0165] As mentioned above, the advantages of Option A include: Rapidly changing environment: When the temperature and pressure change rates are high, it can more accurately reflect the instantaneous non-uniformity. Non-steady-state conditions: System startup, shutdown or rapid adjustment stage. Dynamic response: Better capture instantaneous differences in temperature and pressure, suitable for dynamic adjustment and monitoring. The advantages of Option B include: Steady-state environment: When the temperature and pressure change rates are low, it can more accurately reflect the long-term uniformity. Stable operating conditions: The system has reached a steady state, which is suitable for evaluating long-term uniformity. Simplified algorithm: Applicable to scenarios where the calculation process needs to be simplified.

Claims

1. A steam sterilization system, comprising a sterilization device (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 device (100), and analyzes the change trend of the operating data sent by the control module (110) and / or the alarm threshold based on a preset risk prediction model (230), and determines the failure risk of the sterilization device (100) related to temperature or pressure, wherein when the failure risk reaches the failure risk threshold, a warning instruction is issued, so that the sterilization device (100) can be debugged before a failure occurs. The sterilization equipment (100) is provided 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, wherein: (A) At each sampling moment in the constant pressure stage, the risk prediction model (230) calculates at least two pressure values ​​based on a pressure-temperature conversion formula to obtain corresponding at least two pressure equivalent temperature values, compares a first temperature difference value of the at least two temperature values ​​with a second temperature difference value of the at least two pressure equivalent temperature values, and compares a maximum temperature difference value between the first temperature difference value and the second temperature difference value with a temperature uniformity reference threshold value to determine a temperature uniformity safety margin as a first alarm threshold value, wherein: the formula for the percentage of the temperature uniformity safety margin is: ； Alternatively, (B) at each sampling moment in the constant pressure stage, the risk prediction model (230) compares the temperature values ​​collected by the at least two temperature sensors with the corresponding at least two pressure equivalent temperature values ​​calculated based on the pressure-temperature conversion formula from the at least two pressure values, calculates the maximum temperature difference among the four, and compares the maximum temperature difference with the temperature uniformity reference threshold to determine a temperature uniformity safety margin as the first alarm threshold, wherein: The formula for the percentage of temperature uniformity safety margin is: ； Among them, in scheme A and scheme B, represents a first temperature value collected by the first temperature sensor (101), represents a second temperature value collected by the second temperature sensor (103), Indicates the first pressure equivalent temperature value obtained by converting the first pressure value, represents the second pressure equivalent temperature value obtained by converting the second pressure value, represents time, S represents the end time of the steam pressure being in a steady state; TUBT represents the temperature uniformity benchmark threshold; Indicates the time period when the steam pressure in the sterilization chamber is maintained in a stable state.

2. The steam sterilization system according to claim 1, characterized in that: The pressure equivalent temperature value is determined by the saturated steam pressure and temperature conversion formula: ; Wherein: the pressure equivalent temperature value T represents the converted temperature of the pressure value of saturated steam; the constant A=42.6776K is used to correct the temperature value; the constant B=-3892.70K is used to correct the influence of pressure on temperature; P represents the absolute pressure of saturated steam measured by the pressure sensor; the constant C=9.48654 is used to perform offset correction on the result after taking the logarithm of the pressure value.

3. The steam sterilization system according to claim 1, characterized in that: For the pressure equivalent temperature value, the analysis module (200) sets another pressure equivalent temperature value corresponding to the steam temperature measured by at least two pressure sensors based on the following formula: ; Among them, k represents the conversion coefficient; the other pressure equivalent temperature value is used for shutdown critical warning to avoid serious failure of equipment.

4. The steam sterilization system according to claim 2, characterized in that: The alarm threshold is set based on the temperature uniformity safety margin and the temperature fluctuation margin; wherein, during the sterilization process, the maximum difference of the pressure equivalent temperature values ​​converted by the temperature sensor and the pressure sensor at each moment is compared, and the maximum difference is compared with the temperature uniformity reference threshold; if the maximum difference exceeds the temperature uniformity reference threshold, an alarm is triggered; or, if the difference between the maximum and minimum values ​​of the pressure equivalent temperature values ​​of the temperature sensor and the pressure sensor exceeds the sterilization temperature fluctuation threshold, an alarm is triggered; The fault risk threshold is set based on the stability and fluctuation of temperature and pressure during the sterilization process; wherein, the temperature uniformity safety margin is calculated at each moment in the sterilization process, and the maximum temperature difference in the whole process is counted; the difference between the maximum and minimum values ​​of the pressure equivalent temperature values ​​converted by the temperature sensor and the pressure sensor during the sterilization holding stage is calculated; the stability of the overall temperature control is evaluated by combining the temperature uniformity reference threshold and the sterilization temperature fluctuation threshold; and the fault risk threshold for the risk prediction model (230) is set based on historical data and experience.

5. The steam sterilization system according to claim 1 or 2, characterized in that: The risk prediction model (230) calculates the temperature fluctuation margin in the following manner: During the entire sterilization holding phase of the sterilization device (100), the temperature value of the temperature sensor at each moment is recorded. and , and record the pressure equivalent temperature value converted by the pressure sensor and , choose these four at time The maximum and minimum values ​​in , calculate the difference between the maximum and minimum values: ， The difference between the maximum value and the minimum value is compared with the sterilization temperature fluctuation threshold value to obtain the percentage of the temperature fluctuation margin as the second alarm threshold value.

6. The steam sterilization system according to claim 5, characterized in that: The risk prediction model (230) calculates the percentage of the temperature fluctuation margin using the formula: ； in, represents a first temperature value collected by the first temperature sensor (101), represents a second temperature value collected by the second temperature sensor (103), represents a first pressure value collected by the first pressure sensor (102), represents a second pressure value collected by a second pressure sensor (104); Indicates time; represents the conversion coefficient, S represents the end time when the steam pressure is in a steady state, and STFT represents the sterilization temperature fluctuation threshold.

7. The steam sterilization system according to claim 5, characterized in that: The sterilization device (100) has or is additionally provided with a monitoring module for verifying and / or monitoring the sterilization effect.

8. The steam sterilization system according to claim 5, characterized in that: The analysis module (200) determines the fault condition of the temperature sensor and / or the pressure sensor based on the change and abnormality of the temperature uniformity safety margin and the temperature fluctuation margin.

9. A steam sterilization method using the steam sterilization system according to any one of claims 1 to 8, characterized in that: The method comprises: Based on a preset risk prediction model (230), the change trend of the operating data and / or the alarm threshold sent by the control module (110) are analyzed to determine the failure risk of the sterilization equipment (100) related to temperature or pressure, wherein a warning instruction is issued when the failure risk reaches the failure risk threshold, so that the sterilization equipment (100) can be debugged before the failure occurs.

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