Sterilization effect detection system for steam sterilizer

Abstract

The invention relates to the technical field of sterilization equipment, in particular to a sterilization effect detection system for a steam sterilizer, which is used for solving the problems that the sterilization effect of the steam sterilizer cannot be comprehensively evaluated, comprehensive evaluation cannot be performed by combining physical parameters and biological indexes, and the judgment result cannot be ensured to be scientific and reliable in the prior art. The sterilization effect evaluation module calculates the F0 value and the survival rate of the self-luminous biological indicator to comprehensively evaluate the sterilization effect of the steam sterilizer, the trapezoidal method is adopted to calculate the F0 value, and the corrected real-time luminous intensity and a calibration curve fitted by experimental data are utilized to ensure the data accuracy, so that the accuracy of the steam sterilizer is improved. Physical parameters and biological indexes are combined for comprehensive evaluation, the sterilization effect is divided into unqualified, qualified and excellent grades by presetting scale factor coefficients, rapid judgment and operation guidance are facilitated, and based on a strict mathematical model, it is ensured that the judgment result is scientific and reliable.

Description

Technical Field

[0001] The present invention relates to the technical field of sterilization equipment, and more specifically, to a sterilization effect detection system for a steam sterilizer. Background Art

[0002] Steam sterilizer is a commonly used sterilization equipment in the biomedical engineering industry. Its sterilization effect is directly related to the quality and safety of the product. Traditional sterilization effect detection methods often have problems such as long detection cycle, delayed results, and strong subjectivity. It is difficult to meet the needs of modern production for fast and accurate detection of sterilization effects.

[0003] The patent application with reference publication number CN116421761A discloses a sterilization evaluation method for a moist heat sterilizer, which comprises the following steps: S1, multiple detection parts are arranged in a sterilization chamber, each detection part is arranged in a different area of ​​the sterilization chamber, the sterilization effect of the sterilization steam in different areas of the sterilization chamber is detected by the detection parts, the detection results are obtained, and the detection results of the detection parts are transmitted to the control module; S2, the detection results transmitted by each detection part are analyzed by the control module, and whether the detection result with the worst sterilization effect meets the sterilization requirements is analyzed, if the sterilization requirements are met, the sterilization is stopped, otherwise the sterilization steam is continued to be introduced for sterilization, and the sterilization evaluation method of the moist heat sterilizer can optimize the sterilization process, ensure that the sterilization requirements are met, and improve the sterilization efficiency, thereby shortening the actual required sterilization time;

[0004] However, the above-mentioned reference patent evaluates the sterilization situation by cumulatively calculating the equivalent sterilization time. The control module can set the subsequent sterilization time according to the duration of the initial sterilization, thereby shortening the time and improving the sterilization efficiency. However, it cannot comprehensively evaluate the sterilization effect of the steam sterilizer, cannot combine physical parameters and biological indicators for comprehensive evaluation, cannot ensure that the evaluation results are scientific and reliable, and cannot accurately design the objective function, multi-objective optimization to minimize the survival rate and maximize F 0 values, cannot fully consider actual needs and equipment safety constraints, cannot ensure the effectiveness and feasibility of optimal parameters, and cannot guarantee sterile quality and production efficiency.

[0005] Therefore, we propose a sterilization effect detection system for steam sterilizer to address the above problems. Summary of the invention

[0006] The purpose of the present invention is to provide a sterilization effect detection system for steam sterilizers, which solves the problem that the existing technology cannot comprehensively evaluate the sterilization effect of steam sterilizers, cannot combine physical parameters and biological indicators for comprehensive evaluation, cannot ensure that the evaluation results are scientific and reliable, and cannot accurately design the objective function, multi-objective optimization to minimize the survival rate and maximize F 0The value cannot fully consider the actual needs and equipment safety constraints, cannot ensure the effectiveness and feasibility of the optimal parameters, and cannot guarantee the sterile quality and production efficiency.

[0007] The purpose of the present invention is achieved through the following technical solutions:

[0008] A sterilization effect detection system for a steam sterilizer, applied to a sterilization detection platform, comprising:

[0009] A data acquisition preprocessing module is used to collect the operating data of the steam sterilizer during operation and the luminous intensity data of the self-luminous biological indicator after sterilization is completed, and perform preprocessing operations on the collected operating data;

[0010] The sterilization effect evaluation module calculates the F value of the sterilization process based on the collected operation data and luminescence intensity data. 0 value and the survival rate of the self-luminous biological indicator after sterilization, the comprehensive F 0 The sterilization effect of the steam sterilizer is judged by the value and the survival rate of the self-luminous biological indicator;

[0011] Sterilization parameter optimization module, based on the calculated F 0 The values ​​and survival rate of the self-luminous biological indicator are used to optimize the sterilization parameters of the steam sterilizer and generate the best operation plan for the steam sterilizer;

[0012] The safety protection module is used to monitor the key operating parameters of the steam sterilizer in real time. When an abnormality is detected, an alarm is immediately triggered and corresponding measures are taken to deal with it.

[0013] As a preferred embodiment of the present invention, the sterilization effect evaluation module calculates the F of the sterilization process. 0 The specific process of the value is as follows:

[0014] Obtain the pre-processed operating data, which includes operating temperature, operating humidity, operating pressure and operating time. Collect the operating temperature at equal time intervals, which are recorded as △t and the operating temperature as T. i , i = 0, 1, 2, ..., n, represents the operating temperature at each time point, and the operating time is recorded as t i ,t i =i×Δt, representing the moment at each time point;

[0015] Calculate F using the trapezoidal method 0 The trapezoidal method is a numerical integration method that uses the trapezoidal approximation of the integrand to calculate the integral value. The specific calculation steps are as follows:

[0016] S1: Input operating temperature data T i and time interval △t;

[0017] S2: Set reference temperature T ck and temperature coefficient z;

[0018] S3: Calculate the value of the trapezoidal approximation integrand at each time point

[0019] S4: The calculated f(T i ) value, and calculate F using the following formula 0 value:

[0020]

[0021] As a preferred embodiment of the present invention, the specific process of the sterilization effect evaluation module calculating the survival rate of the self-luminous biological indicator after sterilization is completed is as follows:

[0022] Obtain luminous intensity data, which includes the initial luminous intensity I 0 , Real-time luminous intensity I s and background light intensity I b , calculate the corrected real-time luminous intensity I by the following formula xs :I xs =I s -I b ;

[0023] A calibration curve between the luminescence intensity I and the number of viable bacteria N is established. The calibration curve is expressed by the following expression:

[0024] N=f(I)=a×e bI +c, where f is the functional relationship of the calibration curve, and a, b and c are fitting coefficients;

[0025] The survival rate S of the self-luminous biological indicator after sterilization is calculated by the following formula:

[0026] Where N 0 is the number of viable bacteria before sterilization, N s The number of viable bacteria after sterilization.

[0027] As a preferred embodiment of the present invention, the specific process of the sterilization effect evaluation module evaluating the sterilization effect of the steam sterilizer is as follows:

[0028] Get the calculated F 0 The value and the survival rate S of the self-luminous biological indicator are used to calculate the sterilization effect evaluation coefficient MXX. The sterilization effect evaluation coefficient MXX is compared with the preset first sterilization effect evaluation coefficient threshold and the preset second sterilization effect evaluation coefficient threshold. The preset first sterilization effect evaluation coefficient threshold is less than the preset second sterilization effect evaluation coefficient threshold:

[0029] If the sterilization effect evaluation coefficient MXX is less than the preset first sterilization effect evaluation coefficient threshold, it indicates that the sterilization effect of the steam sterilizer is unqualified and there is a risk of bacterial survival;

[0030] If the sterilization effect evaluation coefficient MXX is greater than or equal to the preset first sterilization effect evaluation coefficient threshold, and the sterilization effect evaluation coefficient MXX is less than the preset second sterilization effect evaluation coefficient threshold, it indicates that the sterilization effect of the steam sterilizer is qualified and meets the basic sterilization requirements;

[0031] If the sterilization effect evaluation coefficient MXX is greater than or equal to the preset second sterilization effect evaluation coefficient threshold, it indicates that the sterilization effect of the steam sterilizer is excellent and the expected bacteria killing effect is achieved.

[0032] As a preferred embodiment of the present invention, the specific process of the sterilization parameter optimization module using the optimization algorithm to optimize the sterilization parameters of the steam sterilizer is as follows:

[0033] Get the calculated F 0 Value and survival rate S of self-luminous bioindicator;

[0034] The sterilization parameters are defined as the vector z = [MW, MY, MS] T , where MW is the sterilization temperature, MY is the sterilization pressure, and MS is the sterilization time;

[0035] The optimization algorithm is used to optimize the sterilization parameters. The optimization goal is to minimize the survival rate S of the self-luminous biological indicator and maximize F 0 value, and at the same time satisfy the sterilization constraints, the objective function is:

[0036] f(z)=v1·S(z)+v2·(F 0,target -F 0 (z)), where S(z) is the survival rate of the self-luminous biological indicator calculated by inputting the sterilization parameter z, and F 0 (z) is the F calculated by inputting sterilization parameter z 0 Value, F 0,target For the target F 0 value, v1 and v2 are weight coefficients;

[0037] Constraints:

[0038] As a preferred embodiment of the present invention, the process of the sterilization effect evaluation module solving the objective function and generating the optimal operation plan of the steam sterilizer includes:

[0039] The steps to solve the objective function are as follows:

[0040] T1: Initialization: randomly generate a set of sterilization parameters z;

[0041] T2: Calculate the objective function: calculate f(z) according to the mathematical model;

[0042] T3: Evaluate constraints: Check whether the sterilization parameters meet the constraints;

[0043] T4: Update parameters: Update sterilization parameters using optimization algorithm;

[0044] T5: Iteration: Repeat steps T2-T4 until the convergence condition or the maximum number of iterations is reached;

[0045] By solving the objective function, the optimal sterilization parameter vector z is obtained * =[MW * , MY * , M.S. * ] T , according to the optimal sterilization parameter vector z * Generate optimal operating plans for steam sterilizers.

[0046] As a preferred embodiment of the present invention, the process of the security protection module monitoring key operating parameters in real time and processing them includes:

[0047] Obtain key operating parameters of the steam sterilizer, including operating temperature, operating pressure and energy consumption, generate a monitoring cycle, and divide the monitoring cycle into multiple monitoring periods;

[0048] Obtain the operating temperature change rate of the steam sterilizer in multiple monitoring periods. The operating temperature change rate represents the ratio between the operating temperature change amount and the length of the corresponding time period. This is used to construct a set A of operating temperature change rates, and the average of the difference between the maximum subset and the minimum subset in set A is recorded as the operating temperature change rate difference YWC;

[0049] By using the method of calculating the operating temperature change rate difference YWC, the operating pressure change rate difference YYC and the energy consumption change rate difference NXC can be obtained.

[0050] As a preferred embodiment of the present invention, the process of immediately triggering an alarm and taking corresponding measures to handle the abnormality when the security protection module detects the abnormality includes:

[0051] The operating temperature change rate difference YWC, the operating pressure change rate difference YYC and the energy consumption change rate difference NXC are obtained, and the safety warning coefficient AYX is calculated by the formula. The safety warning coefficient AYX is compared with the preset safety warning coefficient threshold:

[0052] If the safety warning coefficient AYX is less than the preset safety warning coefficient threshold, no signal is generated;

[0053] If the safety warning coefficient AYX is greater than or equal to the preset safety warning coefficient threshold, an alarm signal is generated and sent to the sterilization detection platform;

[0054] When the sterilization detection platform receives an alarm signal, it immediately takes corresponding measures to deal with it.

[0055] Compared with the prior art, the advantages of the present invention are:

[0056] (1) In the present invention, F is calculated by the sterilization effect evaluation module 0 The F value and the survival rate of the self-luminous biological indicator are used to comprehensively evaluate the sterilization effect of the steam sterilizer. The trapezoidal method is used to calculate the F 0 The corrected real-time luminous intensity and the calibration curve fitted by the experimental data are used to ensure data accuracy. A comprehensive evaluation is conducted in combination with physical parameters and biological indicators. The sterilization effect is divided into three levels: unqualified, qualified and excellent through the preset proportional factor coefficient, which is convenient for quick judgment and operation guidance. Based on a strict mathematical model, the evaluation results are ensured to be scientific and reliable.

[0057] (2) In the present invention, the objective function can be accurately designed through the sterilization parameter optimization module, and the multi-objective optimization minimizes the survival rate and maximizes F 0 value, flexibly adjust the weight to balance the effect and energy consumption, use advanced optimization algorithms to automatically find the optimal solution, comprehensively consider the actual needs and equipment safety constraints, ensure the effectiveness and feasibility of the optimal parameters, and efficiently iterate the solution starting from random initial parameters, continuously calculate the objective function, evaluate the constraints and update the parameters until the global optimal solution is found, promote continuous process optimization, and ensure sterile quality and production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 is a system block diagram of the present invention;

[0059] Figure 2 For calculating F in the present invention 0 Step flow chart of the value;

[0060] Figure 3 It is a flow chart of the steps for solving the objective function in the present invention. DETAILED DESCRIPTION

[0061] The following will combine the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all the embodiments. All other embodiments obtained by ordinary technicians in this field without creative work based on the embodiments of the present invention belong to the scope of protection of the present invention.

[0062] Embodiment 1: Figure 1 , Figure 2 and Figure 3 As shown, the present invention proposes a sterilization effect detection system for a steam sterilizer, which is applied to a sterilization detection platform and includes:

[0063] The data acquisition preprocessing module is used to collect the operation data of the steam sterilizer during operation and the luminescence intensity data of the self-luminous biological indicator after the sterilization is completed, and perform preprocessing operations on the collected operation data; the preprocessing operations of the operation data include but are not limited to data cleaning, filtering and normalization; the self-luminous biological indicator is a bacterial spore containing a specific luciferase gene, which has strong heat resistance and can simulate the microorganisms that are most difficult to kill during the sterilization process. The working mechanism is: after the sterilization process is completed, if these spores are still alive, the luciferase inside them will react with the added luciferin to generate a light signal; by detecting the intensity of this light signal, it is possible to indirectly determine whether the spores are alive, and then evaluate the effectiveness of the sterilization process; if the sterilization is thorough, the luciferase in the spores will be inactivated, and no light signal will be generated or the generated light signal will be extremely weak;

[0064] The data acquisition preprocessing module can significantly improve the quality and availability of data, thereby enhancing the reliability and effectiveness of the entire sterilization effect evaluation system. This module supports more accurate sterilization effect evaluation through high-quality data input, promotes the simplification and automation of operating procedures, and provides a scientific basis for parameter optimization and continuous improvement. These advantages work together to ensure that the steam sterilizer can operate in an efficient and safe state, and guarantee the sterile quality and production efficiency of the product.

[0065] The sterilization effect evaluation module calculates the F value of the sterilization process based on the collected operation data and luminescence intensity data. 0 value and the survival rate of the self-luminous biological indicator after sterilization, the comprehensive F 0 The sterilization effect of the steam sterilizer is judged by the value and the survival rate of the self-luminous biological indicator;

[0066] The sterilization effect evaluation module calculates the F 0 The specific process of the value is as follows:

[0067] Obtain the pre-processed operating data, which includes operating temperature, operating humidity, operating pressure and operating time. Collect the operating temperature at equal time intervals, which are recorded as △t and the operating temperature as T. i , i = 0, 1, 2, ..., n, represents the operating temperature at each time point, and the operating time is recorded as t i ,t i =i×Δt, representing the moment at each time point;

[0068] Calculate F using the trapezoidal method 0 The trapezoidal method is a numerical integration method that uses the trapezoidal approximation of the integrand to calculate the integral value. The specific calculation steps are as follows:

[0069] S1: Input operating temperature data T i and time interval △t;

[0070] S2: Set reference temperature T ck (e.g. 121°C) and temperature coefficient z (usually 10°C, to be determined according to the specific situation and the biological indicator used);

[0071] S3: Calculate the value of the trapezoidal approximation integrand at each time point

[0072] S4: The calculated f(T i ) value, and calculate F using the following formula 0 value:

[0073]

[0074] The specific process of the sterilization effect evaluation module calculating the survival rate of the self-luminous biological indicator after sterilization is completed is as follows:

[0075] Obtain luminous intensity data, which includes the initial luminous intensity I 0 , Real-time luminous intensity I s and background light intensity I b , calculate the corrected real-time luminous intensity I by the following formula xs :I xs =I s -I b ;

[0076] A calibration curve between the luminescence intensity I and the number of viable bacteria N is established. The calibration curve is expressed by the following expression:

[0077] N=f(I)=a×e bI +c, where f is the functional relationship of the calibration curve, which can be obtained by fitting experimental data, a, b and c are all fitting coefficients, and the values ​​of the fitting coefficients a, b and c are determined by the nonlinear least squares method. The process of solving the fitting coefficients by the nonlinear least squares method is a common method in the prior art and will not be elaborated on here;

[0078] The survival rate S of the self-luminous biological indicator after sterilization is calculated by the following formula:

[0079] Where N 0 is the number of viable bacteria before sterilization, N sis the number of viable bacteria after sterilization;

[0080] The following is an example of establishing a calibration curve:

[0081] The experimental conditions were set as follows: temperature 37°C, humidity 50%, simulating the laboratory environment;

[0082] Measure the background light intensity I b , I b =10RLU;

[0083] Prepare a series of live bacterial samples with different concentrations:

[0084] Sample A: N 1 =1×10 3 CFU / ml;

[0085] Sample B: N 1 =5×10 3 CFU / ml;

[0086] Sample C: N 1 =1×10 4 CFU / ml;

[0087] Sample D: N 1 =5×10 4 CFU / ml;

[0088] Sample E: N 1 =1×10 5 CFU / ml;

[0089] Measure the real-time luminous intensity I of each sample s , using Formula I xs =I s -I b Calculate the corrected real-time luminous intensity;

[0090] Sample A average I s =110RLU, then I xs =100RLU;

[0091] Sample B average I s =510RLU, then I xs =500RLU;

[0092] Sample C Average I s =1010RLU, then I xs =1000RLU;

[0093] Sample D Average I s =5010RLU, then I xs =5000RLU;

[0094] Sample E Average I s =10010RLU, then I xs =10000RLU;

[0095] Using the data points obtained above (I xs , N) performs nonlinear least squares fitting to find the best values ​​of a, b, and c. After fitting, a=1000, b=0.001, and c=0 are obtained.

[0096] The specific process of the sterilization effect evaluation module to evaluate the sterilization effect of the steam sterilizer is as follows:

[0097] Get the calculated F 0 The sterilization effect evaluation coefficient MXX is calculated by the following formula:

[0098] Wherein, e1 and e2 are both preset proportional factor coefficients, and both e1 and e2 are greater than 0. The sterilization effect evaluation coefficient MXX is compared with the preset first sterilization effect evaluation coefficient threshold and the preset second sterilization effect evaluation coefficient threshold. The preset first sterilization effect evaluation coefficient threshold is less than the preset second sterilization effect evaluation coefficient threshold:

[0099] If the sterilization effect evaluation coefficient MXX is less than the preset first sterilization effect evaluation coefficient threshold, it indicates that the sterilization effect of the steam sterilizer is unqualified and there is a risk of bacterial survival;

[0100] If the sterilization effect evaluation coefficient MXX is greater than or equal to the preset first sterilization effect evaluation coefficient threshold, and the sterilization effect evaluation coefficient MXX is less than the preset second sterilization effect evaluation coefficient threshold, it indicates that the sterilization effect of the steam sterilizer is qualified and meets the basic sterilization requirements;

[0101] If the sterilization effect evaluation coefficient MXX is greater than or equal to the preset second sterilization effect evaluation coefficient threshold, it indicates that the sterilization effect of the steam sterilizer is excellent and the expected bacteria killing effect is achieved;

[0102] The sterilization effect evaluation module accurately calculates F 0 The module uses the trapezoidal method to calculate the F value and the survival rate of the self-luminous biological indicator to comprehensively evaluate the sterilization effect of the steam sterilizer. 0 The module can adapt to different needs by flexibly setting the reference temperature and temperature coefficient; at the same time, the corrected real-time luminous intensity (minus the background light intensity) and the calibration curve based on experimental data fitting are used to ensure the high accuracy of the conversion from luminous intensity to the number of viable bacteria. In addition, the module not only relies on the physical parameter F 0The module not only combines biological indicators (survival rate of self-luminous biological indicators) with values ​​to provide a comprehensive sterilization effect evaluation, and divides the sterilization effect evaluation coefficient into three levels: unqualified, qualified and excellent by setting preset proportional factor coefficients, which is convenient for quickly judging the sterilization effect and guiding subsequent operations; based on strict mathematical models and statistical methods (such as nonlinear least squares method), the module ensures the scientificity and reliability of the evaluation results, and the intuitive presentation of results makes it easy for operators to understand and execute; finally, through historical data analysis and regular verification, it promotes continuous optimization and improvement of production processes, ensures that the system is always in the best state, and guarantees the sterile quality and production efficiency of the products; these advantages work together to make the sterilization effect evaluation module a key component to ensure the efficient and safe operation of the steam sterilizer, and effectively improves the reliability and operability of the sterilization process.

[0103] Sterilization parameter optimization module, based on the calculated F 0 The values ​​and survival rate of the self-luminous biological indicator are used to optimize the sterilization parameters of the steam sterilizer and generate the best operation plan for the steam sterilizer;

[0104] The specific process of the sterilization parameter optimization module using the optimization algorithm to optimize the sterilization parameters of the steam sterilizer is as follows:

[0105] Get the calculated F 0 Value and survival rate S of self-luminous bioindicator;

[0106] The sterilization parameters are defined as the vector z = [MW, MY, MS] T , where MW is the sterilization temperature, MY is the sterilization pressure, and MS is the sterilization time;

[0107] The optimization algorithm is used to optimize the sterilization parameters. The optimization goal is to minimize the survival rate S of the self-luminous biological indicator and maximize F 0 value, and at the same time satisfy the sterilization constraints, the objective function is:

[0108] f(z)=v1·S(z)+v2·(F 0,target -F 0 (z)), where S(z) is the survival rate of the self-luminous biological indicator calculated by inputting the sterilization parameter z, and F 0 (z) is the F calculated by inputting sterilization parameter z 0 Value, F 0,target For the target F 0 Value, v1 and v2 are weight coefficients used to balance F 0 Value and survival rate of self-luminous biological indicator, the weight coefficient needs to be adjusted according to actual needs;

[0109] Constraints:

[0110] The process of solving the objective function and generating the optimal operation plan of the steam sterilizer by the sterilization effect evaluation module includes:

[0111] The steps to solve the objective function are as follows:

[0112] T1: Initialization: randomly generate a set of sterilization parameters z;

[0113] T2: Calculate the objective function: calculate f(z) according to the mathematical model;

[0114] T3: Evaluate constraints: Check whether the sterilization parameters meet the constraints;

[0115] T4: Update parameters: Update sterilization parameters using optimization algorithm;

[0116] T5: Iteration: Repeat steps T2-T4 until the convergence condition or the maximum number of iterations is reached;

[0117] By solving the objective function, the optimal sterilization parameter vector z is obtained * =[MW * , MY * , M.S. * ] T , according to the optimal sterilization parameter vector z * Generate the best operation plan for steam sterilizer, including:

[0118] Adjust the sterilization temperature MW during the current steam sterilizer operation to the optimal sterilization temperature MW * ;

[0119] Adjust the sterilization pressure MY during the current steam sterilizer operation to the optimal sterilization temperature MY * ;

[0120] Adjust the sterilization time MS during the current steam sterilizer operation to the optimal sterilization temperature MS * ;

[0121] The sterilization parameter optimization module calculates F 0 The optimization algorithm is used to optimize the sterilization parameters of the steam sterilizer and generate the best operation plan. Its main advantages include: accurate objective function design, minimizing the survival rate of the self-luminous biological indicator and maximizing F through multi-objective optimization. 0value, and flexibly adjust the weights of different objectives according to actual needs to ensure a balance between sterilization effect and energy consumption; the module uses advanced optimization algorithms to automatically find the optimal solution, reduce manual intervention, and improve the scientificity and accuracy of decision-making; in the optimization process, full consideration is given to the actual needs and equipment safety constraints to ensure that the generated optimal parameters are both effective and feasible; the efficient iterative solution process starts with a set of randomly generated initial parameters, and continuously calculates the objective function, evaluates the constraints and updates the parameters until the global optimal solution is found to ensure efficient convergence; based on the optimal parameter vector, a specific optimal operation plan is generated, including adjusting the sterilization temperature, pressure and time, which is intuitive and easy to operate, and easy for operators to understand and execute; in addition, the module supports historical data analysis and regular verification, promotes continuous optimization and improvement of production processes, ensures that the system is always in the best state, and guarantees the sterile quality and production efficiency of the product; these advantages make this module a key component to ensure the efficient and safe operation of the steam sterilizer, and significantly improve the reliability and production efficiency of the sterilization process.

[0122] Embodiment 2: The technical solution of the embodiment of the present invention is different from that of embodiment 1 in that:

[0123] like Figure 1 As shown, the safety protection module is used to monitor the key operating parameters of the steam sterilizer in real time, and immediately trigger an alarm and take corresponding measures to deal with it when an abnormality is detected;

[0124] The process of real-time monitoring and processing of key operating parameters by the security protection module includes:

[0125] Obtain key operating parameters of the steam sterilizer, including operating temperature, operating pressure and energy consumption, generate a monitoring cycle, and divide the monitoring cycle into multiple monitoring periods;

[0126] Obtain the operating temperature change rate of the steam sterilizer in multiple monitoring periods. The operating temperature change rate represents the ratio between the operating temperature change amount and the length of the corresponding time period. This is used to construct a set A of operating temperature change rates, and the average of the difference between the maximum subset and the minimum subset in set A is recorded as the operating temperature change rate difference YWC;

[0127] Using the method of calculating the operating temperature change rate difference YWC, the operating pressure change rate difference YYC and the energy consumption change rate difference NXC can be obtained in the same way;

[0128] When the safety protection module detects an abnormality, it immediately triggers an alarm and takes corresponding measures to deal with the process, including:

[0129] The operating temperature change rate difference YWC, operating pressure change rate difference YYC and energy consumption change rate difference NXC are obtained, and the safety warning coefficient AYX is calculated by the formula:

[0130] AYX=h1*YMC+h2*YYC+h3*NXC;

[0131] Among them, h1, h2 and h3 are weight coefficients. The size of the weight coefficient is determined according to historical data. The safety warning coefficient AYX is compared with the preset safety warning coefficient threshold:

[0132] If the safety warning coefficient AYX is less than the preset safety warning coefficient threshold, no signal is generated;

[0133] If the safety warning coefficient AYX is greater than or equal to the preset safety warning coefficient threshold, an alarm signal is generated and sent to the sterilization detection platform;

[0134] When the sterilization detection platform receives an alarm signal, it immediately takes corresponding measures to deal with it. The specific contents of the treatment measures are as follows:

[0135] Automatically adjust the heating or cooling system when the temperature deviates slightly, and shut down the system immediately when the temperature deviates seriously;

[0136] Automatic exhaust at high pressure, steam replenishment at low pressure, and forced shutdown of equipment at dangerous pressure levels;

[0137] Record energy consumption data for subsequent analysis, identify potential problems, adjust operating parameters to improve energy efficiency, and arrange for technicians to conduct detailed inspections to check whether there are hardware failures or areas that need repair;

[0138] The safety protection module has the advantages of real-time monitoring, multi-parameter comprehensive evaluation, adjustable weight coefficient, hierarchical processing, data recording and analysis, active early warning and perfect alarm mechanism; it monitors the key operating parameters of the steam sterilizer (temperature, pressure, energy consumption) in real time, and comprehensively considers their change rate, and calculates the safety warning coefficient in combination with the adjustable weight coefficient to achieve timely early warning and hierarchical processing of abnormal situations; the system can also record energy consumption data for subsequent analysis, improve energy efficiency and assist in fault diagnosis, and ultimately improve the safety and reliability of the sterilization process.

[0139] The above are only preferred specific implementation modes of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical solutions and improved concepts of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A sterilization effect detection system for a steam sterilizer, applied to a sterilization detection platform, characterized in that: include: A data acquisition preprocessing module is used to collect the operating data of the steam sterilizer during operation and the luminous intensity data of the self-luminous biological indicator after sterilization is completed, and perform preprocessing operations on the collected operating data; The sterilization effect evaluation module calculates the F0 value of the sterilization process and the survival rate of the self-luminous biological indicator after sterilization based on the collected operation data and luminous intensity data, and comprehensively evaluates the sterilization effect of the steam sterilizer based on the F0 value and the survival rate of the self-luminous biological indicator; The sterilization parameter optimization module uses an optimization algorithm to optimize the sterilization parameters of the steam sterilizer based on the calculated F0 value and the survival rate of the self-luminous biological indicator, and generates the best operation plan for the steam sterilizer; The safety protection module is used to monitor the key operating parameters of the steam sterilizer in real time. When an abnormality is detected, an alarm is immediately triggered and corresponding measures are taken to deal with it.

2. A sterilization effect detection system for a steam sterilizer according to claim 1, characterized in that: The specific process of the sterilization effect evaluation module calculating the F0 value of the sterilization process is as follows: Obtain the pre-processed operating data, which includes operating temperature, operating humidity, operating pressure and operating time. Collect the operating temperature at equal time intervals, which are recorded as △t and the operating temperature as T. i , i = 0, 1, 2, ..., n, represents the operating temperature at each time point, and the operating time is recorded as t i ,t i =i×Δt, representing the moment at each time point; The F0 value is calculated using the trapezoidal method. The trapezoidal method is a numerical integration method that uses trapezoidal approximation to calculate the integral value. The specific calculation steps are as follows: S1: Input operating temperature data T i and time interval △t; S2: Set reference temperature T ck and temperature coefficient z; S3: Calculate the value of the trapezoidal approximation integrand at each time point S4: The calculated f(T i ) value is substituted into the formula below to calculate the F0 value:

3. A sterilization effect detection system for a steam sterilizer according to claim 2, characterized in that: The specific process of the sterilization effect evaluation module calculating the survival rate of the self-luminous biological indicator after sterilization is completed is as follows: Obtain luminous intensity data, which includes initial luminous intensity I0, real-time luminous intensity I s and background light intensity I b , calculate the corrected real-time luminous intensity I by the following formula xs :I xs =I s -I b ; A calibration curve between the luminescence intensity I and the number of viable bacteria N is established. The calibration curve is expressed by the following expression: N=f(I)=a×e bI +c, where f is the functional relationship of the calibration curve, and a, b and c are fitting coefficients; The survival rate S of the self-luminous biological indicator after sterilization is calculated by the following formula: Where N0 is the number of viable bacteria before sterilization, N s The number of viable bacteria after sterilization.

4. A sterilization effect detection system for a steam sterilizer according to claim 3, characterized in that: The specific process of the sterilization effect evaluation module evaluating the sterilization effect of the steam sterilizer is as follows: The calculated F0 value and the survival rate S of the self-luminous biological indicator are obtained, and the sterilization effect evaluation coefficient MXX is calculated by the formula. The sterilization effect evaluation coefficient MXX is compared with the preset first sterilization effect evaluation coefficient threshold and the preset second sterilization effect evaluation coefficient threshold. The preset first sterilization effect evaluation coefficient threshold is less than the preset second sterilization effect evaluation coefficient threshold: If the sterilization effect evaluation coefficient MXX is less than the preset first sterilization effect evaluation coefficient threshold, it indicates that the sterilization effect of the steam sterilizer is unqualified and there is a risk of bacterial survival; If the sterilization effect evaluation coefficient MXX is greater than or equal to the preset first sterilization effect evaluation coefficient threshold, and the sterilization effect evaluation coefficient MXX is less than the preset second sterilization effect evaluation coefficient threshold, it indicates that the sterilization effect of the steam sterilizer is qualified and meets the basic sterilization requirements; If the sterilization effect evaluation coefficient MXX is greater than or equal to the preset second sterilization effect evaluation coefficient threshold, it indicates that the sterilization effect of the steam sterilizer is excellent and the expected bacteria killing effect is achieved.

5. A sterilization effect detection system for a steam sterilizer according to claim 1, characterized in that: The specific process of the sterilization parameter optimization module using the optimization algorithm to optimize the sterilization parameters of the steam sterilizer is as follows: Obtain the calculated F0 value and the survival rate S of the self-luminous biological indicator; The sterilization parameters are defined as the vector z = [MW, MY, MS] T , where MW is the sterilization temperature, MY is the sterilization pressure, and MS is the sterilization time; The optimization algorithm is used to optimize the sterilization parameters. The optimization goal is to minimize the survival rate S of the self-luminous biological indicator and maximize the F0 value while satisfying the sterilization constraints. The objective function is: f(z)=v1·S(z)+v2·(F 0,target -F0(z)), where S(z) is the survival rate of the self-luminous biological indicator calculated by inputting the sterilization parameter z, F0(z) is the F0 value calculated by inputting the sterilization parameter z, and F 0,target is the target F0 value, v1 and v2 are weight coefficients; Constraints:

6. A sterilization effect detection system for a steam sterilizer according to claim 5, characterized in that: The process of the sterilization effect evaluation module solving the objective function and generating the optimal operation plan of the steam sterilizer includes: The steps to solve the objective function are as follows: T1: Initialization: randomly generate a set of sterilization parameters z; T2: Calculate the objective function: calculate f(z) according to the mathematical model; T3: Evaluate constraints: Check whether the sterilization parameters meet the constraints; T4: Update parameters: Update sterilization parameters using optimization algorithm; T5: Iteration: Repeat steps T2-T4 until the convergence condition or the maximum number of iterations is reached; By solving the objective function, the optimal sterilization parameter vector z is obtained * =[MW * , MY * , M.S. * ] T , according to the optimal sterilization parameter vector z * Generate optimal operating plans for steam sterilizers.

7. A sterilization effect detection system for a steam sterilizer according to claim 1, characterized in that: The process of real-time monitoring of key operating parameters by the security protection module and processing thereof includes: Obtain key operating parameters of the steam sterilizer, including operating temperature, operating pressure and energy consumption, generate a monitoring cycle, and divide the monitoring cycle into multiple monitoring periods; Obtain the operating temperature change rate of the steam sterilizer in multiple monitoring periods. The operating temperature change rate represents the ratio between the operating temperature change amount and the length of the corresponding time period. This is used to construct a set A of operating temperature change rates, and the average of the difference between the maximum subset and the minimum subset in set A is recorded as the operating temperature change rate difference YWC; By using the method of calculating the operating temperature change rate difference YWC, the operating pressure change rate difference YYC and the energy consumption change rate difference NXC can be obtained.

8. A sterilization effect detection system for a steam sterilizer according to claim 7, characterized in that: The process of immediately triggering an alarm and taking corresponding measures to handle the abnormality when the security protection module detects an abnormality includes: The operating temperature change rate difference YWC, the operating pressure change rate difference YYC and the energy consumption change rate difference NXC are obtained, and the safety warning coefficient AYX is calculated by the formula. The safety warning coefficient AYX is compared with the preset safety warning coefficient threshold: If the safety warning coefficient AYX is less than the preset safety warning coefficient threshold, no signal is generated; If the safety warning coefficient AYX is greater than or equal to the preset safety warning coefficient threshold, an alarm signal is generated and sent to the sterilization detection platform; When the sterilization detection platform receives an alarm signal, it immediately takes corresponding measures to deal with it.

Images