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] A 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 products. Traditional sterilization effect detection methods often have problems such as long detection cycles, lagging results, and strong subjectivity, making it difficult to meet the requirements of modern production for rapid and accurate detection of sterilization effects.

[0003] The patent application with the publication number CN116421761A discloses a sterilization evaluation method for a moist heat sterilizer. This sterilization evaluation method for a moist heat sterilizer includes the following steps: S1. Set multiple detection components in the sterilization chamber, and each detection component is set in different areas of the sterilization chamber. Detect the sterilization effect of the sterilization steam in different areas of the sterilization chamber through the detection components to obtain detection results, and transmit the detection results of the detection components to the control module; S2. Analyze the detection results transmitted by each detection component through the control module, and analyze whether the detection result with the worst sterilization effect meets the sterilization requirements. If it meets the sterilization requirements, stop sterilization; otherwise, continue to introduce sterilization steam for sterilization. This sterilization evaluation method for a moist heat sterilizer can optimize the sterilization process, ensure that while meeting the sterilization requirements, improve the sterilization efficiency, thereby shortening the actual required sterilization time;

[0004] However, the above reference patent evaluates the sterilization situation by cumulatively calculating the equivalent sterilization duration. The control module can set the subsequent sterilization duration according to the duration of the initial sterilization, thereby shortening the time and improving the sterilization efficiency, but it cannot comprehensively evaluate the sterilization effect of the steam sterilizer, cannot conduct a comprehensive evaluation by combining physical parameters and biological indicators, cannot ensure the scientific reliability of the judgment result, and at the same time cannot accurately design the objective function, multi-objective optimization to minimize the survival rate and maximize the F 0 value, cannot comprehensively consider the actual requirements and equipment safety constraints, cannot ensure the effectiveness and feasibility of the optimal parameters, and cannot guarantee the aseptic quality and production efficiency.

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

[0006] The purpose of the present invention is to provide a sterilization effect detection system for a steam sterilizer, which solves the problems that the prior art cannot comprehensively evaluate the sterilization effect of the steam sterilizer, cannot conduct a comprehensive evaluation by combining physical parameters and biological indicators, cannot ensure the scientific reliability of the judgment result, and at the same time cannot accurately design the objective function, multi-objective optimization to minimize the survival rate and maximize the F 0The value cannot comprehensively consider the actual requirements and equipment safety constraints, and cannot ensure the effectiveness and feasibility of the optimal parameters, nor can it guarantee the aseptic quality and production efficiency.

[0007] The object of the present invention is achieved by the following technical solutions:

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

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

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

[0011] A sterilization parameter optimization module, based on the calculated F 0 value and the survival rate of the self-luminous biological indicator, uses an optimization algorithm to optimize the sterilization parameters of the steam sterilizer and generate the best operation plan for the steam sterilizer;

[0012] A safety protection module, which is used to monitor the key operation parameters of the steam sterilizer in real time, and immediately trigger an alarm and take corresponding measures for processing when an abnormality is detected.

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

[0014] Obtain the preprocessed operation data. The operation data includes operation temperature, operation humidity, operation pressure and operation time. The operation temperature is collected at equal time intervals. The equal time interval is denoted as △t, and the operation temperature is denoted as T i , i = 0, 1, 2,..., n, representing the operation temperature at each time point, and the operation time is denoted as t i , t i = i×Δt, representing the moment at each time point;

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

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

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

[0018] S3: Calculate the trapezoidal approximation integral function value at each time point

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

[0020]

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

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

[0023] Establish a calibration curve between the luminescence intensity I and the viable cell count N, and 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 all fitting coefficients;

[0025] Calculate the survival rate S of the self-luminous biological indicator after sterilization through the following formula:

[0026] where N 0 is the viable cell count before sterilization, and N s is the viable cell count after sterilization.

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

[0028] Obtain the calculated F 0 value and the survival rate S of the self-luminous biological indicator, calculate the sterilization effect evaluation coefficient MXX through the formula, and compare the sterilization effect evaluation coefficient MXX 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 achieves the expected bacterial killing effect.

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

[0033] Obtain the calculated F 0 value and the survival rate S of the self-luminescent biological indicator;

[0034] Define the sterilization parameters 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] Use an optimization algorithm to optimize the sterilization parameters. The optimization goal is to minimize the survival rate S of the self-luminescent biological indicator and maximize the F 0 value, while satisfying the sterilization constraint conditions. 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-luminescent biological indicator calculated from the input sterilization parameter z, and F 0 (z) is the F 0 value calculated from the input sterilization parameter z, F 0,target is the target F 0 value, and both v1 and v2 are weight coefficients;

[0037] Constraint conditions:

[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 solution steps of 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 the constraint conditions: Check whether the sterilization parameters meet the constraint conditions;

[0043] T4: Update the parameters: Use the optimization algorithm to update the sterilization parameters;

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

[0045] The optimal sterilization parameter vector z is obtained by solving the objective function * =[MW * , MY * , MS * T , and according to the optimal sterilization parameter vector z * Generate the best operation plan for the steam sterilizer.

[0046] As a preferred embodiment of the present invention, the process of the safety protection module for real - time monitoring and processing of key operation parameters includes:

[0047] Obtain the key operation parameters of the steam sterilizer. The key operation parameters include the operating temperature, operating pressure, and energy consumption. Generate a monitoring period and divide the monitoring period into multiple monitoring time slots;

[0048] Obtain the operating temperature change rate of the steam sterilizer within multiple monitoring time slots. The operating temperature change rate represents the ratio of the operating temperature change amount to the duration of the corresponding time period. Construct a set A of the operating temperature change rate, and record the mean value of the difference between the largest subset and the smallest subset in the set A as the operating temperature change rate difference YWC;

[0049] Using the same method to calculate 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 the safety protection module immediately triggering an alarm and taking corresponding measures for processing when an abnormality is detected includes:

[0051] Obtain the operating temperature change rate difference YWC, the operating pressure change rate difference YYC, and the energy consumption change rate difference NXC. Calculate the safety warning coefficient AYX through a formula, and compare the safety warning coefficient AYX 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 the alarm signal, corresponding measures are immediately taken for processing.

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

[0056] (1) In the present invention, the sterilization effect evaluation module calculates the F 0 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 used to calculate the F 0 value, and the calibrated curve fitted with the corrected real-time luminescence intensity and experimental data is used to ensure data accuracy. The sterilization effect is comprehensively evaluated by combining physical parameters and biological indicators. Through the preset proportional factor coefficient, the sterilization effect is divided into three grades: unqualified, qualified, and excellent, which is convenient for quick judgment and operation guidance. Based on a strict mathematical model, the judgment result is ensured to be scientific and reliable;

[0057] (2) In the present invention, the sterilization parameter optimization module can accurately design the objective function, multi-objective optimization minimizes the survival rate and maximizes the F 0 value, flexibly adjusts the weight to balance the effect and energy consumption, uses an advanced optimization algorithm to automatically find the optimal solution, comprehensively considers the actual requirements and equipment safety constraints, ensures the effectiveness and feasibility of the optimal parameters, and the efficient iterative solution starts from random initial parameters, continuously calculates the objective function, evaluates the constraints, and updates the parameters until the global optimal solution is found, promoting the continuous optimization of the process and ensuring the aseptic quality and production efficiency. Description of the Drawings

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

[0059] Figure 2 is the step flow chart for calculating the F 0 value in the present invention;

[0060] Figure 3 is the step flow chart for solving the objective function in the present invention. Detailed Embodiments

[0061] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention; obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0062] Example 1: As shown in Figure 1 , Figure 2 and Figure 3 , a sterilization effect detection system for a steam sterilizer proposed by the present invention is applied to a sterilization detection platform, including:

[0063] A data acquisition and preprocessing module is used to collect the operation data during the operation of the steam sterilizer and the luminescence intensity data of the self-luminous biological indicator after sterilization, 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, and these spores have strong heat resistance and can simulate the microorganisms that are most difficult to be killed during sterilization. The working mechanism is as follows: after the sterilization process ends, if these spores still survive, 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 survive, 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 and preprocessing module can significantly improve the quality and usability of the data, thereby enhancing the reliability and effectiveness of the entire sterilization effect evaluation system. This module supports more accurate evaluation of the sterilization effect through high-quality data input, promotes the simplification and automation of the operation process, 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, guaranteeing the aseptic quality and production efficiency of the product.

[0065] A sterilization effect judgment module calculates the F 0 value of the sterilization process and the survival rate of the self-luminous biological indicator after sterilization, and comprehensively judges the sterilization effect of the steam sterilizer based on the F 0 value and the survival rate of the self-luminous biological indicator;

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

[0067] Obtain the preprocessed operation data. The operation data includes operation temperature, operation humidity, operation pressure, and operation time. The operation temperature is collected at equal time intervals, and the equal time interval is denoted as Δt. The operation temperature is denoted as T i , i = 0, 1, 2,..., n, representing the operation temperature at each time point. The operation time is denoted as t i , t i = i×Δt, representing the moment at each time point;

[0068] Calculating F using the trapezoidal method 0 value. The trapezoidal method is a numerical integration method that calculates the integral value by approximating the integrand with trapezoids. The specific calculation steps are as follows:

[0069] S1: Input the operating temperature data T i and the time interval Δt;

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

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

[0072] S4: Substitute the calculated f(T i ) value into the following formula to calculate the F 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 as follows:

[0075] Obtain the luminescence intensity data, which includes the initial luminescence intensity I 0 , the real-time luminescence intensity I s and the background light intensity I b . Calculate the corrected real-time luminescence intensity I xs through the following formula: I xs = I s - I b ;

[0076] Establish a calibration curve between the luminescence intensity I and the viable cell count N. 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. 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 means in the prior art and will not be elaborated here;

[0078] Calculate the survival rate S of the self-luminous biological indicator after sterilization through the following formula:

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

[0080] The following gives a demonstration example of establishing a calibration curve:

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

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

[0083] Prepare a series of viable bacteria 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 luminescence intensity I of each sample s , and use the formula I xs = I s - I b to calculate the corrected real-time luminescence intensity;

[0090] Average I of sample A s = 110 RLU, then I xs = 100 RLU;

[0091] Average I of sample B s = 510 RLU, then I xs = 500 RLU;

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

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

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

[0095] Using the obtained data points (I xs , N) to perform non - linear least - squares fitting to find the best values of a, b, and c. After fitting, a = 1000, b = 0.001, c = 0.

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

[0097] Obtain the calculated F 0 value and the survival rate S of the self - luminous biological indicator, and calculate the sterilization effect evaluation coefficient MXX through the following formula:

[0098] where both e1 and e2 are preset proportional factor coefficients, and both e1 and e2 are greater than 0. Compare the sterilization effect evaluation coefficient MXX 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 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 achieves the expected bacterial killing effect;

[0102] The sterilization effect evaluation module comprehensively evaluates the sterilization effect of the steam sterilizer by accurately calculating the F 0 value and the survival rate of the self - luminous biological indicator, and has significant advantages. This module accurately calculates the F 0 value using the trapezoidal method numerical integration, and adapts to different requirements by flexibly setting the reference temperature and temperature coefficient; at the same time, using the corrected real - time luminescence intensity (subtracting the background light intensity) and the calibration curve fitted based on experimental data to ensure high accuracy in the conversion from luminescence intensity to the number of viable bacteria. In addition, the module not only depends on the physical parameter F 0The value, combined with biological indicators (the survival rate of self-luminescent biological indicators), provides a comprehensive evaluation of the sterilization effect. By setting a preset proportional factor coefficient, the sterilization effect judgment coefficient is divided into three levels: unqualified, qualified, and excellent, which facilitates a quick judgment of the sterilization effect and guides subsequent operations. Based on strict mathematical models and statistical methods (such as non-linear least squares method), this module ensures the scientificity and reliability of the judgment results. At the same time, the intuitive result presentation makes it easy for operators to understand and execute. Finally, through historical data analysis and regular verification, it promotes the continuous optimization and improvement of the production process, ensures that the system is always in the best state, and guarantees the aseptic quality and production efficiency of the products. These advantages work together to make the sterilization effect judgment module a key component to ensure the efficient and safe operation of the steam sterilizer, effectively improving the reliability and operability of the sterilization process.

[0103] The sterilization parameter optimization module, based on the calculated F 0 value and the survival rate of self-luminescent biological indicators, uses an optimization algorithm 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] Obtain the calculated F 0 value and the survival rate S of the self-luminescent biological indicator;

[0106] Define the sterilization parameters 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] Use the optimization algorithm to optimize the sterilization parameters. The optimization objective is to minimize the survival rate S of the self-luminescent biological indicator and maximize the F 0 value, while satisfying the sterilization constraint conditions. 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-luminescent biological indicator calculated from the input sterilization parameter z, F 0 (z) is the F 0 value calculated from the input sterilization parameter z, F 0,target is the target F 0 value, and v1 and v2 are both weight coefficients used to balance the F 0 value and the survival rate of the self-luminescent biological indicator. The weight coefficients need to be adjusted according to actual requirements;

[0109] Constraint conditions:

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

[0111] The solution steps of 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 the constraint conditions: Check whether the sterilization parameters meet the constraint conditions;

[0115] T4: Update the parameters: Use the optimization algorithm to update the sterilization parameters;

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

[0117] The optimal sterilization parameter vector z * = [MW * , MY * , MS * T , and according to the optimal sterilization parameter vector z * generate the optimal operation plan for the steam sterilizer. The content of the plan includes:

[0118] Adjust the sterilization temperature MW in the current working process of the steam sterilizer to the optimal sterilization temperature MW * ;

[0119] Adjust the sterilization pressure MY in the current working process of the steam sterilizer to the optimal sterilization temperature MY * ;

[0120] Adjust the sterilization time MS in the current working process of the steam sterilizer to the optimal sterilization temperature MS * ;

[0121] The sterilization parameter optimization module optimizes the sterilization parameters of the steam sterilizer by calculating the F 0 value and the survival rate of the self - luminous biological indicator, and uses the optimization algorithm to generate the optimal operation plan; its main advantages include: precise objective function design, simultaneously minimizing the survival rate of the self - luminous biological indicator and maximizing F 0 ​Values, and flexibly adjust the weights of different objectives according to actual needs to ensure the balance between sterilization effect and energy consumption; this module uses advanced optimization algorithms to automatically find the optimal solution, reduce manual intervention, and improve the scientificity and accuracy of decision-making; during the optimization process, fully consider the constraints of actual needs and equipment safety to ensure that the generated optimal parameters are both effective and feasible; the efficient iterative solution process starts from a randomly generated set of initial parameters, and by continuously calculating the objective function, evaluating the constraints, and updating the parameters until the global optimal solution is found, ensuring efficient convergence; based on the optimal parameter vector, generate a specific best operation plan, including adjusting the sterilization temperature, pressure, and time, which is intuitive and easy to operate, facilitating operators to understand and execute; in addition, this module supports historical data analysis and regular verification, promotes the continuous optimization and improvement of the production process, ensures that the system is always in the best state, and guarantees the aseptic quality and production efficiency of products; these advantages make this module a key component to ensure the efficient and safe operation of steam sterilizers, significantly improving the reliability and production efficiency of the sterilization process.

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

[0123] As Figure 1 shown, a 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 for processing when an abnormality is detected;

[0124] The process of the safety protection module monitoring the key operating parameters in real time and processing them includes:

[0125] Obtain the key operating parameters of the steam sterilizer. The key operating parameters include operating temperature, operating pressure, and energy consumption. Generate a monitoring period and divide the monitoring period into multiple monitoring time periods;

[0126] Obtain the operating temperature change rate of the steam sterilizer during multiple monitoring time periods. The operating temperature change rate represents the ratio of the operating temperature change amount to the corresponding time period duration. Based on this, construct a set A of the operating temperature change rate, and record the mean value of the difference between the largest subset and the smallest subset in set A 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] The process of the safety protection module immediately triggering an alarm and taking corresponding measures for processing when an abnormality is detected includes:

[0129] Obtain the operating temperature change rate difference YWC, the operating pressure change rate difference YYC, and the energy consumption change rate difference NXC, and calculate the safety warning coefficient AYX through the formula:

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

[0131] Where h1, h2, and h3 are all weight coefficients, and the magnitudes of the weight coefficients are determined according to historical data. The safety warning coefficient AYX is compared with a 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 the alarm signal, corresponding measures are immediately taken for processing. The specific content of the processing measures is as follows:

[0135] Automatically adjust the heating or cooling system when the temperature has a slight deviation, and perform an emergency shutdown when the temperature has a serious deviation;

[0136] Automatically exhaust air when the pressure is high, supplement steam when the pressure is low, and forcibly shut down the equipment at a dangerous pressure level;

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

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

[0139] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution of the present invention and its improved concept, makes equivalent substitutions or changes, and 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.

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