A method and system for real-time evaluation of sterilization intensity of a sterilization kettle

Abstract

This invention relates to the field of autoclave sterilization technology, specifically disclosing a method and system for real-time evaluation of sterilization intensity in an autoclave. The method includes: simultaneously collecting the steam condensate flow rate and non-condensable gas partial pressure within the autoclave to generate dynamic thermodynamic characteristic pairs; and outputting the heat flux density in real time through thermodynamic state inversion. Simultaneously, the instantaneous true temperature at the center of the object to be sterilized is acquired and time-domain aligned with the heat flux density to calculate the lag time constant. Combining the lag time constant, heat flux density, and material thermal diffusivity, the temperature gradient distribution field is iteratively corrected until convergence. For the cold spot region of the object to be sterilized, lethal equivalent displacement calculation is performed in real time, and the sterilization intensity value is accumulated hourly. The accumulated intensity is compared with a preset target threshold to dynamically determine the sterilization endpoint state, achieving precise control. This invention can improve the accuracy of real-time evaluation of autoclave sterilization intensity.

Description

Technical Field

[0001] This invention relates to the field of sterilization technology, and in particular to a method and system for real-time evaluation of sterilization intensity in a sterilization autoclave. Background Technology

[0002] Currently, it is difficult to simultaneously collect steam condensate flow rate and non-condensable gas partial pressure and construct dynamic thermodynamic characteristics during the sterilization process of autoclave. It is impossible to obtain accurate real-time heat flux density through thermodynamic state inversion. The temperature field calculation does not combine the material heat transfer hysteresis characteristics for iterative correction. The sterilization intensity assessment relies on single-point temperature rather than real-time lethal equivalent calculation of cold point area. The assessment accuracy is low and the sterilization endpoint cannot be dynamically determined.

[0003] For example, relying solely on the surface temperature of the vessel or the temperature of a single point in the material to estimate the sterilization intensity, while ignoring the thermal resistance caused by non-condensable gases and the lag in the internal temperature gradient of the material, can lead to inaccurate calculations of the sterilization intensity in cold spots, resulting in either insufficient or excessive sterilization.

[0004] Therefore, existing technologies can only rely on empirical parameters and single-point monitoring to estimate sterilization intensity, which cannot achieve real-time, accurate and stable assessment and control, and cannot meet the high-precision operation requirements of the sterilization process. There is an urgent need for a technical solution that can simultaneously collect thermal characteristics, invert heat flux, iteratively correct the temperature field, complete the real-time accumulation of cold point lethal equivalent and dynamically determine the sterilization endpoint, so as to make up for the deficiencies of existing technologies. Summary of the Invention

[0005] This invention provides a method and system for real-time evaluation of the sterilization intensity of a sterilization autoclave, in order to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides a method for real-time evaluation of the sterilization intensity of a sterilization autoclave, comprising:

[0007] S1: Simultaneously acquire steam condensate flow rate data and non-condensable gas partial pressure data in the inner cavity of the sterilizer to generate dynamic thermodynamic characteristic pairs of the sterilizer;

[0008] S2: Perform thermodynamic state inversion on the dynamic thermodynamic characteristics and output the real-time heat flux density value of the sterilization vessel;

[0009] S3: Obtain the instantaneous true temperature value of the sterilized object at the center of the sterilization vessel, and align the instantaneous true temperature value with the real-time heat flux density value in the time domain to obtain the lag time constant of the aligned instantaneous true temperature value relative to the aligned real-time heat flux density value.

[0010] S4: Combining the hysteresis time constant, the real-time heat flux density value, and the thermal diffusivity of the sterilized substance, the temperature gradient distribution field of the sterilized substance is iteratively corrected using the obtained results;

[0011] S5: By correcting the gradient field, the cold point region of the sterilized object is replaced with lethal equivalent in real time to obtain the cumulative sterilization intensity value of the sterilization vessel.

[0012] S6: Compare the cumulative sterilization intensity value with the preset target sterilization intensity threshold to determine the sterilization endpoint state of the sterilization autoclave.

[0013] Preferably, the step of simultaneously acquiring steam condensate flow rate data and non-condensable gas partial pressure data within the inner cavity of the sterilizer to generate a dynamic thermodynamic characteristic pair of the sterilizer includes:

[0014] The analog flow signal output by the flow sensing element deployed in the steam condensate discharge pipeline in the inner cavity of the sterilizer is collected, and the analog flow signal is converted from analog to digital to generate the steam condensate flow data of the sterilizer.

[0015] The voltage signal corresponding to the non-condensable gas is synchronously acquired from the gas partial pressure sensing element deployed in the gas phase region of the sterilizer, and the voltage signal is conditioned and quantized to obtain the non-condensable gas partial pressure data of the sterilizer.

[0016] Using the acquisition time points of the steam condensate flow rate data and the non-condensable gas partial pressure data as the correlation benchmark, the steam condensate flow rate data and the non-condensable gas partial pressure data are paired and combined to obtain the dynamic thermodynamic characteristic pair of the sterilization vessel.

[0017] Preferably, the step of performing thermodynamic state inversion on the dynamic thermodynamic characteristics and outputting the real-time heat flux density value of the sterilization vessel includes:

[0018] The dynamic thermodynamic feature pairs are analyzed to separate the condensate flow rate value corresponding to the steam condensate flow rate data and the gas partial pressure value corresponding to the non-condensable gas partial pressure data.

[0019] The condensate flow rate value is input into a pre-generated condensation heat release mapping relationship, and the steam condensation heat release power base value corresponding to the condensate flow rate value is output through the condensation heat release mapping relationship.

[0020] The gas partial pressure value is input into a pre-generated thermal resistance correction mapping relationship, and the thermal resistance correction coefficient of the non-condensable gas corresponding to the gas partial pressure value is output through the thermal resistance correction mapping relationship.

[0021] Using the non-condensable gas thermal resistance correction coefficient, the base value of the steam condensation heat release power is subjected to thermal resistance compensation processing to generate the effective condensation heat release power value of the sterilization vessel.

[0022] Based on the internal geometric characteristic parameters of the sterilization vessel, the effective condensation heat release power value is converted into the heat flow rate per unit area of ​​the sterilization vessel;

[0023] The heat flux per unit area is checked for temporal consistency, and abnormal spikes caused by fluctuations in instantaneous condensate discharge are filtered out. The real-time heat flux density value of the sterilization vessel is then output.

[0024] Preferably, the step of obtaining the instantaneous true temperature value of the substance to be sterilized at the center of the sterilization vessel, and aligning the instantaneous true temperature value with the real-time heat flux density value in the time domain to obtain the lag time constant of the aligned instantaneous true temperature value relative to the aligned real-time heat flux density value includes:

[0025] The resistance value output by the temperature sensing element buried at the geometric center of the object to be sterilized is collected, and the resistance value is converted into the instantaneous true temperature value of the object to be sterilized.

[0026] Simultaneously extract the first time series corresponding to the real-time heat flux density value and the second time series corresponding to the instantaneous real temperature value;

[0027] Using the real-time heat flux density value as a reference signal, a temperature response segment with the highest waveform similarity to the reference signal is searched on the second time series to determine the time offset of the temperature response segment relative to the reference signal.

[0028] Based on the time offset, the second time series is shifted as a whole so that the instantaneous true temperature value and the real-time heat flux density value correspond point by point on the time axis after the shift, thereby obtaining the aligned instantaneous true temperature value and aligned real-time heat flux density value of the sterilization vessel.

[0029] The delay time between the real-time heat flux density value after alignment and the instantaneous true temperature value after alignment is measured to generate the delay time sequence of the sterilization vessel.

[0030] The median value of the delay sequence is extracted by statistical median filtering and used as the hysteresis time constant of the sterilization vessel.

[0031] Preferably, the step of using the real-time heat flux density value as a reference signal to search for the temperature response segment with the highest waveform similarity to the reference signal in the second time series, and determining the time offset of the temperature response segment relative to the reference signal, includes:

[0032] Set the window width of the sliding time window, and set the window width to be equal to the time length of the reference signal;

[0033] By using the window width, candidate temperature response segments of the second time series are extracted to generate a candidate segment set for the sterilization autoclave;

[0034] The Pearson correlation coefficient between the candidate temperature response segments in the candidate segment set and the reference signal is calculated to obtain the correlation coefficient sequence of the sterilization vessel. The formula for calculating the Pearson correlation coefficient is as follows:

[0035] ;

[0036] In the formula, The Pearson correlation coefficient value is mentioned. For the reference signal at the 1st The instantaneous amplitude at each sampling moment, The arithmetic mean of the reference signal. For the candidate temperature response segment in the first The instantaneous amplitude at each sampling moment, The arithmetic mean of the candidate temperature response segments. The total number of sampling points contained in the reference signal and the candidate temperature response segment;

[0037] Locate the candidate temperature response segment corresponding to the maximum value in the correlation coefficient sequence, and denote it as the temperature response segment of the reference signal waveform;

[0038] The time offset is determined based on the difference between the start time of the temperature response segment and the start time of the reference signal.

[0039] Preferably, the step of iteratively correcting the temperature gradient distribution field of the sterilized substance by combining the hysteresis time constant with the real-time heat flux density value and the thermal diffusivity of the sterilized substance, includes:

[0040] The thermal diffusivity of the sterilized substance is determined based on the ratio between its mass, specific heat capacity, and volume.

[0041] The hysteresis time constant, the real-time heat flux density value, and the thermal diffusivity are combined to form the input parameter set of the sterilization vessel;

[0042] Using the real-time heat flux density value as the thermal excitation boundary condition of the bacterium being sterilized, and combining it with the thermal diffusivity, the initial temperature gradient distribution field of the bacterium being sterilized is constructed.

[0043] The hysteresis time constant is mapped to the heat transfer phase delay factor of the bactericide;

[0044] The temperature value at the corresponding position in the initial temperature gradient distribution field is adjusted layer by layer by the heat transfer phase delay factor to generate the first corrected temperature gradient distribution field of the sterilization vessel.

[0045] Replace the initial temperature gradient distribution field with the first-corrected temperature gradient distribution field until the difference between the corrected temperature gradient distribution fields obtained after two adjacent corrections reaches a stable state.

[0046] The corrected temperature gradient distribution field in the stable state is output as the temperature gradient distribution field of the sterilization autoclave.

[0047] Preferably, the step of adjusting the temperature value at the corresponding position in the initial temperature gradient distribution field layer by layer by using the heat transfer phase delay factor to generate the primary corrected temperature gradient distribution field of the sterilization vessel includes:

[0048] The initial temperature gradient distribution field is divided into virtual thin layers along the radial direction of the bactericide from the surface to the center;

[0049] Based on the phase delay factor, the temperature decay ratio of the virtual thin layer is determined, wherein the temperature decay ratio is positively correlated with the depth of the virtual thin layer from the surface;

[0050] The temperature decay ratio is coupled with the initial temperature value of the virtual thin layer to obtain the first corrected temperature value of the virtual thin layer.

[0051] The temperature gradient distribution field of the sterilization vessel is generated by topologically reconstructing the first-corrected temperature value according to spatial order.

[0052] Preferably, the step of performing real-time lethal equivalent replacement on the cold point region of the sterilized object through the corrected gradient field to obtain the cumulative sterilization intensity value of the sterilization vessel includes:

[0053] The spatial coordinates of the lowest temperature in the corrected gradient field are identified as the cold point region, and the real-time cold point temperature value corresponding to the cold point region is extracted.

[0054] Based on the real-time cold spot temperature value, the instantaneous lethality benchmark value is obtained from the preset lethality comparison relationship, and the lethality efficiency compensation factor is determined based on the temperature difference between the cold spot area and the surface area of ​​the sterilized object.

[0055] The instantaneous lethality benchmark value and the lethality compensation factor are weighted and fused to generate the real-time displacement lethality value of the cold spot region.

[0056] The real-time displacement mortality rate value is integrated and accumulated along the time axis to obtain the cumulative bactericidal intensity value.

[0057] Preferably, the step of comparing the cumulative sterilization intensity value with a preset target sterilization intensity threshold to determine the sterilization endpoint state of the sterilization autoclave includes:

[0058] Read the pre-stored target sterilization intensity threshold, wherein the target sterilization intensity threshold is determined by the target sterilization safety level of the sterilized object and the initial microbial load level;

[0059] The cumulative sterilization intensity value is compared with the target sterilization intensity threshold.

[0060] When the cumulative sterilization intensity value is not less than the target sterilization intensity threshold, a sterilization completion instruction for the sterilization vessel is generated, and the sterilization endpoint status of the sterilization vessel is determined according to the sterilization completion instruction.

[0061] When the cumulative sterilization intensity value is less than the target sterilization intensity threshold, the current cumulative value is retained, and the process returns to S1 to continue acquiring data. S1 to S6 are then repeated until the cumulative sterilization intensity value is not less than the target sterilization intensity threshold.

[0062] To address the above problems, the present invention also provides a real-time evaluation system for the sterilization intensity of a sterilization autoclave, the system comprising:

[0063] The feature extraction module is used to simultaneously acquire steam condensate flow rate data and non-condensable gas partial pressure data in the inner cavity of the sterilization vessel, and generate dynamic thermodynamic feature pairs of the sterilization vessel.

[0064] The heat flux inversion module is used to perform thermodynamic state inversion on the dynamic thermodynamic characteristics and output the real-time heat flux density value of the sterilization vessel.

[0065] The lag time determination module is used to obtain the instantaneous true temperature value of the sterilized object at the center of the sterilization vessel, and to align the instantaneous true temperature value with the real-time heat flux density value in the time domain to obtain the lag time constant of the aligned instantaneous true temperature value relative to the aligned real-time heat flux density value.

[0066] The comprehensive iteration module is used to integrate the lag time constant, the real-time heat flux density value, and the thermal diffusivity of the sterilized substance, and to iteratively correct the temperature gradient distribution field of the sterilized substance based on the obtained results.

[0067] The sterilization dose determination module is used to perform real-time lethal equivalent replacement on the cold point region of the sterilized object through the corrected gradient field to obtain the cumulative sterilization intensity value of the sterilization vessel.

[0068] The sterilization status assessment module is used to compare the cumulative sterilization intensity value with a preset target sterilization intensity threshold to determine the sterilization endpoint status of the sterilization autoclave.

[0069] Compared with the prior art, the present invention has the following beneficial effects: The present invention synchronously collects the steam condensate flow rate and non-condensable gas partial pressure and constructs a dynamic thermodynamic characteristic pair. Through thermodynamic state inversion, it accurately outputs the real-time heat flux density. Combined with the real temperature of the center of the sterilized object, it completes the time domain alignment and lag time constant calculation. Relying on the thermal diffusivity coefficient to iteratively correct the temperature gradient distribution field, it significantly improves the real-time performance and calculation accuracy of the sterilization intensity assessment of the sterilization autoclave, and ensures a higher degree of matching between the temperature field and thermodynamic parameters.

[0070] Based on the corrected temperature gradient field, the cold spot area of ​​the object to be sterilized is accurately located, and the lethal equivalent is replaced in real time and the sterilization intensity is accumulated over time. The sterilization endpoint is dynamically determined according to the preset threshold, so as to achieve precise control of the sterilization process and stable achievement of sterilization intensity, effectively ensuring the reliability of sterilization effect and improving the safety and standardization of sterilization operation of autoclave. Attached Figure Description

[0071] Figure 1 This is a flowchart illustrating a method for real-time evaluation of sterilization intensity in a sterilization autoclave, provided in an embodiment of the present invention.

[0072] Figure 2 A functional module diagram of a real-time evaluation system for sterilization intensity of a sterilization autoclave provided in an embodiment of the present invention;

[0073] Figure 3 This is a technical logic diagram of a method for real-time evaluation of sterilization intensity in a sterilization autoclave, provided in an embodiment of the present invention.

[0074] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0075] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0076] This application provides a method for real-time evaluation of the sterilization intensity of an autoclave. The executing entity of this method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the method for real-time evaluation of the sterilization intensity of an autoclave can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0077] Reference Figure 1 The diagram shown is a flowchart illustrating a method for real-time evaluation of sterilization intensity in a sterilizing autoclave according to an embodiment of the present invention. In this embodiment, the method for real-time evaluation of sterilization intensity in a sterilizing autoclave includes:

[0078] S1: Simultaneously acquire steam condensate flow rate data and non-condensable gas partial pressure data in the inner cavity of the sterilizer to generate dynamic thermodynamic characteristic pairs of the sterilizer;

[0079] In this embodiment of the invention, the step of simultaneously acquiring steam condensate flow rate data and non-condensable gas partial pressure data within the inner cavity of the sterilization vessel to generate a dynamic thermodynamic characteristic pair of the sterilization vessel includes:

[0080] The analog flow signal output by the flow sensing element deployed in the steam condensate discharge pipeline in the inner cavity of the sterilizer is collected, and the analog flow signal is converted from analog to digital to generate the steam condensate flow data of the sterilizer.

[0081] The voltage signal corresponding to the non-condensable gas is synchronously acquired from the gas partial pressure sensing element deployed in the gas phase region of the sterilizer, and the voltage signal is conditioned and quantized to obtain the non-condensable gas partial pressure data of the sterilizer.

[0082] Using the acquisition time points of the steam condensate flow rate data and the non-condensable gas partial pressure data as the correlation benchmark, the steam condensate flow rate data and the non-condensable gas partial pressure data are paired and combined to obtain the dynamic thermodynamic characteristic pair of the sterilization vessel.

[0083] It should be noted that the flow sensing element uses an electromagnetic flow sensor, based on Faraday's law of electromagnetic induction. The flow of steam condensate cuts through the internal magnetic field, generating a continuous analog flow signal of 0 to 5 volts. This analog flow signal is transmitted to a 16-bit analog-to-digital converter (ADC) with a sampling frequency of 10 Hz. The ADC conversion rule is: the digital signal value equals the analog voltage signal value divided by 5 volts, then multiplied by 65535. For example, an analog signal of 2.5 volts corresponds to a digital signal of 32767. This digital signal is the steam condensate flow data, in cubic meters per hour, and is sampled and converted every 100 milliseconds.

[0084] The gas partial pressure sensing element employs an electrochemical sensor, outputting a 0-10 volt voltage signal proportional to the partial pressure of the non-condensable gas. First, it passes through an RC low-pass filter consisting of a 10 kΩ resistor and a 1 μF capacitor to filter out high-frequency interference above 100 Hz. Then, it is amplified by a factor of 2 by an operational amplifier non-inverting amplifier circuit to bring the signal to 0-20 volts. The conditioned signal is then transmitted to an 8-bit quantizer. The quantization rule is: the quantized value equals the conditioned voltage signal value divided by 20 volts, then multiplied by 255. For example, 10 volts after conditioning corresponds to a quantized value of 127. This quantized digital signal is the non-condensable gas partial pressure data, in kPa, with a sampling frequency of 10 Hz.

[0085] A timestamp mechanism for system clock synchronization is set in the data processing module. Based on the main control system clock of the sterilizer, a unique timestamp accurate to the millisecond level is generated every 100 milliseconds. When collecting steam condensate flow data and non-condensable gas partial pressure data, the corresponding timestamp is bound and stored with the data respectively. Using the timestamp as the association benchmark, two sets of data with the same timestamp are paired one by one. For example, the timestamp 2011-05-11-10-30-00-100 corresponds to 0.8 cubic meters per hour flow data and 5 kPa partial pressure data. After pairing, a dynamic thermodynamic feature pair of (0.8 cubic meters per hour, 5 kPa) is obtained. All data pairing is completed in the order of collection, and finally a set of dynamic thermodynamic feature pairs of sterilizer containing two sets of data corresponding to the unique timestamp is obtained.

[0086] This step, by simultaneously collecting steam condensate flow rate and non-condensable gas partial pressure and generating dynamic thermodynamic characteristic pairs, can accurately reflect the real-time thermodynamic state inside the sterilization vessel, providing reliable raw data for subsequent thermodynamic inversion, effectively eliminating errors caused by single signal acquisition, improving the accuracy of heat flux density calculation, laying a stable foundation for temperature field correction, cold point lethality assessment, and sterilization endpoint determination, and significantly improving the accuracy and real-time performance of the overall evaluation method.

[0087] S2: Perform thermodynamic state inversion on the dynamic thermodynamic characteristics and output the real-time heat flux density value of the sterilization vessel;

[0088] In this embodiment of the invention, the step of performing thermodynamic state inversion on the dynamic thermodynamic characteristics and outputting the real-time heat flux density value of the sterilization vessel includes:

[0089] The dynamic thermodynamic feature pairs are analyzed to separate the condensate flow rate value corresponding to the steam condensate flow rate data and the gas partial pressure value corresponding to the non-condensable gas partial pressure data.

[0090] The condensate flow rate value is input into a pre-generated condensation heat release mapping relationship, and the steam condensation heat release power base value corresponding to the condensate flow rate value is output through the condensation heat release mapping relationship.

[0091] The gas partial pressure value is input into a pre-generated thermal resistance correction mapping relationship, and the thermal resistance correction coefficient of the non-condensable gas corresponding to the gas partial pressure value is output through the thermal resistance correction mapping relationship.

[0092] Using the non-condensable gas thermal resistance correction coefficient, the base value of the steam condensation heat release power is subjected to thermal resistance compensation processing to generate the effective condensation heat release power value of the sterilization vessel.

[0093] Based on the internal geometric characteristic parameters of the sterilization vessel, the effective condensation heat release power value is converted into the heat flow rate per unit area of ​​the sterilization vessel;

[0094] The heat flux per unit area is checked for temporal consistency, and abnormal spikes caused by fluctuations in instantaneous condensate discharge are filtered out. The real-time heat flux density value of the sterilization vessel is then output.

[0095] The dynamic thermodynamic features of the sterilization vessel are parsed and split according to the preset data field structure. The specific values ​​corresponding to the steam condensate flow rate data are extracted from the dynamic thermodynamic feature pairs to form the condensate flow rate value. At the same time, the specific values ​​corresponding to the non-condensable gas partial pressure data are extracted from the same dynamic thermodynamic feature pairs to form the gas partial pressure value. For example, a set of dynamic thermodynamic feature pairs contains 0.9 cubic meters per hour flow rate data and 6 kPa partial pressure data. After parsing, the corresponding condensate flow rate value and gas partial pressure value can be obtained.

[0096] The pre-construction process of the condensation-exothermic mapping relationship is as follows: Under the standard operating conditions of the sterilization autoclave, defined as a steam pressure of 0.2 MPa, a sterilization temperature of 121 degrees Celsius, and a non-condensable gas partial pressure of 0 kPa, 10 gradient values ​​are set for the condensate flow rate: 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, and 1.0 cubic meters per hour. For each flow rate gradient, the corresponding steam condensation-exothermic power is directly measured using a heat flow meter. Each gradient is measured three times, and the arithmetic mean is taken. Using the flow rate value and the measured exothermic power value as data points, a mapping table is constructed using a piecewise linear interpolation method: the flow rate interval from 0 to 1.0 cubic meters per hour is evenly divided into 10 sub-intervals, and a linear function is used to connect the two endpoints within each sub-interval, which is then stored as a data table structure. When any condensate flow rate value is input, the sub-interval is first located, and then the corresponding steam condensation heat release power base value is calculated according to the linear interpolation formula. For example, if the condensate flow rate value is 0.9 cubic meters per hour, and the endpoints of the interval are 0.8 corresponding to 260 kilowatts and 1.0 corresponding to 300 kilowatts, then the interpolation yields 280 kilowatts.

[0097] Furthermore, the pre-construction process of the thermal resistance correction mapping relationship is as follows: The steam pressure is maintained at 0.2 MPa and the condensate flow rate is fixed at 0.5 cubic meters per hour within the sterilization vessel. Six gradients are set for the partial pressure of non-condensable gases: 0, 2, 4, 6, 8, and 10 kPa. For each partial pressure gradient, the actual effective condensation heat release power is measured using a heat flow meter and divided by the heat release power when there is no non-condensable gas to obtain the thermal resistance correction coefficient. For example, the coefficient is 1.00 at 0 kPa, 0.97 at 2 kPa, 0.94 at 4 kPa, 0.92 at 6 kPa, 0.90 at 8 kPa, and 0.88 at 10 kPa. Using the partial pressure values ​​and corresponding coefficients as data points, a continuous mapping relationship is constructed using monotonic cubic spline interpolation and stored as a data table. When any gas partial pressure value is input, the corresponding non-condensable gas thermal resistance correction coefficient is obtained through cubic spline interpolation.

[0098] Thermal resistance compensation is performed according to the fixed calculation rule that the effective condensation heat release power value is equal to the base value of steam condensation heat release power multiplied by the thermal resistance correction coefficient of non-condensable gas. The two sets of obtained values ​​are directly multiplied precisely, and the calculation result directly generates the effective condensation heat release power value of the sterilization vessel. For example, when the base value of steam condensation heat release power is 280 kW and the correction coefficient is 0.92, the calculated value is 257.6 kW, which is used as the effective condensation heat release power value.

[0099] The geometric characteristic parameters of the sterilizer cavity are measured and stored in advance. The geometric characteristic parameters are limited to the total effective heat exchange area of ​​the cavity. The heat flow rate per unit area is calculated by performing a precise division operation according to the fixed conversion logic that the effective condensation heat release power is divided by the total effective heat exchange area of ​​the cavity. The result of the calculation directly generates the heat flow rate per unit area. For example, when the effective condensation heat release power is 257.6 kW and the total effective heat exchange area is 12 square meters, the heat flow rate per unit area is calculated to be 21.47 kW per square meter.

[0100] A fixed-time sliding window mean filtering algorithm is adopted: the window length is set to 5 consecutive sampling points, and the arithmetic mean of the heat flux per unit area within the window is calculated as the baseline value. The allowable deviation range is set to 20% of the baseline value. When the absolute value of the difference between the current value and the baseline value exceeds the allowable deviation range, it is judged as an abnormal peak and is removed. After removal, the previous valid value is used as the replacement; otherwise, the current value is retained as the valid real-time heat flux density value output.

[0101] This step involves thermodynamic inversion of dynamic thermodynamic characteristics, combined with condensation heat release and thermal resistance correction to achieve accurate heat release power calculation. This eliminates the interference of non-condensable gases on heat transfer and outputs stable and reliable real-time heat flux density values. It provides accurate thermodynamic input for subsequent temperature field iterative correction and cold point lethality calculation, significantly improving the accuracy and stability of the overall sterilization intensity assessment.

[0102] S3: Obtain the instantaneous true temperature value of the sterilized object at the center of the sterilization vessel, and align the instantaneous true temperature value with the real-time heat flux density value in the time domain to obtain the lag time constant of the aligned instantaneous true temperature value relative to the aligned real-time heat flux density value.

[0103] In this embodiment of the invention, the step of obtaining the instantaneous true temperature value of the substance to be sterilized at the center of the sterilization vessel, and aligning the instantaneous true temperature value with the real-time heat flux density value in the time domain to obtain the hysteresis time constant of the aligned instantaneous true temperature value relative to the aligned real-time heat flux density value includes:

[0104] The resistance value output by the temperature sensing element buried at the geometric center of the object to be sterilized is collected, and the resistance value is converted into the instantaneous true temperature value of the object to be sterilized.

[0105] Simultaneously extract the first time series corresponding to the real-time heat flux density value and the second time series corresponding to the instantaneous real temperature value;

[0106] Using the real-time heat flux density value as a reference signal, a temperature response segment with the highest waveform similarity to the reference signal is searched on the second time series to determine the time offset of the temperature response segment relative to the reference signal.

[0107] Based on the time offset, the second time series is shifted as a whole so that the instantaneous true temperature value and the real-time heat flux density value correspond point by point on the time axis after the shift, thereby obtaining the aligned instantaneous true temperature value and aligned real-time heat flux density value of the sterilization vessel.

[0108] The delay time between the real-time heat flux density value after alignment and the instantaneous true temperature value after alignment is measured to generate the delay time sequence of the sterilization vessel.

[0109] The median value of the delay sequence is extracted by statistical median filtering and used as the hysteresis time constant of the sterilization vessel.

[0110] The step of using the real-time heat flux density value as a reference signal to search for the temperature response segment with the highest waveform similarity to the reference signal in the second time series, and determining the time offset of the temperature response segment relative to the reference signal, includes:

[0111] Set the window width of the sliding time window, and set the window width to be equal to the time length of the reference signal;

[0112] By using the window width, candidate temperature response segments of the second time series are extracted to generate a candidate segment set for the sterilization autoclave;

[0113] The Pearson correlation coefficient between the candidate temperature response segments in the candidate segment set and the reference signal is calculated to obtain the correlation coefficient sequence of the sterilization vessel. The formula for calculating the Pearson correlation coefficient is as follows:

[0114] ;

[0115] In the formula, The Pearson correlation coefficient value is mentioned. For the reference signal at the 1st The instantaneous amplitude at each sampling moment, The arithmetic mean of the reference signal. For the candidate temperature response segment in the first The instantaneous amplitude at each sampling moment, The arithmetic mean of the candidate temperature response segments. The total number of sampling points contained in the reference signal and the candidate temperature response segment;

[0116] Locate the candidate temperature response segment corresponding to the maximum value in the correlation coefficient sequence, and denote it as the temperature response segment of the reference signal waveform;

[0117] The time offset is determined based on the difference between the start time of the temperature response segment and the start time of the reference signal.

[0118] A PT100 platinum resistance temperature sensor is embedded in the geometric center of the object to be sterilized and is completely fitted to it. Its output is a resistance value that is linearly related to temperature. The resistance value is collected using a four-wire measurement method and substituted into a fixed conversion formula "instantaneous true temperature value = (collected resistance value - 100 ohms) / 0.385" for calculation. For example, when the collected resistance value is 119.25 ohms, (119.25-100) / 0.385 = 50 degrees Celsius, which is the instantaneous true temperature value of the object to be sterilized. The acquisition frequency is consistent with the real-time heat flux density value to ensure time synchronization.

[0119] In the data processing module, the real-time heat flux density values ​​are arranged in order of acquisition time to form a first time series with time on the horizontal axis and heat flux density values ​​on the vertical axis; at the same time, the instantaneous real temperature values ​​are arranged in the corresponding time order to form a second time series with time on the horizontal axis and instantaneous real temperature values ​​on the vertical axis. The time interval between the two series is 100 milliseconds to ensure that the time dimensions correspond.

[0120] Using the real-time heat flux density of the first time series as the reference signal, and setting 10 frames of data as a sliding comparison window, the window is moved frame by frame on the second time series. For each frame, the cosine similarity between the temperature data within the window and the corresponding segment of the reference signal is calculated. The window data with the highest similarity is the matched temperature response segment, and the difference between its start time and the start time of the reference signal is recorded. The difference is the time offset. For example, if the reference signal starts at 10:30:00.000 and the matched segment starts at 10:30:00.500, the offset is 500 milliseconds. The cosine similarity is calculated by multiplying the two data and dividing by the product of their squares and square roots.

[0121] Using the time offset as a reference, the second time series is shifted as a whole along the positive direction of the time axis. The shift duration is equal to the offset, keeping the relative time sequence of temperature values ​​within the series unchanged. After the shift, each temperature value time point is completely consistent with the corresponding heat flux density value time point of the first time series. After point-by-point correspondence, the aligned instantaneous true temperature value and the aligned real-time heat flux density value are obtained.

[0122] The slope mutation detection method is used to identify the temperature rise inflection point of two aligned sequences, record the corresponding inflection point timestamps and calculate the difference, which is the single delay duration. All corresponding inflection point differences are calculated in chronological order to form a delay duration sequence. For example, the heat flux inflection point is 10:30:01.000 and the temperature inflection point is 10:30:01.600, with a delay duration of 600 milliseconds.

[0123] Furthermore, the method for detecting the inflection point of temperature rise is as follows: For the aligned real-time heat flux density value sequence, calculate the slope between two adjacent points, with the unit of slope being degrees Celsius per second. The inflection point of heat flux rise is defined as the point where the slope abruptly changes from less than 0.5 degrees Celsius per second to greater than or equal to 0.5 degrees Celsius per second. For the aligned instantaneous true temperature value sequence, similarly calculate the slope between two adjacent points, defining the inflection point of temperature rise as the point where the slope abruptly changes from less than 0.2 degrees Celsius per second to greater than or equal to 0.2 degrees Celsius per second.

[0124] A statistical median filtering algorithm with a 5-frame window is used to process the delay time sequence. The window slides frame by frame, and the median value of the 5 values ​​in the window is taken as the output after sorting in each frame. The sequence is traversed to complete the filtering. The median value of all output values ​​after filtering is extracted, which is the lag time constant of the sterilization autoclave. For example, if the filtered output is 580, 600, 610, 590, and 600 milliseconds, the median value of 600 milliseconds is the lag time constant.

[0125] First, obtain the reference signal: the time length of the first time series corresponding to the real-time heat flux density value. Set the window width of the sliding time window to be exactly equal to the time length of the reference signal. The specific value of the window width is equal to the total number of frames contained in the reference signal multiplied by the time interval of a single frame. For example, if the reference signal is 10 frames of data and the time interval of a single frame is 100 milliseconds, the window width is set to 1000 milliseconds (10 × 100 milliseconds).

[0126] Using the set window width as a standard, starting from the beginning of the second time series, data is captured frame by frame along the positive direction of the time axis. Each frame captured is a segment of temperature data equal to the window width, which is used as a candidate temperature response segment. All captured candidate temperature response segments are summarized to form a candidate segment set for the sterilization autoclave. For example, if the second time series has a total of 20 frames of data and the window width is 10 frames, 11 candidate temperature response segments can be captured to form a candidate segment set.

[0127] The Pearson correlation coefficient calculation method is used to calculate the correlation coefficient value between each candidate temperature response segment in the candidate segment set and the reference signal one by one. The calculation steps are as follows: first, calculate the average value of all data of the candidate segment and the average value of all data of the reference signal respectively; then calculate the difference between each candidate segment data and its own average value and the difference between the reference signal data and its own average value; then calculate the sum of the products of all corresponding differences, and divide it by the product of the square root of the sum of squares of the differences of the candidate segments and the square root of the sum of squares of the differences of the reference signal. The result is the Pearson correlation coefficient value. All calculation results are summarized to form the correlation coefficient sequence of the sterilization vessel.

[0128] The correlation coefficient sequence is traversed and compared to select the candidate temperature response segment with the largest value in the sequence. The temperature response segment corresponding to the largest correlation coefficient is the one with the highest similarity to the reference signal waveform. This temperature response segment is marked as the temperature response segment corresponding to the reference signal waveform. For example, if the maximum value in the correlation coefficient sequence is 0.98, its corresponding candidate segment is the target temperature response segment.

[0129] The start time of the reference signal and the start time of the marked temperature response segment are pre-recorded. The time difference between the two start time points is calculated by subtracting the start time of the reference signal from the start time of the temperature response segment. If the result is positive, it means that the temperature response segment lags behind the reference signal. The calculated time difference is the time offset of the temperature response segment relative to the reference signal. For example, if the start time of the reference signal is 10:30:00.000 and the start time of the temperature response segment is 10:30:00.600, the time offset is 600 milliseconds.

[0130] The instantaneous amplitude of the reference signal at each sampling time is taken from the time series data of the real-time heat flux density value of each sampling point. All amplitude data within a fixed time window are extracted point by point according to the sampling time sequence to obtain a complete set of values.

[0131] The arithmetic mean of the reference signal is calculated by summing the instantaneous amplitudes of the real-time heat flux density corresponding to all sampling times within a fixed time window, and then dividing by the total number of sampling points contained in the window.

[0132] The instantaneous amplitude of the candidate temperature response fragment at each sampling time is taken from the temperature time series data of the second time series with the same window width. The temperature values ​​at the corresponding positions are extracted point by point according to the same sampling time series to form a complete set of values.

[0133] The arithmetic mean of the candidate temperature response segments is calculated by summing the instantaneous temperature amplitudes corresponding to all sampling times within the extracted candidate temperature response segments, and then dividing by the total number of sampling points contained in the segment.

[0134] The total number of sampling points contained in the reference signal and the candidate temperature response segment is a fixed total number of sampling points obtained by dividing the preset sliding time window according to a uniform sampling interval, and the number of sampling points of the two sets of comparison data is kept completely consistent and fixed throughout the process.

[0135] The overall calculation logic of the Pearson correlation coefficient is as follows: First, calculate the difference between the instantaneous amplitude of the reference signal and its own arithmetic mean at each sampling time, and the difference between the instantaneous amplitude of the candidate temperature response segment and its own arithmetic mean. Then, multiply the two sets of differences at the same sampling time in turn and sum all the products. At the same time, calculate the sum of the squares of the differences in amplitude of the reference signal and the sum of the squares of the differences in amplitude of the candidate temperature response segment, and perform square root operations on each. Finally, divide the sum of the numerators by the product of the two sets of square root operations to obtain the final correlation coefficient result.

[0136] The overall role of the Pearson correlation coefficient is to quantitatively characterize the similarity of waveform change trends between the reference signal composed of real-time heat flux density and the candidate temperature response segment. The closer the value is to the fixed maximum value, the more consistent the synchronous change characteristics of the two sets of time-series waveforms are, and this is used as the sole criterion for selecting the optimal matching temperature response segment.

[0137] This step collects the true temperature of the center of the sterilized object and aligns it with the heat flux density in the time domain to accurately calculate the lag time constant. This objectively reflects the lag characteristics of material heat transfer and provides key time parameters for subsequent iterative correction of the temperature gradient field. This improves the fit and accuracy of the temperature field calculation, thereby enhancing the overall accuracy of the assessment of cold point lethality and sterilization intensity.

[0138] By using equal-length windows to precisely match the Pearson correlation coefficient, temperature response segments can be quickly located and time offsets can be determined, improving time-domain alignment accuracy and providing a reliable basis for calculating lag time constants. This makes the time-series matching of temperature signals and heat flux signals more accurate, thereby improving the overall accuracy of temperature field correction and sterilization intensity assessment.

[0139] S4: Combining the hysteresis time constant, the real-time heat flux density value, and the thermal diffusivity of the sterilized substance, the temperature gradient distribution field of the sterilized substance is iteratively corrected using the obtained results;

[0140] In this embodiment of the invention, the step of iteratively correcting the temperature gradient distribution field of the sterilized substance by combining the hysteresis time constant, the real-time heat flux density value, and the thermal diffusivity of the sterilized substance with the obtained result includes:

[0141] The thermal diffusivity of the sterilized substance is determined based on the ratio between its mass, specific heat capacity, and volume.

[0142] The hysteresis time constant, the real-time heat flux density value, and the thermal diffusivity are combined to form the input parameter set of the sterilization vessel;

[0143] Using the real-time heat flux density value as the thermal excitation boundary condition of the bacterium being sterilized, and combining it with the thermal diffusivity, the initial temperature gradient distribution field of the bacterium being sterilized is constructed.

[0144] The hysteresis time constant is mapped to the heat transfer phase delay factor of the bactericide;

[0145] The temperature value at the corresponding position in the initial temperature gradient distribution field is adjusted layer by layer by the heat transfer phase delay factor to generate the first corrected temperature gradient distribution field of the sterilization vessel.

[0146] Replace the initial temperature gradient distribution field with the first-corrected temperature gradient distribution field until the difference between the corrected temperature gradient distribution fields obtained after two adjacent corrections reaches a stable state.

[0147] The corrected temperature gradient distribution field in the stable state is output as the temperature gradient distribution field of the sterilization autoclave.

[0148] The step of adjusting the temperature value at the corresponding position in the initial temperature gradient distribution field layer by layer through the heat transfer phase delay factor to generate the primary corrected temperature gradient distribution field of the sterilization vessel includes:

[0149] The initial temperature gradient distribution field is divided into virtual thin layers along the radial direction of the bactericide from the surface to the center;

[0150] Based on the phase delay factor, the temperature decay ratio of the virtual thin layer is determined, wherein the temperature decay ratio is positively correlated with the depth of the virtual thin layer from the surface;

[0151] The temperature decay ratio is coupled with the initial temperature value of the virtual thin layer to obtain the first corrected temperature value of the virtual thin layer.

[0152] The temperature gradient distribution field of the sterilization vessel is generated by topologically reconstructing the first-corrected temperature value according to spatial order.

[0153] First, the mass of the substance to be sterilized is measured using an electronic balance. Then, the volume of the substance is measured using a volumetric measurement method. Finally, the specific heat capacity of the substance is measured using a differential scanning calorimeter. The thermal diffusivity is calculated according to the ratio of specific heat capacity multiplied by mass and then divided by volume. That is, the thermal diffusivity is equal to (specific heat capacity × mass) ÷ volume. For example, if the specific heat capacity of the substance to be sterilized is 4.2 kJ / kg Celsius, the mass is 5 kg, and the volume is 0.005 cubic meters, the calculated result is (4.2 × 5) ÷ 0.005 = 4200 kJ / m³ Celsius. The calculated result is the thermal diffusivity of the substance to be sterilized.

[0154] The three sets of data—the acquired hysteresis time constant, the real-time heat flux density, and the thermal diffusivity of the sterilized substance—are organized in a fixed order and entered into a preset data table in the data processing module. The three sets of data correspond one-to-one and maintain time synchronization, forming the input parameter set of the sterilization autoclave. For example, the hysteresis time constant is 600 milliseconds, the real-time heat flux density is 21.47 kilowatts per square meter, and the thermal diffusivity is 4200 kilojoules per cubic meter of degree Celsius. Entering these three values ​​into the corresponding table constitutes the input parameter set.

[0155] Furthermore, the finite difference method is used to solve the three-dimensional heat conduction equation. This equation describes the rate of temperature change over time as equal to the thermal diffusivity multiplied by the sum of the second derivatives of the temperature in the three spatial directions. The object to be sterilized is divided into a uniform hexahedral mesh, with each mesh having a side length of 2 mm. The boundary conditions are set as follows: the heat flux input to the mesh cells on the surface of the object to be sterilized is equal to the real-time heat flux density. The initial conditions are set as follows: before the sterilization process begins, the temperature inside the object to be sterilized is uniform everywhere, equal to the initial temperature. An explicit time-progression method is used to solve the problem, with a time step of 0.5 seconds. Iterative calculations are performed until the temperature distribution reaches a thermally stable state. At this point, the temperature value at each mesh node is obtained, and the temperature values ​​of all mesh nodes together constitute the initial temperature gradient distribution field.

[0156] A linear mapping algorithm is used to convert the lag time constant into a heat transfer phase delay factor. The mapping rule is that the heat transfer phase delay factor is equal to the lag time constant divided by 1000. The time unit is converted from milliseconds to seconds. For example, if the lag time constant is 600 milliseconds, the heat transfer phase delay factor obtained after conversion is 0.6.

[0157] Using the heat transfer phase delay factor as the adjustment benchmark, the temperature values ​​of each grid in the initial temperature gradient distribution field are adjusted layer by layer by layer from the grid on the surface of the sterilized object to the central grid. The attenuation ratio of the temperature value in each layer is equal to the heat transfer phase delay factor. The adjustment formula is that the adjusted temperature value is equal to the initial temperature value multiplied by (1 minus the heat transfer phase delay factor). For example, if the initial temperature value is 80 degrees Celsius and the heat transfer phase delay factor is 0.6, the adjusted temperature value is 80 × (1 - 0.6) = 32 degrees Celsius. After all grids are adjusted, a corrected temperature gradient distribution field is generated.

[0158] Calculate the difference between the first-corrected temperature gradient distribution field and the previous-corrected temperature gradient distribution field (the initial temperature gradient distribution field during the first calculation). The difference is calculated as the difference between the absolute values ​​of the corresponding grid temperature values ​​of the two distribution fields. Then, calculate the arithmetic mean of the differences of the absolute values ​​of all grids. Compare the arithmetic mean with a preset stability threshold (set to 0.5 degrees Celsius). If the average value is greater than the threshold, the first-corrected temperature gradient distribution field is used as the new initial temperature gradient distribution field, and the above decay adjustment steps are repeated. If the average value is less than or equal to the threshold, it is determined that a stable state has been reached.

[0159] When the difference between two consecutive corrected temperature gradient distribution fields reaches a stable state, the iterative correction operation is stopped, and the corrected temperature gradient distribution field that is now in a stable state is directly output as the temperature gradient distribution field of the sterilization vessel.

[0160] The initial temperature gradient distribution field is based on the geometric center of the object to be sterilized, and is divided radially from the outer surface to the center at equal intervals to create multiple continuous and non-overlapping virtual thin layers. Each virtual thin layer has a fixed radial thickness and independent spatial position attributes, completely covering the entire radial area of ​​the object to be sterilized.

[0161] A fixed linear correlation rule is established based on the heat transfer phase delay factor. The radial depth from the virtual thin layer to the outer surface of the sterilized object is used as the independent variable. The larger the depth value, the larger the temperature attenuation ratio value is assigned. The assignment is completed by a unique linear incremental mapping relationship throughout the process. For example, for every unit increase in radial depth, the temperature attenuation ratio value increases synchronously by a fixed step size. Finally, a unique temperature attenuation ratio value is matched for each virtual thin layer.

[0162] The initial temperature field value corresponding to a single virtual thin layer and the matched temperature decay ratio value are substituted into the fixed coupling operation logic. The operation method is to multiply the initial temperature value of the virtual thin layer by the difference between the unit value and the temperature decay ratio value. The same coupling operation is performed on each virtual thin layer one by one. The value obtained after the operation is directly set as the first correction temperature value of the corresponding virtual thin layer.

[0163] Strictly following the spatial arrangement order of the sterilized material from the surface to the center, the first-corrected temperature values ​​calculated from each virtual thin layer are systematically spliced ​​and recombined while maintaining their original topological positions, completely restoring the three-dimensional spatial temperature gradient arrangement structure. After recombination, the overall first-corrected temperature gradient distribution field of the sterilization vessel is formed. Using the geometric center of the sterilized material as a reference, the initial temperature gradient distribution field is uniformly divided radially from the outer surface inwards towards the center, sequentially dividing into multiple continuous, non-overlapping virtual thin layers. Each virtual thin layer has a fixed radial thickness and independent spatial position attributes, completely covering the entire radial area of ​​the sterilized material.

[0164] A fixed linear correlation rule is established based on the heat transfer phase delay factor. The radial depth from the virtual thin layer to the outer surface of the sterilized object is used as the independent variable. The larger the depth value, the larger the temperature attenuation ratio value is assigned. The assignment is completed by a unique linear incremental mapping relationship throughout the process. For example, for every unit increase in radial depth, the temperature attenuation ratio value increases synchronously by a fixed step size. Finally, a unique temperature attenuation ratio value is matched for each virtual thin layer.

[0165] The initial temperature field value corresponding to a single virtual thin layer and the matched temperature decay ratio value are substituted into the fixed coupling operation logic. The operation method is to multiply the initial temperature value of the virtual thin layer by the difference between the unit value and the temperature decay ratio value. The same coupling operation is performed on each virtual thin layer one by one. The value obtained after the operation is directly set as the first correction temperature value of the corresponding virtual thin layer.

[0166] Strictly following the spatial arrangement order of the sterilized material from the surface to the center, the first-corrected temperature values ​​calculated by each virtual thin layer are orderly spliced ​​and recombined while maintaining their original topological positions, thus completely restoring the three-dimensional spatial temperature gradient arrangement structure. After the recombination is completed, the first-corrected temperature gradient distribution field of the sterilization vessel is formed as a whole.

[0167] This step combines the hysteresis time constant, heat flux density, and thermal diffusivity to iteratively correct the temperature gradient field, which can truly reflect the hysteresis characteristics of heat transfer inside the material, improve the accuracy of temperature field calculation, accurately restore the temperature distribution of the sterilized object, and provide a reliable basis for cold spot identification, lethal equivalent replacement, and sterilization intensity assessment, thus greatly improving the accuracy and stability of the overall assessment method.

[0168] By adjusting the temperature field through radial stratification and phase retardation factor attenuation layer by layer, the hysteresis effect of heat transfer inside the material can be accurately reproduced, making the initial temperature gradient field more consistent with the actual heat exchange process, improving the reliability of the first-correction temperature gradient field, providing accurate temperature basis for subsequent cold spot identification, lethal equivalent replacement and sterilization intensity calculation, and improving the overall evaluation accuracy.

[0169] S5: By correcting the gradient field, the cold point region of the sterilized object is replaced with lethal equivalent in real time to obtain the cumulative sterilization intensity value of the sterilization vessel.

[0170] In this embodiment of the invention, the step of performing real-time lethal equivalent replacement on the cold point region of the sterilized object through the corrected gradient field to obtain the cumulative sterilization intensity value of the sterilization vessel includes:

[0171] The spatial coordinates of the lowest temperature in the corrected gradient field are identified as the cold point region, and the real-time cold point temperature value corresponding to the cold point region is extracted.

[0172] Based on the real-time cold spot temperature value, the instantaneous lethality benchmark value is obtained from the preset lethality comparison relationship, and the lethality efficiency compensation factor is determined based on the temperature difference between the cold spot area and the surface area of ​​the sterilized object.

[0173] The instantaneous lethality benchmark value and the lethality compensation factor are weighted and fused to generate the real-time displacement lethality value of the cold spot region.

[0174] The real-time displacement mortality rate value is integrated and accumulated along the time axis to obtain the cumulative bactericidal intensity value.

[0175] Traverse all spatial coordinates of the corrected temperature gradient distribution field, compare the temperature value corresponding to each coordinate point by point, and filter out the spatial coordinates with the lowest temperature value. The spatial coordinates are the cold point regions. At the same time, extract the temperature value corresponding to the spatial coordinates as the real-time cold point temperature value of the cold point region. For example, if the lowest temperature in the corrected gradient field is 65 degrees Celsius, the corresponding spatial coordinates are the cold point regions, and the real-time cold point temperature value is 65 degrees Celsius.

[0176] The preset lethality comparison relationship is constructed in the following way: Real-time lethality data under common cold point temperature gradients of the sterilized organisms are collected. A one-to-one data comparison table is established and stored with the real-time cold point temperature value on the horizontal axis and the instantaneous lethality rate on the vertical axis. The extracted real-time cold point temperature value is substituted into the corresponding comparison table to accurately match and obtain the corresponding instantaneous lethality benchmark value. Simultaneously, the temperature difference between the cold point area and the surface area of ​​the sterilized organism is calculated. A linear mapping rule is adopted, where the larger the temperature difference, the smaller the lethality compensation factor. The mapping formula is that the lethality compensation factor equals 1 minus (temperature difference divided by 100). For example, if the temperature difference is 5 degrees Celsius, the compensation factor is 1 - (5 / 100) = 0.95.

[0177] A fixed-weighted fusion algorithm is adopted, with the instantaneous lethality benchmark value weighted at 0.8 and the lethality efficiency compensation factor weighted at 0.2. The weighted fusion calculation method is to multiply the instantaneous lethality benchmark value by 0.8 and add the lethality efficiency compensation factor multiplied by 0.2. The sum of the two is the real-time replacement lethality value of the cold spot area. For example, if the instantaneous lethality benchmark value is 0.8 per minute and the compensation factor is 0.95, the calculated value is 0.8×0.8+0.95×0.2=0.83 per minute, which is the real-time replacement lethality value.

[0178] Using the time axis of the sterilization process as a reference, a fixed integration time interval of 100 milliseconds is set. The real-time displacement lethality value corresponding to each time interval is multiplied by the time interval to obtain the sterilization contribution value of a single time interval. The sterilization contribution values ​​of all time intervals are accumulated and summed along the time axis. The accumulated result is the cumulative sterilization intensity value of the sterilization vessel. For example, if the contribution value of each time interval is 0.083, and there are 100 intervals in total, the sum is 8.3, which is the cumulative sterilization intensity value.

[0179] This step accurately locates the cold point based on the corrected temperature gradient field and performs real-time replacement of lethal equivalent. It calculates the sterilization contribution with the cold point temperature as the core, which is consistent with the actual sterilization mechanism. The calculation rationality is improved by combining temperature difference compensation. The cumulative sterilization intensity value is obtained through time-series integration, which provides a reliable basis for the determination of the sterilization endpoint and significantly improves the accuracy and reliability of the overall assessment.

[0180] S6: Compare the cumulative sterilization intensity value with the preset target sterilization intensity threshold to determine the sterilization endpoint state of the sterilization autoclave.

[0181] In this embodiment of the invention, comparing the cumulative sterilization intensity value with a preset target sterilization intensity threshold to determine the sterilization endpoint state of the sterilization autoclave includes:

[0182] Read the pre-stored target sterilization intensity threshold, wherein the target sterilization intensity threshold is determined by the target sterilization safety level of the sterilized object and the initial microbial load level;

[0183] The cumulative sterilization intensity value is compared with the target sterilization intensity threshold.

[0184] When the cumulative sterilization intensity value is not less than the target sterilization intensity threshold, a sterilization completion instruction for the sterilization vessel is generated, and the sterilization endpoint status of the sterilization vessel is determined according to the sterilization completion instruction.

[0185] When the cumulative sterilization intensity value is less than the target sterilization intensity threshold, the current cumulative value is retained, and the process returns to S1 to continue acquiring data. S1 to S6 are then repeated until the cumulative sterilization intensity value is not less than the target sterilization intensity threshold.

[0186] The target sterilization intensity threshold is determined and stored in advance using the following method: First, the target sterilization safety level of the substance to be sterilized is determined. For example, for food products, the target sterilization safety level is commercial sterility. The initial microbial load level of the substance to be sterilized is measured by microbial counting method, where the unit is colony forming units per gram. The target sterilization intensity threshold is calculated according to the formula: "Target sterilization intensity threshold = initial microbial load level × inactivation multiple corresponding to the target sterilization safety level". For example, if the initial microbial load level is 1000 colony forming units per gram and the inactivation multiple corresponding to the target safety level is 1000, the target sterilization intensity threshold is calculated to be 1000000. The value is then stored in the data processing module. When reading, the pre-stored value, i.e., the target sterilization intensity threshold, is retrieved directly.

[0187] The numerical precision comparison method is adopted to compare the cumulative bactericidal intensity value calculated in real time with the target bactericidal intensity threshold one-to-one. During the comparison, the units of the two are kept consistent: both are colony formation unit inactivation equivalents. The relationship between the current cumulative bactericidal intensity value and the target bactericidal intensity threshold is compared synchronously at each moment to ensure that the comparison results are accurate.

[0188] When the comparison result shows that the cumulative sterilization intensity value is not less than the target sterilization intensity threshold, the data processing module immediately generates a sterilization completion instruction in a fixed format. The sterilization completion instruction includes a sterilization completion identifier and the current cumulative sterilization intensity value. Based on the sterilization completion instruction, the data processing module directly determines and outputs the sterilization endpoint status of the sterilization autoclave. For example, if the cumulative sterilization intensity value is 1,050,000 and the target threshold is 1,000,000, then the sterilization completion instruction is generated and the sterilization endpoint status is determined.

[0189] When the comparison result shows that the cumulative sterilization intensity value is less than the target sterilization intensity threshold, the data processing module retains the current cumulative sterilization intensity value, does not generate any instructions, and triggers a return instruction. The control system returns to the initial data acquisition step, continues to acquire steam condensate flow data and non-condensable gas partial pressure data, and repeats all steps from data acquisition to cumulative sterilization intensity value calculation until the cumulative sterilization intensity value is not less than the target sterilization intensity threshold.

[0190] This step sets a target threshold based on the material safety level and initial bacterial count. It achieves closed-loop judgment by comparing the cumulative sterilization intensity in real time. If the target is met, a sterilization completion command is output. If the target is not met, the judgment is automatically iterated and continued. This avoids insufficient or excessive sterilization and makes the entire evaluation process a reliable closed loop, significantly improving the sterilization control accuracy and operational safety.

[0191] like Figure 2 The diagram shown is a functional block diagram of a real-time evaluation system for sterilization intensity of a sterilization autoclave provided in an embodiment of the present invention.

[0192] The real-time sterilization intensity evaluation system for autoclaves described in this invention can be installed in electronic devices. Depending on the functions implemented, the real-time sterilization intensity evaluation system may include a feature pair extraction module, a heat flux inversion module, a lag time determination module, a comprehensive iteration module, a sterilization amount determination module, and a sterilization status evaluation module. The modules described in this invention can also be referred to as units, which are a series of computer program segments that can be executed by the processor of an electronic device and perform a fixed function, stored in the memory of the electronic device.

[0193] In this embodiment, the functions of each module / unit are as follows:

[0194] The feature extraction module is used to simultaneously acquire steam condensate flow rate data and non-condensable gas partial pressure data in the inner cavity of the sterilization vessel, and generate dynamic thermodynamic feature pairs of the sterilization vessel.

[0195] The heat flux inversion module is used to perform thermodynamic state inversion on the dynamic thermodynamic characteristics and output the real-time heat flux density value of the sterilization vessel.

[0196] The lag time determination module is used to obtain the instantaneous true temperature value of the sterilized object at the center of the sterilization vessel, and to align the instantaneous true temperature value with the real-time heat flux density value in the time domain to obtain the lag time constant of the aligned instantaneous true temperature value relative to the aligned real-time heat flux density value.

[0197] The integrated iteration module is used to integrate the lag time constant, the real-time heat flux density value, and the thermal diffusivity of the sterilized substance, and to iteratively correct the temperature gradient distribution field of the sterilized substance based on the obtained results.

[0198] The sterilization dose determination module is used to perform real-time lethal equivalent replacement on the cold point region of the sterilized object through the corrected gradient field to obtain the cumulative sterilization intensity value of the sterilization vessel.

[0199] The sterilization status assessment module is used to compare the cumulative sterilization intensity value with a preset target sterilization intensity threshold to determine the sterilization endpoint status of the sterilization autoclave.

[0200] In the several embodiments provided by this invention, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0201] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0202] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0203] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0204] This application embodiment can employ an artificial intelligence-based technology to acquire and process relevant data. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0205] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for real-time evaluation of the sterilization intensity of a sterilization autoclave, characterized in that, The method includes: S1: Simultaneously acquire steam condensate flow rate data and non-condensable gas partial pressure data in the inner cavity of the sterilizer to generate dynamic thermodynamic characteristic pairs of the sterilizer; S2: Perform thermodynamic state inversion on the dynamic thermodynamic characteristics and output the real-time heat flux density value of the sterilization vessel; S3: Obtain the instantaneous true temperature value of the sterilized object at the center of the sterilization vessel, and align the instantaneous true temperature value with the real-time heat flux density value in the time domain to obtain the lag time constant of the aligned instantaneous true temperature value relative to the aligned real-time heat flux density value. S4: Combining the hysteresis time constant, the real-time heat flux density value, and the thermal diffusivity of the sterilized substance, the temperature gradient distribution field of the sterilized substance is iteratively corrected using the obtained results; S5: By correcting the gradient field, the cold point region of the sterilized object is replaced with lethal equivalent in real time to obtain the cumulative sterilization intensity value of the sterilization vessel. S6: Compare the cumulative sterilization intensity value with the preset target sterilization intensity threshold to determine the sterilization endpoint state of the sterilization autoclave.

2. The method for real-time evaluation of sterilization intensity in a sterilization autoclave as described in claim 1, characterized in that, The process of simultaneously acquiring steam condensate flow rate data and non-condensable gas partial pressure data within the inner cavity of the sterilization vessel to generate dynamic thermodynamic characteristic pairs of the sterilization vessel includes: The analog flow signal output by the flow sensing element deployed in the steam condensate discharge pipeline in the inner cavity of the sterilizer is collected, and the analog flow signal is converted from analog to digital to generate the steam condensate flow data of the sterilizer. The voltage signal corresponding to the non-condensable gas is synchronously acquired from the gas partial pressure sensing element deployed in the gas phase region of the sterilizer, and the voltage signal is conditioned and quantized to obtain the non-condensable gas partial pressure data of the sterilizer. Using the acquisition time points of the steam condensate flow rate data and the non-condensable gas partial pressure data as the correlation benchmark, the steam condensate flow rate data and the non-condensable gas partial pressure data are paired and combined to obtain the dynamic thermodynamic characteristic pair of the sterilization vessel.

3. The method for real-time evaluation of sterilization intensity in a sterilization autoclave as described in claim 1, characterized in that, The step of performing thermodynamic state inversion on the dynamic thermodynamic characteristics and outputting the real-time heat flux density value of the sterilization vessel includes: The dynamic thermodynamic feature pairs are analyzed to separate the condensate flow rate value corresponding to the steam condensate flow rate data and the gas partial pressure value corresponding to the non-condensable gas partial pressure data. The condensate flow rate value is input into a pre-generated condensation heat release mapping relationship, and the steam condensation heat release power base value corresponding to the condensate flow rate value is output through the condensation heat release mapping relationship. The gas partial pressure value is input into a pre-generated thermal resistance correction mapping relationship, and the thermal resistance correction coefficient of the non-condensable gas corresponding to the gas partial pressure value is output through the thermal resistance correction mapping relationship. Using the non-condensable gas thermal resistance correction coefficient, the base value of the steam condensation heat release power is subjected to thermal resistance compensation processing to generate the effective condensation heat release power value of the sterilization vessel. Based on the internal geometric characteristic parameters of the sterilization vessel, the effective condensation heat release power value is converted into the heat flow rate per unit area of ​​the sterilization vessel; The heat flux per unit area is checked for temporal consistency, and abnormal spikes caused by fluctuations in instantaneous condensate discharge are filtered out. The real-time heat flux density value of the sterilization vessel is then output.

4. The method for real-time evaluation of sterilization intensity in a sterilization autoclave as described in claim 1, characterized in that, The step of obtaining the instantaneous true temperature value of the sterilized object at the center of the sterilization vessel, and aligning the instantaneous true temperature value with the real-time heat flux density value in the time domain to obtain the lag time constant of the aligned instantaneous true temperature value relative to the aligned real-time heat flux density value includes: The resistance value output by the temperature sensing element buried at the geometric center of the object to be sterilized is collected, and the resistance value is converted into the instantaneous true temperature value of the object to be sterilized. Simultaneously extract the first time series corresponding to the real-time heat flux density value and the second time series corresponding to the instantaneous real temperature value; Using the real-time heat flux density value as a reference signal, a temperature response segment with the highest waveform similarity to the reference signal is searched on the second time series to determine the time offset of the temperature response segment relative to the reference signal. Based on the time offset, the second time series is shifted as a whole so that the instantaneous true temperature value and the real-time heat flux density value correspond point by point on the time axis after the shift, thereby obtaining the aligned instantaneous true temperature value and aligned real-time heat flux density value of the sterilization vessel. The delay time between the real-time heat flux density value after alignment and the instantaneous true temperature value after alignment is measured to generate the delay time sequence of the sterilization vessel. The median value of the delay sequence is extracted by statistical median filtering and used as the hysteresis time constant of the sterilization vessel.

5. The method for real-time evaluation of sterilization intensity in a sterilization autoclave as described in claim 4, characterized in that, The step of using the real-time heat flux density value as a reference signal to search for the temperature response segment with the highest waveform similarity to the reference signal in the second time series, and determining the time offset of the temperature response segment relative to the reference signal, includes: Set the window width of the sliding time window, and set the window width to be equal to the time length of the reference signal; By using the window width, candidate temperature response segments of the second time series are extracted to generate a candidate segment set for the sterilization autoclave; The Pearson correlation coefficient between the candidate temperature response segments in the candidate segment set and the reference signal is calculated to obtain the correlation coefficient sequence of the sterilization vessel. The formula for calculating the Pearson correlation coefficient is as follows: ； In the formula, The Pearson correlation coefficient value is mentioned. For the reference signal at the 1st The instantaneous amplitude at each sampling moment, The arithmetic mean of the reference signal. For the candidate temperature response segment in the first The instantaneous amplitude at each sampling moment, The arithmetic mean of the candidate temperature response segments. The total number of sampling points contained in the reference signal and the candidate temperature response segment; Locate the candidate temperature response segment corresponding to the maximum value in the correlation coefficient sequence, and denote it as the temperature response segment of the reference signal waveform; The time offset is determined based on the difference between the start time of the temperature response segment and the start time of the reference signal.

6. The method for real-time evaluation of sterilization intensity in a sterilization autoclave as described in claim 1, characterized in that, The step of iteratively correcting the temperature gradient distribution field of the sterilized substance by combining the hysteresis time constant, the real-time heat flux density value, and the thermal diffusivity of the sterilized substance with the obtained result includes: The thermal diffusivity of the sterilized substance is determined based on the ratio between its mass, specific heat capacity, and volume. The hysteresis time constant, the real-time heat flux density value, and the thermal diffusivity are combined to form the input parameter set of the sterilization vessel; Using the real-time heat flux density value as the thermal excitation boundary condition of the bacterium being sterilized, and combining it with the thermal diffusivity, the initial temperature gradient distribution field of the bacterium being sterilized is constructed. The hysteresis time constant is mapped to the heat transfer phase delay factor of the bactericide; The temperature value at the corresponding position in the initial temperature gradient distribution field is adjusted layer by layer by the heat transfer phase delay factor to generate the first corrected temperature gradient distribution field of the sterilization vessel. Replace the initial temperature gradient distribution field with the first-corrected temperature gradient distribution field until the difference between the corrected temperature gradient distribution fields obtained after two adjacent corrections reaches a stable state. The corrected temperature gradient distribution field in the stable state is output as the temperature gradient distribution field of the sterilization autoclave.

7. The method for real-time evaluation of sterilization intensity in a sterilization autoclave as described in claim 6, characterized in that, The step of adjusting the temperature value at the corresponding position in the initial temperature gradient distribution field layer by layer through the heat transfer phase delay factor to generate the primary corrected temperature gradient distribution field of the sterilization vessel includes: The initial temperature gradient distribution field is divided into virtual thin layers along the radial direction of the bactericide from the surface to the center; Based on the phase delay factor, the temperature decay ratio of the virtual thin layer is determined, wherein the temperature decay ratio is positively correlated with the depth of the virtual thin layer from the surface; The temperature decay ratio is coupled with the initial temperature value of the virtual thin layer to obtain the first corrected temperature value of the virtual thin layer. The temperature gradient distribution field of the sterilization vessel is generated by topologically reconstructing the first-corrected temperature value according to spatial order.

8. The method for real-time evaluation of sterilization intensity in a sterilization autoclave as described in claim 1, characterized in that, The step of performing real-time lethal equivalent replacement on the cold point region of the sterilized object through the corrected gradient field to obtain the cumulative sterilization intensity value of the sterilization vessel includes: The spatial coordinates of the lowest temperature in the corrected gradient field are identified as the cold point region, and the real-time cold point temperature value corresponding to the cold point region is extracted. Based on the real-time cold spot temperature value, the instantaneous lethality benchmark value is obtained from the preset lethality comparison relationship, and the lethality efficiency compensation factor is determined based on the temperature difference between the cold spot area and the surface area of ​​the sterilized object. The instantaneous lethality benchmark value and the lethality compensation factor are weighted and fused to generate the real-time displacement lethality value of the cold spot region. The real-time displacement mortality rate value is integrated and accumulated along the time axis to obtain the cumulative bactericidal intensity value.

9. The method for real-time evaluation of sterilization intensity in a sterilization autoclave as described in claim 1, characterized in that, The step of comparing the cumulative sterilization intensity value with a preset target sterilization intensity threshold to determine the sterilization endpoint state of the sterilization autoclave includes: Read the pre-stored target sterilization intensity threshold, wherein the target sterilization intensity threshold is determined by the target sterilization safety level of the sterilized object and the initial microbial load level; The cumulative sterilization intensity value is compared with the target sterilization intensity threshold. When the cumulative sterilization intensity value is not less than the target sterilization intensity threshold, a sterilization completion instruction for the sterilization vessel is generated, and the sterilization endpoint status of the sterilization vessel is determined according to the sterilization completion instruction. When the cumulative sterilization intensity value is less than the target sterilization intensity threshold, the current cumulative value is retained, and the process returns to S1 to continue acquiring data. S1 to S6 are then repeated until the cumulative sterilization intensity value is not less than the target sterilization intensity threshold.

10. A real-time evaluation system for the sterilization intensity of a sterilization autoclave, characterized in that, The system for implementing the real-time evaluation method for sterilization intensity of a sterilizing autoclave as described in claim 1 includes: The feature extraction module is used to simultaneously acquire steam condensate flow rate data and non-condensable gas partial pressure data in the inner cavity of the sterilization vessel, and generate dynamic thermodynamic feature pairs of the sterilization vessel. The heat flux inversion module is used to perform thermodynamic state inversion on the dynamic thermodynamic characteristics and output the real-time heat flux density value of the sterilization vessel. The lag time determination module is used to obtain the instantaneous true temperature value of the sterilized object at the center of the sterilization vessel, and to align the instantaneous true temperature value with the real-time heat flux density value in the time domain to obtain the lag time constant of the aligned instantaneous true temperature value relative to the aligned real-time heat flux density value. The comprehensive iteration module is used to integrate the lag time constant, the real-time heat flux density value, and the thermal diffusivity of the sterilized substance, and to iteratively correct the temperature gradient distribution field of the sterilized substance based on the obtained results. The sterilization dose determination module is used to perform real-time lethal equivalent replacement on the cold point region of the sterilized object through the corrected gradient field to obtain the cumulative sterilization intensity value of the sterilization vessel. The sterilization status assessment module is used to compare the cumulative sterilization intensity value with a preset target sterilization intensity threshold to determine the sterilization endpoint status of the sterilization autoclave.

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