A method and system for optimizing temperature-pressure curve of medical high-pressure steam sterilizer

Abstract

The present application relates to the technical field of high-pressure steam sterilizers, and particularly relates to a medical high-pressure steam sterilizer temperature and pressure curve optimization method and system. The method comprises the following steps: obtaining bacterial characteristic data and environmental parameters by using a sensor to generate an original data sequence; performing data cleaning and standardization processing on the original data sequence to form a high-quality data set; training a sterilization process model based on the high-quality data set by using a deep learning algorithm to generate temperature, pressure and time optimization curves suitable for different bacteria; inputting the optimization curves into a real-time control system to adjust the operating parameters of the high-pressure steam sterilizer; dynamically adjusting the operating parameters according to the equipment operation data during the sterilization process, and generating a sterilization effect evaluation report. Through the deep learning algorithm and the real-time feedback mechanism, the present application realizes dynamic optimization of the sterilization process, reduces energy consumption, and has the beneficial effects of high sterilization efficiency, energy saving and environmental protection, strong self-adaptation capability and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of high-pressure steam sterilizers, and in particular to a method and system for optimizing a temperature and pressure curve of a medical high-pressure steam sterilizer. Background Art

[0002] In the sterilization process of medical equipment, high-pressure steam sterilizers are one of the most commonly used sterilization equipment. They mainly kill various pathogens on the surface and inside the equipment through a high-temperature and high-pressure steam environment. However, in the existing technology, high-pressure steam sterilizers generally use fixed temperature, pressure and time parameters for sterilization, and cannot be precisely adjusted according to the heat resistance and sterilization requirements of different bacterial species. This fixed parameter sterilization method may lead to incomplete sterilization effect and cannot meet the needs of killing multiple bacterial species in complex medical scenarios. At the same time, it may cause unnecessary energy waste due to excessive heating.

[0003] With the advancement of medical technology and the improvement of hospital infection prevention and control standards, higher requirements are being placed on sterilizers: on the one hand, precise control of temperature, pressure, and time parameters is required to ensure effective sterilization of multiple drug-resistant bacteria; on the other hand, energy use needs to be optimized to reduce sterilization costs and equipment energy consumption. Therefore, how to use intelligent algorithms to generate dynamic sterilization curves based on real-time bacterial characteristic data and environmental parameters, and achieve precise control through real-time feedback, has become a core issue that needs to be addressed in current technology. Summary of the Invention

[0004] The present invention provides a method and system for optimizing the temperature and pressure curve of a medical high-pressure steam sterilizer to solve the problem of how to generate dynamic temperature, pressure, and time optimization curves based on bacterial characteristic data, environmental parameters, and equipment operation data in medical equipment through a deep learning algorithm, and adjust the operating parameters of the high-pressure steam sterilizer in real time to ensure the effective killing of different bacterial species and optimize energy utilization efficiency.

[0005] In order to solve the above technical problems, the present invention provides a method for optimizing the temperature and pressure curve of a medical high-pressure steam sterilizer, comprising:

[0006] Sensors are used to obtain bacterial characteristic data and environmental parameters to generate raw data sequences;

[0007] Performing data cleaning and standardization on the raw data sequence to generate a high-quality data set;

[0008] Based on the high-quality data set, a deep learning algorithm is used to train a sterilization process model to generate temperature, pressure, and time optimization curves suitable for different bacterial species;

[0009] Inputting the optimization curve into a real-time control system to adjust the operating parameters of the high-pressure steam sterilizer;

[0010] During the sterilization process, dynamic adjustments are made based on the equipment operation data, and a sterilization effect evaluation report is generated.

[0011] Furthermore, the step of obtaining bacterial characteristic data and environmental parameters includes:

[0012] The types, numbers and population densities of bacteria are collected through sensors;

[0013] Obtain environmental parameters, including initial temperature, initial humidity, and initial pressure;

[0014] The bacterial data and environmental parameters are integrated to form a complete raw data sequence.

[0015] Furthermore, the steps of data cleaning and standardization include:

[0016] removing noise and outliers from the raw data sequence;

[0017] Standardizing the data to convert data of different dimensions into a unified standard;

[0018] Generate datasets suitable for deep learning model training.

[0019] Furthermore, the step of training the sterilization process model includes:

[0020] Extracting key features from the dataset, including strain type, strain heat resistance, and environmental characteristics;

[0021] Based on the extracted features, a sterilization process model is trained using a deep learning algorithm;

[0022] The optimized curves of temperature, pressure and time adapted to different bacterial species were obtained.

[0023] Furthermore, the deep learning algorithm includes a convolutional neural network and / or a long short-term memory network, the input of the sterilization process model is the bacterial strain characteristics and environmental characteristics, and the output is an optimized temperature, pressure and time curve.

[0024] Furthermore, the step of inputting the optimization curve into the real-time control system includes:

[0025] The generated temperature, pressure and time optimization curves are input into the real-time control system of the high-pressure steam sterilizer;

[0026] The operating parameters of the sterilizer are initialized according to the optimization curve, including initial temperature, initial pressure and initial time.

[0027] Furthermore, the real-time control system includes:

[0028] A sensor module that collects equipment operation data;

[0029] an execution module for adjusting the operating parameters of the equipment;

[0030] A processing module that updates the optimization curve based on device feedback data.

[0031] Furthermore, the step of dynamic adjustment includes:

[0032] Monitor the sterilizer's operating status in real time and obtain equipment operating data, including temperature, pressure, and humidity;

[0033] Adjusting operating parameters according to the deviation between the equipment operating data and the optimization curve;

[0034] Through the feedback closed-loop mechanism, the optimization curve is dynamically updated and the equipment operation is adjusted.

[0035] Furthermore, the step of dynamic adjustment further includes:

[0036] Calculate the deviation between the actual sterilization effect and the target value and correct the operating parameters;

[0037] The corrected parameters are input into the real-time control system to update the equipment operating status.

[0038] Furthermore, a medical high-pressure steam sterilizer temperature and pressure curve optimization system is provided, the system comprising:

[0039] Data acquisition module, used to collect bacterial characteristic data, environmental parameters and equipment operation data through high-precision sensors;

[0040] A data processing module, used to clean and standardize the data and extract key features;

[0041] A deep learning modeling module is used to train the sterilization process model based on the processed data and generate temperature, pressure and time optimization curves;

[0042] A real-time control and feedback module for inputting the optimization curve into the equipment control system and adjusting operating parameters;

[0043] The data analysis and evaluation module is used to analyze equipment operation data, generate sterilization effect evaluation reports and provide optimization suggestions.

[0044] The following are its main beneficial effects:

[0045] (1) Dynamically adapt to the needs of killing multiple bacterial species. Based on bacterial characteristic data and environmental parameters, the present invention generates optimization curves adapted to different bacterial species through a deep learning model, which can accurately adjust the operating parameters of the sterilizer. Compared with traditional fixed parameter methods, it can effectively kill bacterial species with different heat and pressure resistance, avoiding the problem of incomplete sterilization.

[0046] (2) Improve the accuracy and efficiency of the sterilization process. This invention achieves closed-loop optimization control by real-time monitoring of equipment operating data and dynamic adjustment based on feedback. The optimized curve can accurately match the operating status of the equipment, reduce temperature or pressure deviations during the sterilization process, and improve the accuracy and efficiency of sterilization.

[0047] (3) By adopting a method of real-time adjustment and optimization of energy consumption, the energy consumption data of equipment operation is analyzed and the heating and pressurization process is dynamically controlled, thus reducing energy waste during the sterilization process. Compared with traditional methods, the present invention can significantly improve the energy utilization efficiency of sterilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 A schematic flow chart of a method for optimizing a temperature and pressure curve of a medical high-pressure steam sterilizer provided in an embodiment of the present application;

[0049] Figure 2 This is a structural block diagram of a medical high-pressure steam sterilizer temperature and pressure curve optimization system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0050] Unless otherwise defined, all technical and scientific terms used herein have the same meanings as commonly understood by those skilled in the art to which this application belongs. The terms used in the specification of the application are for the purpose of describing specific embodiments only and are not intended to limit this application. The terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of this application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0051] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0052] Example 1: Reference Figure 1 , is a schematic flow chart of a method for optimizing a temperature and pressure curve of a medical high-pressure steam sterilizer provided by an embodiment of the present invention. The process may include at least steps S100-S500:

[0053] S100: Using sensors to obtain bacterial characteristic data and environmental parameters to generate a raw data sequence.

[0054] S200: Perform data cleaning and standardization on the original data sequence to generate a high-quality data set.

[0055] S300: Based on the high-quality data set, a deep learning algorithm is used to train a sterilization process model to generate temperature, pressure, and time optimization curves suitable for different bacterial species.

[0056] S400: Input the optimization curve into a real-time control system to adjust the operating parameters of the high-pressure steam sterilizer.

[0057] S500: Make dynamic adjustments based on equipment operation data during the sterilization process and generate a sterilization effect evaluation report.

[0058] Step S100 at least includes steps S110-S130:

[0059] S110. Obtain the bacterial type and quantity data collected by the internal sensor of the medical device, and collect environmental parameters at the same time to obtain a complete original data sequence.

[0060] First, the system collects data from sensors within the medical device to determine the type, quantity, and growth of bacteria in the environment. This data is measured in real time by high-precision sensors, recording bacterial density, type (e.g., family and genus), and the current bacterial count. Sensor data is fed into the system as a data stream.

[0061] Set the data output by the sensor to D bacteria ={b1,b2,…,b n}, where b i represents the number or density of the i-th bacteria, n represents the number of bacterial species, D bacteria The distribution and quantity characteristics of bacteria in the environment are the core input data for optimizing sterilization parameters. Bacterial species are represented by taxonomic codes, and each type of bacteria has its own unique identifier.

[0062] At the same time, the sensors inside the equipment will also collect environmental parameter data, such as temperature, humidity, pressure, etc. These environmental factors have a greater impact on the sterilization process. Set the environmental data to D env ={T,H,P}, where T represents temperature, H represents humidity, and P represents pressure. The data is collected into the system through a real-time monitoring system and combined with the bacterial data to form a complete raw data sequence. That is:

[0063] D raw ={D bacteria ,D env}={b1,b2,…,b n ,T,H,P};

[0064] All bacterial data and environmental data are integrated into a raw data sequence D raw ,This sequence contains various types of input data, which serves as the basis for subsequent processing.,This data sequence will be passed to the next step of data preprocessing.

[0065] S120: Remove noise and outliers from the raw data, standardize the data, and obtain a high-quality data set suitable for deep learning model training.

[0066] From the original data sequence D raw In the process of noise and outliers detection and elimination, we firstly carry out noise and outliers detection and elimination. In order to improve the data quality, we use appropriate algorithms (such as mean deviation method, Z-score standardization, etc.) to remove outliers that are beyond the normal range. bacteria If some values ​​are significantly higher or lower than the normal range, they are marked as outliers and removed. env ,If the temperature, humidity and pressure values ​​exceed the set threshold range, it will be ,considered as abnormal and will be corrected or eliminated.

[0067] For data X, by calculating its standard deviation σ X and mean μ X , use the Z-score method to filter out outliers:

[0068]

[0069] Set the threshold to Z X >3 or Z X When <-3, the data are considered abnormal.

[0070] After removing outliers, the data is standardized. Standardization is to convert data of different dimensions into a unified standard so that it can be input into the deep learning model. Specifically, for the bacterial count data b i , temperature data T, humidity data H and pressure data P are standardized by subtracting the mean from the data and dividing by the standard deviation:

[0071]

[0072] Among them, μ X is the mean of each data dimension, σ X is the standard deviation, b' i is the standardized bacterial data, and T', H', and P' are the standardized environmental parameter data.

[0073] Through the above processing, we get a high-quality data set after denoising and standardization, denoted as D cleaned ={b'1,b'2,…,b' n,T',H',P'}, this dataset can be used as input data for subsequent deep learning model training.

[0074] S130: Extract key features from the high-quality data and annotate them to obtain a feature dataset for deep learning model training.

[0075] From the standardized high-quality dataset D cleaned In the data set b', the key features that are highly related to the sterilization process are extracted. i , characteristics can be extracted according to the growth patterns of different bacterial species, such as bacterial species type (classification data), changing trends in bacterial numbers, high temperature resistance of bacterial species, etc.; for environmental data T′, H′, P′, characteristics of the impact of temperature and humidity changes on the sterilization process can be extracted.

[0076] Specifically, for temperature and humidity data, the sliding window method is used to calculate its statistical characteristics such as mean, standard deviation and maximum value, and the temperature and humidity change characteristics f are obtained. T ,f H For bacterial data, time series features f can be extracted through changes in bacterial density and species variation. b :

[0077] f T =mean(T′),f H =std(H),f b =trend(b′ i );

[0078] Among them, mean represents the calculated mean, std represents the standard deviation, and trend represents time series trend analysis.

[0079] The extracted feature data is annotated, including the sterilization curves and environmental parameter ranges corresponding to different bacterial species. Based on historical sterilization experimental data and known bacterial sterilization standards, each sample data is labeled, such as the sterilization effect corresponding to different sterilization temperatures, pressures, and times. This annotated data will become an important basis for subsequent deep learning model training.

[0080] After feature extraction and labeling, the feature dataset D for deep learning model training is obtained. features ={f b ,f T ,f H ,f P}, the dataset contains characteristic information of bacterial species, environmental changes, and sterilization effects, and is ready to be passed to the next model training stage.

[0081] Step S200 at least includes steps S210-S230:

[0082] S210. Based on the cleaned and preprocessed data set and the extracted features thereof, a deep learning algorithm is used to train a sterilization process model to obtain a preliminary model of temperature, pressure, and time optimization curves that can adapt to different bacterial species.

[0083] In this step, based on the aforementioned high-quality dataset D cleaned ={b'1,b'2,…,b' n ,T',H',P'}, and the key features D extracted from the data features ={f b ,f T ,f H ,f P}, trained using deep learning algorithms (such as convolutional neural networks and long short-term memory networks). The model aims to generate temperature, pressure, and time optimization curves that can adapt to different bacterial species.

[0084] Specifically, the input data D features This will be input as a training data set into the deep learning model, which will learn the nonlinear relationship between bacterial strain characteristics and environmental parameters to optimize the temperature, pressure and time during the sterilization process.

[0085] Assume that the deep learning model is f model , the input of the model is f model (f b ,f T ,f H ,f P ), the output is the corresponding temperature, pressure and time curve parameters, recorded as T opt ,P opt ,τ opt , representing the optimized temperature, pressure and time curves, respectively.

[0086] {T opt ,P opt ,τ opt}=f model (f b ,f T ,f H ,f P );

[0087] Get the preliminary trained model: After completing the training of the deep learning algorithm, the generated model is the preliminary trained sterilization process model The model can predict the appropriate sterilization temperature, pressure and time curves based on the characteristic data of different bacterial species.

[0088] Model evaluation and validation: During the training process, the preliminary model is evaluated using the validation dataset to ensure that the model can generate reasonable temperature, pressure, and time curves to meet the requirements of different sterilization conditions.

[0089] S220: Based on the deep learning model obtained by the preliminary training, tuning is performed to optimize its parameters and architecture to obtain an optimized deep learning model.

[0090] Parameter Optimization: After obtaining a preliminarily trained model, further optimization of model parameters is performed. The goal of optimization is to reduce training error and improve the model's ability to generalize to new data. Model performance can be improved by adjusting model hyperparameters such as the learning rate, number of layers, and number of nodes, and by using optimization algorithms such as Adam and RMSprop.

[0091] Assume that the optimized model is The key to the optimization process is to optimize the weight and bias parameters w through the back propagation algorithm and gradient descent method. i and b i , to minimize the loss function L(f model ), the loss function is defined as the error measure between the model output and the actual target (i.e., the sterilization effect curve):

[0092]

[0093] Among them, y i is the target value, x i is the input feature, is the optimized deep learning model, and L is the loss function, which represents the error between the model output and the target.

[0094] Model architecture optimization: Based on the performance of the deep learning model, adjust the model architecture (such as increasing or decreasing the number of network layers, changing the activation function, etc.) to ensure that the model can better adapt to the characteristics of different types of bacteria and environmental changes.

[0095] Furthermore, the optimized architecture was evaluated using a validation dataset to ensure that it could generate sterilization curves more accurately and achieve the predetermined goals.

[0096] Get the optimized deep learning model: After the optimization process, the optimized deep learning model is obtained This model can predict more accurate temperature, pressure and time curves during the sterilization process based on different bacterial species and environmental characteristics.

[0097] S230. Input different bacterial strain data into the optimized model, verify the output effect of the model, and evaluate its performance and reliability under different sterilization conditions.

[0098] Input different strains of bacterial data for model prediction: Input data from different strains into the optimized deep learning model The input data includes the characteristic data of different strains f b ,f T ,f H ,f P Based on the characteristics, the model will output different temperature, pressure and time curves, which will be provided to the equipment system as the optimized parameters in the sterilization process.

[0099]

[0100] Verify the model output: Compare the temperature, pressure, and time curves generated by the model with the results of actual sterilization experiments to verify that the output meets expectations. Specifically, compare the differences between the model-predicted curves and experimental data to evaluate the model's adaptability and accuracy for different bacterial species and sterilization conditions.

[0101] Validation was performed using experimental data from different bacterial species to ensure that the model could generate a reasonable sterilization curve for each bacterial species and that the sterilization process could completely kill all bacteria.

[0102] Evaluate model reliability: Conduct a comprehensive assessment of the model's reliability, particularly its performance under different sterilization conditions, such as the impact of environmental changes like temperature and pressure on sterilization effectiveness. Through multiple validations, ensure the model's stability and accuracy, and assess its applicability and generalization capabilities.

[0103] Model evaluation formula:

[0104]

[0105] Among them, y i is the actual sterilization result, x i is the input feature data, is the optimized model, Error is the prediction error, and N is the number of validation samples.

[0106] Step S300 at least includes steps S310-S330:

[0107] S310: Input characteristic data of different types of bacterial strains according to the optimized deep learning model to obtain temperature, pressure and time curves suitable for the bacterial strains.

[0108] Input characteristic data of different strains: According to the optimized deep learning model Input the characteristic data of different types of bacteria. Specifically, the input data includes the key features f extracted from the previous step b ,f T ,fH ,f P ,in:

[0109] f b Characteristics indicating bacterial species, bacterial numbers, or density;

[0110] f T Represents temperature-related characteristics (such as average temperature, temperature fluctuation, etc.);

[0111] f H Indicates humidity-related features;

[0112] f P Indicates pressure-related characteristics.

[0113] Input data f b ,f T ,f H ,f P The optimized model Calculate and generate the adaptation temperature, pressure and time curve required for the strain in:

[0114] To adapt to the optimized temperature curve of this strain;

[0115] To adapt to the optimized pressure curve of this strain;

[0116] To adapt to the optimized time curve of this strain.

[0117]

[0118] Generate temperature, pressure, and time curves suitable for the bacterial species: Through the output results of the deep learning model, the temperature, pressure, and time optimization curve required for each bacterial species is obtained. The curve will be used in the control system in the subsequent sterilization process to ensure that the sterilization process can be accurately executed and achieve the desired effect.

[0119] S320: Based on the input data, calculate and generate temperature, pressure and time optimization curves suitable for the sterilization process of different bacterial species.

[0120] Calculate based on the input bacterial species characteristic data: Further, the input different bacterial species characteristic data f b ,f T ,f H ,f P Perform in-depth calculations to generate temperature, pressure, and time optimization curves for each strain. The characteristic data of each bacterial species will correspond to a unique sterilization curve. The output obtained through the calculation is the optimized parameters of each bacterial species, specifically:

[0121] An optimized temperature curve generated based on input data;

[0122] An optimized pressure curve generated based on input data;

[0123] is the optimized time curve generated based on the input data.

[0124] Calculate the optimized curve: Use mathematical formulas to calculate the sterilization curve suitable for each bacterial species. This calculation combines the characteristics of the bacterial species with environmental parameters such as temperature and humidity to ensure that the generated sterilization curve can cope with different bacterial species and provide accurate sterilization results. The formula calculation here will use the previously obtained optimized parameters and characteristic data:

[0125]

[0126] The optimization curve will be transmitted to the real-time control system for execution.

[0127] Generate an optimization curve for the sterilization process: The curve obtained by the calculation Integrate it into the operating system of the sterilization process to ensure that the temperature, pressure and time can be adjusted in real time according to the needs of different bacterial species during the sterilization process.

[0128] S330. Fine-tune the generated sterilization curve based on real-time environmental feedback information.

[0129] Obtain real-time environmental feedback information: During the actual sterilization process, obtain real-time environmental feedback information, such as parameters such as temperature, pressure, and humidity inside the equipment. Use sensors to monitor the operating status of the sterilizer in real time and collect real-time data about the sterilization process. The feedback information includes but is not limited to:

[0130] T env The temperature inside the device is measured in real time;

[0131] P env The pressure inside the device is measured in real time;

[0132] H env Real-time measurement of humidity.

[0133] This real-time feedback serves as a dynamic input for fine-tuning previously generated sterilization profiles.

[0134] Fine-tuning the sterilization curve: based on real-time feedback information env ,P env ,Henv , adjust the previously generated sterilization curve. Specifically, if the temperature, pressure or humidity in the device deviates from the preset range, the system will adjust the temperature based on the feedback information pressure and time to ensure that the sterilization process is still carried out in an optimized state.

[0135] For example, if the temperature T measured at a certain moment env If the temperature exceeds the preset value, the system will adjust the temperature curve To compensate for temperature changes.

[0136] Update and generate a new sterilization curve: Ultimately, the system will regenerate and update the sterilization curve based on the real-time adjusted parameters, so that the sterilization process can adapt to real-time environmental changes at every moment, ensuring the consistency and reliability of the sterilization effect. It will be input into the real-time control system of the equipment to ensure the precise execution of the sterilization process.

[0137] Step S400 at least includes steps S410-S430:

[0138] S410 , inputting the temperature, pressure and time curves generated from the S300 module into the real-time control system of the high-pressure steam sterilizer to adjust the equipment operating parameters.

[0139] Input optimization curve to real-time control system: Optimized temperature, pressure and time curve generated by the S300 module The optimized parameters are input into the real-time control system of the high-pressure steam sterilizer. Specifically, the optimization curve provides the key control parameters in the sterilization process, which correspond to the set temperature T when the sterilizer is running. set , pressure P set and duration τ set ,Right now:

[0140]

[0141] Equipment parameter initialization: The real-time control system initializes the equipment operating parameters according to the input optimization curve, sets the initial temperature, pressure and sterilization time of the high-pressure steam sterilizer, and enables the corresponding control algorithm to ensure that the equipment operates according to the optimization curve.

[0142] Parameter Transfer and Integration: Each point in the optimization curve serves as a dynamic input to the device, ensuring the sterilizer accurately adjusts temperature and pressure at each point in time, synchronized with the sterilization time. Throughout the process, the real-time control system continuously obtains the current target parameters from the curve to control the sterilizer's operation.

[0143] S420. During the sterilization process, monitor the equipment status of the high-pressure steam sterilizer in real time and provide feedback on the equipment operation status.

[0144] Real-time monitoring of equipment status: During the sterilization process, the built-in sensors and monitoring system of the equipment are used to collect the operating status parameters of the sterilizer in real time, including the actual temperature T actual , actual pressure P actual , steam flow F actual Etc. The collected data forms a real-time feedback sequence:

[0145] D feedback ={T actual ,P actual ,F actual}

[0146] Compare the set value with the actual value: the actual parameter D feedback and the target parameter T in the optimization curve set ,P set ,τ set Perform real-time comparisons and calculate deviations to assess the accuracy of equipment operation.

[0147] The deviance is assumed to be calculated as:

[0148] ΔT=T actual -T set ,ΔP=P actual -P set ;

[0149] Where ΔT and ΔP are the deviations in temperature and pressure, respectively.

[0150] Feedback data recording: During real-time monitoring, all feedback data and their corresponding time points are recorded to form a time series for subsequent dynamic adjustment steps. The data will be analyzed in real time and dynamically adjusted by the control system.

[0151] S430: Dynamically adjust the operating parameters of the device according to the real-time feedback data.

[0152] Analyze real-time feedback data: Based on the feedback data collected by S420 module D feedback The deviations ΔT and ΔP from the optimization curve target values ​​are used to analyze the device's operating status. If the deviation exceeds the set threshold, the device's operating parameters need to be adjusted to bring the actual operating status closer to the optimization curve target value.

[0153] Assume the adjustment formula is:

[0154] T adjust =T set -k T ·ΔT,Padjust =P set -k P ΔP

[0155] Among them, k T and k P is the adjustment coefficient for temperature and pressure, T adjust and P adjust are the adjusted temperature and pressure set points, respectively.

[0156] Dynamically adjust equipment parameters: adjust the temperature T adjust and pressure P adjust This data is fed into the real-time control system to update the equipment's operating parameters, ensuring that the equipment follows the optimization curve in real time. The dynamically adjusted parameter sequence is continuously updated over time.

[0157] Feedback closed-loop control: After the adjustment is completed, the operating status parameter D of the equipment is collected again feedback , and repeatedly compares the deviation from the target value of the optimization curve to form a closed-loop control process. This iterative adjustment ensures that the actual operation of the sterilizer always stays close to the optimization curve.

[0158] Step S500 at least includes steps S510-S530:

[0159] S510. During the sterilization process, collect all relevant operating data.

[0160] Data collection and storage: During the sterilization process, high-precision sensors and data acquisition systems are used to collect equipment operating data in real time, including but not limited to:

[0161] Actual temperature data T actual (t): The temperature monitored in real time inside the device.

[0162] Actual pressure data P actual (t): Real-time pressure inside the device.

[0163] Humidity data H actual (t): Ambient humidity information.

[0164] Equipment status data S device (t): The operating status, operation mode and operation cycle of the equipment.

[0165] Energy consumption data E actual (t): Real-time energy consumption of the equipment during the sterilization process.

[0166] The data is recorded in the form of time series, forming a complete sterilization process data set:

[0167] D process ={T actual(t), P actual (t), H actual (t), S device (t), E actual (t)};

[0168] Data storage and formatting: The collected data is formatted into standardized data sequences for further data analysis and model evaluation.

[0169] S520, based on the collected sterilization process data, analyze the sterilization effect and energy saving effect, evaluate whether the sterilization reaches the expected target, and optimize the energy use efficiency.

[0170] Data preprocessing and analysis preparation: The collected sterilization process data D process is input into the data analysis module, removes possible outliers, and standardizes the data to ensure uniform dimensions.

[0171] Assuming the data standardization formula is as follows:

[0172]

[0173] Where X(t) represents the operating data (such as temperature, pressure, etc.). μ X represents the mean of the data.

[0174] σ X represents the standard deviation of the data. Sterilization effect analysis formula: According to the sterilization curve and actual operating data, calculate the sterilization effect parameters such as the sterilization factor F0(t), the formula is:

[0175]

[0176] Where T actual (t) represents the actual measured temperature sequence. T ref represents the reference temperature. z represents the heat resistance coefficient of the bacteria. τ set is the duration of the sterilization cycle.

[0177] The target value of the sterilization process is If , the sterilization effect is considered to meet the standard.

[0178] Energy saving data calculation: Analyze the energy consumption E actual (t) and calculate the total energy consumption:

[0179]

[0180] And calculate the energy efficiency coefficient EefficiencyE efficiency Eefficiency:

[0181]

[0182] If the energy efficiency coefficient E efficiency If the energy saving effect exceeds the energy saving target value set by the system, the energy saving effect is considered to have met the standard.

[0183] S530: Integrate the sterilization results obtained through analysis with the energy-saving data to generate a detailed sterilization effect evaluation report.

[0184] Data integration and report generation: Sterilization effect analysis results (such as F0(t), sterilization target achievement rate, equipment operation time, etc.) and energy saving data (such as E total , energy efficiency) to generate a unified data report.

[0185] The sterilization effect report should include the following contents:

[0186] Sterilization process overview: Record essential parameters such as equipment operating time, sterilization cycle, target temperature and pressure.

[0187] Operation data summary: lists the key indicators of the equipment during actual operation, including the average value and fluctuation range of temperature, pressure, energy consumption, etc.

[0188] Sterilization effect analysis: Display the time variation curve of the sterilization factor F0(t) to confirm whether the sterilization effect meets the standard.

[0189] Energy saving analysis report: calculate energy saving efficiency E efficiency , compare the theoretical energy consumption of the equipment with the actual energy consumption.

[0190] Optimization suggestions: Based on operating data, analyze potential optimization space, such as reducing unnecessary heating time, reducing steam loss, etc.

[0191] Report output and storage:

[0192] The sterilization effect evaluation report is output in the form of electronic documents or equipment operation logs and stored in the equipment management system for subsequent analysis and improvement.

[0193] Example 2: Figure 2 FIG. 1 shows a structural block diagram of a temperature and pressure curve optimization system for a medical high-pressure steam sterilizer according to an embodiment of the present invention. Figure 2 As shown, the system may include:

[0194] The data acquisition module 10 collects data related to the sterilization process through high-precision sensors, including bacterial characteristic data, environmental data and equipment operation data.

[0195] Obtain real-time data on the type and quantity of bacteria inside medical equipment, and record bacterial species characteristics and density.

[0196] Collect environmental parameters, including temperature, humidity, and pressure, as input references for the sterilization process.

[0197] Integrate bacterial and environmental data to form a complete raw data sequence for subsequent data processing.

[0198] Data processing module 20, clean and standardize the collected raw data, extract key features and generate high-quality data sets.

[0199] Remove noise and outliers to ensure data validity and consistency.

[0200] Standardize the data, convert different dimensional data to a unified standard.

[0201] Extract key features (such as bacterial heat resistance, temperature and humidity trend, etc.), and label the target parameters corresponding to different sterilization conditions.

[0202] Deep learning modeling module 30, based on the processed high-quality data set, train the sterilization process deep learning model, generate temperature, pressure and time optimization curve suitable for different bacteria.

[0203] Use high-quality data sets and feature sets to build sterilization process optimization models through deep learning algorithms.

[0204] Optimize model parameters and architecture to generate sterilization optimization curves suitable for different bacteria.

[0205] Verify the output effect of the model to ensure that the model can reliably predict the temperature, pressure and time curve under different sterilization conditions.

[0206] Real-time control and feedback module 40, input the generated optimization curve into the real-time control system of the high-pressure steam sterilizer, and dynamically adjust the operating parameters according to real-time feedback.

[0207] Input the optimization curve into the control system to set the initial operating parameters of the sterilizer.

[0208] Real-time monitoring of equipment operating status, collecting operating data (such as temperature, pressure, humidity, etc.).

[0209] Adjust operating parameters according to device feedback, dynamically update sterilization curve, form closed-loop control.

[0210] Data analysis and reporting module 50, analyze the actual operation data and energy saving effect of the sterilization process, generate sterilization effect evaluation report.

[0211] Collect and analyze the actual operation data (such as sterilization factors, energy consumption data, etc.) in the sterilization process.

[0212] Calculate the sterilization target achievement rate and energy efficiency coefficient, and evaluate the sterilization effect and energy-saving performance.

[0213] Integrate analysis results, generate a sterilization effect evaluation report, and provide optimization suggestions.

[0214] Beneficial effects: (1) High-precision sterilization: The optimization model trained by the deep learning algorithm can dynamically adjust the temperature, pressure and time parameters to ensure that the sterilization process is accurate and effective.

[0215] (2) Energy saving and environmental protection: By analyzing sterilization energy efficiency data, the sterilization process is optimized and energy consumption is significantly reduced.

[0216] (3) Strong adaptability: Based on a real-time feedback mechanism, the system can dynamically adjust sterilization parameters to adapt to different bacterial species and environmental changes, thereby improving the consistency and reliability of the sterilization effect.

[0217] (4) Data support optimization: The sterilization effect evaluation report provides detailed data support and provides a reference basis for subsequent equipment optimization and improvement.

[0218] Obviously, the embodiments described above are only some of the embodiments of the present application, rather than all of the embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing the embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application has been described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions described in the aforementioned specific embodiments, or to make equivalent replacements for some of the technical features therein. Any equivalent structure made using the contents of the present application specification and the accompanying drawings, directly or indirectly used in other related technical fields, is also within the scope of patent protection of the present application.

Claims

1. A method for optimizing the temperature and pressure curve of a medical high-pressure steam sterilizer, characterized in that: include: Sensors are used to obtain bacterial characteristic data and environmental parameters to generate raw data sequences; bacterial characteristic data include bacterial species, quantity, and population density; Environmental parameters include initial temperature, initial humidity, and initial pressure; Performing data cleaning and standardization on the raw data sequence to generate a high-quality data set; Based on the high-quality data set, a deep learning algorithm is used to train a sterilization process model to generate temperature, pressure, and time optimization curves suitable for different bacterial species; Inputting the optimization curve into a real-time control system to adjust the operating parameters of the high-pressure steam sterilizer; specifically comprising: During the sterilization process, the actual parameters collected are compared with the target parameters T in the optimization curve. set , P set Perform real-time comparison, calculate temperature deviation ΔT and pressure deviation ΔP, and analyze the operating status of the equipment. If the deviation exceeds the set threshold, the operating parameters of the equipment need to be adjusted to make the actual operating status close to the target value of the optimization curve; The adjustment formula is: T adjust =T set -k T ·ΔT P adjust =P set -k P ·ΔP Among them, k T and k P is the adjustment coefficient of temperature and pressure; T adjust and P adjust are the adjusted temperature and pressure set values ​​respectively; T set P is the set temperature when the sterilizer is running; set The set pressure for the sterilizer during operation; The adjusted temperature T adjust and pressure P adjust Input into the real-time control system to update the operating parameters of the equipment; During the sterilization process, dynamic adjustments are made based on the equipment operation data, and a sterilization effect evaluation report is generated; the sterilization effect evaluation report includes the calculation of the sterilization effect parameter sterilization factor F0(t), the formula is: Among them, F0(t) is the bactericidal factor; T actual (t) represents the actual measured temperature sequence; T ref represents the reference temperature; z represents the heat resistance coefficient of the strain; τ set is the duration of the sterilization cycle; Calculating Energy Efficiency System E efficiency , the formula is: Among them, E efficiency is the sterilization energy efficiency coefficient; E total is the total energy consumption; Integrate the analyzed sterilization results with energy-saving data to generate a detailed sterilization effect evaluation report.

2. The method for optimizing the temperature and pressure curve of a medical high-pressure steam sterilizer according to claim 1, wherein: The step of inputting the optimization curve into the real-time control system comprises: The generated temperature, pressure and time optimization curves are input into the real-time control system of the high-pressure steam sterilizer; The operating parameters of the sterilizer are initialized according to the optimization curve, including initial temperature, initial pressure and initial time.

3. A medical high-pressure steam sterilizer temperature and pressure curve optimization system, applied to the method according to any one of claims 1-2, characterized in that: include: Data acquisition module, used to collect bacterial characteristic data, environmental parameters and equipment operation data through high-precision sensors; The data processing module is used to clean and standardize the collected raw data, extract key features and generate high-quality data sets; A deep learning modeling module is used to train a deep learning model of the sterilization process based on processed high-quality data sets, generating temperature, pressure, and time optimization curves suitable for different bacterial species; The real-time control and feedback module inputs the generated optimization curve into the real-time control system of the high-pressure steam sterilizer and dynamically adjusts the operating parameters according to the real-time feedback; The data analysis and reporting module is used to analyze the actual operating data and energy-saving effects of the sterilization process and generate a sterilization effect evaluation report.

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