A method, system, equipment and medium for humidification control of a sterilizer

Abstract

This invention provides a humidification control method, system, device, and medium for sterilizers, relating to the field of sterilizer control technology. The method includes acquiring internal environmental parameters and equipment data of the sterilizer, inputting the equipment data into a three-dimensional computer design model to construct a geometric model; using computational fluid dynamics software, inputting the geometric model and environmental parameters of the sterilizer, and performing numerical simulation to obtain the steam flow pattern and distribution data inside the sterilizer; identifying cold spots inside the sterilizer using an algorithm, analyzing the relationship between the cold spots and humidification control parameters, and obtaining the identification results; dynamically adjusting the humidification amount through a feedback control mechanism to optimize the control parameters; and evaluating and adjusting the optimized control parameters based on real-time monitoring data and user feedback to complete the humidification control of the sterilizer. This invention not only improves the quality and efficiency of the sterilization process but also reduces energy consumption and operating costs.

Description

Technical Field

[0001] This invention relates to the field of sterilizer control technology, and more specifically, to a sterilizer humidification control method, system, device, and medium. Background Technology

[0002] With the development of medical technology and the increasing demand for sterile environments, sterilizers, as indispensable equipment in hospitals, laboratories, and other similar settings, directly impact the effectiveness of disinfection and sterilization. Currently, humidification control methods for sterilizers primarily rely on experience and simple feedback adjustments, which often lack precision and adaptability. However, in practical applications, the uneven flow and distribution of steam within the sterilizer frequently creates cold spots, leading to incomplete sterilization. Existing technologies typically address this issue through static parameter settings and periodic manual checks, but this method is inefficient and difficult to adapt to changes in operating conditions and loading status. Summary of the Invention

[0003] The purpose of this invention is to provide a humidification control method, system, device, and medium for sterilizers to improve the aforementioned problems. To achieve the above objective, the technical solution adopted by this invention is as follows:

[0004] Firstly, this application provides a method for obtaining internal environmental parameters and equipment data of the sterilizer based on its working environment and design parameters, and inputting the equipment data into a three-dimensional computer design model to construct a geometric model. The environmental parameters include temperature, humidity and pressure, and the equipment data includes the dimensions of the sterilizer, the location of the steam inlet and the load distribution.

[0005] Using computational fluid dynamics software, the geometric model and environmental parameters of the sterilizer are input. After numerical simulation processing, including mesh generation, boundary condition setting and solution calculation, the steam flow pattern and distribution data inside the sterilizer have been obtained. The steam flow pattern includes the steam velocity, flow direction and temperature distribution, and the distribution data includes the spatial distribution of steam concentration and temperature inside the sterilizer.

[0006] Based on the simulated steam flow and distribution data, the algorithm identifies the cold spot areas inside the sterilizer and analyzes the relationship between the cold spot areas and the humidification control parameters. The humidification control strategy is then optimized to obtain the identification results. The cold spot areas are those where the steam temperature is below a set threshold. The humidification control parameters include steam input rate, humidification rate, and load distribution.

[0007] Based on the identification results, an intelligent humidification control logic is designed to dynamically adjust the humidification amount and optimize the control parameters through a feedback control mechanism until the steam flow and distribution inside the sterilizer are uniform and the cold spot area is minimized. The optimized control parameters are then evaluated and adjusted based on real-time monitoring data and user feedback to complete the humidification control of the sterilizer.

[0008] Preferably, the computational fluid dynamics software is used to input the geometric model and environmental parameters of the sterilizer, and through numerical simulation processing, including mesh generation, boundary condition setting, and solution calculation, to obtain the steam flow pattern and distribution data inside the sterilizer, including:

[0009] Based on the design drawings and dimensional parameters of the sterilizer, a three-dimensional geometric model of the sterilizer was constructed using CAD software. The CAD model was then imported into CFD software for geometric repair and simplification. The Delaunay triangulation method was used to generate the internal mesh, and local mesh refinement technology was employed to optimize the mesh density in key areas. The three-dimensional geometric model includes the cavity, steam inlet, exhaust outlet, and internal shelves.

[0010] Based on the working parameters of the sterilizer, boundary conditions are set, the initial temperature and humidity inside the sterilizer are set, the initial state is simulated, and the turbulent characteristics of the steam flow inside the sterilizer are defined using the Reynolds-averaged equation and the turbulence model.

[0011] Based on the physical properties of steam, the thermodynamic and hydrodynamic control equations of steam in the sterilizer are established, and a phase change model of steam is introduced according to the control equations. After the Dufour effect and Soret effect analysis, a steam flow and heat transfer model considering the influence of phase change is obtained.

[0012] A semi-implicit method using pressure-coupled equations is used to solve the steam flow and heat transfer model, extracting key flow and heat transfer data.

[0013] Based on the flow and heat transfer data, the backward Eulerian method was applied, and after time discretization, the data on the steam velocity, flow direction, temperature, and humidity distribution were obtained.

[0014] Preferably, the step of identifying cold spot regions inside the sterilizer using an algorithm based on simulated steam flow and distribution data, analyzing the relationship between cold spot regions and humidification control parameters, optimizing the humidification control strategy, and obtaining the identification results includes:

[0015] Based on the steam flow and distribution data obtained from numerical simulation, after data cleaning and standardization, outliers and noise interference were removed. Then, empirical mode decomposition was used to extract key features of steam flow from the raw data. These key features include the key distribution features of flow velocity, flow direction, temperature and humidity, resulting in feature extraction results.

[0016] The DBSCAN clustering algorithm was used to perform cluster analysis on the feature extraction results. The cluster analysis adopted the local distribution characteristics of steam temperature in the sterilizer and divided the internal area of ​​the sterilizer into cold point and non-cold point areas. A threshold Tth for identifying cold point areas was set.

[0017] Based on data from cold and non-cold areas, the Pearson correlation coefficient was used to quantify the correlation between the cold area and each humidification control parameter, and the correlation analysis results were obtained.

[0018] The correlation analysis results are processed by an optimization algorithm to obtain the minimized objective function F, and the optimal combination of humidification control parameters is obtained, thus yielding the optimized humidification control strategy.

[0019] Preferably, based on the identification results, an intelligent humidification control logic is designed to dynamically adjust the humidification amount and optimize control parameters through a feedback control mechanism until uniform steam flow and distribution inside the sterilizer and minimization of the cold spot area are achieved. This includes:

[0020] Based on the identified cold spot areas and the results of correlation analysis, a control performance evaluation model is obtained after defining performance indicators and constructing objective functions. Performance indicators are designed, including steam distribution uniformity index U and cold spot area coverage index C.

[0021] Based on performance indicators and control performance evaluation models, the error-compensated control algorithm uses the difference between the performance indicators and the preset target as a feedback signal to dynamically adjust the humidification amount.

[0022] Based on the feedback control algorithm, an optimized humidification control strategy is obtained through real-time monitoring and adaptive adjustment. The control gain β is dynamically adjusted based on real-time control performance indicators to achieve the goal of uniform steam distribution and minimizing the cold spot area.

[0023] Secondly, this application also provides a sterilizer humidification control system, comprising:

[0024] The construction module is used to obtain the internal environmental parameters and equipment data of the sterilizer based on the working environment and design parameters of the sterilizer, and input the equipment data into the three-dimensional computer design model to build the geometric model. The environmental parameters include temperature, humidity and pressure, and the equipment data includes the size of the sterilizer, the location of the steam inlet and the load distribution.

[0025] Processing module: Used to input the geometric model and environmental parameters of the sterilizer using computational fluid dynamics software, and through numerical simulation processing, including mesh generation, boundary condition setting and solution calculation, to obtain the steam flow pattern and distribution data inside the sterilizer. The steam flow pattern includes the steam velocity, flow direction and temperature distribution, and the distribution data includes the spatial distribution of steam concentration and temperature inside the sterilizer.

[0026] The optimization identification module is used to identify cold spot areas inside the sterilizer based on the simulated steam flow and distribution data, analyze the relationship between cold spot areas and humidification control parameters, optimize the humidification control strategy, and obtain the identification results. The cold spot area is the area where the steam temperature is lower than the set threshold. The humidification control parameters include steam input rate, humidification amount, and load distribution.

[0027] Control module: Based on the identification results, it designs intelligent humidification control logic to dynamically adjust the humidification amount and optimize control parameters through a feedback control mechanism until the steam flow and distribution inside the sterilizer are uniform and the cold spot area is minimized; and it evaluates and adjusts the optimized control parameters based on real-time monitoring data and user feedback to complete the humidification control of the sterilizer.

[0028] Thirdly, this application also provides a sterilizer humidification control device, comprising:

[0029] memory for storing computer programs;

[0030] A processor is configured to implement the steps of the sterilizer humidification control method when executing the computer program.

[0031] Fourthly, this application also provides a readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the sterilizer-based humidification control method described above.

[0032] The beneficial effects of this invention are as follows:

[0033] This invention, through the implementation of intelligent humidification control logic, enables precise regulation of steam flow and distribution within the sterilizer, thereby improving sterilization efficiency. Because the humidification control parameters are optimized, ideal sterilization conditions can be achieved more quickly.

[0034] By minimizing cold spot areas and optimizing the uniformity of steam distribution, this invention ensures that every area in the sterilization process meets the necessary sterilization standards, reducing the risk of sterilization failure due to uneven temperature distribution. The intelligent control system dynamically adjusts the humidification amount based on real-time data, avoiding over-humidification or under-humidification, thereby reducing energy and water waste and achieving energy conservation and consumption reduction.

[0035] The ability of this invention to adaptively adjust the control gain β enables the system to adapt to different working conditions and sterilizer loading, enhancing the system's versatility and adaptability; by optimizing steam flow and distribution, it reduces thermal stress on the internal structure of the sterilizer, helping to extend the equipment's service life.

[0036] The intelligent control system of this invention dynamically adjusts the humidification amount based on real-time data, avoiding over-humidification or under-humidification, thereby reducing the waste of energy and water resources and achieving energy conservation and consumption reduction. According to the characteristics and loading mode of different sterilized items, this invention can customize personalized sterilization solutions to achieve more precise and efficient sterilization results.

[0037] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing embodiments of the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings. Attached Figure Description

[0038] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 This is a schematic diagram of the humidification control method for a sterilizer as described in an embodiment of the present invention;

[0040] Figure 2 This is a schematic diagram of the humidification control system for the sterilizer described in an embodiment of the present invention;

[0041] Figure 3 This is a schematic diagram of the humidification control device for the sterilizer described in an embodiment of the present invention.

[0042] In the diagram: 701, Construction module; 702, Processing module; 7021, First processing unit; 7022, Setting unit; 7023, Analysis module unit; 7024, Extraction unit; 7025, Second processing unit; 703, Optimization and identification module; 7031, Decomposition unit; 7032, Clustering unit; 7033, Correlation analysis unit; 7034, Third processing unit; 704, Control module; 7041, Construction unit; 7042, Adjustment unit; 7043, Optimization unit; 800, Sterilizer humidification control device; 801, Processor; 802, Memory; 803, Multimedia component; 804, I / O interface; 805, Communication component. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0044] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0045] Example 1:

[0046] This embodiment provides a method for controlling the humidification of a sterilizer.

[0047] See Figure 1 The figure shows that the method includes steps S100, S200, S300 and S400.

[0048] S100. Based on the working environment and design parameters of the sterilizer, obtain the internal environmental parameters and equipment data of the sterilizer, and input the equipment data into the three-dimensional computer design model to construct the geometric model. The environmental parameters include temperature, humidity and pressure, and the equipment data includes the dimensions of the sterilizer, the location of the steam inlet and the load distribution.

[0049] It should be noted that the geometric model details the internal structure of the sterilizer, including but not limited to the cavity shape, steam inlet, exhaust outlet, internal obstructions (such as shelves or partitions), and the geometry of the items loaded for sterilization. In the CAD model, all features are accurately represented to their actual dimensions to facilitate subsequent mesh generation and fluid dynamics analysis.

[0050] S200. Using computational fluid dynamics software, the geometric model and environmental parameters of the sterilizer are input. After numerical simulation processing, including mesh generation, boundary condition setting and solution calculation, the steam flow pattern and distribution data inside the sterilizer have been obtained. The steam flow pattern includes the steam velocity, flow direction and temperature distribution. The distribution data includes the spatial distribution of steam concentration and temperature inside the sterilizer.

[0051] It is understood that step S200 includes S201, S202, S203, S204, and S205:

[0052] S201. Based on the design drawings and dimensional parameters of the sterilizer, a three-dimensional geometric model of the sterilizer is constructed using CAD software. The CAD model is then imported into CFD software for geometric repair and simplification. The Delaunay triangulation method is used to generate an internal mesh, and local mesh refinement technology is employed to optimize the mesh density in key areas. The three-dimensional geometric model includes the cavity, steam inlet, exhaust outlet, and internal shelves.

[0053] The sterilizer geometry model generated in the CAD software is imported into the CFD preprocessing software. Geometric repair is performed to eliminate any non-manifold geometric errors and complex features are simplified to reduce model complexity, while preserving details of the steam inlet and the expected cold spot area.

[0054] During the mesh refinement process, a leading-edge advancement mesh generation algorithm is used to mesh the entire sterilizer cavity. This algorithm starts from the surface of the geometry and grows progressively inward to form a polygonal mesh. The mesh refinement process involves: before refinement, sensitivity analysis is performed to determine the areas requiring refinement, involving preliminary simulations at different mesh densities to assess the impact of mesh size on the simulation results. Local mesh refinement algorithms are applied in the steam inlet and expected cold spot regions, including geometry-based mesh refinement. Near the steam inlet, the mesh is refined according to the following formula:

[0055]

[0056] Where λnew and λold are the feature lengths of the new and old meshes, respectively, dold and dnew are the mesh sizes of the corresponding regions, and n is the user-defined refinement level.

[0057] A growth factor and number of layers are defined to control the mesh refinement from the steam inlet outwards. The growth factor determines the rate of increase in mesh size per layer, while the number of layers determines the depth of the refined region. In some cases, adaptive mesh refinement techniques may be required, which involves dynamically adjusting the mesh size according to certain criteria during the simulation. For example, the mesh can be automatically refined in high-gradient regions of steam flow. Boundary layer meshes are created at the steam inlet and cavity walls to capture boundary layer effects. This involves generating denser mesh layers near the walls and using wall functions or low Reynolds number models to simulate the flow in the near-wall region. Therefore, the above steps ensure that the mesh inside the sterilizer, especially at the steam inlet and the expected cold spot region, is sufficiently refined to accurately capture key characteristics of steam flow and heat transfer.

[0058] S202. Based on the working parameters of the sterilizer, set the boundary conditions, set the initial temperature and humidity inside the sterilizer, simulate the initial state, and define the turbulent characteristics of the steam flow inside the sterilizer using the Reynolds-averaged equation and turbulence model.

[0059] It should be noted that boundary conditions are set based on the sterilizer's operating parameters and environmental conditions. This includes the no-slip condition on the inner wall of the chamber (u = v = 0), the fixed temperature and velocity conditions at the steam inlet, and the pressure conditions at the exhaust outlet. An initial state is also set, typically assuming that the air inside the chamber is stationary at the initial moment, with uniform temperature and humidity distribution consistent with environmental conditions. The turbulent characteristics of the steam flow are defined using the Reynolds-averaged Navier-Stokes equations (RANS) and turbulence models, such as the k-ε or k-ω models. It is understood that turbulence models can describe the randomness and disorder of the flow, which is crucial for predicting the mixing and heat transfer characteristics of the flow. Step S202 ensures the accuracy and reliability of the numerical simulation, making the simulation results closer to reality and providing a solid foundation for subsequent establishment of governing equations and numerical solutions.

[0060] S203. Based on the physical properties of steam, establish the thermodynamic and hydrodynamic control equations of steam in the sterilizer, and introduce the phase change model of steam according to the control equations. After the Dufour effect and Soret effect analysis, obtain the steam flow and heat transfer model considering the phase change effect.

[0061] Understandably, based on the physical properties of steam, such as density, viscosity, specific heat capacity, and thermal conductivity, governing equations for thermodynamics and fluid mechanics are established. These equations include, but are not limited to, the continuity equation (mass conservation), the momentum conservation equation (Navier-Stokes equation), and the energy conservation equation. Considering the phase change characteristics of steam at different temperatures, a phase change model is introduced. This may include the release or absorption of latent heat, and the impact of phase change on flow and heat transfer. Furthermore, through the Dufour and Soret effects, the influence of temperature and concentration gradients on steam flow and heat transfer is considered, resulting in a more accurate model of steam flow and heat transfer. By measuring steam pressure and density at different temperatures, the thermodynamic property model can be verified and calibrated.

[0062] S204. Use a semi-implicit method with pressure coupling equations to solve the steam flow and heat transfer model and extract key flow and heat transfer data.

[0063] It should be noted that a numerical solver, such as the SIMPLE algorithm, is used to solve the established governing equations. The SIMPLE algorithm is a pressure-coupled numerical solution method that can effectively handle the coupling effects in flow and heat transfer problems. During the solution process, the governing equations need to be discretized, transforming continuous differential equations into discrete algebraic equations. This includes, but is not limited to, using the finite volume method or the finite element method, which are existing technologies and will not be elaborated upon here. Through numerical solutions, the velocity, temperature, and humidity fields of the steam flow can be obtained, providing a basis for subsequent data analysis and optimization.

[0064] S205. Based on the flow and heat transfer data, the backward Euler method is applied, and after time discretization, the flow velocity, flow direction, temperature and humidity distribution data of the steam are obtained.

[0065] It should be noted that, based on the flow and heat transfer data, the backward Euler method is applied for time discretization. The backward Euler method is a single-step time integration method suitable for solving unsteady flow and heat transfer problems. Time discretization involves discretizing the continuous time domain into a series of time steps, solving the transient flow and heat transfer problem at each time step. Therefore, at each time step, the flow and heat transfer states need to be updated, including updates to physical quantities such as velocity, pressure, temperature, and humidity. For example, for rapidly changing processes, a smaller time step may be needed to capture transient characteristics; while for slowly changing processes, a larger time step can be used to reduce computational load. Furthermore, the choice of time discretization method can also affect the stability and accuracy of the numerical solution, requiring optimization based on specific circumstances.

[0066] S300. Based on the simulated steam flow and distribution data, the algorithm identifies the cold spot area inside the sterilizer and analyzes the relationship between the cold spot area and the humidification control parameters. The humidification control strategy is optimized to obtain the identification result. The cold spot area is the area where the steam temperature is lower than the set threshold. The humidification control parameters include steam input rate, humidification amount and load distribution.

[0067] It is understood that step S300 includes S301, S302, S303, and S304, wherein:

[0068] S301. Based on the steam flow and distribution data obtained from numerical simulation, after data cleaning and standardization, outliers and noise interference are removed. Then, empirical mode decomposition is used to extract key features of steam flow from the original data. These key features include key distribution features of flow velocity, flow direction, temperature and humidity, and feature extraction results are obtained.

[0069] It should be noted that, based on the raw steam flow and distribution data obtained from numerical simulations, data cleaning is first performed to remove outliers from the dataset. This includes identifying and deleting or filling in missing data points, as well as identifying and handling abnormal temperature or humidity readings that are outside the normal operating range. The data is then standardized to eliminate the influence of different dimensions and magnitudes. Standardization methods include Z-score standardization or min-max standardization, ensuring that all feature data are on the same scale.

[0070] The standardized data is then processed using the EMD algorithm to extract intrinsic patterns. EMD decomposes complex data into a series of Intrinsic Mode Functions (IMFs), each representing a fundamental oscillation mode. Key features reflecting steam flow characteristics are identified from the IMFs obtained through EMD decomposition. These key features include the distribution characteristics of flow velocity, flow direction, temperature, and humidity, which are representative indicators of steam flow characteristics. By analyzing the physical meaning and statistical properties of the IMFs, the features most relevant to sterilizer performance are determined. For example, the IMF representing the highest energy content might be selected to represent the main steam flow mode, or the IMF most relevant to temperature distribution might be selected to characterize heat transfer characteristics. Finally, the extracted key features are integrated to form the feature extraction results, providing quantitative data support for subsequent cold spot region identification and humidification control parameter optimization.

[0071] S302. The DBSCAN clustering algorithm is used to perform cluster analysis on the feature extraction results. The cluster analysis uses the local distribution characteristics of steam temperature inside the sterilizer to divide the internal area of ​​the sterilizer into cold point and non-cold point areas. The threshold Tth for identifying cold point areas is set and calculated using the following formula:

[0072] Tcold = min(Tregion)

[0073] In the formula, Tregion represents the temperature distribution within a specific region, and Tcold represents the lowest temperature within that region. It is compared with Tth to determine the cold point.

[0074] S303. Based on data from both cold and non-cold point regions, the Pearson correlation coefficient is applied to quantify the correlation between the cold point region and each humidification control parameter, yielding the correlation analysis results. The calculation formula is as follows:

[0075]

[0076] In the formula, Tcold is the temperature of the cold point region, P is the humidification parameter, r is the correlation coefficient of the humidification coefficient, and Tˉcold and Pˉ are the average values ​​of Tcold and P, respectively.

[0077] S304. The correlation analysis results are processed by an optimization algorithm to obtain the minimized objective function F, and the optimal combination of humidification control parameters is obtained, thereby obtaining the optimized humidification control strategy.

[0078] It should be noted that key features, such as minimum or average temperature, are selected from the cold spot region data. These features characterize the thermal state of the cold spot region. Then, humidification control parameters are defined, including those affecting steam flow and distribution, such as steam input rate, humidification rate, and load distribution. In step S304, based on the correlation analysis results, an objective function F is constructed. This function reflects the non-correlation between humidification control parameters and the temperature of the cold spot region. The goal is to minimize F to optimize the humidification control parameters. A suitable optimization algorithm is selected, including but not limited to genetic algorithms, particle swarm optimization, or simulated annealing algorithms, to search for the optimal combination of humidification control parameters. The objective function is input into the optimization algorithm, the search range and optimization criteria for the parameters are defined, the optimization algorithm is run to search for parameters, and the output of the optimization algorithm is analyzed to obtain the optimal combination of humidification control parameters. These parameters minimize the objective function F. Based on the optimization results, an optimized humidification control strategy is formed. This strategy includes adjusting the steam input rate, humidification rate, and load distribution to reduce the cold spot region and improve sterilization efficiency. Finally, the optimized humidification control strategy is prepared for implementation, including updating the parameter settings of the control system and making necessary hardware adjustments. Therefore, through steps S303 and S304, the correlation between the cold spot region and the humidification control parameters can be systematically analyzed, and the optimal combination of control parameters can be found through optimization algorithms, thereby formulating a more effective humidification control strategy.

[0079] S400: Based on the identification results, an intelligent humidification control logic is designed to dynamically adjust the humidification amount and optimize the control parameters through a feedback control mechanism until the steam flow and distribution inside the sterilizer are uniform and the cold spot area is minimized. The optimized control parameters are then evaluated and adjusted based on real-time monitoring data and user feedback to complete the humidification control of the sterilizer.

[0080] It is understood that step S400 includes S401, S402, and S403, wherein:

[0081] S401. Based on the identified cold spot areas and the results of correlation analysis, after defining performance indicators and constructing objective functions, a control performance evaluation model is obtained, and performance indicators are designed, including steam distribution uniformity index U and cold spot area coverage index C.

[0082] It should be noted that, based on the cold spot region data obtained through previous numerical simulations and empirical mode decomposition, the location and characteristics of the cold spot region are determined. By utilizing the correlation analysis results between the cold spot region and the humidification control parameters, the influence of different parameters on the cold spot region is understood. A control performance evaluation model is constructed, using U and C as key inputs to evaluate the effectiveness of the current humidification control strategy. This model will be used to guide subsequent control logic design and parameter optimization, ensuring that the humidification control strategy can effectively reduce the cold spot region and improve the uniformity of steam distribution.

[0083] S402. Based on performance indicators and a control performance evaluation model, the error-compensated control algorithm uses the difference between the performance indicators and the preset target as a feedback signal to dynamically adjust the humidification rate. The calculation formula is as follows:

[0084] H add (t+1)=H add (t)+β·(U target -U(t))

[0085] In the formula, Hadd(t) is the humidification amount at time tt, Utarget is the target uniformity index, U(t) is the current uniformity index, and β is the control gain;

[0086] S403. Based on the feedback control algorithm, an optimized humidification control strategy is obtained through real-time monitoring and adaptive adjustment. The control gain β is dynamically adjusted based on real-time control performance indicators to achieve the goal of uniform steam distribution and minimizing the cold spot area.

[0087] It should be noted that the humidification control strategy is optimized by combining real-time monitoring data and adaptively adjusted control gain. The steam input rate, humidification rate, and load distribution are adjusted to respond to real-time changes in steam distribution. The optimized humidification control strategy is then applied to the closed-loop control system for iterative optimization. The system will continuously run, collecting data, evaluating performance indicators, adjusting control gain, and optimizing the control strategy. The iterative optimization of the humidification control strategy is verified to ensure that it achieves the goals of uniform steam distribution and minimizing the cold spot area. If these goals are achieved, the current strategy is maintained; otherwise, adjustments and optimizations continue.

[0088] Example 2:

[0089] like Figure 2 As shown, this embodiment provides a sterilizer humidification control system. (See attached image) Figure 2 The system includes:

[0090] Module 701: Used to obtain the internal environmental parameters and equipment data of the sterilizer based on the working environment and design parameters of the sterilizer, and input the equipment data into the three-dimensional computer design model to build the geometric model. The environmental parameters include temperature, humidity and pressure, and the equipment data includes the size of the sterilizer, the location of the steam inlet and the load distribution.

[0091] Processing module 702: Used to input the geometric model and environmental parameters of the sterilizer using computational fluid dynamics software, and through numerical simulation processing, including mesh generation, boundary condition setting and solution calculation, to obtain the steam flow pattern and distribution data inside the sterilizer. The steam flow pattern includes the steam velocity, flow direction and temperature distribution, and the distribution data includes the spatial distribution of steam concentration and temperature inside the sterilizer.

[0092] Optimization identification module 703: Based on the simulated steam flow and distribution data, it identifies the cold spot area inside the sterilizer through an algorithm, analyzes the relationship between the cold spot area and the humidification control parameters, optimizes the humidification control strategy, and obtains the identification result. The cold spot area is the area where the steam temperature is lower than the set threshold. The humidification control parameters include steam input rate, humidification amount, and load distribution.

[0093] Control module 704: Based on the identification results, it designs intelligent humidification control logic to dynamically adjust the humidification amount and optimize control parameters through a feedback control mechanism until the steam flow and distribution inside the sterilizer are uniform and the cold spot area is minimized; and it evaluates and adjusts the optimized control parameters based on real-time monitoring data and user feedback, thereby completing the humidification control of the sterilizer.

[0094] Specifically, the processing module 702 includes:

[0095] First processing unit 7021: Used to construct a three-dimensional geometric model of the sterilizer using CAD software based on the design drawings and dimensional parameters of the sterilizer, import the CAD model into CFD software for geometric repair and simplification, generate an internal mesh using the Delaunay triangulation method, and optimize the mesh density of key areas using local mesh refinement technology; the three-dimensional geometric model includes the cavity, steam inlet, exhaust outlet and internal shelves.

[0096] Setting unit 7022: is used to set boundary conditions, set the initial temperature and humidity inside the sterilizer according to the working parameters of the sterilizer, simulate the initial state, and define the turbulent characteristics of steam flow inside the sterilizer using the Reynolds-averaged equation and turbulence model.

[0097] Analysis module unit 7023: It is used to establish the control equations of thermodynamics and fluid dynamics of steam in the sterilizer based on the physical properties of steam, and introduce the phase change model of steam according to the control equations. After the Dufour effect and Soret effect analysis, the steam flow and heat transfer model considering the phase change effect is obtained.

[0098] Extraction Unit 7024: Used to solve the steam flow and heat transfer model using a semi-implicit method with pressure coupling equations, and extract key flow and heat transfer data;

[0099] The second processing unit 7025 is used to obtain the steam velocity, flow direction, temperature and humidity distribution data by applying the backward Euler method and time discretization processing based on the flow and heat transfer data.

[0100] Specifically, the optimized recognition module 703 includes:

[0101] Decomposition unit 7031: Based on the steam flow and distribution data obtained from numerical simulation, after data cleaning and standardization, outliers and noise interference are removed, and empirical mode decomposition is used to extract key features of steam flow from the original data. The key features include key distribution features of flow velocity, flow direction, temperature and humidity, and the feature extraction results are obtained.

[0102] Clustering unit 7032: Used to perform cluster analysis on the feature extraction results using the DBSCAN clustering algorithm. The cluster analysis uses the local distribution characteristics of steam temperature inside the sterilizer to divide the internal area of ​​the sterilizer into cold point and non-cold point regions. The threshold Tth for identifying cold point regions is set and calculated using the following formula:

[0103] Tcold = min(Tregion)

[0104] In the formula, Tregion represents the temperature distribution within a specific region, and Tcold represents the lowest temperature within that region. It is compared with Tth to determine the cold point.

[0105] Correlation analysis unit 7033: Used to quantify the correlation between the cold point area and various humidification control parameters based on data from both cold and non-cold point areas, applying the Pearson correlation coefficient to obtain the correlation analysis results. The calculation formula is as follows:

[0106]

[0107] In the formula, Tcold is the temperature of the cold point region, P is the humidification parameter, r is the correlation coefficient of the humidification coefficient, and Tˉcold and Pˉ are the average values ​​of Tcold and P, respectively.

[0108] The third processing unit 7034 is used to perform optimization algorithm processing on the correlation analysis results to obtain the minimized objective function F, and to obtain the optimal combination of humidification control parameters, thereby obtaining the optimized humidification control strategy.

[0109] Specifically, the control module 704 includes:

[0110] Building unit 7041: is used to obtain a control performance evaluation model and design performance indicators based on the identified cold spot areas and correlation analysis results, after defining performance indicators and constructing objective functions. The performance indicators include steam distribution uniformity index U and cold spot area coverage index C.

[0111] Adjustment unit 7042: Used for dynamically adjusting the humidification amount based on performance indicators and control performance evaluation models, using the difference between the performance indicators and preset targets as feedback signals in the error-compensated control algorithm. The calculation formula is as follows:

[0112] H add (t+1)=H add (t)+β·(U target -U(t))

[0113] In the formula, Hadd(t) is the humidification amount at time tt, Utarget is the target uniformity index, U(t) is the current uniformity index, and β is the control gain;

[0114] Optimization unit 7043: Based on the feedback control algorithm, through real-time monitoring and adaptive adjustment, it obtains an optimized humidification control strategy and dynamically adjusts the control gain β based on real-time control performance indicators, thereby achieving the goal of uniform steam distribution and minimizing the cold spot area.

[0115] It should be noted that the specific methods by which each module performs operations in the system described in the above embodiments have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0116] Example 3:

[0117] Corresponding to the above method embodiments, this embodiment also provides a sterilizer humidification control device. The sterilizer humidification control device described below and the sterilizer humidification control method described above can be referred to in correspondence.

[0118] Figure 3 This is a block diagram illustrating a sterilizer humidification control device 800 according to an exemplary embodiment. Figure 3 As shown, the sterilizer humidification control device 800 includes a processor 801 and a memory 802. The sterilizer humidification control device 800 also includes one or more of a multimedia component 803, an I / O interface 804, and a communication component 805.

[0119] The processor 801 controls the overall operation of the sterilizer humidification control device 800 to complete all or part of the steps in the sterilizer humidification control method described above. The memory 802 stores various types of data to support the operation of the sterilizer humidification control device 800. This data may include, for example, instructions for any application or method operating on the sterilizer humidification control device 800, as well as application-related data such as contact data, sent and received messages, images, audio, video, etc. The memory 802 can be implemented using any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touchscreen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in the memory 802 or transmitted via the communication component 805. The audio component also includes at least one speaker for outputting audio signals. I / O interface 804 provides an interface between processor 801 and other interface modules, such as keyboards, mice, or buttons. These buttons can be virtual or physical. Communication component 805 is used for wired or wireless communication between the sterilizer humidification control device 800 and other devices. Wireless communication includes, for example, Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination thereof. Therefore, the corresponding communication component 805 may include a Wi-Fi module, a Bluetooth module, or an NFC module.

[0120] In an exemplary embodiment, the sterilizer humidification control device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the sterilizer humidification control method described above.

[0121] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, which, when executed by a processor, implement the steps of the sterilizer humidification control method described above. For example, the computer-readable storage medium may be the memory 802 including the program instructions described above, which may be executed by the processor 801 of the sterilizer humidification control device 800 to complete the sterilizer humidification control method described above.

[0122] Example 4:

[0123] Corresponding to the above method embodiments, this embodiment also provides a readable storage medium. The readable storage medium described below can be referred to in conjunction with the sterilizer humidification control method described above.

[0124] A computer program is stored on a readable storage medium, and when executed by a processor, the computer program implements the steps of the sterilizer humidification control method of the above method embodiments.

[0125] Specifically, the readable storage medium can be a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, or any other readable storage medium capable of storing program code.

[0126] In summary, this invention achieves precise control of steam flow and distribution inside the sterilizer through numerical simulation and intelligent control technology. First, computational fluid dynamics (CFD) software is used to simulate the steam flow and distribution inside the sterilizer. Then, empirical mode decomposition (EMD) is used to extract key steam flow characteristics. Based on these characteristics and correlation analysis, an intelligent humidification control logic is developed. This logic employs an adaptive PID control algorithm and reinforcement learning strategy to adjust the humidification amount and control parameters in real time to achieve uniform steam distribution and minimize cold spot areas. This can significantly improve the operating efficiency and sterilization quality of the sterilizer while reducing energy consumption and operating costs.

[0127] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0128] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for controlling humidification in a sterilizer, characterized in that, include: Based on the working environment and design parameters of the sterilizer, the internal environmental parameters and equipment data of the sterilizer are obtained, and the equipment data is input into the three-dimensional computer design model to construct the geometric model. The environmental parameters include temperature, humidity and pressure, and the equipment data includes the size of the sterilizer, the location of the steam inlet and the load distribution. Using computational fluid dynamics software, the geometric model and environmental parameters of the sterilizer are input. After numerical simulation processing, including mesh generation, boundary condition setting and solution calculation, the steam flow pattern and distribution data inside the sterilizer have been obtained. The steam flow pattern includes the steam velocity, flow direction and temperature distribution, and the distribution data includes the spatial distribution of steam concentration and temperature inside the sterilizer. Based on the simulated steam flow and distribution data, the algorithm identifies the cold spot areas inside the sterilizer and analyzes the relationship between the cold spot areas and the humidification control parameters. The humidification control strategy is then optimized to obtain the identification results. The cold spot areas are those where the steam temperature is below a set threshold. The humidification control parameters include steam input rate, humidification rate, and load distribution. Based on the identification results, an intelligent humidification control logic is designed to dynamically adjust the humidification amount and optimize the control parameters through a feedback control mechanism until the steam flow and distribution inside the sterilizer are uniform and the cold spot area is minimized. The optimized control parameters are then evaluated and adjusted based on real-time monitoring data and user feedback to complete the humidification control of the sterilizer. The process of identifying cold spot regions inside the sterilizer based on simulated steam flow and distribution data using an algorithm, analyzing the relationship between these cold spot regions and humidification control parameters, optimizing the humidification control strategy, and obtaining the identification results includes: Based on the steam flow and distribution data obtained from numerical simulation, after data cleaning and standardization, outliers and noise interference were removed. Then, empirical mode decomposition was used to extract key features of steam flow from the raw data. These key features include the key distribution features of flow velocity, flow direction, temperature and humidity, resulting in feature extraction results. The DBSCAN clustering algorithm was used to perform cluster analysis on the feature extraction results. The cluster analysis utilized the local distribution characteristics of steam temperature within the sterilizer to divide the internal area of ​​the sterilizer into cold and non-cold point regions. A threshold Tth for identifying cold point regions was set and calculated using the following formula: Tcold = min(Tregion) In the formula, Tregion represents the temperature distribution within a specific region, and Tcold represents the lowest temperature within that region. It is compared with Tth to determine the cold point. Based on data from both cold and non-cold point regions, the Pearson correlation coefficient was applied to quantify the correlation between the cold point region and various humidification control parameters, yielding the correlation analysis results. The calculation formula is as follows: In the formula, Tcold is the temperature of the cold spot region, P is the humidification parameter, and r is the correlation coefficient of the humidification coefficient. and These are the average values ​​of Tcold and P, respectively. The correlation analysis results are processed by an optimization algorithm to obtain the minimized objective function F, and the optimal combination of humidification control parameters is obtained, thus yielding the optimized humidification control strategy. The intelligent humidification control logic, based on the identification results, is designed to dynamically adjust the humidification amount and optimize control parameters through a feedback control mechanism until uniform steam flow and distribution within the sterilizer and minimization of the cold spot area are achieved. This includes: Based on the identified cold spot areas and the results of correlation analysis, a control performance evaluation model is obtained after defining performance indicators and constructing objective functions. Performance indicators are designed, including steam distribution uniformity index U and cold spot area coverage index C. Based on performance indicators and a control performance evaluation model, the error-compensated control algorithm uses the difference between the performance indicators and the preset target as a feedback signal to dynamically adjust the humidification rate. The calculation formula is as follows: H add (t+1)=H add (t)+β·(U target -U(t)) In the formula, Hadd(t) is the humidification amount at time t, Utarget is the target uniformity index, U(t) is the current uniformity index, and β is the control gain; Based on the feedback control algorithm, an optimized humidification control strategy is obtained through real-time monitoring and adaptive adjustment. The control gain β is dynamically adjusted based on real-time control performance indicators to achieve the goal of uniform steam distribution and minimizing the cold spot area.

2. The sterilizer humidification control method according to claim 1, characterized in that, The process utilizes computational fluid dynamics software, inputting the geometric model of the sterilizer and environmental parameters. Through numerical simulation, including mesh generation, boundary condition setting, and solution calculation, the steam flow patterns and distribution data inside the sterilizer are obtained, including: Based on the design drawings and dimensional parameters of the sterilizer, a three-dimensional geometric model of the sterilizer was constructed using CAD software. The CAD model was then imported into CFD software for geometric repair and simplification. The Delaunay triangulation method was used to generate the internal mesh, and local mesh refinement technology was employed to optimize the mesh density in key areas. The three-dimensional geometric model includes the cavity, steam inlet, exhaust outlet, and internal shelves. Based on the working parameters of the sterilizer, boundary conditions are set, the initial temperature and humidity inside the sterilizer are set, the initial state is simulated, and the turbulent characteristics of the steam flow inside the sterilizer are defined using the Reynolds-averaged equation and the turbulence model. Based on the physical properties of steam, the thermodynamic and hydrodynamic control equations of steam in the sterilizer are established, and a phase change model of steam is introduced according to the control equations. After the Dufour effect and Soret effect analysis, a steam flow and heat transfer model considering the influence of phase change is obtained. A semi-implicit method using pressure-coupled equations is used to solve the steam flow and heat transfer model, extracting key flow and heat transfer data. Based on the flow and heat transfer data, the backward Eulerian method was applied, and after time discretization, the data on the steam velocity, flow direction, temperature, and humidity distribution were obtained.

3. A sterilizer humidification control system, based on the sterilizer humidification control method of claim 1, characterized in that, include: The construction module is used to obtain the internal environmental parameters and equipment data of the sterilizer based on the working environment and design parameters of the sterilizer, and input the equipment data into the three-dimensional computer design model to build the geometric model. The environmental parameters include temperature, humidity and pressure, and the equipment data includes the size of the sterilizer, the location of the steam inlet and the load distribution. Processing module: Used to input the geometric model and environmental parameters of the sterilizer using computational fluid dynamics software, and through numerical simulation processing, including mesh generation, boundary condition setting and solution calculation, to obtain the steam flow pattern and distribution data inside the sterilizer. The steam flow pattern includes the steam velocity, flow direction and temperature distribution, and the distribution data includes the spatial distribution of steam concentration and temperature inside the sterilizer. The optimization identification module is used to identify cold spot areas inside the sterilizer based on the simulated steam flow and distribution data, analyze the relationship between cold spot areas and humidification control parameters, optimize the humidification control strategy, and obtain the identification results. The cold spot area is the area where the steam temperature is lower than the set threshold. The humidification control parameters include steam input rate, humidification amount, and load distribution. Control module: Based on the identification results, it designs intelligent humidification control logic to dynamically adjust the humidification amount and optimize control parameters through a feedback control mechanism until the steam flow and distribution inside the sterilizer are uniform and the cold spot area is minimized; and it evaluates and adjusts the optimized control parameters based on real-time monitoring data and user feedback to complete the humidification control of the sterilizer. The optimized recognition module includes: Decomposition unit: Based on the steam flow and distribution data obtained from numerical simulation, after data cleaning and standardization, outliers and noise interference are removed, and empirical mode decomposition is used to extract key features of steam flow from the original data. These key features include the key distribution features of flow velocity, flow direction, temperature and humidity, and the feature extraction results are obtained. Clustering Unit: Used to perform cluster analysis on the feature extraction results using the DBSCAN clustering algorithm. The cluster analysis utilizes the local distribution characteristics of steam temperature within the sterilizer to divide the internal area of ​​the sterilizer into cold and non-cold point regions. A threshold Tth for identifying cold point regions is set, calculated using the following formula: Tcold = min(Tregion) In the formula, Tregion represents the temperature distribution within a specific region, and Tcold represents the lowest temperature within that region. It is compared with Tth to determine the cold point. Correlation Analysis Unit: Used to quantify the correlation between the cold point area and various humidification control parameters based on data from both cold and non-cold point areas, applying the Pearson correlation coefficient to obtain the correlation analysis results. The calculation formula is as follows: In the formula, Tcold is the temperature of the cold spot region, P is the humidification parameter, and r is the correlation coefficient of the humidification coefficient. and These are the average values ​​of Tcold and P, respectively. The third processing unit is used to process the correlation analysis results using optimization algorithms to obtain the minimized objective function F, and to obtain the optimal combination of humidification control parameters, thereby obtaining the optimized humidification control strategy. The control module includes: Construction Unit: Based on the identified cold spot areas and correlation analysis results, the unit is used to define performance indicators and construct objective functions to obtain a control performance evaluation model and design performance indicators, including steam distribution uniformity index U and cold spot area coverage index C. Adjustment Unit: Used to dynamically adjust the humidification rate based on performance indicators and control performance evaluation models. The control algorithm, after error compensation, uses the difference between the performance indicators and the preset target as a feedback signal. The calculation formula is as follows: H add (t+1)=H add (t)+β·(U target -U(t)) In the formula, Hadd(t) is the humidification amount at time t, Utarget is the target uniformity index, U(t) is the current uniformity index, and β is the control gain; The optimization unit is used to obtain an optimized humidification control strategy based on the feedback control algorithm through real-time monitoring and adaptive adjustment, and to dynamically adjust the control gain β based on real-time control performance indicators, so as to achieve the goal of uniform steam distribution and minimizing the cold spot area.

4. The sterilizer humidification control system according to claim 3, characterized in that, The processing module includes: The first processing unit is used to construct a three-dimensional geometric model of the sterilizer using CAD software based on the design drawings and dimensional parameters of the sterilizer. The CAD model is then imported into CFD software for geometric repair and simplification. The internal mesh is generated using the Delaunay triangulation method, and the mesh density in key areas is optimized using local mesh refinement technology. The three-dimensional geometric model includes the cavity, steam inlet, exhaust outlet, and internal shelves. Setting unit: Used to set boundary conditions, set the initial temperature and humidity inside the sterilizer according to the working parameters of the sterilizer, simulate the initial state, and define the turbulent characteristics of steam flow inside the sterilizer using the Reynolds-averaged equation and turbulence model. Analysis module unit: Based on the physical properties of steam, establish the control equations of thermodynamics and fluid dynamics of steam in the sterilizer, and introduce the phase change model of steam according to the control equations. After the Dufour effect and Soret effect analysis, the steam flow and heat transfer model considering the phase change effect is obtained. Extraction Unit: Used to solve the steam flow and heat transfer model using a semi-implicit method with pressure coupling equations, and extract key flow and heat transfer data; The second processing unit is used to obtain the steam velocity, flow direction, temperature, and humidity distribution data by applying the backward Eulerian method and time discretization based on the flow and heat transfer data.

5. A humidification control device for a sterilizer, characterized in that, include: memory for storing computer programs; A processor for executing the computer program to implement the sterilizer humidification control method as described in any one of claims 1 to 2.

6. A readable storage medium, characterized in that: The readable storage medium stores a computer program that, when executed by a processor, implements the sterilizer humidification control method as described in any one of claims 1 to 2.

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