Humidification control method, system and equipment for sterilizer and medium

Abstract

The invention provides a sterilizer humidification control method, system, equipment and medium, and relates to the technical field of sterilizer control, and the sterilizer humidification control method comprises the following steps: obtaining environmental parameters and equipment data in a sterilizer, inputting the equipment data into a three-dimensional computer design model, and constructing a geometric model; the method comprises the following steps: inputting a geometric model and environmental parameters of the sterilizer by utilizing computational fluid dynamics software, and carrying out numerical simulation treatment to obtain a steam flow form and distribution data in the sterilizer; identifying a cold spot area in the sterilizer through an algorithm, and analyzing the relationship between the cold spot area and the humidification control parameters to obtain an identification result; the humidification amount is dynamically adjusted through a feedback control mechanism, and control parameters are optimized; performance evaluation and adjustment are carried out on the optimized control parameters according to real-time monitoring data and user feedback, and then humidification control over the sterilizer is completed. The quality and the efficiency of the sterilization process are improved, and meanwhile, the energy consumption and the operation cost are reduced.

Description

Technical Field

[0001] The present invention relates to the field of sterilizer control technology, and in particular to a sterilizer humidification control method, system, equipment and medium. Background Art

[0002] With the development of medical technology and the increasing demand for sterile environment, sterilizers are indispensable equipment in hospitals, laboratories and other places, and their performance directly affects the disinfection and sterilization effects. At present, the humidification control method of sterilizers mainly relies on experience and simple feedback adjustment, which often lacks accuracy and adaptability. However, in actual applications, due to the uneven flow and distribution of steam inside the sterilizer, cold spots often appear, resulting in incomplete sterilization. The existing technology usually uses static parameter settings and periodic manual inspections to solve this problem, but this method is inefficient and difficult to adapt to changes in different operating conditions and loading conditions. Summary of the invention

[0003] The purpose of the present invention is to provide a sterilizer humidification control method, system, device and medium to improve the above problems. In order to achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0004] In a first aspect, the present application provides obtaining environmental parameters and equipment data inside a sterilizer according to the working environment and design parameters of the sterilizer, and inputting the equipment data into a three-dimensional computer design model to construct a geometric model, wherein the environmental parameters include temperature, humidity and pressure, and the equipment data include the size of the sterilizer, the location of the steam input port and the load distribution;

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

[0006] According to the steam flow and distribution data obtained by simulation, the cold spot area inside the sterilizer is identified through an algorithm, and the relationship between the cold spot area and the humidification control parameters is analyzed to optimize the humidification control strategy to obtain the identification result, where the cold spot area is the area where the steam temperature is lower than the set threshold, and the humidification control parameters include steam input speed, humidification amount and load distribution;

[0007] Based on the identification results, an intelligent humidification control logic is designed to dynamically adjust the humidification amount through a feedback control mechanism and optimize the control parameters until the steam flow and distribution inside the sterilizer are uniform and the cold spot area is minimized. The performance of the optimized control parameters is 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 after numerical simulation processing, including meshing, boundary condition setting and solution calculation, the steam flow form and distribution data inside the sterilizer have been obtained, including:

[0009] According to the design drawings and size parameters of the sterilizer, the CAD software was used to build the 3D geometric model of the sterilizer. The CAD model was imported into the CFD software for geometric repair and simplification. The Delaunay triangulation method was used to generate the internal mesh, and the local mesh refinement technology was used to optimize the mesh density in the key area. The 3D geometric model includes the cavity, steam inlet, exhaust port and internal shelf.

[0010] According to 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 turbulence characteristics of the steam flow in the sterilizer are defined using the Reynolds average equation and turbulence model;

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

[0012] The steam flow and heat transfer models are solved using a semi-implicit method of pressure-coupled equations to extract key flow and heat transfer data;

[0013] According to the flow and heat transfer data, the backward Euler method is applied and the flow velocity, flow direction, temperature and humidity distribution data of the steam are obtained after time discretization.

[0014] Preferably, the steam flow and distribution data obtained by simulation are used to identify the cold spot area inside the sterilizer through an algorithm, and the relationship between the cold spot area and the humidification control parameters is analyzed to optimize the humidification control strategy to obtain the identification results, which include:

[0015] According to the steam flow and distribution data obtained by numerical simulation, after data cleaning and standardization, outliers and noise interference are eliminated, and empirical mode decomposition is used to extract the key features of steam flow from the original data, where the key features include the key distribution features of flow velocity, flow direction, temperature and humidity, and obtain the feature extraction results;

[0016] The DBSCAN clustering algorithm is used to perform cluster analysis on the feature extraction results. The cluster analysis uses the local distribution characteristics of the steam temperature in the sterilizer to divide the internal area of ​​the sterilizer into cold spot and non-cold spot areas, where the threshold Tth for cold spot area identification is set;

[0017] Based on the data of cold spot and non-cold spot areas, the Pearson correlation coefficient is used to quantify the correlation between the cold spot area and each humidification control parameter, and the correlation analysis results are obtained;

[0018] The correlation analysis results are processed by an optimization algorithm to obtain the minimized objective function F and the best combination of humidification control parameters, thereby obtaining the optimized humidification control strategy.

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

[0020] According to the identified cold spot area and correlation analysis results, after the performance index definition and objective function construction, the control performance evaluation model is obtained and the performance index is designed, where the performance index includes the steam distribution uniformity index U and the cold spot area coverage index C;

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

[0022] According to the feedback control algorithm, the optimized humidification control strategy is obtained through real-time monitoring and adaptive adjustment, and the control gain β is dynamically adjusted based on the real-time control performance index, so as to achieve the goal of uniform steam distribution and minimize the cold spot area.

[0023] In a second aspect, the present application also provides a sterilizer humidification control system, comprising:

[0024] Construction module: used to obtain the environmental parameters and equipment data inside the sterilizer according to the working environment and design parameters of the sterilizer, and input the equipment data into the three-dimensional computer design model to build a geometric model, where the environmental parameters include temperature, humidity and pressure, and the equipment data includes the size of the sterilizer, the position of the steam inlet and the load distribution;

[0025] Processing module: used to use computational fluid dynamics software to input the geometric model and environmental parameters of the sterilizer, and after numerical simulation processing, including meshing, boundary condition setting and solution calculation, the steam flow form and distribution data inside the sterilizer have been obtained, wherein the steam flow form includes the flow velocity, flow direction and temperature distribution of the steam, and the distribution data includes the spatial distribution of steam concentration and temperature inside the sterilizer;

[0026] Optimization identification module: It is used to identify the cold spot area inside the sterilizer through an algorithm based on the steam flow and distribution data obtained by simulation, and analyze the relationship between the cold spot area and the humidification control parameters, optimize the humidification control strategy, and obtain the identification result, where the cold spot area is the area where the steam temperature is lower than the set threshold, and the humidification control parameters include steam input speed, humidification amount and load distribution;

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

[0028] In a third aspect, the present application also provides a sterilizer humidification control device, comprising:

[0029] Memory for storing computer programs;

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

[0031] In a fourth aspect, the present application further provides a readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the above-mentioned sterilizer humidification control method are implemented.

[0032] The beneficial effects of the present invention are:

[0033] Through the implementation of intelligent humidification control logic, the present invention can achieve accurate regulation of steam flow and distribution inside the sterilizer, thereby improving sterilization efficiency. Since 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, the present invention ensures that each area in the sterilization process can meet the necessary sterilization standards and reduces the risk of sterilization failure due to uneven temperature distribution; the intelligent control system dynamically adjusts the humidification amount according to real-time data to avoid over-humidification or under-humidification, thereby reducing the waste of energy and water resources and achieving energy saving and consumption reduction.

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

[0036] The intelligent control system of the present invention dynamically adjusts the humidification amount according to real-time data, avoids over-humidification or under-humidification, thereby reducing the waste of energy and water resources and achieving energy saving and consumption reduction. The present invention can customize personalized sterilization plans according to the characteristics and loading modes of different sterilized items to achieve more accurate and efficient sterilization effects.

[0037] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or be understood by practicing the embodiments of the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0039] Figure 1 It is a schematic flow chart of the sterilizer humidification control method described in an embodiment of the present invention;

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

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

[0042] In the figure: 701, building 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, building 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 DESCRIPTION

[0043] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0044] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0045] Embodiment 1:

[0046] This embodiment provides a sterilizer humidification control method.

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

[0048] S100. According to the working environment and design parameters of the sterilizer, obtain the environmental parameters and equipment data inside the sterilizer, and input the equipment data into a three-dimensional computer design model to construct a geometric model, where 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.

[0049] It should be noted that the geometric model describes the internal structure of the sterilizer in detail, including but not limited to the cavity shape, steam inlet, exhaust port, internal obstacles (such as shelves or partitions), and the geometry of the sterilized items loaded. In the CAD model, ensure that all features are accurately represented according to actual size to facilitate subsequent meshing 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 grid division, boundary condition setting and solution calculation, the steam flow form and distribution data inside the sterilizer have been obtained. The steam flow form includes the steam flow rate, flow direction and temperature distribution, and the distribution data includes the spatial distribution of steam concentration and temperature inside the sterilizer.

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

[0052] S201. Based on the design drawings and dimensional parameters of the sterilizer, use CAD software to build a three-dimensional geometric model of the sterilizer, import the CAD model into the CFD software, perform geometric repair and simplification, generate the internal mesh using the Delaunay triangulation method, and use the local mesh refinement technology to optimize the mesh density in the key area; the three-dimensional geometric model includes the cavity, steam inlet, exhaust port and internal shelf;

[0053] The sterilizer geometry model generated in the CAD software was imported into the CFD pre-processing software. Geometry repair was performed to eliminate any non-manifold geometry errors and complex features were simplified to reduce model complexity while retaining details of the steam inlet and expected cold spot areas.

[0054] During the mesh refinement process, the frontier progress mesh generation algorithm is used to mesh the entire sterilizer cavity. This algorithm starts from the surface of the geometric body and gradually grows inward to form a polygonal mesh. Among them, the mesh refinement process is: before mesh refinement, a sensitivity analysis is performed to determine the areas that need to be refined, which involves running preliminary simulations at different mesh densities to evaluate the impact of mesh size on simulation results. At the steam inlet and expected cold spot areas, a local mesh refinement algorithm is applied, which includes mesh refinement based on geometric features. Near the steam inlet, the mesh is refined according to the following formula:

[0055]

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

[0057] Define a growth factor and number of layers to control the degree of mesh refinement from the steam inlet outward, where the growth factor determines the rate of increase in mesh size per layer, and the number of layers determines the depth of the refined area. In some cases, it may be necessary to apply adaptive mesh refinement techniques, which involve dynamically adjusting the mesh size based on certain criteria during the simulation. For example, the mesh can be automatically refined in areas of high gradient steam flow. At the steam inlet and cavity walls, create a boundary layer mesh to capture boundary layer effects, which involves generating a denser mesh layer near the wall and using wall functions or low Reynolds number models to simulate the flow in the near-wall area. Therefore, the above steps can ensure that the mesh inside the sterilizer, especially the steam inlet and expected cold spot areas, is fine enough to accurately capture the key features of steam flow and heat transfer.

[0058] S202. According to 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 turbulence characteristics of the steam flow in the sterilizer are defined by using the Reynolds average equation and the turbulence model;

[0059] It should be noted that the boundary conditions are set according to the operating parameters and environmental conditions of the sterilizer. This includes the no-slip condition of the inner wall of the cavity (u=v=0), the fixed temperature and velocity conditions of the steam inlet, and the pressure conditions of the exhaust port, and the initial state is set. It is usually assumed that the air in the cavity is still at the initial moment, and the temperature and humidity are evenly distributed, consistent with the 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 model. It can be understood that the turbulence model can describe the randomness and disorder of the flow, which is crucial to 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 the actual situation, and provides a solid foundation for the subsequent establishment of control equations and numerical solutions.

[0060] S203. Based on the physical properties of steam, the control equations of thermodynamics and fluid mechanics of steam in the sterilizer are established, and the phase change model of steam is introduced according to the control equations. After the analysis of Dufour effect and Soret effect, the steam flow and heat transfer model considering the influence of phase change is obtained;

[0061] It is understood that the governing equations of thermodynamics and fluid mechanics are established based on the physical properties of steam, such as density, viscosity, specific heat capacity, and thermal conductivity, including but not limited to the continuity equation (mass conservation), momentum conservation equation (Navier-Stokes equation), and energy conservation equation. Considering the phase change characteristics of steam at different temperatures, a phase change model is introduced, which may include the release or absorption of latent heat, and the effect of phase change on flow and heat transfer; and through the analysis of the Dufour effect and Soret effect, the effect of temperature gradient and concentration gradient on steam flow and heat transfer is considered, thereby obtaining a more accurate steam flow and heat transfer model. By measuring the steam pressure and density at different temperatures, the model of thermodynamic properties can be verified and calibrated.

[0062] S204, using a semi-implicit method of the pressure coupled 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 control equations. The SIMPLE algorithm is a pressure-coupled numerical solution method that can effectively handle the coupling effects in flow and heat transfer problems; in the solution process, the control equations need to be discretized, and the continuous differential equations need to be converted into discrete algebraic equations, including but not limited to the use of finite volume methods or finite element methods. This is a prior art and will not be elaborated on here. Through numerical solution, the velocity field, temperature field, and humidity field of 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 to obtain the flow velocity, flow direction, temperature and humidity distribution data of the steam after time discretization.

[0065] It should be noted that the backward Euler method is used for time discretization based on the flow and heat transfer data, where the backward Euler method is a single-step time integration method suitable for solving unsteady flow and heat transfer problems. The time discretization process involves discretizing the continuous time domain into a series of time steps, and solving the transient problems of flow and heat transfer at each time step. Therefore, at each time step, the state of flow and heat transfer needs to be updated, including the update of physical quantities such as velocity, pressure, temperature and humidity. For example, for a rapidly changing process, a smaller time step may be required to capture transient characteristics; for a slowly changing process, a larger time step can be used to reduce the amount of calculation. In addition, the choice of time discretization method may also affect the stability and accuracy of the numerical solution, and needs to be optimized according to the specific situation.

[0066] S300. Based on the steam flow and distribution data obtained by simulation, the cold spot area inside the sterilizer is identified through an algorithm, and the relationship between the cold spot area and the humidification control parameters is analyzed, the humidification control strategy is optimized, and the identification result is obtained, where the cold spot area is the area where the steam temperature is lower than the set threshold, and the humidification control parameters include steam input speed, humidification amount and load distribution.

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

[0068] S301. Based on the steam flow and distribution data obtained by numerical simulation, the data is cleaned and standardized to remove outliers and noise interference, and the key features of steam flow are extracted from the original data using empirical mode decomposition, where the key features include key distribution features of flow velocity, flow direction, temperature and humidity, to obtain feature extraction results;

[0069] It should be noted that, based on the original steam flow and distribution data obtained by numerical simulation, data cleaning is first performed to remove outliers in the data set, including identifying and deleting or filling missing data points, and identifying and processing abnormal temperature or humidity readings that are beyond the normal operating range. The data is standardized to eliminate the impact of different dimensions and magnitudes. The standardization methods include Z-score standardization or minimum-maximum standardization, and ensure that all feature data are on the same scale.

[0070] The standardized data is then processed using the EMD algorithm to extract the intrinsic patterns in the data. EMD can decompose complex data into a series of intrinsic mode functions, each IMF represents a basic oscillation mode of the data, and identify the key features reflecting the steam flow characteristics from the IMFs decomposed by EMD. The key features include the distribution characteristics of flow rate, flow direction, temperature and humidity, which are representative indicators of steam flow characteristics. By analyzing the physical meaning and statistical characteristics of IMFs, the features most relevant to the performance of the sterilizer are determined. For example, the IMF representing the highest energy content may be selected to represent the main mode of steam flow, or the IMF most relevant to the temperature distribution may be selected to characterize the heat transfer characteristics. Finally, the extracted key features are integrated to form feature extraction results, which provide quantitative data support for the subsequent cold spot area identification and humidification control parameter optimization.

[0071] S302, using the DBSCAN clustering algorithm to perform cluster analysis on the feature extraction results. The cluster analysis uses the local distribution characteristics of the steam temperature in the sterilizer to divide the internal area of ​​the sterilizer into cold spot and non-cold spot areas, wherein a threshold Tth for cold spot area identification is set, using the following calculation formula:

[0072] Tcold=min(Tregion)

[0073] Where Tregion is the temperature distribution in a specific region, Tcold is the lowest temperature in the region, and is compared with Tth to determine the cold spot;

[0074] S303, based on the data of the cold spot and non-cold spot areas, the Pearson correlation coefficient is applied to quantify the correlation between the cold spot area and each humidification control parameter, and the correlation analysis result is obtained, and the calculation formula is as follows:

[0075]

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

[0077] S304, performing optimization algorithm processing on the correlation analysis result to obtain the minimized objective function F and the best humidification control parameter combination, and then obtaining the optimized humidification control strategy.

[0078] It should be noted that key features, such as the lowest temperature value or the average temperature, are selected from the cold spot area data, which can characterize the thermal state of the cold spot area. Then, the humidification control parameters are defined, which include the steam input speed, humidification amount, and load distribution. In step S304, based on the correlation analysis results, an objective function F is constructed, which reflects the non-correlation between the humidification control parameters and the temperature of the cold spot area. The goal is to minimize F to optimize the humidification control parameters, and select a suitable optimization algorithm, including but not limited to genetic algorithm, particle swarm optimization or simulated annealing algorithm, to search for the optimal humidification control parameter combination, input the objective function into the optimization algorithm, define the search range and optimization criteria of the parameters, run the optimization algorithm to search for parameters, analyze the output of the optimization algorithm, and obtain the best humidification control parameter combination, which parameters make the objective function F reach the minimum value, and form an optimized humidification control strategy based on the optimization results, wherein this strategy includes adjusting the steam input speed, humidification amount and load distribution, etc., to reduce the cold spot area and improve the sterilization efficiency, and finally prepare to implement the optimized humidification control strategy, 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 area and the humidification control parameters can be systematically analyzed, and the best control parameter combination can be found through the optimization algorithm, so as to formulate a more effective humidification control strategy.

[0079] S400. Based on the identification results, design intelligent humidification control logic to dynamically adjust the humidification amount through the feedback control mechanism and optimize the control parameters until the steam flow and distribution inside the sterilizer are uniform and the cold spot area is minimized; and perform performance evaluation and adjustment on the optimized control parameters based on real-time monitoring data and user feedback to complete the humidification control of the sterilizer.

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

[0081] S401, according to the identified cold spot area and the correlation analysis results, after defining the performance index and constructing the objective function, a control performance evaluation model is obtained, and the performance index is designed, wherein the performance index includes a steam distribution uniformity index U and a cold spot area coverage index C;

[0082] It should be noted that the location and characteristics of the cold spot area are determined based on the cold spot area data obtained through numerical simulation and empirical mode decomposition. The correlation analysis results between the cold spot area and the humidification control parameters are used to understand the influence of different parameters on the cold spot area. A control performance evaluation model is constructed, and U and C are used 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 to ensure that the humidification control strategy can effectively reduce the cold spot area and improve the uniformity of steam distribution.

[0083] S402, based on the performance index and the control performance evaluation model, the control algorithm after error compensation uses the difference between the performance index and the preset target as a feedback signal to dynamically adjust the humidification amount. The calculation formula is as follows:

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

[0085] Where 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. According to the feedback control algorithm, after real-time monitoring and adaptive adjustment, an optimized humidification control strategy is obtained, and the control gain β is dynamically adjusted based on the real-time control performance index, so as to achieve the goals of uniform steam distribution and minimization of cold spot areas.

[0087] It should be noted that the humidification control strategy is optimized by combining real-time monitoring data and adaptively adjusted control gains. The steam input rate, humidification amount, and load distribution are adjusted to respond to real-time changes in steam distribution. The optimized humidification control strategy is applied to the closed-loop control system to achieve iterative optimization. The system will continue to run, continuously collect data, evaluate performance indicators, adjust control gains, and optimize control strategies. Verify whether the iteratively optimized humidification control strategy has achieved the goal of uniform steam distribution and minimizing cold spot areas. If so, maintain the current strategy; if not, continue to adjust and optimize.

[0088] Embodiment 2:

[0089] like Figure 2 As shown, this embodiment provides a sterilizer humidification control system, see Figure 2 The system comprises:

[0090] Construction module 701: used to obtain the environmental parameters and equipment data inside the sterilizer according to the working environment and design parameters of the sterilizer, and input the equipment data into the three-dimensional computer design model to construct a geometric model, wherein the environmental parameters include temperature, humidity and pressure, and the equipment data include the size of the sterilizer, the position of the steam input port and the load distribution;

[0091] Processing module 702: used to use computational fluid dynamics software to input the geometric model and environmental parameters of the sterilizer, and obtain the steam flow form and distribution data inside the sterilizer through numerical simulation processing, including grid division, boundary condition setting and solution calculation, wherein the steam flow form includes the flow velocity, flow direction and temperature distribution of the steam, and the distribution data includes the spatial distribution of steam concentration and temperature inside the sterilizer;

[0092] Optimization identification module 703: used to identify the cold spot area inside the sterilizer through an algorithm according to the steam flow and distribution data obtained by simulation, and analyze the relationship between the cold spot area and the humidification control parameters, optimize the humidification control strategy, and obtain the identification result, wherein the cold spot area is the area where the steam temperature is lower than the set threshold, and the humidification control parameters include steam input speed, humidification amount and load distribution;

[0093] Control module 704: used to design intelligent humidification control logic based on the identification results, dynamically adjust the humidification amount through the feedback control mechanism, and optimize the control parameters until the steam flow and distribution inside the sterilizer are uniform and the cold spot area is minimized; and perform performance evaluation and adjustment on 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] The first processing unit 7021 is used to construct a three-dimensional geometric model of the sterilizer using CAD software according to the design drawings and size parameters of the sterilizer, import the CAD model into the CFD software, perform geometric repair and simplification, generate an internal mesh using the Delaunay triangulation method, and optimize the mesh density in key areas using local mesh refinement technology; wherein the three-dimensional geometric model includes a cavity, a steam input port, an exhaust port, and an internal shelf;

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

[0097] Analysis module unit 7023: used to establish the control equations of thermodynamics and fluid mechanics 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 influence of phase change is obtained;

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

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

[0100] Specifically, the optimization identification module 703 includes:

[0101] Decomposition unit 7031: used for extracting key features of steam flow from the original data through data cleaning and standardization based on the steam flow and distribution data obtained by numerical simulation, removing outliers and noise interference, and using empirical mode decomposition, wherein the key features include key distribution features of flow velocity, flow direction, temperature and humidity, and obtaining feature extraction results;

[0102] Clustering unit 7032: used to perform cluster analysis on the feature extraction results using the DBSCAN clustering algorithm. The clustering analysis uses the local distribution characteristics of the steam temperature in the sterilizer to divide the internal area of ​​the sterilizer into cold spot and non-cold spot areas, wherein the threshold Tth for cold spot area identification is set, and the following calculation formula is used:

[0103] Tcold=min(Tregion)

[0104] Where Tregion is the temperature distribution in a specific region, Tcold is the lowest temperature in the region, and is compared with Tth to determine the cold spot;

[0105] Correlation analysis unit 7033: used to quantify the correlation between the cold spot area and each humidification control parameter based on the data of the cold spot area and the non-cold spot area by applying the Pearson correlation coefficient to obtain the correlation analysis result. The calculation formula is as follows:

[0106]

[0107] In the formula, Tcold is the temperature of the cold spot area, P is the humidification parameter, r is the correlation coefficient of the humidification coefficient, 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 result to obtain the minimized objective function F and the best humidification control parameter combination, and then obtain the optimized humidification control strategy.

[0109] Specifically, the control module 704 includes:

[0110] Construction unit 7041: used to obtain a control performance evaluation model and design performance indicators according to the identified cold spot area and correlation analysis results, through performance indicator definition and objective function construction, wherein the performance indicators include a steam distribution uniformity index U and a cold spot area coverage index C;

[0111] Adjustment unit 7042: Based on the performance index and the control performance evaluation model, the control algorithm after error compensation uses the difference between the performance index and the preset target as a feedback signal to dynamically adjust the humidification amount. The calculation formula is as follows:

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

[0113] Where 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: is used to obtain an optimized humidification control strategy according to the feedback control algorithm through real-time monitoring and adaptive adjustment, and dynamically adjust the control gain β based on the real-time control performance index, so as to achieve the goal of uniform steam distribution and minimization of cold spot areas.

[0115] It should be noted that, regarding the system in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0116] Embodiment 3:

[0117] Corresponding to the above method embodiment, a sterilizer humidification control device is also provided in this embodiment. The sterilizer humidification control device described below and the sterilizer humidification control method described above can refer to each other.

[0118] Figure 3 8 is a block diagram of 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 is used to control the overall operation of the sterilizer humidification control device 800 to complete all or part of the steps in the above-mentioned sterilizer humidification control method. The memory 802 is used to store various types of data to support the operation of the sterilizer humidification control device 800, and these data may include, for example, instructions for any application or method operating on the sterilizer humidification control device 800, and application-related data, such as contact data, sent and received messages, pictures, audio, video, etc. The memory 802 can be implemented by 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 memory, flash memory, disk or optical disk. The multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touch screen, 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 signal may be further stored in the memory 802 or sent via the communication component 805. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 804 provides an interface between the processor 801 and other interface modules, and the above-mentioned other interface modules can be a keyboard, a mouse or a button, etc. These buttons can be virtual buttons or physical buttons. The communication component 805 is used for wired or wireless communication between the sterilizer humidification control device 800 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G or 4G, or a combination of one or more of them, so the corresponding communication component 805 can include: a Wi-Fi module, a Bluetooth module or an NFC module.

[0120] In an exemplary embodiment, the sterilizer humidification control device 800 can be implemented by one or more application specific integrated circuits (Application Specific Integrated Circuit, referred to as ASIC), digital signal processors (Digital Signal Processor, referred to as DSP), digital signal processing devices (Digital Signal Processing Device, referred to as DSPD), programmable logic devices (Programmable Logic Device, referred to as PLD), field programmable gate arrays (Field Programmable Gate Array, referred to as FPGA), controllers, microcontrollers, microprocessors or other electronic components to execute the above-mentioned sterilizer humidification control method.

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

[0122] Embodiment 4:

[0123] Corresponding to the above method embodiment, a readable storage medium is also provided in this embodiment. The readable storage medium described below and the sterilizer humidification control method described above can refer to each other.

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

[0125] The readable storage medium may specifically be a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or other readable storage medium that can store program codes.

[0126] In summary, the present invention realizes precise regulation 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, and then the key steam flow characteristics are extracted through empirical mode decomposition (EMD); based on these characteristics and correlation analysis, an intelligent humidification control logic is developed. This logic adopts an adaptive PID control algorithm and a reinforcement learning strategy to adjust the humidification amount and control parameters in real time to achieve the goal of uniform steam distribution and minimization of cold spot areas, which can significantly improve the operating efficiency and sterilization quality of the sterilizer while reducing energy consumption and operating costs.

[0127] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

[0128] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A sterilizer humidification control method, It is characterized in that include: According to the working environment and design parameters of the sterilizer, the environmental parameters and equipment data inside the sterilizer are obtained, and the equipment data is input into a three-dimensional computer design model to construct a geometric model, wherein 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; By using computational fluid dynamics software, the geometric model and environmental parameters of the sterilizer are input, and after numerical simulation processing, including meshing, boundary condition setting and solution calculation, the steam flow form and distribution data inside the sterilizer have been obtained. The steam flow form includes the flow velocity, flow direction and temperature distribution of the steam, and the distribution data includes the spatial distribution of steam concentration and temperature inside the sterilizer. According to the steam flow and distribution data obtained by simulation, the cold spot area inside the sterilizer is identified through an algorithm, and the relationship between the cold spot area and the humidification control parameters is analyzed to optimize the humidification control strategy to obtain the identification result, where the cold spot area is the area where the steam temperature is lower than the set threshold, and the humidification control parameters include steam input speed, humidification amount and load distribution; Based on the identification results, an intelligent humidification control logic is designed to dynamically adjust the humidification amount through a feedback control mechanism and optimize the control parameters until the steam flow and distribution inside the sterilizer are uniform and the cold spot area is minimized. The performance of the optimized control parameters is evaluated and adjusted based on real-time monitoring data and user feedback to complete the humidification control of the sterilizer.

2. The sterilizer humidification control method according to claim 1, It is characterized in that The computational fluid dynamics software is used to input the geometric model and environmental parameters of the sterilizer. After numerical simulation processing, including meshing, boundary condition setting and solution calculation, the steam flow form and distribution data inside the sterilizer have been obtained, including: According to the design drawings and size parameters of the sterilizer, the CAD software was used to build the 3D geometric model of the sterilizer. The CAD model was imported into the CFD software for geometric repair and simplification. The Delaunay triangulation method was used to generate the internal mesh, and the local mesh refinement technology was used to optimize the mesh density in the key area. The 3D geometric model includes the cavity, steam inlet, exhaust port and internal shelf. According to 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 turbulence characteristics of the steam flow in the sterilizer are defined using the Reynolds average equation and turbulence model; Based on the physical properties of steam, the control equations of thermodynamics and fluid mechanics of steam in the sterilizer are established, and the phase change model of steam is introduced according to the control equations. After the analysis of Dufour effect and Soret effect, the steam flow and heat transfer model considering the influence of phase change is obtained. The steam flow and heat transfer models are solved using a semi-implicit method of pressure-coupled equations to extract key flow and heat transfer data; According to the flow and heat transfer data, the backward Euler method is applied and the flow velocity, flow direction, temperature and humidity distribution data of the steam are obtained after time discretization.

3. The sterilizer humidification control method according to claim 1, It is characterized in that According to the steam flow and distribution data obtained by simulation, the cold spot area inside the sterilizer is identified by algorithm, and the relationship between the cold spot area and the humidification control parameters is analyzed, the humidification control strategy is optimized, and the identification results are obtained, including: According to the steam flow and distribution data obtained by numerical simulation, after data cleaning and standardization, outliers and noise interference are eliminated, and empirical mode decomposition is used to extract the key features of steam flow from the original data, where the key features include the key distribution features of flow velocity, flow direction, temperature and humidity, and obtain the feature extraction results; The DBSCAN clustering algorithm is used to perform cluster analysis on the feature extraction results. The cluster analysis uses the local distribution characteristics of the steam temperature in the sterilizer to divide the internal area of ​​the sterilizer into cold spot and non-cold spot areas. The threshold Tth for cold spot area identification is set by the following calculation formula: Tcold=min(Tregion) Where Tregion is the temperature distribution in a specific region, Tcold is the lowest temperature in the region, and is compared with Tth to determine the cold spot; Based on the data of cold spot and non-cold spot areas, the Pearson correlation coefficient is used to quantify the correlation between the cold spot area and each humidification control parameter, and the correlation analysis results are obtained. The calculation formula is as follows: In the formula, Tcold is the temperature of the cold spot area, P is the humidification parameter, r is the correlation coefficient of the humidification coefficient, Tˉcold and Pˉ 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 best combination of humidification control parameters, thereby obtaining the optimized humidification control strategy.

4. The sterilizer humidification control method according to claim 1, It is characterized in that Based on the identification results, the intelligent humidification control logic is designed to dynamically adjust the humidification amount through the feedback control mechanism and optimize the control parameters until the steam flow and distribution inside the sterilizer are uniform and the cold spot area is minimized, including: According to the identified cold spot area and correlation analysis results, after the performance index definition and objective function construction, the control performance evaluation model is obtained and the performance index is designed, where the performance index includes the steam distribution uniformity index U and the cold spot area coverage index C; Based on the performance index and the control performance evaluation model, the error-compensated control algorithm uses the difference between the performance index and the preset target as a feedback signal to dynamically adjust the humidification amount. The calculation formula is as follows: Hada(t+1)=Hadd(t)+β·(Utarget-U(t)) Where 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; According to the feedback control algorithm, the optimized humidification control strategy is obtained through real-time monitoring and adaptive adjustment, and the control gain β is dynamically adjusted based on the real-time control performance index, so as to achieve the goal of uniform steam distribution and minimize the cold spot area.

5. A sterilizer humidification control system, based on the sterilizer humidification control method according to claim 1, It is characterized in that include: Construction module: used to obtain the environmental parameters and equipment data inside the sterilizer according to the working environment and design parameters of the sterilizer, and input the equipment data into the three-dimensional computer design model to build a geometric model, where the environmental parameters include temperature, humidity and pressure, and the equipment data includes the size of the sterilizer, the position of the steam inlet and the load distribution; Processing module: used to use computational fluid dynamics software to input the geometric model and environmental parameters of the sterilizer, and after numerical simulation processing, including meshing, boundary condition setting and solution calculation, the steam flow form and distribution data inside the sterilizer have been obtained, wherein the steam flow form includes the flow velocity, flow direction and temperature distribution of the steam, and the distribution data includes the spatial distribution of steam concentration and temperature inside the sterilizer; Optimization identification module: It is used to identify the cold spot area inside the sterilizer through an algorithm based on the steam flow and distribution data obtained by simulation, and analyze the relationship between the cold spot area and the humidification control parameters, optimize the humidification control strategy, and obtain the identification result, where the cold spot area is the area where the steam temperature is lower than the set threshold, and the humidification control parameters include steam input speed, humidification amount and load distribution; Control module: used to design intelligent humidification control logic based on the identification results, dynamically adjust the humidification amount through the feedback control mechanism, and optimize the control parameters until the steam flow and distribution inside the sterilizer are uniform and the cold spot area is minimized; and the optimized control parameters are evaluated and adjusted according to the real-time monitoring data and user feedback to complete the humidification control of the sterilizer.

6. The sterilizer humidification control system according to claim 5, It is 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 according to the design drawings and size parameters of the sterilizer, import the CAD model into the CFD software, perform geometric repair and simplification, generate the internal mesh using the Delaunay triangulation method, and use the local mesh refinement technology to optimize the mesh density in the key area; the three-dimensional geometric model includes the cavity, steam inlet, exhaust port and internal shelf; Setting unit: used to set boundary conditions according to the working parameters of the sterilizer, set the initial temperature and humidity inside the sterilizer, simulate the initial state, and define the turbulence characteristics of the steam flow in the sterilizer using the Reynolds average equation and turbulence model; Analysis module unit: used to establish the control equations of thermodynamics and fluid mechanics 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 influence of phase change is obtained; Extraction unit: used to solve the steam flow and heat transfer model using the semi-implicit method of the pressure-coupled equations to extract key flow and heat transfer data; The second processing unit is used to obtain the flow velocity, flow direction, temperature and humidity distribution data of the steam by applying the backward Euler method according to the flow and heat transfer data and performing time discretization processing.

7. The sterilizer humidification control system according to claim 5, It is characterized in that The optimization identification module includes: Decomposition unit: It is used to extract the key features of steam flow from the original data through data cleaning and standardization based on the steam flow and distribution data obtained by numerical simulation, and remove outliers and noise interference. The key features include the key distribution features of flow velocity, flow direction, temperature and humidity, and obtain the feature extraction results. Clustering unit: used to perform cluster analysis on the feature extraction results using the DBSCAN clustering algorithm. The clustering analysis uses the local distribution characteristics of the steam temperature in the sterilizer to divide the internal area of ​​the sterilizer into cold spot and non-cold spot areas. The threshold Tth for cold spot area identification is set using the following calculation formula: Tcold=min(Tregion) Where Tregion is the temperature distribution in a specific region, Tcold is the lowest temperature in the region, and is compared with Tth to determine the cold spot; Correlation analysis unit: It is used to quantify the correlation between the cold spot area and each humidification control parameter based on the data of the cold spot and non-cold spot area, and obtain the correlation analysis result by applying the Pearson correlation coefficient. The calculation formula is as follows: In the formula, Tcold is the temperature of the cold spot area, P is the humidification parameter, r is the correlation coefficient of the humidification coefficient, Tˉcold and Pˉ are the average values ​​of Tcold and P respectively; The third processing unit is used to perform optimization algorithm processing on the correlation analysis result to obtain the minimized objective function F and the best humidification control parameter combination, and then obtain the optimized humidification control strategy.

8. The sterilizer humidification control system according to claim 5, It is characterized in that The control module includes: Construction unit: used to obtain the control performance evaluation model and design performance indicators according to the identified cold spot area and correlation analysis results, through performance indicator definition and objective function construction, where the performance indicators include steam distribution uniformity index U and cold spot area coverage index C; Adjustment unit: Based on the performance index and control performance evaluation model, the control algorithm after error compensation uses the difference between the performance index and the preset target as a feedback signal to dynamically adjust the humidification amount. The calculation formula is as follows: Hadd(t+1)=Hada(t)+β·(Utarget-U(t)) Where 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; Optimization unit: It is used to obtain the optimized humidification control strategy according to the feedback control algorithm through real-time monitoring and adaptive adjustment, and dynamically adjust the control gain β based on the real-time control performance index, so as to achieve the goal of uniform steam distribution and minimize the cold spot area.

9. A sterilizer humidification control device, It is characterized in that include: Memory for storing computer programs; A processor, used to implement the sterilizer humidification control method as claimed in any one of claims 1 to 4 when executing the computer program.

10. A readable storage medium, Features: The readable storage medium stores a computer program, and when the computer program is executed by a processor, the sterilizer humidification control method according to any one of claims 1 to 4 is implemented.

Images