Simulation optimization design method for structural size parameters of intelligent range hood

Abstract

The invention relates to the technical field of structural simulation, in particular to a simulation optimization design method for structural size parameters of an intelligent range hood. The method comprises the following steps: acquiring kitchen environment sensing data, and performing random environment generation to obtain a random kitchen environment data set; acquiring structural data of the range hood, and performing aerodynamic simulation to obtain aerodynamic simulation data of the range hood; estimating the environmental fitness of the range hood structure according to the aerodynamic simulation data of the range hood, so as to obtain environmental fitness data of the range hood structure; predicting the performance of the range hood according to the environmental fitness data of the range hood structure so as to obtain optimized performance prediction data of the pipeline structure of the range hood; and carrying out pipeline structure size performance optimization on the optimized range hood pipeline structure performance prediction data and the range hood performance evaluation data so as to obtain an optimal range hood pipeline structure model. The performance of the range hood can be effectively improved, and the operation noise is effectively reduced.

Description

Technical Field

[0001] The invention relates to the technical field of structural simulation, and in particular to a simulation optimization design method for structural dimension parameters of an intelligent range hood. Background Art

[0002] With the continuous development of modern kitchen equipment and the improvement of users' requirements for kitchen environment air quality, the performance and design of range hoods as an important part of kitchen appliances have received more and more attention. The main function of the range hood is to effectively remove the fumes and odors in the kitchen, thereby improving the air quality of the kitchen. However, the design of traditional range hoods mostly relies on experience or preliminary theoretical calculations, and fails to fully consider the complexity of the internal flow field of the range hood and various dynamic changes in actual use, resulting in its performance not being able to reach the optimal level. The performance of the range hood is mainly affected by factors such as its structural size, fan power, and exhaust duct. The traditional range hood design method is mainly optimized in the following ways: first, the performance of the range hood is tested through static experiments to obtain performance data under different structures and size configurations; second, the design is based on engineering experience, and the parameters such as air volume, air pressure, and noise of the range hood are estimated through empirical formulas; finally, it relies on some simple theoretical models to perform basic fluid mechanics calculations. Although these methods can meet the basic design requirements to a certain extent, they have significant defects. Traditional design methods often ignore the dynamic changes of the flow field during the operation of the range hood. In actual operation, parameters such as the air flow rate at the air inlet and outlet, and oil fume concentration of the range hood will change with the change of usage conditions. Therefore, the traditional method based on static calculation and empirical formula cannot accurately simulate the actual performance of the range hood under different usage conditions, resulting in a large difference between the design results and the actual effects. Summary of the invention

[0003] Based on this, it is necessary for the present invention to provide a simulation optimization design method for the structural dimension parameters of an intelligent range hood to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a simulation optimization design method for the structural dimension parameters of an intelligent range hood includes the following steps:

[0005] Step S1: Acquire kitchen environment sensor data, and perceive kitchen environment changes based on the kitchen environment sensor data, thereby obtaining a real-time kitchen environment parameter data set; generate a random environment for the real-time kitchen environment parameter data set, thereby obtaining a random kitchen environment data set;

[0006] Step S2: acquiring range hood structural data, and performing range hood duct topology analysis on the range hood structural data, thereby obtaining a range hood duct structural model; performing aerodynamic simulation on a random kitchen environment data set through the range hood duct structural model, thereby obtaining range hood aerodynamic simulation data;

[0007] Step S3: performing range hood performance evaluation according to the range hood aerodynamic simulation data, thereby obtaining range hood performance evaluation data, and estimating the range hood structural environmental adaptability of the range hood performance evaluation data and the random kitchen environment data set, thereby obtaining range hood structural environmental adaptability data;

[0008] Step S4: performing multi-objective optimization of the pipe structure size of the range hood pipe structure model according to the range hood structure environmental adaptability data, thereby obtaining an optimized range hood pipe structure model set; performing range hood performance prediction on the random kitchen environment parameter data set by optimizing the range hood pipe structure model set, thereby obtaining optimized range hood pipe structure performance prediction data;

[0009] Step S5: Calculate the performance error of the optimized range hood duct structure performance prediction data and the range hood performance evaluation data to obtain the range hood structure performance error data, and optimize the duct structure size performance of the optimized range hood duct structure model set according to the range hood structure performance error data to obtain the optimal range hood duct structure model.

[0010] Optionally, step S1 specifically includes:

[0011] Step S11: acquiring kitchen environment sensor data, and performing data preprocessing on the kitchen environment sensor data, thereby obtaining kitchen environment sensor data to be analyzed;

[0012] Step S12: integrating kitchen environment characteristics of the kitchen environment sensor data to be analyzed, thereby obtaining kitchen environment characteristic data;

[0013] Step S13: performing environmental change pattern recognition based on the kitchen environment feature data, thereby obtaining a real-time kitchen environment parameter data set;

[0014] Step S14: performing Monte Carlo simulation on the real-time kitchen environment parameter data set to obtain a random kitchen environment parameter data set;

[0015] Step S15: Perform kitchen environment scene simulation according to the random kitchen environment parameter data set to obtain a random kitchen environment data set.

[0016] Optionally, step S13 is specifically:

[0017] Perform kitchen environment time series change analysis based on kitchen environment characteristic data to obtain kitchen temperature and humidity change data and kitchen fume concentration change data;

[0018] Performing periodic pattern recognition on kitchen fume concentration change data to obtain fume concentration change pattern data;

[0019] Calculating the temperature and humidity change slope of the kitchen temperature and humidity change data to obtain the kitchen temperature and humidity change slope data, and identifying the temperature and humidity change pattern based on the kitchen temperature and humidity change slope data to obtain the kitchen temperature and humidity change pattern data;

[0020] Integrate the kitchen environment change pattern data with the oil smoke concentration change pattern data and the kitchen temperature and humidity change pattern data to obtain the kitchen environment change pattern data;

[0021] According to the kitchen environment change pattern data, the kitchen environment sensor data to be analyzed is subjected to real-time kitchen environment change pattern recognition, thereby obtaining the kitchen environment real-time environment change pattern data;

[0022] The real-time kitchen environment parameter data is integrated with the real-time environmental change pattern data of the kitchen environment to obtain a real-time kitchen environment parameter data set.

[0023] Optionally, step S2 specifically includes:

[0024] Step S21: acquiring range hood structure data, and performing data preprocessing on the range hood structure data, thereby obtaining standardized range hood structure data;

[0025] Step S22: extracting the range hood structure features from the standardized range hood structure data, thereby obtaining range hood component structure dimension parameter data and range hood component description data;

[0026] Step S23: performing a range hood duct topology analysis according to the range hood component structural dimension parameter data, thereby obtaining a range hood duct topology model;

[0027] Step S24: assigning the range hood duct component attributes to the range hood duct topology structure model according to the range hood component description data, thereby obtaining the range hood duct structure model;

[0028] Step S25: performing aerodynamic simulation on the random kitchen environment data set through the range hood duct structure model, thereby obtaining range hood aerodynamic simulation data.

[0029] Optionally, step S23 is specifically:

[0030] Step S231: classifying the components of the range hood according to the structural dimension parameter data of the range hood components, thereby obtaining the structural dimension parameter data of the pipeline part and the structural parameter data of the fan part;

[0031] Step S232: Calculating the range hood duct structure similarity according to the duct part structure dimension parameter data, thereby obtaining the range hood duct structure similarity data;

[0032] Step S233: extracting the fan duct connection port structure from the fan part structural parameter data, thereby obtaining the fan duct connection port structure data, and performing pipeline structure similarity calculation on the pipeline part structural dimension parameter data and the fan duct connection port structure data, thereby obtaining the fan duct connection port similarity data;

[0033] Step S234: integrating the range hood duct connection relationship according to the range hood duct structure similarity data and the fan duct connection port similarity data, thereby obtaining the range hood component duct connection relationship data;

[0034] Step S235: performing a range hood duct topology structure analysis based on the range hood component duct connection relationship data, thereby obtaining a range hood duct topology structure model.

[0035] Optionally, step S3 specifically includes:

[0036] The simulation feature extraction is performed based on the range hood aerodynamic simulation data, so as to obtain the simulated air pressure distribution data, the simulated air velocity distribution data and the simulated oil fume concentration distribution data;

[0037] Estimating the range hood suction power based on the simulated air pressure distribution data and the simulated air velocity distribution data, thereby obtaining the range hood suction power data;

[0038] The range hood pressure difference is estimated based on the simulated air pressure distribution data and the range hood suction data, thereby obtaining the range hood wind pressure difference data;

[0039] Performing an oil fume concentration change analysis based on the simulated oil fume concentration distribution data, thereby obtaining the range hood simulated oil fume concentration change data, and performing range hood exhaust efficiency calculation on the range hood simulated oil fume concentration change data, thereby obtaining the range hood exhaust efficiency data;

[0040] The range hood operation noise is estimated according to the simulated air velocity distribution data, thereby obtaining the range hood operation noise data;

[0041] The range hood performance is weightedly scored based on the range hood suction data, range hood wind pressure difference data, range hood exhaust efficiency data, and range hood operation noise data, thereby obtaining range hood performance evaluation data;

[0042] The range hood structural environmental adaptability is estimated based on the range hood performance evaluation data and random kitchen environment data sets to obtain the range hood structural environmental adaptability data.

[0043] Optionally, the estimation of the environmental adaptability of the range hood structure in step S3 is specifically as follows:

[0044] The performance error of the range hood in a random kitchen environment is calculated according to the range hood performance evaluation data and the random kitchen environment data set, so as to obtain the performance error data of the range hood in a random environment;

[0045] Quantify the kitchen environment differences of random kitchen environment data sets to obtain kitchen environment parameter difference data;

[0046] The kitchen environment impact is quantified based on the random environment range hood performance error data and the kitchen environment parameter difference data, so as to obtain the range hood performance error factor set;

[0047] Scoring the environmental adaptability of the range hood performance according to the range hood performance error factor set, thereby obtaining the environmental adaptability data of the range hood performance;

[0048] Based on the environmental adaptability data of the range hood performance, the range hood duct structure model is simulated and scored to obtain the range hood structure environmental adaptability data.

[0049] Optionally, step S4 is specifically:

[0050] Step S41: clustering the range hood structures with high environmental adaptability according to the range hood structure environmental adaptability data, thereby obtaining the range hood structure data with high environmental adaptability;

[0051] Step S42: confirming the pipeline structure constraint conditions based on the range hood pipeline structure model, thereby obtaining the range hood pipeline structure constraint conditions;

[0052] Step S43: extracting the range hood structure performance characteristics of the range hood structure data with high environmental adaptability according to the range hood performance evaluation data, thereby obtaining the range hood structure data with high suction power, the range hood structure data with high wind pressure difference, the range hood structure data with high smoke exhaust efficiency, and the range hood structure data with high operating noise;

[0053] Step S44: constructing a high-performance range hood structural parameter space according to the high suction range hood structural data, the high wind pressure difference range hood structural data, the high smoke exhaust efficiency range hood structural data, and the high operating noise range hood structural data;

[0054] Step S45: performing optimal structural parameter combination on the high-performance range hood structural parameter space through the range hood duct structural model, thereby obtaining a range hood duct structural model with minimized operation noise and a range hood duct structural model with maximized energy efficiency;

[0055] Step S46: merging the model data sets of the range hood duct structure model with minimized operation noise and the range hood duct structure model with maximized energy efficiency, thereby obtaining an optimized range hood duct structure model set;

[0056] Step S47: performing range hood performance prediction on the random kitchen environment parameter data set by optimizing the range hood duct structure model set, thereby obtaining optimized range hood duct structure performance prediction data.

[0057] Optionally, step S45 is specifically:

[0058] According to the structural constraints of the range hood pipeline, the range hood structural parameter space of the high-performance range hood is constrained, so as to obtain the range hood structural constraint parameter space;

[0059] The range hood pipeline structure model is used to combine the operating noise minimizing structural parameters in the range hood structure constraint parameter space, thereby obtaining the operating noise minimizing structural parameter group;

[0060] The range hood duct structure model is used to perform random structural parameter combinations on the range hood structure constraint parameter space, thereby obtaining a random structural parameter group;

[0061] Based on the random structural parameter group, the range hood airflow resistance and pressure drop are simulated by using the range hood duct structure model, so as to obtain the random structural parameter group simulation data;

[0062] The random structural energy efficiency is estimated for the random structural parameter group simulation data, thereby obtaining the random structural energy efficiency data, and the maximum energy efficiency structural parameter combination is selected based on the random structural energy efficiency data, thereby obtaining the structural parameter combination with the maximum energy efficiency;

[0063] Adaptively adjust the structural parameters of the range hood duct structure model according to the running noise minimization structural parameter group, so as to obtain the range hood duct structure model with minimized running noise;

[0064] According to the energy-efficiency-maximizing structural parameter combination, the structural parameters of the range hood duct structure model are adaptively adjusted to obtain the energy-efficiency-maximizing range hood duct structure model.

[0065] Optionally, step S5 specifically includes:

[0066] Step S51: Calculating the performance error of the optimized range hood pipeline structure performance prediction data and the range hood performance evaluation data, thereby obtaining range hood structure performance error data;

[0067] Step S52: quantifying the influence of pipeline structural parameters according to the range hood structural performance error data, thereby obtaining a range hood performance error influence factor;

[0068] Step S53: selecting parameters to be optimized for the range hood duct structure based on the range hood performance error influencing factor, thereby obtaining parameter data to be optimized for the range hood duct structure;

[0069] Step S54: performing iterative optimization of structural dimension parameters of the optimized range hood duct structure model set according to the range hood duct structure parameter data to be optimized, thereby obtaining a performance structure optimization model set.

[0070] The present invention can significantly improve the performance and use effect of the range hood by comprehensively integrating kitchen environment data and range hood structure performance data. By acquiring and processing kitchen environment sensor data, real-time perception of kitchen environment changes can be achieved, and random kitchen environment data sets can be generated by Monte Carlo simulation and other means, which provides more real and diverse environmental data support for subsequent range hood performance evaluation. On this basis, through the preprocessing and feature extraction of range hood structure data, combined with the range hood pipeline topology model, accurate simulation of the range hood pipeline system can be achieved to reflect its operating state in different kitchen environments, thereby obtaining the aerodynamic simulation data of the range hood. This type of simulation can not only simulate the key performance indicators of the range hood, such as wind pressure, oil fume concentration, and suction, but also predict the exhaust efficiency and noise level of the range hood in different environments, which is helpful for optimization through multi-dimensional performance evaluation. By estimating the environmental adaptability of the range hood structure, the adaptability of the range hood in a specific kitchen environment can be quantified, thereby guiding the subsequent pipeline structure optimization. Further multi-objective optimization of pipeline structure size can adjust the pipeline structure in a targeted manner according to different performance requirements, optimize the airflow path and wind pressure distribution of the range hood, and improve its comprehensive performance. Through the error calculation of the performance prediction data, the structural parameters that affect the performance of the range hood can be accurately identified and optimized to ensure that the final range hood pipeline structure can show the best performance in different kitchen environments. Finally, through the analysis of performance error data and pipeline structure optimization, the optimal range hood pipeline structure model can be obtained, thereby greatly improving the suction, exhaust efficiency and energy efficiency of the range hood, and effectively reducing the operating noise. This method can not only improve the performance of the range hood in actual use, but also significantly improve the kitchen air quality, meeting the high performance requirements of modern users for kitchen appliances. In addition, the optimization method of the present invention has high flexibility and scalability, can adapt to different kitchen environments and changes in the range hood structure, and provides strong technical support for the customized design and intelligent regulation of the range hood. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0072] Figure 1 A schematic diagram of the steps of the simulation optimization design method of the structural dimension parameters of the intelligent range hood of the present invention;

[0073] Figure 2 Detailed step flow diagram of step S1 in the present invention;

[0074] Figure 3 Detailed step flow diagram of step S2 in the present invention;

[0075] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0076] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0077] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0078] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0079] To achieve this, please refer to Figures 1 to 3 The present invention provides a simulation optimization design method for the structural dimension parameters of an intelligent range hood, the method comprising the following steps:

[0080] Step S1: Acquire kitchen environment sensor data, and perceive kitchen environment changes based on the kitchen environment sensor data, thereby obtaining a real-time kitchen environment parameter data set; generate a random environment for the real-time kitchen environment parameter data set, thereby obtaining a random kitchen environment data set;

[0081] In this embodiment, the kitchen environment sensor data can be obtained by installing multiple sensors in multiple arranged kitchen scenes, such as temperature and humidity sensors, oil fume concentration sensors, airflow sensors, etc., to monitor the environmental parameters of the kitchen in real time. These sensors transmit the collected data to the control system for processing. The system removes noise and standardizes the data through data preprocessing, thereby obtaining an accurate real-time kitchen environment parameter data set. For example, the temperature, humidity, and oil fume concentration data obtained by the sensor will be integrated into a comprehensive data set. Random environment generation uses these environmental data to create a variety of simulated environments in the computer system to generate different kitchen environment data sets, such as temperature and humidity, oil fume concentration changes, etc. These data sets will be used in subsequent aerodynamic simulation and performance prediction.

[0082] Step S2: acquiring range hood structural data, and performing range hood duct topology analysis on the range hood structural data, thereby obtaining a range hood duct structural model; performing aerodynamic simulation on a random kitchen environment data set through the range hood duct structural model, thereby obtaining range hood aerodynamic simulation data;

[0083] In this embodiment, the acquisition of the range hood structural data can obtain detailed structural information of the range hood through 3D scanning technology or design drawings, covering the range hood's pipe diameter, length, fan position, suction size and other data. Next, the range hood pipe topology structure is analyzed, and the topology analysis software (such as Autodesk Revit or other professional CAE tools) is used to simulate the range hood pipe layout, so as to construct the range hood pipe structure model. These models include the connectivity relationship of the pipes, the connection between the fan and the pipe, etc. Based on this structural model, the system performs aerodynamic simulation through fluid dynamics simulation software (such as ANSYSFluent) to simulate the air flow of the range hood in different kitchen environments, and then obtains simulation data such as air flow rate, pressure distribution and oil fume concentration distribution, which will provide a basis for the evaluation of the range hood performance.

[0084] Step S3: performing range hood performance evaluation according to the range hood aerodynamic simulation data, thereby obtaining range hood performance evaluation data, and estimating the range hood structural environmental adaptability of the range hood performance evaluation data and the random kitchen environment data set, thereby obtaining range hood structural environmental adaptability data;

[0085] In this embodiment, the performance evaluation of the range hood can be performed by simulating the environment and actual kitchen usage data, and using a fluid dynamics model to evaluate the performance of the range hood, specifically including the suction force of the fan, exhaust efficiency, and emission effect of fume concentration. By comparing the simulation results with the actual data, the exhaust effect and performance indicators of the range hood are evaluated, and the structural environmental adaptability of the range hood is further calculated based on these evaluation data. The structural environmental adaptability estimation can be performed by comprehensively evaluating the operating effect of the range hood under different kitchen environments (such as kitchen fume concentration, airflow velocity, humidity, etc.) to determine the adaptability and performance of the range hood in different environments. Through these steps, the performance of the range hood under different environmental conditions can be judged and optimized.

[0086] Step S4: performing multi-objective optimization of the pipe structure size of the range hood pipe structure model according to the range hood structure environmental adaptability data, thereby obtaining an optimized range hood pipe structure model set; performing range hood performance prediction on the random kitchen environment parameter data set by optimizing the range hood pipe structure model set, thereby obtaining optimized range hood pipe structure performance prediction data;

[0087] In this embodiment, multi-objective optimization of the pipe structure size is performed based on the environmental adaptability data of the range hood structure, and it is necessary to determine the optimization objectives, such as minimizing wind pressure loss, maximizing energy efficiency, and minimizing noise. On this basis, advanced optimization methods such as genetic algorithms and particle swarm optimization algorithms are used to perform optimization calculations based on various constraints of the range hood pipe structure (such as space limitations, installation locations, etc.) to find the optimal pipe structure parameters. During the optimization process, multiple dimensions such as the size, material, and layout of the pipe will be adjusted to improve the overall performance of the range hood in different kitchen environments. By predicting the performance of the range hood on the optimized range hood pipe structure model set, the optimization effect can be further verified, and the performance of the optimized pipe structure in a random kitchen environment, such as suction, oil fume concentration removal efficiency, energy efficiency, etc., can be predicted, thereby determining the performance of the optimal pipe structure.

[0088] Step S5: Calculate the performance error of the optimized range hood duct structure performance prediction data and the range hood performance evaluation data to obtain the range hood structure performance error data, and optimize the duct structure size performance of the optimized range hood duct structure model set according to the range hood structure performance error data to obtain the optimal range hood duct structure model.

[0089] In this embodiment, the performance error calculation is performed on the performance prediction data of the optimized range hood duct structure and the range hood performance evaluation data. Specifically, the performance of the optimized range hood duct structure model will be verified through simulation or experimental data, for example, by measuring the suction force, oil fume concentration emission, wind pressure and other indicators of the range hood, and calculating the error between it and the expected performance. This performance error data can reflect the shortcomings of the optimized design, and then guide subsequent optimization and improvement. Based on these performance error data, the system will further optimize the performance of the size and layout of the pipeline structure to ensure that the performance of the range hood is more in line with actual needs. For example, when it is found that the exhaust efficiency is not up to standard, the fan position, pipe elbow angle, etc. are adjusted to improve the exhaust efficiency of the range hood, and finally the optimal range hood duct structure model is obtained.

[0090] Optionally, step S1 specifically includes:

[0091] Step S11: acquiring kitchen environment sensor data, and performing data preprocessing on the kitchen environment sensor data, thereby obtaining kitchen environment sensor data to be analyzed;

[0092] In this embodiment, the kitchen environment sensor data can be obtained by installing a variety of environmental sensors in the arranged multiple kitchen scenes, such as temperature and humidity sensors, oil fume concentration sensors, carbon dioxide sensors and air flow sensors, etc., to collect various data of the kitchen environment in real time. The process of data preprocessing includes removing outliers, noise filtering and standardization. For example, the temperature and humidity sensors may be affected by external factors such as opening and closing doors and windows in a short period of time, causing data fluctuations. During data preprocessing, these interferences are eliminated by setting reasonable thresholds and filtering algorithms (such as Kalman filtering) to ensure that accurate kitchen environment sensor data is obtained. These processed data will form the kitchen environment sensor data set to be analyzed, providing a basis for subsequent analysis.

[0093] Step S12: integrating kitchen environment characteristics of the kitchen environment sensor data to be analyzed, thereby obtaining kitchen environment characteristic data;

[0094] In this embodiment, the kitchen environment sensor data set to be analyzed can be integrated by processing different sensor data to obtain comprehensive kitchen environment feature data. For example, by integrating the data of multiple sensors such as temperature and humidity, fume concentration, and airflow velocity, the temporal variation characteristics of the data are extracted, such as the temperature fluctuation range, humidity change rate, peak value and fluctuation period of fume concentration, etc., so as to construct multi-dimensional feature data of the kitchen environment. These data can not only reflect the current state of the kitchen environment, but also provide a basis for subsequent pattern recognition, ensuring that the change trends under different environments can be identified during the analysis process.

[0095] Step S13: performing environmental change pattern recognition based on the kitchen environment feature data, thereby obtaining a real-time kitchen environment parameter data set;

[0096] In this embodiment, environmental change pattern recognition is performed based on statistical analysis of kitchen environment characteristic data. For example, a time series analysis method (such as an autoregressive moving average model ARMA) is used to model data such as temperature and humidity changes and oil fume concentration changes in the kitchen environment, thereby identifying the periodic change pattern of the kitchen environment. By identifying these change patterns, the environmental parameters in the kitchen can be predicted in real time, and a real-time kitchen environment parameter data set can be generated. These data sets can reflect the changing state of the kitchen at different time points. For example, the oil fume concentration will fluctuate over time during the cooking process, and the temperature and humidity will also change due to the progress of cooking activities. This information is crucial for the optimization control of kitchen equipment.

[0097] Step S14: performing Monte Carlo simulation on the real-time kitchen environment parameter data set to obtain a random kitchen environment parameter data set;

[0098] In this embodiment, the application of Monte Carlo simulation can generate a variety of possible kitchen environment scenarios by constructing a kitchen environment change model based on random sampling. Specifically, first, environmental parameters (such as temperature, humidity, fume concentration, etc.) are modeled according to the statistical distribution of historical data or sensor data. Then, the Monte Carlo method is used to randomly generate different combinations of kitchen environment parameters to simulate various possible kitchen environment changes. For example, by generating a large amount of temperature and humidity change and fume concentration change data, the possible states of the kitchen environment under different cooking behaviors are simulated. These simulation data will form a random kitchen environment parameter data set, which provides a basis for subsequent scene simulation and optimization.

[0099] Step S15: Perform kitchen environment scene simulation according to the random kitchen environment parameter data set to obtain a random kitchen environment data set.

[0100] In this embodiment, the kitchen environment scene simulation can be performed by computer simulation software (such as CFD computational fluid dynamics simulation software) after obtaining a random kitchen environment parameter data set. For example, the simulated fume concentration changes, temperature and humidity fluctuations and other data are input into the CFD software to simulate the fume diffusion, airflow, humidity changes and the like in different kitchen environments. Through these simulations, the performance of equipment such as range hoods and ventilation systems in different kitchen environments, as well as changes in air quality in the kitchen, can be predicted. These simulation results will provide a variety of different kitchen environment scenes, forming a complete random kitchen environment data set, providing reliable data support for subsequent range hood optimization, performance prediction and environmental adaptability analysis.

[0101] Optionally, step S13 is specifically:

[0102] Perform kitchen environment time series change analysis based on kitchen environment characteristic data to obtain kitchen temperature and humidity change data and kitchen fume concentration change data;

[0103] In this embodiment, the kitchen environment time series change analysis is performed based on the kitchen environment characteristic data. Specifically, time series data analysis methods such as sliding average method, weighted moving average method or trend line analysis can be used to perform time series processing on the data of various sensors in the kitchen (such as temperature and humidity sensors, oil fume concentration sensors, etc.). By analyzing the temperature and humidity change data, the changing patterns of the kitchen environment within a day or a week can be identified, such as the temperature and humidity fluctuation trends in the morning and evening, the impact of cooking activities on oil fume concentration, etc. Based on this, the kitchen temperature and humidity change data and the kitchen oil fume concentration change data will be extracted, and the internal laws of environmental parameter changes will be further analyzed through time series analysis, such as how the oil fume concentration increases with the cooking time, and how the temperature and humidity change with the cooking process.

[0104] Performing periodic pattern recognition on kitchen fume concentration change data to obtain fume concentration change pattern data;

[0105] In this embodiment, when the periodic pattern recognition of the oil fume concentration change is performed on the kitchen oil fume concentration change data, a periodic analysis method is used, such as Fourier transform or wavelet transform. These technologies can identify the periodicity of the change of the oil fume concentration in the kitchen, such as the fluctuation frequency of the oil fume concentration between the start-up and shutdown of the range hood, and the concentration change pattern during the cooking process. These methods can help identify the regular changes in the oil fume concentration, such as the time period from the start-up of the oil fume concentration to the maximum value during the cooking process, and the subsequent concentration decline trend, so as to obtain the oil fume concentration change pattern data, and then provide data support for the control strategy of the range hood.

[0106] Calculating the temperature and humidity change slope of the kitchen temperature and humidity change data to obtain the kitchen temperature and humidity change slope data, and identifying the temperature and humidity change pattern based on the kitchen temperature and humidity change slope data to obtain the kitchen temperature and humidity change pattern data;

[0107] In the present embodiment, when the temperature and humidity change slope calculation is performed in the kitchen, the rate of change of the temperature and humidity data can be calculated using the numerical differentiation method. Specifically, the temperature and humidity change speed (slope) at each time point is calculated using the neighborhood difference method or the time series difference method, so as to obtain the rate information of the temperature and humidity change in the kitchen. According to the calculated slope data, the trend and speed of the temperature and humidity change in the kitchen can be analyzed. For example, during the cooking process, the kitchen temperature will rise rapidly, while the change of humidity is relatively slow. Further, by analyzing the temperature and humidity change slope data, cluster analysis and other technologies are used to perform temperature and humidity change pattern recognition, and the typical patterns of temperature and humidity changes under different environments in the kitchen are identified, such as low humidity and high temperature patterns and high humidity and low temperature patterns, so as to obtain the kitchen temperature and humidity change pattern data.

[0108] Integrate the kitchen environment change pattern data with the oil smoke concentration change pattern data and the kitchen temperature and humidity change pattern data to obtain the kitchen environment change pattern data;

[0109] In this embodiment, when the kitchen environment change pattern is integrated, the oil fume concentration change pattern data and the kitchen temperature and humidity change pattern data are firstly fused in multiple dimensions. Through data fusion technology, such as principal component analysis (PCA) or multidimensional scalar analysis (MDS), these two types of data are fused into a comprehensive kitchen environment change pattern data set. By identifying different patterns, the system can accurately reflect the overall environmental changes in the kitchen, for example, in the case of high oil fume concentration, whether the kitchen temperature also rises accordingly, and how the humidity changes. This integration helps to provide a global perspective for the kitchen environment control system, effectively combining the relationship between temperature and humidity and oil fume concentration changes, and forming a complete environmental change model.

[0110] According to the kitchen environment change pattern data, the kitchen environment sensor data to be analyzed is subjected to real-time kitchen environment change pattern recognition, thereby obtaining the kitchen environment real-time environment change pattern data;

[0111] In this embodiment, when the real-time environment change pattern recognition of the kitchen environment is performed based on the kitchen environment change pattern data, machine learning techniques such as support vector machine (SVM) or neural network methods can be used to perform real-time pattern recognition on the kitchen environment sensor data collected in real time. These models can learn the characteristics of different environmental patterns through training data, such as the oil fume pattern generated during cooking and the humidity pattern generated during the cleaning process. Through the trained model, the real-time change pattern in the kitchen environment can be identified, and the sudden change of the environment, such as a sharp increase in oil fume concentration or a rapid change in temperature, can be captured in time, thereby generating real-time environment change pattern data of the kitchen environment.

[0112] The real-time kitchen environment parameter data is integrated with the real-time environmental change pattern data of the kitchen environment to obtain a real-time kitchen environment parameter data set.

[0113] In this embodiment, when the real-time kitchen environment parameters are integrated for the real-time environment change pattern data of the kitchen environment, the real-time identified environment change pattern can be merged with other data (such as temperature and humidity, fume concentration, etc.) obtained by the sensor through a data fusion algorithm. The specific operation is to integrate various types of real-time data into an accurate real-time kitchen environment parameter data set through methods such as weighted average or Bayesian inference. For example, the real-time environment change pattern recognition data shows that when the fume concentration increases, the temperature and humidity in the kitchen also rise rapidly. The system will combine these two types of data to predict the overall condition of the current kitchen environment and obtain a more comprehensive and accurate real-time kitchen environment data set, which provides data support for the optimization and adjustment of kitchen equipment such as range hoods.

[0114] Optionally, step S2 specifically includes:

[0115] Step S21: acquiring range hood structure data, and performing data preprocessing on the range hood structure data, thereby obtaining standardized range hood structure data;

[0116] In this embodiment, when obtaining the range hood structural data and performing data preprocessing, the size, material, connection method and other data of each range hood component can be collected. These data usually come from product design drawings, 3D modeling software or directly obtained from physical measurements. In the data preprocessing stage, in order to ensure the accuracy of subsequent analysis, all data are first standardized. Specifically, the Z-score standardization method can be used to unify the numerical range of each parameter into a standard range. For example, all dimensional data are uniformly converted into numerical values ​​in meters, and each numerical value is subtracted from its mean and then divided by the standard deviation, thereby eliminating the influence of different dimensions. In this way, it can be ensured that the structural data of all range hood components have the same scale, which is convenient for subsequent feature extraction and modeling analysis.

[0117] Step S22: extracting the range hood structure features from the standardized range hood structure data, thereby obtaining range hood component structure dimension parameter data and range hood component description data;

[0118] In this embodiment, when extracting the structural features of the range hood from the standardized range hood structural data, the main focus is on the geometric dimensions and functional characteristics of each component. The components of the range hood include fans, pipes, filters, air outlets, etc. Each component has different dimensional parameters (such as diameter, length, thickness, etc.) and descriptive data (such as material, functional type, etc.). Specifically, the key dimensions of each component can be automatically extracted from the standardized data by writing an algorithm, such as the inner diameter, length and connection method of the pipe; the air volume and power of the fan and other characteristics. For the descriptive data, the functions of each part of the range hood can be extracted, such as "high-efficiency filtration" and "low noise". These features will play an important role in the subsequent pipeline topology analysis and simulation.

[0119] Step S23: performing a range hood duct topology analysis according to the range hood component structural dimension parameter data, thereby obtaining a range hood duct topology model;

[0120] In this embodiment, when performing the topological structure analysis of the range hood duct according to the structural dimension parameter data of the range hood components, it is first necessary to establish the topological structure of the duct according to the connection method between the components. Specifically, the connection relationship and flow direction of the range hood duct can be analyzed by graph theory algorithms, such as the Dijkstra algorithm. For example, the fan is connected to the exhaust port through a duct, and there may be multiple elbows, filters and other components in the duct. The analysis of the topological structure needs to take into account the physical position and direction of these connections, so as to form a complete network model to ensure that the flow path of the duct can accurately reflect the actual connection between the various components and the direction of air flow.

[0121] Step S24: assigning the range hood duct component attributes to the range hood duct topology structure model according to the range hood component description data, thereby obtaining the range hood duct structure model;

[0122] In this embodiment, when assigning range hood duct component attributes to the range hood duct topology structure model according to the range hood component description data, it is necessary to combine the standardized component features with the topology structure model. Each duct component (such as elbows, straight pipes, filters, etc.) has different aerodynamic properties, such as drag coefficient, cross-sectional area, length, etc. These properties can be assigned through an existing aerodynamic database or through experimental data. For example, the drag coefficient of a straight pipe can refer to standard literature data, while the corresponding drag coefficient of an elbow needs to be calculated based on its angle and radius. Through this process, the properties of each duct component of the range hood can be associated with the topology model to obtain a complete range hood duct structure model.

[0123] Step S25: performing aerodynamic simulation on the random kitchen environment data set through the range hood duct structure model, thereby obtaining range hood aerodynamic simulation data.

[0124] In this embodiment, when the range hood duct structure model is used to perform aerodynamic simulation on the random kitchen environment data set, the simulation environment is set to contain the actual kitchen parameters of the random kitchen environment data set, such as temperature, humidity, air flow velocity, etc. The computational fluid dynamics (CFD) simulation model can be used to simulate the flow of air in the range hood duct, including the pressure distribution, flow velocity distribution, and changes in oil fume concentration of the air. This simulation process will be calculated based on the topological structure model of the range hood duct and the aerodynamic properties of each component to predict the performance of the range hood in different kitchen environments. Through this process, the range hood aerodynamic simulation data obtained will provide an important basis for the optimization of range hood performance and help designers understand the impact of different kitchen environments on range hood performance.

[0125] Optionally, step S23 is specifically:

[0126] Step S231: classifying the components of the range hood according to the structural dimension parameter data of the range hood components, thereby obtaining the structural dimension parameter data of the pipeline part and the structural parameter data of the fan part;

[0127] In this embodiment, the components of the range hood are classified according to the structural dimension parameter data of the range hood components, and the range hood is divided into a pipe part and a fan part. The pipe part usually includes components such as straight pipes, elbows, and pipe joints, and its structural dimension parameters include inner diameter, length, bending angle, etc.; the fan part includes the type of fan, air volume, power, connection port size, etc. Through classification, the structural data of the pipeline system and the fan system can be clearly distinguished, which facilitates subsequent structural analysis and optimization. For example, for the pipe part, the standardized dimension parameters include an inner diameter of 100mm, a length of 500mm, etc., while the fan parameters of the fan part may include a power of 50W, a connection port diameter of 120mm, etc.

[0128] Step S232: Calculating the range hood duct structure similarity according to the duct part structure dimension parameter data, thereby obtaining the range hood duct structure similarity data;

[0129] In this embodiment, the similarity calculation of the range hood duct structure is performed based on the structural dimension parameter data of the duct part, and the similarity is mainly calculated by analyzing the geometric shapes and dimensions of different range hood duct parts. Specifically, the dimensions of the duct parts can be compared using a shape similarity algorithm or a distance measurement method based on Euclidean distance. For example, when calculating the similarity of two ducts with the same inner diameter and different lengths, the evaluation can be based on their geometric dimension differences and overall layout. Through this process, it is possible to determine which duct parts have a high structural similarity, which helps to select appropriate duct components during the design optimization process.

[0130] Step S233: extracting the fan duct connection port structure from the fan part structural parameter data, thereby obtaining the fan duct connection port structure data, and performing pipeline structure similarity calculation on the pipeline part structural dimension parameter data and the fan duct connection port structure data, thereby obtaining the fan duct connection port similarity data;

[0131] In this embodiment, the fan pipe connection port structure is extracted from the structural parameter data of the fan part. The connection port of the fan and the pipe is usually an important part of the fan and the pipe connection. Its size, shape, sealing method, etc. will affect the efficiency of air flow and the performance of the fan. When extracting the structural data of these connection ports, the diameter and shape (such as circular, elliptical, etc.) of the fan access port and the way of connection with the pipe (such as flange connection, threaded connection, etc.) can be referred to. Then, the pipeline structure similarity calculation is performed using the structural dimension parameter data of the pipeline part and the structural data of the fan pipe connection port, mainly by comparing the geometric dimensions of the connection port and the matching degree of the pipe to evaluate the adaptability of the fan connection port and the pipeline part. For example, if the inner diameter of the pipe is 100mm and the diameter of the fan connection port is 105mm, the size difference between them can be calculated to obtain similarity data, thereby providing a basis for the correct connection of the pipe and the fan.

[0132] Step S234: integrating the range hood duct connection relationship according to the range hood duct structure similarity data and the fan duct connection port similarity data, thereby obtaining the range hood component duct connection relationship data;

[0133] In this embodiment, the range hood duct connection relationship is integrated based on the range hood duct structure similarity data and the fan duct connection port similarity data. The connection relationship between the duct part and the fan part is systematically integrated to form a complete range hood duct connection network. Specifically, the duct part and the fan part of the range hood can be modeled through the graph structure in graph theory, and the duct part and the fan part of the range hood can be regarded as nodes in the graph, and the edges between the nodes represent the connection relationship between the duct and the fan. By combining the similarity data, a connection plan can be determined to ensure that the connection between the duct and the fan is tighter and seamless, and avoid the mismatch problem between the duct structures.

[0134] Step S235: performing a range hood duct topology structure analysis based on the range hood component duct connection relationship data, thereby obtaining a range hood duct topology structure model.

[0135] In this embodiment, the range hood pipeline topology structure analysis is performed based on the pipeline connection relationship data of the range hood components. The purpose of this analysis is to construct the pipeline topology structure model of the range hood through the integrated pipeline connection relationship data. Through the topology structure analysis, the air circulation path, the relative position of the pipeline and the fan, the relative angle of each pipeline and other information can be clarified. It can be achieved by using the computational fluid dynamics (CFD) method or by other structural analysis tools such as finite element analysis (FEA). Specifically, by using the existing data to input into the pipeline topology analysis model, the air flow path of the entire system and the possible air resistance can be calculated, thereby providing accurate structural data for the design and optimization of the range hood. The range hood pipeline topology structure analysis is performed using the integrated pipeline connection relationship data of the range hood components. Through this analysis, the pipeline topology structure model of the range hood can be constructed, which contains information such as the air flow path, the relative position between each pipeline component, the connection angle of the pipeline, etc. The construction of this topology structure model can be achieved by computational fluid dynamics (CFD) simulation or by finite element analysis (FEA), so as to obtain accurate predictions of air flow, pressure loss and energy efficiency.

[0136] Optionally, step S3 specifically includes:

[0137] The simulation feature extraction is performed based on the range hood aerodynamic simulation data, so as to obtain the simulated air pressure distribution data, the simulated air velocity distribution data and the simulated oil fume concentration distribution data;

[0138] In this embodiment, feature extraction is performed based on the aerodynamic simulation data of the range hood. The simulation data includes air flow pressure, flow rate, and oil fume concentration. These data are obtained by numerical simulation tools (such as CFD). After calculating the flow field, the air pressure distribution, flow rate distribution, and oil fume concentration distribution data of each point in the three-dimensional space are obtained. For example, through simulation, it is obtained that the air pressure at a certain position in the range hood pipeline is 120Pa, the flow rate is 3m / s, and the oil fume concentration is 80mg / m 3 The focus of this step is to extract these key features from the complex aerodynamic simulation results to facilitate subsequent performance estimation and optimization.

[0139] Estimating the range hood suction power based on the simulated air pressure distribution data and the simulated air velocity distribution data, thereby obtaining the range hood suction power data;

[0140] In this embodiment, the simulated air pressure distribution data and air velocity distribution data are used to estimate the suction of the range hood. The suction calculation is based on a comprehensive analysis of the pressure difference and the velocity distribution, assuming that the fan suction of the range hood is determined by the combined effect of the negative pressure generated by the fan and the flow velocity in the pipeline. Through a mathematical model, such as the Bernoulli equation, combined with the simulation results, the suction value of the range hood under different working conditions can be obtained. For example, in a certain kitchen environment, the simulation results show that the suction of the range hood is 250m 3 / h, and the flow velocity distribution shows that there is a stronger air flow velocity at the pipe inlet, which further indicates that the suction force is greater at this position.

[0141] The range hood pressure difference is estimated based on the simulated air pressure distribution data and the range hood suction data, thereby obtaining the range hood wind pressure difference data;

[0142] In this embodiment, the wind pressure difference of the range hood is estimated based on the simulated air pressure distribution data of the range hood and the estimated suction data. The wind pressure difference refers to the difference in air pressure between the inside and outside of the range hood, which usually affects the adsorption efficiency and exhaust capacity of oil smoke. The wind pressure difference of the range hood is calculated by combining the pressure distribution data in the simulation with the suction value using a preset differential pressure sensor model. Suction usually refers to the air flow rate per unit time, usually in cubic meters per hour (m 3 / h). According to the Bernoulli equation and the principle of fluid mechanics, there is a certain relationship between the suction generated by the fan and the wind pressure difference. The specific formula can be expressed as: Q = K\times\sqrt{\Delta P}; Q is the flow rate, \DeltaP is the wind pressure difference, and K is a constant related to the fan performance and pipeline resistance. For example, if the pressure at a certain location is 110Pa and the suction is 250m 3 / h, then according to the above formula, we can get the relationship between wind pressure difference and suction power. Assuming that we know the suction power of the range hood (for example, 250m 3 / h) and the relevant parameters of the fan (i.e. constant K), we can reversely calculate the wind pressure difference to be 48.2Pa, indicating that the smoke exhaust capacity of the range hood here is relatively strong.

[0143] Performing an oil fume concentration change analysis based on the simulated oil fume concentration distribution data, thereby obtaining the range hood simulated oil fume concentration change data, and performing range hood exhaust efficiency calculation on the range hood simulated oil fume concentration change data, thereby obtaining the range hood exhaust efficiency data;

[0144] In this embodiment, the change of oil fume concentration is analyzed based on the simulated oil fume concentration distribution data. The distribution of oil fume concentration reflects the diffusion of oil fume in the kitchen. By analyzing the simulation data, the change of oil fume concentration at different time and space points is obtained. Combined with environmental factors such as wind speed and temperature, the change law of oil fume concentration is analyzed, and the exhaust efficiency of the range hood is further calculated. This process can be achieved by establishing an exhaust efficiency model, combining the change of oil fume concentration with the suction and exhaust volume of the range hood, and evaluating the exhaust efficiency. For example, the simulation shows that the range hood can reduce the oil fume concentration in the kitchen by 70% under a certain setting, then the exhaust efficiency data output by this step is 70%.

[0145] The range hood operation noise is estimated according to the simulated air velocity distribution data, thereby obtaining the range hood operation noise data;

[0146] In this embodiment, the operating noise of the range hood is estimated based on the simulated air velocity distribution data. The noise is mainly caused by the flow of air in the fan and the pipe. Through the velocity and fluid noise calculation model, combined with the velocity distribution data, the noise generated by the range hood during operation can be estimated. For example, if the flow velocity at a certain position is 2.5m / s and the fan operates at a higher speed, the calculation results show that the operating noise of the range hood is 55dB. This step can be further used to optimize the noise control design of the range hood.

[0147] The range hood performance is weightedly scored based on the range hood suction data, range hood wind pressure difference data, range hood exhaust efficiency data, and range hood operation noise data, thereby obtaining range hood performance evaluation data;

[0148] In this embodiment, the suction data, wind pressure difference data, smoke exhaust efficiency data and operating noise data of the range hood are combined to perform a weighted performance score of the range hood. Each performance data is weighted according to its importance, and then the final performance evaluation score is obtained by weighted average. For example, the suction of the range hood accounts for 30% of the total score, the smoke exhaust efficiency accounts for 40%, and the operating noise accounts for 30%. Finally, a comprehensive performance score, such as 90 points, is obtained, indicating that the range hood performs well in a specific environment.

[0149] The range hood structural environmental adaptability is estimated based on the range hood performance evaluation data and random kitchen environment data sets to obtain the range hood structural environmental adaptability data.

[0150] In this embodiment, the performance evaluation data of the range hood and the random kitchen environment data set are used to estimate the structural environmental adaptability. This process needs to consider the impact of changes in the temperature, humidity, fume concentration, etc. of the kitchen environment on the performance of the range hood. Based on the matching degree between environmental factors and performance evaluation data, the adaptability of the range hood under different environmental conditions is estimated. For example, in an environment with high humidity, the suction and exhaust efficiency of the range hood may be affected to a certain extent, so the structural adaptability may be reduced. By establishing an environmental adaptability evaluation model, the adaptability of the range hood can be quantitatively analyzed.

[0151] Optionally, the estimation of the environmental adaptability of the range hood structure in step S3 is specifically as follows:

[0152] The performance error of the range hood in a random kitchen environment is calculated according to the range hood performance evaluation data and the random kitchen environment data set, so as to obtain the performance error data of the range hood in a random environment;

[0153] In this embodiment, the performance error of the range hood in a random kitchen environment is calculated based on the range hood performance evaluation data and the random kitchen environment data set. It is assumed that there are performance evaluation data of the range hood (including multiple dimensions such as suction, exhaust efficiency, noise, etc.) and kitchen environment data sets based on different environmental conditions (such as kitchen temperature, humidity, fume concentration, etc.). By comparing and analyzing these data, the error value of the performance of the same range hood in each kitchen environment can be obtained. For example, in a hot and humid kitchen environment, the suction of the range hood will decrease, resulting in an increase in performance error. Then, these error data are summarized and counted to obtain the performance error data of the range hood in a random environment, which helps to further optimize the design of the range hood.

[0154] Quantify the kitchen environment differences of random kitchen environment data sets to obtain kitchen environment parameter difference data;

[0155] In this embodiment, the kitchen environment differences are quantified for random kitchen environment data sets. In order to quantify the differences between different kitchen environments, temperature, humidity, fume concentration, etc. can be set as main parameters, and these parameters can be standardized. For example, the kitchen temperature varies between 20°C and 30°C, and the humidity fluctuates between 50% and 90%. By quantifying the differences in these environmental parameters, kitchen environment parameter difference data can be obtained to describe the specific differences in different kitchen environments, which will affect the working efficiency and performance of the range hood.

[0156] The kitchen environment impact is quantified based on the random environment range hood performance error data and the kitchen environment parameter difference data, so as to obtain the range hood performance error factor set;

[0157] In this embodiment, by associating kitchen environment differences with the performance errors of the range hood, it is possible to identify which environmental factors have the greatest impact on the performance of the range hood. For example, excessive fume concentration causes the fan to be overloaded, resulting in insufficient suction, thereby reducing the exhaust efficiency of the range hood. Statistical methods are used to extract and quantify error factors to obtain a range hood performance error factor set. This factor set can quantify the sources of errors under different kitchen environmental conditions and help to further optimize the range hood design. For example, for kitchen A, the temperature difference causes the suction of the range hood to decrease by 10%, the humidity difference causes the fan efficiency to decrease by 5%, and the fume concentration difference causes the exhaust efficiency to decrease by 8%. By analyzing these data, the impact of each environmental factor on the performance of the range hood can be calculated to obtain an error factor set. For example, the error factor of fume concentration is 0.1, the error factor of humidity is 0.05, and the error factor of temperature is 0.08. These error factors can help identify the key factors that affect the performance of the range hood under different kitchen environments and provide a basis for subsequent optimization.

[0158] Scoring the environmental adaptability of the range hood performance according to the range hood performance error factor set, thereby obtaining the environmental adaptability data of the range hood performance;

[0159] In this embodiment, the environmental adaptability of the range hood is scored using an error factor set. For example, assume that the range hood performs poorly in a high humidity environment and performs better in a low temperature environment. Based on the performance error factors of each range hood, the adaptability score of the range hood in different kitchen environments is calculated. For example, in a kitchen with a humidity of 60%, the temperature difference causes the suction power of the range hood to decrease by 10%, the humidity difference causes the fan efficiency to decrease by 5%, and the oil fume concentration difference causes the exhaust efficiency to decrease by 8%. The error factor of the oil fume concentration is 0.1, the error factor of the humidity is 0.05, and the error factor of the temperature is 0.08, and the environmental adaptability of the range hood is the weighted average of the three error factors. In a kitchen with a temperature of 38 degrees Celsius, the temperature difference causes the range hood's suction to drop by 15%, the humidity difference causes the fan efficiency to drop by 9%, and the fume concentration difference causes the exhaust efficiency to drop by 5%. The error factor of fume concentration is 0.15, the error factor of humidity is 0.09, and the error factor of temperature is 0.05. The environmental adaptability of the range hood is the weighted average of the three error factors. The performance environmental adaptability of the range hood is the average of multiple environmental adaptabilities. This score can help evaluate the adaptability of the range hood under different environmental conditions and identify parameters and conditions that perform poorly in specific environments.

[0160] Based on the environmental adaptability data of the range hood performance, the range hood duct structure model is simulated and scored to obtain the range hood structure environmental adaptability data.

[0161] In this embodiment, based on the performance fitness data of the range hood in different environments, its pipeline structure is simulated and scored. For example, suppose that the range hood pipeline is designed as a straight pipeline, but there will be a large wind pressure loss in a high humidity environment. At this time, the simulation can reveal the effectiveness of the pipeline design in different environments. For example, in a kitchen with high humidity, the wind pressure loss of the pipeline is 20Pa, resulting in a 10% decrease in the suction power of the range hood. In this case, the optimized pipeline structure needs to add a moisture-proof function or increase the fan power to reduce the impact of humidity on the pipeline efficiency. In this way, the simulation scoring of the range hood pipeline structure can provide direction for the optimization of the pipeline design, and finally obtain a range hood structure environmental adaptability data set, which reflects the adaptability and performance of the range hood structure under different environmental conditions.

[0162] Optionally, step S4 is specifically:

[0163] Step S41: clustering the range hood structures with high environmental adaptability according to the range hood structure environmental adaptability data, thereby obtaining the range hood structure data with high environmental adaptability;

[0164] In this embodiment, the environmental fitness data of multiple range hood structures are clustered and analyzed based on multiple kitchen environmental conditions (such as temperature, humidity, fume concentration, etc.). A clustering algorithm (such as K-means or hierarchical clustering) is used to group the range hood structures according to the fitness data. Assume that in kitchen A, the range hood has a strong suction and has a higher fitness score when the humidity is 60%, while in kitchen B, the range hood has a lower suction and has a lower fitness score when the humidity is 80%. The range hood structures with high fitness in multiple environments are then clustered. Through these data clustering, the range hood structures with higher fitness scores are classified as high fitness range hood structures, thereby forming a group of environmental high fitness range hood structure data. This process can be achieved through an algorithm to match the range hood structure with its fitness score in different environments, thereby screening out the range hood structure that is most suitable for a specific environment.

[0165] Step S42: confirming the pipeline structure constraint conditions based on the range hood pipeline structure model, thereby obtaining the range hood pipeline structure constraint conditions;

[0166] In this embodiment, based on the range hood duct structure model, the constraints of the range hood duct structure are determined according to the performance indicators such as the size of the duct, wind pressure, and suction. For example, for a certain model of range hood, the maximum length of the duct cannot exceed 3 meters to avoid wind pressure loss caused by an excessively long duct; the diameter of the duct needs to meet the minimum requirement of 120 mm to ensure sufficient air flow; at the same time, the duct material needs to be corrosion-resistant and able to withstand higher oil fume concentrations. By confirming these constraints, the structural constraint data of the range hood duct is obtained. These constraints provide specific parameter ranges and performance requirements for the subsequent optimization and design of the duct structure, ensuring the efficient operation of the range hood in different kitchen environments.

[0167] Step S43: extracting the range hood structure performance characteristics of the range hood structure data with high environmental adaptability according to the range hood performance evaluation data, thereby obtaining the range hood structure data with high suction power, the range hood structure data with high wind pressure difference, the range hood structure data with high smoke exhaust efficiency, and the range hood structure data with high operating noise;

[0168] In this embodiment, after determining the structural data of the high-adaptability range hood, the key high-performance structural features are extracted through the performance evaluation data of the range hood (such as suction, wind pressure, smoke exhaust efficiency, noise, etc.). By analyzing these data, the corresponding structural feature data of high suction, high wind pressure difference, high smoke exhaust efficiency and high operating noise can be extracted. In this way, the structural parameters that best reflect its superior performance are extracted from the structure of the high-adaptability range hood, providing data support for the subsequent optimization of the range hood pipeline structure.

[0169] Step S44: constructing a high-performance range hood structural parameter space according to the high suction range hood structural data, the high wind pressure difference range hood structural data, the high smoke exhaust efficiency range hood structural data, and the high operating noise range hood structural data;

[0170] In this embodiment, a high-performance range hood structural parameter space is constructed based on the data of high suction, high wind pressure, smoke exhaust efficiency and operating noise. Specifically, the above characteristic parameters are used as coordinate axes to form a four-dimensional parameter space. In this space, each point represents a combination of range hood structures. For example, a point represents a range hood with a suction of 350m 3 / h, wind pressure is 90Pa, smoke exhaust efficiency is 98%, and noise is 55dB. By analyzing this parameter space, we can identify which parameter combinations can provide the best performance, and provide a reference for the subsequent optimization of the range hood duct structure to find the optimal structural parameter combination.

[0171] Step S45: performing optimal structural parameter combination on the high-performance range hood structural parameter space through the range hood duct structural model, thereby obtaining a range hood duct structural model with minimized operation noise and a range hood duct structural model with maximized energy efficiency;

[0172] In this embodiment, the optimal combination of high-performance range hood structural parameters is performed through optimization algorithms (such as genetic algorithms, particle swarm optimization, etc.). For example, suppose that for a range hood, the goal is to minimize operating noise and improve energy efficiency. Through simulation and optimization, the optimal combination of structural parameters is obtained. For example, a larger diameter pipe is selected to reduce wind resistance, the design of the fan is adjusted to reduce noise, and the exhaust system is improved to improve energy efficiency. The results obtained through optimization correspond to the range hood duct structure models that minimize noise and maximize energy efficiency, respectively, providing an optimal structure that meets specific goals.

[0173] Step S46: merging the model data sets of the range hood duct structure model with minimized operation noise and the range hood duct structure model with maximized energy efficiency, thereby obtaining an optimized range hood duct structure model set;

[0174] In this embodiment, after the optimization of the range hood duct structure for noise minimization and energy efficiency maximization is completed, the data of the two optimization models are integrated. By merging the data of the range hood duct structure model for noise minimization and the range hood duct structure model for energy efficiency maximization, a model set containing multiple optimization schemes is generated. This integrated model can not only balance between noise and energy efficiency, but also provide multiple solutions for practical applications. For example, the most suitable model can be selected for application according to the requirements of the specific kitchen environment (such as low noise requirements or high energy efficiency requirements).

[0175] Step S47: performing range hood performance prediction on the random kitchen environment parameter data set by optimizing the range hood duct structure model set, thereby obtaining optimized range hood duct structure performance prediction data.

[0176] In this embodiment, the optimized range hood duct structure model set is used to predict the range hood performance for random environmental parameters in different kitchen environments (such as kitchen A and kitchen B). For example, in kitchen A, the temperature is 30°C, the humidity is 60%, and the fume concentration is high; in kitchen B, the temperature is 22°C, the humidity is 50%, and the fume concentration is low. By inputting these environmental data into the optimized duct structure model, the performance indicators such as suction, exhaust efficiency, wind pressure and noise of the range hood in these two environments are predicted. Based on these prediction results, the performance of the range hood in different kitchen environments can be evaluated, providing a basis for consumers to choose a suitable range hood.

[0177] Optionally, step S45 is specifically:

[0178] According to the structural constraints of the range hood pipeline, the range hood structural parameter space of the high-performance range hood is constrained, so as to obtain the range hood structural constraint parameter space;

[0179] In this embodiment, the high-performance range hood structural parameter space is constrained according to the pipeline structural constraints of the range hood (such as the maximum length of the pipeline, the minimum diameter, material requirements, the maximum wind pressure bearing capacity, etc.). For example, assume that the pipeline of the range hood cannot exceed 3 meters, the minimum diameter of the pipeline must be 120mm, and the wind pressure cannot exceed 100Pa. These constraints are introduced into the structural parameter space model of the range hood, and the structural points that meet these constraints are screened out from the original high-performance structural parameter space to obtain the range hood structural constraint parameter space. This space only contains those range hood structural data sets that meet specific constraints and have higher performance, providing an effective parameter space for subsequent optimization.

[0180] The range hood pipeline structure model is used to combine the operating noise minimizing structural parameters in the range hood structure constraint parameter space, thereby obtaining the operating noise minimizing structural parameter group;

[0181] In this embodiment, based on the range hood duct structure model, the structural constraint parameter space is optimized through optimization algorithms (such as particle swarm optimization, genetic algorithm, etc.), with the goal of minimizing the operating noise of the range hood. For example, suppose that through analysis, it is found that factors such as the smoothness of the inner wall of the duct, the speed of the fan, and the curvature of the duct have a great influence on the noise. Through algorithm search, noise minimization is taken as the optimization goal. Under the premise of meeting the structural constraints, the structural parameter combination with the lowest noise is selected, such as a larger duct diameter, a lower speed fan, and a duct design with fewer bends, and finally the "operating noise minimization structural parameter group" is obtained. These parameter groups can be used for further performance optimization in subsequent model adjustments.

[0182] The range hood duct structure model is used to perform random structural parameter combinations on the range hood structure constraint parameter space, thereby obtaining a random structural parameter group;

[0183] In this embodiment, the range hood duct structure model is used to randomly sample the structural constraint parameter space, and a random number generator is set to randomly combine multiple parameters such as duct diameter, duct length, and fan speed. These random parameter groups may include various range hood duct configurations. For example, under the structural constraint condition, a random structural parameter group will be obtained: the duct diameter is 140 mm, the duct length is 2.5 meters, and the fan speed is 2000 RPM. Through multiple random samplings, multiple random structural parameter groups are obtained, which provides a large number of different range hood structure solutions for subsequent performance analysis.

[0184] Based on the random structural parameter group, the range hood airflow resistance and pressure drop are simulated by using the range hood duct structure model, so as to obtain the random structural parameter group simulation data;

[0185] In this embodiment, based on the obtained multiple random structural parameter groups, the range hood duct structure model is used to perform numerical simulation of airflow resistance and pressure drop. During the simulation process, the airflow flow simulation is performed on each random structural parameter group through numerical simulation software (such as ANSYS Fluent or CFD simulation tool) to calculate the resistance and pressure drop of the airflow when it flows in the duct. For example, for a certain random structural parameter group, it may be simulated that the resistance of the airflow when flowing in the structure is 20Pa and the pressure drop is 5Pa. Each random structural parameter group will obtain a set of airflow resistance and pressure drop data to evaluate its actual operating performance.

[0186] The random structural energy efficiency is estimated for the random structural parameter group simulation data, thereby obtaining the random structural energy efficiency data, and the maximum energy efficiency structural parameter combination is selected based on the random structural energy efficiency data, thereby obtaining the structural parameter combination with the maximum energy efficiency;

[0187] In this embodiment, energy efficiency estimation is performed on the simulation data of the random structural parameter group obtained in the step. The energy efficiency estimation can be based on the ratio of calculated energy consumption to airflow efficiency. For example, the energy efficiency value of each random structural parameter group is obtained by calculating the flow velocity, pressure drop, and power consumed by the fan of the airflow in the pipeline. Assume that a certain random structural parameter group exhibits higher energy efficiency in the simulation, that is, consumes less power and has a lower pressure drop. By comparing the energy efficiency data of multiple random structural parameter groups, the structural parameter group with the best energy efficiency is selected, that is, the structural parameter combination with the maximum energy efficiency. This optimization process usually involves ranking the energy efficiency of each structure and finally selecting the structural configuration with the best energy efficiency.

[0188] Adaptively adjust the structural parameters of the range hood duct structure model according to the running noise minimization structural parameter group, so as to obtain the range hood duct structure model with minimized running noise;

[0189] In this embodiment, the range hood duct structure model is adaptively adjusted based on the obtained operating noise minimization structural parameter group. The goal of this adjustment process is to further reduce the operating noise of the range hood while ensuring that the performance is not affected. For example, in the simulation, it was found that a certain duct design produced greater noise at the bend. After adjustment, the airflow in the duct is made smoother by increasing the length of the duct or changing the installation angle of the fan, thereby reducing the noise. In this step, the duct bending angle, fan speed, and air velocity distribution in the model can be adjusted until the most suitable noise minimization structural model is found.

[0190] According to the energy-efficiency-maximizing structural parameter combination, the structural parameters of the range hood duct structure model are adaptively adjusted to obtain the energy-efficiency-maximizing range hood duct structure model.

[0191] In this embodiment, based on the obtained energy efficiency maximization structural parameter combination, the structural parameters of the range hood duct structure model are adaptively adjusted, with the goal of further improving energy efficiency. The key to this process is to optimize the flow characteristics of the duct and reduce energy loss. For example, in the energy efficiency maximization scheme, the power of the fan, the diameter of the duct, and the choice of materials are adjusted to make the airflow flow smoother and reduce friction and energy loss. It is assumed that by adjusting the fan speed and the duct material, energy consumption can be reduced by 5%, thereby improving the energy efficiency of the range hood. The adjusted model will ultimately be reflected as the range hood duct structure design that is most suitable for specific environmental conditions, ensuring maximum energy efficiency.

[0192] Optionally, step S5 specifically includes:

[0193] Step S51: Calculating the performance error of the optimized range hood pipeline structure performance prediction data and the range hood performance evaluation data, thereby obtaining range hood structure performance error data;

[0194] In this embodiment, the performance prediction data of the optimized range hood duct structure model (such as the simulated airflow rate, pressure difference, exhaust efficiency, etc.) are compared and analyzed with the range hood performance evaluation data (such as the actual measured value) to calculate the structural performance error. For example, assuming that the range hood suction power predicted in the simulation is 300m 3 / h, but in actual test it is 280m 3 / h, error is 20m 3 / h; in actual operation, the wind pressure difference is 80Pa, while the simulation data is 85Pa, with an error of 5Pa. By statistically analyzing these error data, the overall structural performance error data can be obtained. These error data will serve as the basis for subsequent optimization and help identify the design parts that need to be improved.

[0195] Step S52: quantifying the influence of pipeline structural parameters according to the range hood structural performance error data, thereby obtaining a range hood performance error influence factor;

[0196] In this embodiment, based on the range hood structural performance error data, the impact of various parameters of the range hood pipeline structure (such as pipeline diameter, length, fan speed, number of pipeline elbows, etc.) is quantified through regression analysis, sensitivity analysis and other methods. For example, through analysis, it is found that the increase in pipeline diameter has a significant impact on the improvement of smoke exhaust efficiency, but too large a pipeline diameter will increase system resistance, thereby affecting suction; although the increase in fan speed can improve suction, it will lead to increased noise. By quantifying the impact of each structural parameter, the performance error influencing factor of each pipeline structural parameter can be obtained. For example, the pipeline diameter has a greater impact on the smoke exhaust efficiency (the impact factor is 0.6), while the impact factor of the fan speed is 0.3. These data provide a clear direction for subsequent optimization.

[0197] Step S53: selecting parameters to be optimized for the range hood duct structure based on the range hood performance error influencing factor, thereby obtaining parameter data to be optimized for the range hood duct structure;

[0198] In this embodiment, based on the obtained range hood performance error influencing factors, the pipeline structural parameters that need to be optimized are selected. For example, if in the quantitative analysis of the performance error influencing factors, it is found that the pipeline diameter and the fan speed are the key factors affecting the smoke exhaust efficiency and suction, then these parameters will be selected as parameters to be optimized. Assuming that the pipeline diameter is 150mm and the fan speed is 2200RPM, these two parameters will be marked as parameters to be optimized. Through further optimization algorithms, these parameters to be optimized will be refined into specific data sets, such as adjusting the pipeline diameter range to 130mm to 170mm and the fan speed range to 2000RPM to 2400RPM, in order to find the optimal structural parameters in the subsequent optimization process.

[0199] Step S54: performing iterative optimization of structural dimension parameters of the optimized range hood duct structure model set according to the range hood duct structure parameter data to be optimized, thereby obtaining a performance structure optimization model set.

[0200] In this embodiment, the above-determined range hood duct structure parameter data to be optimized is used to iteratively optimize the structural dimension parameters of the optimized range hood duct structure model using an optimization algorithm (such as a particle swarm optimization algorithm, a genetic algorithm, or a simulated annealing algorithm). For example, based on the two parameters to be optimized, namely, the pipe diameter and the fan speed, a genetic algorithm is used to continuously adjust the combination of these two parameters, and evaluate their impact on the performance of the range hood (such as noise, energy efficiency, exhaust efficiency, etc.). Each iteration will calculate the performance error of the current parameter combination, and gradually reduce the error value until the optimal structural dimension combination is found. For example, after multiple iterations, the optimization result may be a pipe diameter of 160mm and a fan speed of 2300RPM. This combination shows the best exhaust efficiency and the lowest noise in the performance test. During the optimization process, the multiple models obtained will be aggregated into a "performance structure optimization model set" to provide an optimization solution for the final range hood design.

[0201] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0202] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A simulation optimization design method for the structural dimension parameters of an intelligent range hood, characterized in that: The following steps are involved: Step S1: Acquire kitchen environment sensor data, and perceive kitchen environment changes based on the kitchen environment sensor data, thereby obtaining a real-time kitchen environment parameter data set; generate a random environment for the real-time kitchen environment parameter data set, thereby obtaining a random kitchen environment data set; Step S2: acquiring range hood structural data, and performing range hood duct topology analysis on the range hood structural data, thereby obtaining a range hood duct structural model; performing aerodynamic simulation on a random kitchen environment data set through the range hood duct structural model, thereby obtaining range hood aerodynamic simulation data; Step S3: performing range hood performance evaluation according to the range hood aerodynamic simulation data, thereby obtaining range hood performance evaluation data, and estimating the range hood structural environmental adaptability of the range hood performance evaluation data and the random kitchen environment data set, thereby obtaining range hood structural environmental adaptability data; Step S4: performing multi-objective optimization of the pipe structure size of the range hood pipe structure model according to the range hood structure environmental adaptability data, thereby obtaining an optimized range hood pipe structure model set; performing range hood performance prediction on the random kitchen environment parameter data set by optimizing the range hood pipe structure model set, thereby obtaining optimized range hood pipe structure performance prediction data; Step S5: Calculate the performance error of the optimized range hood duct structure performance prediction data and the range hood performance evaluation data to obtain the range hood structure performance error data, and optimize the duct structure size performance of the optimized range hood duct structure model set according to the range hood structure performance error data to obtain the optimal range hood duct structure model.

2. The simulation optimization design method of the intelligent range hood structural dimension parameters according to claim 1 is characterized in that: Step S1 is specifically as follows: Step S11: acquiring kitchen environment sensor data, and performing data preprocessing on the kitchen environment sensor data, thereby obtaining kitchen environment sensor data to be analyzed; Step S12: integrating kitchen environment characteristics of the kitchen environment sensor data to be analyzed, thereby obtaining kitchen environment characteristic data; Step S13: performing environmental change pattern recognition based on the kitchen environment feature data, thereby obtaining a real-time kitchen environment parameter data set; Step S14: performing Monte Carlo simulation on the real-time kitchen environment parameter data set to obtain a random kitchen environment parameter data set; Step S15: Perform kitchen environment scene simulation according to the random kitchen environment parameter data set to obtain a random kitchen environment data set.

3. The simulation optimization design method of the intelligent range hood structural dimension parameters according to claim 2 is characterized in that: Step S13 is specifically as follows: Perform kitchen environment time series change analysis based on kitchen environment characteristic data to obtain kitchen temperature and humidity change data and kitchen fume concentration change data; Performing periodic pattern recognition on kitchen fume concentration change data to obtain fume concentration change pattern data; Calculating the temperature and humidity change slope of the kitchen temperature and humidity change data to obtain the kitchen temperature and humidity change slope data, and identifying the temperature and humidity change pattern based on the kitchen temperature and humidity change slope data to obtain the kitchen temperature and humidity change pattern data; Integrate the kitchen environment change pattern data with the oil smoke concentration change pattern data and the kitchen temperature and humidity change pattern data to obtain the kitchen environment change pattern data; According to the kitchen environment change pattern data, the kitchen environment sensor data to be analyzed is subjected to real-time kitchen environment change pattern recognition, thereby obtaining the kitchen environment real-time environment change pattern data; The real-time kitchen environment parameter data is integrated with the real-time environmental change pattern data of the kitchen environment to obtain a real-time kitchen environment parameter data set.

4. The simulation optimization design method of the intelligent range hood structural dimension parameters according to claim 1 is characterized in that: Step S2 is specifically as follows: Step S21: acquiring range hood structure data, and performing data preprocessing on the range hood structure data, thereby obtaining standardized range hood structure data; Step S22: extracting the range hood structure features from the standardized range hood structure data, thereby obtaining range hood component structure dimension parameter data and range hood component description data; Step S23: performing a range hood duct topology analysis according to the range hood component structural dimension parameter data, thereby obtaining a range hood duct topology model; Step S24: assigning range hood duct component attributes to the range hood duct topology structure model according to the range hood component description data, thereby obtaining the range hood duct structure model; Step S25: performing aerodynamic simulation on the random kitchen environment data set through the range hood duct structure model, thereby obtaining range hood aerodynamic simulation data.

5. The simulation optimization design method of the intelligent range hood structural dimension parameters according to claim 4 is characterized in that: Step S23 is specifically as follows: Step S231: classifying the components of the range hood according to the structural dimension parameter data of the range hood components, thereby obtaining the structural dimension parameter data of the pipeline part and the structural parameter data of the fan part; Step S232: Calculating the range hood duct structure similarity according to the duct part structure dimension parameter data, thereby obtaining the range hood duct structure similarity data; Step S233: extracting the fan duct connection port structure from the fan part structural parameter data, thereby obtaining the fan duct connection port structure data, and performing pipeline structure similarity calculation on the pipeline part structural dimension parameter data and the fan duct connection port structure data, thereby obtaining the fan duct connection port similarity data; Step S234: integrating the range hood duct connection relationship according to the range hood duct structure similarity data and the fan duct connection port similarity data, thereby obtaining the range hood component duct connection relationship data; Step S235: performing a range hood duct topology structure analysis based on the range hood component duct connection relationship data, thereby obtaining a range hood duct topology structure model.

6. The simulation optimization design method of the intelligent range hood structural dimension parameters according to claim 1 is characterized in that: Step S3 is specifically as follows: The simulation feature extraction is performed based on the range hood aerodynamic simulation data, so as to obtain the simulated air pressure distribution data, the simulated air velocity distribution data and the simulated oil fume concentration distribution data; Estimating the suction power of the range hood based on the simulated air pressure distribution data and the simulated air velocity distribution data, thereby obtaining the suction power data of the range hood; The range hood pressure difference is estimated based on the simulated air pressure distribution data and the range hood suction data, thereby obtaining the range hood wind pressure difference data; Performing an oil fume concentration change analysis based on the simulated oil fume concentration distribution data, thereby obtaining the range hood simulated oil fume concentration change data, and performing range hood exhaust efficiency calculation on the range hood simulated oil fume concentration change data, thereby obtaining the range hood exhaust efficiency data; The range hood operation noise is estimated according to the simulated air velocity distribution data, thereby obtaining the range hood operation noise data; The range hood performance is weightedly scored based on the range hood suction data, range hood wind pressure difference data, range hood exhaust efficiency data, and range hood operation noise data, thereby obtaining range hood performance evaluation data; The range hood structural environmental adaptability is estimated based on the range hood performance evaluation data and random kitchen environment data sets to obtain the range hood structural environmental adaptability data.

7. The simulation optimization design method of the intelligent range hood structural dimension parameters according to claim 6 is characterized in that: The estimation of the environmental adaptability of the range hood structure in step S3 is specifically as follows: The performance error of the range hood in a random kitchen environment is calculated according to the range hood performance evaluation data and the random kitchen environment data set, so as to obtain the performance error data of the range hood in a random environment; Quantify the kitchen environment differences of random kitchen environment data sets to obtain kitchen environment parameter difference data; The kitchen environment impact is quantified based on the random environment range hood performance error data and the kitchen environment parameter difference data, so as to obtain the range hood performance error factor set; Scoring the environmental adaptability of the range hood performance according to the range hood performance error factor set, thereby obtaining the environmental adaptability data of the range hood performance; Based on the environmental adaptability data of the range hood performance, the range hood duct structure model is simulated and scored to obtain the range hood structure environmental adaptability data.

8. The simulation optimization design method of the intelligent range hood structural dimension parameters according to claim 1 is characterized in that: Step S4 is specifically as follows: Step S41: clustering the range hood structures with high environmental adaptability according to the range hood structure environmental adaptability data, thereby obtaining the range hood structure data with high environmental adaptability; Step S42: confirming the pipeline structure constraint conditions based on the range hood pipeline structure model, thereby obtaining the range hood pipeline structure constraint conditions; Step S43: extracting the range hood structure performance characteristics of the range hood structure data with high environmental adaptability according to the range hood performance evaluation data, thereby obtaining the range hood structure data with high suction power, the range hood structure data with high wind pressure difference, the range hood structure data with high smoke exhaust efficiency, and the range hood structure data with high operating noise; Step S44: constructing a high-performance range hood structural parameter space according to the high suction range hood structural data, the high wind pressure difference range hood structural data, the high smoke exhaust efficiency range hood structural data, and the high operating noise range hood structural data; Step S45: performing optimal structural parameter combination on the high-performance range hood structural parameter space through the range hood duct structural model, thereby obtaining a range hood duct structural model with minimized operation noise and a range hood duct structural model with maximized energy efficiency; Step S46: merging the model data sets of the range hood duct structure model with minimized operation noise and the range hood duct structure model with maximized energy efficiency, thereby obtaining an optimized range hood duct structure model set; Step S47: performing range hood performance prediction on the random kitchen environment parameter data set by optimizing the range hood duct structure model set, thereby obtaining optimized range hood duct structure performance prediction data.

9. The simulation optimization design method of the intelligent range hood structural dimension parameters according to claim 8 is characterized in that: Step S45 is specifically as follows: According to the structural constraints of the range hood pipeline, the range hood structural parameter space of the high-performance range hood is constrained, so as to obtain the range hood structural constraint parameter space; The range hood pipeline structure model is used to combine the operating noise minimizing structural parameters in the range hood structure constraint parameter space, thereby obtaining the operating noise minimizing structural parameter group; The range hood duct structure model is used to perform random structural parameter combinations on the range hood structure constraint parameter space, thereby obtaining a random structural parameter group; Based on the random structural parameter group, the range hood airflow resistance and pressure drop are simulated by using the range hood duct structure model, so as to obtain the random structural parameter group simulation data; The random structural energy efficiency is estimated for the random structural parameter group simulation data, thereby obtaining the random structural energy efficiency data, and the maximum energy efficiency structural parameter combination is selected based on the random structural energy efficiency data, thereby obtaining the structural parameter combination with the maximum energy efficiency; Adaptively adjust the structural parameters of the range hood duct structure model according to the running noise minimization structural parameter group, so as to obtain the range hood duct structure model with minimized running noise; According to the energy-efficiency-maximizing structural parameter combination, the structural parameters of the range hood duct structure model are adaptively adjusted to obtain the energy-efficiency-maximizing range hood duct structure model.

10. The simulation optimization design method of the intelligent range hood structural dimension parameters according to claim 1, characterized in that: Step S5 is specifically as follows: Step S51: Calculating the performance error of the optimized range hood pipeline structure performance prediction data and the range hood performance evaluation data, thereby obtaining range hood structure performance error data; Step S52: quantifying the influence of pipeline structural parameters according to the range hood structural performance error data, thereby obtaining a range hood performance error influence factor; Step S53: selecting parameters to be optimized for the range hood duct structure based on the range hood performance error influencing factor, thereby obtaining parameter data to be optimized for the range hood duct structure; Step S54: performing iterative optimization of structural dimension parameters of the optimized range hood duct structure model set according to the range hood duct structure parameter data to be optimized, thereby obtaining a performance structure optimization model set.

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