Cigarette factory process air conditioner airflow field modeling analysis method

Abstract

The invention discloses a cigarette factory process air conditioner airflow flow field modeling analysis method, which comprises the following steps of: acquiring simulation boundary condition data, solving a basic control equation to obtain environment field distribution data by combining a physical model and a fluid characteristic model, and performing airflow organization simulation by combining the simulation boundary condition data, obtaining a current simulation result and a related simulation result; the collected actual measurement data is compared with the current simulation result of the corresponding point location, and when a first deviation condition is met, the collection of the simulation boundary condition data is executed again; when the first deviation condition is not met, the current simulation result is compared with the preset temperature and humidity standard, and when the second deviation condition is met, according to the comparison result and the related simulation result, the current air supply combination parameter is adjusted, and collection of the simulation boundary condition data is executed again; and when the second deviation condition is not met, a regulation and control scheme of the cigarette factory process air conditioner is generated based on the current air supply combination parameters, energy conservation and consumption reduction are achieved, and stable operation of the air conditioner system is guaranteed.

Description

Technical Field

[0001] This invention relates to the fields of automation in cigarette production and air conditioning control technology, and in particular to a method for modeling and analyzing the airflow field of process air conditioning in cigarette factories. Background Technology

[0002] The porous media materials used in cigarette production are extremely sensitive to air temperature and humidity. Ambient temperature and humidity directly affect the moisture content of the materials, which in turn determines indicators such as cigarette weight, draw resistance, and hardness. Maintaining ambient temperature and humidity within the specified range and ensuring stable airflow distribution during cigarette processing are key measures to guarantee cigarette quality.

[0003] Currently, cigarette factories mostly use central air conditioning systems paired with temperature and humidity sensors to control the environment. The sensors monitor temperature and humidity data in the controlled area in real time, and the system automatically adjusts the air conditioning operation based on preset algorithms. However, existing technologies have many shortcomings in practical applications, making it difficult to achieve ideal control effects. On the one hand, factory air conditioning systems are prone to significant deviations from design conditions due to factors such as untimely maintenance, climate change, and fluctuations in equipment operation. The complex layout of equipment in production workshops, with differences in process tasks and stored materials in different areas, leads to varying temperature and humidity standards, resulting in uneven indoor heating and cooling, strong localized drafts, and dead zones, reducing the stability of regional temperature and humidity. On the other hand, the control effect of central air conditioning systems is constrained by various factors, making it difficult to guarantee stable temperature and humidity in production areas or achieve energy conservation and consumption reduction, thus reducing the reliability of air conditioning control. Summary of the Invention

[0004] This invention provides a method for modeling and analyzing the airflow field of a cigarette factory's process air conditioning system, in order to solve the problems of large deviation between the actual temperature and humidity distribution in the controlled area and the design state after air conditioning control, low stability of temperature and humidity in the controlled area, difficulty in achieving energy saving and consumption reduction, and low reliability of air conditioning control.

[0005] According to one aspect of the present invention, a method for controlling the process air conditioning in a cigarette factory is provided, comprising:

[0006] The simulated boundary condition data of the controlled region are collected, and the basic governing equations are solved by combining the pre-constructed physical model and fluid characteristic model that match the controlled region to obtain the environmental field distribution data of the controlled region.

[0007] Based on the simulated boundary condition data and environmental field distribution data, airflow organization simulation is performed to obtain the current simulation results of each preset point in the controlled area and the relevant simulation results matched with multiple sets of air supply combination parameters.

[0008] Collect measured data at each preset point within the controlled area, compare the measured data with the current simulation results at the corresponding point, and when the first deviation condition is met, return to the operation of collecting the simulation boundary condition data of the controlled area;

[0009] When the first deviation condition is not met, the current simulation result is compared with the preset temperature and humidity standard. When the second deviation condition is met, the current air supply combination parameters are adjusted according to the comparison result between the current simulation result and the preset temperature and humidity standard, as well as the relevant simulation results matched with multiple sets of air supply combination parameters. Then, the operation of collecting simulation boundary condition data of the controlled area is returned.

[0010] When the second deviation condition is not met, a control scheme for the process air conditioning of the cigarette factory is generated based on the current air supply combination parameters.

[0011] According to another aspect of the present invention, a control device for a process air conditioning system in a cigarette factory is provided, comprising:

[0012] The distributed solution module is used to collect simulated boundary condition data of the controlled region and, in combination with a pre-built physical model and fluid characteristic model that match the controlled region, solve the basic governing equations to obtain the environmental field distribution data of the controlled region.

[0013] The airflow simulation module is used to simulate airflow organization based on simulated boundary condition data and environmental field distribution data, and to obtain the current simulation results of each preset point in the controlled area as well as the relevant simulation results matched with multiple sets of air supply combination parameters.

[0014] The first verification module is used to collect measured data of each preset point within the controlled area and compare the measured data with the current simulation results of the corresponding point. When the first deviation condition is met, it returns to the operation of collecting the simulated boundary condition data of the controlled area.

[0015] The second verification module is used to compare the current simulation results with the preset temperature and humidity standards when the first deviation condition is not met, and to adjust the current air supply combination parameters based on the comparison results of the current simulation results with the preset temperature and humidity standards and the relevant simulation results matched with multiple sets of air supply combination parameters when the second deviation condition is met, and then return to the operation of collecting the simulation boundary condition data of the controlled area.

[0016] The scheme generation module is used to generate a control scheme for the process air conditioning of the cigarette factory based on the current air supply combination parameters when the second deviation condition is not met.

[0017] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0018] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the control method for the process air conditioning of a cigarette factory according to any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions, the computer instructions being configured to cause a processor to execute and implement the control method for the process air conditioning of a cigarette factory as described in any embodiment of the present invention.

[0020] According to another aspect of the present invention, a computer program product is also provided, including a computer program that, when executed by a processor, implements the steps of the method as described in any embodiment of the present invention.

[0021] The technical solution of this invention collects simulated boundary condition data from multiple points in the controlled area, solves the basic control equations using a matched physical model and fluid characteristic model to obtain environmental field distribution data, acquires current simulation results and related simulation results corresponding to multiple sets of air supply combination parameters through airflow organization simulation, and analyzes the correlation between air supply combination parameters and the environmental field, temperature, and humidity of the controlled area, providing a basis for parameter adjustment and improving the accuracy of temperature and humidity control to meet the needs of cigarette production processes. A first deviation verification step, comparing measured data from preset points with the current simulation results, forms the first closed loop of simulation, measurement, and correction, correcting simulation deviations and model adaptation errors, enhancing the reliability of simulation results, and reducing trial-and-error in control. Cost; After the simulation results pass the first round of verification, a second round of deviation comparison is conducted with the preset temperature and humidity standards. If the standards are not met, the current parameters are adjusted and iterated again based on the simulation results of multiple sets of air supply combination parameters. This dynamically adapts to the controlled area, continuously optimizes the air supply combination parameters, reduces the deviation between the actual temperature and humidity distribution and the design state, and ensures uniform and stable temperature and humidity in the area. The final air supply combination parameters are selected through simulation and iterative adjustment of multiple sets of air supply combination parameters to avoid redundant operation of the air conditioning system. The control scheme generated based on the final parameters enables the air conditioning system to output cooling, heating and air volume on demand, achieving energy saving and consumption reduction, ensuring long-term stable operation of the air conditioning system, improving the reliability of air conditioning control, and providing a continuous and reliable temperature and humidity environment for cigarette production.

[0022] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a flowchart of a method for controlling a process air conditioner in a cigarette factory according to Embodiment 1 of the present invention;

[0025] Figure 2 This is a flowchart of another method for controlling the process air conditioning in a cigarette factory according to Embodiment 2 of the present invention;

[0026] Figure 3 This is a flowchart of another method for controlling a process air conditioner in a cigarette factory according to Embodiment 3 of the present invention;

[0027] Figure 4 This is a schematic diagram illustrating the control of a process air conditioning system in a cigarette factory, applicable to an embodiment of the present invention.

[0028] Figure 5 This is a schematic diagram of the structure of a control device for a process air conditioner in a cigarette factory according to Embodiment 4 of the present invention;

[0029] Figure 6 This is a schematic diagram of the structure of an electronic device for implementing the control method of the process air conditioning in a cigarette factory according to an embodiment of the present invention. Detailed Implementation

[0030] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0031] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0032] Example 1

[0033] Figure 1 This is a flowchart of a method for controlling a process air conditioner in a cigarette factory, provided in Embodiment 1 of the present invention. This embodiment is applicable to the control of process air conditioners. The method can be executed by a control device for the process air conditioner in a cigarette factory. This control device can be implemented in hardware and / or software and is generally configured in an electronic device. For example... Figure 1 As shown, the method includes:

[0034] S110. Collect simulated boundary condition data of the controlled area, and combine it with the pre-constructed physical model and fluid characteristic model that match the controlled area to solve the basic control equations and obtain the environmental field distribution data of the controlled area.

[0035] In this embodiment of the invention, the simulated boundary condition data can be specifically understood as: parameters obtained through actual measurements to define the boundary state of the numerical simulation, which can be the temperature values ​​at locations such as the air outlet and equipment surface in the controlled area. The physical model can be specifically understood as: a three-dimensional mesh model constructed based on data such as the spatial dimensions, enclosure structure, and equipment layout of the controlled area, used to restore the true spatial morphology of the controlled area.

[0036] Fluid characteristic models can be specifically understood as a set of models used to describe the characteristics of mixed fluids within a controlled region, such as turbulence models, used to modify the basic governing equations to adapt to real fluid flow scenarios. Basic governing equations can be specifically understood as equations describing the fundamental laws of fluid motion, which may include mass conservation equations, momentum conservation equations, and energy conservation equations, etc. Environmental field distribution data can be specifically understood as environmental field distribution data reflecting the airflow and temperature and humidity distribution within a controlled region, obtained through numerical calculations, such as spatial distribution data of the flow field, temperature field, humidity field, or velocity field across the entire controlled region.

[0037] Specifically, the measured simulated boundary condition data are substituted into the pre-constructed physical model and fluid characteristic model to numerically solve the basic governing equations. The simulated boundary condition data defines the initial boundary state of the simulation. The physical model determines the spatial range and shape of the calculation. The fluid characteristic model corrects the basic governing equations to better reflect real fluid characteristics. For example, the turbulence model quantifies the impact of turbulent fluctuations in the airflow within the cigarette workshop caused by equipment operation or air supply from vents, generating turbulent viscosity parameters that are introduced into the momentum equation to correct calculation errors in fluid momentum transfer. The corrected basic governing equations are then jointly solved using numerical calculation methods, outputting environmental field distribution data reflecting the airflow and temperature and humidity distribution within the controlled area.

[0038] Optionally, based on the above embodiments, the fluid characteristic model may include a turbulence model, wall functions, and component transport models;

[0039] Accordingly, based on the above embodiments, and combining a pre-constructed physical model and fluid characteristic model matched with the controlled region, the fundamental governing equations are solved to obtain the environmental field distribution data of the controlled region, which may include:

[0040] Based on a pre-built physical model, fluid property model, and simulated boundary condition data that match the controlled region, the turbulence fluctuations of the airflow within the controlled region are quantified through the turbulence model to generate turbulent viscosity parameters.

[0041] By pre-setting the boundary interaction law between the fluid and the wall, as well as the equipment surface, the wall function generates flow velocity and temperature gradient parameters at the wall.

[0042] The mass conservation relationship of the mixed fluid is decomposed by the component transport model to generate component transport parameters.

[0043] Based on the aforementioned turbulent viscosity parameters, wall boundary parameters, and component transport parameters, combined with the spatial morphology data of the physical model and the simulated boundary condition data, the adapted and corrected mass equation, momentum equation, and energy equation are solved jointly using numerical calculation methods to obtain the flow field distribution data, temperature field distribution data, humidity field distribution data, and velocity field distribution data of the entire controlled region, which serve as the environmental field distribution data.

[0044] In this embodiment of the invention, the turbulence model can be specifically understood as a numerical calculation model used to describe the turbulent flow characteristics of airflow. Its purpose is to quantify the influence of irregular fluctuations in airflow caused by air supply from the air outlet and equipment operation disturbances on fluid momentum transfer. For example, the Realizable k-ɛ turbulence model is a method that quantifies the intensity of fluid turbulent fluctuations by solving the transport equations of turbulent kinetic energy (k) and dissipation rate (ɛ), generating turbulent viscosity parameters that fit the momentum equation calculation. Specifically, the turbulent viscosity parameters can be understood as parameters calculated by the turbulence model, used to correct the momentum equation, and reflecting the influence of turbulent fluctuations on fluid momentum transfer.

[0045] Wall functions can be understood as methods to simplify numerical calculations of fluid boundary regions such as workshop walls or production equipment surfaces. Their purpose is to address the computational inefficiency caused by the need for extremely dense meshes in direct solutions due to drastic changes in fluid velocity and temperature within the boundary layer. For example, the Standard Wall Treatment method, by pre-setting the velocity and temperature gradient distribution near the wall, can solve the equations for the boundary region without requiring finer meshes. The velocity and temperature gradient parameters at the wall can be understood as being generated by the wall function and serving as boundary conditions for solving the momentum and energy equations in the wall region, thus simplifying the calculation process.

[0046] The component transport model can be understood as a computational model for multi-component fluid mixtures. It breaks down the total mass conservation of the mixture into the mass conservation of each component. For example, for a fluid mixture consisting of air and water vapor, it allows for the separate calculation of the diffusion and convection processes of each component. Component transport parameters, generated by the component transport model, supplement the mass equations to achieve accurate solutions for the humidity field distribution.

[0047] Flow field distribution data can be specifically understood as: the distribution data of airflow motion state within the controlled area obtained by jointly solving the modified control equations. This data represents a set of parameters such as airflow pressure and flow direction at different spatial locations within the controlled area. It directly reflects the presence of vortex dead zones and airflow short-circuiting issues within the controlled area and can be used to optimize the layout of air conditioning supply and return air vents. Temperature field distribution data can be specifically understood as: a set of air temperature values ​​representing different spatial locations within the controlled area. It covers the entire controlled area, including temperature values ​​in key areas such as tobacco storage areas, production and processing areas, and the area surrounding equipment. This data reflects temperature gradients and uneven heating within the controlled area and is used to determine whether the temperature environment of the controlled area meets the requirements of the cigarette production process.

[0048] Humidity field distribution data can be specifically understood as: the set of values ​​for relative humidity or water vapor volume fraction at different spatial locations within a controlled area. This data, particularly relevant to the moisture content sensitivity of tobacco shreds in cigarette production, reflects the humidity status in key areas such as tobacco storage and feeding zones, ensuring tobacco quality and preventing excessively dry or wet tobacco. Velocity field distribution data can be specifically understood as: the set of values ​​for airflow velocity at different spatial locations within a controlled area. This data clarifies the distribution of airflow strength within the controlled area, identifying issues such as excessively strong drafts or low airflow velocities leading to humidity accumulation. This data is used to optimize air conditioning parameters and ensure a stable airflow environment in the workshop.

[0049] Specifically, the fluid characteristic model includes a turbulence model, wall functions, and component transport models. Based on the spatial morphology of the controlled region reconstructed by the physical model, and combined with measured data related to the air outlet in the simulated boundary condition data, the influence of airflow turbulence fluctuations is quantified through the turbulence model to generate turbulent viscosity parameters for correcting the momentum equation. For example, taking the flow field simulation in a cigarette manufacturing workshop as an example, an achievable k-ε turbulence model is adopted. First, based on the spatial shape of the workshop space and equipment layout determined by the physical model, combined with the measured data such as the air velocity and temperature at the air outlet in the simulation boundary condition data, the initial conditions for turbulence calculation are set. Then, by solving the turbulent kinetic energy (k) transport equation and the dissipation rate (ε) transport equation, the turbulence intensity values ​​at different locations in the workshop are obtained. According to the turbulence viscosity coefficient calculation formula, the turbulence intensity is transformed into a turbulence viscosity parameter. When the equation is corrected, this parameter can be introduced into the viscous force term of the momentum equation to replace the calculation term that only considers laminar viscosity in the original equation. This corrects the defect that the conventional momentum equation cannot reflect the influence of turbulent fluctuations on momentum transfer, so that the equation can adapt to the real turbulent flow state in the workshop.

[0050] Based on the boundary positions of the fluid with walls and equipment located by the physical model, and combined with the measured surface temperature data of the walls and equipment in the simulated boundary conditions data, the boundary action law is preset through the wall function to generate the velocity and temperature gradient parameters at the wall to simplify the solution conditions of the momentum equation and energy equation in the boundary region. For example, based on the boundary locations of equipment such as walls, drying machines, and shredders in a cigarette workshop, located using a physical model, and combined with measured wall surface temperatures and equipment shell temperatures from simulated boundary condition data, the standard wall function method is used to pre-determine the velocity and temperature distribution patterns of the fluid near the wall. For velocity, the velocity gradient change from the no-slip boundary at the wall to the mainstream region is determined according to the semi-empirical formula of the wall function. For temperature, the temperature gradient near the wall is determined by combining the heat transfer characteristics between the wall and the fluid, thereby generating velocity and temperature gradient parameters at the wall. When solving the momentum and energy equations, these parameters can be directly substituted as boundary conditions, eliminating the need for extremely dense meshing and complex flow calculations within the boundary layer in the wall region, thus simplifying the solution conditions of the equations in the boundary region and reducing computational complexity.

[0051] Based on the mixed fluid distribution region defined by the physical model, and combined with the measured temperature and humidity data of the air outlet in the simulated boundary condition data, the mass conservation relationship between air and water vapor is decomposed using a component transport model. This generates component transport parameters to supplement the mass equation for accurate solution of the humidity field. For example, based on the distribution region of the mixed fluid of air and water vapor in the cigarette workshop defined by the physical model, and combined with the measured temperature and humidity data of the air outlet in the simulated boundary condition data, the initial volume fraction of water vapor in the mixed fluid is determined. Using the component transport model, the total mass conservation equation is decomposed into mass conservation equations for two components: air and water vapor. By solving the convection and diffusion transport equations of the two components, component transport parameters such as the diffusion coefficient and convection velocity of air and water vapor at different locations in the workshop are calculated. These parameters can be added to the decomposed component mass equation to determine the mass transfer process of the two components. This solves the problem that conventional mass equations can only calculate the total mass change and cannot reflect the humidity distribution, thus achieving accurate solution of the humidity field in the workshop.

[0052] Based on the above parameters, combined with the spatial morphology data of the physical model and the simulated boundary condition data, the adapted and corrected mass, momentum and energy equations are solved jointly using numerical calculation methods to obtain the flow field, temperature field, humidity field and velocity field distribution data of the entire controlled area, which are used as environmental field distribution data.

[0053] By quantifying the effects of airflow turbulence fluctuations using a turbulence model to generate turbulent viscosity parameters, the momentum equation can be modified to better reflect the actual turbulent flow state of the airflow within the controlled area caused by equipment operation and air supply from vents. This avoids the flow field calculation deviations caused by neglecting turbulence fluctuations in conventional equations. By pre-setting the boundary interaction laws between the fluid and the walls and equipment surfaces using wall functions, the velocity and temperature gradient parameters at the walls are generated. This simplifies the solution conditions of the momentum and energy equations in the boundary region, eliminating the need for excessive mesh refinement in the wall region. This significantly reduces computational power consumption while ensuring computational accuracy, adapting to the boundary calculation needs of complex equipment layouts in controlled areas. By separating the mass of air and water vapor using a component transport model, the calculation can be performed more efficiently. The constant relation generates component transport parameters, which can supplement the mass equation to achieve accurate solutions for the humidity field. This solves the problem that the conventional mass equation can only calculate the total mass change and cannot reflect the humidity distribution, thus meeting the stringent humidity requirements of cigarette production. Finally, by jointly solving the modified mass, momentum, and energy equations, the obtained global flow field, temperature field, humidity field, and velocity field distribution data can comprehensively and accurately reflect the airflow and temperature and humidity distribution in the controlled area. This provides reliable data support for subsequent airflow organization simulation, air supply parameter optimization, and control scheme formulation, thereby improving the accuracy and scientific nature of process air conditioning control, ensuring the stability of cigarette production quality, and reducing air conditioning energy consumption.

[0054] S120. Based on the simulated boundary condition data and environmental field distribution data, perform airflow organization simulation to obtain the current simulation results of each preset point in the controlled area and the relevant simulation results matched with multiple sets of air supply combination parameters.

[0055] In this embodiment of the invention, airflow organization simulation can be specifically understood as: simulating the flow, diffusion, and temperature and humidity distribution patterns of airflow within a controlled area under different air supply parameters based on numerical simulation. Air supply combination parameters can be specifically understood as: combinations of air supply-related parameters of the air conditioning system, which may include key parameters such as air outlet location, air supply temperature, air supply velocity, or air supply direction. Current simulation results can be specifically understood as: data such as airflow state, temperature and humidity values, or pressure values ​​at each preset point in the controlled area, calculated based on the initially set air supply parameters. Related simulation results can be specifically understood as: airflow and temperature and humidity data at each preset point calculated for each set of parameters after adjusting the air supply combination parameters to obtain multiple sets of different parameter schemes.

[0056] Specifically, the physical model of the controlled region is imported into the fluid dynamics simulation software. The simulated boundary condition data is set as the boundary input for the simulation calculation, the environmental field distribution data is set as the initial calculation state of the model, and a set of initial air supply combination parameters (such as air outlet velocity, air supply temperature, and air supply direction) are set. The airflow organization simulation calculation is performed to obtain the airflow state and temperature and humidity values ​​at each preset point in the controlled region under these parameters, which is the current simulation result.

[0057] By adjusting the specific values ​​or combinations of the air supply parameters, multiple sets of differentiated air supply combination parameters are generated to form different air supply schemes. The above simulation calculation steps are repeated for each scheme to obtain the simulation data of each preset point corresponding to each set of air supply combination parameters, that is, the relevant simulation results matched with multiple sets of air supply combination parameters, thus completing the simulation of airflow and temperature and humidity distribution under different air supply schemes.

[0058] Optionally, based on the above embodiments, airflow organization simulation is performed according to simulated boundary condition data and environmental field distribution data to obtain the current simulation results of each preset point within the controlled area and the relevant simulation results matched with multiple sets of air supply combination parameters, which may include:

[0059] Based on the simulated boundary condition data and environmental field distribution data, airflow organization simulation is performed to obtain the current simulated airflow state, current simulated temperature and humidity values, and current simulated pressure values ​​at each preset point in the controlled area, which are used as the current simulation results.

[0060] Multiple sets of differentiated air supply combination parameters are constructed by adjusting the air supply parameters sequentially while keeping the supply and return air outlet heights constant, and by adjusting the installation heights of the supply and return air outlets sequentially while keeping the supply air parameters constant.

[0061] For each set of air supply combination parameters, airflow organization simulation is performed separately. The relevant simulated airflow state, relevant simulated temperature and humidity values, and relevant simulated pressure values ​​are collected at each preset point as relevant simulation results that match the set of air supply combination parameters.

[0062] In this embodiment of the invention, the preset points can be specifically understood as: measurement or simulated monitoring points pre-selected at different horizontal positions and different height levels in the controlled area to accurately capture the distribution characteristics of environmental parameters in the controlled area. The air supply parameters can be specifically understood as: the control parameters of the air supply link of the air conditioning system, such as air supply temperature, air supply volume, air supply velocity, air supply direction, and air supply humidity. These parameters collectively determine the diffusion, heat exchange, and mass transfer processes of the airflow after it enters the workshop, and are used to optimize airflow organization to achieve temperature and humidity control in the workshop.

[0063] Specifically, the physical model of the controlled region is imported into the fluid dynamics simulation software. The simulation boundary condition data is set as the simulation boundary, and the environmental field distribution data is set as the initial state. The airflow organization simulation is performed to obtain the airflow state, temperature and humidity values, and pressure values ​​at preset points at different locations and heights in the controlled region, which are used as the current simulation results.

[0064] Then, using the controlled variable method, multiple sets of differentiated and unique air supply combination parameters were constructed by sequentially adjusting air supply parameters such as supply air temperature, supply air volume, and supply air velocity while keeping the supply and return air outlet heights fixed, and by sequentially adjusting the installation heights of the supply and return air outlets while keeping the supply air parameters fixed. For each set of air supply combination parameters, the corresponding parameters were simultaneously adjusted in the software, and simulation calculations were repeated. Relevant simulated airflow states, temperature, humidity, and pressure values ​​at preset points under each set of parameters were collected to form relevant simulation results matching each set of parameters. The current simulation results, relevant simulation results, and corresponding air supply combination parameters determined the correlation between the air supply combination parameters and the flow field distribution (such as airflow state, temperature, humidity, and pressure).

[0065] By collecting airflow status, temperature, humidity, and pressure values ​​at preset points in the controlled area to form the current simulation results, the current environmental field distribution under the initial air supply conditions in the controlled area can be accurately grasped, and the advantages and disadvantages of the current airflow organization can be identified, providing a benchmark for subsequent parameter optimization. Multiple sets of differentiated air supply combination parameters are constructed using a controlled variable method that adjusts air supply parameters by fixing the height of the supply and return air inlets, and by adjusting the height of the supply and return air inlets by fixing the supply air parameters. This effectively eliminates mutual interference between parameters, accurately locates the influence weight of single parameter changes on airflow organization, and avoids the problem of ambiguous optimization direction caused by simultaneous adjustment of multiple parameters. Simulations are conducted separately for each set of air supply combination parameters, and corresponding simulation results are collected. This allows for the systematic acquisition of environmental field distribution data under different parameter combinations, providing data support for subsequent comparative analysis of the correlation between air supply combination parameters and flow field distribution. This enables the selection of the optimal air supply scheme that suits the process requirements of different areas in the controlled area, improving the accuracy and scientific nature of air conditioning system control, ensuring the stability of temperature, humidity, and airflow distribution in the workshop, reducing air conditioning energy consumption, and balancing the quality requirements and energy-saving goals of cigarette production.

[0066] S130. Collect measured data of each preset point within the controlled area, and compare the measured data with the current simulation results of the corresponding point. When the first deviation condition is met, return to the operation of collecting the simulation boundary condition data of the controlled area.

[0067] In this embodiment of the invention, the measured data can be specifically understood as: real working condition data such as temperature and humidity values ​​or airflow speed values ​​collected on-site at preset points in a controlled area (such as a cigarette manufacturing or packaging workshop) using detection equipment such as temperature and humidity sensors or anemometers. The first deviation condition can be specifically understood as: a pre-set threshold standard for judging whether the deviation between the simulation result and the measured data is within an acceptable range. If the deviation value exceeds the threshold, the simulation result is judged to be unqualified.

[0068] Specifically, real-world operating condition data, such as temperature, humidity, and airflow velocity, are collected at each preset point within the controlled area using testing equipment. These measured data are then compared one by one with the current simulation results at the corresponding points to calculate the deviation, thereby verifying the accuracy of the airflow organization simulation results. The calculated deviation is compared with a preset allowable range. If the deviation exceeds this range, the first deviation condition is met, and the process returns to collecting simulated boundary condition data for the controlled area. The measured boundary condition data is then re-added and substituted into the model for solution. Airflow organization simulation is performed based on the corrected results. This comparison and verification process is repeated until the deviation between the simulation results and the measured data meets the preset allowable range, thus completing the verification of the simulation results.

[0069] S140. When the first deviation condition is not met, the current simulation result is compared with the preset temperature and humidity standard. When the second deviation condition is met, the current air supply combination parameters are adjusted according to the comparison result between the current simulation result and the preset temperature and humidity standard, as well as the relevant simulation results matched with multiple sets of air supply combination parameters. The process then returns to the operation of collecting simulation boundary condition data of the controlled area.

[0070] In this embodiment of the invention, the preset temperature and humidity standard can be specifically understood as: a temperature and humidity threshold for the controlled area established according to the requirements of the cigarette production process, used to determine whether the environment of the controlled area meets the production requirements. The second deviation condition can be specifically understood as: the deviation between the current simulated temperature and humidity result that has passed verification and the preset temperature and humidity standard exceeds the allowable range, that is, the temperature and humidity of the controlled area under the current air supply conditions cannot meet the production requirements.

[0071] Specifically, when the deviation between the measured data and the current simulation result at the corresponding point is within the preset allowable range (does not meet the first deviation condition), meaning the accuracy of the simulation result has been verified, the temperature and humidity values ​​in the current simulation result are compared with the preset temperature and humidity standards. If the comparison result shows that the temperature and humidity deviation exceeds the process allowable range (meets the second deviation condition), then based on the deviation between the current simulation result and the preset temperature and humidity standards, as well as the relevant simulation results corresponding to multiple sets of air supply combination parameters, the direction of parameter adjustment is analyzed and determined. The current air supply combination parameters are adjusted by adjusting the opening degree of the outlet pipe valve and the air supply angle, etc., while optimizing the sensor placement and number.

[0072] For example, if the simulated temperature is higher than the upper limit of the standard, it is determined that the supply air temperature needs to be reduced or the supply air volume needs to be increased; if the simulated humidity is lower than the lower limit of the standard, it is determined that the supply air humidity needs to be increased or the supply air speed needs to be adjusted. Retrieve relevant simulation results corresponding to multiple sets of supply air combination parameters, and analyze the influence of different supply air parameters (such as supply air temperature, supply air volume, and supply air speed) and changes in the height of the supply and return air vents on the temperature and humidity field. For example, analyze the relationship between the increase in supply air volume and the decrease in the average temperature of the workshop, and the relationship between the increase in the height of the supply and return air vents and the percentage increase in the uniformity of humidity in the upper area. Combine the deviation and the influence patterns, analyze and determine the direction and magnitude of parameter adjustments, and make targeted adjustments to the current supply air combination parameters. For example, when the temperature in a local area of ​​the workshop exceeds the standard and relevant simulation results show that increasing the supply air volume can effectively reduce the temperature, while maintaining the supply air vent height unchanged, increase the current supply air volume by a preset percentage, and at the same time fine-tune the supply air temperature to adapt to the requirements of coordinated temperature and humidity control.

[0073] For key areas where deviations exceed the limits in the simulation results, the number of sensors can be increased to improve the density and frequency of data collection in these areas, ensuring that the real environmental parameters of these areas can be accurately captured during subsequent simulation verification. Sensors can be moved from their original blind spots or locations with weak data representativeness (such as airflow dead zones or equipment-obstructed areas) to core locations with active airflow and sensitivity to temperature and humidity changes (such as installing them near the air outlet after adjusting the air outlet height, and at key monitoring points at different height levels in the workshop). This ensures that the collected measured data can comprehensively and accurately reflect the environmental field distribution of the controlled area, providing data support for subsequent model correction, boundary condition supplementation, and air supply parameter optimization, thereby improving the efficiency and accuracy of iterative optimization.

[0074] Return to the operation of collecting simulated boundary condition data of the controlled area, resubmit the adjusted parameters to carry out the model solution, airflow organization simulation and experimental verification process, and iterate and optimize the airflow organization flow field in the controlled area until the simulated temperature and humidity distribution results meet the preset standards for cigarette production.

[0075] S150. When the second deviation condition is not met, generate a control scheme for the process air conditioning of the cigarette factory based on the current air supply combination parameters.

[0076] Specifically, when the deviation between the verified current simulated temperature and humidity results and the preset temperature and humidity standards for cigarette production is within the allowable range (not meeting the second deviation condition), it indicates that the current air supply combination parameters can ensure that the airflow organization and temperature and humidity distribution in the controlled area meet the requirements of the cigarette production process. Based on the current air supply combination parameters, the specific operating parameter control methods of the air conditioning system are determined (such as adjusting the opening degree of the air supply valve, the height of the air supply outlet, and the frequency of temperature and humidity monitoring), generating a control scheme that can directly guide the stable operation of the process air conditioning system in the cigarette factory.

[0077] The technical solution of this invention collects simulated boundary condition data from multiple points in the controlled area, solves the basic control equations using a matched physical model and fluid characteristic model to obtain environmental field distribution data, acquires current simulation results and related simulation results corresponding to multiple sets of air supply combination parameters through airflow organization simulation, and analyzes the correlation between air supply combination parameters and the environmental field, temperature, and humidity of the controlled area, providing a basis for parameter adjustment and improving the accuracy of temperature and humidity control to meet the needs of cigarette production processes. A first deviation verification step, comparing measured data from preset points with the current simulation results, forms the first closed loop of simulation, measurement, and correction, correcting simulation deviations and model adaptation errors, enhancing the reliability of simulation results, and reducing trial-and-error in control. Cost; After the simulation results pass the first round of verification, a second round of deviation comparison is conducted with the preset temperature and humidity standards. If the standards are not met, the current parameters are adjusted and iterated again based on the simulation results of multiple sets of air supply combination parameters. This dynamically adapts to the controlled area, continuously optimizes the air supply combination parameters, reduces the deviation between the actual temperature and humidity distribution and the design state, and ensures uniform and stable temperature and humidity in the area. The final air supply combination parameters are selected through simulation and iterative adjustment of multiple sets of air supply combination parameters to avoid redundant operation of the air conditioning system. The control scheme generated based on the final parameters enables the air conditioning system to output cooling, heating and air volume on demand, achieving energy saving and consumption reduction, ensuring long-term stable operation of the air conditioning system, improving the reliability of air conditioning control, and providing a continuous and reliable temperature and humidity environment for cigarette production.

[0078] Example 2

[0079] Figure 2 This is a flowchart of another method for controlling the process air conditioning in a cigarette factory according to Embodiment 2 of the present invention. This embodiment is a refinement of the method for controlling the process air conditioning in a cigarette factory as described in the above embodiments. Figure 2 As shown, the method includes:

[0080] S210. Collect spatial dimension data of the controlled area, enclosure structure data of the controlled area, equipment layout data of the controlled area, material storage data and heat and moisture dissipation data of the controlled area, and construct a three-dimensional physical model matching the controlled area based on the collected data.

[0081] In this embodiment of the invention, the enclosure structure data can be specifically understood as: the material (such as color steel plate or concrete) and thickness parameters of the enclosing structure such as walls, floors, and ceilings of the controlled area (such as a cigarette workshop), which is the basis for calculating the heat conduction and heat exchange of the area. The equipment layout data can be specifically understood as: the coordinates and dimensions of the placement of production equipment (such as drying machines and cutting machines) within the controlled area, used to recreate the space occupied by the equipment and the interaction boundary with airflow in the model. The material storage data can be specifically understood as: the distribution data of storage areas for materials such as tobacco shreds and leaves, which is the basis for analyzing the distribution of heat and moisture load in the area. The heat and moisture dissipation data can be specifically understood as: the quantitative data of heat dissipation during equipment operation and moisture evaporation from materials within the controlled area, which is a key parameter for setting the heat and moisture boundary conditions in numerical simulation.

[0082] S220. Based on the three-dimensional physical model, perform mesh discretization to form a mesh model as a pre-constructed physical model that matches the controlled region.

[0083] In this embodiment of the invention, the mesh discretization can be understood as the process of decomposing a continuous three-dimensional physical model into a large number of discrete micro-units (mesh), with each mesh providing an independent solution space for numerical computation. The mesh model can be understood as the three-dimensional model after discretization, which serves as the direct computational carrier for subsequent fluid numerical simulations.

[0084] Specifically, data on the spatial dimensions of the controlled area (such as the dimensions of the length, width and height of the space), the material and thickness of the enclosure structure such as walls and the ground, the placement and dimensions of equipment such as the shredder and the shredder, the distribution of material storage areas, and the heat and moisture dissipation data of equipment operation and material moisture release.

[0085] Based on the collected data, a 3D physical model capable of reconstructing the physical spatial morphology of the controlled area is constructed using 3D modeling software. This model clarifies the spatial location and boundary relationships of each entity within the area, presenting the spatial boundaries, equipment placement, and material distribution. The 3D physical model is then meshed. Optimization methods, such as adjusting mesh density and using denser meshes in critical areas and simplification in non-critical areas, transform the 3D physical model into a mesh model. For example, dense meshes are used in critical areas sensitive to airflow and temperature / humidity changes, such as around air vents and material storage areas, while sparse meshes are used in non-critical areas with stable airflow, such as workshop corners. Adjusting the mesh density balances computational accuracy and efficiency. This pre-constructed physical model, matching the controlled area, provides the computational basis for subsequent numerical simulations.

[0086] S230. Collect simulated boundary condition data of the controlled area, and combine it with the pre-constructed physical model and fluid characteristic model that match the controlled area to solve the basic control equations and obtain the environmental field distribution data of the controlled area.

[0087] S240. Based on the simulated boundary condition data and environmental field distribution data, perform airflow organization simulation to obtain the current simulation results of each preset point in the controlled area and the relevant simulation results matched with multiple sets of air supply combination parameters.

[0088] S250: Collect measured data of each preset point within the controlled area, and compare the measured data with the current simulation results of the corresponding point. When the first deviation condition is met, return to the operation of collecting the simulation boundary condition data of the controlled area.

[0089] S260. When the first deviation condition is not met, the current simulation result is compared with the preset temperature and humidity standard. When the second deviation condition is met, the current air supply combination parameters are adjusted according to the comparison result between the current simulation result and the preset temperature and humidity standard, as well as the relevant simulation results matched with multiple sets of air supply combination parameters. The process then returns to the operation of collecting the simulation boundary condition data of the controlled area.

[0090] S270. When the second deviation condition is not met, generate a control scheme for the process air conditioning of the cigarette factory based on the current air supply combination parameters.

[0091] Optionally, based on the above embodiments, the control scheme for the process air conditioning of the cigarette factory generated based on the current air supply combination parameters may include:

[0092] Based on the current air supply combination parameters, combined with the current simulation results, relevant simulation results, and the process tasks of each sub-region within the controlled area, the controlled area is divided into at least one temperature and humidity control zone, and the temperature and humidity protection priority of each zone is determined to generate a matching hierarchical control scheme.

[0093] Based on the collected data on equipment layout in the controlled area, heat and moisture emission data, current simulation results, and related simulation results, the installation configuration information of each preset point is determined, and a matching optimized layout scheme for measuring points is generated based on the installation configuration information.

[0094] Based on the hierarchical control scheme, the optimized layout of measuring points, and the current air supply combination parameters, a process air conditioning control scheme for the cigarette factory is generated.

[0095] In this embodiment of the invention, temperature and humidity control zones can be specifically understood as: independent areas with different temperature and humidity control objectives, divided according to factors such as differences in process tasks and material temperature and humidity sensitivity among sub-regions within the controlled area, such as the tobacco storage area, feeding and processing area, and finished product temporary storage area in a cigarette workshop. Temperature and humidity assurance priority can be specifically understood as: a priority ranking of control zones based on their process importance and the material's tolerance to temperature and humidity fluctuations; zones with higher priority must prioritize ensuring stable temperature and humidity that meets standards.

[0096] The tiered control scheme can be understood as follows: differentiated control strategies are developed based on the priority and temperature / humidity requirements of different control zones. This is achieved by setting control thresholds in layers and adjusting air supply parameters in stages, thus enabling precise control of each zone. Installation configuration information can be understood as the set of parameters determining the installation height, number, and location of sensor measuring points for accurate monitoring of the temperature and humidity status of each control zone.

[0097] Specifically, based on the current air supply combination parameters, combined with the current simulation results corresponding to these parameters, the relevant simulation results of multiple sets of air supply combination parameter matching, and the process tasks of each sub-region within the controlled area (such as the low humidity requirements for tobacco storage, the constant temperature requirements for the feeding process, and the temperature and humidity sensitivity differences of different materials), the controlled area is divided into at least one temperature and humidity control zone. At the same time, according to the production process requirements and material temperature and humidity sensitivity of each zone, the corresponding temperature and humidity protection priority is determined (such as the priority of the tobacco storage area is higher than that of the finished product temporary storage area), thereby generating a hierarchical control scheme for differentiated management of different zones.

[0098] For example, for the highest priority core areas (such as the tobacco storage area in the cigarette workshop), a smaller range of temperature and humidity control thresholds and more frequent parameter adjustments are set. A combination of fixed air outlet height and dynamic adjustment of air supply temperature and volume is used to ensure that the temperature and humidity remain stable within the process standard range. For medium priority production and processing areas (such as the feeding area), with the goal of ensuring process continuity, a moderate range of control thresholds is set. Several gradients are divided according to the deviation of the temperature and humidity of the controlled area from the preset standard, and different air supply speed levels are set accordingly. When the temperature and humidity deviation reaches a certain gradient threshold, the air supply speed is increased. The temperature is adjusted to the corresponding level to achieve a control method that uses tiered adjustment of air supply speed and on-demand fine-tuning of humidity. For auxiliary zones with lower priority (such as finished product storage areas), a wider range of control thresholds and low-cost basic control methods are adopted (such as using a fixed air supply parameter operation mode, manually adjusting the opening of the air supply valve only when the temperature and humidity exceed the preset wide threshold, without the need for additional automatic control equipment). At the same time, a linkage control mechanism between zones is established. When the temperature and humidity of the core zone fluctuate, the air conditioning system resources are prioritized to ensure the needs of the core zone, thus forming a hierarchical control scheme for differentiated management of different zones.

[0099] Based on the collected data on the layout of equipment in the controlled area, heat and humidity emission data, current simulation results, and related simulation results, points that can reflect the true temperature and humidity status of each control zone are selected. The installation configuration information such as the installation height, number of installations, and installation location of these preset points is determined, and a matching optimized layout scheme for measuring points is generated based on this configuration information.

[0100] For example, based on the collected equipment layout data of the controlled area, the distribution location of equipment and the heat and humidity interference area are determined. Combined with the heat and humidity emission data, the concentrated points of heat and humidity load are identified. Then, based on the temperature and humidity fluctuation patterns of each point in the current simulation results and related simulation results, as well as the distribution characteristics of airflow state, points with stable airflow, strong temperature and humidity representativeness, and no interference from equipment obstruction are selected in each control zone. For the high-priority core zone, the installation height of the measuring points (covering the key height level from the ground to the ceiling), the number of installations (ensuring no less than 1 measuring point per preset unit area), and the installation location (evenly distributed in the center and edge of the area) are determined according to the multi-level high-density principle. For the medium-priority production and processing zone, the installation height of the measuring points (matching the material stacking height), the number of installations (covering key process points), and the installation location (avoiding equipment operation interference areas) are determined in combination with the process flow. For the low-priority auxiliary zone, the basic installation configuration information is determined by the low-density full coverage principle. Finally, based on the preset point installation configuration information of each zone, an optimized layout scheme of measuring points adapted to the entire controlled area is generated.

[0101] Based on the zonal control strategy, temperature and humidity control thresholds, and linkage control rules in the hierarchical control scheme, the sensor installation height, quantity, location, and data acquisition frequency requirements in the measurement point optimization layout scheme, as well as the parameters such as supply and return air outlet height, supply air temperature, supply air volume, and supply air velocity in the current supply air combination parameters, the supply air parameter configuration standards corresponding to each control zone, the linkage logic between sensor monitoring data and air conditioning system control actions, the parameter adjustment process under different operating conditions, and the emergency response plan are determined. At the same time, combined with the actual production situation such as the production shifts and equipment operating hours of the cigarette factory, the implementation details and operation and maintenance requirements of the scheme are supplemented to generate the process air conditioning control scheme for the cigarette factory.

[0102] By dividing temperature and humidity control zones based on current air supply parameters, simulation results, and process tasks in each sub-region, and setting priority levels, a hierarchical control scheme is generated. This allows for differentiated control based on the process requirements of different zones, prioritizing the temperature and humidity stability of core areas such as tobacco storage areas. This avoids issues like substandard parameters in core areas or wasted energy in non-core areas due to indiscriminate control. By determining the installation configuration information of preset points based on equipment layout, heat and moisture dissipation data, and simulation results, and generating an optimized measurement point layout scheme, the scheme ensures that sensors accurately capture the true temperature and humidity status of each zone, providing reliable data monitoring support for the implementation of the control scheme and reducing control delays or misjudgments caused by unreasonable measurement point layout. Finally, the hierarchical control scheme, the optimized measurement point layout scheme, and the current air supply parameters are integrated to form a complete process air conditioning control scheme. This enables closed-loop management of monitoring, control, and feedback, improving the accuracy and scientific nature of process air conditioning control in cigarette factories, ensuring the quality stability of cigarette production, and reducing the overall energy consumption of the air conditioning system through differentiated control, thus balancing production efficiency and energy-saving goals.

[0103] Furthermore, based on the above embodiments, before generating the process air conditioning control scheme for the cigarette factory based on the hierarchical control scheme, the optimized layout of measuring points, and the current air supply combination parameters, it may further include:

[0104] Based on heat and moisture dissipation data and historical environmental temperature and humidity data, a heat and moisture load model of the controlled area related to seasonal climate is constructed, and a matching variable fresh air volume operation strategy is determined according to the heat and moisture load model and the current season.

[0105] Based on the temperature and humidity requirements of each temperature and humidity control zone in the hierarchical control scheme, a variable frequency control scheme matching the equipment components of the air conditioning system in each temperature and humidity control zone is generated.

[0106] Accordingly, based on the above embodiments, a process air conditioning control scheme for a cigarette factory is generated based on the hierarchical control scheme, the optimized layout of measuring points, and the current air supply combination parameters. This scheme may include:

[0107] Based on the hierarchical control scheme, the optimized layout of measuring points, the variable fresh air volume operation strategy, the variable frequency control scheme, and the current air supply combination parameters, a process air conditioning control scheme for a cigarette factory is generated.

[0108] In this embodiment of the invention, the heat and humidity load model can be specifically understood as: a mathematical model that reflects the correlation between seasonal climate and heat and humidity load in the region, which is constructed by combining the heat and humidity emission data of the controlled area and historical data such as the external environmental temperature and humidity of different seasons. It can be used to predict the trend of heat and humidity load changes in the region under different seasons.

[0109] The variable fresh air volume (VAV) operation strategy can be understood as: dynamically adjusting the amount of fresh air introduced into the air conditioning system based on the heat and humidity load model and the current season. For example, reducing fresh air intake when the heat and humidity load is high, and increasing fresh air intake when the heat and humidity load is low, thereby reducing the energy consumption of the air conditioning system in processing air. The variable frequency drive (VFD) control scheme can be understood as: a plan to adjust the operating frequency of equipment components such as fans, pumps, and compressors in the air conditioning system according to the temperature and humidity requirements of each temperature and humidity control zone. The aim is to adapt to the load requirements of each zone and avoid redundant full-load operation of the equipment.

[0110] Specifically, based on heat and moisture dissipation data of the controlled area (such as heat dissipation from equipment operation, material storage, and personnel activities), combined with historical data on ambient temperature and humidity for different seasons and climates (such as the four seasons and typical climate types like high temperature and high humidity and low temperature and low humidity), heat and moisture dissipation data are used as the heat and moisture input for the area, and historical data on ambient temperature and humidity are used as the boundary input for influencing heat and moisture exchange in the area. Through data fitting and parameter correction, a mathematical correlation between ambient temperature and humidity, seasonal climate characteristics, and heat and moisture load in the controlled area is established. The calculation accuracy of the model is verified by substituting historical data, and the model parameters are iteratively optimized to construct a heat and moisture load model that can accurately reflect the correspondence between seasonal climate change and heat and moisture load in the controlled area.

[0111] The ambient temperature and humidity data for the current season are input into the established heat and humidity load model to calculate the theoretical heat and humidity load value of the controlled area under this season. The air handling load variation patterns of the air conditioning system under different fresh air intake ratios are compared to determine the correlation between fresh air intake and system energy consumption. Based on the temperature and humidity thresholds required by the process, the boundary conditions for adjusting the fresh air intake are determined: reduce the fresh air intake in seasons with high heat and humidity loads and increase the fresh air intake in seasons with low heat and humidity loads to reduce the air conditioning system's handling load. Simultaneously, a gradient threshold for fresh air intake adjustment is set to avoid sudden changes in fresh air intake causing regional temperature and humidity fluctuations exceeding the process's allowable range, thus forming a variable fresh air intake operation strategy that matches the current season and regional heat and humidity load.

[0112] Based on the temperature and humidity requirements of each temperature and humidity control zone in the hierarchical control scheme, a variable frequency control scheme is generated to match the equipment components such as fans, water pumps and compressors of each zone's air conditioning system. By adjusting the operating frequency of the equipment, the scheme adapts to the zone load and avoids redundant operation.

[0113] By integrating the hierarchical control scheme, the optimized layout of measuring points, the variable fresh air volume operation strategy, the variable frequency adjustment scheme, and the current air supply combination parameters, a complete process air conditioning control scheme for cigarette factories is formed.

[0114] Understandably, during the actual operation of the air conditioning system according to this scheme, real-time temperature and humidity data are collected through the optimized temperature and humidity measurement points determined by the optimized measurement point layout scheme. Combined with the energy consumption monitoring data of the air conditioning system, the energy-saving effect is continuously evaluated. If the energy-saving effect does not meet the preset requirements, the variable fresh air volume parameters and the variable frequency operation parameters of the equipment components are dynamically adjusted based on the real-time collected temperature and humidity data and energy consumption data to further optimize the energy-saving operation strategy and achieve continuous iteration and upgrading of the control scheme.

[0115] By constructing a heat and humidity load model based on heat and humidity dissipation data and historical environmental temperature and humidity data, the impact of seasonal climate on the heat and humidity load of the controlled area can be accurately quantified. Combined with the variable fresh air volume operation strategy determined for the current season, the cooling, heating, and dehumidification loads of the air conditioning system can be reduced by reducing the fresh air intake during peak heat and humidity load periods and increasing the fresh air intake during off-peak periods, thus making full use of natural environmental conditions to reduce the cooling, heating, and dehumidification loads of the air conditioning system and achieving initial energy savings. The variable frequency control scheme generated according to the needs of each temperature and humidity control zone adopts variable frequency operation mode for equipment components such as fans, water pumps, and compressors, which can match the zone load demand as needed and avoid energy waste caused by full-load redundant operation of equipment. Finally, the process air conditioning control scheme, which integrates the hierarchical control scheme, the optimized layout of measuring points, the variable fresh air volume operation strategy, the variable frequency control scheme, and the current air supply combination parameters, not only ensures that the temperature and humidity of each zone of the cigarette factory are stable and meet the process standards, but also reduces the overall operating energy consumption of the air conditioning system, taking into account both production stability and energy-saving economy.

[0116] The technical solution of this invention constructs a three-dimensional physical model by collecting comprehensive data on the spatial dimensions, enclosure structure, equipment layout, material storage, and heat and moisture dissipation of the controlled area. This model is then discretized into a grid model, which not only fully reproduces the real spatial form, complex equipment layout, material distribution, and heat and moisture load sources of the cigarette workshop, ensuring a high degree of consistency with the actual production scenario and avoiding subsequent simulation deviations caused by distorted scenario replication, but also provides a reliable spatial carrier for solving environmental field distribution data and simulating airflow organization. Furthermore, by selectively adjusting the grid density, the grid can be densified in key areas such as the equipment perimeter and air supply / return vents, while simplifying the grid in non-critical areas. This ensures the calculation accuracy of key areas while avoiding the waste of computing power caused by full-area densification, improving numerical calculation efficiency, shortening simulation time, and reducing the overall modeling and analysis time and computing power costs. The model, supported by comprehensive data, can flexibly adapt to the layout differences and heat and moisture load characteristics of different cigarette workshops, such as tobacco processing and packaging, without requiring the reconstruction of the basic model. It can be fine-tuned to adapt to diverse production scenarios, improving the model's versatility and feasibility. The system collects simulated boundary condition data from multiple points within the controlled area. Combined with a matched physical model and fluid characteristic model, it solves the fundamental control equations to obtain environmental field distribution data. Airflow organization simulation yields the current simulation results and related simulation results corresponding to multiple sets of air supply combination parameters. A first deviation verification step involves comparing the current simulation results with measured data from preset points to correct simulation deviations and model adaptation errors. After the simulation results pass the first round of verification, a second round of deviation comparison is performed with preset temperature and humidity standards. If the standards are not met, the current parameters are adjusted based on the simulation results of multiple sets of air supply combination parameters, and the system iterates again to dynamically adapt to the controlled area. Through simulation and iterative adjustment of multiple sets of air supply combination parameters, the final air supply combination parameters are selected, achieving energy saving and consumption reduction, ensuring the long-term stable operation of the air conditioning system, improving the reliability of air conditioning control, and providing a continuous and reliable temperature and humidity environment for cigarette production.

[0117] Example 3

[0118] Figure 3 This is a flowchart of another method for controlling the process air conditioning in a cigarette factory, provided in Embodiment 3 of the present invention. This embodiment is a refinement of the "collecting simulated boundary condition data of the controlled area" in the above embodiments. Figure 3 As shown, the method includes:

[0119] S310. Multiple measurements are taken at preset points of air outlets in the controlled area, preset points on the surface of production equipment, and multiple evenly distributed preset points on walls and the ground to obtain raw data of multiple air outlet temperatures, equipment surface temperatures, and wall and ground surface temperatures.

[0120] S320. Calculate the statistical values ​​corresponding to each preset point based on the original data, and use them as simulated boundary condition data. Combine the pre-constructed physical model and fluid characteristic model that match the controlled area to solve the basic control equations and obtain the environmental field distribution data of the controlled area.

[0121] Specifically, in order to obtain the actual air supply status and boundary temperature parameters of the controlled area, professional equipment such as hot-wire anemometers, thermometers and infrared thermal imagers were used to collect air supply outlet temperature data at preset points; equipment surface temperature data at preset points on the production equipment surface; and building envelope surface temperature data at multiple evenly distributed preset points on the walls and ground, and summarizing multiple sets of raw measured data.

[0122] The collected raw data is preprocessed to remove outliers caused by instrument malfunctions or external interference. Statistical values ​​(such as average values) are calculated for the valid data at each preset point to construct simulated boundary condition data. This simulated boundary condition data is then substituted into a pre-constructed physical model that matches the spatial morphology and equipment layout of the controlled area. Combined with a fluid characteristic model describing fluid flow and heat exchange, the fundamental control equations of fluid mechanics and heat and mass transfer are numerically solved to obtain environmental field distribution data that reflects the spatial distribution of parameters such as temperature and airflow velocity within the controlled area.

[0123] S330. Based on the simulated boundary condition data and environmental field distribution data, perform airflow organization simulation to obtain the current simulation results of each preset point in the controlled area and the relevant simulation results matched with multiple sets of air supply combination parameters.

[0124] S340. Collect measured data of each preset point in the controlled area and compare the measured data with the current simulation results of the corresponding point. When the first deviation condition is met, return to the operation of performing multiple measurements at the preset points of the air outlet, the preset points on the surface of the production equipment, and the multiple evenly distributed preset points on the walls and the ground in the controlled area.

[0125] S350. When the first deviation condition is not met, the current simulation result is compared with the preset temperature and humidity standard. When the second deviation condition is met, the current air supply combination parameters are adjusted according to the comparison result between the current simulation result and the preset temperature and humidity standard, as well as the relevant simulation results matched with multiple sets of air supply combination parameters. The operation of performing multiple actual measurements at the preset points of the air supply outlet in the controlled area, the preset points on the surface of the production equipment, and multiple evenly distributed preset points on the walls and the ground is then performed.

[0126] S360. When the second deviation condition is not met, generate a control scheme for the process air conditioning of the cigarette factory based on the current air supply combination parameters.

[0127] Figure 4 This is a schematic diagram illustrating the control of a process air conditioning system in a cigarette factory, applicable to an embodiment of the present invention. Figure 4 As shown, using the temperature and humidity requirements of the controlled area, the layout of the air conditioning system, and the heat and humidity dissipation of equipment and materials as initial inputs, a three-dimensional physical model accurately reproducing the workshop morphology is constructed and meshed. Subsequently, numerical simulations of airflow organization are conducted. The accuracy of the simulation results is then verified by on-site measurements of temperature and humidity data from air outlets, equipment surfaces, and the enclosure structure. If the simulation deviation exceeds the allowable range, the air supply combination parameters are adjusted and the simulation is repeated. Once the simulation results are verified as satisfactory and meet the preset temperature and humidity standards for cigarette production, further temperature and humidity control zones are defined based on the process tasks and material sensitivity of each sub-area, and optimal control measures are determined. First, a hierarchical control scheme is generated. At the same time, a seasonal heat and humidity load model is constructed by combining heat and humidity dissipation and historical environmental data to formulate a variable fresh air volume operation strategy. Then, a variable frequency control scheme for equipment is generated according to the needs of each zone. Finally, the optimized layout of measuring points and the current air supply combination parameters are integrated to form a complete process air conditioning control scheme. After the scheme is put into actual operation, temperature, humidity and energy consumption data are continuously collected to evaluate the process assurance and energy saving effect. If the requirements are not met, the parameters are dynamically adjusted and the scheme is iteratively optimized. If the requirements are met, the system enters a stable energy-saving operation state, realizing full-process control of zone control, energy saving optimization and dynamic iteration.

[0128] The technical solution of this invention involves conducting multiple field measurements at preset points on the air outlets, production equipment surfaces, and walls and floors within a controlled area. This collects multiple sets of raw data on the air outlet temperature, equipment surface temperature, and wall and floor surface temperatures. The statistical values ​​corresponding to each preset point are then calculated as simulated boundary condition data. This approach comprehensively captures the temperature distribution characteristics of different spatial locations within the controlled area, focusing on key areas such as air outlets and equipment surfaces that significantly influence airflow and temperature / humidity distribution. It avoids the inconsistency of single-point, single-measurement data, ensuring that the collected data accurately reflects the temperature boundary conditions in the actual production scenario. Statistical processing can eliminate random errors and outliers in the measurement process, reduce the impact of factors such as equipment precision and environmental interference on the data, improve the stability and accuracy of simulated boundary condition data, and thus avoid deviations in environmental field distribution data caused by noise in the original data, ensuring the credibility of subsequent airflow organization simulation results. Boundary conditions constructed based on multi-point statistical data can replicate the temperature gradient distribution of air outlets, equipment, and walls, making the solution of fluid characteristic models and basic control equations more consistent with the actual airflow heat transfer and mass transfer laws, reducing the deviation between simulation and real operating conditions caused by boundary condition distortion, and providing a data foundation for iterative optimization of air supply combination parameters and the formulation of control schemes. By combining the matched physical model and fluid characteristic model to solve the basic control equations, environmental field distribution data is obtained. The current simulation results and the relevant simulation results corresponding to multiple sets of air supply combination parameters are obtained through airflow organization simulation. The first deviation verification step is to correct the simulation deviation and model adaptation error by collecting measured data from preset points and comparing them with the current simulation results. After the simulation results pass the first round of verification, a second round of deviation comparison is performed with preset temperature and humidity standards. If the standards are not met, the current parameters are adjusted and iterated again based on the simulation results of multiple sets of air supply combination parameters to dynamically adapt to the controlled area. The final air supply combination parameters are selected through simulation and iterative adjustment of multiple sets of air supply combination parameters, so as to achieve energy saving and consumption reduction, ensure the long-term stable operation of the air conditioning system, improve the reliability of air conditioning control, and provide a continuous and reliable temperature and humidity environment for cigarette production.

[0129] Example 4

[0130] Figure 5 This is a schematic diagram of the control device for a process air conditioner in a cigarette factory, provided in Embodiment 4 of the present invention. Figure 5 As shown, the device includes:

[0131] The distributed solution module 510 is used to collect simulated boundary condition data of the controlled region and, in combination with a pre-built physical model and fluid characteristic model that match the controlled region, solve the basic control equations to obtain the environmental field distribution data of the controlled region.

[0132] The airflow simulation module 520 is used to simulate airflow organization based on simulated boundary condition data and environmental field distribution data, and to obtain the current simulation results of each preset point in the controlled area as well as the relevant simulation results matched with multiple sets of air supply combination parameters.

[0133] The first verification module 530 is used to collect measured data of each preset point in the controlled area and compare the measured data with the current simulation results of the corresponding point. When the first deviation condition is met, it returns to the operation of collecting the simulation boundary condition data of the controlled area.

[0134] The second verification module 540 is used to compare the current simulation result with the preset temperature and humidity standard when the first deviation condition is not met, and to adjust the current air supply combination parameters according to the comparison result of the current simulation result with the preset temperature and humidity standard and the relevant simulation results matched with multiple sets of air supply combination parameters when the second deviation condition is met, and return to the operation of collecting the simulation boundary condition data of the controlled area.

[0135] The scheme generation module 550 is used to generate a control scheme for the process air conditioning of the cigarette factory based on the current air supply combination parameters when the second deviation condition is not met.

[0136] The technical solution of this invention collects simulated boundary condition data from multiple points in the controlled area, solves the basic control equations using a matched physical model and fluid characteristic model to obtain environmental field distribution data, acquires current simulation results and related simulation results corresponding to multiple sets of air supply combination parameters through airflow organization simulation, and analyzes the correlation between air supply combination parameters and the environmental field, temperature, and humidity of the controlled area, providing a basis for parameter adjustment and improving the accuracy of temperature and humidity control to meet the needs of cigarette production processes. A first deviation verification step, comparing measured data from preset points with the current simulation results, forms the first closed loop of simulation, measurement, and correction, correcting simulation deviations and model adaptation errors, enhancing the reliability of simulation results, and reducing trial-and-error in control. Cost; After the simulation results pass the first round of verification, a second round of deviation comparison is conducted with the preset temperature and humidity standards. If the standards are not met, the current parameters are adjusted and iterated again based on the simulation results of multiple sets of air supply combination parameters. This dynamically adapts to the controlled area, continuously optimizes the air supply combination parameters, reduces the deviation between the actual temperature and humidity distribution and the design state, and ensures uniform and stable temperature and humidity in the area. The final air supply combination parameters are selected through simulation and iterative adjustment of multiple sets of air supply combination parameters to avoid redundant operation of the air conditioning system. The control scheme generated based on the final parameters enables the air conditioning system to output cooling, heating and air volume on demand, achieving energy saving and consumption reduction, ensuring long-term stable operation of the air conditioning system, improving the reliability of air conditioning control, and providing a continuous and reliable temperature and humidity environment for cigarette production.

[0137] Furthermore, based on the above embodiments, the control device for the process air conditioning in a cigarette factory may further include: a three-dimensional modeling module and a mesh generation module, wherein:

[0138] The 3D modeling module is used to collect spatial dimension data, enclosure structure data, equipment layout data, material storage data, and heat and moisture dissipation data of the controlled area before collecting simulated boundary condition data of the controlled area, and to construct a 3D physical model that matches the controlled area based on the collected data.

[0139] The mesh generation module is used to discretize the three-dimensional physical model into a mesh model, which serves as a pre-built physical model that matches the controlled region.

[0140] Based on the above embodiments, the distributed solution module 510 is specifically used for:

[0141] Multiple measurements were taken at preset points of air outlets in the controlled area, preset points on the surface of production equipment, and multiple evenly distributed preset points on walls and the ground to obtain raw data of multiple air outlet temperatures, equipment surface temperatures, and wall and ground surface temperatures.

[0142] Calculate the statistical values ​​corresponding to each preset point based on the original data, and use them as simulated boundary condition data.

[0143] Based on the above embodiments, the fluid property model includes a turbulence model, wall functions, and a component transport model;

[0144] Accordingly, based on the above embodiments, the distributed solver module 510 is further used for:

[0145] Based on a pre-built physical model, fluid property model, and simulated boundary condition data that match the controlled region, the turbulence fluctuations of the airflow within the controlled region are quantified through the turbulence model to generate turbulent viscosity parameters.

[0146] By pre-setting the boundary interaction law between the fluid and the wall, as well as the equipment surface, the wall function generates flow velocity and temperature gradient parameters at the wall.

[0147] The mass conservation relationship of the mixed fluid is decomposed by the component transport model to generate component transport parameters.

[0148] Based on the aforementioned turbulent viscosity parameters, wall boundary parameters, and component transport parameters, combined with the spatial morphology data of the physical model and the simulated boundary condition data, the adapted and corrected mass equation, momentum equation, and energy equation are solved jointly using numerical calculation methods to obtain the flow field distribution data, temperature field distribution data, humidity field distribution data, and velocity field distribution data of the entire controlled region, which serve as the environmental field distribution data.

[0149] Based on the above embodiments, the airflow simulation module 520 is specifically used for:

[0150] Based on the simulated boundary condition data and environmental field distribution data, airflow organization simulation is performed to obtain the current simulated airflow state, current simulated temperature and humidity values, and current simulated pressure values ​​at each preset point in the controlled area, which are used as the current simulation results.

[0151] Multiple sets of differentiated air supply combination parameters are constructed by adjusting the air supply parameters sequentially while keeping the supply and return air outlet heights constant, and by adjusting the installation heights of the supply and return air outlets sequentially while keeping the supply air parameters constant.

[0152] For each set of air supply combination parameters, airflow organization simulation is performed separately. The relevant simulated airflow state, relevant simulated temperature and humidity values, and relevant simulated pressure values ​​are collected at each preset point as relevant simulation results that match the set of air supply combination parameters.

[0153] Based on the above embodiments, the scheme generation module 550 is specifically used for:

[0154] Based on the current air supply combination parameters, combined with the current simulation results, relevant simulation results, and the process tasks of each sub-region within the controlled area, the controlled area is divided into at least one temperature and humidity control zone, and the temperature and humidity protection priority of each zone is determined to generate a matching hierarchical control scheme.

[0155] Based on the collected data on equipment layout in the controlled area, heat and moisture emission data, current simulation results, and related simulation results, the installation configuration information of each preset point is determined, and a matching optimized layout scheme for measuring points is generated based on the installation configuration information.

[0156] Based on the hierarchical control scheme, the optimized layout of measuring points, and the current air supply combination parameters, a process air conditioning control scheme for the cigarette factory is generated.

[0157] Optionally, based on the above embodiments, the scheme generation module 550 may include: a hot and humid season unit and a frequency conversion regulation unit, wherein:

[0158] The hot and humid season unit is used to construct a heat and humidity load model of the controlled area related to the seasonal climate based on heat and humidity emission data and historical environmental temperature and humidity data before generating the process air conditioning control scheme of the cigarette factory based on the hierarchical control scheme, the optimized layout of measuring points and the current air supply combination parameters. Based on the heat and humidity load model and the current season, the matching variable fresh air volume operation strategy is determined.

[0159] The variable frequency control unit is used to generate a variable frequency control scheme that matches the equipment components of the air conditioning system in each temperature and humidity control zone according to the temperature and humidity requirements of each temperature and humidity control zone in the hierarchical control scheme.

[0160] Accordingly, based on the above embodiments, the solution generation module 550 is further used for:

[0161] Based on the hierarchical control scheme, the optimized layout of measuring points, the variable fresh air volume operation strategy, the variable frequency control scheme, and the current air supply combination parameters, a process air conditioning control scheme for a cigarette factory is generated.

[0162] The control device for the process air conditioning of a cigarette factory provided in this embodiment of the invention can execute the control method for the process air conditioning of a cigarette factory provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0163] The collection, storage, use, processing, transmission, provision, and disclosure of user personal information involved in the technical solution disclosed herein comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0164] Example 5

[0165] Figure 6 A schematic diagram of an electronic device 10, which can be used to implement embodiments of the present invention, is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0166] like Figure 6As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0167] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0168] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the control method of process air conditioning in a cigarette factory, namely:

[0169] The simulated boundary condition data of the controlled region are collected, and the basic governing equations are solved by combining the pre-constructed physical model and fluid characteristic model that match the controlled region to obtain the environmental field distribution data of the controlled region.

[0170] Based on the simulated boundary condition data and environmental field distribution data, airflow organization simulation is performed to obtain the current simulation results of each preset point in the controlled area and the relevant simulation results matched with multiple sets of air supply combination parameters.

[0171] Collect measured data at each preset point within the controlled area, compare the measured data with the current simulation results at the corresponding point, and when the first deviation condition is met, return to the operation of collecting the simulation boundary condition data of the controlled area;

[0172] When the first deviation condition is not met, the current simulation result is compared with the preset temperature and humidity standard. When the second deviation condition is met, the current air supply combination parameters are adjusted according to the comparison result between the current simulation result and the preset temperature and humidity standard, as well as the relevant simulation results matched with multiple sets of air supply combination parameters. Then, the operation of collecting simulation boundary condition data of the controlled area is returned.

[0173] When the second deviation condition is not met, a control scheme for the process air conditioning of the cigarette factory is generated based on the current air supply combination parameters.

[0174] In some embodiments, the method for controlling the process air conditioning in a cigarette factory can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the method for controlling the process air conditioning in a cigarette factory described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the method for controlling the process air conditioning in a cigarette factory by any other suitable means (e.g., by means of firmware).

[0175] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0176] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0177] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0178] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0179] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0180] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0181] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0182] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for controlling the process air conditioning in a cigarette factory, characterized in that, include: The simulated boundary condition data of the controlled region are collected, and the basic governing equations are solved by combining the pre-constructed physical model and fluid characteristic model that match the controlled region to obtain the environmental field distribution data of the controlled region. Based on the simulated boundary condition data and environmental field distribution data, airflow organization simulation is performed to obtain the current simulation results of each preset point in the controlled area and the relevant simulation results matched with multiple sets of air supply combination parameters. Collect measured data at each preset point within the controlled area, compare the measured data with the current simulation results at the corresponding point, and when the first deviation condition is met, return to the operation of collecting the simulation boundary condition data of the controlled area; When the first deviation condition is not met, the current simulation result is compared with the preset temperature and humidity standard. When the second deviation condition is met, the current air supply combination parameters are adjusted according to the comparison result between the current simulation result and the preset temperature and humidity standard, as well as the relevant simulation results matched with multiple sets of air supply combination parameters. Then, the operation of collecting simulation boundary condition data of the controlled area is returned. When the second deviation condition is not met, a control scheme for the process air conditioning of the cigarette factory is generated based on the current air supply combination parameters.

2. The method according to claim 1, characterized in that, Before collecting simulated boundary condition data for the controlled area, the following steps are also included: Collect spatial dimension data, enclosure structure data, equipment layout data, material storage data, and heat and moisture dissipation data of the controlled area, and construct a three-dimensional physical model that matches the controlled area based on the collected data; Based on the three-dimensional physical model, a mesh is discretized to form a mesh model, which serves as a pre-constructed physical model that matches the controlled region.

3. The method according to claim 1, characterized in that, Collect simulated boundary condition data for the controlled region, including: Multiple measurements were taken at preset points of air outlets in the controlled area, preset points on the surface of production equipment, and multiple evenly distributed preset points on walls and the ground to obtain raw data of multiple air outlet temperatures, equipment surface temperatures, and wall and ground surface temperatures. Calculate the statistical values ​​corresponding to each preset point based on the original data, and use them as simulated boundary condition data.

4. The method according to claim 1, characterized in that, The fluid property model includes a turbulence model, wall functions, and a component transport model; Accordingly, by combining a pre-constructed physical model and fluid characteristic model that match the controlled region, the basic governing equations are solved to obtain the environmental field distribution data of the controlled region, including: Based on a pre-built physical model, fluid property model, and simulated boundary condition data that match the controlled region, the turbulence fluctuations of the airflow within the controlled region are quantified using the turbulence model to generate turbulent viscosity parameters. By pre-setting the boundary interaction law between the fluid and the wall, as well as the equipment surface, the wall function generates flow velocity and temperature gradient parameters at the wall. The mass conservation relationship of the mixed fluid is decomposed by the component transport model to generate component transport parameters. Based on the aforementioned turbulent viscosity parameters, wall boundary parameters, and component transport parameters, combined with the spatial morphology data of the physical model and the simulated boundary condition data, the adapted and corrected mass equation, momentum equation, and energy equation are solved jointly using numerical calculation methods to obtain the flow field distribution data, temperature field distribution data, humidity field distribution data, and velocity field distribution data of the entire controlled region, which serve as the environmental field distribution data.

5. The method according to claim 1, characterized in that, Based on simulated boundary condition data and environmental field distribution data, airflow organization simulation is performed to obtain the current simulation results of each preset point within the controlled area, as well as the relevant simulation results matched with multiple sets of air supply combination parameters, including: Based on the simulated boundary condition data and environmental field distribution data, airflow organization simulation is performed to obtain the current simulated airflow state, current simulated temperature and humidity values, and current simulated pressure values ​​at each preset point in the controlled area, which are used as the current simulation results. Multiple sets of differentiated air supply combination parameters are constructed by adjusting the air supply parameters sequentially while keeping the supply and return air outlet heights constant, and by adjusting the installation heights of the supply and return air outlets sequentially while keeping the supply air parameters constant. For each set of air supply combination parameters, airflow organization simulation is performed separately. The relevant simulated airflow state, relevant simulated temperature and humidity values, and relevant simulated pressure values ​​are collected at each preset point as relevant simulation results that match the set of air supply combination parameters.

6. The method according to claim 2, characterized in that, Based on the current air supply combination parameters, a control scheme for the process air conditioning of the cigarette factory is generated, including: Based on the current air supply combination parameters, combined with the current simulation results, relevant simulation results, and the process tasks of each sub-region within the controlled area, the controlled area is divided into at least one temperature and humidity control zone, and the temperature and humidity protection priority of each zone is determined to generate a matching hierarchical control scheme. Based on the collected data on equipment layout in the controlled area, heat and moisture emission data, current simulation results, and related simulation results, the installation configuration information of each preset point is determined, and a matching optimized layout scheme for measuring points is generated based on the installation configuration information. Based on the hierarchical control scheme, the optimized layout of measuring points, and the current air supply combination parameters, a process air conditioning control scheme for the cigarette factory is generated.

7. The method according to claim 6, characterized in that, Before generating the process air conditioning control scheme for the cigarette factory based on the hierarchical control scheme, the optimized layout of measuring points, and the current air supply combination parameters, the following steps are also included: Based on heat and moisture dissipation data and historical environmental temperature and humidity data, a heat and moisture load model of the controlled area related to seasonal climate is constructed, and a matching variable fresh air volume operation strategy is determined according to the heat and moisture load model and the current season. Based on the temperature and humidity requirements of each temperature and humidity control zone in the hierarchical control scheme, a variable frequency control scheme matching the equipment components of the air conditioning system in each temperature and humidity control zone is generated. Accordingly, based on the hierarchical control scheme, the optimized layout of measuring points, and the current air supply combination parameters, a process air conditioning control scheme for the cigarette factory is generated, including: Based on the hierarchical control scheme, the optimized layout of measuring points, the variable fresh air volume operation strategy, the variable frequency control scheme, and the current air supply combination parameters, a process air conditioning control scheme for a cigarette factory is generated.

8. An electronic device, characterized in that, The electronic device includes: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the control method of the process air conditioning in a cigarette factory according to any one of claims 1-7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the control method for the process air conditioning of a cigarette factory as described in any one of claims 1-7.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the control method for the process air conditioning of a cigarette factory according to any one of claims 1-7.

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