Clean room air quality control method, apparatus, device, and storage medium

Abstract

The application provides a clean room air quality control method, device, equipment and storage medium, and relates to the technical field of clean room environment regulation. The method comprises the following steps: based on a three-dimensional geometric model, a grid division result and a turbulent flow model of a clean room, a clean room CFD simulation model is established, an initial simulation result is obtained, a multi-objective optimization model is input, and an optimized control scheme is obtained; wherein, constraint conditions of the multi-objective optimization model comprise cleanliness constraints, air flow uniformity constraints and economic constraints; the optimized control scheme comprises FFU arrangement positions and FFU arrangement densities; fitness evaluation indexes are determined according to the optimized control scheme, and fitness is calculated according to the fitness evaluation indexes; wherein, the fitness evaluation indexes comprise pollutant removal efficiency, pollution source contribution rate coverage indexes and air flow uniformity indexes; when the fitness meets a set condition, the FFU distribution is adjusted according to the optimized control scheme. The application can optimize the number of FFUs and energy consumption while meeting cleanliness requirements.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of clean room environment regulation, and in particular to a clean room air quality control method, device, equipment and storage medium. BACKGROUND

[0002] A clean room is a key facility for controlling the concentration of pollutants in the air, and is widely used in industries such as semiconductors, pharmaceuticals, biological engineering, and precision manufacturing, which have strict requirements for environmental cleanliness. In order to maintain the cleanliness of the clean room, it is usually necessary to control particulate matter, chemical pollutants and microorganisms in the air through reasonable air flow organization and high-efficiency filtration systems. In the clean room, Fan Filter Units (FFU) as the main provider of clean air, through the built-in fan, the air is purified through the high-efficiency filter and then delivered to the clean room, thereby achieving strict control of air cleanliness. The reasonable arrangement of FFU directly affects the air flow characteristics and cleanliness level of the clean room, and is a key link in the design of the clean room.

[0003] At present, the arrangement design of FFU in the clean room mostly adopts an empirical method. Common designs include uniform arrangement on the ceiling, functional partition arrangement, and adjustment arrangement after operation, etc. However, the uniform arrangement method is simple and easy to implement, but it fails to optimize the characteristics of pollutant diffusion, and is prone to cause excessive redundancy or local loss of control of cleanliness; the functional partition arrangement, although it designs the density difference according to the cleanliness requirements of the region, usually relies on the experience and rough estimation of the designer, and lacks scientific and systematic optimization strategies. Some researches introduce Computational Fluid Dynamics (CFD) simulation technology to assist in analyzing the air flow distribution, but most of them still focus on the simulation of air flow characteristics, and do not fully combine the pollutant diffusion characteristics. An important challenge in clean room design is the complex and variable diffusion behavior of pollutants, and its diffusion path is jointly affected by air flow distribution, air supply and exhaust port position, pollutant source position and release characteristics. The existing FFU design scheme in the clean room is difficult to optimize the number of FFUs and energy consumption while meeting the cleanliness requirements. SUMMARY

[0004] The embodiments of the present application provide a clean room air quality control method, device, equipment and storage medium to solve the problem that the existing FFU design scheme in the clean room is difficult to optimize the number of FFUs and energy consumption while meeting the cleanliness requirements.

[0005] In a first aspect, the embodiments of the present application provide a clean room air quality control method, comprising:

[0006] Based on a clean room three-dimensional geometric model, a meshing result and a turbulent flow model, a clean room CFD simulation model is established, and based on the clean room CFD simulation model, an initial simulation result is obtained; wherein the initial simulation result includes a pollutant concentration distribution, an airflow distribution, a high-risk area marking and a pollution source contribution rate;

[0007] The initial simulation result is input into a multi-objective optimization model to obtain an optimized control scheme; wherein constraint conditions of the multi-objective optimization model include cleanliness constraints, airflow uniformity constraints and economic constraints; and the optimized control scheme includes FFU arrangement positions and FFU arrangement densities;

[0008] According to the optimized control scheme, an adaptability evaluation index is determined, and an adaptability is calculated according to the adaptability evaluation index; wherein the adaptability evaluation index includes a pollutant removal efficiency, a pollution source contribution rate coverage index and an airflow uniformity index;

[0009] When the adaptability meets a set condition, the optimized control scheme is output to adjust FFU distribution according to the optimized control scheme.

[0010] In a possible implementation, the multi-objective optimization model takes maximum cleanliness compliance rate, optimal airflow distribution uniformity and minimum FFU number and energy consumption as objective functions.

[0011] In a possible implementation, determining the adaptability evaluation index according to the optimized control scheme includes:

[0012] The clean room CFD simulation model is used to simulate the optimized control scheme to obtain a pollutant concentration drop amount, an airflow distribution and a pollution source contribution rate after FFU updated arrangement;

[0013] A pollutant removal efficiency is determined according to a ratio of the pollutant concentration drop amount to an original pollutant concentration drop amount, a pollution source contribution rate coverage index is calculated according to the pollution source contribution rate and a weight of a pollution source to each area, and an airflow uniformity index is calculated according to the airflow distribution.

[0014] In a possible implementation, the adaptability calculation formula is:

[0015]

[0016] wherein, is a pollutant removal efficiency, is a pollution source contribution rate coverage index, is an airflow uniformity index, , , is a dynamic weight factor.

[0017] In a possible implementation, when the fitness does not satisfy the set condition, the method further includes:

[0018] According to the FFU arrangement position and the FFU arrangement density corresponding to the previous optimization control scheme and the optimization control scheme, the FFU arrangement position and the FFU arrangement density are updated, to obtain updated FFU arrangement position and FFU arrangement density as the updated optimization control scheme.

[0019] The operation of determining the fitness evaluation index according to the optimization control scheme and the operation thereafter are performed.

[0020] In a possible implementation, the updated formula corresponding to the FFU arrangement position is:

[0021]

[0022] wherein, is a path fusion coefficient; is the FFU arrangement position corresponding to the previous optimization control scheme; is the optimization control scheme.

[0023] In a possible implementation, the set condition is that a difference between the fitness corresponding to the optimization control scheme and the fitness corresponding to the previous optimization control scheme is less than a set threshold.

[0024] In a possible implementation, after the operation of determining the fitness evaluation index according to the optimization control scheme, the method further includes:

[0025] The difference between each index in the fitness evaluation index and each index in the previous fitness evaluation index is calculated.

[0026] According to the difference between each index and the corresponding threshold, the weight of each index in the fitness evaluation index calculation formula is adjusted.

[0027] In a possible implementation, the adjusting the weight of each index in the fitness evaluation index calculation formula according to the difference between each index and the corresponding threshold includes:

[0028] When the index with the difference less than the corresponding threshold is included, the weight of the index with the difference greater than or equal to the corresponding threshold is increased.

[0029] In a second aspect, an embodiment of the present application provides a clean room air quality control device, which includes:

[0030] The simulation module is configured to establish a clean room CFD simulation model based on a three-dimensional geometric model of the clean room, a meshing result and a turbulent flow model, and obtain an initial simulation result based on the clean room CFD simulation model, wherein the initial simulation result comprises a pollutant concentration distribution, an air flow distribution, a high-risk area mark and a pollution source contribution rate;

[0031] The optimization model is configured to input the initial simulation result into a multi-objective optimization model to obtain an optimized control scheme, wherein constraint conditions of the multi-objective optimization model comprise a cleanliness constraint, an air flow uniformity constraint and an economic constraint, and the optimized control scheme comprises an FFU arrangement position and an FFU arrangement density;

[0032] The fitness calculation module is configured to determine a fitness evaluation index according to the optimized control scheme, and calculate a fitness according to the fitness evaluation index, wherein the fitness evaluation index comprises a pollutant removal efficiency, a pollution source contribution rate coverage index and an air flow uniformity index.

[0033] The output module is configured to output the optimized control scheme to adjust the FFU distribution according to the optimized control scheme when the fitness satisfies a set condition.

[0034] In a third aspect, an electronic device is provided, which includes a memory and a processor, the memory stores a computer program, and the processor implements the method in the first aspect or any possible implementation manner of the first aspect when executing the computer program.

[0035] In a fourth aspect, a computer readable storage medium is provided, which stores a computer program, and the computer program is executed by a processor to implement the method in the first aspect or any possible implementation manner of the first aspect.

[0036] In the embodiments of the present application, the initial pollutant concentration, air flow distribution and other key data are obtained by establishing a clean room CFD simulation model, the FFU arrangement is optimized by combining a multi-objective optimization model, the effect of the scheme is evaluated through fitness indexes including pollutant removal efficiency, pollution source contribution rate coverage and air flow uniformity, and finally the FFU distribution is adjusted according to the fitness. This process realizes closed-loop control from simulation analysis to optimization decision, can accurately identify the pollution risk and air flow problem of the clean room, can balance cleanliness, air flow uniformity and economy through multi-objective constraints, avoids the blindness of traditional empirical arrangement, makes the FFU arrangement more scientific and targeted, and effectively improves the accuracy and efficiency of clean room air quality control. BRIEF DESCRIPTION OF DRAWINGS

[0037] Figure 1 is an application scenario diagram of the clean room air quality control method provided by the embodiments of the present application.

[0038] Figure 2 is a structure diagram of the clean room air quality control device provided by the embodiment of the present application.

[0039] Figure 3 is a structure diagram of the clean room air quality control device provided by the embodiment of the present application.

[0040] Figure 4 is a schematic diagram of an electronic device provided by the embodiment of the present application. DETAILED DESCRIPTION

[0041] The traditional design method usually ignores the pollution source analysis, that is, the dynamic diffusion path and influence range of the pollutants from the release source to different areas. Therefore, in actual operation, the high pollution risk area is often not effectively covered, resulting in insufficient performance of the clean room. In addition, the existing design is difficult to optimize the number of FFUs and energy consumption while meeting the cleanliness requirement. Although uniform arrangement or blind increase of FFU density can improve the cleanliness to a certain extent, it will greatly increase the operation cost and energy consumption, and the partition arrangement method is difficult to accurately control the diffusion of pollution sources.

[0042] In view of the above problems, a scientific and systematic FFU arrangement design method is urgently needed. In the design stage of the clean room, the optimal FFU arrangement scheme is determined by simulating the diffusion path of the pollutants and combining the dynamic optimization technology. In this way, not only the targeted cleanliness control can be realized in the high risk area, but also the waste of resources caused by excessive design can be avoided, so as to balance the performance and economy of the clean room. Based on the above problems, the embodiment of the present application proposes an FFU arrangement method combining pollution source analysis and simulation optimization, which provides a new solution for the design of the clean room.

[0043] The embodiments of the present application will be described in detail below with reference to the drawings.

[0044] Figure 1 is an application scenario diagram of the clean room air quality control method provided by the embodiment of the present application.

[0045] As shown in Figure 1 , the terminal device is equipped with a CFD simulation model. Before the FFU system is built, the CFD simulation model will carry out simulation work on the clean room, and generate an optimized control scheme of FFU arrangement according to the simulation result, so as to realize reasonable arrangement of the FFU and maintain the air flow characteristics and cleanliness level of the clean room.

[0046] Figure 2 is an implementation flowchart of the clean room air quality control method provided by the embodiment of the present application, as shown in Figure 2 , comprising the following steps:

[0047] S201, based on the clean room three-dimensional geometric model, the grid division result and the turbulent flow model, a clean room CFD simulation model is established, and based on the clean room CFD simulation model, an initial simulation result is obtained; wherein the initial simulation result includes the pollutant concentration distribution, the airflow distribution, the high-risk area marking and the pollution source contribution rate.

[0048] The execution subject of each embodiment of the present application can be a server, a processor, a microprocessor, etc. with data processing function. In actual implementation process, the specific implementation mode of the execution subject can be selected according to actual needs, and the present embodiment does not make special limitation as long as it is a device with data processing function.

[0049] In the present embodiment, by combining the pollutant tracing analysis and the CFD simulation technology in the clean room design stage, the diffusion path and distribution characteristics of the pollutants are dynamically simulated, so as to scientifically identify the high-risk area and the airflow dead angle in the clean room.

[0050] Before the FFU arrangement project is carried out in the initial stage of the clean room design, the air quality control effect of the FFU arrangement scheme on the clean room is simulated and simulated through the clean room CFD simulation model, so as to achieve the effect of meeting the cleanliness requirement while optimizing the number of FFUs and energy consumption. Specifically, by inputting the clean room design parameters, the pollution source assumption and the initial arrangement scheme, the clean room CFD simulation model is constructed to provide support for subsequent simulation and optimization.

[0051] Firstly, the clean room design parameters are determined according to the preliminary design drawings and functional requirements of the clean room, including: geometric structure parameters, functional area division and airflow design parameters.

[0052] In one possible implementation, the geometric structure parameters include:

[0053] Clean room size (length, width, height): ;

[0054] Air supply port and exhaust port position: , ;

[0055] Area of air supply and exhaust port: , ;

[0056] (2) Functional area division, including:

[0057] High cleanliness area (such as photolithography area, key process equipment area);

[0058] Medium cleanliness area (such as auxiliary equipment area, personnel activity area);

[0059] Low cleanliness area (such as peripheral storage or buffer area);

[0060] where each region is labeled as and cleanliness requirements (ISO class or maximum allowable concentration ).

[0061] (3) Airflow design parameters include: total supply air volume and exhaust rate:

[0062] The formula for calculating the total supply air volume is:

[0063]

[0064] where, is the number of air changes per hour; is the clean room volume.

[0065] The exhaust rate .

[0066] Secondly, in the design stage, due to the unknown actual pollution source position, the possible pollution source distribution needs to be simulated through reasonable assumptions. The pollution source distribution includes: equipment pollution source, personnel activity pollution source and dynamic release model of pollution source. The equipment pollution source position and the pollution release amount are determined corresponding to the equipment pollution source; the activity path and the release amount are determined corresponding to the personnel activity pollution source.

[0067] In one possible implementation, (1) Equipment pollution source position: according to the preliminary process equipment layout diagram, the pollution source point .

[0068] The formula for calculating the equipment pollution source release amount is:

[0069]

[0070] where, is the maximum pollution release amount of the equipment (determined by the equipment type); is the dynamic operation mode of the equipment (such as intermittent release or constant release).

[0071] (2) Personnel activity pollution source activity path is the typical path simulated from the entrance to the process operation area.

[0072] The path is represented by a parameterized curve:

[0073]

[0074] Release amount:

[0075]

[0076] where, : number of personnel; : the release amount per unit time per person.

[0077] (3) The total pollution source release amount calculation formula corresponding to the dynamic release model of pollution source is:

[0078]

[0079] Wherein, is the dynamic pollution generated by personnel activities; is the three-dimensional product of Dirac function; when the spatial coordinates coincide with the source position, the function takes a non-zero value; is the spatial position coordinate of the th pollution source; is the release amount of the th pollution source at time .

[0080] Then, the initial FFU arrangement scheme lays the foundation for subsequent optimization, assuming that the initial FFU arrangement scheme includes the initial FFU arrangement density and the initial FFU operating parameters:

[0081] (1) The initial FFU arrangement density is:

[0082]

[0083] Wherein, is the initial FFU number; is the clean room ceiling area.

[0084] (2) The initial FFU operating parameters include the initial wind speed of each FFU and the FFU coverage area.

[0085] FFU coverage area:

[0086]

[0087] Wherein, is the initial FFU number; is the clean room ceiling area.

[0088] Finally, after completing the above simulation process, the clean room CFD simulation model outputs the following data:

[0089] Clean room geometry and airflow parameters:

[0090]

[0091] Pollution source distribution and dynamic release amount:

[0092]

[0093] Initial FFU arrangement:

[0094]

[0095] These data will be used as input conditions for subsequent pollutant dispersion simulation and FFU optimization.

[0096] Next, the CFD tool is used to simulate the pollutant dispersion path and concentration distribution in the cleanroom, identify high-risk areas, insufficient cleanliness areas, and dispersion paths, and provide the basis for subsequent FFU arrangement optimization.

[0097] Among them, the pollutant dispersion modeling includes determining the convection-diffusion equation, boundary conditions and initial conditions.

[0098] (1) The pollutant concentration distribution is described by the convection-diffusion equation:

[0099]

[0100] where, C is the pollutant concentration (unit: mg / m3); V is the air flow velocity (calculated by CFD simulation); D is the diffusion coefficient of the pollutant; S is the source term of the pollutant.

[0101] (2) Boundary conditions:

[0102] Air supply port: , The pollutant concentration of clean air is assumed to be zero.

[0103] Air exhaust port: ; Wall and obstacle:

[0104] For pollutant concentration: no flux boundary condition .

[0105] For air flow: no-slip boundary condition .

[0106] (3) Initial conditions:

[0107] The initial pollutant concentration is assumed to be zero, or there is a background concentration.

[0108] Air flow simulation includes: CFD simulation settings and simulation output.

[0109] (1) CFD simulation settings

[0110] Geometry Model: Establish a three-dimensional geometric model based on the cleanroom structure, including air supply outlets, exhaust outlets, obstacles, FFU locations, etc.

[0111] Meshing: To ensure simulation accuracy, use structured mesh or adaptive meshing to densify the pollution sources and key areas.

[0112] Turbulence Model: Use a turbulence model suitable for cleanroom airflow (such as or LES model).

[0113] (2) Simulation Output:

[0114] Airflow velocity distribution ; Pollutant concentration distribution .

[0115] Further, according to the CFD simulation results, analyze the pollutant diffusion path, identify high-risk areas and airflow dead angles.

[0116] (1) High concentration area identification: define the area where the concentration exceeds the target value as a high-risk area:

[0117]

[0118] Where, is the concentration target value.

[0119] Calculate the volume of the high-risk area:

[0120]

[0121] (2) Pollution source contribution analysis: trace the diffusion path of the pollution source and analyze the pollution contribution rate of each pollution source to different areas.

[0122]

[0123] Where, is the contribution rate of the pollution source to the target point; is the pollution source .

[0124] (3) Airflow dead angle identification: judge whether the airflow velocity is lower than the set threshold :

[0125]

[0126] Record the dead angle position and range.

[0127] Based on the analysis results, output the pollutant concentration distribution map, air flow distribution map, high-risk area data and pollution source contribution rate. Among them, the pollutant concentration distribution map aims to output the pollutant concentration distribution map in the clean room, marking the high concentration area and its range. The air flow distribution map aims to output the air flow velocity field distribution, marking the air flow dead angle position. The high-risk area data includes: the volume of the high concentration area And the coordinate range of the high concentration area . The pollution source contribution rate aims to output the contribution rate of each pollution source to the high concentration area .

[0128] In the specific implementation process, the output data form is:

[0129] Pollutant concentration distribution: ; Air flow distribution: ; High-risk area marking: ; Pollution source contribution rate: .

[0130] S202, input the initial simulation results into the multi-objective optimization model to obtain the optimized control scheme; wherein the constraint conditions of the multi-objective optimization model include cleanliness constraint, air flow uniformity constraint and economic constraint; the optimized control scheme includes: FFU arrangement position and FFU arrangement density.

[0131] In the specific implementation process, the initial simulation results including pollutant concentration distribution, air flow distribution, high-risk area marking and pollution source contribution rate are used to design a multi-objective optimization model in combination with the pollutant high concentration area and air flow distribution, and the FFU position and density are dynamically adjusted to obtain the optimized control scheme.

[0132] In the specific implementation process, the multi-objective optimization model takes the maximum cleanliness compliance rate, the optimal air flow distribution uniformity, and the minimum FFU number and energy consumption as the objective function. The cleanliness constraint: ; The air flow uniformity constraint: ; The economic constraint: .

[0133] Among them, the cleanliness constraint corresponds to the pollutant concentration in each area must be lower than the target value, the air flow uniformity constraint corresponds to the air flow velocity cannot be lower than the minimum threshold after the FFU is arranged, and the economic constraint corresponds to the number of FFUs cannot exceed the budget.

[0134] In the process of obtaining the optimized control scheme, the arrangement is adjusted according to the high-risk area and the partition arrangement density is adjusted.

[0135] (1) Adjusting the arrangement according to the high-risk area includes:

[0136] In the area where the pollutant concentration is higher than the target value , increase the FFU arrangement density.

[0137] In the area where the air flow velocity is lower than the threshold value Arrange FFU, supplement air flow circulation.

[0138] (2) The zoning arrangement density adjustment includes setting different FFU arrangement densities according to the cleanliness requirements of the area.

[0139] The FFU arrangement density in the high cleanliness area is:

[0140]

[0141] Wherein, is the area of the area; is the release amount of the pollution source; is the target air flow velocity.

[0142] The FFU arrangement density in the medium cleanliness area is:

[0143]

[0144] Wherein, is a proportionality coefficient (usually 0.5-0.8).

[0145] The FFU arrangement density in the low cleanliness area is:

[0146]

[0147] Wherein, is the minimum density of the system.

[0148] S203, determine the fitness evaluation index according to the optimization control scheme, and calculate the fitness according to the fitness evaluation index; wherein the fitness evaluation index includes: pollutant removal efficiency, pollution source contribution rate coverage index and air flow uniformity index.

[0149] In the embodiments of the present application, in order to ensure that the cleanliness, air flow uniformity and economy meet the expected requirements, after the optimization control scheme is determined, the optimization control scheme is further evaluated, and in order to meet the evaluation requirements of the optimization control scheme, the fitness and the corresponding evaluation index are set.

[0150] The traditional objective function only takes minimizing the concentration deviation and the number of FFUs as the target, and the embodiments of the present application are different from the traditional scheme, and introduce the pollutant removal efficiency, the pollution source contribution rate coverage index and the air flow uniformity index.

[0151] S204, when the fitness meets the set condition, output the optimization control scheme, so as to adjust the FFU distribution according to the optimization control scheme.

[0152] In the implementation process, the fitness is used to guide the iterative update of the optimization control scheme to ensure that the FFU distribution scheme effectively improves the accuracy and efficiency of clean room air quality control. When the fitness meets the set conditions, the corresponding optimization control scheme is output as the final scheme.

[0153] In this embodiment, the initial pollutant concentration, airflow distribution and other key data are obtained by establishing a clean room CFD simulation model, the FFU arrangement is optimized by combining a multi-objective optimization model, the scheme effect is evaluated by the fitness indexes including pollutant removal efficiency, pollution source contribution rate coverage and airflow uniformity, and finally the FFU distribution is adjusted according to the fitness. This process realizes closed-loop control from simulation analysis to optimization decision, which can accurately identify the pollution risk and airflow problem of the clean room, balance the cleanliness, airflow uniformity and economy through multi-objective constraints, avoid the blindness of traditional empirical arrangement, make the FFU arrangement more scientific and targeted, and effectively improve the accuracy and efficiency of clean room air quality control.

[0154] In one possible implementation, the fitness evaluation index is determined according to the optimization control scheme, including:

[0155] The clean room CFD simulation model is used to simulate the optimization control scheme to obtain the pollutant concentration reduction, airflow distribution and pollution source contribution rate after the FFU is updated and arranged;

[0156] The pollutant removal efficiency is determined according to the ratio of the pollutant concentration reduction to the original pollutant concentration reduction, the pollution source contribution rate coverage index is calculated according to the pollution source contribution rate and the weight of the pollution source to each region, and the airflow uniformity index is calculated according to the airflow distribution.

[0157] Among them, the pollutant removal efficiency is introduced to increase the fitness index of the interruption of the FFU arrangement to the diffusion path of the pollutant:

[0158]

[0159] Among them, is the pollutant concentration reduction after the FFU is arranged; is the removal efficiency, the higher the better.

[0160] The pollution source contribution rate index is introduced to guide the FFU to preferentially cover the area with high pollution source contribution rate:

[0161]

[0162] Among them, is the pollution source weight to a certain region; For regional coordinates; maximize the FFU coverage density of high-weight regions to ensure that critical contamination paths are controlled.

[0163] The airflow distribution uniformity index is introduced, aiming to increase airflow uniformity as part of the optimization target:

[0164]

[0165] wherein, : average velocity of cleanroom airflow.

[0166] In the implementation process, when determining the fitness evaluation index according to the optimization control scheme, the following data is first taken as input data:

[0167] Contaminant concentration distribution: .

[0168] Airflow distribution: .

[0169] High-risk area marking: .

[0170] FFU initial arrangement scheme: .

[0171] Then, initialization is performed based on the contaminant tracing path, including: contaminant source path weight initialization and regional stratification initialization.

[0172] Among them, the contaminant source path weight initialization aims to assign weights to the diffusion paths of each contaminant source:

[0173]

[0174] wherein, is the change amount of the contaminant concentration of a certain region; is the weight of the contaminant source to a certain region.

[0175] Regions with higher weights are initially arranged with more FFUs, thereby prioritizing the control of the diffusion paths of major contaminant sources.

[0176] Among them, the regional stratification initialization arrangement aims to divide the cleanroom into high, medium, and low risk layers (combined with the high-risk areas and airflow dead zones output in step two), and assign an initial FFU arrangement density:

[0177]

[0178] wherein, is the risk adjustment coefficient, which takes a larger value for high-risk areas; is the weight of the contaminant source to a certain region.

[0179] In the embodiment, the method obtains the pollutant concentration change, airflow distribution and pollution source contribution rate after the FFU is updated and arranged through CFD simulation, and then quantitatively calculates the pollutant removal efficiency, pollution source contribution rate coverage index and airflow uniformity index. The index calculation method based on simulation data ensures the objectivity and accuracy of the fitness evaluation index, can truly reflect the inhibition effect of the FFU arrangement scheme on the diffusion of pollutants, the coverage degree of high-risk pollution sources and the optimization of airflow distribution, provides a reliable basis for subsequent fitness calculation and scheme adjustment, and improves the scientificity of the optimization process.

[0180] In a possible implementation, the fitness calculation formula is:

[0181]

[0182] wherein, the pollutant removal efficiency, the pollution source contribution rate coverage index, the airflow uniformity index, , , is a dynamic weight factor.

[0183] wherein, , , is a dynamic weight factor, and the introduction of the dynamic weight factor can flexibly adjust the importance of each index according to the actual optimization demand.

[0184] For example, the weight of the pollutant removal efficiency can be increased in a high pollution risk area, and the weight of the airflow uniformity index can be increased when the airflow dead angle is prominent, so that the fitness calculation is more in line with the actual control demand of the clean room, and the optimization process is guided to develop in a more optimal comprehensive performance direction.

[0185] In the embodiment, the fitness calculation formula integrates the pollutant removal efficiency, the pollution source contribution rate coverage index and the airflow uniformity index through the dynamic weight factor, so that the three form a synergistic effect in the fitness evaluation.

[0186] In a possible implementation, when the fitness does not meet the set condition, the method further comprises:

[0187] updating the FFU arrangement position and the FFU arrangement density according to the optimization control scheme and the FFU arrangement position and the FFU arrangement density corresponding to the last optimization control scheme to obtain the updated FFU arrangement position and the updated FFU arrangement density as the updated optimization control scheme;

[0188] performing the operation of determining the fitness evaluation index according to the optimization control scheme and the subsequent operation.

[0189] In the present embodiment, when the fitness does not satisfy the set condition, a new scheme is generated by updating the FFU arrangement position and density of the current and last optimization control scheme, and the fitness is recalculated, forming an iterative optimization mechanism. This updating method can combine the advantages of different schemes, avoid the limitations of a single scheme, gradually approach the optimal solution through continuous iteration, effectively improve the optimization depth of the FFU arrangement scheme, and ensure that the final scheme achieves a better balance in cleanliness, airflow uniformity and economy.

[0190] In a possible implementation, the optimization update formula corresponding to the FFU arrangement position is:

[0191]

[0192] wherein, is a path fusion coefficient; is the FFU arrangement position corresponding to the last optimization control scheme; is the optimization control scheme.

[0193] When the value of a is large, the new scheme retains more reasonable layout of the last scheme; when the value of a is small, the new scheme absorbs more optimization ideas of the current scheme. This controllable fusion method makes the adjustment of the FFU arrangement position more targeted, which helps to gradually optimize the coverage effect of the high-risk area in the iteration process and improve the rationality of the scheme.

[0194] In the present embodiment, the optimization update formula corresponding to the FFU arrangement position is weighted fusion of the arrangement positions of the current and last schemes through the path fusion coefficient, so that the new scheme can flexibly adjust the influence weight of the two schemes according to actual needs.

[0195] In a possible implementation, the optimization update formula corresponding to the FFU arrangement density is:

[0196]

[0197] wherein, is an adjustment value calculated from the risk coefficient and the removal efficiency; , is the risk coefficient, is calculated according to the proportion of the pollutant concentration exceeding the standard in the high-risk area, is the target value of the regional pollutant concentration, is the removal efficiency, and is a dynamic weight factor for automatically adjusting the relative weight of risk control and removal efficiency at different stages, and The FFU arrangement density can be determined according to actual process experience or simulation iteration.

[0198] wherein, is a dynamic adjustment amount of the FFU arrangement density, used to correct the FFU arrangement in real time according to the pollution risk and removal efficiency in the optimization iteration. In the specific implementation, the , data output by the CFD simulation software are calculated and , and and are calculated , which are used for the next round of FFU arrangement density calculation to realize automatic generation selection optimization.

[0199] In a possible implementation, the condition is set as that a difference between the fitness corresponding to the optimization control scheme and the fitness corresponding to the last optimization control scheme is less than a set threshold.

[0200] In the specific implementation, the condition is set as that

[0201]

[0202] wherein, is a convergence determination threshold; is the fitness corresponding to the current optimization control scheme; is the fitness corresponding to the last optimization control scheme.

[0203] In the embodiment, the condition is defined as that a difference between the fitnesses of the two optimization control schemes is less than a set threshold, which can not only avoid that the scheme is not optimal due to insufficient iteration times, but also prevent resource waste caused by excessive iteration, so as to ensure that the iteration is stopped in time when the fitness tends to be stable, improve the optimization efficiency while ensuring the optimization effect, and make the FFU arrangement scheme reach the expected performance at a reasonable calculation cost.

[0204] In a possible implementation, after the fitness evaluation index is determined according to the optimization control scheme, the method further includes:

[0205] corresponding to each index in the fitness evaluation index is calculated respectively;

[0206] According to the difference of each index and the corresponding threshold, the weight of each index in the fitness evaluation index calculation formula is adjusted.

[0207] Optionally, according to the difference of each index and the corresponding threshold, the weight of each index in the fitness evaluation index calculation formula is adjusted, including:

[0208] ​The weight of the index with the difference value greater than or equal to the corresponding threshold is increased when the index with the difference value less than the corresponding threshold is included.

[0209] For example, when the difference value of the pollutant removal efficiency is large and does not reach the threshold value, the weight thereof in the fitness calculation formula can be increased to guide the optimization process to preferentially improve the pollutant removal effect; when the airflow uniformity index has reached the standard, the weight thereof can be reduced to reduce unnecessary optimization resource investment. This dynamic adjustment mechanism makes the optimization process more targeted, and can flexibly adjust the optimization focus according to the actual performance of each index to accelerate convergence to a scheme with better comprehensive performance.

[0210] In the embodiment, the dynamic optimization of the weight is realized by calculating the difference value of each index in the fitness evaluation index from the last time and adjusting the weight of each index according to the difference value and the corresponding threshold.

[0211] In order to ensure that the optimization control scheme output in step S204 meets the requirements, the optimization control scheme can be imported into the CFD model again to verify the pollutant concentration and airflow distribution, and compare the number of FFUs, energy consumption and cleanliness improvement effect of the initial scheme and the optimal scheme. Specifically, whether the FFU arrangement position and the FFU arrangement density in the optimization control scheme meet the following requirements:

[0212] The pollutant concentration of each region is lower than the target value; the airflow distribution is uniform and there is no obvious dead angle; the number of FFU operation and the energy consumption meet the economic requirements.

[0213] It should be understood that the size of the serial number of each step in the above embodiment does not mean the order of execution, and the execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present application.

[0214] The following is a device embodiment of the present application. For details not described in detail, please refer to the corresponding method embodiments described above.

[0215] Figure 3 A structure diagram of a clean room air quality control device provided by an embodiment of the present application is shown. For ease of illustration, only parts related to the embodiment of the present application are shown, and are described in detail as follows:

[0216] As shown in Figure 3 The clean room air quality control device 3 includes:

[0217] The simulation module 301 is configured to establish a clean room CFD simulation model based on a three-dimensional geometric model of the clean room, a grid division result and a turbulent flow model, and obtain an initial simulation result based on the clean room CFD simulation model; wherein the initial simulation result includes a pollutant concentration distribution, an airflow distribution, a high-risk area mark and a pollution source contribution rate.

[0218] The optimization model 302 is used for inputting the initial simulation result into a multi-objective optimization model to obtain an optimized control scheme; wherein, the constraint conditions of the multi-objective optimization model include cleanliness constraint, air flow uniformity constraint and economy constraint; and the optimized control scheme includes FFU arrangement position and FFU arrangement density.

[0219] The fitness calculation module 303 is used for determining a fitness evaluation index according to the optimized control scheme, and calculating fitness according to the fitness evaluation index; wherein, the fitness evaluation index includes pollutant removal efficiency, pollution source contribution rate coverage index and air flow uniformity index.

[0220] The output module 304 is used for outputting the optimized control scheme when the fitness satisfies a set condition, so as to adjust the FFU distribution according to the optimized control scheme.

[0221] In a possible implementation, the multi-objective optimization model takes the maximum cleanliness compliance rate, the optimal air flow distribution uniformity and the minimum FFU number and energy consumption as objective functions.

[0222] In a possible implementation, the fitness calculation module 303 is specifically used for:

[0223] The optimized control scheme is simulated by using a clean room CFD simulation model to obtain the pollutant concentration drop, air flow distribution and pollution source contribution rate after the FFU is updated and arranged;

[0224] The pollutant removal efficiency is determined according to the ratio of the pollutant concentration drop to the original pollutant concentration drop, the pollution source contribution rate coverage index is calculated according to the pollution source contribution rate and the weight of the pollution source to each region, and the air flow uniformity index is calculated according to the air flow distribution.

[0225] In a possible implementation, the fitness calculation formula is as follows:

[0226]

[0227] wherein, is the pollutant removal efficiency, is the pollution source contribution rate coverage index, is the air flow uniformity index, , , is a dynamic weight factor.

[0228] In a possible implementation, when the fitness does not satisfy the set condition, the fitness calculation module 303 is further used for:

[0229] The FFU arrangement position and the FFU arrangement density corresponding to the previous optimization control scheme are updated according to the optimization control scheme to obtain updated FFU arrangement position and FFU arrangement density as the updated optimization control scheme.

[0230] The fitness calculation module 303 determines the fitness evaluation index according to the updated optimization control scheme, and calculates the fitness according to the fitness evaluation index; the output module 304 outputs the optimization control scheme when the fitness meets the set condition.

[0231] In a possible implementation, the updated optimization formula corresponding to the FFU arrangement position is:

[0232]

[0233] wherein, is a path fusion coefficient; is the FFU arrangement position corresponding to the previous optimization control scheme; is the optimization control scheme.

[0234] In a possible implementation, the set condition is that the fitness difference between the optimization control scheme and the previous optimization control scheme is less than a set threshold.

[0235] In a possible implementation, the fitness calculation module 303 is further configured to, after determining the fitness evaluation index according to the optimization control scheme, calculate the difference between each index in the fitness evaluation index and each index in the previous fitness evaluation index, respectively; and adjust the weight of each index in the fitness evaluation index calculation formula according to the difference between each index and the corresponding threshold.

[0236] In a possible implementation, the fitness calculation module 303 is specifically configured to, when the index whose difference is less than the corresponding threshold is included, increase the weight of the index whose difference is greater than or equal to the corresponding threshold.

[0237] In the embodiment, the initial pollutant concentration, airflow distribution and other key data are obtained by establishing a clean room CFD simulation model, the FFU arrangement is optimized by combining a multi-objective optimization model, the effect of the scheme is evaluated through the fitness indexes including the pollutant removal efficiency, the pollution source contribution rate coverage and the airflow uniformity, and finally the FFU distribution is adjusted according to the fitness. This process realizes closed-loop control from simulation analysis to optimization decision, can accurately identify the pollution risk and airflow problem of the clean room, balances the cleanliness, airflow uniformity and economy through multi-objective constraints, avoids the blindness of traditional empirical arrangement, makes the FFU arrangement more scientific and targeted, and effectively improves the accuracy and efficiency of clean room air quality control.

[0238] Figure 4is a schematic diagram of an electronic device provided by an embodiment of the present application. As shown in Figure 4 The electronic device 4 of this embodiment includes a processor 40 and a memory 41. The memory 41 stores a computer program 42. The processor 40 implements the steps in each of the above method embodiments when executing the computer program 42. Alternatively, the processor 40 implements the functions of each module / unit in each of the above apparatus embodiments when executing the computer program 42.

[0239] For example, the computer program 42 can be divided into one or more modules / units, which are stored in the memory 41 and executed by the processor 40 to complete the present application. The one or more modules / units can be a series of computer program instruction segments capable of completing a specific function, which are used to describe the execution process of the computer program 42 in the electronic device 4.

[0240] The electronic device 4 can include, but is not limited to, the processor 40 and the memory 41. Those skilled in the art can understand that the electronic device 4 can include more or less components, or combine certain components, or different components, for example, the electronic device 4 can also include an input / output device, a network access device, a bus, etc. Figure 4 The electronic device 4 is only an example and does not constitute a limitation on the electronic device 4, and can include more or less components than the diagram, or combine certain components, or different components, for example, the electronic device 4 can also include an input / output device, a network access device, a bus, etc.

[0241] The processor 40 can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0242] The memory 41 can be an internal storage unit of the electronic device 4, such as a hard disk or a memory of the electronic device 4. The memory 41 can also be an external storage device of the electronic device 4, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. equipped on the electronic device 4. Further, the memory 41 can include both the internal storage unit and the external storage device of the electronic device 4. The memory 41 is used to store the computer program 42 and other programs and data required by the electronic device 4. The memory 41 can also be used to temporarily store data that has been output or will be output.

[0243] For the convenience and brevity of description, only the above-mentioned division of functional modules / units is exemplified, and in actual application, the above-mentioned functions can be completed by different functional modules / units according to needs. The above-mentioned modules / units can be realized in the form of hardware, software or a combination of hardware and software.

[0244] The embodiment of the present application also provides a computer readable storage medium, which stores a computer program. When the computer program is executed by a processor, the method in each method embodiment described above is implemented.

[0245] The embodiment of the present application also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the method in each method embodiment described above is implemented.

[0246] The computer program includes computer program code, which can be in the form of source code, object code, executable files or some intermediate forms. The computer readable medium can include any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, Read-Only Memory (ROM), Random Access Memory (RAM), electric carrier wave signal, telecommunication signal and software distribution medium, etc.

[0247] In the above embodiments, the description of each embodiment has its own focus, and the parts not described or recorded in detail in a certain embodiment can be referred to the relevant description of other embodiments. If there is no special description and no logical conflict, the terms and / or descriptions of different embodiments are consistent and can be mutually referenced. The technical features in different embodiments can be combined to form new embodiments according to their inherent logical relationship.

[0248] The above examples are only used to illustrate the technical solutions of the present application, but not limit the present application; although the present application has been described in detail with reference to the foregoing examples, those ordinarily skilled in the art should understand: the technical solutions recorded in the foregoing examples can still be modified, or some technical features can be replaced equivalently; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A clean room air quality control method, characterized by, The method comprises the following steps: Based on the three-dimensional geometric model, the grid division result and the turbulence model of the clean room, a clean room CFD simulation model is established, and an initial simulation result is obtained based on the clean room CFD simulation model; wherein the initial simulation result comprises a pollutant concentration distribution, an airflow distribution, a high-risk area marking and a pollution source contribution rate; wherein the high-risk area is an area where the pollutant concentration exceeds a concentration target value; The initial simulation result is input into a multi-objective optimization model to obtain an optimized control scheme; wherein the constraint conditions of the multi-objective optimization model comprise a cleanliness constraint, an airflow uniformity constraint and an economic constraint; and the optimized control scheme comprises an FFU arrangement position and an FFU arrangement density; According to the optimized control scheme, an adaptability evaluation index is determined, and the adaptability is calculated according to the adaptability evaluation index; wherein the adaptability evaluation index comprises a pollutant removal efficiency, a pollution source contribution rate coverage index and an airflow uniformity index; When the adaptability meets a set condition, the optimized control scheme is output to adjust the FFU distribution according to the optimized control scheme; The determination of the adaptability evaluation index according to the optimized control scheme comprises: The optimized control scheme is simulated by using the clean room CFD simulation model to obtain a pollutant concentration drop amount, an airflow distribution and a pollution source contribution rate after the FFU is updated and arranged; The pollutant removal efficiency is determined according to the ratio of the pollutant concentration drop amount to an original pollutant concentration drop amount, the pollution source contribution rate coverage index is calculated according to the pollution source contribution rate and the weight of the pollution source to each area, and the airflow uniformity index is calculated according to the airflow distribution; The adaptability calculation formula is as follows: wherein, is a pollutant removal efficiency, is a pollution source contribution rate coverage index, is an air flow uniformity index, , , is a dynamic weight factor; The pollution source contribution rate coverage index is used to guide the FFU to preferentially cover the area with a higher pollution source contribution rate: wherein, is a pollution source a weight for a region; is a region coordinate; maximize FFU coverage density in high-weight regions, ensure critical pollution paths are controlled.

2. The method of claim 1, wherein, When the adaptability does not meet the set condition, the following steps are further included: According to the FFU arrangement position and the FFU arrangement density corresponding to the optimized control scheme and the last optimized control scheme, the FFU arrangement position and the FFU arrangement density after optimization update are obtained as the updated optimized control scheme; The operation of determining the adaptability evaluation index according to the optimized control scheme and the subsequent operations are performed.

3. The method of claim 2, wherein, The optimization update formula corresponding to the FFU arrangement position is as follows: wherein, is a path fusion coefficient; is a last time optimization control scheme corresponding FFU arrangement position; is the optimization control scheme.

4. The method of claim 2, wherein, The set condition is that the difference between the adaptability corresponding to the optimized control scheme and the last optimized control scheme is less than a set threshold.

5. The method of claim 2, wherein, After the adaptability evaluation index is determined according to the optimized control scheme, the following steps are further included: The difference between each index in the adaptability evaluation index and each index in the last adaptability evaluation index is calculated respectively; According to the difference between each index and the corresponding threshold, the weight of each index in the adaptability evaluation index calculation formula is adjusted.

6. A clean room air quality control apparatus, comprising: The method comprises the following steps: A simulation module is used to establish a clean room CFD simulation model based on a three-dimensional geometric model, a grid division result and a turbulence model of a clean room, and obtain an initial simulation result based on the clean room CFD simulation model; wherein the initial simulation result comprises a pollutant concentration distribution, an airflow distribution, a high-risk area marking and a pollution source contribution rate; An optimization model is used to input the initial simulation result into a multi-objective optimization model to obtain an optimized control scheme; wherein the constraint conditions of the multi-objective optimization model include cleanliness constraint, airflow uniformity constraint and economy constraint; and the optimized control scheme includes FFU arrangement position and FFU arrangement density; An adaptability calculation module is configured to determine an adaptability evaluation index according to the optimized control scheme, and to calculate adaptability according to the adaptability evaluation index; wherein the adaptability evaluation index includes pollutant removal efficiency, pollution source contribution rate coverage index and airflow uniformity index; An output module is configured to output the optimized control scheme to adjust FFU distribution according to the optimized control scheme when the adaptability meets a set condition. The adaptability calculation module is specifically configured to: simulate the optimized control scheme by using the clean room CFD simulation model to obtain pollutant concentration drop, airflow distribution and pollution source contribution rate after FFU is updated and arranged; determine pollutant removal efficiency according to the ratio of the pollutant concentration drop to the original pollutant concentration drop, calculate pollution source contribution rate coverage index according to the pollution source contribution rate and the weight of pollution source to each area, and calculate airflow uniformity index according to the airflow distribution; The adaptability calculation formula is as follows: wherein, is a pollutant removal efficiency, is a pollution source contribution rate coverage index, is an air flow uniformity index, , , is a dynamic weight factor; The pollution source contribution rate coverage index is used to guide FFU to preferentially cover the area with higher pollution source contribution rate: wherein, is a source of pollution a weight for a region; is a region coordinate; maximize FFU coverage density in high-weight regions, ensuring critical pollution paths are controlled.

7. An electronic device, comprising: The computer readable storage medium stores a computer program, and the processor executes the computer program to implement the method in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the processor executes the computer program to implement the method in any one of claims 1 to 5.

Images