Clean air pollution detection method, device, terminal equipment and storage medium

Abstract

The present application relates to the field of clean production and provides a clean air pollution detection method, apparatus, terminal device and storage medium. The method comprises: obtaining a first air sampling sequence of key detection points in a clean room in a scenario without metal utensils or in a scenario where metal utensils are statically placed; obtaining a second air sampling sequence of key detection points in a clean room in a scenario where metal utensils are dynamically simulated and used in the clean room; and determining the comprehensive pollution contribution index of metal utensils to the clean air of the clean room based on the first air sampling sequence and the second air sampling sequence. The present application can achieve comprehensive and accurate detection of the pollution effects of metal utensils on the clean air of the clean room, and can provide accurate and reliable technical and data support for the formulation of scientific and reasonable metal utensils usage specifications to ensure the air quality of the clean room.

Description

Technical Field

[0001] The present application relates to the field of clean production, and in particular to a clean air pollution detection method, apparatus, terminal equipment and storage medium. Background Art

[0002] A clean room (such as a semiconductor laboratory clean room) is a special environment that usually requires strict control of pollutants such as particulate matter and metal impurities in the air to ensure high product quality and the smooth progress of the production process.

[0003] During cleanroom laboratory operations, metal instruments are often used. These instruments can generate pollutants such as particles and metal impurities through various mechanisms (such as physical wear and chemical reactions), thus affecting the air quality of the cleanroom environment.

[0004] Currently, there is a lack of comprehensive and accurate detection methods for the impact of metal appliances on the air quality of clean room environments. Summary of the Invention

[0005] In view of this, the embodiments of the present application provide a clean air pollution detection method, apparatus, terminal device and storage medium to address the lack of a comprehensive and accurate detection method for the impact of metal appliances on the air quality of the clean room environment in the prior art.

[0006] A first aspect of an embodiment of the present application provides a clean air pollution detection method, comprising:

[0007] Obtain the first air sampling sequence of key inspection points in the clean room in a scenario without metal utensils or in a scenario where metal utensils are statically placed;

[0008] Obtain a second air sampling sequence at key inspection points in a clean room under a dynamic simulation scenario of using metal utensils;

[0009] Based on the first air sampling sequence and the second air sampling sequence, a comprehensive pollution contribution index of the metal appliance to the clean air of the clean room is determined.

[0010] A second aspect of the embodiments of the present application provides a clean air pollution detection device, comprising:

[0011] A first acquisition module is configured to acquire a first air sampling sequence at key detection points of the clean room in a scenario without metal utensils or in a scenario with metal utensils statically placed;

[0012] The second acquisition module is configured to acquire a second air sampling sequence of key detection points in the clean room under a dynamic simulation scenario of using metal utensils in the clean room;

[0013] The determination module is configured to determine a comprehensive pollution contribution index of the metal appliance to the clean air of the clean room based on the first air sampling sequence and the second air sampling sequence.

[0014] According to a third aspect of an embodiment of the present application, a terminal device is provided, comprising: a memory and a processor, wherein the memory is used to store a computer program; and the processor is used to execute the method of the first aspect when calling the computer program.

[0015] According to a fourth aspect of an embodiment of the present application, a readable storage medium is provided, on which a program is stored, and when the program is executed by a processor, the method of the first aspect is implemented.

[0016] According to a fifth aspect of the embodiments of the present application, a program product is provided. When the program product is run on a clean air pollution detection device, the device executes the method of the first aspect.

[0017] Compared with the prior art, the embodiments of the present application have at least the following advantages: by obtaining a first air sampling sequence at key detection points in a clean room in a scenario without metal utensils or with metal utensils statically placed; obtaining a second air sampling sequence at key detection points in a clean room in a scenario of dynamic simulation of the use of metal utensils; and determining the comprehensive pollution contribution index of the metal utensils to the clean air in the clean room based on the first air sampling sequence and the second air sampling sequence, thereby achieving comprehensive and accurate detection of the pollution impact of metal utensils on the clean air in the clean room. The detection results of the embodiments of the present application can provide accurate and reliable technical and data support for formulating scientific and reasonable metal utensils usage regulations to ensure clean room air quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0019] Figure 1 This is a flow chart of a clean air pollution detection method provided in an embodiment of the present application;

[0020] Figure 2 is a schematic diagram of a sampling time axis provided in an embodiment of the present application;

[0021] Figure 3 This is a schematic structural diagram of a clean air pollution detection device provided in an embodiment of the present application;

[0022] Figure 4This is a structural diagram of a terminal device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0023] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0024] A clean air pollution detection method and device according to an embodiment of the present application will be described in detail below with reference to the accompanying drawings.

[0025] At present, traditional clean air pollution detection methods are difficult to comprehensively and accurately evaluate the comprehensive impact of metal appliances on the clean air quality of clean rooms through various mechanisms (such as physical wear, chemical reactions, etc.) during use.

[0026] In view of this, an embodiment of the present application provides a clean air pollution detection method, which collects a first air sampling sequence at key detection points in a clean room in a scenario where there are no metal appliances in the clean room or where metal appliances are statically placed; collects a second air sampling sequence at key detection points in the clean room in a scenario where metal appliances are dynamically simulated and used in the clean room; and determines the comprehensive pollution contribution index of metal appliances to the clean air in the clean room based on the first air sampling sequence and the second air sampling sequence, thereby achieving comprehensive and accurate detection of the pollution impact of metal appliances on the clean air in the clean room.

[0027] Figure 1 This is a flow chart of a clean air pollution detection method provided in an embodiment of the present application. This method can be executed by a terminal device.

[0028] See also Figure 1 The clean air pollution detection method provided in the embodiment of the present application includes the following steps:

[0029] Step S101: obtaining a first air sampling sequence of key detection points in a clean room in a scene without metal appliances or in a scene with metal appliances statically placed.

[0030] The no-metal-equipment scenario usually refers to a clean environment in which no metal equipment is introduced into the clean room, that is, a scenario in which the clean room is in an empty state.

[0031] The static placement of metal appliances scenario usually refers to the scenario where metal appliances are introduced into the clean room but are in a static placement state.

[0032] Key detection points are usually the points (areas) that are most representative and effective in evaluating the air quality of clean rooms, mainly including key downwind areas such as the surface of metal appliances (within 5 cm), the operator's breathing zone (at a height of about 170 cm), and the laboratory return air outlet.

[0033] Table 1 lists the relevant information of some metal tools commonly used in laboratories (mainly including material, main components and surface roughness).

[0034] Table 1 Information on commonly used metal utensils in laboratories

[0035]

[0036] As an example, in a cleanroom without metal appliances, a laser particle counter (RION KC-24 particle counter, with a detection particle size of 0.5µm) is used to collect the particle concentration (number of particles, unit: particles / m) at each key detection point in the cleanroom (such as a Class 100 cleanroom) at a preset sampling time interval (such as 2 hours). 3 ), air samples were collected at each key detection point simultaneously, and the metal element concentration of the air sample was analyzed using ICP-MS (inductively coupled plasma mass spectrometer, model Agilent 8900). At the same time, the relative humidity of the clean room corresponding to each sampling time point was recorded and monitored continuously for 24 hours.

[0037] For example, assuming that the sampling interval is 2 hours and the monitoring is continuous for 24 hours, the continuous sampling time of 24 hours can be divided into the following 12 sampling time points: 0:00, 2:00, 4:00, 6:00, ..., 24:00, and each sampling time point is recorded as (corresponding to sampling time point 0:00), (corresponding to sampling time point 2:00), (corresponding to sampling time point 4:00),..., (Corresponding sampling time point 24:00).

[0038] Furthermore, the particle concentration, metal element concentration, and relative humidity of the clean room at each key detection point collected at each sampling time point are sorted to obtain the first air sampling sequence: ,in, Indicates key detection points , Indicates the collection time point 0:00, Indicates the time of collection Collect key detection points The concentration of particulate matter, Indicates the time of collection Collect key detection points The metal element concentration, Indicates the collection time point Corresponding clean room relative humidity; Indicates the collection time point 24:00, Indicates the time of collection Collect key detection points The concentration of particulate matter, Indicates the time of collection Collect key detection points The metal element concentration, Indicates the collection time point Corresponding clean room relative humidity; Indicates key detection points , Indicates the time of collection Collect key detection points The concentration of particulate matter, Indicates the time of collection Collect key detection points The metal element concentration is 1, Indicates the time of collection Collect key detection points The concentration of particulate matter, Indicates the time of collection Collect key detection points The concentration of metal elements; Indicates key detection points , Indicates the time of collection Collect key detection points The concentration of particulate matter, Indicates the time of collection Collect key detection points The metal element concentration, Indicates the time of collection Collect key detection points The concentration of particulate matter, Indicates the time of collection Collect key detection points concentration of metal elements.

[0039] Step S102 : obtaining a second air sampling sequence of key detection points in the clean room under a dynamic simulation scenario of using metal appliances in the clean room.

[0040] Cleanroom dynamic simulation of the use of metal tools usually refers to the dynamic simulation of the actual operation process of using metal tools in the clean room (such as instrument collision, friction, movement, etc.).

[0041] As an example, the operating procedures for dynamically simulating the use of metal utensils in a clean room (such as simulating instrument collision) are as follows:

[0042] 1) Experimental equipment and materials:

[0043] ①Metal instruments to be tested: 316L stainless steel tweezers, Ti-6Al-4V titanium alloy tweezers, high-purity aluminum alloy (Al6061-T6) tweezers, 17-4PH hardened stainless steel tweezers, nconel718 nickel-based alloy tweezers, chrome-plated 304 electroplated tweezers.

[0044] ② Collision test platform: includes a fixing fixture, an angle adjustment device and a reciprocating oscillator, etc.

[0045] ③Collision reference object: PFA (perfluoroalkoxy resin) beaker.

[0046] 2) Collision operation parameter settings:

[0047] Collision angle: set to three standard angles of 45±2°, 90±2° and 135±2° to simulate different operation modes.

[0048] Collision frequency: Set the horizontal frequency of the reciprocating oscillator to 60r / min.

[0049] Collision contact surface: the working end of the metal tool (such as the tip of the tweezers) and the main body of the metal tool (such as the middle part of the tweezers).

[0050] 3) Dynamically simulate the actual use of each metal tool to be tested according to the following standard operating procedures:

[0051] ① Preparation: Install the collision test platform at the key detection points in the clean room, then use powder-free clean gloves to install the metal device to be tested on the fixed fixture of the collision test platform and adjust it to the specified collision angle.

[0052] ②Environment stabilization: Wait for the laser particle counter reading to stabilize and record baseline data for at least 10 minutes.

[0053] ③Parameter setting: Set the collision frequency and collision contact surface according to the test plan.

[0054] ④Execute collision: Start the reciprocating oscillator to make the metal tool to be tested collide with the collision reference object according to the preset parameters.

[0055] ⑤ Data Collection: Before and after the collision (for at least 30 minutes), a laser particle counter was used to continuously record the particle concentration at each key detection point. Air samples were collected and the metal element concentrations in the air samples were measured using an ICP-MS (Agilent 8900). The air samples were collected as follows: an impactor (30 mL 2% nitric acid) was connected in series with an air sampling pump. Air samples were collected at key detection points at a flow rate of 0.2 L / min for 45 minutes, and the temperature and humidity were recorded.

[0056] ⑥ Repeat verification: Repeat the test for each set of parameter settings at least 3 times to ensure the reliability of the collected data.

[0057] During the above-mentioned operation of simulating the collision of the equipment, the following matters need to be noted:

[0058] 1) Before the collision test, all instruments must undergo a standardized cleaning process and be equilibrated in a clean environment for at least 12 hours.

[0059] 2) Operators must wear complete clean room work clothes and receive standardized operation training.

[0060] 3) If a sudden peak in particulate matter concentration exceeding a preset threshold (such as three times the Class 100 requirement) is detected, the experiment should be suspended immediately and the cause should be identified.

[0061] 4) Maintain stable environmental parameters during the collision process: temperature 20℃±1℃, relative humidity 45%±5%RH.

[0062] 5) Regularly check the surface condition of the collision reference object. If obvious wear is found, replace it to ensure consistency of experimental conditions.

[0063] The second air sampling sequence at the key detection points of the clean room can be obtained by referring to the method of obtaining the first air sampling sequence in the above embodiment.

[0064] Step S103 : determining a comprehensive pollution contribution index of the metal appliance to the clean air in the clean room based on the first air sampling sequence and the second air sampling sequence.

[0065] The comprehensive pollution contribution index is used to characterize the comprehensive pollution contribution of particulate matter and metal elements released by metal appliances to the clean air in the clean room.

[0066] The technical solution provided in the embodiment of the present application collects a first air sampling sequence at key detection points in a clean room in a scenario without metal appliances or in a scenario where metal appliances are statically placed; collects a second air sampling sequence at key detection points in a clean room in a scenario where metal appliances are dynamically simulated for use in the clean room; and determines the comprehensive pollution contribution index of metal appliances to the clean air in the clean room based on the first air sampling sequence and the second air sampling sequence, thereby achieving comprehensive and accurate detection of the pollution impact of metal appliances on the clean air in the clean room.

[0067] In a special environment like a cleanroom, accurately setting test points is crucial to obtaining reliable data. Some existing test solutions lack scientific rationality in the setting of test points and cannot well represent the air conditions of the entire cleanroom.

[0068] In some embodiments, before the step of acquiring a first air sampling sequence at key inspection points of a clean room in a scenario without metal appliances or in a scenario where metal appliances are statically placed, the step further includes:

[0069] Under the dynamic simulation of using metal utensils in the clean room, the third air sampling sequence is collected from multiple high-interest detection points in the clean room;

[0070] Determine, based on the third air sampling sequence, a spatial correlation coefficient between the pollutant concentration corresponding to each high-concern detection point and the operation area, and select at least one first candidate detection point from the high-concern detection points based on the spatial correlation coefficient;

[0071] Inputting the third air sampling sequence into a preset multivariate regression model, outputting the predicted pollutant concentration corresponding to each high-concern detection point, and selecting at least one second candidate detection point from the high-concern detection points based on the predicted pollutant concentration;

[0072] Determine key detection points based on the first candidate detection point and the second candidate detection point.

[0073] High-profile monitoring points are typically those locations (areas) that are highly representative and effective in evaluating cleanroom air quality. They primarily include three types of points: near-source monitoring points, personnel exposure zones, and system feedback points. Near-source monitoring points primarily include the 5±1 cm area on the surface of metal objects (this point most directly captures initial releases of pollutants from metal objects) and the approximately 10 cm area above the center of the operating platform (this point covers the potential combined impact of multiple metal objects). The personnel exposure zone primarily includes the area around 170 cm above the operator's breathing zone (this point is primarily used to assess the potential health impact of pollutants) and the area around 120 cm above the work area boundary (this point is primarily used to monitor the spread of pollutants to non-core areas). System feedback points primarily include the main return air vent (this point is primarily used to monitor the overall contamination level leaving the work area) and the area approximately 30 cm downstream of the supply air vent (this point is primarily used to assess the background baseline and system filtration efficiency).

[0074] In practical applications, high-risk detection points are initially identified based on the cleanroom's cleanliness level requirements. Then, according to the aforementioned method, metal instrument use (e.g., simulated instrument collision) is dynamically simulated in the cleanroom. A laser particle counter is used to continuously monitor the particle concentration at each high-risk detection point for 24 hours (sampling once per minute). Air samples are simultaneously collected for metal element analysis to determine the metal element concentration at each high-risk detection point. The relative humidity of the cleanroom corresponding to each sampling time point is also recorded.

[0075] The third air sampling sequence of the high-interest detection points in the clean room may be obtained by referring to the method for obtaining the first air sampling sequence in the above embodiment.

[0076] Next, the third air sampling sequence is processed using multivariate spatial statistical methods (such as using a covariance matrix to calculate the overall spatial correlation of multiple variables) to calculate the spatial correlation coefficient between the pollutant concentration (including particulate matter and metal elements) corresponding to each high-concern detection point and the operating area (i.e., the high-concern detection point). It is then determined whether the spatial correlation coefficient corresponding to each high-concern detection point is greater than or equal to a preset threshold (which can be flexibly set based on actual conditions, for example, any value between 0.7 and 1). High-concern detection points with spatial correlation coefficients greater than or equal to the preset threshold are identified as first candidate detection points.

[0077] The collected third air sampling sequence is input into a pre-trained multivariate regression model for processing, and the predicted pollutant concentration corresponding to each high-concern detection point (including the predicted concentration of particulate matter and the predicted concentration of metal elements) is output. Based on the predicted pollutant concentration, at least one second candidate detection point whose data changes can most accurately predict the overall clean air pollution level of the clean room is screened from multiple high-concern detection points.

[0078] Finally, the union of the first candidate detection points and the second candidate detection points is determined as the key detection points. For example, assuming that the high-interest detection points include six points A, B, C, D, E, and F, the first candidate detection points selected according to the above implementation include two points {A, B}, and the second candidate detection points selected according to the above implementation include three points {A, B, C}. Among them, the union of the set {A, B} and the set {A, B, C} is {A, B, C}. Therefore, the key detection points can be determined to be the three points A, B, and C.

[0079] Through the above method, it can be ensured that the selected key detection points have the greatest representativeness and effectiveness in evaluating the clean room air quality, which is conducive to improving the comprehensiveness and accuracy of subsequent detection of the pollution impact of metal utensils on the clean air of the clean room.

[0080] In some embodiments, determining a comprehensive pollution contribution index of metal appliances to clean air in a clean room based on the first air sampling sequence and the second air sampling sequence includes:

[0081] determining a baseline concentration of particulate matter and a baseline concentration of metal elements in the clean air of the clean room based on the first air sampling sequence;

[0082] Determine, based on the second air sampling sequence, the particle indicative concentration and the metal element indicative concentration in the clean air of the clean room under the dynamic simulation scenario of using metal utensils in the clean room;

[0083] Based on the baseline concentration of particulate matter, the baseline concentration of metal elements, the indicative concentration of particulate matter and the indicative concentration of metal elements, the comprehensive pollution contribution index of metal appliances to the clean air in the clean room is determined.

[0084] The baseline concentration of particulate matter refers to the concentration of particulate matter in clean air in a clean room without metal utensils or with metal utensils placed statically.

[0085] In clean room scenarios with no metal utensils or where metal utensils are statically placed, the concentration of particulate matter in the clean air does not differ much.

[0086] As an example, the particle sampling concentration corresponding to each sampling time point in the first air sampling sequence may be extracted, and then the average of the particle sampling concentration may be calculated to obtain the particle baseline concentration.

[0087] In some embodiments, the second air sampling sequence includes a plurality of sampling time points, and a particulate matter sampling concentration, a metal element sampling concentration, and a clean air relative humidity corresponding to each sampling time point;

[0088] Based on the second air sampling sequence, determine the indicative concentration of particulate matter and the indicative concentration of metal elements in the clean air of the clean room under the dynamic simulation scenario of using metal utensils in the clean room, including:

[0089] Determine, based on the particle sampling concentration and metal element sampling concentration corresponding to each sampling time point in the second air sampling sequence, the particle release peak concentration and metal element release peak concentration in the clean air of the clean room under the dynamic simulation scenario of using metal utensils in the clean room;

[0090] The sampling time point corresponding to the peak concentration of particulate matter release is determined as the first target time point, the clean air relative humidity corresponding to the peak concentration of particulate matter release is determined as the first target relative humidity, the sampling time point corresponding to the peak concentration of metal element release is determined as the second target time point, and the clean air relative humidity corresponding to the peak concentration of metal element release is determined as the second target relative humidity;

[0091] Determine the indicative concentration of particulate matter in the clean air of the clean room under the dynamic simulation of using metal utensils in the clean room based on the peak concentration of particulate matter release, the first target time point, and the first target relative humidity;

[0092] Based on the peak concentration of metal element release, the second target time point and the second target relative humidity, the indicator concentration of metal elements in the clean air of the clean room under the dynamic simulation scenario of using metal utensils in the clean room is determined.

[0093] As an example, assume that the key inspection points in the clean room include those close to the surface of metal appliances (within 5 cm) (hereinafter referred to as "key inspection points"). ”), operator breathing zone (hereinafter referred to as “key detection points ”), laboratory return air outlet (hereinafter referred to as “key detection points ”). The data collection method is as follows: at the beginning of the dynamic simulation of the operation using metal tools ( =0), 30 seconds after the dynamic simulation of using metal tools ( =30 s), 1 minute ( =1 min), 2 minutes ( = 2 min), 5 minutes ( =5 min)、10 minutes( =10 min)、15 minutes( =15 min)、30 min( =30min)、60 minutes( =60 min)、120 min( =120 min)、180 min( =180 min), these 11 sampling time points respectively collect, analyze and record the key detection points of the clean room 、 、 The corresponding particulate matter sampling concentration and metal element sampling concentration, as well as the relative humidity of the clean room, are sorted to obtain the second air sampling sequence:

[0094] ,in, 、 、 Represent key detection points 、 、 ; Indicates the sampling time point Collect key detection points The concentration of particulate matter sampled; Indicates the sampling time point Collect key detection points The concentration of particulate matter sampled; Indicates the sampling time point Collect key detection points The concentration of particulate matter sampled; Indicates the sampling time point Collect key detection points The metal element sampling concentration; Indicates the sampling time point Collect key detection points The metal element sampling concentration; Indicates the sampling time point Collect key detection points The metal element sampling concentration; Indicates the sampling time point Collect key detection points The concentration of particulate matter sampled; Indicates the sampling time point Collect key detection points The concentration of particulate matter sampled; Indicates the sampling time point Collect key detection points The concentration of particulate matter sampled; Indicates the sampling time point Collect key detection points The metal element sampling concentration; Indicates the sampling time point Collect key detection points The metal element sampling concentration; Indicates the sampling time point Collect key detection points The metal element sampling concentration; Indicates the sampling time point The collected relative humidity of the clean room; Indicates the sampling time point The collected relative humidity of the clean room.

[0095] Extract the key detection points in the second air sampling sequence ~ At the sampling time point ~ The corresponding particulate matter sampling concentration is ~ ,..., ~ , and compare their sizes, and determine the maximum particle sampling concentration as the peak concentration of particle release in the clean air of the clean room under the dynamic simulation of using metal utensils in the clean room. For example, assuming that the maximum particle sampling concentration is at the sampling time point Collect key detection points The concentration of particulate matter sampled , then you can It is determined as the peak concentration of particulate matter released into the clean air of the clean room under the dynamic simulation of the use of metal utensils in the clean room.

[0096] It is understandable that referring to the above method, the peak concentration of metal element release in the clean air of the clean room under the dynamic simulation of using metal utensils in the clean room is determined, which will not be repeated here.

[0097] In some embodiments, determining an indicative concentration of particulate matter in clean air of a clean room in a dynamic simulation of using metal appliances in the clean room based on a peak concentration of particulate matter release, a first target time point, and a first target relative humidity includes:

[0098] determining a first influence coefficient and a reference humidity, wherein the first influence coefficient is used to characterize the influence of a change in clean air relative humidity on a relative change in a peak concentration of particulate matter emission;

[0099] Correcting the peak concentration of particulate matter release based on the first target relative humidity, the first influence coefficient, and the reference humidity to obtain a corrected peak concentration of particulate matter release;

[0100] Based on the corrected peak concentration of particulate matter release and the first target time point, the indicator concentration of particulate matter in the clean air of the clean room under the dynamic simulation scenario of using metal utensils in the clean room is determined.

[0101] Reference humidity (can be expressed as ) is usually 55%RH.

[0102] The first impact coefficient, or the effect of clean air relative humidity on peak particulate matter emission concentration, represents the relative change in peak particulate matter emission concentration caused by a unit change in clean air relative humidity (1% RH). When the first impact coefficient is greater than 0, it indicates that an increase in clean air relative humidity increases particulate matter emission.

[0103] As an example, the corrected peak concentration of particulate matter release can be calculated according to the following formula (1).

[0104] (1);

[0105] In formula (1), represents the peak concentration of particulate matter release after correction, represents the peak concentration of particulate matter release, represents the first influence coefficient, Indicates the first target relative humidity, Indicates reference humidity.

[0106] The above method can be used to calculate the corrected peak concentration of particulate matter release based on the peak concentration of metal element release, the third influence coefficient (the influence coefficient of clean air relative humidity on the peak concentration of metal element release, which represents the relative change ratio of the peak concentration of metal element release caused by a unit change in clean air relative humidity (1%RH)), the second target relative humidity, and the reference humidity. The above method will not be repeated here.

[0107] Compared with the traditional exponential decay model (assuming a constant decay rate and ignoring the influence of environmental conditions), the embodiment of the present application introduces environmental factors (relative humidity of the clean room) as parameters to correct the peak concentration of particulate matter release. This can more accurately describe the sensitivity of the particulate matter concentration and metal element concentration of metal utensils to environmental factors (such as relative humidity of the clean room), which is conducive to improving the accuracy of the detection results.

[0108] In some embodiments, determining an indicative concentration of particulate matter in clean air of a clean room in a dynamic simulation of using metal appliances in the clean room based on the corrected peak concentration of particulate matter release and the first target time point includes:

[0109] Determining a first particle attenuation constant and a second influence coefficient under reference humidity conditions, wherein the second influence coefficient is used to characterize the effect of changes in clean air relative humidity on the residence time of particles in the air;

[0110] calculating a second particle attenuation constant under a first target relative humidity condition based on the first particle attenuation constant, the second influence coefficient, and the reference humidity;

[0111] Based on the corrected peak concentration of particle release, the first target time point and the second particle attenuation constant, the indicator concentration of particles in the clean air of the clean room under the dynamic simulation scenario of using metal utensils in the clean room is determined.

[0112] The first particle attenuation constant refers to the relative reduction rate of particle concentration per unit time under reference humidity (55% RH) conditions, and the unit is min -1 The inverse of the first particle decay constant represents the particle concentration dropping to the initial value e -1 (about 36.8%) of the time required.

[0113] The second impact coefficient, or the effect of cleanroom relative humidity on the particle attenuation process, characterizes the effect of changes in cleanroom relative humidity on the persistence of particles in the air. When the second impact coefficient is greater than 0, it indicates that an increase in cleanroom relative humidity increases the residence time of particles in the air.

[0114] As an example, the second particle attenuation constant may be calculated according to the following formula (2).

[0115] (2);

[0116] In formula (2), represents the second particle attenuation constant, represents the first particle attenuation constant, represents the second influence coefficient, Indicates the first target relative humidity, Indicates reference humidity.

[0117] The embodiment of the present application uses an exponential function to describe the relationship between the particle attenuation constant and the relative humidity of the clean room, which can capture the nonlinear slowdown of the particle concentration decay rate under high humidity conditions, which cannot be expressed by the linear correction model.

[0118] In some embodiments, determining an indicative concentration of particulate matter in clean air of a clean room in a clean room dynamic simulation scenario of using metal appliances based on the corrected peak concentration of particulate matter release, the first target time point, and the second particulate matter attenuation constant includes:

[0119] Based on the first target time point, determine the particle decay time in a cleanroom dynamic simulation scenario using metal utensils;

[0120] Based on the corrected peak concentration of particle release, the second particle decay constant and the particle decay time, the indicative concentration of particles in the clean air of the clean room under the dynamic simulation scenario of using metal utensils in the clean room is determined.

[0121] The particle decay time refers to the time a metal device can continue to be used after reaching its peak particle emission concentration (during which the concentration of particles emitted by the metal device decays). Specifically, the particle decay time is the time a metal device continues to be used after reaching its peak particle emission concentration at the first target time point.

[0122] Combine Figure 2 , continue with the above example, assuming that in the dynamic simulation of the operation of metal tools, including the sampling time point ~ , where at the sampling time point When the metal appliance reaches the peak concentration of particulate matter release, the sampling time point Determine as the first target time point. At the first target time point (sampling time point The time it takes to continue using metal utensils after the first target time point (sampling time point) is the particle decay time. ) and then continued to use metal utensils until the sampling time point , then the particle decay time is the first target time point (sampling time point ) and sampling time points The time difference between them.

[0123] As an example, the concentration of particulate matter in the clean air of the clean room in a dynamic simulation of using metal appliances can be calculated according to the following formula (3).

[0124] (3);

[0125] In formula (3), Indicates the concentration of particulate matter. represents the peak concentration of particulate matter release after correction, represents the second particle attenuation constant, Indicates the decay time of particles.

[0126] The embodiment of the present application introduces an environmental factor (relative humidity of the clean room) to correct the peak concentration of particulate matter release from metal appliances, and simultaneously introduces a second particulate matter attenuation constant to further adjust the corrected peak concentration of particulate matter release from metal appliances, thereby more accurately reflecting the complex influence of the relative humidity of the clean room on the entire process of particulate matter release from metal appliances.

[0127] It can be understood that, referring to the above method, based on the peak concentration of metal element release, the second target time point and the second target relative humidity, the indicator concentration of metal elements in the clean air of the clean room under the dynamic simulation of using metal utensils in the clean room is calculated, which will not be repeated here.

[0128] Through the above method, the pollution risk of particulate matter and metal elements released by metal utensils to the clean room air quality under different environmental conditions (relative humidity of the clean room) can be accurately predicted. Especially for the actual clean room environment with rapid humidity fluctuations, the above method can more accurately predict the pollution risk of metal utensils to the clean room air quality, providing a scientific basis for preventive control measures.

[0129] Verified by relevant experiments, the above scheme can cover the particle / element release dynamics data within the clean room relative humidity range of 25%~85%RH, with a prediction accuracy within ±12%, which is far better than the ±35% error range of traditional models under varying humidity conditions.

[0130] In some embodiments, determining a comprehensive pollution contribution index of metal appliances to clean air in a clean room based on the baseline concentration of particulate matter, the baseline concentration of metal elements, the indicative concentration of particulate matter, and the indicative concentration of metal elements includes:

[0131] Calculating a first pollution contribution index based on a preset first weight coefficient, a particulate matter baseline concentration, and a particulate matter indicator concentration;

[0132] Calculating a second pollution contribution index based on a preset second weight coefficient, the baseline concentration of the metal element, and the indicative concentration of the metal element; wherein the sum of the first weight coefficient and the second weight coefficient is 1;

[0133] The comprehensive pollution contribution index of the metal appliance to the clean air in the clean room is determined according to the first pollution contribution index and the second pollution contribution index.

[0134] The first weight coefficient and the second weight coefficient may be determined according to the extent to which the particulate matter and metal elements released by the metal appliance affect the clean air of the clean room.

[0135] As an example, the comprehensive pollution contribution index of metal appliances to the clean air in the clean room can be calculated according to the following formula (4).

[0136] (4);

[0137] In formula (4), Indicates the comprehensive pollution contribution index of metal appliances to the clean air in the clean room; 、 represent the first weight coefficient and the second weight coefficient respectively; Indicates the indicative concentration of particulate matter; represents the baseline concentration of particulate matter; Indicates the indicative concentration of metal elements; Indicates the baseline concentration of metal elements.

[0138] The first pollution contribution index is the The second pollution contribution index is the .

[0139] In some embodiments, the method further comprises:

[0140] Determine the contamination risk level of the clean room based on the comprehensive contamination contribution index;

[0141] Develop operating specifications for the use of metal utensils in clean rooms based on the level of contamination risk.

[0142] As an example, the pollution risk level classification of clean rooms is shown in Table 2:

[0143] Table 2 Classification of pollution risk levels in clean rooms

[0144]

[0145] After calculating the comprehensive pollution contribution index, the pollution risk level corresponding to the comprehensive pollution contribution index can be determined by consulting Table 2 above, and the operating specifications for the use of metal utensils in the clean room (including the material selection of metal utensils, the usage time of metal utensils, cleaning methods, operating procedures, etc.) can be formulated or adjusted based on the pollution risk level.

[0146] As an example, verify the pollution effect of the metal tool under test (316L stainless steel tweezers) on the clean air of the clean room.

[0147] (1) Experimental equipment and materials:

[0148] Table 3 Experimental equipment and materials

[0149]

[0150] (2) The testing process is as follows:

[0151] ①Baseline data collection:

[0152] When the clean room is running without load (no metal equipment to be tested is introduced), each key test point in the clean room (key test point) is tested, analyzed and recorded every 30 minutes or 1 hour. : Close to the surface of metal appliances (within 5cm), key detection points : Operator breathing zone (height 170cm), key detection points : The particle concentration (particle size is 0.5μm), metal element concentration, and clean room relative humidity at the laboratory return air outlet are monitored continuously for 24 hours.

[0153] ②Static pollution release test:

[0154] Introduce the metal instruments to be tested listed in Table 3 above into the clean room (introduce one group of metal instruments to be tested for each test). Specifically, place the metal instruments to be tested on the central laboratory table in the clean room (with a spacing of ≥1m). Detect, analyze, and record the particle concentration (particle size of 0.5µm), metal element concentration, and relative humidity of the clean room at each key detection point every 2 hours.

[0155] The test data obtained in the above step ① or ② are sorted to obtain a first air sampling sequence.

[0156] Taking the metal tool 316L stainless steel tweezers as an example, the test results of the static pollution release test are shown in Table 4. Among them, the particle sampling concentration is the statistical particle size of 0.5um particles (unit: pieces / m 3 During the entire test period, the relative humidity in the clean room was maintained at 55% RH.

[0157] Table 4 Static contamination release test results of 316L stainless steel tweezers

[0158]

[0159] ③ Dynamic simulation of the use of metal tools (mechanical friction test):

[0160] According to the aforementioned operating procedures for dynamic simulation of using metal tools in a clean room, before performing the collision (sampling time point =0), detect, analyze and record the particle concentration (particle size is 0.5µm), metal element concentration and relative humidity of the clean room at each key detection point. Then, according to the experimental operation procedures, use each set of metal utensils to pre-treat the actual laboratory samples (simulate use and wear). After the operation is completed, within 30 seconds (sampling time point = 30 s), 1 minute (sampling time point =1 min), 2 minutes (sampling time point =2 min), 5 min (sampling time point =5 min), 10 minutes (sampling time point =10 min), 15 min (sampling time point =15 min), 30 min (sampling time point =30 min), 60 min (sampling time point =60 min), 120 min (sampling time point =120 min), 180 min (sampling time point =180 min) to detect, analyze and record the particle concentration (particle size is 0.5µm), metal element concentration and relative humidity of the clean room at each key detection point in the clean room.

[0161] The test data obtained in step ③ above are sorted to obtain a second air sampling sequence.

[0162] Taking the metal tool 316L stainless steel tweezers as an example, the test data of the dynamic simulation operation test process is shown in Table 5. Among them, the particle sampling concentration is the statistical particle size of 0.5um particles (unit: pieces / m 3 During the entire test period, the relative humidity in the clean room was maintained at 55% RH.

[0163] Table 5 Dynamic simulation operation test data of 316L stainless steel tweezers

[0164]

[0165] ④Data analysis:

[0166] Extract the particle concentrations corresponding to each sampling time point in the first air sampling sequence, and then calculate the average of the particle concentrations to obtain the particle baseline concentration. Extract the metal element concentrations corresponding to each sampling time point in the first air sampling sequence, and then calculate the average of the metal element concentrations to obtain the metal element baseline concentration.

[0167] The second air sampling sequence corresponding to each group of metal appliances is processed to obtain a comprehensive pollution contribution index corresponding to each group of metal appliances.

[0168] ⑤ Make suggestions for improvement:

[0169] Based on the data analysis results of step ④ above, relevant suggestions for improving the operating specifications of metal utensils in the clean room can be given, such as selecting appropriate metal utensils based on the data analysis results.

[0170] In addition, the cleanroom air purification system can be optimized and designed based on the data analysis results. Specifically, based on the pollution characteristics and spatial distribution patterns of metal tools, the purification system can be upgraded to be more precise and intelligent in the following aspects:

[0171] 1) Optimization of air inlet and outlet location design: Based on the particle and metal element concentration distribution data from multi-point detection, the spatial propagation path of pollutants can be accurately modeled, providing a scientific basis for the layout of air inlets and outlets.

[0172] 2) CFD Simulation Driven by Key-Point Data: This method uses multi-point monitoring data (particularly at three key locations: 5 cm from the instrument surface, the operator's breathing zone, and the laboratory's return air vent) to accurately simulate the airflow within the cleanroom. The simulation results determine the optimal air supply vent location to create a "directional airflow path" from critical work areas directly to the return air vent, minimizing the residence time of contaminants in the workspace.

[0173] 3) Zoning Air Supply Strategy: Based on the varying contamination risks of areas where metal tools of different materials are used (e.g., data shows 316L stainless steel areas as high-risk, and titanium alloy areas as medium-to-low-risk), a zoning air supply strategy is implemented, with higher air changes (ACH) and airflow velocities configured in high-risk areas. Experimental data shows that increasing the ACH in high-risk 316L stainless steel areas from the standard 40 ACH to 60 ACH shortens the time it takes for particulate matter concentrations to return to baseline by approximately 35%.

[0174] 4) Optimizing Air Inlet Geometry: Based on particle size distribution characteristics (e.g., the characteristic particle size distribution generated by different materials), optimize air outlet geometry parameters, including air outlet aperture, grille angle, and density. For example, for the high proportion of large-sized particles (≥0.5μm, accounting for 13.9%) generated by 17-4PH hardened stainless steel, a capture airflow with appropriate momentum can be designed. For pollution sources dominated by small particles, such as titanium alloys, a laminar diffusion air outlet design is more suitable.

[0175] All of the above optional technical solutions can be combined in any way to form optional embodiments of the present application, and will not be described in detail here.

[0176] The following are device embodiments of the present application, which can be used to implement the method embodiments of the present application. For details not disclosed in the device embodiments of the present application, please refer to the method embodiments of the present application.

[0177] Figure 3 This is a structural diagram of a clean air pollution detection device provided in an embodiment of the present application.

[0178] See also Figure 3The clean air pollution detection device 300 provided in the embodiment of the present application includes:

[0179] The first acquisition module 301 is configured to acquire a first air sampling sequence at key detection points of a clean room in a scenario without metal appliances or in a scenario with metal appliances statically placed;

[0180] The second acquisition module 302 is configured to acquire a second air sampling sequence of key detection points in the clean room under a dynamic simulation scenario of using metal tools in the clean room;

[0181] The determination module 303 is configured to determine a comprehensive pollution contribution index of the metal appliance to the clean air in the clean room based on the first air sampling sequence and the second air sampling sequence.

[0182] The technical solution provided by the embodiment of the present application uses a first acquisition module 301 to acquire a first air sampling sequence at key inspection points in a clean room in a scenario without metal utensils or with metal utensils statically placed. A second acquisition module 302 acquires a second air sampling sequence at key inspection points in a clean room in a scenario where metal utensils are dynamically simulated and used. Based on the first and second air sampling sequences, a determination module 303 determines the comprehensive pollution contribution index of metal utensils to the clean air in the clean room, thereby achieving comprehensive and accurate detection of the pollution impact of metal utensils on the clean air in the clean room. The detection results of the embodiment of the present application can provide accurate and reliable technical and data support for formulating scientific and reasonable regulations for the use of metal utensils to ensure the air quality of clean rooms.

[0183] Figure 4 This is a structural diagram of a terminal device provided in an embodiment of the present application.

[0184] See also Figure 4 The terminal device 400 of this embodiment includes: a processor 401 and a memory 402, the memory 402 is used to store a computer program 403, and the processor 401 is used to execute the steps in the above-mentioned various method embodiments when calling the computer program 403.

[0185] The terminal device 400 may be an electronic device such as a desktop computer, a notebook, a PDA, or a cloud server. The terminal device 400 may include but is not limited to a processor 401 and a memory 402. Those skilled in the art will appreciate that Figure 4 The terminal device 400 is merely an example and does not limit the terminal device 400 . The terminal device 400 may include more or fewer components than shown in the figure, or different components.

[0186] The processor 401 may be a central processing unit (CPU), or 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.

[0187] Memory 402 can be an internal storage unit of terminal device 400, such as a hard disk or memory of terminal device 400. Memory 402 can also be an external storage device of terminal device 400, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. equipped on terminal device 400. Memory 402 can also include both an internal storage unit of terminal device 400 and an external storage device. Memory 402 is used to store computer programs and other programs and data required by the electronic device.

[0188] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0189] If the integrated module / unit is implemented as a software functional unit and sold or used as a standalone product, it can be stored in a readable storage medium. Based on this understanding, the present application can implement all or part of the process steps in the above-mentioned method embodiments by using a computer program to instruct the relevant hardware. The computer program can be stored in a readable storage medium, and when executed by a processor, the computer program can implement the steps of each of the above-mentioned method embodiments. The computer program can include computer program code, which can be in source code form, object code form, executable file, or some intermediate form. Computer-readable media can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, removable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunications signals, and software distribution media. It should be noted that the content included in computer-readable media can be appropriately increased or decreased based on the requirements of legislation and patent practice in a jurisdiction. For example, in some jurisdictions, based on legislation and patent practice, computer-readable media does not include electrical carrier signals or electrical signals.

[0190] An embodiment of the present application further provides a computer-readable storage medium on which a program is stored. When the program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0191] Through the description of the above embodiments, it is clear to those skilled in the art that the present application can be implemented by means of software plus necessary general-purpose hardware, and of course it can also be implemented by dedicated hardware including application-specific integrated circuits, dedicated CPUs, dedicated memories, dedicated components, etc. In general, all functions performed by computer programs can be easily implemented with corresponding hardware, and the specific hardware structures used to implement the same function can also be various, such as analog circuits, digital circuits or dedicated circuits, etc. However, for the present application, software program implementation is a better implementation method in most cases. Based on such an understanding, the technical solution of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a readable storage medium, such as a computer's floppy disk, USB flash drive, mobile hard disk, ROM, RAM, magnetic disk or optical disk, etc., including a number of instructions to enable a computer device (which can be a personal computer or network device, etc.) to execute the method of each embodiment of the present application.

[0192] In the above embodiments, all or part of the embodiments may be implemented by software, hardware, firmware, or any combination thereof. When implemented by software, all or part of the embodiments may be implemented in the form of a computer program product.

[0193] In the above embodiments, the computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on the computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website, computer, training equipment or data center to another website, computer, training equipment or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that a computer can store or a data storage device such as a training equipment, data center, etc. that includes one or more available media integrations. The available medium can be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)).

[0194] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0195] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A clean air pollution detection method, characterized in that: include: Obtain the first air sampling sequence of key inspection points in the clean room in a scenario without metal utensils or in a scenario where metal utensils are statically placed; Acquire a second air sampling sequence at key detection points in the clean room under a dynamic simulation scenario of using metal utensils in the clean room; determining a comprehensive pollution contribution index of metal appliances to the clean air of the clean room based on the first air sampling sequence and the second air sampling sequence; Determining a comprehensive pollution contribution index of metal appliances to the clean air of the clean room based on the first air sampling sequence and the second air sampling sequence includes: determining a baseline concentration of particulate matter and a baseline concentration of metal elements in the clean air of the clean room based on the first air sampling sequence; Determining, based on the second air sampling sequence, an indicative concentration of particulate matter and an indicative concentration of metal elements in the clean air of the clean room under a dynamic simulation scenario of using metal appliances in the clean room; Determining a comprehensive pollution contribution index of metal appliances to the clean air of the clean room based on the particulate matter baseline concentration, the metal element baseline concentration, the particulate matter indicative concentration, and the metal element indicative concentration; The second air sampling sequence includes a plurality of sampling time points, and a particulate matter sampling concentration, a metal element sampling concentration, and a clean air relative humidity corresponding to each sampling time point; Determining, based on the second air sampling sequence, an indicative concentration of particulate matter and an indicative concentration of metal elements in the clean air of the clean room in a dynamic simulation scenario of using metal appliances in the clean room, including: Determining, based on the particle sampling concentration and the metal element sampling concentration corresponding to each sampling time point in the second air sampling sequence, the peak particle release concentration and the peak metal element release concentration in the clean air of the clean room under the dynamic simulation scenario of using metal utensils in the clean room; Determine the sampling time point corresponding to the peak concentration of particulate matter release as the first target time point, determine the clean air relative humidity corresponding to the peak concentration of particulate matter release as the first target relative humidity, determine the sampling time point corresponding to the peak concentration of metal element release as the second target time point, and determine the clean air relative humidity corresponding to the peak concentration of metal element release as the second target relative humidity; Determining an indicative concentration of particulate matter in the clean air of the clean room under a dynamic simulation of using metal appliances in the clean room based on the peak concentration of particulate matter release, the first target time point, and the first target relative humidity; Determining, based on the peak concentration of metal element release, the second target time point, and the second target relative humidity, an indicative concentration of the metal element in the clean air of the clean room in a dynamic simulation scenario of using metal utensils in the clean room; Determining an indicative concentration of particulate matter in clean air of the clean room in a dynamic simulation of using metal appliances in the clean room based on the peak concentration of particulate matter release, the first target time point, and the first target relative humidity includes: determining a first influence coefficient and a reference humidity, wherein the first influence coefficient is used to characterize the influence of a change in clean air relative humidity on a relative change in a peak concentration of particulate matter release; Correcting the peak concentration of particulate matter release based on the first target relative humidity, the first influence coefficient, and the reference humidity to obtain a corrected peak concentration of particulate matter release; Based on the corrected peak concentration of particulate matter release and the first target time point, an indicative concentration of particulate matter in the clean air of the clean room under a dynamic simulation scenario of using metal appliances in the clean room is determined.

2. The method according to claim 1, characterized in that Determining an indicative concentration of particulate matter in clean air of the clean room in a dynamic simulation of using metal appliances in the clean room based on the corrected peak concentration of particulate matter release and the first target time point includes: determining a first particle attenuation constant and a second influence coefficient under the reference humidity condition, wherein the second influence coefficient is used to characterize the effect of a change in the relative humidity of clean air on the residence time of particles in the air; calculating a second particle attenuation constant under the first target relative humidity condition based on the first particle attenuation constant, the second influence coefficient, and the reference humidity; Based on the corrected peak concentration of particle release, the first target time point and the second particle attenuation constant, an indicative concentration of particles in the clean air of the clean room under a dynamic simulation scenario of using metal appliances in the clean room is determined.

3. The method according to claim 2, characterized in that Determining an indicative concentration of particulate matter in clean air of the clean room in a dynamic simulation of using metal appliances in the clean room based on the corrected peak concentration of particulate matter release, the first target time point, and the second particulate matter attenuation constant includes: Determining, based on the first target time point, a particle decay time in a cleanroom dynamic simulation scenario of using metal utensils; Based on the corrected particle release peak concentration, the second particle decay constant and the particle decay time, an indicative concentration of particles in the clean air of the clean room under a dynamic simulation scenario of using metal appliances in the clean room is determined.

4. The method according to claim 1 or 3, characterized in that Based on the particle baseline concentration, the metal element baseline concentration, the particle indicator concentration, and the metal element indicator concentration, a comprehensive pollution contribution index of the metal appliance to the clean air of the clean room is determined, including: Calculating a first pollution contribution index based on a preset first weight coefficient, the particulate matter baseline concentration, and the particulate matter indicator concentration; Calculating a second pollution contribution index based on a preset second weight coefficient, the baseline concentration of the metal element, and the indicative concentration of the metal element; wherein the sum of the first weight coefficient and the second weight coefficient is 1; A comprehensive pollution contribution index of the metal appliance to the clean air of the clean room is determined according to the first pollution contribution index and the second pollution contribution index.

5. The method according to claim 1, wherein Before the step of obtaining the first air sampling sequence at key detection points of the clean room in a scenario without metal utensils or in a scenario where metal utensils are statically placed, the step further includes: Obtain a third air sampling sequence of multiple high-interest detection points in the clean room under a dynamic simulation scenario of using metal utensils in the clean room; Determining, based on the third air sampling sequence, a spatial correlation coefficient between the pollutant concentration corresponding to each of the high-concern detection points and the operation area, and selecting at least one first candidate detection point from the high-concern detection points based on the spatial correlation coefficient; Inputting the third air sampling sequence into a preset multivariate regression model, outputting a predicted pollutant concentration corresponding to each of the high-concern detection points, and selecting at least one second candidate detection point from the high-concern detection points based on the predicted pollutant concentration; Determine key detection points based on the first candidate detection points and the second candidate detection points.

6. The method according to claim 1, characterized in that The method further comprises: Determining the pollution risk level of the clean room based on the comprehensive pollution contribution index; Based on the pollution risk level, formulate operating specifications for the use of metal utensils in the clean room.

7. A clean air pollution detection device, characterized in that: include: A first acquisition module is configured to acquire a first air sampling sequence at key detection points of the clean room in a scenario without metal utensils or in a scenario with metal utensils statically placed; The second acquisition module is configured to acquire a second air sampling sequence at key detection points of the clean room in a dynamic simulation scenario of using metal utensils in the clean room; a determination module configured to determine a comprehensive pollution contribution index of metal appliances to the clean air of the clean room based on the first air sampling sequence and the second air sampling sequence; Determining a comprehensive pollution contribution index of metal appliances to the clean air of the clean room based on the first air sampling sequence and the second air sampling sequence includes: determining a baseline concentration of particulate matter and a baseline concentration of metal elements in the clean air of the clean room based on the first air sampling sequence; Determining, based on the second air sampling sequence, an indicative concentration of particulate matter and an indicative concentration of metal elements in the clean air of the clean room under a dynamic simulation scenario of using metal appliances in the clean room; Determining a comprehensive pollution contribution index of metal appliances to the clean air of the clean room based on the particulate matter baseline concentration, the metal element baseline concentration, the particulate matter indicative concentration, and the metal element indicative concentration; The second air sampling sequence includes a plurality of sampling time points, and a particulate matter sampling concentration, a metal element sampling concentration, and a clean air relative humidity corresponding to each sampling time point; Determining, based on the second air sampling sequence, an indicative concentration of particulate matter and an indicative concentration of metal elements in the clean air of the clean room in a dynamic simulation scenario of using metal appliances in the clean room, including: Determining, based on the particle sampling concentration and the metal element sampling concentration corresponding to each sampling time point in the second air sampling sequence, the peak particle release concentration and the peak metal element release concentration in the clean air of the clean room under the dynamic simulation scenario of using metal utensils in the clean room; Determine the sampling time point corresponding to the peak concentration of particulate matter release as the first target time point, determine the clean air relative humidity corresponding to the peak concentration of particulate matter release as the first target relative humidity, determine the sampling time point corresponding to the peak concentration of metal element release as the second target time point, and determine the clean air relative humidity corresponding to the peak concentration of metal element release as the second target relative humidity; Determining an indicative concentration of particulate matter in the clean air of the clean room under a dynamic simulation of using metal appliances in the clean room based on the peak concentration of particulate matter release, the first target time point, and the first target relative humidity; Determining, based on the peak concentration of metal element release, the second target time point, and the second target relative humidity, an indicative concentration of the metal element in the clean air of the clean room in a dynamic simulation scenario of using metal utensils in the clean room; Determining an indicative concentration of particulate matter in clean air of the clean room in a dynamic simulation of using metal appliances in the clean room based on the peak concentration of particulate matter release, the first target time point, and the first target relative humidity includes: determining a first influence coefficient and a reference humidity, wherein the first influence coefficient is used to characterize the influence of a change in clean air relative humidity on a relative change in a peak concentration of particulate matter release; Correcting the peak concentration of particulate matter release based on the first target relative humidity, the first influence coefficient, and the reference humidity to obtain a corrected peak concentration of particulate matter release; Based on the corrected peak concentration of particulate matter release and the first target time point, an indicative concentration of particulate matter in the clean air of the clean room under a dynamic simulation scenario of using metal appliances in the clean room is determined.

8. A terminal device, characterized in that: include: a memory and a processor, wherein the memory is used to store a computer program; The processor is configured to execute the method according to any one of claims 1 to 6 when calling the computer program.

9. A readable storage medium having a program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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