Method and system for detecting surface particle pollution
By combining collection, extraction, filtration, and microscopic analysis techniques with a pre-trained model, the problem of inaccurate detection of particulate matter contamination on product surfaces in existing technologies has been solved, enabling rapid, convenient, and accurate calculation of particulate matter coverage.
Patent Information
- Application Number
- CN202410996153.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-07-24
- Publication Date
- 2026-01-27
AI Technical Summary
Existing technologies cannot reflect the state of particulate matter contamination on product surfaces in real time and accurately, resulting in inaccurate test results.
The sampler collects particulate matter from the surface of the target object, extracts it, and filters it onto a filter membrane. Microscopic analysis is used to determine the size and quantity of the particulate matter, and the particulate matter coverage is calculated using a pre-trained target model.
It enables rapid, convenient, and accurate detection of particulate matter contamination on product surfaces, reduces the impact of human factors, and improves detection efficiency and accuracy.
Smart Images
Figure CN121409822A_ABST
Abstract
Description
Technical Field
[0001] This disclosure generally relates to the field of quantitative detection, and specifically to a method and system for detecting surface particulate contamination. Background Technology
[0002] With the advancement of science and technology, the scientific performance requirements of high-end technological products are becoming increasingly stringent regarding pollutants. Particulate matter pollution, as one of the most common pollutants, has a crucial impact on the performance of related technological products, especially for precision optical components and space science satellites. Cleanliness control has become a very important aspect of product development. For scientific satellites, the surface cleanliness of the product is directly and closely related to the scientific objectives of the satellite's scientific payload. To ensure its scientific performance and output, the cleanliness requirements during payload development are extremely stringent, and pollution control engineering has received widespread attention. Comprehensive pollution control has become a systematic project that runs through the entire process of satellite design, manufacturing, testing, launch preparation, and on-orbit operation.
[0003] Currently, the most commonly used international method for pollution monitoring is to monitor various processes in product development using PFO (Particle Fall Out, photometer) samples. The working mode is to first take initial pollution readings on several PFO samples before implementing the process, then place the samples around the product to be tested, and finally take the final pollution readings on the samples after the process is completed (which usually takes a long time). The difference between the two is then taken as the amount of particulate pollution accumulated in the product to be tested during the process.
[0004] However, the above method can only serve as a reference for the amount of particulate matter contamination on the surface of the product under test during the process. It cannot represent the actual amount of particulate matter contamination on the product under test, nor can it reflect the particulate matter contamination status of the product under test at a certain point in time. Therefore, it is imperative to develop a method that can reflect the actual particulate matter contamination status on the product surface. Summary of the Invention
[0005] In view of the above-mentioned defects or deficiencies in the prior art, it is desirable to provide a detection and system for surface particulate contamination that can quickly and conveniently determine the degree of contamination on the surface of a target object in its current state.
[0006] The first aspect provides a method for detecting surface particulate contamination, the method comprising:
[0007] The sampler collects particulate matter from the surface of the target object.
[0008] The particulate matter collected by the collector is extracted to obtain a mixture;
[0009] The mixture is filtered through an initial filter membrane to obtain a target filter membrane, the target filter membrane containing the particulate matter.
[0010] The size range of each particle on the target filter membrane is determined by microscopic analysis, and the number of particles corresponding to each size range is determined.
[0011] The coverage rate of particles on the surface of the target object is determined based on the size range of each particle, the number of particles corresponding to each size range, and the target model.
[0012] This application provides a method for detecting surface particulate contamination. The method involves directly collecting particulate matter from the surface of a target object, then transferring the particulate matter adhering to the adhesive surface of the collector to a filter membrane for easier analysis through extraction and filtration. Further, microscopic analysis is used to statistically determine the quantity and size of particles in various size ranges. Finally, the particulate matter data is input into a pre-trained target model to calculate the surface particulate matter coverage, obtaining the final determined result. This application uses a pre-trained model to calculate the particulate matter coverage of the target object surface, eliminating human intervention. The target model is obtained after multiple training iterations, ensuring the accuracy and stability of its calculations. Furthermore, the detection process for surface particulate matter does not require complex operations, thus improving detection efficiency.
[0013] The second aspect provides a detection system, the system comprising:
[0014] A collector used to collect particulate matter from the surface of a target object;
[0015] An extractor is used to extract the particulate matter collected by the collector to obtain a mixture.
[0016] A filter for filtering the mixture;
[0017] A microanalyzer is used to determine the size range of each particle on the target filter membrane through microanalysis techniques, and to determine the number of particles corresponding to each size range.
[0018] A server is used to determine the coverage of particles on the surface of the target object based on the size range of each particle, the number of particles corresponding to each size range, and the target model. Attached Figure Description
[0019] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:
[0020] Figure 1A schematic diagram of the structure of a surface particulate contamination detection system provided in this application;
[0021] Figure 2 A flowchart illustrating the steps of a method for detecting surface particulate contamination provided in this application;
[0022] Figure 3 A flowchart illustrating the steps of a method for detecting surface particulate contamination provided in this application.
[0023] Explanation of reference numerals in the attached diagram:
[0024] 100. Collector; 200. Extractor; 300. Filter; 400. Microanalyzer; 500. Server. Detailed Implementation
[0025] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.
[0026] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.
[0027] Please refer to Figure 1 , Figure 1 This is a schematic diagram of a surface particulate contamination detection system provided in this application. The system includes a collector 100, an extractor 200, a filter 300, a microanalyzer 400, and a server 500. The collector 100 collects particulate matter from the surface of a target object. The surface of the collector 100 is then extracted to obtain a mixture containing particulate matter. This mixture is further filtered by the filter 300, leaving the particulate matter on a filter membrane. The microanalyzer 400 analyzes the filter membrane to determine the size of each particulate and the number of particles belonging to the same size range. Finally, based on the size of each particulate and the number of particles belonging to the same size range, the target model on the server 500 calculates the particulate matter coverage rate of the target object's surface. This coverage rate can then be used as the detection result for particulate contamination detection on the target object's surface.
[0028] like Figure 2 As shown, Figure 2 The flowchart illustrates the steps of a method for detecting surface particulate contamination provided in an exemplary embodiment of this application. Taking the application of this method to the aforementioned system as an example, the method includes the following steps:
[0029] Step S20: Collect particulate matter from the surface of the target object using the collector 100;
[0030] The collector 100 is a device capable of collecting particulate matter from the surface of a target object. For example, a sample particulate matter collector 100 may be provided. The collector 100 includes an adhesive surface with a certain degree of stickiness, allowing it to collect particulate matter from the surface of the target object by means of adhesion.
[0031] The target object can be any focusing lens that requires particulate contamination detection; no specific limitations are imposed here. As the most important optical component in X-ray astronomy satellites, the cleanliness control of the focusing lens is directly and closely related to the scientific objectives of the satellite payload. Therefore, it is necessary to detect particulate contamination on the surface of the focusing lens to provide important data support for improving the focusing lens.
[0032] In an optional embodiment, such as Figure 3 As shown, Figure 3 An embodiment of a method for collecting particulate matter from the surface of a target object using a collector 100, as provided in an exemplary embodiment of this application, includes the following steps:
[0033] Step S201: The surface of the target object is pasted through the adhesive surface of the collector 100;
[0034] As mentioned above, the collector 100 includes an adhesive surface, which is sticky. Therefore, the collector 100 collects particulate matter from the surface of the target object by using the adhesiveness of the adhesive surface to stick the particulate matter off the target object.
[0035] In addition, the adhesive surface of the collector 100 has a certain degree of elasticity. This feature allows the adhesive surface to better adhere to the surface of the target object, and more accurately collect particles from the surface of the target object.
[0036] Therefore, during the process of collecting particulate matter from the surface of the target object, the collector 100 first completely adheres its adhesive surface to the surface of the target object. During the adhesion process, it can start from the edge of the adhesive surface and then slowly adhere in one direction to the other. This avoids air bubbles within the adhesive surface that could lead to incomplete particulate matter collection, and also prevents the adhesive surface from being too firmly attached and difficult to remove.
[0037] After the adhesive is applied, the adhesive surface can be pressed with a pressing plate or other tools to avoid areas that are not in contact with the target object surface, ensuring that the adhesive surface of the collector 100 is in close contact with the target object surface, so as to accurately collect the particulate matter on the target object surface.
[0038] It should be noted that when pressing the adhesive surface of the collector 100, the pressure must be controlled to avoid damaging the target object. This can be prevented by adding a compressed sponge over the adhesive surface.
[0039] Step S202: After determining that the surface of the target object is completely covered by the adhesive surface of the collector 100, the adhesive surface of the collector 100 is separated from the surface of the target object.
[0040] Once the surface of the target object is completely covered by the adhesive surface of the collector 100, the adhesive surface of the collector 100 can be removed from the surface of the target object, thus separating the adhesive surface of the collector 100 from the surface of the target object. Because the adhesive surface of the collector 100 is adhesive, by adhering the collector 100 to the surface of the target object, particulate matter on the target object can be transferred to the adhesive surface of the collector 100. Subsequent processing and analysis of the particulate matter on the adhesive surface of the collector 100 are sufficient to obtain the detection results of particulate pollutants on the surface of the target object. The collector 100 can completely collect particulate matter, and the residual particulate matter on the surface after collection does not exceed 5 ppm.
[0041] In an optional embodiment, after collecting particulate matter from the target object surface through the adhesive surface of the collector 100, the adhesive surface can be sealed with a sealing cap. This prevents particulate matter not belonging to the target object surface from entering the adhesive surface of the collector 100 during movement of the collector 100, thereby affecting the accuracy of contaminant detection on the target object surface. Optionally, the sealing cap can be part of the collector 100 itself.
[0042] Step S30: Extract the particulate matter collected by the collector 100 to obtain a mixture;
[0043] Based on the above description, after the collector 100 collects particulate matter from the surface of the target object by using the adhesive surface provided thereon, the first step is to transfer the particulate matter from the adhesive surface of the collector 100. The transfer process involves further transferring the particulate matter to the filter membrane in the filter, which facilitates subsequent analysis by the microanalyzer 400.
[0044] The specific steps of the transfer are as follows: First, the particulate matter collected by the collector 100 is extracted. The extraction process includes cleaning the adhesive surface of the collector 100 with a cleaning agent. For example, alcohol, isopropanol, or similar substances can be used as the cleaning agent. In this way, the particulate matter on the adhesive surface of the collector 100 is washed into a corresponding container, where the mixture includes the cleaning agent and the particulate matter washed off the adhesive surface.
[0045] The extraction process includes, for example:
[0046] 1. Select the cleaning method and parameters, and clean the test piece on the adhesive surface of the collector 100 three times in a row using the same method (i.e., the same cleaning method and the same cleaning agent), and record the number of particles obtained.
[0047] 2. When the ratios of the particulate matter data obtained in the second and third cleaning cycles to the particulate matter data obtained in the first and second cycles are both less than 0.1, it indicates that the cleaning method meets the requirements.
[0048] 3. If, after three cleanings, at least one of the two particulate data ratios is not less than 0.1, the cleaning method and parameters need to be changed and the verification repeated until the above requirements are met. This is considered the correct cleaning method for the same type of collector 100 as the test piece. The collector 100 to be extracted is then cleaned using this method, and the mixtures after the three cleanings are combined as the final mixture.
[0049] It should be noted here that, through multiple rounds of testing, this application can design a complete process or equipment for cleaning the adhesive surface of the collector 100 based on the results of multiple rounds of testing, and establish a standardized cleaning process, thereby enabling automated cleaning, avoiding parameters affected by human factors, and improving the efficiency and accuracy of detection.
[0050] Step S40: Filter the mixture through an initial filter membrane to obtain a target filter membrane, which contains particulate matter.
[0051] As mentioned above, after cleaning the adhesive surface of the collector 100 and determining the correct cleaning method, a corresponding mixture is obtained. This mixture contains only the cleaning agent and the particulate matter on the adhesive surface. The cleaning agent needs to be filtered out by filtration so that the particulate matter can be transferred to the filter membrane.
[0052] The difference between the initial filter membrane and the target filter membrane is that the target filter membrane includes particulate matter washed off from the adhesive surface of the collector 100. The initial filter membrane has a mesh structure with gaps between the meshes. The mesh gap of the initial filter membrane is greater than or equal to 1 μm and less than or equal to 5 μm. For example, the mesh gap of the initial filter membrane in this application is 5 micrometers.
[0053] The initial pore size of a filter membrane is a key factor determining its filtration efficiency and application range. Filter membrane pore sizes can range from very small (e.g., nanofiltration membranes have pore sizes between 0.001 μm and 0.01 μm) to relatively large (e.g., microfiltration membranes typically have pore sizes between 1 μm and 10 μm). Choosing the appropriate pore size is crucial for ensuring effective filtration. For example, commonly used laboratory filter membrane pore sizes include 0.22 μm, 0.45 μm, and 0.8 μm, each suitable for different analytical testing needs. Therefore, when selecting a filter membrane, the optimal pore size must be determined based on the specific application scenario and filtration requirements.
[0054] This application may select the pore size of the initial filter membrane based on the size range of particles generally attached to the target object. For example, this application may select a filter membrane with a 47 mm pore size and a 5 μm mesh gap as the initial filter membrane.
[0055] Filtration of the mixture involves passing it through an initial filter membrane. Because the initial filter membrane has pores, the cleaning agent in the mixture can pass through while the particles washed off the adhesive surface of the collector 100 are blocked on the initial filter membrane, thus obtaining the target filter membrane.
[0056] In an optional embodiment, during the filtration of the mixture, the present application may simultaneously stir the mixture with a glass rod while evacuating the initial filter membrane using an air pump. On one hand, stirring with a glass rod helps to filter particulate matter as centrally as possible within the initial filter membrane, preventing particulate matter from adhering to areas outside the measurable region of the initial filter membrane. On the other hand, evacuating the air from the filtration environment during the filtration process creates a low-pressure environment, thereby accelerating the filtration rate and improving filtration efficiency.
[0057] Understandably, the pumping rate of the initial filter membrane needs to be controlled to prevent excessive pressure difference across the membrane from allowing particles smaller than a certain size to pass through, thus affecting the accuracy of the detection. For example, for a 47mm diameter filter membrane, the pumping rate could be controlled to be less than or equal to 200 L / min.
[0058] In another optional embodiment, the server 500 can control the opening and closing of the air pump and its pumping rate. The server 500 and the air pump can communicate. When the air pump needs to be used, the server 500 sends an opening command to the air pump, including the pumping rate in the command. After receiving the opening command from the server 500, the air pump parses the command to obtain the corresponding pumping rate and begins operation at that rate. Correspondingly, when filtering is complete, the server 500 can send a closing command to the air pump, and the air pump stops operating upon receiving the closing command.
[0059] By controlling the air pump through the server 500, the air extraction can be automated, realizing an automated detection process and improving detection efficiency.
[0060] Of course, the filtration of the mixture can also be performed through the corresponding filter 300. The filter 300 can also be controlled by the server 500. When filtration is required, the server 500 can send a corresponding working command to the sub-component of the filter 300 that holds the container containing the mixed filtrate, causing the sub-component to pour the mixed filtrate onto the initial filter membrane at a preset pouring speed until all the mixed filtrate in the container has been poured out. Similarly, the sub-component can also return to its initial position according to the stop working command sent by the server 500. The specific process will not be described in detail again.
[0061] Therefore, the entire filtration process can be automated, avoiding human intervention, which not only improves filtration efficiency but also enhances detection accuracy.
[0062] It is understood that the filtration operation can be performed once or multiple times, depending on the application scenario. In an exemplary embodiment, the filtration operation is performed three times, with the filtrate from the previous filtration being placed back onto the initial filter membrane each time. This process is repeated three times to ensure that all particles in the mixture are transferred to the initial filter membrane. The filter membrane obtained after the three filtrations is the target filter membrane. The target filter membrane can be encapsulated using a filter membrane slide for protection and subsequent analysis.
[0063] Step S50: Determine the size of each particle on the target filter membrane using microscopic analysis techniques, and determine the number of particles whose size belongs to the same size range.
[0064] Among them, microscopic analysis technology can be used to determine the size and quantity of each particle on the target filter membrane and to perform metal / fiber analysis after high-resolution grid scanning of the target filter membrane.
[0065] This application allows for the initial high-resolution gridded scanning of the target filter membrane using a microscope. This magnifies the particles on the filter membrane, facilitating the subsequent determination of the size and quantity of each particle and the performance of metal / fiber analysis. For example, the microscope may be equipped with a high-resolution automated microscopy system, which automates the aforementioned operations.
[0066] It should be noted that particulate matter can have various sizes, such as 1μm, 5μm, 10μm, 25μm, 30μm, 50μm, 70μm, 100μm, 120μm, 150μm, 170μm, 180μm, and 200μm. As can be seen, particulate matter has many sizes and a large size range. Calculating the coverage coefficient for each size individually would be computationally intensive and not very meaningful. Therefore, we can divide particulate matter by size range, for example, grouping particles with sizes between 1-10μm together. The particles are grouped into one set; particles with a size range of 10-25 μm are grouped into one set; particles with a size range of 25-50 μm are grouped into one set; particles with a size range of 50-100 μm are grouped into one set; particles with a size range of 100-150 μm are grouped into one set; particles with a size range of 150-200 μm are grouped into one set, etc.
[0067] Therefore, this application can first divide the particle size range according to the size of each particle to obtain multiple size ranges. The above is only an example; there are other ways to divide the particle size range, which are specifically determined according to the size of each particle on the target filter membrane and the resolution of the microscopic analysis system used. No specific limitations are made here.
[0068] Based on the above operations, multiple size ranges can be obtained, each corresponding to a certain number of particles. This allows us to determine the number of particles for each size range, as shown in Table 1 below.
[0069]
[0070] Table 1
[0071] As shown in the figure above, this application can divide the size range of particulate matter into 6 size ranges.
[0072] Step S60: Determine the coverage rate of particles on the surface of the target object based on the size range of each particle, the number of particles corresponding to each size range, and the target model.
[0073] The target model can be pre-trained and stored in the corresponding server 500. When it is necessary to determine the coverage of particulate matter on the target surface, it can be directly called.
[0074] The target model can be based on The surface particulate matter coverage of the target object is calculated, where n . i C represents the number of particles corresponding to the size range. i is the coverage coefficient corresponding to the size range of particulate matter, and m is the number of size range divisions.
[0075] Therefore, this application can input the coverage coefficient corresponding to the size range of each particle and the number of particles corresponding to each size range into the target model to obtain the coverage of particles on the surface of the target object.
[0076] It should be noted here that the coverage coefficient is determined by preliminary experiments, generating a correspondence information which is stored in the corresponding memory address on the server. When it is necessary to determine the coverage of target surface particles, the server can read the correspondence information from the corresponding memory address and match the coverage coefficient corresponding to the size range of each particle from the correspondence information, thereby obtaining the coverage coefficient corresponding to the size range of each particle. For example, as shown in Table 2, Table 2 contains the correspondence information provided in this application:
[0077] Particle size (micrometers) <![CDATA[Coverage coefficient C i > 1-10 0.01737 10-25 0.1528 25-50 0.7078 50-100 2.435 100-150 5.186 150-200 7.484
[0078] Table 2
[0079] After obtaining the coverage coefficients corresponding to the size ranges of each particulate matter using the above method, the target model can calculate the particulate matter coverage of the target surface using the following formula:
[0080] 0.01737n1+0.1528n2+0.7078n3+2.435n4+5.186n5+7.484n6
[0081] In another embodiment, before collecting particulate matter from the surface of the target object using the collector 100, this application determines whether the detection environment meets a preset environmental cleanliness level. This application may determine whether the detection environment meets the preset environmental cleanliness level using an environmental cleanliness detector. For example, this application may determine that the detection environment meets the preset environmental cleanliness level if the environmental cleanliness level of the detection environment is determined to be Class 1000 or better than that of a Class 1000 cleanroom. The preset environmental cleanliness level can then be determined by referring to the environmental cleanliness level of a Class 1000 cleanroom.
[0082] Alternatively, this application can perform the following operations after confirming that the testing environment meets the preset environmental cleanliness level:
[0083] The collector 100 collects particulate matter from the surface of the target object.
[0084] The particulate matter collected by the collector 100 is extracted to obtain a mixture;
[0085] The mixture is filtered through an initial filter membrane to obtain a target filter membrane, the target filter membrane containing the particulate matter.
[0086] The size range of each particle on the target filter membrane was determined by microscopic analysis, and the number of particles corresponding to each size range was determined.
[0087] The coverage of particles on the surface of the target object is determined based on the size range of each particle, the number of particles corresponding to each size range, and the target model.
[0088] This application provides a method for detecting surface particulate contamination. The method involves directly collecting particulate matter from the surface of a target object, then transferring the particulate matter adhering to the adhesive surface of the collector to a filter membrane for easier analysis through extraction and filtration. Further, microscopic analysis is used to statistically determine the quantity and size of particles in various size ranges. Finally, the particulate matter data is input into a pre-trained target model to calculate the surface particulate matter coverage, obtaining the final determined result. This application uses a pre-trained model to calculate the particulate matter coverage of the target object surface, eliminating human intervention. The target model is obtained after multiple training iterations, ensuring the accuracy and stability of its calculations. Furthermore, the detection process for surface particulate matter does not require complex operations, thus improving detection efficiency.
[0089] It should be noted that although the operation of the method of the present invention is described in a specific order in the accompanying drawings, this does not require or imply that the operations must be performed in that specific order, or that all the operations shown must be performed in order to achieve the desired result.
[0090] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the inventive concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this application.
Claims
1. A method for detecting surface particulate contamination, characterized in that, The method includes: The sampler collects particulate matter from the surface of the target object. The particulate matter collected by the collector is extracted to obtain a mixture; The mixture is filtered through an initial filter membrane to obtain a target filter membrane, the target filter membrane containing the particulate matter. The size range of each particle on the target filter membrane is determined by microscopic analysis, and the number of particles corresponding to each size range is determined. The coverage rate of particles on the surface of the target object is determined based on the size range of each particle, the number of particles corresponding to each size range, and the target model.
2. The detection method according to claim 1, characterized in that, The determination of the particle coverage rate on the surface of the target object based on the size range of each particle, the number of particles corresponding to each size range, and the target model includes: Based on the size range of each particle, the coverage coefficient corresponding to the size range of each particle is matched from the correspondence information, wherein the correspondence information includes the size range of multiple particles and the coverage coefficient corresponding to the size range of multiple particles; The coverage coefficient corresponding to each size range of the particles and the number of particles corresponding to each size range are input into the target model to obtain the coverage of particles on the surface of the target object. The target model is based on... The surface particulate coverage of the target object is obtained by calculating the relational formula, where n i C represents the number of particles corresponding to the size range. i is the coverage coefficient corresponding to the size range of particulate matter, and m is the number of size range divisions.
3. The detection method according to claim 1, characterized in that, The process of collecting particulate matter from the surface of the target object using a collector includes: The target object is attached to the adhesive surface of the collector; After confirming that the adhesive surface of the collector fully covers the surface of the target object, the adhesive surface of the collector is separated from the surface of the target object.
4. The detection method according to claim 3, characterized in that, The method further includes: After separating the adhesive surface of the collector from the surface of the target object, the adhesive surface is sealed with a sealing cap.
5. The detection method according to claim 3, characterized in that, The extraction process of the particulate matter collected by the collector to obtain a mixture includes: According to the corresponding attenuation curve, the adhesive surface of the collector is cleaned with the corresponding cleaning agent to obtain the mixture, which includes the cleaning agent and the particulate matter on the adhesive surface.
6. The detection method according to claim 1, characterized in that, The method further includes: During the filtration of the mixture through the initial filter membrane, the initial filter membrane is evacuated using an air pump.
7. The detection method according to claim 6, characterized in that, The pumping speed of the air pump is less than or equal to 200 L / min.
8. The detection method according to claim 1, characterized in that, The mesh gap of the initial filter membrane is greater than or equal to 1 μm and less than or equal to 5 μm.
9. The detection method according to claim 1, characterized in that, The method further includes: Before collecting particulate matter from the surface of the target object using a collector, it is determined whether the detection environment meets the preset environmental cleanliness level.
10. A detection system, characterized in that, The system includes: A collector used to collect particulate matter from the surface of a target object; An extractor is used to extract the particulate matter collected by the collector to obtain a mixture. A filter for filtering the mixture; A microanalyzer is used to determine the size range of each particle on the target filter membrane through microanalysis techniques, and to determine the number of particles corresponding to each size range. A server is used to determine the coverage of particles on the surface of the target object based on the size range of each particle, the number of particles corresponding to each size range, and the target model.