A method, device and medium for detecting coating effect of a three-way catalyst

Abstract

The application provides a coating effect detection method, device and medium for a three-way catalyst, the method comprising: obtaining a detection data vector corresponding to each group of detection sensors according to a plurality of detection data obtained by each group of detection sensors within a target time period corresponding to each group of detection sensors; obtaining a matching degree between each two detection data vectors according to the plurality of detection data vectors; if the standard deviation of a plurality of matching degrees corresponding to each detection data vector is less than a preset standard deviation threshold, determining that the coating of the ventilation channel of the honeycomb carrier of the catalyst to be detected is uniformly coated; by dividing the honeycomb carrier of the catalyst to be detected into a plurality of channel regions and respectively performing ventilation tests on each channel region, the coating state of the material coating of each channel region is determined, and by considering the gas purification effect of the local region of the honeycomb carrier, the coating effect detection of the three-way catalyst is more accurate.

Description

Technical Field

[0001] This invention relates to the field of three-way catalytic converter testing, and in particular to a method, equipment, and medium for testing the coating effect of a three-way catalytic converter. Background Technology

[0002] The three-way catalytic converter is a crucial purification device installed in the automotive exhaust system. Located between the exhaust manifold and muffler of the engine, it converts harmful gases such as carbon monoxide, hydrocarbons, and nitrogen oxides emitted from the engine into harmless carbon dioxide, water, and nitrogen through oxidation and reduction reactions, thus purifying the exhaust gases. The interior of the three-way catalytic converter is a honeycomb ceramic carrier. The inner wall of each vent channel of this carrier is coated with rare and precious metal materials (platinum, rhodium, palladium, etc.) used for gas purification. When exhaust gases pass through these vent channels, the rare and precious metal materials convert harmful gases into harmless ones. Therefore, the uniformity of the rare and precious metal coating directly affects the contact efficiency between the exhaust gases and the catalyst, as well as the purification performance. A more uniform coating results in better gas purification. Thus, it is necessary to test the material coating of the manufactured three-way catalytic converters to determine their purification effectiveness.

[0003] Current methods for detecting the coating condition of three-way catalytic converters include endoscopic inspection, cross-sectional microscopic imaging, and ultrasonic thickness measurement. These methods all detect the overall coating condition of the three-way catalytic converter. However, in actual use, even if the overall coating condition of some three-way catalytic converters passes the test, the lifespan of the three-way catalytic converter will be shortened due to localized rapid aging. Therefore, current methods for detecting the coating condition of three-way catalytic converters do not perform localized coating inspection on the honeycomb ceramic carrier of the three-way catalytic converter, resulting in low detection accuracy. Summary of the Invention

[0004] To address the aforementioned technical problems, the technical solution adopted by this invention is as follows: According to one aspect of this application, a method for detecting the coating effect of a three-way catalytic converter is provided, which is applied to a detection system. The detection system is connected to several sets of detection sensors. Each set of detection sensors includes an oxygen sensor and a temperature sensor. Each set of detection sensors corresponds to a channel area on the honeycomb carrier of the catalytic converter to be tested. Each channel area is composed of several ventilation channels on the honeycomb carrier. The oxygen sensor corresponding to any channel area is used to obtain the oxygen content of the gas flowing in the channel area, and the temperature sensor corresponding to any channel area is used to obtain the temperature value of the gas flowing in the channel area. The methods for testing the coating effect of three-way catalytic converters include: Step S100: Based on the detection data obtained by each group of detection sensors within the target time period corresponding to each group of detection sensors, obtain the detection data vector corresponding to each group of detection sensors; the target time period corresponding to any group of detection sensors is the time period for conducting ventilation tests on the pore area corresponding to that group of detection sensors; the duration of the target time period corresponding to each group of detection sensors is equal; the ventilation test is the process of passing a mixed gas with a preset gas temperature, preset gas flow rate, and preset gas concentration through several ventilation pores in the pore area; the preset gas flow rate is greater than the standard gas flow rate of the catalyst to be tested; the mixed gas consists of nitrogen oxides, carbon monoxide, and hydrocarbons; Step S200: Based on several detection data vectors, obtain the matching degree between every two detection data vectors; Step S300: If the standard deviation of several matching degrees corresponding to each detection data vector is less than the preset standard deviation threshold, then it is determined that the coating of the ventilation channel of the honeycomb carrier of the catalyst to be tested is uniform.

[0005] According to another aspect of this application, a non-transitory computer-readable storage medium is provided, wherein the storage medium stores at least one instruction or at least one program, the at least one instruction or the at least one program being loaded and executed by a processor to implement the aforementioned method for detecting the coating effect of a three-way catalytic converter.

[0006] According to another aspect of this application, an electronic device is provided, including a processor and the aforementioned non-transitory computer-readable storage medium.

[0007] The present invention has at least the following beneficial effects: The coating effect detection method for the three-way catalytic converter of the present invention first obtains a detection data vector corresponding to each pore area based on several detection data obtained by the oxygen sensor and temperature sensor during the time period of the ventilation test for each pore area. Based on several detection data vectors, the matching degree between each pair of detection data vectors is obtained. If the standard deviation of several matching degrees corresponding to each detection data vector is less than a preset standard deviation threshold, it indicates that the gas purification effect of several pore areas on the honeycomb carrier of the catalytic converter under test is similar during the ventilation test. It is then determined that the coating of the ventilation pores of the honeycomb carrier of the catalytic converter under test is uniform. By dividing the honeycomb carrier of the catalytic converter under test into several pore areas and conducting ventilation tests on each pore area separately, the coating state of the material coating in each pore area is determined. By considering the gas purification effect of the local area of ​​the honeycomb carrier, the coating effect detection of the three-way catalytic converter is made more accurate. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 A flowchart of a method for detecting the coating effect of a three-way catalytic converter provided in an embodiment of the present invention. Detailed Implementation

[0010] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0011] This application proposes a method for detecting the coating effect of a three-way catalytic converter, applied to a detection system. The detection system is connected to several sets of detection sensors, each set including an oxygen sensor and a temperature sensor. Each set of detection sensors corresponds to a channel region on the honeycomb carrier of the catalytic converter to be tested. Each channel region consists of several venting channels on the honeycomb carrier, dividing the honeycomb carrier into several channel regions to ensure that the coating is detected on each venting channel of the honeycomb carrier. The oxygen sensor corresponding to any channel region is used to obtain the oxygen content of the gas flowing in that channel region, and the temperature sensor corresponding to any channel region is used to obtain the temperature value of the gas flowing in that channel region.

[0012] Among them, such as Figure 1 As shown, the method for detecting the coating effect of the three-way catalytic converter proposed in this application includes: Step S100: Based on the detection data obtained by each group of detection sensors within the target time period corresponding to each group of detection sensors, obtain the detection data vector corresponding to each group of detection sensors. The target time period corresponding to any set of detection sensors is the time period for conducting ventilation tests on the pore area corresponding to that set of detection sensors.

[0013] The target time period corresponding to each set of detection sensors is of equal duration.

[0014] The ventilation test is the process of passing a mixture of gases with preset gas temperature, preset gas flow rate, and preset gas concentration through several ventilation channels in the channel area.

[0015] The preset gas flow rate is greater than the standard gas flow rate of the catalyst under test. Setting the gas flow rate to be greater than the standard gas flow rate is to reduce the gas diffusion rate. Because the gas flow rate for the ventilation test is high, the gas in the ventilation channel for the ventilation test only flows through that ventilation channel and will not flow to other ventilation channels that have not been tested, making the subsequent test data more accurate and improving the accuracy of subsequent tests.

[0016] The mixture consists of nitrogen oxides, carbon monoxide, and hydrocarbons, and its concentration is the same as that of the compounds in automobile exhaust.

[0017] Furthermore, step S100 includes step S110: Step S110: Obtain several sets of detection data acquired by several detection sensors within the corresponding target time period, and determine the detection data vectors A1, A2, ..., A corresponding to the several sets of detection sensors. i ,...,A h Where i = 1, 2, ..., h; h is the number of groups of detection sensors; A i Let be the detection data vector corresponding to the i-th group of detection sensors; A i =(A i1 A i2 ,...,A ig ,...,A if ); g=1,2,...,f; f is the number of data collection moments within the target time period; the duration between any two adjacent data collection moments within the target time period is equal; A ig The detection data obtained by the i-th group of detection sensors at the g-th data acquisition time within the target time period corresponding to the i-th group of detection sensors; A ig =(A ig1 A ig2 A ig1 Let A be the oxygen content data acquired by the oxygen sensor in the i-th group of detection sensors at the g-th data acquisition time within the target time period corresponding to the i-th group of detection sensors; ig2 The temperature value data obtained by the temperature sensor in the i-th group of detection sensors at the g-th data acquisition time within the target time period corresponding to the i-th group of detection sensors.

[0018] Step S200: Based on several detection data vectors, obtain the matching degree between every two detection data vectors; Furthermore, step S200 includes step S210: Step S210: Compare any two detection data vectors to obtain several matching degree lists B1, B2, ..., B i ,...,B h Among them, B i This is a list of matching degrees for the detection data vectors corresponding to the i-th group of detection sensors; B i =(B i1 B i2 ,...,B ie ,...,B ih );e=1,2,...,h;B ie For A i and A e The degree of matching between them.

[0019] Step S300: If the standard deviation of several matching degrees corresponding to each detection data vector is less than the preset standard deviation threshold, then it is determined that the coating of the ventilation channel of the honeycomb carrier of the catalyst to be tested is uniform.

[0020] Furthermore, step S300 includes steps S310-S320: Step S310: Calculate the standard deviation of several matching degrees in each matching degree list to obtain a standard deviation list C=(C1,C2,...,C...). i ,...,C h ); where C i For B i1 B i2 ,...,B ie ,...,B ih Standard deviation; Furthermore, step S310 also includes steps S311-S315: Step S311, if C i If the value is greater than or equal to C0, then the i-th group of detection sensors is determined as the target sensor, and the channel region corresponding to the i-th group of detection sensors is determined as the target region; C0 is the preset standard deviation threshold. Step S312: Perform a second test on the target area; The secondary test involves passing a mixture of gases with preset gas temperature, preset gas flow rate, and preset gas concentration through several ventilation channels in the target area.

[0021] The duration of the secondary test was longer than the duration of the ventilation test.

[0022] When conducting a secondary test on the target area, the temperature of the mixed gas in several ventilation channels of the target area is directly proportional to the ventilation duration.

[0023] If C iIf C ≥ C0, it means that the standard deviation of several matching degrees in the matching degree list of the detection data vector corresponding to the i-th group of detection sensors is greater than the preset standard deviation threshold. This indicates that the data of these matching degrees is unstable, meaning that there are differences between several sets of detection data when the ventilation area corresponding to the i-th group of detection sensors is tested. When the coating of the ventilation channel is uniformly coated, the detection data during the ventilation test should be stable, and the difference should be less than the preset threshold. Therefore, if there are differences between several sets of detection data when the ventilation area corresponding to the i-th group of detection sensors is tested, it means that the coating of the ventilation area corresponding to the i-th group of detection sensors may be uneven. In this case, a second test with a longer test time is required to accurately detect the target area.

[0024] Step S313: Based on the detection data obtained by the target sensor during the secondary test in the target area, obtain the secondary data vector corresponding to the target sensor. Step S313 includes step S3131: Step S3131: Acquire several detection data points obtained by the target sensor during the secondary test in the target area, and determine the secondary data vector D=(D1,D2,...,D) corresponding to the target sensor. a ,...,D b ); where a=1,2,...,b; b is the number of data collection moments within the time period corresponding to the second test; D a The detection data is obtained at the a-th data acquisition time when the target sensor performs a secondary test in the target area; D a =(D a1 D a2 );D a1 The oxygen content data obtained by the oxygen sensor of the target sensor at the a-th data acquisition time during the secondary test in the target area; D a2 The temperature value data is obtained by the temperature sensor of the target sensor at the a-th data acquisition time during the secondary test in the target area.

[0025] Step S314: Input the quadratic data vector into the preset neural network model to obtain the result identifier output by the neural network model; The neural network model is trained using detection data obtained from secondary tests on several pore regions with different coating coverage rates. The neural network model is determined according to steps S3141-S3143. Step S3141: Perform secondary tests on several test pore areas with different coating coverage rates to obtain several training data vectors E1, E2, ..., Ej ,...,E k Where j=1,2,...,k; k is the number of test channel regions; the coating coverage of the vents in several test channel regions is different; E j Let be the training data vector corresponding to the j-th test channel region; E j =(E j1 E j2 ,...,E ja ,...,E jb ); E ja =(E ja1 E ja2 ); E ja1 For the oxygen sensor corresponding to the j-th test channel region, the oxygen content data is obtained at the a-th data acquisition time during the secondary test in the j-th test channel region; E ja2 The temperature sensor corresponding to the j-th test channel region acquires the temperature value data at the a-th data acquisition time during the secondary test process in the j-th test channel region. Step S3142: Obtain the preset training identifier F corresponding to the j-th test channel region. j When F j When F is the first identifier, it indicates that the coating of the ventilation channel in the j-th test channel region is uniformly applied; when F j When the second identifier is used, it indicates that the coating of the ventilation channel in the j-th test channel region is uneven. Step S3143, E j As input samples, F j As output labels, supervised training is performed on a pre-defined convolutional neural network to obtain a neural network model.

[0026] The method for supervised training of convolutional neural networks can be achieved using existing model training methods.

[0027] Step S315: If the result identifier is the first identifier, determine that the coating of the ventilation channel in the target area is uniform. When the result identifier is characterized as the second identifier, it is determined that the coating of the ventilation channels in the target area is uneven.

[0028] Step S320: If MAX(C) < C0, then it is determined that the coating of the venting channels of the honeycomb carrier of the catalyst to be tested is uniform. MAX() is a preset function for determining the maximum value.

[0029] The coating effect detection method for the three-way catalytic converter of the present invention first obtains a detection data vector corresponding to each pore area based on several detection data obtained by the oxygen sensor and temperature sensor during the time period of the ventilation test for each pore area. Based on several detection data vectors, the matching degree between each pair of detection data vectors is obtained. If the standard deviation of several matching degrees corresponding to each detection data vector is less than a preset standard deviation threshold, it indicates that the gas purification effect of several pore areas on the honeycomb carrier of the catalytic converter under test is similar during the ventilation test. It is then determined that the coating of the ventilation pores of the honeycomb carrier of the catalytic converter under test is uniform. By dividing the honeycomb carrier of the catalytic converter under test into several pore areas and conducting ventilation tests on each pore area separately, the coating state of the material coating in each pore area is determined. By considering the gas purification effect of the local area of ​​the honeycomb carrier, the coating effect detection of the three-way catalytic converter is made more accurate.

[0030] Embodiments of the present invention also provide a computer program product including program code, which, when the program product is run on an electronic device, causes the electronic device to perform the steps of the methods described above according to various exemplary embodiments of the present invention.

[0031] Furthermore, although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps.

[0032] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, mobile terminal, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0033] In an exemplary embodiment of this disclosure, an electronic device capable of implementing the above-described method is also provided.

[0034] Those skilled in the art will understand that various aspects of the present invention can be implemented as systems, methods, or program products. Therefore, various aspects of the present invention can be specifically implemented in the following forms: entirely in hardware, entirely in software (including firmware, microcode, etc.), or in a combination of hardware and software, collectively referred to herein as “circuit,” “module,” or “system.”

[0035] An electronic device according to this embodiment of the invention. The electronic device is merely an example and should not be construed as limiting the functionality or scope of the embodiments of the invention.

[0036] Electronic devices are manifested in the form of general-purpose computing devices. Components of an electronic device may include, but are not limited to: at least one processor, at least one memory, and buses connecting different system components (including memory and processor).

[0037] The storage device stores program code that can be executed by the processor to perform the steps described in the "Exemplary Methods" section above, according to various exemplary embodiments of the present invention.

[0038] The storage may include readable media in the form of volatile storage, such as random access memory (RAM) and / or cache memory, and may further include read-only memory (ROM).

[0039] The storage may also include programs / utilities having a set (at least one) of program modules, including but not limited to: an operating system, one or more applications, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0040] A bus can represent one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus that uses any of the various bus architectures.

[0041] Electronic devices can also communicate with one or more external devices (such as keyboards, pointing devices, Bluetooth devices, etc.), one or more devices that enable users to interact with the electronic device, and / or any device that enables the electronic device to communicate with one or more other computing devices (such as routers, modems, etc.). This communication can be performed through input / output (I / O) interfaces. Furthermore, electronic devices can also communicate with one or more networks (such as local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via network adapters.

[0042] In exemplary embodiments of this disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the methods described above is stored. In some possible embodiments, various aspects of the invention may also be implemented as a program product comprising program code that, when the program product is run on a terminal device, causes the terminal device to perform the steps of the various exemplary embodiments of the invention described in the "Exemplary Methods" section of this specification.

[0043] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0044] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting programs for use by or in conjunction with an instruction execution system, apparatus, or device.

[0045] The program code contained on the readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0046] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0047] Furthermore, the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0048] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0049] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for detecting the coating effect of a three-way catalytic converter, characterized in that, The system is used in a detection system connected to several sets of detection sensors. Each set of detection sensors includes an oxygen sensor and a temperature sensor. Each set of detection sensors corresponds to a channel region on the honeycomb carrier of the catalyst to be detected. Each channel region is composed of several ventilation channels on the honeycomb carrier. The oxygen sensor corresponding to any channel region is used to obtain the oxygen content of the gas flowing in the channel region, and the temperature sensor corresponding to any channel region is used to obtain the temperature value of the gas flowing in the channel region. The method for detecting the coating effect of the three-way catalytic converter includes: Step S100: Based on the detection data acquired by each group of detection sensors within the target time period corresponding to each group of detection sensors, obtain the detection data vector corresponding to each group of detection sensors; the target time period corresponding to any group of detection sensors is the time period for performing ventilation tests on the pore area corresponding to that group of detection sensors; the duration of the target time period corresponding to each group of detection sensors is equal; the ventilation test is the process of passing a mixed gas with a preset gas temperature, preset gas flow rate, and preset gas concentration through several ventilation pores in the pore area; the preset gas flow rate is greater than the standard gas flow rate of the catalyst to be tested; the mixed gas consists of nitrogen oxides, carbon monoxide, and hydrocarbons; Step S200: Based on the plurality of detection data vectors, obtain the matching degree between every two detection data vectors; Step S300: If the standard deviation of several matching degrees corresponding to each detection data vector is less than the preset standard deviation threshold, then it is determined that the coating of the ventilation channel of the honeycomb carrier of the catalyst to be tested is uniform. Wherein, step S100 includes step S110: Step S110: Obtain several sets of detection data acquired by the detection sensors within the corresponding target time period, and determine the detection data vectors A1, A2, ..., A corresponding to the several sets of detection sensors. i ,...,A h Where i = 1, 2, ..., h; h is the number of groups of the detection sensors; A i Let be the detection data vector corresponding to the detection sensor in the i-th group; A i =(A i1 A i2 ,...,A ig ,...,A if ); g = 1, 2, ..., f; f is the number of data acquisition moments within the target time period; the duration between any two adjacent data acquisition moments within the target time period is equal; A ig The detection data obtained by the detection sensor in the i-th group at the g-th data acquisition time within the target time period corresponding to the detection sensor in the i-th group; A ig =(A ig1 A ig2 A ig1 A represents the oxygen content data acquired by the oxygen sensor in the i-th group of detection sensors at the g-th data acquisition time within the target time period corresponding to the i-th group of detection sensors; ig2 The temperature value data obtained by the temperature sensor in the i-th group of detection sensors at the g-th data acquisition time within the target time period corresponding to the i-th group of detection sensors; Wherein, step S200 includes step S210: Step S210: Compare any two of the detected data vectors to obtain several matching degree lists B1, B2, ..., B i ,...,B h Among them, B i This is a list of matching degrees for the detection data vectors corresponding to the detection sensors in the i-th group; B i =(B i1 B i2 ,...,B ie ,...,B ih );e=1,2,...,h;B ie For A i and A e The degree of matching between them; Wherein, step S300 includes steps S310-S320: Step S310: Calculate the standard deviation of several matching degrees in each of the matching degree lists to obtain a standard deviation list C=(C1,C2,...,C...). i ,...,C h ); where C i For B i1 B i2 ,...,B ie ,...,B ih Standard deviation; Step S320: If MAX(C) < C0, then it is determined that the coating of the ventilation channel of the honeycomb carrier of the catalyst to be tested is uniform; where MAX() is a preset maximum value determination function; C0 is a preset standard deviation threshold. Step S310 further includes steps S311-S315: Step S311, if C i If ≥C0, then the detection sensor in the i-th group is determined as the target sensor, and the channel region corresponding to the detection sensor in the i-th group is determined as the target region; Step S312: Perform a secondary test on the target area; the secondary test is a process of ventilating a mixed gas with a preset gas temperature, preset gas flow rate, and preset gas concentration through several ventilation channels in the target area; the duration of the secondary test is longer than the duration of the ventilation test. Step S313: Based on the detection data obtained by the target sensor during the secondary test in the target area, obtain the secondary data vector corresponding to the target sensor. Step S314: Input the secondary data vector into a preset neural network model to obtain the result identifier output by the neural network model; the neural network model is trained based on the detection data obtained by performing secondary tests on several pore areas with different coating coverage rates. Step S315: If the result identifier is characterized as the first identifier, determine that the coating of the ventilation channel in the target area is uniformly applied; If the result identifier is characterized as the second identifier, it is determined that the coating of the ventilation channels in the target area is uneven.

2. The method according to claim 1, characterized in that, Step S313 includes: Step S3131: Obtain several detection data points acquired by the target sensor during the secondary test in the target area, and determine the secondary data vector D=(D1,D2,...,D) corresponding to the target sensor. a ,...,D b ); where a=1,2,...,b; b is the number of data acquisition moments within the time period corresponding to the secondary test; D a The detection data is obtained by the target sensor at the a-th data acquisition time when the target sensor performs a secondary test in the target area; D a =(D a1 D a2 );D a1 The oxygen content data obtained by the oxygen sensor of the target sensor at the a-th data acquisition time during the secondary test in the target area; D a2 The temperature value data is obtained by the temperature sensor of the target sensor at the a-th data acquisition time during the secondary test in the target area.

3. The method according to claim 2, characterized in that, The neural network model is determined according to the following steps: Step S3141: Perform secondary tests on several test pore areas with different coating coverage rates to obtain several training data vectors E1, E2, ..., E j ,...,E k Where j = 1, 2, ..., k; k is the number of test channel regions; the coating coverage of the ventilation channels in several test channel regions is different; E j Let be the training data vector corresponding to the j-th test channel region; E j =(E j1 E j2 ,...,E ja ,...,E jb ); E ja =(E ja1 E ja2 ); E ja1 For the oxygen sensor corresponding to the j-th test channel region, the oxygen content data is obtained at the a-th data acquisition time during the secondary test in the j-th test channel region; E ja2 The temperature sensor corresponding to the j-th test channel region acquires the temperature value data at the a-th data acquisition time during the secondary test process in the j-th test channel region. Step S3142: Obtain the preset training identifier F corresponding to the j-th test channel region. j When F j When the first identifier is used, it indicates that the coating of the ventilation channel in the j-th test channel region is uniformly applied; when F j When the second identifier is used, it indicates that the coating of the ventilation channel in the j-th test channel region is uneven. Step S3143, E j As input samples, F j As output labels, supervised training is performed on a pre-defined convolutional neural network to obtain the neural network model.

4. The method according to claim 3, characterized in that, When conducting a secondary test on the target area, the temperature of the mixed gas in several ventilation channels of the target area is directly proportional to the ventilation duration.

5. A non-transitory computer-readable storage medium, characterized in that, The storage medium stores at least one instruction or at least one program segment, which is loaded and executed by a processor to implement the method as described in any one of claims 1-4.

6. An electronic device, characterized in that, Includes a processor and the non-transitory computer-readable storage medium as described in claim 5.

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