High-efficiency denitration agent quality detection method

Abstract

The present invention provides a method for quality inspection of an efficient denitration agent, comprising the following steps: in response to the completion of the manufacture of a raw material substrate, sequentially numbering the raw material substrate, and determining whether the raw material substrate meets a preset sampling inspection condition; in response to meeting the preset sampling inspection condition, determining the structure and configuring a light source unit based on the determined structural characteristics; controlling the light source unit to emit light and configuring a photographing unit for the raw material substrate; controlling the photographing unit to take an image to obtain a substrate image; performing region recognition on the substrate image, and determining a substrate type corresponding to the raw material substrate based on the respective region shapes corresponding to the respective light regions located in the substrate image; and retrieving a preset quality determination strategy corresponding to the substrate type to perform quality inspection on the raw material substrate. The present invention at least improves the inspection efficiency.

Description

Technical Field

[0001] The present invention relates to data processing technologies, and particularly to an efficient method for detecting the quality of denitration agents. Background Art

[0002] A denitration agent, also known as a denitration additive, is a chemical used to remove harmful nitrogen oxides from gases. The following is a detailed introduction to denitration agents:

[0003] 1. Working principle: The denitration agent undergoes a chemical reduction reaction with nitrogen oxides to convert them into harmless gases such as nitrogen and water vapor, enabling the waste gas generated in industrial production to meet the discharge standards, eliminating the hidden dangers of pollutants from the source, and protecting the environment.

[0004] 2. Main types: Denitration agents are mainly divided into two categories: polymer denitration agents and low-temperature denitration agents. In addition, according to different denitration principles, they can also be divided into reduction method denitration agents and oxidation method denitration agents. Among them, polymer denitration agents: contain functional polymer reduction materials and auxiliary components such as emulsifiers, dispersants, slow-release agents, activators, and penetrants, and can achieve efficient denitration effects at relatively low temperatures, with advantages such as high denitration rate, wide temperature adaptability, safe operation, and no toxic side effects. Low-temperature denitration agents: are suitable for the denitration process in low-temperature environments, with characteristics such as simple operation and high denitration efficiency. Reduction method denitration agents: reduce nitrogen oxides (NOx) to nitrogen (N2) and water through the reduction principle. Oxidation method denitration agents: convert nitrogen oxides (NOx) into nitrates through the oxidation principle to achieve the purpose of removing nitrogen oxides. Commonly used oxidation method denitration agents include aluminum powder, iron powder, rare earth alumina, and rare earth iron oxide, etc., which have the advantages of high denitration efficiency and low cost.

[0005] 3. Application fields: Denitration agents are widely used in multiple fields to reduce nitrogen oxide emissions and protect the environment and human health. Specifically, they can include: Coal-fired power plants: Denitration agents are added to flue gas to reduce nitrogen oxide emissions. Industrial production: Many industrial processes also generate nitrogen oxides, and denitration agents can also reduce their emissions. Automobile exhaust treatment: Denitration agents can be directly added to automobile exhaust to reduce nitrogen oxide emissions. Wastewater treatment: Denitration agents can also be used in wastewater treatment to reduce the nitrogen oxide content in wastewater.

[0006] The inventors found in their research that during the production of denitration agents, corresponding spot checks are carried out on the denitration agents to ensure that the quality of the denitration agents meets the production requirements. However, since the existing detection process for denitration agents usually involves multiple steps and complex operations, this increases the time cost and labor cost of detection, and thus reduces the corresponding product production efficiency. Summary of the Invention

[0007] Based on the above problems, the present invention is proposed to provide an efficient denitration agent quality detection method and system that overcomes the above problems or at least partially solves the above problems.

[0008] According to one aspect of the present invention, there is provided an efficient denitration agent quality detection method, including the following steps:

[0009] In response to the completion of the manufacture of the raw material substrate for the denitration agent, sequentially number the raw material substrate according to the current number sequence, and determine whether the raw material substrate meets the preset sampling inspection conditions based on the obtained substrate number;

[0010] In response to the raw material substrate meeting the preset sampling inspection conditions, determine the structure of the raw material substrate, and configure a light source unit for the first ventilation surface of the raw material substrate based on the determined structural characteristics;

[0011] Control the light source unit to irradiate light towards the first ventilation surface, and configure a photographing unit for the second ventilation surface of the raw material substrate that is oppositely arranged to the first ventilation surface;

[0012] Control the photographing unit to take an image of the second ventilation surface to obtain a substrate image;

[0013] Perform region recognition on the substrate image, and determine the substrate type corresponding to the raw material substrate based on the respective region shapes corresponding to the respective light regions located in the substrate image;

[0014] Retrieve a preset quality determination strategy corresponding to the substrate type to perform quality detection on the raw material substrate.

[0015] Optionally, in the method according to the present invention, determining whether the raw material substrate meets the preset sampling inspection conditions based on the obtained substrate number includes:

[0016] Decompose the substrate number to obtain a primary number and a secondary number;

[0017] Determine the production grade corresponding to the raw material substrate based on the primary number, and determine the sampling inspection frequency corresponding to the production grade based on a preset grade sampling inspection rule, where different production grades correspond to different sampling inspection frequencies;

[0018] Compare the secondary number with each sampling inspection number corresponding to the sampling inspection frequency;

[0019] In response to the secondary number being equal to any sampling inspection number corresponding to the sampling inspection frequency, determine the raw material substrate as meeting the preset sampling inspection conditions;

[0020] In response to the secondary number not being equal to any sampling number corresponding to the sampling frequency, determine the sampling number having a positive proximity relationship with the secondary number as the target number, and determine the number difference between the target number and the secondary number;

[0021] When the number difference is less than or equal to a preset difference, retrieve the sampling result of the substrate number equal to the target number from the historical sampling library, and when the sampling result is unqualified, determine that the raw material substrate corresponding to the secondary number meets the preset sampling conditions.

[0022] Optionally, in the method according to the present invention, the method further includes:

[0023] In response to the sampling quantity corresponding to the same production grade reaching the preset sampling quantity, determine the sampling results corresponding to the preset sampling quantity as the same sampling batch;

[0024] In response to the obtained sampling batches reaching the preset batch quantity, determine the sampling quality corresponding to the sampling batch based on the sampling results in the same sampling batch;

[0025] Sort the sampling batches based on the chronological order, and determine whether there is a gradually decreasing trend or a gradually increasing trend in each of the sampling qualities based on the obtained sampling order;

[0026] If there is a gradually increasing trend, perform a decreasing training on the sampling frequency;

[0027] If there is a gradually decreasing trend, perform an increasing training on the sampling frequency;

[0028] Wherein, the trained sampling frequency is obtained through the following formula:

[0029]

[0030] Wherein, q + is the number of times of increasing training of the sampling frequency k s , ∈ is the training constant value of the sampling frequency k s , q - is the number of times of decreasing training of the sampling frequency k s , k w is the sampling frequency after training.

[0031] Optionally, in the method according to the present invention, in response to the raw material substrate meeting the preset sampling conditions, determine the structure of the raw material substrate, and configure the light source unit on the first ventilation surface of the raw material substrate based on the determined structural characteristics, including:

[0032] Determine any ventilation surface of the raw material substrate as the first ventilation surface, and perform image acquisition on the first ventilation surface to obtain a raw material ventilation image;

[0033] Perform binarization processing on the raw material ventilation image to obtain each substrate pixel point corresponding to the first pixel value and each noise pixel point corresponding to the second pixel value;

[0034] Obtain the first quantity corresponding to each substrate pixel point and the second quantity corresponding to each noise pixel point, and determine the substrate density corresponding to the raw material substrate based on the first quantity and the second quantity;

[0035] Perform length detection on the raw material substrate based on a preset extension length strategy, and determine the substrate length corresponding to the raw material substrate;

[0036] Perform fusion calculation based on the substrate density and the substrate length, and configure a light source unit for the first ventilation surface of the raw material substrate based on the obtained substrate coefficient.

[0037] Optionally, in the method according to the present invention, performing length detection on the raw material substrate based on a preset extension length strategy to determine the substrate length corresponding to the raw material substrate includes:

[0038] Take the image center point of the raw material ventilation image as the coordinate origin, establish an image coordinate system corresponding to the raw material ventilation image, and obtain each image coordinate point constituting the raw material ventilation image based on the image coordinate system;

[0039] Among each image coordinate point, determine each coordinate point corresponding to the horizontal coordinate extreme value and the vertical coordinate extreme value as each detection coordinate point, and determine each detection position point in the first ventilation surface corresponding to the raw material ventilation image that has a mapping relationship with each detection coordinate point respectively;

[0040] Determine the ventilation surface of the raw material substrate that has an opposite relationship with the first ventilation surface as the second ventilation surface, take the second ventilation surface as the horizontal plane, perform distance detection on each detection position point for distance information, and obtain each distance detection value corresponding to each detection position point respectively;

[0041] Among each distance detection value, determine the detection value corresponding to the minimum value as the reference detection value, and determine the detection position point corresponding to the reference detection value as the reference position point;

[0042] Determine the point-plane distance between the second ventilation surface and the reference position point along the extension direction perpendicular to the second ventilation surface of the raw material substrate as the substrate length corresponding to the raw material substrate.

[0043] Optionally, in the method according to the present invention, a fusion calculation is performed based on the substrate density and the substrate length, and a light source unit is configured for the first ventilation surface of the raw substrate based on the obtained substrate coefficient, including:

[0044] Normalize the substrate density and the substrate length respectively to obtain corresponding first density coefficients and first length coefficients;

[0045] Multiply the first density coefficient and the first length coefficient by the retrieved density weight value and length weight value respectively to obtain second density coefficients and second length coefficients;

[0046] Superpose the second density coefficient and the second length coefficient to obtain a substrate coefficient corresponding to the raw substrate, and retrieve a preset light source comparison table, where the light source comparison table includes different coefficient intervals and light source specifications respectively corresponding to the different coefficient intervals.

[0047] Traverse the preset light source comparison table, determine the light source specification corresponding to the substrate coefficient based on the coefficient interval including the substrate coefficient, and configure the light source unit for the first ventilation surface of the raw substrate based on the light source specification.

[0048] Optionally, in the method according to the present invention, region recognition is performed on the substrate image, and the substrate type corresponding to the raw substrate is determined based on the respective region shapes corresponding to each light region located in the substrate image, including:

[0049] Perform pixel recognition on the obtained substrate image to obtain each light pixel point corresponding to the light source light in the substrate image, and perform pixel connection based on connectivity on each light pixel point to obtain each light region;

[0050] Taking the region center point of each light region as the rotation point, sequentially perform vertical rotation on the light region to obtain a first rotation region, a second rotation region, and a third rotation region corresponding to the same light region, and determine the original region shape corresponding to the light region, the first rotation shape corresponding to the first rotation region, the second rotation shape corresponding to the second rotation region, and the third rotation shape corresponding to the third rotation region as the shape comparison group corresponding to the light region;

[0051] Retrieve the respective standard shapes corresponding to the honeycomb type, the corrugated type, and the plate type, and perform similarity comparison between the respective shape comparison groups corresponding to each light region and the standard shapes;

[0052] In response to the shape similarity between any shape in any shape comparison group and any standard shape being greater than a preset first similarity, determine the light region corresponding to the shape comparison group as the similar shape corresponding to the standard shape;

[0053] In response to the shape ratio of the similar shape corresponding to any standard shape being greater than a preset ratio, determine the substrate type corresponding to the raw material substrate as the type corresponding to the standard shape.

[0054] Optionally, in the method according to the present invention, retrieving a preset quality determination strategy corresponding to the substrate type to perform quality inspection on the raw material substrate includes:

[0055] When the substrate type corresponding to the raw material substrate is a honeycomb type, obtain the respective region contours corresponding to each light region, and disassemble the region contours to obtain the horizontal contour lines corresponding to the horizontal direction and the vertical contour lines corresponding to the vertical direction;

[0056] Determine the dimensions of the horizontal contour lines and the vertical contour lines respectively to obtain the horizontal dimension and the vertical dimension corresponding to the same light region;

[0057] Based on the respective horizontal dimensions corresponding to each light region, determine the horizontal dimension with the smallest corresponding value as the horizontal comparison value, and based on the respective vertical dimensions corresponding to each light region, determine the vertical dimension with the smallest corresponding value as the vertical comparison value;

[0058] Retrieve the horizontal reference value and the vertical reference value corresponding to the honeycomb type, and compare the horizontal reference value and the vertical reference value with the horizontal comparison value and the vertical comparison value respectively;

[0059] In response to any comparison value being less than the corresponding reference value, determine the raw material substrate as unqualified.

[0060] Optionally, in the method according to the present invention, retrieving a preset quality determination strategy corresponding to the substrate type to perform quality inspection on the raw material substrate includes:

[0061] When the substrate type corresponding to the raw material substrate is a plate type or a corrugated type, perform pairwise similarity comparison on each light region, and divide the light regions with a corresponding region similarity greater than a preset second similarity into the same region shape group to obtain each region shape group;

[0062] Determine the area of each light region located in the same region shape group respectively to obtain the respective region areas corresponding to each light region;

[0063] Among the areas of each region, the region area with the smallest corresponding value is determined as the minimum area, the region area with the largest corresponding value is determined as the maximum area, and the average value of the area is calculated based on all the region areas except the minimum area and the maximum area to obtain the area average value;

[0064] In response to the area difference between the minimum area and / or the maximum area and the area average value being greater than the preset area difference, the raw material substrate is determined to be unqualified.

[0065] According to another aspect of the present invention, there is provided a high-efficiency denitration agent quality detection system, including:

[0066] A sampling determination module, configured to, in response to the completion of the manufacture of the raw material substrate based on the denitration agent, sequentially number the raw material substrate according to the current number sequence, and determine whether the raw material substrate meets the preset sampling conditions based on the obtained substrate number;

[0067] A structure determination module, configured to, in response to the raw material substrate meeting the preset sampling conditions, determine the structure of the raw material substrate, and configure a light source unit for the first ventilation surface of the raw material substrate based on the determined structural characteristics;

[0068] A shooting configuration module, configured to control the light source unit to irradiate light towards the first ventilation surface, and configure a shooting unit for the second ventilation surface of the raw material substrate that is arranged opposite to the first ventilation surface;

[0069] An image shooting module, configured to control the shooting unit to shoot an image towards the second ventilation surface to obtain a substrate image;

[0070] A region recognition module, configured to perform region recognition on the substrate image, and determine the substrate type corresponding to the raw material substrate based on the respective region shapes corresponding to the respective light regions located in the substrate image;

[0071] A quality detection module, configured to retrieve a preset quality determination strategy corresponding to the substrate type to perform quality detection on the raw material substrate.

[0072] According to the solution of the present invention, firstly, the method judges the sequential numbering and sampling conditions of the raw material substrate through an automated process, greatly improving the production efficiency and management accuracy, ensuring that every operation is well-documented, and reducing the possibility of human errors. Secondly, by determining the structure of the raw material substrate that meets the sampling conditions and precisely configuring the light source unit and the imaging unit, non-destructive and efficient detection of the internal structural characteristics of the raw material substrate is achieved. This not only improves the detection accuracy but also guarantees the integrity of the raw material substrate, providing a reliable basis for subsequent processing. Furthermore, by using light irradiation and image capture techniques and combining with advanced region recognition algorithms, the light regions and their shapes in the substrate image can be quickly and accurately identified, thereby precisely determining the type of the raw material substrate. The intelligence of this step greatly shortens the product classification time and enhances the flexibility of the production line. Finally, according to the identified substrate type, the corresponding quality inspection strategy is automatically retrieved, making the quality inspection process more scientific and rigorous, effectively ensuring the quality stability and consistency of the denitration agent product, and enhancing the market competitiveness of the product and the corresponding detection efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 FIG. shows a flowchart of an efficient denitration agent quality inspection method according to an embodiment of the present invention;

[0074] Figure 2 FIG. shows a positional relationship diagram among the light source unit, the raw material substrate, and the imaging unit in this embodiment;

[0075] Figure 3 FIG. shows a cross-sectional view of the raw material substrate corresponding to the honeycomb type in this embodiment;

[0076] Figure 4 FIG. shows a cross-sectional view of the raw material substrate corresponding to the plate type in this embodiment;

[0077] Figure 5 FIG. shows a cross-sectional view of the raw material substrate corresponding to the corrugated type in this embodiment;

[0078] Figure 6 FIG. shows a structural block diagram of an efficient denitration agent quality inspection system according to another embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0079] Hereinafter, exemplary embodiments of the present disclosure will be described in more detail with reference to the drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be fully conveyed to those skilled in the art.

[0080] The denitration agent, also known as the denitration additive, is a chemical used to remove harmful nitrogen oxides from gases. The following is a detailed introduction to the denitration agent:

[0081] 1. Working principle: The denitration agent converts nitrogen oxides into harmless gases such as nitrogen and water vapor through a chemical reduction reaction, enabling the exhaust gas generated in industrial production to meet the discharge standards, eliminating the hidden dangers of pollutants from the source, and protecting the environment.

[0082] 2. Main types: Denitration agents are mainly divided into two categories: polymer denitration agents and low-temperature denitration agents. In addition, according to different denitration principles, they can also be divided into reduction method denitration agents and oxidation method denitration agents. Among them, polymer denitration agents: contain functional polymer reduction materials and auxiliary components such as emulsifiers, dispersants, slow-release agents, activators, and penetrants, which can achieve high-efficiency denitration at relatively low temperatures, and have the advantages of high denitration rate, wide applicable temperature range, safe operation, and no toxic side effects. Low-temperature denitration agents: are suitable for the denitration process in low-temperature environments and have the characteristics of simple operation and high denitration efficiency. Reduction method denitration agents: reduce nitrogen oxides (NOx) to nitrogen (N2) and water through the reduction principle. Oxidation method denitration agents: convert nitrogen oxides (NOx) into nitrates through the oxidation principle to achieve the purpose of removing nitrogen oxides. Commonly used oxidation method denitration agents include aluminum powder, iron powder, rare earth alumina, and rare earth iron oxide, etc., which have the advantages of high denitration efficiency and low cost.

[0083] 3. Application fields: Denitration agents are widely used in many fields to reduce nitrogen oxide emissions and protect the environment and human health. Specifically, it can include: Coal-fired power plants: Denitration agents are added to flue gas to reduce nitrogen oxide emissions. Industrial production: Many industrial processes also generate nitrogen oxides, and denitration agents can also reduce their emissions. Automobile exhaust treatment: Denitration agents can be directly added to automobile exhaust to reduce nitrogen oxide emissions. Wastewater treatment: Denitration agents can also be used in wastewater treatment to reduce the nitrogen oxide content in wastewater.

[0084] The inventor found in the research that corresponding spot checks are carried out on the denitration agent during production to ensure that the quality of the denitration agent meets the production requirements. However, since the existing detection process for denitration agents usually involves multiple steps and complex operations, this increases the detection time cost and labor cost, and thus reduces the corresponding product production efficiency.

[0085] To solve the problems existing in the above-mentioned prior art, the inventor proposed the solution of the present invention. An embodiment of the present invention provides a method for detecting the quality of an efficient denitration agent, and this method can be executed in a computing device.

[0086] Figure 1The flowchart of an efficient denitration agent quality detection method according to an embodiment of the present invention is shown. This method is suitable for execution in a computing device, where the computing device can be understood as a terminal device with data processing capabilities, such as a mobile phone or a computer.

[0087] As Figure 1 shown, the efficient denitration agent quality detection method proposed in this embodiment starts from step S102, and in step S102, it includes the following content:

[0088] In response to the completion of the manufacture of the raw material substrate of the denitration agent, sequentially number the raw material substrate according to the current number sequence, and judge whether the raw material substrate meets the preset sampling inspection conditions based on the obtained substrate number.

[0089] For example, in this embodiment, the raw material substrate can be understood as a substrate carrying the denitration agent. After the manufacture of any raw material substrate is completed, the server will sequentially number the raw material substrate according to the current number sequence to make a written record of the manufacturing order of each raw material substrate; since the detection method for the raw material substrate in this embodiment is sampling inspection, during the production process, in order to quickly determine the raw material substrates that need to be sampled, it is possible to judge whether the raw material substrate meets the preset sampling inspection conditions based on the substrate number, so as to perform corresponding quality inspections on the raw material substrates that meet the preset sampling inspection conditions.

[0090] Furthermore, in this embodiment, the above "judging whether the raw material substrate meets the preset sampling inspection conditions based on the obtained substrate number" may further include the following steps:

[0091] Decompose the substrate number to obtain the primary number and the secondary number;

[0092] Determine the production grade corresponding to the raw material substrate based on the primary number, and determine the sampling frequency corresponding to the production grade based on the preset grade sampling rules, where different production grades correspond to different sampling frequencies;

[0093] Compare the secondary number with each sampling number corresponding to the sampling frequency;

[0094] In response to the secondary number being equal to any sampling number corresponding to the sampling frequency, judge the raw material substrate as meeting the preset sampling inspection conditions;

[0095] In response to the secondary number not being equal to any sampling number corresponding to the sampling frequency, determine the sampling number having a positive proximity relationship with the secondary number as the target number, and determine the number difference between the target number and the secondary number;

[0096] When the number difference is less than or equal to a preset difference, retrieve the inspection result of the substrate number equivalent to the target number from the historical inspection library, and when the inspection result is unqualified, determine the raw material substrate corresponding to the secondary number as meeting the preset inspection conditions.

[0097] For example, in this embodiment, since the raw material substrates may correspond to different production batches during the production process, the quality requirements that each production batch needs to meet may also be different, resulting in different inspection frequencies that may correspond to each production batch. Specifically, the higher the quality requirements of any production batch, the higher the corresponding inspection frequency should be, so as to ensure that the quality of this production batch meets the corresponding production conditions; based on the foregoing content, in order to determine the corresponding inspection batches based on the production batches, relevant substrate numbers can be generated based on the relevant data of the production batches, specifically including setting the substrate number as a composite number, including a primary number and a secondary number. Among them, the primary number specifically corresponds to the production level corresponding to the raw material substrate, and there is also a corresponding relationship between the production level and the aforementioned production batch, that is, the higher the production level, the higher the quality requirements of this production batch, and the higher the corresponding inspection frequency; by determining the production level, the corresponding inspection frequency can be determined. For example, the frequency of production level A is 10 times, and the inspection frequency corresponding to production level B can be 20 times; among them, the corresponding relationship between the production level and the inspection frequency can be obtained from the preset level inspection rules pre-stored in the server;

[0098] For the secondary number that constitutes the substrate number, it mainly corresponds to the production order. That is, the secondary number is produced according to the production order. If the production order of the raw material substrate of a certain batch is 101, the corresponding secondary number can be 101;

[0099] It should be noted that in this embodiment, by way of example, the corresponding composition structure of a certain substrate number can be, for example: grade A - 101, where grade A is the primary number and 101 is the secondary number.

[0100] Furthermore, after determining the inspection frequency, the secondary number corresponding to the raw material substrate can be compared with the inspection frequency. When the secondary number is equal to any inspection number corresponding to the inspection frequency, the raw material substrate is determined to meet the preset inspection conditions. For example, when the secondary number of a certain raw material substrate is 10 and the corresponding inspection frequency is 9, this raw material substrate does not meet the preset inspection conditions. When the secondary number of a certain raw material substrate is 18 and the corresponding inspection frequency is 9, since there is an integer multiple relationship between 18 and 9, the raw material substrate corresponding to this secondary number can be determined to meet the preset inspection conditions;

[0101] Here, when the secondary number corresponding to any raw material substrate is not equal to any sampling number corresponding to the sampling frequency, the sampling number having a positive proximity relationship with it can be determined as the target number. Here, the positive proximity relationship should be understood as the number order of the secondary number being greater than the number order of the corresponding sampling number. For example, when the sampling frequency is 10 times per time and the corresponding secondary number is 11, the sampling number 10 can be determined as having a positive proximity relationship with the secondary number at this time; or when the sampling frequency is 10 times per time and the corresponding sampling number is 29, the sampling number 20 can be determined as having a positive proximity relationship with the secondary number at this time.

[0102] After determining the target number, the corresponding difference calculation can be performed between the target number and the secondary number to obtain the corresponding number difference; at the same time, the obtained number difference is compared with the preset difference. When the number difference is less than the preset difference, it indicates that the production order of the raw material substrate corresponding to the secondary number is near the production order of the raw material substrate corresponding to the target number. At this time, the server will retrieve the sampling result of the substrate number equal to the target number from the historical sampling library and determine the corresponding sampling result.

[0103] If the sampling result is unqualified, the raw material substrate corresponding to the secondary number can be judged to meet the preset sampling conditions, and the secondary number can be used for another sampling to further improve the sampling effect of quality; while if the sampling result is qualified, there is no need to use the secondary number for another sampling.

[0104] Furthermore, in this embodiment, based on the above content, it can be known that there is a corresponding sampling batch for each production level, and the setting of the corresponding sampling batch can be based on human experience. In actual operation, the quality inspection of raw material substrates is mainly to continuously improve the production process of raw material substrates, so as to ensure meeting relevant quality requirements. In order to dynamically adjust the sampling batch based on the actual situation to ensure that the production process is supervised through sampling, the following scheme can be implemented:

[0105] In response to the sampling quantity corresponding to the same production level reaching the preset sampling quantity, determine the sampling results corresponding to the preset sampling quantity as the same sampling batch;

[0106] In response to the obtained sampling batches reaching the preset batch quantity, determine the sampling quality corresponding to the sampling batch based on the sampling results in the same sampling batch;

[0107] Sort the sampling batches in chronological order, and judge whether there is a gradually decreasing trend or a gradually increasing trend in the sampling qualities based on the obtained sampling order;

[0108] If a gradually increasing trend appears, perform a reduction training on the sampling frequency.

[0109] If a gradually decreasing trend appears, perform an increase training on the sampling frequency.

[0110] Among them, the trained sampling frequency is obtained through the following formula:

[0111]

[0112] Among them, q + is the sampling frequency k s the number of times of increase training, ∈ is the training constant value of the sampling frequency k s the number of times of decrease training of the sampling frequency q - is the sampling frequency k s the number of times of decrease training, k w is the sampling frequency after training.

[0113] For example, when the sampling quantity corresponding to the same production grade reaches the preset sampling quantity, the sampling results corresponding to the preset sampling quantity can be determined as the same sampling batch; further, when the obtained sampling batches reach the preset batch quantity, it is necessary to determine the sampling quality corresponding to the sampling batch based on the sampling results in the same sampling batch; for example, the preset sampling quantity can be set to 20, and when the sampling quantity corresponding to grade A is 20, it can be correspondingly determined as the same sampling batch, and if there are a total of 100 corresponding sampling results, 5 sampling batches arranged in chronological order can be obtained, and then the sampling quality corresponding to each sampling batch can be obtained in turn, that is, the product qualification rate is determined;

[0114] After determining the sampling quality corresponding to each sampling batch, the sampling batches can be sorted based on the chronological order to obtain the corresponding sampling order, and further, the changing trend of each sampling quality can be judged according to the sampling order. If a gradually decreasing trend appears, it indicates that the corresponding product qualification rate is gradually decreasing. At this time, the corresponding sampling frequency should be increased to improve the supervision effect on the production process; if a gradually increasing trend appears, it indicates that the corresponding product qualification rate is gradually increasing. At this time, the corresponding sampling frequency can be reduced to place the supervision focus on other production grades.

[0115] In step S104, the following content is included:

[0116] In response to the raw material substrate meeting the preset sampling conditions, in response to the raw material substrate meeting the preset sampling conditions, determine the structure of the raw material substrate, and configure the light source unit for the first ventilation surface of the raw material substrate based on the determined structural characteristics.

[0117] For example, when the raw material substrate meets the preset sampling inspection conditions, corresponding quality inspection can be performed on the raw material substrate. In this embodiment, the sampling inspection of the raw material substrate can be carried out based on image acquisition. The reasons are as follows: The raw material substrate produced by the denitrating agent generally has a ventilation structure with a fixed structure (including ventilation surfaces on both sides and a ventilation space between the ventilation surfaces on both sides). If the shape of the ventilation structure is different from the preset shape, it indicates that there will be a certain quality deviation in the raw material substrate. Therefore, the quality inspection of the raw material substrate can be completed based on corresponding image acquisition of the ventilation surface; in addition, since the raw material substrate has a certain size and shape, and the corresponding ventilation structure generally has a plurality of ventilation holes arranged in an array, based on the characteristics of light scattering, transmission, and reflection, when image acquisition is performed on the raw material substrate in a natural light irradiation environment, the natural light will be scattered, transmitted, and reflected in the ventilation structure of the raw material substrate, thereby gradually reducing the light flux exiting from the ventilation surface and being unable to accurately obtain the image content located in the ventilation structure, thus affecting the detection effect of the quality inspection; based on this, a light source unit can be configured for the first ventilation surface of the ventilation structure of the raw material substrate to increase the ambient light brightness so as to be able to perform integrity acquisition on the ventilation structure; in addition, since the scattering, transmission, and reflection of light in the ventilation structure are affected by the structural characteristics of the ventilation structure (such as including the substrate length and the substrate density corresponding to the ventilation holes), when configuring the light source unit, the structural characteristics need to be determined accordingly.

[0118] Furthermore, in this embodiment, the above-mentioned "in response to the raw material substrate meeting the preset sampling inspection conditions, determining the structure of the raw material substrate, and configuring the light source unit for the first ventilation surface of the raw material substrate based on the determined structural characteristics" may further include the following steps:

[0119] Determine any ventilation surface of the raw material substrate as the first ventilation surface, and perform image acquisition on the first ventilation surface to obtain a raw material ventilation image;

[0120] Perform binary processing on the raw material ventilation image to obtain each substrate pixel point corresponding to the first pixel value and each noise pixel point corresponding to the second pixel value;

[0121] Obtain the first quantity corresponding to each substrate pixel point and the second quantity corresponding to each noise pixel point, and determine the substrate density corresponding to the raw material substrate based on the first quantity and the second quantity;

[0122] Perform length detection on the raw material substrate based on a preset extension length strategy to determine the substrate length corresponding to the raw material substrate;

[0123] Perform a fusion calculation based on the substrate density and the substrate length, and configure the light source unit on the first ventilation surface of the raw material substrate based on the obtained substrate coefficient.

[0124] For example, in this embodiment, since the ventilation structure of the raw material substrate generally has two opposite ventilation surfaces, when setting the light source unit for the ventilation surface, any ventilation surface of the raw material substrate can be determined as the first ventilation surface, and an image of the raw material ventilation is collected for the first ventilation surface to obtain the corresponding raw material ventilation image. Here, the image collection method can be performed by placing a corresponding camera directly in front of the first ventilation surface; after obtaining the raw material ventilation image, in order to determine the structure of the raw material substrate, the raw material ventilation image can be binarized to obtain each substrate pixel point corresponding to the first pixel value and each noise pixel point corresponding to the second pixel value. Among them, each noise pixel point can be understood as the pixel point corresponding to the area other than the raw material substrate; through the binarization method, the pixel points in the corresponding substrate area in the raw material ventilation image can be quickly obtained, and further, the substrate density of the corresponding raw material substrate can be determined by the first quantity of the corresponding substrate pixel points and the second quantity of the corresponding noise pixel points; after completing the determination of the substrate density, the substrate length of the corresponding raw material substrate needs to be detected. Here, a corresponding preset extension length strategy is selected to perform the length detection of the raw material substrate, so as to obtain the substrate length corresponding to the raw material substrate; finally, by performing a fusion calculation on the obtained substrate density and substrate length, the light source unit can be configured on the first ventilation surface of the raw material substrate based on the obtained substrate coefficient.

[0125] Furthermore, in this embodiment, the above-mentioned "performing length detection on the raw material substrate based on the preset extension length strategy to determine the substrate length corresponding to the raw material substrate" may further include the following steps:

[0126] Taking the image center point of the raw material ventilation image as the coordinate origin, establish an image coordinate system corresponding to the raw material ventilation image, and obtain each image coordinate point constituting the raw material ventilation image based on the image coordinate system;

[0127] Among each image coordinate point, determine each coordinate point corresponding to the horizontal coordinate extreme value and the vertical coordinate extreme value as each detection coordinate point, and determine each detection point position in the first ventilation surface corresponding to the raw material ventilation image that has a mapping relationship with each detection coordinate point;

[0128] Determine the ventilation surface of the raw material substrate that has an opposite relationship with the first ventilation surface as the second ventilation surface, take the second ventilation surface as the horizontal plane, perform distance detection on the distance information of each detection point position, and obtain each distance detection value corresponding to each detection point position;

[0129] Among the distance detection values, the detection value with the smallest corresponding numerical value is determined as the reference detection value, and the detection point corresponding to the reference detection value is determined as the reference point;

[0130] The point-plane distance between the second ventilation surface and the reference point is determined as the substrate length corresponding to the raw material substrate along the extension direction perpendicular to the second ventilation surface of the raw material substrate.

[0131] For example, in this embodiment, when producing a raw material substrate, due to different manufacturing processes, the two ventilation surfaces included in the ventilation structure of the raw material substrate may not be parallel to each other, resulting in the substrate length of the raw material substrate may not be a fixed value. At this time, it is necessary to perform the length detection of the raw material substrate based on a preset extension length strategy. The specific process is as follows:

[0132] First, the image center point of the raw material ventilation image can be used as the coordinate origin to establish an image coordinate system corresponding to the raw material ventilation image, and further, based on the image coordinate system, each image coordinate point constituting the raw material ventilation image can be obtained;

[0133] Next, among each image coordinate point, the coordinate points corresponding to the horizontal coordinate extreme values (including the corresponding maximum and minimum values) and the vertical coordinate extreme values (including the corresponding maximum and minimum values) are respectively determined as each detection coordinate point. Since each detection coordinate point corresponds to a coordinate extreme value, each detection coordinate point can be understood as being located on the image contour of the raw material ventilation image. Further, since the raw material ventilation image is obtained based on the first ventilation surface, there is a corresponding mapping relationship between the raw material ventilation image and the first ventilation surface. Based on this, each detection point having a mapping relationship with each detection coordinate point can be obtained on the first ventilation surface;

[0134] Then, the ventilation surface of the raw material substrate that has an opposite relationship with the first ventilation surface is determined as the second ventilation surface, and further, with the second ventilation surface as the horizontal plane, distance detection of the distance information of each detection point is performed to obtain each distance detection value corresponding to each detection point; specifically, the corresponding distance detection process can include: an infrared sensor is pre-set at a position above the first ventilation surface, and distance detection is respectively performed on each detection point based on this infrared sensor to obtain the corresponding distance detection values;

[0135] Then, the detection value with the smallest corresponding numerical value among each distance detection value can be determined as the reference detection value, and at the same time, the detection point corresponding to the reference detection value is determined as the reference point; it can be noted that the smaller the corresponding distance detection value, the longer the substrate length corresponding to this detection point should be;

[0136] Finally, a point-plane distance between the second ventilation surface and the reference point position can be obtained along the extension direction perpendicular to the second ventilation surface of the raw material substrate, and the point-plane distance is further determined as the substrate length corresponding to the raw material substrate; it should be noted that since the specific value of the substrate length will affect the configuration of the light source unit, when the substrate length is longer, it means that the light source configuration of the required light source unit should be higher. Therefore, the distance detection value with the smallest corresponding value among the distance detection values needs to be determined as the corresponding reference detection value, and further, the corresponding substrate length is determined based on the reference detection value to ensure that the acquisition requirements for the integrity of the ventilation structure can be met when the light source unit is configured subsequently.

[0137] In this embodiment, as can be seen from the above content, after determining the substrate density and the substrate length, fusion calculation can be performed based on the substrate density and the substrate length, and then the light source unit can be configured for the first ventilation surface of the raw material substrate based on the obtained substrate coefficient. The specific implementation method can be achieved based on the following steps:

[0138] Normalize the substrate density and the substrate length respectively to obtain the corresponding first density coefficient and first length coefficient;

[0139] Perform multiplication calculations on the first density coefficient and the first length coefficient respectively with the retrieved density weight value and length weight value to obtain the second density coefficient and the second length coefficient;

[0140] Perform superposition calculation on the second density coefficient and the second length coefficient to obtain the substrate coefficient corresponding to the raw material substrate, and retrieve a preset light source comparison table. Among them, the light source comparison table includes each different coefficient interval and the light source specifications respectively corresponding to each different coefficient interval.

[0141] Traverse the preset light source comparison table, determine the light source specification corresponding to the substrate coefficient based on the coefficient interval including the substrate coefficient, and configure the light source unit for the first ventilation surface of the raw material substrate based on the light source specification.

[0142] For example, in this embodiment, when obtaining the substrate coefficient, since the substrate density and the substrate length correspond to different measurement units, in order to perform fusion calculation subsequently, the basic density and the substrate length need to be normalized respectively. The specific steps may include retrieving the density normalization value and the length normalization value, and further performing multiplication calculations on the substrate density, the substrate length with the density normalization value and the length normalization value respectively, so as to obtain the corresponding first density coefficient and first length coefficient.

[0143] Further, after obtaining the first density coefficient and the first length coefficient, corresponding density weight values and length weight values can be preset based on the influence degrees of density and length on the configuration of the light source unit. For example, the density weight value can be 0.6 and the length weight value can be 0.4. Then, the first density coefficient and the first length coefficient are respectively multiplied by the density weight value and the length weight value to obtain the corresponding second density coefficient and second length coefficient;

[0144] Next, the second density coefficient and the second length coefficient are superimposed and calculated, and then the substrate coefficient corresponding to the raw material substrate can be obtained. In order to configure the light source unit based on the substrate coefficient, a corresponding preset light source comparison table can also be stored in the server. The preset light source comparison table includes different coefficient intervals and the light source specifications corresponding to the different coefficient intervals respectively;

[0145] Finally, by traversing the preset light source comparison table, the coefficient interval including the substrate coefficient is determined, and further the light source specification corresponding to the substrate coefficient is determined based on this coefficient interval. Thus, the light source unit can be configured for the first ventilation surface of the raw material substrate. It should be noted that the coefficient intervals included in the preset light source comparison table can, for example, include interval A [1, 2], interval B [3, 4], and interval C [5, 6]. When the corresponding substrate coefficient is 3, it can be determined that interval B includes the substrate coefficient. At this time, the corresponding light source unit can be selected based on the light source specification corresponding to interval B. The light source specification can be understood as the relevant configurations such as the light intensity of the corresponding light source unit.

[0146] In step S106, the following content is included:

[0147] Control the light source unit to irradiate light towards the first ventilation surface, and configure a photographing unit for the second ventilation surface of the raw material substrate that is oppositely arranged to the first ventilation surface.

[0148] For example, after completing the configuration of the light source unit, the light source unit can be controlled to irradiate light towards the first ventilation surface so that the light of the light source can be transmitted in the ventilation structure of the raw material substrate. At the same time, in order to obtain the light irradiation situation, a corresponding photographing unit also needs to be arranged at the second ventilation surface that is oppositely arranged to the first ventilation surface to realize image photographing of the light irradiation situation.

[0149] In step S108, the following content is included:

[0150] Control the photographing unit to perform image photographing towards the second ventilation surface to obtain a substrate image.

[0151] For example, after setting the photographing unit, the photographing unit can be controlled to photograph an image of the second ventilation surface on the premise that the light source unit irradiates light toward the first ventilation surface, so as to obtain a corresponding substrate image.

[0152] For example, Figure 2 shows that, in the case of irradiating a raw material substrate with light based on a light source unit, the photographing unit is controlled to photograph an image of the second ventilation surface of the raw material substrate. Among them, the light source unit is arranged on the left side of the raw material substrate and the corresponding number is set to 6, while the photographing unit is arranged on the right side of the raw material substrate.

[0153] In step S110, the following contents are included:

[0154] Perform region recognition on the substrate image, and determine the substrate type corresponding to the raw material substrate based on the respective region shapes corresponding to the respective light regions located in the substrate image.

[0155] For example, in this embodiment, after obtaining the corresponding substrate image, the corresponding light irradiation situation can be obtained based on the substrate image. Since the substrate types of the raw material substrate may include multiple types, such as honeycomb type, corrugated type, and plate type, in order to perform quality inspection on the raw material substrate based on the substrate image, it is necessary to determine the corresponding substrate type based on the substrate image. Here, since the substrate structures corresponding to different substrate types are different, therefore, region recognition can be performed on the substrate image, and the substrate type corresponding to the raw material substrate can be determined based on the respective region shapes corresponding to the respective light regions located in the substrate image.

[0156] Furthermore, in this embodiment, the above-mentioned "perform region recognition on the substrate image, and determine the substrate type corresponding to the raw material substrate based on the respective region shapes corresponding to the respective light regions located in the substrate image" may further include the following steps:

[0157] Perform pixel recognition on the obtained substrate image to obtain the respective light pixel points corresponding to the light source light in the substrate image, and perform pixel connection on the respective light pixel points based on connectivity to obtain respective light regions;

[0158] Taking the region center point of each light region as the rotation point, vertically rotate the light region in sequence to obtain the first rotation region, the second rotation region, and the third rotation region corresponding to the same light region, and determine the original region shape corresponding to the light region, the first rotation shape corresponding to the first rotation region, the second rotation shape corresponding to the second rotation region, and the third rotation shape corresponding to the third rotation region as the shape comparison group corresponding to the light region;

[0159] Retrieve each standard shape corresponding to the honeycomb type, corrugation type, and plate type respectively, and perform a similarity comparison between each shape comparison group corresponding to each light region and each standard shape;

[0160] In response to the shape similarity between any shape in any shape comparison group and any standard shape being greater than a preset first similarity, determine the light region corresponding to the shape comparison group as the similar shape corresponding to the standard shape;

[0161] In response to the shape ratio of the similar shape corresponding to any standard shape being greater than a preset ratio, determine the substrate type corresponding to the raw material substrate as the type corresponding to the standard shape.

[0162] For example, in this embodiment, when performing region recognition on the substrate image, it can be implemented based on the following process:

[0163] First, it is necessary to perform pixel recognition on the obtained substrate image to obtain each light pixel point corresponding to the light source relationship pattern in the substrate image, and further perform pixel connection on adjacent pixel points to obtain the corresponding light regions;

[0164] Next, since the substrate structures included in any substrate type generally show symmetry, in order to group the light regions in the substrate image based on the same region shape, the light regions can be vertically rotated in sequence with the region center point of each light region as the rotation point to obtain the first rotation region, the second rotation region, and the third rotation region corresponding to the same light region, and determine the original region shape corresponding to the light region, the first rotation shape corresponding to the first rotation region, the second rotation shape corresponding to the second rotation region, and the third rotation shape corresponding to the third rotation region as the shape comparison group corresponding to the light region;

[0165] Then, in order to determine the substrate type corresponding to the raw material substrate based on the region shape, it is necessary to retrieve each standard shape corresponding to the honeycomb type, corrugation type, and plate type respectively, and at the same time perform a similarity comparison between the shape comparison groups corresponding to each light region and each standard shape;

[0166] Then, when the shape similarity between any shape in any shape comparison group and any standard shape is greater than a preset first similarity, the light region corresponding to the shape comparison group can be determined as the similar shape corresponding to the standard shape;

[0167] Finally, it is also necessary to determine the shape ratios of the similar shapes corresponding to each standard shape. When the shape ratio corresponding to any standard shape is greater than the preset ratio, it indicates that most of the light regions of the corresponding raw material substrate are similar to this standard shape. At this time, the substrate type corresponding to the raw material substrate can be determined as the type corresponding to this standard shape.

[0168] For example, Figure 3 shows a raw material substrate corresponding to the honeycomb type, Figure 4 shows a raw material substrate corresponding to the plate type, while Figure 5 shows a raw material substrate corresponding to the corrugated type.

[0169] In step S112, the following contents are included:

[0170] Retrieve the preset quality determination strategy corresponding to the substrate type to perform quality inspection on the raw material substrate.

[0171] For example, in this embodiment, after completing the determination of the substrate type of the raw material substrate, the preset quality determination strategy corresponding to this substrate type can be retrieved from the server to perform quality inspection on the raw material substrate, so as to determine whether the raw material substrate is qualified or unqualified.

[0172] Furthermore, in this embodiment, the above-mentioned "retrieve the preset quality determination strategy corresponding to the substrate type to perform quality inspection on the raw material substrate" may further include the following steps:

[0173] When the substrate type corresponding to the raw material substrate is the honeycomb type, obtain the respective region contours corresponding to each light region, and disassemble the region contours to obtain the horizontal contour lines corresponding to the horizontal direction and the vertical contour lines corresponding to the vertical direction;

[0174] Determine the sizes of the horizontal contour lines and the vertical contour lines respectively to obtain the horizontal size and the vertical size corresponding to the same light region;

[0175] Based on the respective horizontal sizes corresponding to each light region, determine the horizontal size with the smallest corresponding value as the horizontal comparison value, and based on the respective vertical sizes corresponding to each light region, determine the vertical size with the smallest corresponding value as the vertical comparison value;

[0176] Retrieve the horizontal reference value and the vertical reference value corresponding to the honeycomb type, and compare the horizontal reference value and the vertical reference value with the horizontal comparison value and the vertical comparison value respectively;

[0177] In response to any comparison value being less than the corresponding reference value, determine the raw material substrate as unqualified.

[0178] For example, in this embodiment, when the substrate type of the raw material substrate is determined to be the honeycomb type, based on the structural characteristics of the honeycomb type, it can be known that the ventilation surface of the honeycomb type is generally composed of ventilation holes arranged in an array and presenting various shapes. Therefore, the quality test of the raw material substrate can be carried out by determining the dimensions of each rectangular area; specifically, the following implementation process can be included:

[0179] First, the area contours corresponding to each light region can be obtained, and each area contour can be disassembled to obtain the horizontal lateral contour line corresponding to the same area contour in the horizontal direction and the vertical longitudinal contour line corresponding to the vertical direction. Among them, the same area contour should include two lateral contour lines and two longitudinal contour lines;

[0180] Next, by determining the dimensions of the lateral contour line and the longitudinal contour line, the lateral dimension and the longitudinal dimension corresponding to the same light region can be obtained, and based on the lateral dimensions corresponding to each light region located on the raw material substrate, the lateral dimension with the smallest corresponding value is determined and defined as the lateral comparison value. Similarly, based on the longitudinal dimensions corresponding to each light region, the longitudinal dimension with the smallest corresponding value is determined as the longitudinal comparison value;

[0181] Then, by retrieving the lateral reference value and the longitudinal reference value corresponding to the honeycomb type stored in the server, and comparing the lateral reference value and the longitudinal reference value with the lateral comparison value and the longitudinal comparison value respectively;

[0182] Finally, when any comparison value (lateral comparison value and / or longitudinal comparison value) is less than the corresponding reference value, it indicates that the dimensions of each light region included in the raw material substrate do not meet the standard, and thus the structure of the quality test of the raw material substrate can be determined as unqualified.

[0183] Similarly, in this embodiment, when the substrate type corresponding to the raw material substrate is the plate type or the corrugated type, the above-mentioned "retrieving the preset quality determination strategy corresponding to the substrate type to perform quality detection on the raw material substrate" can further include the following steps:

[0184] When the substrate type corresponding to the raw material substrate is the plate type or the corrugated type, pairwise similarity comparison is performed on each light region, and the light regions with the corresponding region similarity greater than the preset second similarity are divided into the same region shape group to obtain each region shape group;

[0185] The area of each light region located in the same region shape group is determined respectively to obtain the area of each region corresponding to each light region;

[0186] Among the areas of each region, the region area with the smallest corresponding value is determined as the minimum area, and the region area with the largest corresponding value is determined as the maximum area. Then, an average value calculation is performed based on all the region areas except the minimum area and the maximum area to obtain the area average value;

[0187] In response to the area difference between the minimum area and / or the maximum area and the area average value being greater than a preset area difference, the raw material substrate is determined to be unqualified.

[0188] For example, from the above content, it can be seen that the substrate structures included in any substrate type generally exhibit symmetry. Therefore, a raw material substrate includes multiple light regions with the same or similar shapes. At this time, pairwise similarity comparison can also be performed on each light region. And when the similarity of any obtained region is greater than the preset second similarity stored in the server, it indicates that the two light regions corresponding to the region similarity are of the same or similar shapes. At this time, the two can be classified into the same region shape group, and then each region shape group corresponding to different shapes can be obtained; further, the area of each light region located in the same region shape group can be determined respectively to obtain the region areas corresponding to each light region. Among the region areas, the region area with the smallest corresponding value is determined as the minimum area, and the region area with the largest corresponding value is determined as the maximum area. Then, an average value calculation is further performed on all the remaining region areas to obtain the corresponding area average value. Finally, by calculating the difference between the minimum area and the maximum area and the area average value, the corresponding area difference is obtained. And when any area difference is greater than the preset area difference, it indicates that there are light regions with a large size difference in the raw material substrate. At this time, the raw material substrate needs to be determined to be unqualified.

[0189] According to the solution of the present invention, first, the method judges the sequential numbering and sampling inspection conditions of the raw material substrate through an automated process, greatly improving the production efficiency and management accuracy, ensuring that every operation is based on evidence, and reducing the possibility of human errors; second, by determining the structure of the raw material substrate that meets the sampling inspection conditions and accurately configuring the light source unit and the imaging unit, non-destructive and efficient detection of the internal structural characteristics of the raw material substrate is achieved. This not only improves the detection accuracy but also guarantees the integrity of the raw material substrate, providing a reliable basis for subsequent processing. Furthermore, by using the light irradiation and image capture technology and combining with an advanced region recognition algorithm, the light regions and their shapes located in the substrate image can be quickly and accurately recognized, thereby accurately determining the substrate type of the raw material substrate. The intelligence of this step greatly shortens the product classification time and enhances the flexibility of the production line. Finally, according to the recognized substrate type, the corresponding quality inspection strategy is automatically retrieved, making the quality inspection process more scientific and rigorous, effectively guaranteeing the quality stability and consistency of the denitration agent product, and enhancing the market competitiveness of the product and the corresponding detection efficiency.

[0190] Another embodiment of the present invention provides an efficient denitration agent quality inspection system. Figure 6 For its corresponding system block diagram, as Figure 6 shown, the system includes:

[0191] A sampling inspection determination module, configured to, in response to the completion of the manufacture of the raw material substrate of the denitration agent, sequentially number the raw material substrate according to the current number sequence, and judge whether the raw material substrate meets the preset sampling inspection conditions based on the obtained substrate number;

[0192] A structure determination module, configured to, in response to the raw material substrate meeting the preset sampling inspection conditions, determine the structure of the raw material substrate, and configure the light source unit for the first ventilation surface of the raw material substrate based on the determined structural characteristics;

[0193] A shooting configuration module, configured to control the light source unit to irradiate light towards the first ventilation surface, and configure the imaging unit for the second ventilation surface of the raw material substrate that is oppositely arranged to the first ventilation surface;

[0194] An image shooting module, configured to control the imaging unit to shoot an image towards the second ventilation surface to obtain a substrate image;

[0195] A region recognition module, configured to perform region recognition on the substrate image, and determine the substrate type corresponding to the raw material substrate based on the respective region shapes corresponding to the respective light regions located in the substrate image;

[0196] The quality inspection module is configured to retrieve a preset quality determination strategy corresponding to the type of the substrate and perform quality inspection on the raw substrate.

[0197] In the specification provided herein, the algorithms and displays are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the examples of the present invention. Based on the above description, the structure required to construct such a system is obvious. In addition, the present invention is not directed to any particular programming language. It should be understood that the content of the present invention described herein can be implemented using various programming languages, and the description of a particular language above is for the purpose of disclosing the preferred embodiments of the present invention.

[0198] In the specification provided herein, a large number of specific details are set forth. However, it can be understood that the embodiments of the present invention can be practiced without these specific details. In some instances, well-known methods, structures, and technologies have not been shown in detail so as not to obscure the understanding of this specification.

[0199] Similarly, it should be understood that, in order to streamline the present disclosure and assist in understanding one or more of the various inventive aspects, in the foregoing description of the exemplary embodiments of the present invention, the various features of the present invention are sometimes grouped together in a single embodiment, figure, or description thereof.

[0200] Those skilled in the art should understand that the modules or units or components of the devices in the examples disclosed herein can be arranged in the devices as described in the embodiments, or alternatively can be located in one or more devices different from the devices in the examples. The modules in the foregoing examples can be combined into one module or further divided into multiple sub-modules.

[0201] Those skilled in the art can understand that the modules in the devices of the embodiments can be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments can be combined into one module or unit or component, and further can be divided into multiple sub-modules or sub-units or sub-components.

[0202] In addition, those skilled in the art can understand that, although some of the embodiments described herein include certain features included in other embodiments but not other features, the combination of the features of different embodiments means that it is within the scope of the present invention and forms different embodiments.

[0203] In addition, some of the embodiments described herein are described as methods or combinations of method elements that can be implemented by a processor of a computer system or by other devices performing the functions. Therefore, a processor having the necessary instructions for implementing the method or method elements constitutes an apparatus for implementing the method or method elements. In addition, the elements described herein in the apparatus embodiments are examples of the following apparatus: the apparatus is used to implement the functions performed by the elements for the purpose of implementing the invention.

[0204] As used herein, unless otherwise specified, the use of ordinal numbers "first", "second", "third", etc. to describe ordinary objects only represents different instances of similar objects, and is not intended to imply that the objects so described must have a given order in terms of time, space, sorting, or in any other way.

[0205] Although the present invention has been described in terms of a limited number of embodiments, those skilled in the art in this technical field will appreciate that other embodiments can be contemplated within the scope of the present invention as thus described. In addition, it should be noted that the language used in this specification has been mainly selected for readability and teaching purposes rather than for the purpose of interpreting or limiting the subject matter of the present invention.

Claims

1. A high-efficiency denitrification agent quality detection method, characterized in that: The following steps are involved: In response to completion of manufacturing of a raw material substrate based on a denitrifying agent, sequentially numbering the raw material substrate according to a current numbering sequence, and determining whether the raw material substrate meets a preset sampling condition based on the obtained substrate number, wherein the raw material substrate is a substrate carrying a denitrifying agent; In response to the raw material substrate meeting a preset sampling condition, determining the structure of the raw material substrate, determining any ventilation surface of the raw material substrate as a first ventilation surface, and configuring a light source unit on the first ventilation surface of the raw material substrate based on the determined structural characteristics; Controlling the light source unit to irradiate light toward the first ventilation surface, and configuring a photographing unit on a second ventilation surface of the raw material substrate that is opposite to the first ventilation surface; controlling the shooting unit to shoot an image toward the second ventilation surface to obtain an image of the substrate; Performing region recognition on the substrate image, and determining the type of substrate corresponding to the raw substrate based on the obtained region shapes corresponding to the light regions in the substrate image; A preset quality determination strategy corresponding to the type of substrate is retrieved to perform quality inspection on the raw substrate.

2. The high-efficiency denitrification agent quality detection method according to claim 1, characterized in that: Determining whether the raw substrate meets the preset sampling conditions based on the obtained substrate number includes: Decomposing the substrate number to obtain a primary number and a secondary number; Determining a production grade corresponding to the raw substrate based on the main grade number, and determining a sampling inspection frequency corresponding to the production grade based on a preset grade sampling inspection rule, wherein different production grades correspond to different sampling inspection frequencies; Comparing the secondary number with each sampling inspection number corresponding to the sampling inspection frequency; In response to the secondary number being equal to any sampling inspection number corresponding to the sampling inspection frequency, the raw substrate is determined to meet the preset sampling inspection conditions; In response to the secondary number not being equal to any sampling number corresponding to the sampling frequency, determining a sampling number that is in a positive proximity relationship with the secondary number as a target number, and determining a number difference between the target number and the secondary number, wherein the positive proximity relationship is that the number sequence of the secondary number is greater than the number sequence of the corresponding sampling number; When the number difference is less than or equal to the preset difference, the sampling result of the substrate number equivalent to the target number is retrieved based on the historical sampling library, and if the sampling result is unqualified, the raw material substrate corresponding to the secondary number is judged to meet the preset sampling conditions.

3. The high-efficiency denitrification agent quality detection method according to claim 2, characterized in that: The method further comprises: In response to the number of random inspections corresponding to the same production level reaching a preset number of random inspections, determining the random inspection results corresponding to the preset number of random inspections as the same random inspection batch; In response to the obtained sampling batches reaching a preset number of batches, determining the sampling quality corresponding to the sampling batch based on the sampling results in the same sampling batch; Sort the inspection batches based on chronological order, and determine whether the quality of each inspection shows a gradually decreasing trend or a gradually increasing trend based on the obtained inspection order; If a gradual upward trend occurs, the frequency of random inspections will be reduced; If a gradual downward trend occurs, the frequency of random inspections will be increased; Among them, the frequency of random inspection after training is obtained by the following formula: Among them, q + is the sampling frequency k s Increase the number of training times, ∈ is the sampling frequency k s The training constant value, q - is the sampling frequency k s Reduce the number of training times, k w is the frequency of random inspection after training.

4. The high-efficiency denitrification agent quality detection method according to claim 1, characterized in that: In response to the raw material substrate meeting a preset sampling condition, determining the structure of the raw material substrate, and configuring a light source unit on the first ventilation surface of the raw material substrate based on the determined structural characteristics, including: Capturing an image of the first ventilation surface to obtain a ventilation image of the raw material; Binarizing the raw material ventilation image to obtain substrate pixel points corresponding to the first pixel value and noise pixel points corresponding to the second pixel value; Obtaining a first number corresponding to each substrate pixel and a second number corresponding to each noise pixel, and determining a substrate density corresponding to the raw substrate based on the first number and the second number; Performing length detection on the raw substrate based on a preset extension length strategy to determine a substrate length corresponding to the raw substrate; A fusion calculation is performed based on the substrate density and the substrate length, and a light source unit is configured on the first ventilation surface of the raw substrate based on the obtained substrate coefficient.

5. The high-efficiency denitrification agent quality detection method according to claim 4, characterized in that: Performing length detection on the raw material substrate based on a preset extension length strategy to determine a substrate length corresponding to the raw material substrate includes: Taking the image center point of the raw material ventilation image as the coordinate origin, establishing an image coordinate system corresponding to the raw material ventilation image, and acquiring the image coordinate points constituting the raw material ventilation image based on the image coordinate system; Determine, in each image coordinate point, each coordinate point corresponding to a transverse coordinate extreme value and a longitudinal coordinate extreme value as a detection coordinate point, and determine, in a first ventilation surface corresponding to the raw material ventilation image, each detection point position having a mapping relationship with each detection coordinate point; Determine a ventilation surface of the raw material substrate that is opposite to the first ventilation surface as a second ventilation surface, use the second ventilation surface as a horizontal plane, perform distance detection on each detection point, and obtain distance detection values corresponding to each detection point; Determine the detection value with the smallest corresponding value among the distance detection values as the reference detection value, and determine the detection point corresponding to the reference detection value as the reference point; A point-to-surface distance between the second ventilation surface and the reference point along an extension direction perpendicular to the second ventilation surface of the raw material substrate is determined as a substrate length corresponding to the raw material substrate.

6. The high-efficiency denitrification agent quality detection method according to claim 4, characterized in that: Performing a fusion calculation based on the substrate density and the substrate length, and configuring a light source unit on the first ventilation surface of the raw substrate based on the obtained substrate coefficient, including: Normalizing the substrate density and substrate length respectively to obtain corresponding first density coefficient and first length coefficient; Multiplying the first density coefficient and the first length coefficient by the retrieved density weight value and the retrieved length weight value, respectively, to obtain a second density coefficient and a second length coefficient; The second density coefficient and the second length coefficient are superimposed and calculated to obtain a substrate coefficient corresponding to the raw substrate, and a preset light source comparison table is retrieved, wherein the light source comparison table includes different coefficient intervals and light source specifications corresponding to each different coefficient interval. The preset light source comparison table is traversed, and a light source specification corresponding to the substrate coefficient is determined based on a coefficient interval including the substrate coefficient, and a light source unit is configured on the first ventilation surface of the raw substrate based on the light source specification.

7. The high-efficiency denitrification agent quality detection method according to claim 1, characterized in that: Performing region recognition on the substrate image and determining the type of substrate corresponding to the raw substrate based on the obtained region shapes corresponding to the light regions in the substrate image, including: Performing pixel recognition on the obtained substrate image to obtain light pixel points corresponding to the light source light in the substrate image, and performing pixel connection on each light pixel point based on connectivity to obtain each light area; Taking the center point of each light region as a rotation point, the light regions are sequentially vertically rotated to obtain a first rotation region, a second rotation region, and a third rotation region corresponding to the same light region, and determining the original region shape corresponding to the light region, the first rotation shape corresponding to the first rotation region, the second rotation shape corresponding to the second rotation region, and the third rotation shape corresponding to the third rotation region as a shape comparison group corresponding to the light region; Retrieving standard shapes corresponding to honeycomb, corrugated, and plate types, and performing similarity comparison between each shape comparison group corresponding to each light area and each standard shape; In response to a shape similarity between any shape in any shape comparison group and any standard shape being greater than a preset first similarity, determining the light region corresponding to the shape comparison group as a similar shape corresponding to the standard shape; In response to a shape ratio of similar shapes corresponding to any standard shape being greater than a preset ratio, the type of substrate corresponding to the raw material substrate is determined to be the type corresponding to the standard shape.

8. The high-efficiency denitrification agent quality detection method according to claim 7, characterized in that: Retrieving a preset quality determination strategy corresponding to the type of substrate to perform quality inspection on the raw substrate includes: When the substrate type corresponding to the raw substrate is a honeycomb type, obtaining the contours of each region corresponding to each light region, and decomposing the contours of the region to obtain a horizontal contour line corresponding to the horizontal direction and a longitudinal contour line corresponding to the vertical direction; Determine the dimensions of the horizontal contour line and the vertical contour line respectively to obtain the horizontal dimension and the vertical dimension corresponding to the same light area; Based on the horizontal dimensions corresponding to the light regions, the horizontal dimension with the smallest corresponding value is determined as the horizontal contrast value; based on the vertical dimensions corresponding to the light regions, the vertical dimension with the smallest corresponding value is determined as the vertical contrast value; Retrieving a horizontal reference value and a vertical reference value corresponding to the honeycomb type, and comparing the horizontal reference value and the vertical reference value with the horizontal comparison value and the vertical comparison value respectively; In response to any comparison value being smaller than a corresponding reference value, the raw material substrate is determined to be unqualified.

9. The high-efficiency denitrification agent quality detection method according to claim 7, characterized in that: Retrieving a preset quality determination strategy corresponding to the type of substrate to perform quality inspection on the raw substrate includes: When the substrate type corresponding to the raw substrate is a plate type or a corrugated type, performing a similarity comparison between each pair of light regions, and classifying each light region whose corresponding region similarity is greater than a preset second similarity into the same region shape group, thereby obtaining each region shape group; Determine the area of each light region in the same region shape group, and obtain the area of each region corresponding to each light region; The area of the region with the smallest corresponding value in each region is determined as the minimum area, and the area of the region with the largest corresponding value is determined as the maximum area, and the average value of the area of all regions other than the minimum area and the maximum area is calculated to obtain the area average value; In response to an area difference between the extremely small area and / or the extremely large area and the area average being greater than a preset area difference, the raw material substrate is determined to be unqualified.

10. A high-efficiency denitrification agent quality detection system, characterized in that: include: a random inspection determination module configured to, in response to completion of manufacturing of a raw material substrate based on a denitrifying agent, sequentially number the raw material substrate according to a current numbering sequence, and determine whether the raw material substrate meets a preset random inspection condition based on the obtained substrate number, wherein the raw material substrate is a substrate carrying a denitrifying agent; a structure determination module configured to, in response to the raw material substrate meeting a preset sampling condition, determine the structure of the raw material substrate, determine any ventilation surface of the raw material substrate as a first ventilation surface, and configure a light source unit on the first ventilation surface of the raw material substrate based on the determined structural characteristics; a shooting configuration module configured to control the light source unit to irradiate light toward the first ventilation surface, and to configure a shooting unit on a second ventilation surface of the raw material substrate that is opposite to the first ventilation surface; an image capturing module configured to control the capturing unit to capture an image toward the second ventilation surface to obtain an image of the substrate; a region recognition module configured to perform region recognition on the substrate image and determine the type of substrate corresponding to the raw substrate based on the obtained region shapes corresponding to the light regions in the substrate image; The quality detection module is configured to call a preset quality determination strategy corresponding to the type of the substrate to perform quality detection on the raw material substrate.

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