Intelligent spraying dust suppression method based on multi-environment parameter regulation

Abstract

The application discloses an intelligent spraying dust suppression method based on multi-environment parameter regulation, and specifically comprises the following steps: step one, acquiring pictures of each to-be-monitored position in a tunnel, and determining an overall gray value A of the pictures; step two, acquiring a construction sound signal B in the tunnel, and comparing the construction sound signal B with a standard construction sound; step three, if the gray value A of more than 80% of the area in the pictures is distributed between 50 and 220, and the similarity of the sound signal B and the standard construction sound reaches more than 90%, the spraying dust suppression system is started, and steps four to five are executed after the spraying dust suppression system is turned off; if the above values are not reached, steps three to six are continuously executed. By adopting the method, the tunnel excavation dust is accurately treated, water resources are saved, and dust suppression cost is reduced.

Description

Technical Field

[0001] This invention belongs to the field of dust control technology in tunnel construction, specifically relating to an intelligent spray dust suppression method based on the regulation of multiple environmental parameters. Background Technology

[0002] Long, high-altitude tunnels operate in cold, low-pressure environments, altering dust transport patterns and making precise and efficient dust control during construction particularly crucial. Therefore, optimizing dust control technology is essential for the physical and mental health of workers during high-altitude tunnel excavation.

[0003] Based on the principles of dust control technology and the different processes of dust diffusion within tunnels, dust removal is categorized into techniques such as "reduction, lowering, emission, removal, and obstruction." Conventional ventilation methods are insufficient for effectively controlling dust throughout the tunnel, while individual protective equipment is unsuitable for strenuous physical labor within the tunnel, potentially leading to safety accidents. Research indicates that the most economical and effective method for dust control is the spraying method within water mist dust suppression technology. However, in on-site spray dust suppression, the spray parameters are fixed and cannot be automatically adjusted according to actual conditions, resulting in poor dust suppression effectiveness, water waste, and high costs. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent spray dust suppression method based on the control of multiple environmental parameters, which achieves precise control of dust during tunnel excavation, while saving water resources and reducing dust suppression costs.

[0005] This invention adopts the following technical solution: an intelligent spray dust suppression method based on the control of multiple environmental parameters, the method is as follows:

[0006] Step 1: Obtain images of each location to be monitored within the tunnel, determine the overall grayscale value A of the images, and set the grayscale value of pure white in the images to 255 and the grayscale value of pure black to 0.

[0007] Step 2: Obtain the construction sound signal B inside the tunnel and compare it with the construction sound of the standard operation procedure to obtain the ratio B;

[0008] Step 3: If the grayscale value A of more than 80% of the area in the image is between 50 and 220, and the similarity between the sound signal B and the sound of the standard operation procedure reaches more than 90%, start the spray dust suppression system, and then execute steps 3 to 6 after shutting it down; if the above values ​​are not achieved, continue to execute steps 4 to 6.

[0009] Step 4: Under a certain work procedure, the average dust concentration at the pedestrian breathing surface at different distances from the tunnel face was measured as C0, the corresponding average tunnel wind speed was V0, the ambient air pressure was P0, and the relative humidity was [missing value]. Furthermore, the parameters for the air-water spray system with the highest dust suppression efficiency were determined to be: air pressure: p0, water pressure: p1;

[0010] Under the corresponding operating conditions, the dust concentration C, tunnel wind speed V, ambient air pressure P, and relative humidity at the monitored location inside the tunnel were obtained. For different values, set the offset parameters N1, N2, N3, and N4, and calculate them as follows:

[0011] N1 = C / C0(1);

[0012] N2=V / V0(2);

[0013] N3=P / P0(3);

[0014]

[0015] Where: N1 is the dust concentration offset parameter; N2 is the tunnel wind speed offset parameter; N3 is the ambient air pressure offset parameter; N4 is the relative humidity offset parameter;

[0016] Step 5: Based on the parameters in Step 4, calculate the spray air pressure and water pressure under the specified operating conditions using the following formula:

[0017] p g =(M1N1+M2N2-M2N2-M4N4)×p0(5)

[0018] p w =(M1N1+M2N2-M3N3-M4N4)×p1(6)

[0019] Where, p g p represents the optimal spray pressure under the corresponding operating conditions. w The optimal spray water pressure is set for the corresponding operating conditions.

[0020] The weights of dust concentration, tunnel wind speed, ambient air pressure and relative humidity on the spraying effect are M1, M2, M3 and M4, respectively. The values ​​of M1, M2, M3 and M4 are all between 0 and 3, and M1>M2>M3>M4.

[0021] Step 6: Set the optimal spray air pressure and optimal spray water pressure from Step 2 to set values, and spray water onto the monitoring area inside the tunnel at the set values.

[0022] Furthermore, in step four, three locations at different distances from the working face are selected for the human breathing surface.

[0023] Furthermore, in step six, a spray dust suppression system is used to spray water onto the areas to be monitored inside the tunnel;

[0024] The spray dust suppression system includes: a tunnel excavation dust identification primary response device, a sensing device, and a graded and controlled spray dust removal device;

[0025] The tunnel boring dust identification primary response device includes:

[0026] Multiple smart cameras are installed at various dust source locations within the tunnel, and each smart camera is connected to an OpenCV module. The smart cameras are used to periodically acquire video images of key dust source locations and transmit them to the OpenCV module.

[0027] A sound response sensor is installed near the tunnel face to acquire the sound of processes involving large amounts of dust emission from the tunnel.

[0028] The sensing device includes:

[0029] Multiple dust concentration sensors are installed at the dust source locations and personnel gathering areas in the monitored area within the tunnel to obtain the dust concentration in the monitored area of ​​the tunnel.

[0030] The tunnel wind speed sensors are arranged in multiple groups, spaced apart along the tunnel direction. Each group contains multiple tunnel wind speed sensors located on the same cross-section of the tunnel and arranged at intervals, used to obtain the wind speed at different locations on different tunnel cross-sections.

[0031] An ambient air pressure sensor is installed at the location to be monitored inside the tunnel to obtain the ambient air pressure value inside the tunnel.

[0032] Multiple air humidity sensors are installed at key spray locations and work areas within the tunnel to obtain relative air humidity.

[0033] The data transcoding system is used to receive data acquired by various sensors and convert it into a data type that the intelligent control system can process.

[0034] The graded and controlled spray dust suppression device includes:

[0035] The intelligent control system receives data transmitted from various sensors and, when the dust concentration reaches a set value, calculates and matches the optimal spray parameters based on the tunnel wind speed, air pressure, and relative humidity, and outputs a signal.

[0036] The intelligent control system is also used to perform similarity recognition based on sound signals to identify the type of work being carried out inside the tunnel;

[0037] The tunnel water and gas pipes are installed inside the tunnel, with their water outlets extending to each testing area and connected to spray nozzles; intelligent pressure-controlled solenoid valves are installed on the water supply pipelines, and each intelligent pressure-controlled solenoid valve is connected to the intelligent control system.

[0038] Furthermore, it also includes a multi-degree-of-freedom servo motor, which is connected to the spray nozzle and is used to receive electrical signals from the data output by the intelligent control system to drive the spray nozzle to move.

[0039] The beneficial effects of this invention are as follows: 1. It uses an intelligent camera with machine vision recognition function to capture video data of key dust-generating sources, namely the tunnel face, and automatically captures and analyzes the grayscale of images within a set period of time; it installs a sound response sensor to identify the type of work being carried out in the tunnel, and starts spraying water mist when the grayscale value and sound signal are within the set range. In the early stage of tunnel dust generation, it identifies specific construction procedures and identifies precursors before dust spreads into the tunnel, achieving intelligent spraying with low latency and high efficiency in the early stage of dust diffusion, and accurately controlling dust in the initial stage. 2. It uses various sensors to sense the dust concentration, tunnel wind speed, ambient air pressure and relative humidity in the construction tunnel in real time, determines the optimal spraying parameters, controls the angle of the spray nozzle with the vertical direction through a multi-degree-of-freedom servo motor, and controls the cross-sectional area of ​​the pipeline channel through a solenoid valve to change the nozzle atomization angle and droplet size, achieving precise control of tunnel excavation dust while saving water resources and reducing dust suppression costs. Attached Figure Description

[0040] Figure 1 This is a schematic diagram of the spray dust suppression system in this invention.

[0041] The components include: 1. Smart camera; 2. Dust concentration sensor; 3. Tunnel wind speed sensor; 4. Ambient air pressure sensor; 5. Air humidity sensor; 6. Sound response sensor; 7. Data transcoding system; 8. Intelligent control system; 9. Multi-degree-of-freedom servo motor; 10. Spray nozzle; 11. Smart pressure control solenoid valve; 12. Tunnel water and air pipes; 13. OpenCV1 module. Detailed Implementation

[0042] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments.

[0043] This invention discloses an intelligent spray dust suppression method based on the control of multiple environmental parameters, the method is as follows:

[0044] Step 1: Obtain images of key dust-generating sources inside the tunnel, determine the overall grayscale value A of the images, and set the grayscale value of pure white in the images to 255 and the grayscale value of pure black to 0.

[0045] Specifically, such as Figure 1As shown, intelligent cameras 1 with machine vision recognition capabilities are used to capture video data at key dust-generating sources, namely the tunnel face. Automatic screenshots are taken and analyzed for grayscale values ​​over a set period. Multiple intelligent cameras 1 are used, positioned at the dust source locations within the tunnel face. Each intelligent camera 1 is connected to an OpenCV module 13. The intelligent cameras 1 periodically acquire video images of key dust-generating locations and transmit them to the OpenCV module 13 for grayscale calculation. Using MATLAB image processing capabilities, the grayscale value of pure white in the grayscale image is 255, and the grayscale value of pure black is 0. Based on dust grayscale tests during tunnel blasting, slag removal, and shotcreting processes, the grayscale values ​​range from 50 to 220, serving as a variable to measure tunnel dust concentration. The image is divided into a grid, and the overall grayscale value A of the image at that moment is calculated.

[0046] Step 2: Obtain the construction sound signal B inside the tunnel and compare it with the construction sound of the standard operation procedure to obtain the ratio B;

[0047] Specifically, sound response sensors are installed near the tunnel face. For different stages of the tunnel excavation process, the sound sensor control system is pre-loaded. Based on the sound signal B obtained by the sound response sensor, similarity recognition is performed using filters and residual networks to identify the type of work being carried out inside the tunnel.

[0048] The process of sound similarity recognition is as follows:

[0049] Dataset partitioning and enhancement: First, a dataset of ambient sound inside the tunnel was selected, and the dataset was divided into two parts: training samples and test samples. Then, the sound signals were enhanced.

[0050] FBank feature extraction: The FBank feature extraction method is used to extract relevant features from the audio signal frame by frame on both the training and test samples;

[0051] Training the residual network model: Input the feature matrix extracted from the training samples into the designed residual network model for training. After training, the trained residual network model can be obtained.

[0052] Testing model performance: The feature matrix extracted from the test samples is input into the trained residual network model to obtain the recognition results of the test samples. The network model parameters are then adjusted based on the recognition results to achieve optimal performance.

[0053] Step 3: If over 80% of the grayscale values ​​A in the image are between 50 and 220, and the similarity between the sound signal B and the standard operating procedure sound reaches over 90%, activate the spray dust suppression system and execute steps 3 to 6 after shutting it down. If the above values ​​are not met, continue executing steps 4 to 6. The main function is to identify specific construction procedures in the early stage of tunnel dust generation, identify precursors before dust spreads into the tunnel, and achieve intelligent spray low-delay and high-efficiency response in the early stage of dust diffusion, thus accurately controlling dust in the initial stage.

[0054] If the above values ​​are not reached, continue with steps three through five.

[0055] Step 4: Under a certain work procedure, the average dust concentration at the pedestrian breathing surface at different distances from the tunnel face was measured as C0, the corresponding average tunnel wind speed was V0, the ambient air pressure was P0, and the relative humidity was [missing value]. Furthermore, the parameters for the air-water spray system with the highest dust suppression efficiency were determined to be: air pressure: p0, water pressure: p1;

[0056] Under the corresponding operating conditions, the dust concentration C, tunnel wind speed V, ambient air pressure P, and relative humidity at the monitored location inside the tunnel were obtained. For different values, set the offset parameters N1, N2, N3, and N4, and calculate them as follows:

[0057] N1 = C / C0(1);

[0058] N2=V / V0(2);

[0059] N3=P / P0(3);

[0060]

[0061] Where: N1 is the dust concentration offset parameter; N2 is the tunnel wind speed offset parameter; N3 is the ambient air pressure offset parameter; N4 is the relative humidity offset parameter;

[0062] Among them, dust concentration has a positive correlation with the spray air pressure and water pressure parameters, tunnel wind speed has a positive correlation with the spray air pressure and water pressure parameters, ambient air pressure has a negative correlation with the spray air pressure and water pressure, and relative humidity has a negative correlation with the spray air pressure and water pressure. Based on actual production experience at the tunnel site, the weights of the influence of dust concentration, tunnel wind speed, ambient air pressure, and relative humidity on the spraying effect are determined to be M1, M2, M3, and M4, respectively.

[0063] Due to tunnel air pressure P, wind speed V, and relative humidity It is relatively stable, but it will vary in different work processes. The operation time of a single tunnel process is about 3 hours. Data is acquired once every 3 hours and transmitted to the data transcoding system.

[0064] Step 5: Based on the parameters in Step 4, calculate the spray air pressure and water pressure under the specified operating conditions using the following formula:

[0065] p g =(M1N1+M2N2-M3N3-M4N4)×p0(5);

[0066] p w =(M1N1+M2N2-M3N3-M4N4)×p1(6);

[0067] Where, p g p represents the optimal spray pressure under the corresponding operating conditions. w The optimal spray water pressure is set for the corresponding operating conditions.

[0068] The weights of dust concentration, tunnel wind speed, ambient air pressure and relative humidity on the spraying effect are M1, M2, M3 and M4, respectively. The values ​​of M1, M2, M3 and M4 are all between 0 and 3, and M1>M2>M3>M4.

[0069] Step 6: Set the optimal spray air pressure and optimal spray water pressure from Step 5 to set values, and spray water onto the monitoring area inside the tunnel at the set values.

[0070] In step four, three locations at different distances from the working face are selected for the human breathing surface.

[0071] In step six, a spray dust suppression system is used to spray water onto the areas to be monitored inside the tunnel;

[0072] The spray dust suppression system includes: a primary response device for identifying dust during tunnel excavation, a sensing device, and a graded and controlled spray dust removal device;

[0073] The tunnel boring dust identification primary response device includes:

[0074] Multiple smart cameras 1 are installed at the dust source locations to be monitored within the tunnel. Each smart camera 1 is connected to the OpenCV module 13. The smart cameras 1 are used to periodically acquire video images of key dust source locations and transmit them to the OpenCV module 13.

[0075] The sound response sensor 6 is installed near the tunnel face to acquire the sound of processes with large amounts of dust emanating from the tunnel.

[0076] The sensing device includes:

[0077] Dust concentration sensors 2, multiple of them, are installed at the dust source location and personnel gathering area in the area to be monitored in the tunnel, and are used to obtain the dust concentration in the area to be monitored in the tunnel;

[0078] The tunnel wind speed sensor 3 consists of multiple groups, arranged at intervals along the tunnel direction. Each group contains multiple tunnel wind speed sensors 3, located on the same cross-section of the tunnel and arranged at intervals, to obtain the wind speed at different locations on different tunnel cross-sections.

[0079] Ambient air pressure sensor 4 is installed at the location to be monitored inside the tunnel to obtain the ambient air pressure value inside the tunnel.

[0080] Air humidity sensor 5, multiple of them, are installed at key spray locations and work areas inside the tunnel to obtain the relative humidity of the air;

[0081] The data transcoding system 7 is used to receive data acquired by various sensors and convert it into a data type that the intelligent control system 8 can process.

[0082] The graded and controlled spray dust suppression device includes:

[0083] The intelligent control system 8 is used to receive data transmitted by various sensors and, when the dust concentration reaches a set value, calculates and matches the optimal spray parameters based on the tunnel wind speed, air pressure and relative humidity, and outputs a signal.

[0084] The intelligent control system 8 is also used to perform similarity recognition based on sound signals to identify the type of work being carried out inside the tunnel;

[0085] The tunnel water and air pipe 12 is installed inside the tunnel, with its water outlet extending to each testing area and connected to a spray nozzle; the water supply pipeline is equipped with an intelligent pressure control solenoid valve 11, and each intelligent pressure control solenoid valve is connected to the intelligent control system 8; by controlling the intelligent pressure control solenoid valve 11 to open to different positions, the cross-sectional area of ​​the pipeline is controlled, the pressure of the water supply pipeline is adjusted in stages, and thus the atomization angle and droplet size of the nozzle are changed.

[0086] It also includes a multi-degree-of-freedom servo motor 9, which is connected to the spray nozzle and is used to receive electrical signals from the data output by the intelligent control system 8 to drive the spray nozzle to move. The multi-degree-of-freedom servo motor 9 controls the angle between the spray nozzle 10 and the vertical direction, and adjusts the nozzle diameter, nozzle atomization angle and droplet size through a micro rotary motor.

[0087] In this invention, a smart camera 1 with machine vision recognition function captures video data at the tunnel face. Over a period of time, it automatically captures and analyzes the image grayscale. A sound response sensor 6 is positioned close to the tunnel face. When the grayscale value of the video image rises to a certain level and the sound sensor identifies the sound of a process with a large amount of tunnel dust, the smart pressure control solenoid valve 1 opens, initiating spraying. Sensors for dust concentration, tunnel wind speed, ambient air pressure, and air humidity are placed at appropriate locations in the tunnel to acquire corresponding parameter data. Through a data transcoding device, the relevant data is converted into a data type that the smart control system can recognize and calculate. The smart control system 8 has an internal program that outputs corresponding signals to the degree-of-freedom servo motor 9 or the smart pressure control solenoid valve 11 when the dust concentration, tunnel wind speed, air pressure, and air humidity reach specific values. Based on different values, it matches the optimal spraying parameters. The angle between the spray nozzle and the vertical direction is controlled by the multi-degree-of-freedom servo motor, and the nozzle diameter, nozzle atomization angle, and droplet size are adjusted by a micro-rotary motor. This achieves precise dust control during tunnel excavation while saving water resources and reducing dust suppression costs.

Claims

1. A smart spray dust suppression method based on the control of multiple environmental parameters, characterized in that, The method is as follows: Step 1: Obtain images of each location to be monitored within the tunnel, determine the overall grayscale value A of the images, and set the grayscale value of pure white in the images to 255 and the grayscale value of pure black to 0. Step 2: Obtain the construction sound signal B inside the tunnel and compare it with the construction sound of the standard operation procedure to obtain the ratio B; Step 3: If the grayscale value A of more than 80% of the area in the image is between 50 and 220, and the similarity between the sound signal B and the sound of the standard operation procedure reaches more than 90%, start the spray dust suppression system, and then execute steps 3 to 6 after shutting it down; if the above values ​​are not achieved, continue to execute steps 4 to 6. Step 4: Under a certain work procedure, the average dust concentration at the pedestrian breathing surface at different distances from the tunnel face was measured as C0, the corresponding average tunnel wind speed was V0, the ambient air pressure was P0, and the relative humidity was [missing value]. Furthermore, the parameters for the air-water spray system with the highest dust suppression efficiency were determined to be: air pressure: p0, water pressure: p1; Under the corresponding operating conditions, the dust concentration C, tunnel wind speed V, ambient air pressure P, and relative humidity at the monitored location inside the tunnel were obtained. For different values, set the offset parameters N1, N2, N3, and N4, and calculate them as follows: N1 = C / C0 (1); N2=V / V0 (2); N3 = P / P0 (3); Where: N1 is the dust concentration offset parameter; N2 is the tunnel wind speed offset parameter; N3 is the ambient air pressure offset parameter; N4 is the relative humidity offset parameter; Step 5: Based on the parameters in Step 4, calculate the spray air pressure and water pressure under the specified operating conditions using the following formula: p g =(M1N1+M2N2-M3N3-M4N4)×p0(5) p w =(M1N1+M2N2-M3N3-M4N4)×p1 (6) Where, p g p represents the optimal spray pressure under the corresponding operating conditions. w The optimal spray water pressure is set for the corresponding operating conditions. The weights of dust concentration, tunnel wind speed, ambient air pressure and relative humidity on the spraying effect are M1, M2, M3 and M4, respectively. The values ​​of M1, M2, M3 and M4 are all between 0 and 3, and M1>M2>M3>M4. Step 6: Set the optimal spray air pressure and optimal spray water pressure from Step 5 to set values, and spray water onto the monitoring area inside the tunnel at the set values.

2. The intelligent spray dust suppression method based on multi-environmental parameter control as described in claim 1, characterized in that, In step four, three locations with different distances from the working face are selected for the human breathing surface.

3. The intelligent spray dust suppression method based on multi-environmental parameter control as described in claim 2, characterized in that, In step six, a spray dust suppression system is used to spray water onto the areas to be monitored inside the tunnel; The spray dust suppression system includes: a tunnel excavation dust identification primary response device, a sensing device, and a graded and controlled spray dust removal device; The tunnel boring dust identification primary response device includes: Multiple smart cameras (1) are installed at the dust source locations to be monitored in the tunnel. Each smart camera (1) is connected to the OpenCV module (13). The smart cameras (1) are used to periodically acquire video images of key dust source locations and transmit them to the OpenCV module (13). A sound response sensor (6) is installed near the tunnel face to acquire the sound of processes with large amounts of tunnel dust. The sensing device includes: Dust concentration sensors (2) are multiple and are installed in the dust source location and personnel gathering area of ​​the area to be monitored in the tunnel to obtain the dust concentration of the area to be monitored in the tunnel. The tunnel wind speed sensor (3) is in multiple groups and is arranged at intervals along the tunnel direction. Each group contains multiple tunnel wind speed sensors (3), which are located on the same cross section of the tunnel and are arranged at intervals to obtain the wind speed at different locations on the tunnel cross section. An ambient air pressure sensor (4) is installed at the location to be monitored inside the tunnel to obtain the ambient air pressure value inside the tunnel. Multiple air humidity sensors (5) are installed at key spray locations and work areas inside the tunnel to obtain relative air humidity. The data transcoding system (7) is used to receive the data acquired by each of the sensors and convert it into a data type that the intelligent control system (8) can process; The graded and controlled spray dust removal device includes: The intelligent control system (8) is used to receive data transmitted by each of the sensors and, when the dust concentration reaches the set value, calculates the optimal spray parameters in combination with the tunnel wind speed, air pressure and relative humidity in the tunnel and outputs a signal. The intelligent control system (8) is also used to perform similarity recognition based on sound signals to identify the type of work being carried out in the tunnel; The tunnel water pipe and air pipe (12) are installed inside the tunnel, with the water outlet at its end extending to each testing area and connected to a spray nozzle (10); intelligent pressure control solenoid valves (11) are installed on the water supply pipeline, and each intelligent pressure control solenoid valve (11) is connected to the intelligent control system (8).

4. The intelligent spray dust suppression method based on multi-environmental parameter control as described in claim 3, characterized in that, It also includes a multi-degree-of-freedom servo motor (9), which is connected to the spray nozzle and is used to receive the electrical signals of the data output by the intelligent control system (8) to drive the spray nozzle to move.

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