Tunnel construction safety monitoring and automatic spraying dust falling method

Abstract

The invention relates to the technical field of constructional engineering, in particular to a tunnel construction safety monitoring and automatic spraying dust fall method, which comprises the following steps: acquiring sensing data of a construction area, the sensing data at least comprising dust concentration data, humidity data and airflow data at each detection position; according to the dust concentration data, determining a high dust concentration area and predicted dust concentration data of the high dust concentration area at a future moment; according to the dust concentration data, the predicted dust concentration data, the current position of a spraying device and the position of the high-dust-concentration area, the humidity data and the airflow data are combined, and spraying control logic is determined; according to the spraying control logic, a spraying device is controlled to spray water to the high-dust-concentration area for dust falling. By monitoring the sensing data of the construction area and processing the sensing data to determine the appropriate spraying control logic, the dust falling effect is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of construction engineering, and particularly relates to a method for safety monitoring and automatic spray dust suppression in tunnel construction. Background Art

[0002] In underground engineering, especially in the process of tunnel excavation and mine exploitation, dust control is an important link to ensure construction safety and environmental protection. Traditional dust suppression methods usually rely on manual water spraying, which not only has low efficiency, but also has poor real-time effect, is difficult to achieve full coverage, and has poor dust suppression effect. In addition, manual operation has potential safety hazards, especially in complex underground environments. Therefore, it is of great significance to develop a system that can automatically and continuously monitor dust and perform spray dust suppression, and realize real-time data collection and analysis. Summary of the Invention

[0003] The purpose of the present invention is to provide a method for safety monitoring and automatic spray dust suppression in tunnel construction, which is used to solve the problems of low timeliness and poor dust suppression effect existing in the existing manual water spraying.

[0004] To solve the above technical problems, in the first aspect, the present invention provides a method for safety monitoring and automatic spray dust suppression in tunnel construction, including the following steps:

[0005] Obtain the sensing data of the construction area, and the sensing data at least includes the dust concentration data at each detection position, as well as the humidity data and air flow data;

[0006] According to the dust concentration data, predict the dust concentration data at different undetected positions, and determine the high dust concentration area according to the dust concentration data at each detected position and undetected position; according to the dust concentration data in the high dust concentration area, perform dust concentration prediction to determine the predicted dust concentration data in the high dust concentration area at a future moment;

[0007] According to the dust concentration data, predicted dust concentration data, the current position of the spray device, and the position of the high dust concentration area, and in combination with the humidity data and air flow data, determine the spray control logic, and the spray control logic at least includes: spray flow rate, spraying angle of the nozzle of the spray device, moving speed of the spray device, and water spraying time;

[0008] According to the spray control logic, control the spray device to spray water for dust suppression in the high dust concentration area.

[0009] Combined with the above first aspect, in some possible implementation manners, the method further includes:

[0010] Smoothing the dust concentration data at each detected position obtained to achieve data cleaning, so as to obtain the dust concentration data at each detected position after data cleaning.

[0011] Combined with the above first aspect, in some possible implementation manners, according to the dust concentration data, the Kriging interpolation method is used to predict the dust concentration data at different undetected positions.

[0012] Combined with the above first aspect, in some possible implementation manners, determining a high dust concentration area includes:

[0013] Comparing the dust concentration data at each detected position and undetected position with a dust concentration threshold respectively, and determining the position corresponding to the dust concentration data greater than the dust concentration threshold as the high dust concentration area.

[0014] Combined with the above first aspect, in some possible implementation manners, according to the dust concentration data in the high dust concentration area, a long short-term memory network is used for dust concentration prediction to obtain the predicted dust concentration data in the high dust concentration area at a future moment.

[0015] Combined with the above first aspect, in some possible implementation manners, determining a spray control logic includes:

[0016] Performing data fusion on the dust concentration data at each detected position at the current moment t to obtain the dust concentration data after data fusion at the current moment t;

[0017] Determining the maximum value among the predicted dust concentration data in all high dust concentration areas at a future moment, and determining the high dust concentration area corresponding to the maximum value as the target high dust concentration area;

[0018] According to the dust concentration data after data fusion at the current moment t and in combination with the humidity data at the current moment t, determining the spray flow rate;

[0019] According to the air flow data at the current moment t, determining the spraying angle of the nozzles of the spraying device, where the air flow data at least includes the air flow direction;

[0020] According to the position of the target high dust concentration area and the current position of the spraying device, performing path planning to determine the length of the planned moving path;

[0021] According to the length of the planned moving path and the specified moving time of the spraying device, determining the moving speed of the spraying device;

[0022] According to the difference between the dust concentration data after data fusion at the current moment t and the dust concentration threshold, and in combination with the dust reduction efficiency of the spraying device, determining the spraying time of the spraying device.

[0023] Combined with the first aspect above, in some possible implementation manners, the weighted average method is used to perform data fusion on the dust concentration data at each detection position at the current moment t, so as to obtain the dust concentration data at the current moment t after data fusion.

[0024] Combined with the first aspect above, in some possible implementation manners, according to the position of the target high-dust concentration area and the current position of the spraying device, the A* algorithm is used for path planning to obtain the planned moving path.

[0025] Combined with the first aspect above, in some possible implementation manners, determining the spray flow rate and the water spraying time of the spraying device includes:

[0026] If the dust concentration data at the current moment t after data fusion is less than or equal to the dust concentration threshold, the spray flow rate is set to 0;

[0027] Otherwise, according to the highest dust concentration value and the dust concentration threshold, the dust

[0028] concentration data at the current moment t after data fusion is normalized to obtain the dust concentration normalization result;

[0029] The humidity data at the current moment t is subjected to negative correlation processing to obtain the humidity data after negative correlation processing;

[0030] The dust concentration normalization result and the humidity data after negative correlation processing are weighted and added to obtain the spray flow rate;

[0031] Calculate the ratio of the difference between the dust concentration data at the current moment t after data fusion and the dust concentration threshold to the dust reduction efficiency of the spraying device;

[0032] The calculated ratio is used as the water spraying time of the spraying device.

[0033] Combined with the first aspect above, in some possible implementation manners, using the weighted average method to perform data fusion on the dust concentration data at each detection position at the current moment t includes:

[0034] According to the historical stability, real-time consistency and health status of the sensors for collecting dust concentration data at each detection position, determine the real-time data quality of the sensors for collecting dust concentration data at each detection position;

[0035] According to the real-time data quality and coverage range of the sensors for collecting dust concentration data at each detection position and the position distribution at each detection position, determine the weight at each detection position;

[0036] Based on the weights at each detection location, data fusion is performed on the dust concentration data at each detection location at the current moment t.

[0037] To solve the above technical problems, in a second aspect, the present invention also provides a tunnel construction safety monitoring and automatic spray dust suppression device, which includes:

[0038] A sensing unit for: acquiring sensing data of the construction area, where the sensing data at least includes dust concentration data at each detection location, as well as humidity data and air flow data;

[0039] A data processing unit for: predicting the dust concentration data at different undetected locations according to the dust concentration data, and determining a high dust concentration area according to the dust concentration data at each detected location and undetected location; predicting the dust concentration according to the dust concentration data in the high dust concentration area to determine the predicted dust concentration data of the high dust concentration area at a future moment; determining a spray control logic according to the dust concentration data, predicted dust concentration data, the current position of the spray device, and the position of the high dust concentration area, and combining the humidity data and air flow data, where the spray control logic at least includes: spray flow rate, spraying angle of the nozzle of the spray device, moving speed of the spray device, and water spraying time; controlling the spray device to spray water for dust suppression on the high dust concentration area according to the spray control logic.

[0040] To solve the above technical problems, in a third aspect, the present invention also provides a tunnel construction safety monitoring and automatic spray dust suppression system, including a memory and a processor. The memory is used to store executable program codes, and the processor is used to call and run the executable program codes from the memory, so that the device executes the method in the first aspect or any possible implementation manner of the first aspect.

[0041] To solve the above technical problems, in a fourth aspect, the present invention also provides a computer program product, which includes: computer program codes, when the computer program codes run on a computer, enabling the computer to execute the method in the first aspect or any possible implementation manner of the first aspect.

[0042] To solve the above technical problems, in a fifth aspect, the present invention also provides a computer-readable storage medium, which stores computer program codes, when the computer program codes run on a computer, enabling the computer to execute the method in the first aspect or any possible implementation manner of the first aspect.

[0043] The present invention has the following beneficial effects: The present invention obtains sensing data of the construction area, and the sensing data at least includes dust concentration data, humidity data, and airflow data at each detection position; according to the dust concentration data, a high-dust-concentration area and predicted dust concentration data of the high-dust-concentration area at a future moment are determined; according to the dust concentration data, the predicted dust concentration data, the current position of the spraying device, and the position of the high-dust-concentration area, and in combination with the humidity data and the airflow data, a spraying control logic is determined, and the spraying control logic at least includes: spray flow rate, spraying angle of the nozzle of the spraying device, moving speed of the spraying device, and spraying time; according to the spraying control logic, the spraying device is controlled to spray water to reduce dust in the high-dust-concentration area. By monitoring the sensing data of the construction area and processing the sensing data, the present invention adaptively determines a suitable spraying control logic, ensuring the dust reduction efficiency and effectively improving the dust reduction effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0045] Figure 1 It is a schematic diagram of the basic process of a tunnel construction safety monitoring and automatic spraying dust reduction method according to an embodiment of the present invention;

[0046] Figure 2 It is a schematic diagram of a slide rail and a spraying device according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0047] In order to clearly illustrate the technical features of the present solution, the present invention will be described in detail below through specific embodiments in combination with the drawings.

[0048] The embodiments of the present invention will be described in more detail below with reference to the drawings. Although some embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as limited to the embodiments described herein. On the contrary, these embodiments are provided to more thoroughly and completely understand the present invention. It should be understood that the drawings and embodiments of the present invention are only for exemplary purposes and are not used to limit the protection scope of the present invention.

[0049] It should be understood that the steps recorded in the method embodiments of the present invention can be executed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this regard.

[0050] As used herein, the term "including" and its variations are open-ended, i.e., "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the following description.

[0051] It should be noted that the concepts such as "first", "second", etc. mentioned in the present invention are only used to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependent relationships.

[0052] In the embodiments of the present invention, although operations or steps are described in a specific order in the drawings, it should not be understood that these operations or steps are required to be performed in the specific order shown or in a serial order, or that all the operations or steps shown are required to be performed to obtain the desired result. In the embodiments of the present invention, these operations or steps can be performed serially; they can also be performed in parallel; or a part of these operations or steps can be performed.

[0053] At the same time, it can be understood that the data involved in the technical solution of the present invention (including but not limited to the data itself, the acquisition or use of the data) should comply with the requirements of the corresponding laws, regulations and related regulations. Unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs, and all parameters or indicators in the formulas involved in the present invention are numerical values after normalization to eliminate the influence of dimensions.

[0054] To solve the problems of low efficiency and difficulty in achieving full coverage existing in the existing artificial water spraying, the embodiments of the present invention provide a method for safety monitoring and automatic spraying and dust reduction in tunnel construction. This method monitors the sensing data of the construction area in real time and efficiently reduces dust through an automatic spraying device to ensure construction safety and environmental protection.

[0055] The following will combine with the drawings to introduce in detail a method for safety monitoring and automatic spraying and dust reduction in tunnel construction provided by the embodiments of the present invention.

[0056] Figure 1 The basic flow schematic diagram of a method for safety monitoring and automatic spraying and dust reduction in tunnel construction provided by the embodiments of the present invention is shown, as Figure 1 shown, and this method specifically includes the following steps:

[0057] S100: Obtain the sensing data of the construction area, and the sensing data at least includes the dust concentration data, humidity data and air flow data at each detection position.

[0058] Install sliding rails on the underground vault of the tunnel construction area. The sliding rails are made of corrosion-resistant materials such as stainless steel or high-strength composite materials, and a dust-proof cover is added to prevent dust or gravel from blocking the rails. At the same time, it is designed as a modular structure for easy installation and maintenance.

[0059] A spraying device is installed on the sliding rail. The spraying device can move on the sliding rail and can spray water to reduce dust in specific areas as needed. Figure 2 A schematic diagram of the sliding rail and the spraying device is shown. The spraying device is equipped with multiple nozzles to ensure the uniformity and coverage of water spraying. At the same time, dynamic atomization technology is adopted to control the water mist particle size of the nozzles within 10 - 50 microns, improving the dust capture efficiency. At the same time, the nozzles of the spraying device are designed as rotary multi-functional nozzles, supporting 360° rotation, with a wide and flexible coverage range. The nozzles of the spraying device are also set with multi-mode water spraying functions, including wide-angle spraying and concentrated spraying modes, to meet the needs of different regional dust concentrations. Each nozzle is equipped with an independent control module, and the data processing unit can dynamically adjust the moving speed of the spraying device, as well as the spraying flow rate, spraying angle, and spraying time of the nozzles of the spraying device according to the real-time dust concentration data. For example, when the dust concentration in a certain area is high, the spraying device will increase its moving speed and the amount of water sprayed. An anti-blocking device is also built into the nozzles, which is periodically back-flushed to avoid blockage. To further improve the dust reduction effect, the spraying device supports a multi-point water spraying cooperation mode to achieve staggered coverage in high-dust concentration areas and quickly reduce the dust concentration.

[0060] To improve construction safety, the spraying device is equipped with high-brightness LED lights, and the light brightness can be dynamically adjusted according to the ambient light. Different colors of lights can indicate the current dust concentration situation, such as green indicating safety and red indicating danger. When the dust concentration exceeds the standard, an audible and visual alarm will be triggered, and the tunnel broadcasting system will be linked to remind construction workers to pay attention to safety. In addition, an automatic emergency spraying mode is added, and high-frequency water spraying is immediately started after blasting to quickly suppress dust diffusion.

[0061] A laser particle sensor is installed on the top of the spraying device. The laser particle sensor can follow the spraying device and move along the sliding rail for dust detection. In addition to the laser particle sensors installed on the above-mentioned moving devices along the sliding rail, laser particle sensors are also arranged at various fixed positions in the tunnel construction area. The number and position of the laser particle sensors are deployed according to the layout of the tunnel construction area, which is not limited here. All the laser particle sensors form a networked monitoring to ensure a dust-free coverage of detection. Each laser particle sensor can detect the dust concentration in the air at the corresponding detection position in real time. The unit of the dust concentration is mg / m 3, the dust concentration data at each detection position in the construction area can thus be obtained. Among them, the laser particulate matter sensor installed on the top of the spraying device is mainly used for real-time monitoring and spraying control, while the laser particulate matter sensors arranged at various fixed positions in the tunnel construction area focus more on regional or global data collection and analysis. The two work together to help the system make more accurate responses in a dynamic environment to ensure the safety of construction workers.

[0062] In addition, humidity sensors and air flow sensors are set at appropriate positions in the tunnel construction area to add humidity and air flow monitoring functions. The humidity sensor is used to detect the humidity in the tunnel construction area in real time for subsequent dynamic adjustment of the spraying water volume. The air flow sensor is used to detect the air flow speed and direction in the tunnel construction area. The unit of air flow speed is m / s, and the unit of air flow direction is degree. The air flow speed and direction are collectively referred to as air flow data to help analyze the diffusion direction of dust.

[0063] The laser particulate matter sensor, humidity sensor and air flow sensor together constitute a multi-sensor system. Dust-proof covers and automatic cleaning devices are equipped outside each sensor in the multi-sensor system to regularly clean the attached dust and ensure the accuracy of monitoring data. The introduction of the multi-sensor system not only improves the detection accuracy of dust concentration, but also provides more dimensional information input for the data processing unit.

[0064] The laser particulate matter sensor, humidity sensor and air flow sensor in the multi-sensor system transmit the collected dust concentration data, humidity data and air flow data to the data processing unit by wireless or wired means. High-bandwidth and low-latency communication technology should be used for data transmission to ensure the real-time and integrity of data.

[0065] S200: Predict the dust concentration data at different undetected positions according to the dust concentration data, and determine the high-dust-concentration area according to the dust concentration data at each detected position and undetected position; Predict the dust concentration according to the dust concentration data in the high-dust-concentration area, and determine the predicted dust concentration data of the high-dust-concentration area at a future moment.

[0066] A data processing unit is set up. The data processing unit is the core of the entire system. Its function is to receive real-time sensing data feedback from the multi-sensor system, and perform analysis and processing, so as to generate precise spray control logic based on the sensing data. In implementation, by introducing an automatic control algorithm, the data processing unit can analyze historical dust concentration data, predict high-dust-concentration areas and formulate a water spray strategy in advance. At the same time, the real-time sensing data is used to dynamically optimize the spray path and frequency to achieve precise dust reduction. The data processing unit has the ability to handle multiple tasks, and can process data such as dust concentration, air flow direction, and humidity in real time, and quickly generate spray control instructions. In addition, the data processing unit has fault tolerance and self-adaptive functions. When a sensor in a certain area fails, it will automatically compensate the data of other sensors to ensure the continuity of the dust reduction work. Through cloud data upload, construction management personnel can also remotely view the spray effect report to provide support for subsequent optimization.

[0067] Specifically, after the data processing unit receives the sensing data feedback from the multi-sensor system, it first preprocesses the dust concentration data in the sensing data. The preprocessing includes data cleaning and data fusion, so as to obtain the dust concentration data after preprocessing. By performing data cleaning on the dust concentration data, outliers and noise in the dust concentration data can be removed to ensure the stability of the data. By performing data fusion on the dust concentration data, the comprehensive dust concentration of the entire area can be calculated.

[0068] Furthermore, in the embodiment of the present invention, the sliding average method is used to smooth the dust concentration data to achieve data cleaning:

[0069]

[0070] Among them, C i ′(t) represents the dust concentration data at the i-th detection position after data cleaning at the current moment t; N is the size of the sliding window; C i (t - k) represents the dust concentration data at the i-th detection position at the moment t - k.

[0071] Furthermore, in the embodiment of the present invention, the weighted average method is used to perform data fusion on the dust concentration data after data cleaning:

[0072]

[0073] Among them, C avg (t) represents the dust concentration data at the current moment t after data fusion; C i ′(t) represents the dust concentration data at the i-th detection position after data cleaning at the current moment t; ω iRepresents the weight of the dust concentration data at the i-th detection position. This weight depends on the position and accuracy of the laser particulate matter sensor and can be reasonably set as needed. It is not limited here; n represents the total number of different detection positions, that is, the total number of laser particulate matter sensors, including the movable laser particulate matter sensors on the spray device.

[0074] For the movable laser particulate matter sensor on the spray device, since its detection position changes dynamically with the movement of the slide rail, its weight ω i will be adjusted in real time. The functional relationship of ω i can be expressed as:

[0075] ω i = g(d i , a i , q i )

[0076] The specific calculation method of the weight function is as follows:

[0077]

[0078] Among them, d i is the distance between the i-th movable laser particulate matter sensor and the target detection area (the undetected position currently being evaluated or predicted for dust concentration, that is, the coordinate point (x, y)). The closer the distance, the higher the weight; a i is the coverage area size of the i-th movable laser particulate matter sensor, which is related to the detection accuracy of the sensor; q i is the real-time data quality of the i-th movable laser particulate matter sensor, reflecting the detection stability and credibility of the sensor; α is a constant representing the influence degree of the distance.

[0079] Among them, the determination method of the real-time data quality q i is as follows:

[0080] 1. Definition of core indicators:

[0081] Historical stability s i : Evaluate the stability through the variance of the dust concentration data of the sensor in the most recent T time steps (such as T = 10 minutes). The smaller the variance, the higher s i :

[0082]

[0083] Among them, Var max is the maximum allowable variance preset by the system (set according to historical data statistics); Var is the variance function; C i (t - T) is the dust concentration data of the i-th movable laser particulate matter sensor at time t - T.

[0084] Real-time consistency c i : Evaluate the consistency through the data difference between the i-th sensor and its adjacent N 邻近 sensors. The smaller the difference, the higher the c i :

[0085]

[0086] where α is the attenuation coefficient (e.g., α = 0.1), which controls the sensitivity of the difference to the consistency; N 邻近 is the number of sensors closest to the i-th sensor (e.g., N 邻近 = 3); C i (t) is the dust concentration data of the i-th movable laser particulate sensor at time t; C k (t) is the dust concentration data of the k-th adjacent of the i-th movable laser particulate sensor at time t; exp is the exponential function with the natural constant e as the base.

[0087] Health status h i : Judge according to the sensor self-check signal. The normal status h i = 1, calibration required h i = 0.5, faulty or offline h i = 0.

[0088] 2. Comprehensive calculation q i :

[0089]

[0090] where ω s , ω c and ω h are the weights of historical stability, real-time consistency, and health status respectively. The settings of the three weights can be adjusted according to the scenario, and the sum is 1. In this embodiment, ω s = 0.5, ω c = 0.3, ω h = 0.2.

[0091] 3. Dynamic update rule:

[0092] Recalculate q i each time the slide rail moves or the sensor data is refreshed;

[0093] If the sensor is faulty h i = 0, directly set q i = 0 to exclude the influence of its data.

[0094] Since the positions and quantities of the movable laser particulate matter sensors change with the movement of the slide rail, the system needs to recalculate the weights of each movable laser particulate matter sensor and perform data fusion based on the new sensor positions each time the slide rail moves. In this way, the system can more accurately reflect the dust concentration in different areas.

[0095] Based on the preprocessed dust concentration data, the optimized design of the data processing unit introduces a time series data modeling algorithm, which can effectively identify high dust concentration areas and improve the prediction accuracy of dust concentration. First, a concentration distribution model is established. The dust concentration in the tunnel changes according to time and space. An interpolation algorithm, such as Kriging interpolation, is used to predict the dust concentration at undetected positions:

[0096]

[0097] Among them, C′(x,y,t) represents the dust concentration data at the undetected position (x,y) at the current time t; λ i represents the interpolation weight at the i-th detected position; C i ′(t) represents the dust concentration data at the i-th detected position after data cleaning at the current time t; n represents the total number of different positions, that is, the total number of laser particulate matter sensors at different positions.

[0098] Then, high concentration area identification is carried out. A dust concentration threshold C0 is set, and the dust concentration C i ′(t) at each detected position is compared with this dust concentration threshold C0. If the concentration at a certain position exceeds this dust concentration threshold C0, that position is marked as a high dust concentration area.

[0099] There may be multiple high dust concentration areas. For each high dust concentration area, a time series model, such as Long Short-Term Memory (LSTM), can be used to predict the dust concentration trend based on the dust concentration data at the current time t and the previous times to achieve early spray response. Assume that there are m high dust concentration areas currently, denoted as p1, p2, …, p m , then for any j-th high dust concentration area p j , its dust concentration trend prediction formula is:

[0100]

[0101] Among them, represents the predicted dust concentration data at the j-th high dust concentration area p j at time t + Δt; respectively represent the high dust concentration area p j The dust concentrations at the current time t and time t-Δt. If the high dust concentration area p j is the location where the laser particulate sensor is located, then the dust concentration data refers to the dust concentration data after data cleaning. If the high dust concentration area p j has no sensor coverage, its dust concentration is estimated by an interpolation algorithm (such as Kriging interpolation or inverse distance weighting interpolation, etc.); Δt represents the time step, that is, the time interval during continuous prediction, usually in seconds (s); f represents the prediction function corresponding to the time series model.

[0102] S300: According to the dust concentration data, predicted dust concentration data, the current position of the spraying device, and the position of the high dust concentration area, and in combination with the humidity data and air flow data, determine the spraying control logic, where the spraying control logic at least includes: spray flow rate, spraying angle of the nozzle of the spraying device, moving speed of the spraying device, and spraying time.

[0103] To preferentially process the high-concentration area with the greatest impact on the construction environment, the predicted dust concentration data of all areas can be sorted, and the area with the highest predicted concentration is selected as the target priority response area, that is, the target high dust concentration area. In addition, multiple spraying devices can respond to multiple areas simultaneously according to the prediction results to ensure spraying treatment in different areas at the same time. The allocation of spraying devices can be reasonably adjusted according to the predicted concentration of each area, combined with the area position and the equipment coverage range, using a scheduling algorithm (such as the Hungarian algorithm) to achieve the best response effect.

[0104] To more accurately predict the dust diffusion path, air flow data is introduced:

[0105] [[ID=2,2]]Δx = V(t)·cos(θ(t))·Δt;

[0106] [[ID=2,5]]Δy = V(t)·sin(θ(t))·Δt;

[0107] where, Δx and Δy respectively represent the displacements of dust in the horizontal and vertical directions along the air flow direction within the time interval Δt; V(t) represents the air flow velocity at the current time t; θ(t) represents the angle value corresponding to the air flow direction at the current time t; Δt represents the time step, that is, the time interval during continuous prediction, usually in seconds (s).

[0108] Through the above formula, predict the diffusion direction and path of dust according to the air flow velocity and direction, so as to adjust the spraying range.

[0109] Based on the data analysis results, the data processing unit generates precise spray control logic to ensure that the dust concentration is within a safe range. The spray control logic includes:

[0110] Calculation of the water spray volume: According to the dust concentration data C avg (t) and the humidity H(t) at the current moment t after data fusion, calculate the spray flow rate Q:

[0111]

[0112] where Q represents the spray flow rate; k1 and k2 are adjustment coefficients, and in this embodiment, k1 = 0.8 and k2 = 0.2 are set; C0 represents the dust concentration threshold; C max represents the possible highest dust concentration value in the system, which is used for normalization, with the unit of mg / m 3 ; represents the normalized processing result of the dust concentration excess amount, with the range of [0, 1]. When C avg (t) ≤ C0, it means that the current dust concentration is lower than the safety threshold, and directly set Q = 0, that is, no spraying is carried out; when C avg (t) > C0, calculate the spray flow rate according to the normalization formula.

[0113] Optimization of the nozzle angle: Adjust the spraying angle α of the nozzle according to the angle value θ(t) corresponding to the air flow direction to ensure that the spray covers the high-concentration area:

[0114] α = θ(t) + Δα corr

[0115] where Δα corr represents the environmental disturbance correction value.

[0116] Planning of the moving path of the spray device: According to the position coordinates (x h , y h ) of the target high-dust-concentration area and the current position coordinates (x c , y c ) of the current spray device, use the shortest path algorithm, such as the A* algorithm, to perform path planning. If the shortest path between the position coordinates (x h , y h ) of the target high-dust-concentration area and the current position coordinates (x c , y c ) of the spray device is a straight line, then there is:

[0117]

[0118] where D represents the length of the planned moving path.

[0119] Control the moving speed of the spray device to ensure that the spray device can reach the target high dust concentration area in time:

[0120]

[0121] Among them, v represents the moving speed of the spray device; t r represents the specified moving time of the spray device; D represents the length of the planned moving path.

[0122] Spraying time adjustment: Dynamically adjust the spraying time according to the dust concentration data and spray flow rate:

[0123]

[0124] Among them, T represents the spraying time; R represents the dust reduction efficiency of the spray device, with the unit of mg / m 3 / s; C0 is the dust concentration threshold.

[0125] S400: Control the spray device to spray water and reduce dust in the high dust concentration area according to the described spray control logic.

[0126] According to the spray control logic determined above, the data processing unit generates corresponding spray control instructions. According to the spray control instructions, the spray device can be accurately moved to the high dust concentration area that needs dust reduction, and water is sprayed to reduce dust in the high dust concentration area. In this embodiment, the high dust concentration area that needs dust reduction refers to the target high dust concentration area. During the process of accurately moving the spray device to the high dust concentration area that needs dust reduction, in order to achieve accurate positioning of the spray device, positioning methods such as laser ranging, infrared positioning, or GPS and other high-precision positioning technologies can be used. At the same time, the device can be built-in with an inertial navigation module to ensure real-time calibration of the positioning accuracy of the spray device during the movement on the slide rail.

[0127] In addition, the spray device and the data processing unit also support remote monitoring. Construction personnel can view the dust concentration data and the working status of the spray device in real time through mobile phones or computers, and remotely control and adjust the spraying parameters.

[0128] The above tunnel construction safety monitoring and automatic spray dust reduction method provided by the embodiments of the present invention can achieve the following effects:

[0129] Efficient dust reduction: Real-time monitor the dust concentration through the laser particle sensor, and automatically spray water to reduce dust through the spray device to ensure the cleanliness and safety of the construction environment;

[0130] Real-time monitoring: The laser particle sensor can continuously monitor the dust concentration on the ground and transmit data in real time to ensure the safety during the construction process;

[0131] Multi-point water spraying: The spraying device can be moved to different positions as needed for multi-point water spraying to ensure the uniformity and comprehensiveness of the dust suppression effect.

[0132] Intelligent control: The data processing unit has an intelligent control function and can automatically adjust the spray flow rate, the spraying angle of the nozzles of the spraying device, the moving speed of the spraying device, and the water spraying time according to the dust concentration, thereby improving the dust suppression efficiency.

[0133] Reduce manual intervention: The automated system reduces manual intervention and lowers the work intensity and safety risks of construction workers.

[0134] Based on the same inventive concept, an embodiment of the present invention further provides a tunnel construction safety monitoring and automatic spray dust suppression device, and the device includes:

[0135] A sensing unit for: acquiring sensing data of the construction area, where the sensing data at least includes dust concentration data at each detection position, as well as humidity data and air flow data;

[0136] A data processing unit for: predicting the dust concentration data at different undetected positions according to the dust concentration data, and determining a high dust concentration area according to the dust concentration data at each detected position and undetected position; predicting the dust concentration according to the dust concentration data in the high dust concentration area to determine the predicted dust concentration data of the high dust concentration area at a future moment; determining a spray control logic according to the dust concentration data, the predicted dust concentration data, the current position of the spraying device, and the position of the high dust concentration area, and in combination with the humidity data and the air flow data, where the spray control logic at least includes: spray flow rate, the spraying angle of the nozzles of the spraying device, the moving speed of the spraying device, and the water spraying time; controlling the spraying device to spray water for dust suppression on the high dust concentration area according to the spray control logic.

[0137] It should be noted that: For the device provided in the above embodiment, only the above-mentioned division of each functional module is used for illustration. In actual application, the above functions can be allocated to different functional modules as needed, that is, the internal structure of the computer device is divided into different functional modules to complete all or part of the functions described above.

[0138] Based on the same inventive concept, an embodiment of the present invention further provides a tunnel construction safety monitoring and automatic spray dust suppression system, and the system includes: a memory, a processor, and a computer program stored in the memory and running on the processor. Wherein, when the processor executes the computer program, the system can execute any one of the tunnel construction safety monitoring and automatic spray dust suppression methods described above.

[0139] Embodiments of the present invention can divide the functions of the system according to the above method examples. For example, each function module can be corresponded, or two or more functions can be integrated into one processing module. The above integrated module can be implemented in the form of hardware. It should be noted that the division of modules in this embodiment is illustrative and is only a logical function division. There can be other division methods in actual implementation.

[0140] Based on the same inventive concept, embodiments of the present invention also provide a computer program product, which includes: computer program code. When the computer program code runs on a computer, it causes the computer to execute any one of the tunnel construction safety monitoring and automatic spray dust suppression methods described above.

[0141] Based on the same inventive concept, embodiments of the present invention also provide a computer-readable storage medium, which stores computer program code. When the computer program code runs on a computer, it causes the computer to execute any one of the tunnel construction safety monitoring and automatic spray dust suppression methods described above.

[0142] It should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A method for safety monitoring and automatic spray dust suppression in tunnel construction, characterized in that, The method includes the following steps: Obtain sensing data of the construction area, where the sensing data at least includes dust concentration data at each detection position, as well as humidity data and air flow data; Predict dust concentration data at different undetected positions according to the dust concentration data, and determine high-dust-concentration areas based on the dust concentration data at each detected position and undetected position; Perform dust concentration prediction according to the dust concentration data in the high-dust-concentration areas to determine the predicted dust concentration data in the high-dust-concentration areas at future moments; Determine the spray control logic according to the dust concentration data, predicted dust concentration data, the current position of the spray device, and the position of the high-dust-concentration areas, and combine the humidity data and air flow data. The spray control logic at least includes: spray flow rate, spraying angle of the nozzles of the spray device, moving speed of the spray device, and water spraying time; Control the spray device to spray water for dust reduction in the high-dust-concentration areas according to the spray control logic.

2. A tunneling construction safety monitoring and automatic spray dust suppression method according to claim 1, characterized in that The method further includes: Perform smoothing processing on the dust concentration data at each detected position obtained to achieve data cleaning, so as to obtain the dust concentration data at each detected position after data cleaning.

3. A method for safety monitoring and automatic spray dust suppression in tunnel construction according to claim 1 or 2, characterized in that, Predict the dust concentration data at different undetected positions according to the dust concentration data by using the Kriging interpolation method.

4. A method for tunnel construction safety monitoring and automatic spray dust suppression according to claim 1 or 2, characterized in that Determining the high-dust-concentration areas includes: Compare the dust concentration data at each detected position and undetected position with the dust concentration threshold respectively, and determine the positions corresponding to the dust concentration data greater than the dust concentration threshold as high-dust-concentration areas.

5. A safety monitoring and automatic spray dust suppression method for tunnel construction according to claim 1 or 2, characterized in that, Perform dust concentration prediction on the dust concentration data in the high-dust-concentration areas by using a long short-term memory network to obtain the predicted dust concentration data in the high-dust-concentration areas at future moments.

6. A method for tunnel construction safety monitoring and automatic spray dust suppression according to claim 1 or 2, characterized in that, Determining the spray control logic includes: Perform data fusion on the dust concentration data at each detected position at the current moment t to obtain the dust concentration data after data fusion at the current moment t; Determine the maximum value among the predicted dust concentration data in all high-dust-concentration areas at future moments, and determine the high-dust-concentration area corresponding to the maximum value as the target high-dust-concentration area; Determine the spray flow rate according to the dust concentration data after data fusion at the current moment t and in combination with the humidity data at the current moment t; Determine the spraying angle of the nozzles of the spray device according to the air flow data at the current moment t, where the air flow data at least includes the air flow direction; Perform path planning according to the position of the target high-dust-concentration area and the current position of the spray device to determine the length of the planned moving path; Determine the moving speed of the spray device according to the length of the planned moving path and the specified moving time of the spray device; Determine the water spraying time of the spray device according to the difference between the dust concentration data after data fusion at the current moment t and the dust concentration threshold, and in combination with the dust reduction efficiency of the spray device.

7. A method for safety monitoring and automatic spray dust suppression in tunnel construction according to claim 6, characterized in that Perform data fusion on the dust concentration data at each detected position at the current moment t by using the weighted average method to obtain the dust concentration data after data fusion at the current moment t.

8. A safety monitoring and automatic spray dust suppression method for tunnel construction according to claim 6, characterized in that, According to the position of the target high-dust-concentration area and the current position of the spraying device, the A* algorithm is used for path planning to obtain the planned moving path.

9. A method for safety monitoring and automatic spray dust suppression in tunnel construction according to claim 6, characterized in that, Determine the spray flow rate and the water spraying time of the spraying device, including: If the dust concentration data at the current moment t after data fusion is less than or equal to the dust concentration threshold, set the spray flow rate to 0; Otherwise, according to the highest dust concentration value and the dust concentration threshold, normalize the dust concentration data at the current moment t after data fusion to obtain the normalized dust concentration processing result; Perform negative correlation processing on the humidity data at the current moment t to obtain the negatively correlated humidity data; Perform weighted addition on the normalized dust concentration processing result and the negatively correlated humidity data to obtain the spray flow rate; Calculate the ratio of the difference between the dust concentration data at the current moment t after data fusion and the dust concentration threshold to the dust reduction efficiency of the spraying device; Use the calculated ratio as the water spraying time of the spraying device.

10. A method for tunnel construction safety monitoring and automatic spray dust suppression according to claim 7, characterized in that, Use the weighted average method to perform data fusion on the dust concentration data at each detection position at the current moment t, including: Determine the real-time data quality of the sensors collecting dust concentration data at each detection position according to the historical stability, real-time consistency, and health status of the sensors collecting dust concentration data at each detection position; Determine the weights of each detection position according to the real-time data quality and coverage area size of the sensors collecting dust concentration data at each detection position and the position distribution of each detection position; Based on the weights of each detection position, perform data fusion on the dust concentration data at each detection position at the current moment t.

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