A roadway dust concentration monitoring system and a monitoring method

Abstract

The application relates to the technical field of dust concentration monitoring, and discloses a roadway dust concentration monitoring system and a monitoring method, which comprise the following modules: a multispectral light field sensing module, which is used for constructing a detection field; a synchronous control and data acquisition module, which is used for obtaining projection data; a tomographic reconstruction and concentration field solving module, which is used for obtaining a dust mass concentration distribution diagram and a water mist mass concentration distribution diagram; a time series analysis and diffusion prediction module, which is used for predicting the future spatial position of a high-concentration dust area; and a data processing and output module, which is used for generating a cooperative control instruction. The method comprises the following steps: constructing a detection field and obtaining projection data; separating dust and water mist concentrations based on projection; predicting a dust position; identifying high concentration; and generating a control instruction. By obtaining light intensity attenuation data of two specific wavelengths, the tomographic reconstruction and concentration field solving module solves a mathematical equation group based on the difference between unit mass extinction coefficients of dust and water mist, so that the interference of water mist on dust concentration measurement is effectively eliminated.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of dust concentration monitoring, in particular to a roadway dust concentration monitoring system and a monitoring method. BACKGROUND

[0002] In the underground roadway working environment such as mine and tunnel, dust pollution is a long-term problem that plagues production safety and occupational health. High concentration of dust not only causes serious damage to the respiratory system of workers, induces irreversible occupational diseases such as pneumoconiosis, but also poses a threat to the safety of life and property due to its potential explosion risk.

[0003] At present, in the field of roadway dust concentration monitoring, traditional technologies mainly include sensors based on light scattering and light absorption principles. These devices are usually fixedly installed at specific monitoring points in the roadway, or local area sampling detection is carried out by handheld devices. Their working mode is usually to obtain the average concentration value at a certain point or a certain cross-section light path, and to display these data in real time to the on-site management personnel. When the dust concentration exceeds the standard, the system will trigger an audible and visual alarm to remind the workers to pay attention to safety.

[0004] The existing roadway dust monitoring technology is often accompanied by spray dust reduction operation in the roadway, and the water mist in the air greatly interferes with the light signal, making it difficult for the sensor to accurately distinguish between dust and water mist, resulting in distorted monitoring data and affecting the reliability of decision-making. Most of the existing monitoring devices can only provide discrete single-point or limited area concentration information, and it is difficult to fully present the dust spatial distribution of the entire roadway cross-section. There is a blind area in the perception of local sudden high-concentration dust clusters, it is difficult to fully evaluate the pollution situation, and it lacks the ability to capture the dynamic changes of dust airflow in the roadway, making it difficult to predict the diffusion direction, speed and future position of the dust. Therefore, the present application provides a roadway dust concentration monitoring system and a monitoring method to solve the problems existing in the prior art. SUMMARY

[0005] In view of the deficiencies of the prior art, the present application provides a roadway dust concentration monitoring system and a monitoring method, which solves the problems of insufficient accuracy of existing roadway dust concentration monitoring technology under complex working conditions, difficulty in effectively distinguishing between dust and water mist, lack of dynamic tracking and forward-looking prediction ability, and difficulty in realizing collaborative control with intelligent dust reduction system.

[0006] To achieve the above purpose, the present application provides a roadway dust concentration monitoring system, which comprises:

[0007] A multi-spectral light field sensing module is used to construct a detection field composed of light beams containing multiple specific spectral components on the roadway cross-section to be monitored.

[0008] The synchronous control and data acquisition module is used to drive and control the multispectral light field sensing module and acquire the light intensity attenuation data of the light beam after passing through the cross section of the alley to obtain projection data to represent the integral attenuation.

[0009] The tomographic reconstruction and concentration field calculation module is used to reconstruct a spatial distribution map of the attenuation coefficient representing the tunnel cross section based on the projection data, and to separate the spatial distribution map of the attenuation coefficient into a quantitative dust mass concentration distribution map and a water mist mass concentration distribution map by utilizing the spectral absorption characteristics of different substances.

[0010] The time series analysis and diffusion prediction module is used to calculate a vector field describing the dust motion state by analyzing the dust mass concentration distribution map, and predict the future spatial location of the high-concentration dust area based on the vector field.

[0011] The data processing and output module is used to generate collaborative control commands based on the current location of the high-concentration dust area, the future spatial location of the high-concentration dust area, and the preset light intensity.

[0012] Preferably, the multispectral light field sensing module includes:

[0013] The transmitting unit includes multiple multi-wavelength light source modules, each of which is used to transmit light at a wavelength of [wavelength value missing]. and A light beam, the wavelength of which is and The beam has the following characteristics: the target object and potential optical interference objects are at the same wavelength. and The extinction coefficient per unit mass varies;

[0014] The receiving unit, comprising multiple photodetector arrays, is positioned opposite the transmitting unit and is used to receive the wavelength. and The beam of light.

[0015] Preferably, the synchronization control and data acquisition module includes:

[0016] A signal modulation unit is used to assign a unique pseudo-random code sequence to each laser transmitter and to modulate the drive current of the laser transmitter;

[0017] The synchronous acquisition and demodulation unit is used to receive the mixed signal output by the receiving unit of the multispectral light field sensing module after converting the superimposed light beam into an electrical signal, and to demodulate the light intensity component belonging to a specific optical path by performing correlation operation between the mixed signal and the pseudo-random code sequence.

[0018] a data processing unit configured to calculate projection data based on the light intensity components and a pre-calibrated reference light intensity.

[0019] Preferably, the tomographic reconstruction and concentration field solving module is specifically configured to:

[0020] discretize the cross section of the roadway to be monitored into a two-dimensional pixel grid;

[0021] use an algebraic reconstruction technique to iteratively calculate based on the geometric relationship between the projection data calculated by the synchronous control and data acquisition module and the two-dimensional pixel grid to obtain a total linear attenuation coefficient spatial distribution map corresponding to the two wavelengths respectively.

[0022] Preferably, the tomographic reconstruction and concentration field solving module is further configured to:

[0023] based on the physical model of linear superposition of the attenuation effects caused by dust and water mist, for each pixel point in the two-dimensional pixel grid, solve a binary linear equation system to separate the dust mass concentration distribution map and the water mist mass concentration distribution map:

[0024] ;

[0025] wherein, represents the total linear attenuation coefficient, represents the spatial coordinates of the two-dimensional pixel grid on the cross section of the roadway, represents the time at which the measurement is made, represents the wavelength of the specific light beam used, represents the dust mass concentration to be solved, represents the water mist mass concentration to be solved, represents the unit mass extinction coefficient of dust, represents the unit mass extinction coefficient of water mist.

[0026] Preferably, the time series analysis and diffusion prediction module is specifically configured to:

[0027] use an optical flow analysis method to define a local neighborhood for each pixel point in the dust mass concentration distribution map, and based on the constraint assumption that the motion vector in the local neighborhood remains constant, solve the optical flow constraint equation to calculate a vector field;

[0028] based on the calculated vector field, predict the future spatial position of the high-concentration dust region after a future time step through a linear extrapolation model.

[0029] Preferably, the calculation formula of the linear extrapolation model is:

[0030] ;

[0031] wherein, is the center coordinate of the high-concentration dust cluster at time , represents a motion vector calculated by optical flow analysis, represents a predicted center coordinate of the dust cluster at a future time , represents a short time step in the future.

[0032] Preferably, the data processing and output module is specifically configured to:

[0033] perform threshold segmentation and connected component analysis on the dust mass concentration distribution map to identify the high-concentration dust region and extract the centroid coordinates, coverage area, and peak concentration of the high-concentration dust region as feature parameters;

[0034] based on the extracted feature parameters, assess the current total pollution level.

[0035] Preferably, the data processing and output module is further configured to:

[0036] generate a coordinated control instruction, which includes a target dust-settling region center coordinate obtained by weighted calculation of the current centroid position of the high-concentration dust region and the future spatial position of the high-concentration dust region, and a spraying intensity and duration determined according to the total pollution level.

[0037] The application also provides a roadway dust concentration monitoring method, comprising the following steps:

[0038] S1. Constructing a detection field composed of light beams containing multiple specific spectral components on the roadway section to be monitored, and collecting light intensity attenuation data of the light beams after passing through the roadway section to obtain projection data representing integral attenuation;

[0039] S2. Reconstructing an attenuation coefficient spatial distribution map representing the roadway section based on the projection data, and separating the attenuation coefficient spatial distribution map into quantitative dust mass concentration distribution map and water mist mass concentration distribution map using the spectral absorption characteristics of different substances;

[0040] S3. Calculating a vector field describing the motion state of dust by analyzing the dust mass concentration distribution map, and predicting the future spatial position of the high-concentration dust region based on the vector field;

[0041] S4. Processing the dust mass concentration distribution map to identify the high-concentration dust region, and generating a coordinated control instruction according to the current position of the high-concentration dust region, the future spatial position of the high-concentration dust region, and a preset light intensity. ​

[0042] The application provides a roadway dust concentration monitoring system and a monitoring method, which have the following beneficial effects:

[0043] The application can accurately separate the total attenuation coefficient into independent dust mass concentration distribution and water mist mass concentration distribution by setting a multi-spectrum light field sensing module to obtain light intensity attenuation data of two specific wavelengths and solving a mathematical equation set based on the difference in unit mass extinction coefficient of dust and water mist by a tomographic reconstruction and concentration field solving module, effectively eliminating the optical interference of water mist generated by spray dust reduction on dust concentration measurement in the roadway, and improving the accuracy and reliability of the monitoring result.

[0044] The application can calculate a vector field describing the motion state of the dust by using an optical flow analysis method through a time series analysis and diffusion prediction module, and predict the future spatial position of the high-concentration dust area based on the linear extrapolation model, realize the transition from static concentration presentation to dynamic diffusion trend tracking and forward-looking prediction, and provide a prediction time window for dust reduction operation, which has the foresight prevention and control.

[0045] The application can generate a structured collaborative control instruction containing the target dust reduction area center coordinates, spray intensity and duration by comprehensively analyzing and deciding the current position, future spatial position and pollution level of the high-concentration dust area through a data processing and output module, realize the closed-loop linkage with the downstream intelligent dust reduction device, guide it to carry out predictive, partitioned and graded precise operation, improve the dust reduction efficiency and reduce the water resource consumption. BRIEF DESCRIPTION OF DRAWINGS

[0046] Figure 1 It is a roadway dust concentration monitoring system architecture diagram of the embodiment of the application.

[0047] Figure 2 It is a tomographic reconstruction principle schematic diagram of the embodiment of the application.

[0048] Figure 3 It is an information processing flowchart of the data processing and output module of the embodiment of the application.

[0049] Figure 4 It is a method flowchart of the embodiment of the application. DETAILED DESCRIPTION

[0050] The technical solutions in the embodiments of the application will be described clearly and completely in combination with the drawings of the application specification. Obviously, the described embodiments are only part of the embodiments of the application, not all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the application.

[0051] ReferenceFigure 1 , Figure 1 is a schematic diagram of a roadway dust concentration monitoring system according to an embodiment of the present application. The embodiment of the present application provides a roadway dust concentration monitoring system, which comprises a multi-spectral light field sensing module, a synchronous control and data acquisition module, a tomographic reconstruction and concentration field solving module, a time series analysis and diffusion prediction module, and a data processing and output module.

[0052] The physical environment information of the roadway section is obtained by an active sensing method, and then the information is subjected to multi-level deep processing and analysis, and finally multi-dimensional and structured data which can be directly used for accurate decision-making and control are output. Specifically, the multi-spectral light field sensing module constructs a detection field on the roadway section to be monitored, which is composed of multiple groups of light beams with multiple angles intersecting each other and containing multiple specific spectral components.

[0053] The synchronous control and data acquisition module is used for accurately driving and controlling the multi-spectral light field sensing module, and synchronously acquiring the light intensity attenuation data of the light beams after passing through the roadway section. This module converts the physical effects caused by dust, water mist and other media in the environment into original digital signals for subsequent processing.

[0054] The tomographic reconstruction and concentration field solving module is used for receiving the original light intensity attenuation data output by the synchronous control and data acquisition module. Based on the principle of computer tomography, the tomographic reconstruction and concentration field solving module mathematically inverts the linear projection data from multiple angles to reconstruct a two-dimensional spatial distribution map representing the attenuation coefficient of the entire roadway section. Subsequently, the module uses the prior knowledge that different substances have different absorption characteristics under different spectra to establish and solve a system of mathematical equations, and accurately separates the mixed attenuation coefficient distribution map into independent and quantitative dust mass concentration distribution map and water mist mass concentration distribution map.

[0055] The time series analysis and diffusion prediction module is used for receiving a series of pure dust concentration distribution maps sorted by time continuously output by the previous module. The module calculates a vector field describing the motion state of the dust cloud by analyzing the shape and position changes of the dust cloud between consecutive frames of images, thereby achieving dynamic tracking of the diffusion direction and speed of the dust body, and based on this, establishing a prediction model to calculate the spatial position of the dust body in the future short time.

[0056] The data processing and output module, as the final link of the system, comprehensively analyzes and processes all the information solved and predicted. The data processing and output module automatically identifies and locates the high-concentration dust area, judges its pollution level according to the preset rules, and structurally integrates the multi-dimensional information such as the current position, the predicted position and the pollution level, and finally generates a collaborative control instruction which can be directly used to guide the downstream intelligent dust reduction device to perform advanced, partitioned and hierarchical accurate operation.

[0057] The roadway dust concentration monitoring system provided by the embodiment of the present application comprises a multi-spectrum light field sensing module. The module is the physical sensing front end of the system, and is used for constructing a detection field composed of a large number of multi-angle intersecting light beams on the cross section of the roadway to be monitored, and acquiring the attenuation information of the detection field caused by the existence of the medium (such as dust, water mist) in the roadway in real time.

[0058] The multi-spectrum light field sensing module comprises a transmitting unit, a receiving unit and a support and mounting structure for stably mounting them.

[0059] The transmitting unit is fixed to one side of the roadway, and is composed of a plurality of laser transmitters arranged on a rigid support frame. In order to realize accurate identification of different phases such as dust and water mist, each laser transmitter in the embodiment is a multi-wavelength light source module, which internally integrates at least two laser sources capable of independently emitting light beams of different specific wavelengths. The two laser sources respectively emit light beams with wavelengths of and .

[0060] The selection of wavelengths and is based on the fact that the target monitoring object (such as coal dust) and the potential optical interference object (such as water mist sprayed for dust reduction) commonly found in the roadway have significantly different unit mass extinction coefficients at the two wavelengths. Specifically, one wavelength is in the near-infrared band, in which coal dust particles have strong absorption or scattering characteristics due to their physical and chemical properties; and the other wavelength is in another near-infrared band where water molecules have a characteristic absorption peak. Through such differentiated wavelength configuration, the necessary original physical input is provided for the subsequent algorithm module to decouple and separate the phase and concentration. In addition, the light beams output by each laser transmitter are collimated through an optical system to ensure that they still have a small divergence angle after passing through the entire roadway width, thereby ensuring the accuracy of spatial detection.

[0061] The receiving unit is arranged on the other side of the roadway opposite to the transmitting unit. The receiving unit is composed of one or more linearly arranged photodetector arrays, and the total length and spatial resolution of the array are matched with the transmitting unit to ensure that the light beam signals from all the laser transmitters of the transmitting unit can be received. Each photodetector has high sensitivity and high-speed response characteristics. High sensitivity ensures that even if the light beam passes through a high-concentration dust field and is severely attenuated, an effective light intensity signal can still be detected; the high-speed response characteristic is to cooperate with the signal modulation and demodulation technology adopted by the subsequent synchronous control and data acquisition module to accurately distinguish the light signals from different transmitters.

[0062] The support and mounting structure is used to precisely and securely fix the transmitting and receiving units to the tunnel wall. This structure possesses sufficient mechanical strength and vibration resistance to ensure the long-term stability of the spatial coordinates of all laser emitters and photodetectors in the complex working conditions of a mine. This geometric stability is a necessary prerequisite for subsequent tomographic reconstruction calculations, as it guarantees that the spatial path of each optical path is known and fixed.

[0063] The roadway dust concentration monitoring system provided in this embodiment of the invention further includes a synchronization control and data acquisition module. This synchronization control and data acquisition module is used to precisely drive, coordinate and manage the working timing of the sensing module, while simultaneously acquiring, processing and formatting the raw physical signals output by the sensing module without distortion, providing the necessary high-precision projection dataset for subsequent tomographic reconstruction.

[0064] The synchronization control and data acquisition module includes a timing control unit, a signal modulation unit, a synchronization acquisition and demodulation unit, and a data processing unit.

[0065] The timing control unit is the command center of the entire data acquisition process. To ensure that the receiving unit can clearly distinguish which part of the energy in the composite optical signal received at the same time comes from which specific laser emitter, the timing control unit employs a specific signal multiplexing technique. In a specific embodiment, this multiplexing technique is Code Division Multiplexing (CDM). According to the instructions of the timing control unit, the signal modulation unit assigns a unique pseudo-random code sequence with good autocorrelation and low cross-correlation to each laser emitter. When emitting a beam, the driving current of each laser emitter is high-frequency modulated by this unique code sequence, so that its emitted light intensity carries a digital fingerprint of its own identity.

[0066] Because all laser emitters can be modulated by different code sequences and operate simultaneously, the system can achieve near-instantaneous scanning of the tunnel cross-section. The synchronous acquisition and demodulation unit works closely with the receiving unit. Each photodetector in the receiving unit converts the total light intensity signal received—composed of multiple modulated beams superimposed—into an electrical signal and transmits it to the synchronous acquisition and demodulation unit. This synchronous acquisition and demodulation unit internally contains a correlator array corresponding to the code sequence from the transmitting end. For the signal from the first... The unit combines the mixed signal from the photodetector with the signal from the transmitter. By performing correlation operations on the pseudo-random code sequences used by each laser emitter, the signal belonging solely to the optical path can be accurately demodulated from the mixed signal. The light intensity component. This technical solution based on code division multiplexing and correlation demodulation has extremely strong resistance to inter-channel crosstalk and environmental noise interference.

[0067] The data processing unit converts the demodulated analog electrical signals representing the light intensity of each optical path into digital quantities via a high-speed analog-to-digital converter (ADC), which are recorded as real-time light intensity. ,in These represent different wavelengths. This data processing unit relies on the reference light intensity stored during the system initialization and calibration phase. The integral attenuation of each optical path at both wavelengths, i.e., the projected value, is calculated. Its calculation follows the formula:

[0068] ;

[0069] in, At any moment , wavelength is At that time, along the first The transmitter to the first The optical path of each receiver The projection value; It is the reference light intensity calibrated for this optical path in clean air; At any moment Real-time measured light intensity It represents the natural logarithm.

[0070] After completing the scanning and calculation of a complete cross-section, the data processing unit will process all... The projection values ​​are organized to ultimately output two complete and independent sets of wavelengths. and These correspond to the projected data matrices. These two sets of matrices are then transmitted to the tomographic reconstruction and concentration field calculation module as direct input for all subsequent calculations.

[0071] See Figure 2 , Figure 2 This is a schematic diagram illustrating the principle of tomographic reconstruction according to an embodiment of the present invention. The tunnel dust concentration monitoring system provided in this embodiment further includes a tomographic reconstruction and concentration field calculation module. This module receives two sets of multi-angle projection data matrices output by the synchronization control and data acquisition module, and through performing a series of precise mathematical operations, finally outputs a spatial distribution map of the independent quantitative mass concentrations of dust and water mist within the tunnel cross-section.

[0072] The tomographic reconstruction and concentration field calculation module reconstructs the attenuation field by inverting and reconstructing an attenuation coefficient distribution map that reflects the physical characteristics of the entire two-dimensional cross-section through a series of one-dimensional linear projection data. The module first mathematically discretizes the tunnel cross-section to be monitored into a graph composed of... A two-dimensional grid composed of pixel units. For each pixel... the linear attenuation coefficient inside is assumed to be uniform and unknown, denotes the total number of pixels, denotes the pixel index.

[0073] Based on this discretization model, the integrated attenuation along any light path can be approximated as a weighted sum of the attenuation contributions of the pixels crossed by the light path. Thus, the integral form of the Beer-Lambert law is transformed into a large system of linear equations as follows:

[0074] ;

[0075] where is the measured projection value at wavelength along the light path ; is the average linear attenuation coefficient of the th pixel at time , wavelength ; is the geometric weight factor representing the length of the light path intersecting the th pixel. All these factors together form a sparse matrix uniquely determined by the system geometry.

[0076] To solve this large system of equations, the present embodiment employs the Algebraic Reconstruction Technique (ART) for iterative computation. This technique starts from an initial guess field (e.g., all are zero) and, in a pre-determined order, successively corrects the current image field using the projection value of each light path until the result converges. The correction process of the th iteration follows the formula:

[0077] ;

[0078] where is the index of the projection data currently used for correction; is the iteration number; is a relaxation factor taking values in the interval (0, 2) to control the convergence speed and stability of the iterative process; is the linear attenuation coefficient of the th pixel at the th iteration; is the linear attenuation coefficient of the th pixel at the th iteration; is the linear attenuation coefficient of the The projection value of the optical path; It is a geometric weighting factor, representing the first... The light path and the first The length of the intersection of pixels; It represents the total number of pixels.

[0079] The tomographic reconstruction and concentration field calculation module processes the two sets of wavelengths acquired. The projection data are reconstructed independently using the above process, resulting in two pixel-aligned spatial distribution maps of the bus linear attenuation coefficient at two different wavelengths. and .

[0080] Subsequently, the module calculates the phase concentration. In this step, the total attenuation coefficient at any point on the cross-section of the tunnel is the linear superposition of the attenuation effects produced by the dust and water mist present at that point. For any pixel in the cross-sectional grid... Its physical model can be expressed as:

[0081] ;

[0082] Using the total attenuation coefficients obtained from the previous reconstruction at two different wavelengths, the module establishes and solves a problem for each pixel containing two unknowns. A system of two linear equations in two variables:

[0083] ;

[0084] in, Indicates the bus linear attenuation coefficient; and These are the mass concentrations of dust and water mist to be determined for that pixel; and Dust and water mist at wavelengths The extinction coefficient per unit mass. These two coefficients are physical constants representing the optical properties of a material and can be pre-calibrated experimentally.

[0085] The solution to this system of equations is accomplished through matrix operations. This is achieved by applying matrix operations to equations composed of unit mass extinction coefficients. By inverting the coefficient matrix, the pure dust mass concentration can be accurately resolved at each pixel. and water mist mass concentration The validity of this solution process depends on the wavelength. and The choice of wavelength ensures that dust and water mist have linearly independent absorption characteristics at these two wavelengths, thus ensuring that the coefficient matrix is ​​non-singular.

[0086] Finally, the tomographic reconstruction and concentration field solving module outputs two data images: one is a high-resolution, quantitative dust mass concentration spatial distribution map without water mist interference, and the other is a water mist mass concentration spatial distribution map. The two images are transmitted to a subsequent module for further time series analysis and decision-making.

[0087] The roadway dust concentration monitoring system provided by the embodiment of the present application further comprises a time series analysis and diffusion prediction module. The time series analysis and diffusion prediction module dynamically analyzes a series of time series dust concentration maps output by the tomographic reconstruction and concentration field solving module, so that the monitoring capability is improved from static presentation of the dust distribution of the roadway section to dynamic tracking of the movement law and forward-looking prediction of the short-term future state.

[0088] The time series analysis and diffusion prediction module receives a series of time-sequentially arranged, pure dust mass concentration distribution maps without water mist interference , and is based on an optical flow analysis method. The basic physical assumption of the optical flow analysis method is that, within a sufficiently short time interval , the dust concentration value of a small area in the roadway section is constant, and the apparent movement of the pixel point on the image is caused by the actual physical movement of the dust cloud. This relationship can be described by the following formula:

[0089] ;

[0090] wherein, represents the mass concentration of the dust to be solved at the pixel point; represents the spatial coordinates of the two-dimensional pixel grid on the roadway section; represents time; , represents the displacement increment; represents the time interval.

[0091] A first-order Taylor series expansion is performed on the right side of the above formula, and high-order terms are ignored, to derive the basic constraint equation of the optical flow method:

[0092] ;

[0093] wherein, and are the movement velocity components of the dust cloud in the and directions, i.e., the motion vector to be solved; and represent the spatial gradient of the dust concentration map, which can be obtained by performing a difference operation on the image; represents the temporal gradient of the dust concentration map, which can be obtained by comparing the concentration changes of the current frame and the previous frame image.

[0094] Since the above optical flow constraint equation contains two unknowns To obtain a unique solution to a single equation, this embodiment employs the Lucas-Kanade algorithm. This algorithm introduces an additional local constraint assumption, namely, within a pixel-based... small neighborhood centered on Within, the motion vectors of all pixels All are the same. Based on this assumption, all pixels within this neighborhood together constitute a region about... and The system of overdetermined linear equations is given. Solving this system of equations using the least squares method allows us to calculate the motion vector of the neighborhood center. By performing this calculation on all pixels or key feature points of the entire image, the temporal analysis and diffusion prediction module ultimately generates a two-dimensional motion vector field that corresponds to the dust concentration map and represents the internal motion state of the dust cloud.

[0095] Obtain the current moment motion vector field Subsequently, the time series analysis and diffusion prediction module further performs the diffusion prediction function. For the current concentration map... The centroid of any high-concentration dust agglomerate identified in the sample is denoted as . This time-series analysis and diffusion prediction module can predict the motion vector at a given location over a short time step using a linear extrapolation model. The spatial position after :

[0096] ;

[0097] in, It is a high concentration of dust clumps at all times center coordinates ; It is the motion vector calculated by optical flow analysis at the central coordinate position; It is a prediction that the dust agglomeration will occur in the future. The center coordinates; It indicates a short time step in the future.

[0098] The time-series analysis and diffusion prediction module outputs two key dynamic pieces of information: a motion vector field map representing the overall movement of the current dust field, and predicted data on the future location of high-concentration hazardous areas. These two pieces of information are transmitted to the data processing and output module, serving as the core decision-making basis for achieving proactive and intelligent dust control.

[0099] See Figure 3 ,Figure 3 is an information processing flow chart of a data processing and output module according to an embodiment of the present application. The roadway dust concentration monitoring system and monitoring method provided by the embodiment of the present application further comprises a data processing and output module, which is used for comprehensively processing and decision analysis on the multi-dimensional and high-density information output by each upstream module, and finally generates structured visualized information and collaborative control instructions which can be directly applied to production practice.

[0100] The data processing and output module comprises a high-concentration area identification unit, a pollution level assessment unit and a collaborative control instruction generation unit in implementation.

[0101] The high-concentration area identification unit receives the quantitative dust mass concentration spatial distribution map output by the tomographic reconstruction and concentration field calculation module . In order to automatically identify and locate the dust clusters that pose a threat to safety production, the unit first performs threshold segmentation processing on the input concentration map. By comparing the concentration value of each pixel point in the map with a preset dust concentration safety threshold , all pixel points with a concentration value exceeding the threshold are identified as part of the high-concentration pollution area. Subsequently, the unit uses a connected domain analysis algorithm to aggregate spatially adjacent high-concentration pixel points into independent dust cloud objects that can be parameterized. For each identified independent cloud, the unit calculates and extracts its key geometric and physical features, including the centroid coordinates of the cloud, the coverage area, and the peak concentration in the area.

[0102] The pollution level assessment unit receives the parameterized cloud information output by the high-concentration area identification unit. The unit quantitatively grades the overall pollution state of the current roadway section according to a preset multi-level assessment standard corresponding to the mine safety production regulations.

[0103] In a specific embodiment, the assessment standard is a decision matrix that comprehensively considers the coverage area and peak concentration of the largest dust cloud. By comparing the extracted feature parameters with the grading threshold values in the matrix, the unit finally outputs a pollution level representing the current overall risk level, for example, from level I (clean) to level III (severe pollution).

[0104] The collaborative control instruction generation unit is the decision center of the module, which integrates real-time and predicted information from multiple upstream modules to generate forward-looking and intelligent dust suppression control instructions. The key input information received by the collaborative control instruction generation unit includes:

[0105] the current centroid position of each dust cloud determined by the high-concentration area identification unit ;

[0106] the future predicted positions of these dust clouds output by the temporal analysis and diffusion prediction module ;

[0107] the current overall pollution level output by the pollution level assessment unit.

[0108] The instruction generation logic of the cooperative control instruction generation unit is to change the control target of the dust-settling equipment (such as a partitioned spraying device) from a passive response to the existing high-concentration area to a predictive and covering attack on the current position and future trajectory of the dust cloud. Specifically, the structured control instruction data packet generated by the cooperative control instruction generation unit contains the center coordinates of the target dust-settling area, which are the aiming points obtained by weighting calculation according to and the spraying intensity and duration determined according to the pollution level.

[0109] The data processing and output module outputs two types of information to external systems: one is to dynamically and real-timely display the dust concentration distribution, water mist concentration distribution, and motion vector field representing the diffusion trend in the form of pseudo-color cloud images on the human-computer interaction interface, providing intuitive visual decision support for on-site managers; the other is to send the generated structured cooperative control instruction containing the prospective target position and execution intensity to the downstream intelligent dust-settling and treatment device through the industrial bus, realizing the whole-process, closed-loop, and intelligent treatment of dust.

[0110] Referring to Figure 4 , Figure 4 is a flowchart of a roadway dust concentration monitoring method according to an embodiment of the present application. The present application provides a roadway dust concentration monitoring method, including the following steps:

[0111] S100, system initialization and environment calibration. Under the clean air condition without dust and water mist in the roadway, the roadway dust concentration monitoring system is started, the synchronous control and data acquisition module drives all laser emitters in the multi-spectral light field sensing module to emit light beams of two different wavelengths in turn, and the reference light intensity formed on each photodetector in the receiving unit after passing through the clean air is recorded synchronously, and the data set is stored as a reference for subsequent calculation of light attenuation.

[0112] Real-time monitoring and projection data acquisition. In dust-producing working conditions such as tunneling, the system enters continuous monitoring mode, and the synchronous control and data acquisition module continuously drives the induction module to scan the section at a preset time resolution, continuously acquiring the real-time light intensity attenuated after passing through the roadway section containing a mixture of dust and water mist. Based on the stored reference light intensity and the real-time collected attenuated light intensity, the module calculates the integral attenuation projection value of each light path at the two wavelengths, and forms two sets of projection data matrices corresponding to the wavelengths.

[0113] S200, reconstruction and concentration field separation. The tomographic reconstruction and concentration field calculation module receives two sets of projection data matrices and independently inverts two sets of one-dimensional projection data to two-dimensional total linear attenuation coefficient spatial distribution maps based on algebraic reconstruction technology. Subsequently, the module uses the pre-calibrated unit mass extinction coefficients of dust and water mist for the two wavelengths to establish and solve a system of linear equations for each pixel point in the distribution map, thereby accurately separating the mixed total attenuation coefficient into a quantitative, pure dust mass concentration spatial distribution map and a water mist mass concentration spatial distribution map.

[0114] S300, time series analysis and diffusion trend prediction. The time series analysis and diffusion prediction module continuously receives and processes the output pure dust mass concentration distribution map in time sequence. The time series analysis and diffusion prediction module calculates a two-dimensional motion vector field representing the speed and direction of dust movement within the current section by applying an optical flow estimation algorithm to compare and analyze the shape and position changes of dust clouds between adjacent time frames. Based on the motion vector field, the time series analysis and diffusion prediction module extrapolates the centroid position of the identified high-concentration dust mass to predict its spatial position after a short time step in the future.

[0115] S400, comprehensive decision and instruction information output. The data processing and output module analyzes the current dust concentration map, water mist concentration map, dust motion vector field, and future predicted position of high-concentration areas. The data processing and output module performs threshold segmentation on the dust concentration map, automatically identifies and parameterizes all over-standard areas, and assesses the current total pollution level based on parameters such as the area and peak concentration of over-standard areas. The data processing and output module fuses the current position, predicted position, and pollution level of high-concentration areas to generate structured collaborative control instructions, which include all the information needed to guide downstream dust suppression devices to perform forward-looking, precise positioning, and hierarchical response operations, and simultaneously displays multi-dimensional information on the visualization terminal.

[0116] While embodiments of the application have been shown and described, it is to be understood that the embodiments described are merely exemplary of the principles and application of the present application. Numerous modifications and adaptions can be effected without departing from the spirit and scope of the present application, which is not limited to the exact construction and arrangement described. It is intended, therefore, to cover all modifications and adaptions that fall within the scope of the claims and their equivalents.

Claims

1. A roadway dust concentration monitoring system, characterised in that, The application relates to a dust and water mist concentration field monitoring system. The application relates to a dust and water mist concentration field monitoring system. The application relates to a dust and water mist concentration field monitoring system. The application relates to a dust and water mist concentration field monitoring system. The application relates to a dust and water mist concentration field monitoring system. The application relates to a dust and water mist concentration field monitoring system. The application relates to a dust and water mist concentration field monitoring system. The application relates to a dust and water mist concentration field monitoring system. The application relates to a dust and water mist concentration field monitoring system. The application relates to a dust and water mist concentration field monitoring system. ； wherein, represents the total linear attenuation coefficient, represents the spatial coordinates of the two-dimensional pixel grid on the cross section of the roadway, represents the time at which the measurement was made, represents the specific beam wavelength used, represents the dust mass concentration to be solved, represents the water mist mass concentration to be solved, represents the unit mass extinction coefficient of the dust, represents the unit mass extinction coefficient of the water mist.

2. A roadway dust concentration monitoring system according to claim 1, characterised in that, The application relates to a dust and water mist concentration field monitoring system. a transmitting unit, the transmitting unit comprising a plurality of multi-wavelength light source modules, each multi-wavelength light source module being configured to emit a light beam having wavelengths of and The light beam having wavelengths of and has the following characteristics: the target monitor and the potential optical interferer have different unit mass extinction coefficients at wavelengths of and ​ a receiving unit comprising a plurality of photodetector arrays, the receiving unit being disposed at an opposite position of the transmitting unit for receiving the light beams of the wavelengths and .

3. A roadway dust concentration monitoring system according to claim 1, characterised in that, The application relates to a dust and water mist concentration field monitoring system. The application relates to a dust and water mist concentration field monitoring system. The application relates to a dust and water mist concentration field monitoring system. The application relates to a dust and water mist concentration field monitoring system.

4. A roadway dust concentration monitoring system according to claim 1, characterised in that, The application relates to a dust and water mist concentration field monitoring system. The application relates to a dust and water mist concentration field monitoring system. The application relates to a dust and water mist concentration field monitoring system.

5. A roadway dust concentration monitoring system according to claim 4, characterised in that, The application relates to a dust and water mist concentration field monitoring system. ； wherein, is the center coordinate of the high concentration dust clump at time , denotes the motion vector calculated from the optical flow analysis, denotes the predicted center coordinate of the dust clump at a future time denotes a short time step into the future.​​ 6. A roadway dust concentration monitoring system according to claim 1, characterised in that, The application relates to a dust and water mist concentration field monitoring system. The application relates to a dust and water mist concentration field monitoring system. The application relates to a dust and water mist concentration field monitoring system. The application relates to a dust and water mist concentration field monitoring system. The application relates to a dust and water mist concentration field monitoring system. 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7. A roadway dust concentration monitoring system according to claim 6, characterised in that, The data processing and output module is further configured to: generate a cooperative control instruction, which includes target dust-settling region center coordinates obtained by weighted calculation of a current centroid position of the high-concentration dust region and a future spatial position of the high-concentration dust region, and a spraying intensity and duration determined according to the total pollution level.

8. A method of monitoring the concentration of dust in a roadway, characterised by, The method is applied to a roadway dust concentration monitoring system according to any one of claims 1-7, and the method comprises the following steps: S1. Constructing a detection field composed of light beams containing multiple specific spectral components on a roadway cross section to be monitored, and collecting light intensity attenuation data of the light beams after passing through the roadway cross section to obtain projection data representing integral attenuation; S2. Reconstructing an attenuation coefficient spatial distribution map representing the roadway cross section based on the projection data, and separating the attenuation coefficient spatial distribution map into quantitative dust mass concentration distribution maps and water mist mass concentration distribution maps using spectral absorption characteristics of different substances; S3. Calculating a vector field describing a dust motion state by analyzing the dust mass concentration distribution maps, and predicting a future spatial position of a high-concentration dust region based on the vector field; S4. Processing the dust mass concentration distribution maps to identify a high-concentration dust region, and generating a cooperative control instruction according to a current position of the high-concentration dust region, the future spatial position of the high-concentration dust region, and a preset light intensity.

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