Real-time perception and trace analysis system and method for dust concentration in full domain of fully mechanized working face

Abstract

This invention relates to a real-time sensing and source tracing analysis system and method for dust concentration across the entire longwall mining face, belonging to the field of coal mine safety monitoring technology. The method includes: dust concentration sensor network construction: based on data obtained from dust concentration sensor monitoring, combined with dust diffusion and transport patterns obtained from numerical simulations, and the relationship between dust concentration sensor distribution density and monitoring accuracy obtained from field tests, the installation location and spacing of dust concentration sensors are determined; dynamic source tracing analysis of dust in the breathing zone: based on the real-time concentration monitored by sensors, coal mining machine power, hydraulic support roof pressure, and airflow velocity, a multi-source data fusion model is used to obtain a dust heat map mapping function; visualization display system: integrating an underground GIS map and displaying the dust concentration in the breathing zone as a superimposed heat map, triggering early warnings for areas exceeding limits. This invention achieves dynamic reconstruction of dust concentration distribution and source tracing of dust in the breathing zone under the coupling effect of multiple dust sources.

Description

Technical Field

[0001] This invention belongs to the field of coal mine safety monitoring technology, and relates to a real-time sensing and source tracing analysis system and method for dust concentration in the entire longwall mining face. Background Technology

[0002] Coal mine safety monitoring is a core area for ensuring underground operational safety, directly impacting miners' lives and production efficiency. Dust, a major hazard in fully mechanized mining faces, not only affects worker health but can also trigger explosions; therefore, real-time monitoring and precise dust control are crucial. However, existing methods have significant shortcomings in dust monitoring and control. Traditional monitoring relies on scattered sensor deployments, making it difficult to comprehensively capture the dynamic changes in dust distribution at the working face, resulting in incomplete monitoring data and numerous blind spots. Simultaneously, existing dust source prediction models often neglect dynamic factors such as coal and rock confinement pressure and coal cutting intensity, leading to significant prediction biases and failing to provide a reliable basis for precise dust suppression. Furthermore, the interaction of multiple dust sources makes it difficult to analyze the sources of high-level dust in the breathing zone, hindering targeted control.

[0003] These limitations stem from several interconnected core challenges. Comprehensive dust concentration monitoring is constrained by the optimization of sensor placement; traditional empirical deployments struggle to balance monitoring accuracy and cost, resulting in insufficient data coverage and difficulty in real-time reconstruction of dust distribution at the work surface. Insufficient data coverage further exacerbates the difficulty of dust source tracing, as the lack of comprehensive concentration data makes it difficult to accurately distinguish the contribution ratios of multiple dust sources, such as coal cutting by the mining machine and support movement. This ambiguity in the contribution ratios of dust sources directly hinders the development of precise dust suppression strategies based on dynamic operating conditions, and prevents efficient control of dust suppression equipment such as spray systems.

[0004] Therefore, how to optimize the sensor network layout and combine it with dynamic operating parameters to reconstruct the dust concentration distribution of the fully mechanized mining face in real time and accurately separate the contribution ratio of multiple dust sources has become a key issue in achieving intelligent dust control. Summary of the Invention

[0005] In view of this, the purpose of this invention is to provide a real-time sensing and source tracing analysis system and method for dust concentration in the entire mining face. By optimizing the sensor layout and intelligent analysis algorithm, the system can realize dynamic reconstruction of dust concentration distribution and source tracing of dust in the breathing zone under the coupling effect of multiple dust sources, thus providing a basis for the regulation of intelligent dust control systems.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] Option 1:

[0008] A method for real-time sensing and source tracing analysis of dust concentration across the entire longwall mining face, specifically including the following steps:

[0009] S1: Construction of dust concentration sensor network: Distribute dust concentration sensors in the longwall mining face to realize real-time monitoring of dust concentration in the entire longwall mining face.

[0010] S2: Dynamic source analysis of dust in the breathing zone: Based on the real-time concentration monitored by the sensor, the power P of the coal mining machine, the pressure p of the hydraulic support roof plate, and the airflow velocity v, a multi-source data fusion model is used to obtain the dust heat map mapping function;

[0011] S3: Visualization system: integrates downhole GIS map, displays dust concentration in breathing zone as a heat map, and triggers red alerts in areas exceeding limits.

[0012] Furthermore, in step S1, the sensors are distributed and installed in the fully mechanized mining face. Specifically, this includes: determining the installation location and spacing of the dust concentration sensors based on the data obtained from the dust concentration sensors, combined with the dust diffusion and transport laws obtained from numerical simulations, and the relationship between the distribution density of the dust concentration sensors and the monitoring accuracy obtained from field tests; the dust diffusion and transport laws include the dust transport laws generated by the coal cutting machine and the dust transport laws generated by the hydraulic support column lowering and moving.

[0013] Furthermore, in step S1, the relationship between the dust concentration sensor distribution density and monitoring accuracy obtained from the field test is expressed as follows:

[0014] C=αln(N)+β

[0015] Where C is the monitoring error, N is the number of sensors, and α and β are the fitting coefficients.

[0016] Furthermore, in step S2, the expression for the multi-source data fusion model is:

[0017]

[0018] Here, Heatmap(x,y,t) is a three-dimensional spatiotemporal dynamic heatmap function, representing the visual intensity value of dust concentration at time t and spatial location (x,y,z). Its output is usually a color-coded scalar value (such as RGB or grayscale value), used to intuitively reflect the spatial distribution and temporal evolution of dust; Color(·) is the color mapping function; N is the number of sensors; w i (x,y,z,t) is the sensor weighting function, calculated using the inverse distance weighting method; c i(t) represents the dust concentration measured by the sensor; M(t) and M′(t) represent the dust generation from coal cutting by the coal mining machine and the dust generation from the hydraulic support column lowering and moving, respectively, calculated using the coal mining machine dust generation prediction model and the hydraulic support column lowering and moving dust generation prediction model; D1(x,y,z,t) and D2(x,y,z,t) represent the dust diffusion functions of the coal mining machine and the hydraulic support column lowering and moving, respectively, using the Gaussian diffusion model; C total (x,y,z,t) represents the total dust concentration, which is the linear superposition of the contributions from each dust source.

[0019] Furthermore, in step S2, the expression for the dust generation prediction model of the coal mining machine is:

[0020] M = k1·f a ·η b ·p c ·P d

[0021] Where M is the dust mass generated per unit time by the coal mining machine (g / s), f is the hardness of coal and rock, η is the moisture content of coal and rock mass, p is the roof pressure, P is the power of the coal mining machine, k1 is the correction coefficient for the working condition of the coal mining machine; a, b, c, and d are sensitivity coefficients, all of which have been calibrated experimentally.

[0022] Furthermore, in step S2, the expression for the dust generation prediction model of the hydraulic support column lowering and moving is:

[0023] M′=k2·f m ·η n ·p q ·d avr r

[0024] Where M′ is the dust mass generated per unit time (g / s) of the hydraulic support column lowering and moving, f is the coal and rock hardness, η is the water content of the coal and rock mass, p is the roof pressure, and d avr is the distance between adjacent support gaps, k2 is the correction coefficient for hydraulic support column lowering and moving conditions; m, n, q, and r are sensitivity coefficients, all calibrated experimentally.

[0025] Furthermore, in step S3, the visualization system allows users to click to query the composition of dust sources at any location, namely the contribution ratio of the coal mining machine and the hydraulic support column lowering and moving.

[0026] Option 2:

[0027] A real-time dust concentration sensing and source tracing analysis system for the entire longwall mining face includes dust concentration sensors, signal converters, monitoring substations, switches, ring network switches, a system control center, a server, and a monitoring host. The dust concentration sensors are distributed around the front and rear drums of the coal mining machine and between the hydraulic support columns and the moving frame. The dust concentration sensors wirelessly transmit the collected signals to the signal converters, and the converted signals are then sequentially transmitted via wires to the monitoring substations, switches, and the system control center. The monitoring host is connected to the server, and the server is connected to the system control center through the ring network switches.

[0028] The system control center is based on the real-time sensing and source analysis method of dust concentration across the entire longwall mining face described in Scheme 1.

[0029] Preferably, the sensor in the support area is linked to the support spray system for control, and the spray system is activated when dust exceeds the limit.

[0030] The beneficial effects of this invention are as follows:

[0031] (1) High-precision real-time monitoring of dust across the entire area

[0032] ① Optimize sensor layout: By using distributed sensor layout and economic optimization algorithms, the number of sensors required can be significantly reduced while ensuring small monitoring errors.

[0033] ②The coverage blind spots are reduced, achieving full-area monitoring without dead angles on the working surface.

[0034] (2) Dynamic response and intelligent early warning

[0035] ① Short delay in over-limit alarm: Combining real-time sensor data with a multi-source fusion model, it can quickly trigger early warning.

[0036] ② The linked dust suppression equipment (such as the spray system) has a short response time and supports directional control (such as automatic pressurized spraying when the power of the coal mining machine suddenly increases).

[0037] (3) Precise dust source tracing analysis

[0038] ① High accuracy in separating multiple dust sources: The dust contribution ratio of coal cutting and support movement is quantified by using the coal cutting dust generation model of the coal mining machine and the hydraulic support column lowering and moving dust generation model.

[0039] ② The dynamic heat map intuitively displays the distribution of dust sources and supports clicking to query the composition of dust sources at any location.

[0040] (4) Visualization and decision support

[0041] ①GIS map integrated heat map: Real-time overlay of dust concentration in the breathing zone, and early warning of areas exceeding the limit.

[0042] ② Data-driven dust control strategy: Provides a basis for the regulation of intelligent dust suppression systems (such as prioritizing the treatment of high dust contribution areas from coal mining machines).

[0043] (5) Practical application value

[0044] Economic benefits: Optimizing the sensor layout reduces monitoring costs while still meeting accuracy requirements.

[0045] This invention achieves real-time, high-precision monitoring, rapid source tracing, and intelligent control of dust in fully mechanized mining operations through sensor network optimization, multi-source data fusion models, and dynamic visualization technology, providing an efficient and economical solution for coal mine dust control.

[0046] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0047] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein:

[0048] Figure 1 Flowchart of the method for real-time sensing and source tracing analysis of dust concentration across the entire longwall mining face provided by the present invention;

[0049] Figure 2 A schematic diagram illustrating the principle of dust concentration monitoring and simulation technology at fully mechanized mining surfaces;

[0050] Figure 3 This is a diagram illustrating the source analysis and display of dust at the breathing zone height in the working area of ​​a fully mechanized mining face. Detailed Implementation

[0051] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0052] The accompanying drawings are for illustrative purposes only and are schematic diagrams, not actual pictures. They should not be construed as limiting the invention. To better illustrate the embodiments of the invention, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0053] In the accompanying drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components. In the description of the present invention, it should be understood that if terms such as "upper," "lower," "left," "right," "front," and "rear" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the terms used to describe positional relationships in the drawings are only for illustrative purposes and should not be construed as limiting the present invention. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0054] Please see Figures 1-3 This invention provides a method for real-time sensing and source tracing analysis of dust concentration across the entire longwall mining face, characterized by the following steps:

[0055] S1: Construction of dust concentration sensor network: Distribute dust concentration sensors in the longwall mining face to realize real-time monitoring of dust concentration in the entire longwall mining face.

[0056] The distributed installation of sensors in the longwall mining face specifically includes: determining the installation location and spacing of dust concentration sensors based on data obtained from dust concentration sensors, combined with dust diffusion and transport patterns obtained from numerical simulations, and the relationship between the distribution density of dust concentration sensors and monitoring accuracy obtained from field tests; the dust diffusion and transport patterns include the dust transport patterns generated by coal cutting by the coal mining machine and the dust transport patterns generated by the hydraulic support lowering and moving.

[0057] S2: Dynamic source analysis of dust in the breathing zone: Based on the real-time concentration monitored by sensors, the power P of the coal mining machine, the pressure p of the hydraulic support roof, and the airflow velocity v (obtained through an underground anemometer), a multi-source data fusion model is used to obtain the dust heat map mapping function.

[0058] The expression for the multi-source data fusion model is:

[0059]

[0060] Here, Heatmap(x,y,t) is a three-dimensional spatiotemporal dynamic heatmap function, representing the visual intensity value of dust concentration at time t and spatial location (x,y,z). Its output is usually a color-coded scalar value (such as RGB or grayscale value), used to intuitively reflect the spatial distribution and temporal evolution of dust; Color(·) is the color mapping function; N is the number of sensors; w i (x,y,z,t) is the sensor weighting function, calculated using the inverse distance weighting method; c i (t) represents the dust concentration measured by the sensor; M(t) and M′(t) represent the dust generation from coal cutting by the coal mining machine and the dust generation from the hydraulic support column lowering and moving, respectively, calculated using the coal mining machine dust generation prediction model and the hydraulic support column lowering and moving dust generation prediction model; D1(x,y,z,t) and D2(x,y,z,t) represent the dust diffusion functions of the coal mining machine and the hydraulic support column lowering and moving, respectively, using the Gaussian diffusion model; C total (x,y,z,t) represents the total dust concentration, which is the linear superposition of the contributions from each dust source.

[0061] The expression for the dust generation prediction model of coal mining machine is as follows:

[0062] M = k1·f a ·η b ·p c ·P d

[0063] Where M is the dust mass generated per unit time by the coal mining machine (g / s), f is the hardness of coal and rock, η is the moisture content of coal and rock mass, p is the roof pressure, P is the power of the coal mining machine, k1 is the correction coefficient for the working condition of the coal mining machine; a, b, c, and d are sensitivity coefficients, all of which have been calibrated experimentally.

[0064] The expression for the dust generation prediction model of hydraulic support column lowering and moving is:

[0065] M′=k2·f m ·η n ·p q ·d avr r

[0066] Where M′ is the dust mass generated per unit time (g / s) of the hydraulic support column lowering and moving, f is the coal and rock hardness, η is the water content of the coal and rock mass, p is the roof pressure, and d avr is the distance between adjacent support gaps, k2 is the correction coefficient for hydraulic support column lowering and moving conditions; m, n, q, and r are sensitivity coefficients, all calibrated experimentally.

[0067] S3: Visualization System: Integrates underground GIS map, displays dust concentration in the breathing zone as a heat map overlay, and triggers red alerts in areas exceeding limits; supports clicking to query the composition of dust sources at any location (contribution ratio of coal mining machine / hydraulic support column lowering and moving).

[0068] Example 1:

[0069] In step S1 above, the dust concentration sensor network is constructed using a hierarchical layout for measurement, specifically as follows:

[0070] Core monitoring layer: One sensor is installed within 10m of the front and rear drums of the coal mining machine to monitor instantaneous dust during cutting; a main sensor is installed 30m from the working face in the return airway, and auxiliary sensors are arranged at intervals of 50-100m.

[0071] Auxiliary monitoring layer: One sensor is installed for every 10 sets of hydraulic support columns and between the support columns, suspended 1.5m above the front column of the support to monitor the spray efficiency between the supports; sensors are installed 10-15m downwind of the transport transfer point to capture secondary dust data.

[0072] Economic optimization algorithm: Based on the sensor distribution density-accuracy relationship model (formula: C=αln(N)+β, where C is the monitoring error, N is the number of sensors, and α and β are fitting coefficients), determine the minimum number of sensors to meet the error requirements.

[0073] Example 2:

[0074] A coal mining machine dust generation prediction model based on theoretical analysis and experimental verification:

[0075] 1. Dust generation characteristic characterization system

[0076] Key parameters: Instantaneous dust generation M (mg / s): Dust mass flow rate passing through a 75μm sieve;

[0077] Dispersion index R:

[0078] R = 100exp(-λd) N )

[0079] Where, d N It is negatively correlated with the coal and rock fragility index N (the larger the N value, the finer the dust);

[0080] The proportion of respirable dust η PM2.5 It is positively correlated with the degree of coal body fracture development k.

[0081] 2. Modeling of key influencing factors

[0082] (1) Physical and chemical properties of coal

[0083] Hardness f (Shore hardness): It has a power-law relationship with dust production. Hard coal is prone to forming high-pressure dense cores, which leads to a surge in dust.

[0084] Moisture content η: The binding effect of moisture on dust causes M to decrease exponentially with increasing η;

[0085] Crack development degree k: quantified by CT scan, affecting the proportion of respirable dust generation.

[0086] (2) Operating parameters

[0087] Roof pressure p (MPa): positively correlated with the degree of coal pre-crushing, indirectly reducing cutting energy consumption and dust generation;

[0088] Coal mining machine power P (kW): equivalent coal cutting intensity, its relationship with M is as follows:

[0089] M∝P 0.8 ·v 0.6

[0090] Where v is the traction speed.

[0091] 3. Prediction Model Construction

[0092] The comprehensive prediction equation is obtained through multiple nonlinear regression:

[0093] M = 0.48f 1.2 ·η -0.7 ·p 0.3 ·P 0.8 ·e -0.05u

[0094] Where u is the working face wind speed (m / s), and wind speed > 2m / s plays a dominant role in dust diffusion; the coefficient is calibrated through similar simulation test.

[0095] 4. Dust Dispersion Sub-model

[0096] Relationship between the proportion of respirable dust and coal and rock characteristics:

[0097] η PM2.5 =22.3k 0.4 ·(1-e -0.12f )

[0098] Among them, the degree of fracture development k was measured by an acoustic wave detector.

[0099] 5. Engineering Applications

[0100] Dynamic correction mechanism: Adjust the diffusion coefficient (coefficient for headwind operation × 1.3) according to the windward / co-wind operation of the drum;

[0101] Integration into intelligent control systems: Real-time input of coal quality sensor data (such as online moisture meters) updates predicted values.

[0102] Example 3:

[0103] A dust generation model for hydraulic support column lowering and shifting was constructed based on theoretical analysis and experimental verification.

[0104] 1. Dust generation characteristic characterization parameters

[0105] Instantaneous dust generation (M z (mg / frame·time): Total mass of dust generated during a single column lowering and frame shifting process;

[0106] Scaffold gap leakage rate (β): exponentially related to the spacing (d) between adjacent supports;

[0107] β(d)=0.21e -0.15d (d∈[50,200]mm)

[0108] The proportion of respirable dust (η) z Actual measurements show that 38%–45% of the working face at a mining height of 8.8m can achieve this.

[0109] Respirable dust sub-model:

[0110] η z =0.42·(1-e -0.12f )·(1+v / 15)

[0111] 2. Modeling of key influencing factors

[0112] (1) Structural parameters

[0113] Frame joint spacing d, top beam sealing (for brackets using contact-type side guards);

[0114] (2) Process parameters

[0115] The column lowering speed v (which is linearly related to the dust generation rate) and the top plate pressure p;

[0116] 3. Prediction Model Construction

[0117] Comprehensive multiple regression equation:

[0118]

[0119] 4. Engineering optimization suggestions

[0120] Using automatic dust suppression devices can reduce (Mz); maintaining a gap of ≤100mm between frame seams can reduce the generation of respirable dust.

[0121] Example 4:

[0122] This embodiment provides a real-time dust concentration sensing and source tracing analysis system for the entire longwall mining face, such as... Figure 2As shown, the system includes dust concentration sensors, signal converters, monitoring substations, switches, ring network switches, a system control center, a server, and a monitoring host. The dust concentration sensors are distributed around the front and rear drums of the coal mining machine, and between the hydraulic support columns and movement points. The dust concentration sensors wirelessly transmit the collected signals to the signal converters, which then transmit the converted signals sequentially to the monitoring substations, switches, and the system control center via wired connections. The monitoring host is connected to the server, and the server is connected to the system control center via the ring network switches.

[0123] The system control center analyzes the real-time dust concentration distribution at the breathing zone height of the personnel working area based on data obtained from real-time monitoring, combined with the dust generation capacity and dust transport patterns of the fully mechanized mining face, and other dust prevention-related information. It also analyzes the real-time dust concentration distribution under the individual effects of coal cutting by the coal mining machine and the lowering of the support frame. This data can be integrated into the existing intelligent system for underground fully mechanized mining faces for real-time display.

[0124] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for real-time sensing and source tracing analysis of dust concentration across the entire longwall mining face, characterized in that, The method specifically includes the following steps: S1: Construction of Dust Concentration Sensor Network: Distributed dust concentration sensors are installed in the fully mechanized mining face to achieve real-time monitoring of dust concentration throughout the entire mining face. Specifically, this includes: determining the installation location and spacing of dust concentration sensors based on data obtained from the dust concentration sensors, combined with dust diffusion and transport patterns obtained from numerical simulations, and the relationship between the distribution density of dust concentration sensors and monitoring accuracy obtained from field tests. The dust diffusion and transport patterns include the dust transport patterns generated by coal cutting by the coal mining machine and the dust transport patterns generated by the hydraulic support column lowering and moving. S2: Dynamic Source Tracing Analysis of Dust in the Breathing Zone: Based on the real-time concentration monitored by sensors, coal mining machine power, hydraulic support roof pressure, and airflow velocity, a multi-source data fusion model is used to obtain the dust thermal mapping function; the expression of the multi-source data fusion model is: in, It is a three-dimensional spatiotemporal dynamic heatmap function, representing the time... t Spatial location ( The output of the visual intensity value of dust concentration at point () is a color-coded scalar value. This is a color mapping function; N Number of sensors; The sensor weighting function is calculated using the inverse distance weighting method; The dust concentration is measured by the sensor; and The dust generation from coal cutting by the coal mining machine and the dust generation from the hydraulic support column lowering and moving are respectively calculated using the coal mining machine coal cutting dust generation prediction model and the hydraulic support column lowering and moving dust generation prediction model. and The dust diffusion functions for the coal mining machine and the hydraulic support column lowering and shifting are respectively, using a Gaussian diffusion model; For the total dust concentration, is the linear summation of the contributions from each dust source; S3: Visualization System: Integrates downhole GIS map, displays dust concentration in breathing zone as a heat map overlay, and triggers early warning in areas exceeding limits.

2. The method for real-time sensing and source tracing analysis of dust concentration across the entire longwall mining face according to claim 1, characterized in that, In step S1, the relationship between the dust concentration sensor distribution density and the monitoring accuracy obtained from the field test is expressed as follows: C = αln(N) + β Where C is the monitoring error, N is the number of sensors, and α and β are the fitting coefficients.

3. The method for real-time sensing and source tracing analysis of dust concentration across the entire longwall mining face according to claim 1, characterized in that, In step S2, the expression for the dust generation prediction model of the coal mining machine is: M = k 1 f a η b p c P d in, M The dust mass produced by the coal mining machine per unit time. f Due to the hardness of coal and rock, η The water content of the coal and rock mass. p For the pressure of the top plate, P For the power of the coal mining machine, k 1 represents the correction factor for the coal mining machine's operating conditions; a , b , c , d These are sensitivity coefficients, all of which were calibrated experimentally.

4. The method for real-time sensing and source tracing analysis of dust concentration across the entire longwall mining face according to claim 1, characterized in that, In step S2, the expression for the dust generation prediction model of the hydraulic support column lowering and moving is: M′=k 2 f m η n p q d avr r in, M′ The dust generation per unit time for hydraulic support column lowering and moving is [value missing]. f Due to the hardness of coal and rock, η The water content of the coal and rock mass. p For the pressure of the top plate, d avr The distance between adjacent frame joints. k 2 represents the correction factor for the hydraulic support column lowering and moving operation. m , n , q , r These are sensitivity coefficients, all of which were calibrated experimentally.

5. The method for real-time sensing and source tracing analysis of dust concentration across the entire longwall mining face according to claim 1, characterized in that, In step S3, the visualization system allows users to click to query the composition of dust sources at any location, namely the contribution ratio of the coal mining machine and the hydraulic support column lowering and moving.

6. A real-time dust concentration sensing and source tracing analysis system for the entire longwall mining face, characterized in that, The system includes dust concentration sensors, signal converters, monitoring substations, switches, ring network switches, a system control center, a server, and a monitoring host. The dust concentration sensors are distributed around the front and rear drums of the coal mining machine and between the hydraulic support columns and the moving frame. The dust concentration sensors wirelessly transmit the collected signals to the signal converters, and the converted signals are then sequentially transmitted via wires to the monitoring substations, switches, and the system control center. The monitoring host is connected to the server, and the server is connected to the system control center through the ring network switches. The system control center is based on the real-time sensing and source analysis method for dust concentration across the entire longwall mining face as described in any one of claims 1 to 5.

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