Fully mechanized coal mining face global dust concentration real-time sensing and traceability analysis system and method

Abstract

The invention relates to a fully mechanized coal mining face global dust concentration real-time sensing and traceability analysis system and method, and belongs to the technical field of coal mine safety monitoring. The method comprises the following steps: constructing a dust concentration sensor network: based on data monitored by dust concentration sensors, combining a dust diffusion and migration rule obtained by numerical simulation and a relationship between dust concentration sensor distribution density and monitoring precision obtained by a field test, determining mounting positions and spacing of the dust concentration sensors; according to the real-time concentration monitored by the sensor, the coal mining machine power, the hydraulic support top plate pressure and the air flow speed, a multi-source data fusion model is adopted to obtain a dust thermodynamic diagram mapping function; and the visual display system is used for integrating an underground GIS map, displaying the dust concentration of the respiratory zone in a thermodynamic diagram overlapping manner, and triggering early warning in an over-limit region. According to the invention, dust concentration distribution dynamic reconstruction and respiratory zone dust traceability under the action of multi-dust-source coupling are realized.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of coal mine safety monitoring, and relates to a full-area dust concentration real-time sensing and traceability analysis system and method for a fully-mechanized coal mining face. BACKGROUND

[0002] Coal mine safety monitoring is a core field for ensuring the safety of underground operations, and is directly related to the safety of miners and production efficiency. Dust, as one of the main hazards of fully-mechanized coal mining faces, not only affects the health of workers, but also may cause explosion accidents, so it is crucial to monitor and accurately control dust in real time. However, existing methods have significant limitations in dust monitoring and control. Traditional monitoring relies on scattered sensor placement, which is difficult to fully capture the dynamic changes of dust distribution on the working face, resulting in incomplete monitoring data and a large number of coverage blind spots. At the same time, existing dust source prediction models often ignore dynamic factors such as coal and rock confining pressure and coal cutting intensity, resulting in large prediction errors and providing unreliable basis for accurate dust reduction. In addition, under the interaction of multiple dust sources, the source of high dust in the breathing zone is not clear, making it difficult to achieve targeted control.

[0003] These limitations are due to several interrelated core challenges. The full-area monitoring of dust concentration is limited by the optimization of sensor layout, and traditional empirical placement is difficult to balance monitoring accuracy and economic cost, resulting in insufficient data coverage and difficulty in real-time reconstruction of dust distribution on the working face. 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 proportion of multiple dust sources such as coal cutting by the coal mining machine and support moving. The unclear contribution proportion of dust sources directly hinders the development of accurate dust reduction strategies based on dynamic working conditions, and the dust reduction equipment such as the spray system cannot be efficiently controlled.

[0004] Therefore, how to optimize the layout of the sensor network and combine dynamic working condition parameters to real-time reconstruct the dust concentration distribution on the fully-mechanized coal mining face and accurately separate the contribution proportion of multiple dust sources has become a key problem for intelligent dust prevention. SUMMARY

[0005] Therefore, the purpose of the present application is to provide a full-area dust concentration real-time sensing and traceability analysis system and method for a fully-mechanized coal mining face, which optimizes the layout of sensors and intelligent analysis algorithms to achieve dynamic reconstruction of dust concentration distribution and traceability of dust in the breathing zone under the coupling action of multiple dust sources, and provides control basis for an intelligent dust prevention system.

[0006] To achieve the above purpose, the present application provides the following technical solutions:

[0007] Scheme 1:

[0008] A full-area dust concentration real-time sensing and traceability analysis method for a fully-mechanized coal mining face, specifically comprising the following steps:

[0009] S1: Dust concentration sensor network construction: distributedly install dust concentration sensors at the fully mechanized coal mining face to realize real-time monitoring of the dust concentration in the whole space of the fully mechanized coal mining face;

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

[0011] S3: Visual display system: integrate the underground GIS map and the dust concentration in the respiratory zone to display in a heat map superposition manner, and trigger a red early warning in an over-limit area.

[0012] Further, in step S1, the sensors are distributedly installed at the fully mechanized coal mining face, specifically including: based on the data obtained by the dust concentration sensor monitoring, combining the dust diffusion and migration law obtained by numerical simulation, and the relationship between the distribution density of the dust concentration sensor and the monitoring accuracy obtained by field test, the installation position and spacing of the dust concentration sensor are determined; the dust diffusion and migration law includes the dust migration law generated by the coal mining machine cutting coal and the dust migration law generated by the hydraulic support lowering and shifting the column.

[0013] Further, in step S1, the relationship expression between the distribution density of the dust concentration sensor and the monitoring accuracy obtained by the field test is:

[0014] C = αln(N) + β

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

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

[0017]

[0018] Wherein, Heatmap(x, y, t) is a three-dimensional space-time dynamic heat map function, representing the dust concentration visualization intensity value at time t and space position (x, y, z), and its output is usually a color-coded scalar value (such as RGB or grayscale value), which is used to intuitively reflect the spatial distribution and time evolution of dust; Color(·) is a color mapping function; N is the number of sensors; w i (x, y, z, t) is a sensor weight function, which is calculated by inverse distance weighting method; c i(t) is the dust concentration measured by the sensor; M(t) and M'(t) are the dust production amounts of the coal mining machine cutting coal and the hydraulic support lowering column and moving support, respectively, which are calculated by using the coal mining machine cutting coal dust production prediction model and the hydraulic support lowering column and moving support dust production prediction model, respectively; D1(x, y, z, t) and D2(x, y, z, t) are the dust diffusion functions of the coal mining machine and the hydraulic support lowering column and moving support, respectively, which use the Gaussian diffusion model; C total (x, y, z, t) is the total dust concentration, which is a linear superposition of contributions of various dust sources.

[0019] Further, in step S2, the expression of the coal mining machine cutting coal dust production prediction model is:

[0020] M = k1 f a η b p c P d

[0021] wherein M is the dust production mass per unit time of the coal mining machine (g / s), f is the coal rock hardness, η is the water content of the coal rock body, p is the roof pressure, P is the power of the coal mining machine, k1 is the working condition correction coefficient of the coal mining machine; a, b, c, and d are sensitivity coefficients, which are calibrated through experiments.

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

[0023] M' = k2 f m η n p q d avr r

[0024] wherein M' is the dust production mass per unit time of the hydraulic support lowering column and moving support (g / s), f is the coal rock hardness, η is the water content of the coal rock body, p is the roof pressure, d avr is the distance between adjacent frame joints, k2 is the working condition correction coefficient of the hydraulic support lowering column and moving support; m, n, q, and r are sensitivity coefficients, which are calibrated through experiments.

[0025] Further, in step S3, the visual display system supports click query of the dust source composition at any position, i.e., the contribution proportion of the coal mining machine and the hydraulic support lowering column and moving support.

[0026] Scheme 2:

[0027] A kind of fully mechanized working face global dust concentration real-time sensing and trace analysis system, including dust concentration sensor, signal converter, monitoring substation, switch, ring network switch, system control center, server and monitoring host;The dust concentration sensor is distributedly arranged around the drum before and after coal winning machine, and between hydraulic support descending column and moving support;The dust concentration sensor wirelessly transmits the signal collected to signal converter, and the signal converted by signal converter is sequentially wired to monitoring substation, switch and system control center;Monitoring host is connected with server, and server is connected with system control center by ring network switch.

[0028] The system control center is realized based on the fully mechanized working face global dust concentration real-time sensing and trace analysis method described in scheme 1.

[0029] Preferably, the support area sensor is linked with the support spray system for linkage control, and when dust over-limit is monitored, the spray system is started.

[0030] The beneficial effects of the present application are:

[0031] (1) high-precision global dust real-time monitoring

[0032] ① Optimize sensor layout: through distributed sensor layout and economic optimization algorithm, significantly reduce the number of sensors required while ensuring small monitoring error.

[0033] ② Reduce coverage blind area and realize full-area dead-angle-free monitoring of working face.

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

[0035] ① Short over-limit alarm delay: combine real-time sensor data with multi-source fusion model to quickly trigger early warning.

[0036] ② Linkage dust-settling equipment (such as spray system) has short response time and supports directional control (such as automatic pressure-increasing spray when coal winning machine power suddenly increases).

[0037] (3) Precise dust trace analysis

[0038] ① High accuracy of multi-dust-source contribution separation: through coal winning machine coal cutting dust model and hydraulic support descending column and moving support dust model, the dust contribution proportion of coal cutting and moving support is quantified.

[0039] ② Dynamic heat map visually displays dust source distribution and supports click query of dust source composition at any position.

[0040] (4) Visualization and decision support

[0041] ① GIS map integrated heat map: real-time superposition of breathing zone dust concentration, over-limit area early warning.

[0042] 2) Data-driven dust prevention strategy: provide control basis for intelligent dust reduction system (such as preferential processing of high contribution area of dust source of coal winning machine).

[0043] (5) Actual application value

[0044] Economic benefit: after optimizing the sensor layout, the monitoring cost is reduced, and the accuracy requirement is met at the same time.

[0045] The present application realizes real-time high-precision monitoring, rapid tracing and intelligent control of dust on the fully mechanized working face through sensor network optimization, multi-source data fusion model and dynamic visualization technology, and provides an efficient and economic solution for coal dust control.

[0046] Other advantages, objects and features of the present application will be set forth in part in the following specification, and in part will become apparent to those skilled in the art from a reading of the following specification, or can be learned from practice of the present application. The objects and other advantages of the present application can be realized and attained by the methods and instrumentalities particularly pointed out in the following specification. BRIEF DESCRIPTION OF DRAWINGS

[0047] In order to make the objects, technical solutions and advantages of the present application clearer, the preferred detailed description of the present application will be made below in combination with the drawings, in which:

[0048] Fig. 1 The flow chart of the real-time perception and tracing analysis method of the dust concentration of the fully mechanized working face provided by the present application is shown in the figure.

[0049] Fig. 2 The schematic diagram of the dust concentration monitoring and simulation technology for the fully mechanized working face is shown in the figure.

[0050] Fig. 3 The tracing analysis and display figure of the dust at the breathing height of the personnel working area of the fully mechanized working face is shown in the figure. DETAILED DESCRIPTION

[0051] The embodiments of the present application will be described below through specific concrete examples, and those skilled in the art can easily understand other advantages and effects of the present application from the disclosure of the present specification. The present application can also be implemented or applied through other different specific embodiments, and the details in the present specification can be modified or changed based on different views and applications without departing from the spirit of the present application. It should be noted that the figures provided in the following examples only illustrate the basic concept of the present application in a schematic manner, and the following examples and features in the examples can be combined with each other without conflict.

[0052] In the drawings, only for example, the representation is a schematic diagram, not a physical diagram, and cannot be understood as a limitation of the present application; in order to better illustrate the embodiments of the present application, some components of the drawings may be omitted, enlarged or reduced, and do not represent the size of the actual product; for those skilled in the art, it is understandable that some well-known structures and their descriptions in the drawings may be omitted.

[0053] The same or similar reference numerals in the drawings of the embodiments of the present application correspond to the same or similar components; in the description of the present application, it should be understood that if the terms "upper", "lower", "left", "right", "front", "back" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, only for the convenience of describing the present application and simplifying the description, and not to indicate or imply that the device or element referred to must have a particular orientation, structure and operation, therefore the positional relationship described in the drawings is only for example, and cannot be understood as a limitation of the present application, for those skilled in the art, the specific meaning of the above terms can be understood according to the specific situation.

[0054] Please refer to Figs. 1-3 The present application provides a kind of full domain dust concentration real-time sensing and trace analysis method of fully mechanized working face, it is characterized in that, the method specifically includes the following steps:

[0055] S1: dust concentration sensor network construction: distributedly install dust concentration sensor in fully mechanized working face, realize the real-time monitoring of full domain space dust concentration of fully mechanized working face.

[0056] Distributedly install sensor in fully mechanized working face, specifically includes: based on the data obtained by dust concentration sensor monitoring, in combination with the dust diffusion transport law obtained by numerical simulation, and the relationship between the distribution density and monitoring accuracy of dust concentration sensor obtained by field test, the installation position and interval of dust concentration sensor are determined;The dust diffusion transport law includes the dust transport law of coal cutter coal cutting and the dust transport law of hydraulic support column lowering and moving.

[0057] S2: dynamic trace analysis of respiratory zone dust: according to the real-time concentration monitored by sensor, coal cutter power P, hydraulic support roof pressure p and air flow velocity v (obtained by underground wind speed instrument), adopt multi-source data fusion model to obtain dust thermal map mapping function.

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

[0059]

[0060] where Heatmap(x, y, t) is a three-dimensional spatiotemporal dynamic heat map function, representing the dust concentration visualization intensity value at time t and spatial position (x, y, z), and its output is usually a color-coded scalar value (such as RGB or grayscale value) for intuitively reflecting the spatial distribution and temporal evolution of dust; Color(·) is a color mapping function; N is the number of sensors; w i (x, y, z, t) is a sensor weight function calculated using the inverse distance weighting method; c i (t) is the dust concentration measured by the sensor; M(t) and M'(t) are the dust production amounts of the coal mining machine cutting coal and the hydraulic support lowering column and moving support, respectively, which are calculated using the coal mining machine cutting coal dust production prediction model and the hydraulic support lowering column and moving support dust production prediction model, respectively; D1(x, y, z, t) and D2(x, y, z, t) are the dust diffusion functions of the coal mining machine and the hydraulic support lowering column and moving support, respectively, using the Gaussian diffusion model; C total (x, y, z, t) is the total dust concentration, which is the linear superposition of the contributions of each dust source.

[0061] The expression of the coal mining machine cutting coal dust production prediction model is:

[0062] M = k1·f a ·η b ·p c ·P d

[0063] where M is the dust production mass per unit time of the coal mining machine (g / s), f is the coal rock hardness, η is the water content of the coal rock body, p is the roof pressure, P is the power of the coal mining machine, k1 is the working condition correction coefficient of the coal mining machine; a, b, c, and d are sensitivity coefficients, which are calibrated through experiments.

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

[0065] M' = k2·f m ·η n ·p q ·d avr r

[0066] where M' is the dust production mass per unit time of the hydraulic support lowering column and moving support (g / s), f is the coal rock hardness, η is the water content of the coal rock body, p is the roof pressure, d avr is the distance between adjacent frame joints, k2 is the working condition correction coefficient of the hydraulic support lowering column and moving support; m, n, q, and r are sensitivity coefficients, which are calibrated through experiments.

[0067] S3: Visualization display system: integrate downhole GIS map and respirable dust concentration to display in heat map overlay, trigger red warning in overrun area; support click query of dust source composition (contribution proportion of shearer / hydraulic support) at any position.

[0068] Embodiment 1:

[0069] In the above step S1, the dust concentration sensor network construction adopts hierarchical layout measurement, specifically:

[0070] Core monitoring layer: one sensor is arranged in each of the 10m range of the front and rear rollers of the shearer to monitor the instantaneous dust cutting; the main sensor is arranged at the position 30m away from the working face in the return airway, and the auxiliary sensors are arranged at intervals of 50-100m.

[0071] Auxiliary monitoring layer: one sensor is arranged every 10 groups of supports between the hydraulic support column lowering and support moving, which is hung at a height of 1.5m from the front column of the support to monitor the spraying efficiency between the supports; the sensor is arranged at a position 10-15m away from the downwind side of the transportation transfer point to capture secondary dust raising data.

[0072] Economic optimization algorithm: based on the sensor distribution density-precision relationship model (formula: C = a ln (N) + b, where C is the monitoring error, N is the number of sensors, and a and b are fitting coefficients), the minimum number of sensors is determined to meet the error requirement.

[0073] Embodiment 2:

[0074] Based on theoretical analysis and experimental verification, a shearer coal cutting dust production prediction model is constructed:

[0075] 1. Dust production characteristic representation system

[0076] Core parameters: instantaneous dust production M (mg / s): dust mass flow through a 75μm sieve;

[0077] Dispersion index R:

[0078] R = 100exp (-λd N )

[0079] Wherein, d N is negatively correlated with the coal rock fragmentation index N (the larger the value of N, the finer the dust);

[0080] Respirable dust proportion η PM2.5 : positively correlated with the coal body fissure development degree k.

[0081] 2. Modeling of key influencing factors

[0082] (1) Coal and rock physicochemical properties

[0083] Hardness f (Shore hardness): power law relationship with dust yield, hard coal is easy to form high pressure dense core leading to dust surge;

[0084] Moisture content η: the adhesion of moisture to dust makes M exponentially decay with η;

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

[0086] (2) Working condition parameters

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

[0088] Shearer power P (kW): equivalent cutting intensity, the relationship with M is:

[0089] M∝P 0.8 ·v 0.6

[0090] Where v is the traction speed.

[0091] 3. Construction of prediction model

[0092] The comprehensive prediction equation is obtained by 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), wind speed > 2 m / s plays a leading role in dust diffusion; the coefficient is calibrated by similar simulation test.

[0095] 4. Dust dispersion degree sub-model

[0096] Relationship between respirable dust proportion and coal rock properties:

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

[0098] Where the fracture development degree k is measured by acoustic detector.

[0099] 5. Engineering application

[0100] Dynamic correction mechanism: adjust the diffusion coefficient according to the drum working with / against the wind (coefficient × 1.3 for working with the wind);

[0101] Integrated into intelligent control system: real-time input of coal quality sensor data (such as online moisture meter) to update the prediction value.

[0102] Example 3

[0103] Based on theoretical analysis and experimental verification, a dust production model of hydraulic support descending and shifting column is constructed:

[0104] 1. Characterization parameters of dust production characteristics

[0105] Instantaneous dust production (Mz) (mg / support·time): total dust mass in single descending and shifting column process; z

[0106] Frame joint leakage rate (β): exponential relationship with adjacent support spacing (d);

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

[0108] Respirable dust proportion (η z ): up to 38% to 45% at 8.8m working face;

[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 (supports with contact type side guard);

[0114] (2) Process parameters

[0115] Descending speed v (linear relationship with dust production), roof pressure p;

[0116] 3. Construction of prediction model

[0117] Comprehensive multiple regression equation:

[0118]

[0119] 4. Engineering optimization suggestions

[0120] Using automatic dust falling device can reduce (Mz); keeping frame joint spacing ≤100mm can reduce respirable dust production.

[0121] Example 4

[0122] The embodiment provides a kind of full domain dust concentration real-time sensing and tracing analysis system of fully mechanized coal mining face, such as Fig. 2 ​As shown, the system comprises a dust concentration sensor, a signal converter, a monitoring substation, an exchange, a ring network exchange, a system control center, a server and a monitoring host. The dust concentration sensor is distributedly arranged around the drum before and after the coal mining machine and between the hydraulic support descending column and the movable support; the dust concentration sensor wirelessly transmits the collected signals to the signal converter, and the converted signals are sequentially transmitted to the monitoring substation, the exchange and the system control center through wires; the monitoring host is connected with the server, and the server is connected with the system control center through the ring network exchange.

[0123] The system control center is based on the data obtained by real-time monitoring, combined with the dust production capacity of the fully mechanized working face and the dust migration influence law, and other dust-related information perceived, finally analyzes the real-time dust concentration distribution of the breathing zone height of the fully mechanized working face personnel operation area, and the real-time dust concentration distribution under the condition of the dust source of the coal mining machine cutting coal and the descending column and movable support alone, and can be integrated into the existing underground fully mechanized face intelligent system for real-time display.

[0124] Finally, it should be pointed out that the above embodiments are only used to illustrate the technical solutions of the present application and not to limit it. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced by equivalents without departing from the purpose and scope of the present technical solutions, which should be covered in the scope of the claims of the present application.

Claims

1. A method for real-time perception and source tracing analysis of dust concentration in the entire fully mechanized mining face, characterized by: The method specifically comprises the following steps: S1: Construction of dust concentration sensor network: Dust concentration sensors are distributedly installed in the fully mechanized mining working face to achieve real-time monitoring of dust concentration in the entire space of the fully mechanized mining working face; S2: Dynamic source tracing analysis of breathing zone dust: 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 map mapping function; S3: Visual display system: Integrates underground GIS map, and displays the dust concentration in the breathing zone as a superimposed heat map, triggering an early warning in the exceeding area.

2. The method for real-time perception and source tracing analysis of dust concentration in the entire fully mechanized mining face according to claim 1 is characterized in that: In step S1, sensors are distributedly installed on the fully mechanized mining working face, specifically including: determining the installation position and spacing of the dust concentration sensors based on the data monitored by the dust concentration sensors, in combination with the dust diffusion and migration laws obtained by numerical simulation, and the relationship between the distribution density of the dust concentration sensors and the monitoring accuracy obtained by field tests; the dust diffusion and migration laws include the dust migration laws generated by coal cutting by the coal mining machine and the dust migration laws generated by lowering the hydraulic support and moving the frame.

3. The method for real-time perception and source tracing analysis of dust concentration in the entire fully mechanized mining face according to claim 2 is characterized in that: In step S1, the relationship between the distribution density of dust concentration sensors and 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 fitting coefficients.

4. The method for real-time perception and source tracing analysis of dust concentration in the entire fully mechanized mining face according to claim 1 is characterized in that: In step S2, the expression of the multi-source data fusion model is: Heatmap(x,y,t) is a three-dimensional spatiotemporal dynamic heat map function, which represents the dust concentration visualization intensity value at time t and spatial position (x,y,z). Its output is a color-coded scalar value; Color((·) is a color mapping function; N is the number of sensors; w i (x, y, z, t) is the sensor weight function, which is calculated using the inverse distance weighted method; c i (t) is the dust concentration measured by the sensor; M(t) and M′(t) are the dust production during coal cutting by the shearer and the dust production during hydraulic support column lowering and moving, respectively, which are calculated using the shearer coal cutting dust production prediction model and the hydraulic support column lowering and moving dust production prediction model, respectively; D1(x, y, z, t) and D2(x, y, z, t) are the dust diffusion functions of the shearer and the hydraulic support column lowering and moving, respectively, which are calculated using the Gaussian diffusion model; C total (x, y, z, t) is the total dust concentration, which is the linear superposition of the contributions of each dust source.

5. The method for real-time perception and source tracing analysis of dust concentration in the entire fully mechanized mining face according to claim 4 is characterized in that: In step S2, the expression of the coal mining machine coal cutting dust generation prediction model is: M=k1·f a ·η b ·p c ·P d Among them, M is the dust mass produced per unit time by the coal mining machine, f is the hardness of coal rock, η is the moisture content of coal rock, p is the roof pressure, P is the power of the coal mining machine, k1 is the working condition correction coefficient of the coal mining machine; a, b, c, and d are sensitivity coefficients, all of which are calibrated through experiments.

6. The method for real-time perception and source tracing analysis of dust concentration in the entire fully mechanized mining face according to claim 4 is characterized in that: In step S2, the dust generation prediction model for hydraulic support column lowering and frame moving is expressed as: M′=k2·f m ·η n ·p q ·d avr r Among them, M' is the dust mass generated per unit time of hydraulic support column lowering and moving, f is the hardness of coal rock, η is the water content of coal rock, p is the roof pressure, d avr is the distance between adjacent frame gaps, k2 is the correction coefficient for the hydraulic support column lowering and frame moving working condition; m, n, q, and r are sensitivity coefficients, all of which are calibrated through experiments.

7. The method for real-time perception and source tracing analysis of dust concentration in the entire fully mechanized mining face according to claim 1 is characterized in that: In step S3, the visualization display system supports clicking to query the dust source composition at any location, that is, the contribution ratio of the coal mining machine and the hydraulic support column lowering and frame moving.

8. A real-time perception and source tracing analysis system for dust concentration in the entire fully mechanized mining face, characterized by: The system includes a dust concentration sensor, a signal converter, a monitoring substation, a switch, a ring network switch, 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 column lowering and moving frames. The dust concentration sensors wirelessly transmit the collected signals to the signal converter. The converted signals are then transmitted to the monitoring substation, the switch, and the system control center in turn by wires. The monitoring host is connected to the server, and the server is connected to the system control center via the ring network switch. The system control center is realized based on the real-time perception and source tracing analysis method of the entire dust concentration in the fully mechanized mining working face as described in any one of claims 1 to 7.

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