Coal mining face roof disaster monitoring method and system based on edge computing

Abstract

The application relates to the technical field of coal mining, and provides a coal mining working face roof disaster monitoring method and system based on edge computing. In the method, a plurality of pressure radio frequency sensors are uniformly arranged on hydraulic supports of a coal mining working face to monitor roof pressure data of the coal mining working face and perform wireless transmission; meanwhile, a regional edge gateway is arranged in an air inlet roadway of the coal mining working face to receive roof pressure data monitored by the pressure radio frequency sensors; then, roof pressure data in a time period [t1, t2] received by the regional edge gateway is marked, and an optimal hyperplane is determined according to roof pressure marking data obtained by marking; finally, a vector direction of roof pressure data in a time period [t3, t4] received by the regional edge gateway to the optimal hyperplane is determined, so that the roof pressure data in the time period [t3, t4] is classified, and a roof disaster danger area of the coal mining working face in a production process is determined according to a classification result.

Description

Technical Field

[0001] The present application relates to the field of coal mining technology, and in particular to a method and system for monitoring roof disasters in a coal mining face based on edge computing. Background Art

[0002] Coal mine roof disasters refer to undesirable phenomena such as cracking, collapse, or deformation of the roadway roof during underground coal mining due to factors such as geological conditions and engineering structures. These disasters pose a serious threat to mine safety and the safety of workers. To prevent and reduce the occurrence of coal mine roof disasters, effective geological surveys, roadway support, gas extraction, and safety monitoring are necessary to improve mine safety.

[0003] At present, when monitoring roof disasters, indicator data is usually collected by wired transmission. The data needs to go through a tedious process such as monitoring substations and underground ring networks before it can be uploaded to the ground for analysis, which seriously affects the real-time and timely response capabilities of the data. Moreover, the existing monitoring methods lack sufficient theoretical support, and monitoring during the entire mining cycle of the coal mining face cannot collect data scientifically, accurately and effectively.

[0004] Therefore, there is an urgent need to provide a technical solution to the above-mentioned deficiencies in the existing technology. Summary of the Invention

[0005] The purpose of this application is to provide a coal mining face roof disaster monitoring method and system based on edge computing to solve or alleviate the problems existing in the above-mentioned prior art.

[0006] In order to achieve the above objectives, this application provides the following technical solutions:

[0007] The present application provides a method for monitoring roof disasters in a coal mining face based on edge computing, comprising: step S101, evenly arranging multiple pressure radio frequency sensors on the hydraulic supports of the coal mining face to monitor roof pressure data of the coal mining face and wirelessly transmitting the data through a built-in LoRa module; and, deploying a regional edge gateway in the air intake lane of the coal mining face to receive the roof pressure data monitored by the pressure radio frequency sensors; step S102, the regional edge gateway marking the received roof pressure data within a time period [t1, t2], and determining an optimal hyperplane based on the obtained roof pressure marking data; step S103, determining a vector direction from the roof pressure data within a time period [t3, t4] received by the regional edge gateway to the optimal hyperplane, so as to classify the received roof pressure data within the time period [t3, t4], and determining a roof disaster risk area of ​​the coal mining face during the production process based on the classification result; wherein, the time period [t3, t4] is later than the time period [t1, t2].

[0008] Preferably, in step S102, the received roof pressure data in the time period [t1, t2] is marked based on a clustering algorithm to obtain the roof pressure marked data, and the optimal hyperplane is determined according to the roof pressure marked data.

[0009] Preferably, the step of marking the received roof pressure data within the time period [t1, t2] based on a clustering algorithm comprises:

[0010] According to the formula:

[0011]

[0012] Determine the top plate pressure data x i received in the time period [t1, t2] i To the predetermined mth cluster center c m wherein n is the number of the top plate pressure data in the time period [t1, t2]; n is a positive integer; m=2;

[0013] According to the top plate pressure data x i To the mth cluster center c m The distance d(i, m) of the top plate pressure data x i Perform cluster level labeling.

[0014] Preferably, determining the optimal hyperplane based on the top plate pressure marking data includes: expanding the top plate pressure marking data in a three-dimensional space coordinate system with the working face advancement distance, hydraulic support number, and pressure value as characteristic indicators, and determining the optimal hyperplane based on a preset objective function.

[0015] Preferably, determining the optimal hyperplane based on a preset objective function includes: based on the objective function:

[0016] max margin(W, p)

[0017] Determine the optimal hyperplane; wherein W is the direction vector of the hyperplane obtained after the n roof pressure mark data are expanded in the three-dimensional space coordinate system; p is the range of the hyperplane obtained after the n roof pressure mark data are expanded in the three-dimensional space coordinate system;

[0018] According to the formula:

[0019]

[0020] Determine the jth top plate pressure mark data X( j ) to the shortest distance of the hyperplane obtained by expanding the n top plate pressure mark data in the three-dimensional space coordinate system;

[0021] And according to the formula:

[0022]

[0023] and:

[0024] Y (j) (W T ·X (j) +p)>0

[0025] Where Y (j) is an intermediate variable, for the top plate pressure mark data X( j ) are assigned values ​​so that different top plate pressure mark data are distributed on both sides of the hyperplane obtained after the n top plate pressure mark data are expanded in the three-dimensional space coordinate system; a and b respectively represent different clustering levels of the top plate pressure mark data.

[0026] Preferably, in step S103, according to the formula:

[0027] f(X (i) )=W T ·X (j) +p

[0028] Determine the roof pressure data X received by the regional edge gateway within the time period [t3, t4] (i) The vector value f(X (i) );

[0029] According to the top plate pressure data X (i) The vector value f(X(i) ) is positive or negative, the top plate pressure data X (i) to classify.

[0030] The embodiment of the present application further provides a coal mining face roof disaster monitoring system based on edge computing, including:

[0031] a data acquisition unit configured to evenly arrange multiple pressure radio frequency sensors on the hydraulic supports of the coal mining face to monitor roof pressure data of the coal mining face and wirelessly transmit the data via a built-in LoRa module; and a regional edge gateway arranged in the air intake lane of the coal mining face to receive the roof pressure data monitored by the pressure radio frequency sensors;

[0032] a hyperplane unit configured to mark the received roof pressure data within the time period [t1, t2] by the regional edge gateway, and determine an optimal hyperplane based on the obtained roof pressure marked data;

[0033] A disaster determination unit is configured to determine the vector direction of the roof pressure data within the time period [t3, t4] received by the regional edge gateway to the optimal hyperplane, so as to classify the roof pressure data within the received time period [t3, t4], and determine the roof disaster risk area of ​​the coal mining working face during the production process according to the classification result; wherein, the time period [t3, t4] is later than the time period [t1, t2].

[0034] Beneficial effects:

[0035] In the coal mining face roof disaster monitoring method based on edge computing provided in an embodiment of the present application, multiple pressure radio frequency sensors are evenly arranged on the hydraulic support of the coal mining face to monitor the roof pressure data of the coal mining face and transmit it wirelessly through the built-in LoRa module; at the same time, a regional edge gateway is arranged in the air intake lane of the coal mining face to receive the roof pressure data monitored by the pressure radio frequency sensor; then, the roof pressure data within the time period [t1, t2] received by the regional edge gateway is marked, and the optimal hyperplane is determined based on the roof pressure mark data obtained by the marking; finally, the vector direction of the roof pressure data within the time period [t3, t4] received by the regional edge gateway to the optimal hyperplane is determined to classify the received roof pressure data within the time period [t3, t4], and the roof disaster risk area of ​​the coal mining face during the production process is determined based on the classification result.

[0036] Therefore, the collected roof pressure data is transmitted by wireless transmission, and the collected data is analyzed and processed through the working face area edge gateway, so as to obtain the roof activity pattern of the working face in a scientific, real-time and accurate manner, determine the risk range of roof disasters, and greatly improve the timeliness of the working face roof disaster analysis; at the same time, the roof pressure data is processed in real time through the regional edge gateway to realize real-time active perception and intelligent analysis, form the best decision, and divide the risk range of roof disasters, so as to more effectively prevent and control the working face roof disasters. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The drawings in the specification, which constitute a part of this application, are used to provide further understanding of this application. The illustrative embodiments of this application and their descriptions are used to explain this application and do not constitute improper limitations on this application.

[0038] in:

[0039] Figure 1 A schematic flow chart of a method for monitoring roof disasters in a coal mining face based on edge computing according to some embodiments of the present application;

[0040] Figure 2 A schematic diagram of the arrangement of a roof pressure data acquisition device provided according to some embodiments of the present application;

[0041] Figure 3 A schematic diagram of classification of roof pressure data of a coal mining face provided according to some embodiments of the present application;

[0042] Figure 4 A structural schematic diagram of a coal mining face roof disaster monitoring system based on edge computing provided according to some embodiments of the present application. DETAILED DESCRIPTION

[0043] The present application will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments. Each example is provided by way of explanation of the present application and does not limit the present application. In fact, it will be clear to those skilled in the art that modifications and variations can be made in the present application without departing from the scope or spirit of the present application. For example, a feature shown or described as part of one embodiment can be used in another embodiment to produce yet another embodiment. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present invention should fall within the scope of protection of the embodiments of the present invention.

[0044] Currently, in the monitoring method of roof disaster, the stability of the roadway surrounding rock needs to be evaluated before monitoring, and the monitoring range of the dangerous area of the surrounding rock is determined according to the evaluation result of the stability of the surrounding rock, and then the heavy disaster area of the roadway roof is determined, and the monitoring instrument is arranged in the heavy disaster area for monitoring. However, this method lacks verification of the reliability of the evaluation index and index threshold, and does not consider the difference of the index threshold under different geological conditions, which leads to a large deviation of the monitoring result compared with the actual situation, and the reliability of the monitoring is poor.

[0045] In the monitoring method of collecting data through sensors, using switches and monitoring substations to transmit data to the ground, the ground server evaluates the roof state through mathematical models and index analysis to give different early warning signals, but because the data needs to be transmitted from underground to the ground, the data response time is long, which leads to low timeliness and poor accuracy of the monitoring result.

[0046] Based on this, the application provides a roof disaster monitoring method for a coal mining face based on edge computing, which uses a wireless transmission method to collect index data, analyzes and processes the collected data at the data end of the working face through a regional edge gateway of the working face, and scientifically, timely and accurately obtains the roof activity law of the working face to determine the dangerous range of the roof disaster. Figures 1 to 3 As shown in the figure, the roof disaster monitoring method for the coal mining face based on edge computing comprises:

[0047] Step S101, uniformly arranging a plurality of pressure radio frequency sensors on the hydraulic support of the coal mining face to monitor the roof pressure data of the coal mining face and wirelessly transmitting the roof pressure data through the built-in LoRa module; and arranging a regional edge gateway in the air inlet lane of the coal mining face to receive the roof pressure data monitored by the pressure radio frequency sensor.

[0048] In the application, the pressure radio frequency sensors are uniformly arranged on the working face hydraulic support to completely cover the monitoring of the roof pressure of the coal mining face; wherein the pressure radio frequency sensor has a built-in wireless LoRa module for wireless communication to wirelessly transmit the monitored roof pressure data.

[0049] In the air inlet lane of the coal mining face, a regional edge gateway, a power distribution box, a mobile substation and the like are arranged, wherein the regional edge gateway, the power distribution box and the mobile substation are electrically connected to ensure normal power supply of the regional edge gateway. The regional edge gateway receives the roof pressure time monitored by the pressure radio frequency sensor and performs real-time processing and analysis underground to greatly improve the real-time performance and effectiveness of data monitoring.

[0050] Step S102: The regional edge gateway marks the received roof pressure data within the time period [t1, t2], and determines the optimal hyperplane based on the obtained roof pressure marked data.

[0051] In this application, the pressure RF sensor monitors the roof pressure of the coal mining face and sends it to the regional edge gateway via wireless transmission. After the regional edge gateway receives the roof pressure data sent by the pressure RF sensor, it divides the received roof pressure data into two parts. The earlier part is used as sample data to seek the optimal hyperplane, and the later part is classified according to the determined optimal hyperplane to determine the roof disaster risk area of ​​the coal mining face during the production process.

[0052] Alternatively, an optimal hyperplane is determined using historical roof pressure data collected from the coal mining face. The optimal hyperplane is then used to classify the roof pressure data collected in real time by the pressure RF sensor to identify areas at risk of roof disasters within the coal mining face during production. In other words, the roof pressure data within the time period [t1, t2] can be either the older portion of the real-time roof pressure data collected by the pressure RF sensor or the historical roof pressure data from the coal mining face.

[0053] Here, the roof pressure data received in the time period [t1, t2] are marked based on the clustering algorithm to obtain roof pressure marked data. Specifically, according to the formula:

[0054]

[0055] Determine the i-th top plate pressure data x received within the time period [t1, t2] i To the predetermined mth cluster center c m Where n is the number of roof pressure data in the time period [t1, t2], n is a positive integer, m = 2. Then, according to the i-th roof pressure data x i To the mth cluster center c m The distance of the top plate pressure data x i Perform cluster level labeling.

[0056] Repeat the cycle to distribute the top plate pressure data x i The system moves to the nearest cluster center and updates the cluster center cyclically until the cluster center no longer changes significantly or the number of iterations reaches the set value. Based on the two cluster centers, the roof pressure data with low values ​​is classified as "normal" level, recorded as "a", and the roof pressure data with high values ​​is classified as "dangerous" level, recorded as "b".

[0057] Next, the marked roof pressure data is expanded in a three-dimensional space coordinate system using the working face advancement distance, hydraulic support number, and pressure value as characteristic indicators. Based on the distribution characteristics of the data, a hyperplane will exist in the space to separate high pressure value data from low pressure value data. In order to accurately classify these two types of data, it is necessary to ensure that the distance between the points on the boundary of the two types of data and the hyperplane is maximized. Therefore, based on the preset objective function:

[0058] max margin(W, p)

[0059] Determine the optimal hyperplane. Where W is the direction vector of the hyperplane obtained by expanding the n roof pressure marker data in the three-dimensional space coordinate system; p is the range of the hyperplane obtained by expanding the n roof pressure marker data in the three-dimensional space coordinate system.

[0060] Here, the objective function must satisfy two constraints: first, the distance from the point in the data set to the hyperplane is the shortest; second, it ensures that data with different labels are distributed twice on the hyperplane. Based on this, in this application, according to the formula:

[0061]

[0062] Determine the jth top plate pressure mark data X( j ) to the shortest distance of the hyperplane obtained by expanding the n top plate pressure mark data in the three-dimensional space coordinate system;

[0063] And according to the formula:

[0064]

[0065] and:

[0066] Y (j) (W T ·X (j) +p)>0

[0067] Where a and b represent the different clustering levels of the roof pressure mark data, respectively. "a" means that the roof pressure data is classified as the "normal" level, that is, the roof pressure at the corresponding point is in the safe range; "b" means that the roof pressure data is classified as the "dangerous" level, that is, the roof pressure at the corresponding point is in the dangerous range. T It represents the transpose of the direction vector W of the hyperplane obtained after the n top plate pressure mark data are expanded in the three-dimensional space coordinate system.

[0068] Y (j) is the intermediate variable, for the top plate pressure mark data X (j)The assignment is performed so that different roof pressure mark data are distributed on both sides of a hyperplane obtained by unfolding n roof pressure mark data in a three-dimensional space coordinate system. An intermediate variable Y is introduced (j) The assignment is performed on different types of data, so that different types of data can be accurately classified on both sides of the hyperplane.

[0069] In step S103, a vector direction of the roof pressure data in the time period [t3, t4] received by the area edge gateway to the optimal hyperplane is determined, so as to classify the roof pressure data in the time period [t3, t4] received by the area edge gateway, and determine the roof disaster danger area of the coal mining face in the production process according to the classification result.

[0070] In this application, the part of the roof pressure data collected by the pressure radio frequency sensor in real time, i.e. the roof pressure data in the time period [t1, t2], or the historical data of the roof pressure of the coal mining face, is taken as sample data, the optimal hyperplane is determined, and the roof pressure data collected by the pressure radio frequency sensor in real time or the part of the roof pressure data collected by the pressure radio frequency sensor in real time, i.e. the roof pressure data in the time period [t3, t4] is classified. That is, the roof pressure data in the time period [t3, t4] can be all of the roof pressure data collected by the pressure radio frequency sensor in real time, or the data later than the time period [t1, t2] in the time period [t3, t4].

[0071] The optimal hyperplane is determined by the roof pressure data in the time period [t1, t2], and then the roof pressure data X (i) is unfolded in a three-dimensional space coordinate system, and then the roof pressure data X (i) is substituted into the equation of the determined optimal hyperplane:

[0072] W T ·X (j) +p=0

[0073] Through calculation:

[0074] f(X (i) )=W T ·X (j) +p

[0075] The vector value f(X (i) ) of the roof pressure data X (i) in the time period [t3, t4] received by the area edge gateway to the optimal hyperplane is determined.

[0076] Then, according to the vector value f(X (i) ) of the roof pressure data X (i)) is positive or negative, and has an impact on the top plate pressure data X (i) Specifically, when f(X (i) )>0, the top plate pressure data X (i) Classified as "dangerous" when (X (i) )<0, the top plate pressure data X (i) Classified as "Normal".

[0077] Finally, based on the classification results of the roof pressure data in the time period [t3, t4], the roof disaster danger zone of the coal mining face during the production process is determined. That is, the area corresponding to the roof pressure data classified as "dangerous" in the time period [t3, t4] is the roof disaster danger zone of the coal mining face during the production process.

[0078] By demarcating roof hazard risk zones within coal mining faces during production, we can reveal the step distance and duration of pressure at the coal mining face. Pressure at the coal mining face typically varies periodically, better reflecting the patterns of roof activity. This allows for real-time processing of roof pressure data through regional edge gateways, enabling real-time active sensing and intelligent analysis. This allows for optimal decision-making, demarcating roof hazard risk zones, and more effectively preventing and managing roof hazard events at the coal mining face.

[0079] The embodiment of the present application also provides a coal mining face roof disaster monitoring system based on edge computing, such as Figure 3 As shown, the monitoring system includes:

[0080] The data acquisition unit 301 is configured to evenly arrange multiple pressure RF sensors on the hydraulic supports of the coal mining face to monitor the roof pressure data of the coal mining face and transmit it wirelessly via the built-in LoRa module; and to deploy a regional edge gateway in the air intake lane of the coal mining face to receive the roof pressure data monitored by the pressure RF sensors;

[0081] The hyperplane unit 302 is configured as a regional edge gateway to mark the received roof pressure data within the time period [t1, t2] and determine the optimal hyperplane based on the obtained roof pressure data;

[0082] The disaster determination unit 303 is configured to determine the vector direction of the roof pressure data within the time period [t3, t4] received by the regional edge gateway to the optimal hyperplane, so as to classify the roof pressure data within the received time period [t3, t4], and determine the roof disaster risk area of ​​the coal mining working face during the production process according to the classification result; wherein, the time period [t3, t4] is later than the time period [t1, t2].

[0083] The coal mining face roof disaster monitoring system based on edge computing provided by the embodiments of the present application can implement the steps and processes of any of the above coal mining face roof disaster monitoring methods based on edge computing, and achieve the same technical effects, which will not be described one by one here.

[0084] In the description of the present application, the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the present description, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any suitable manner in any one or more embodiments or examples.

[0085] The above only describes the preferred embodiments of the present application and is not intended to limit the present application. For those skilled in the art, the present application can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for monitoring roof disasters in coal mining faces based on edge computing, characterized in that: include: Step S101: evenly distributing multiple pressure radio frequency sensors on the hydraulic supports of the coal mining face to monitor roof pressure data of the coal mining face and wirelessly transmitting the data through a built-in LoRa module; and distributing a regional edge gateway in the air intake lane of the coal mining face to receive the roof pressure data monitored by the pressure radio frequency sensors; Step S102: The regional edge gateway receives the time period Marking the roof pressure data within the , and determining the optimal hyperplane according to the obtained roof pressure marked data; Step S103: The time period received by the regional edge gateway Roof pressure data Expand the top plate pressure data in the three-dimensional space coordinate system Substitute into the determined optimal hyperplane equation: Where, For time period A direction vector of the optimal hyperplane obtained by expanding the top plate pressure mark data in a three-dimensional space coordinate system; is the range of the optimal hyperplane obtained after the top plate pressure mark data is expanded in the three-dimensional space coordinate system; By calculation: Determine the time period received by the regional edge gateway The roof pressure data within The vector value to the optimal hyperplane After that, according to the top plate pressure data Vector value to the optimal hyperplane The positive and negative of the top plate pressure data to classify; when When the top plate pressure data Classified into hazard categories and time periods The area corresponding to the roof pressure data classified as dangerous category is the roof disaster danger area of ​​the coal mining face during production; wherein, the time period Later than the stated time period .

2. The method for monitoring coal mining face roof disasters based on edge computing according to claim 1, characterized in that: In step S102, Based on the clustering algorithm, the received time period The top plate pressure data in the image is marked to obtain the top plate pressure marked data, and the optimal hyperplane is determined according to the top plate pressure marked data.

3. The method for monitoring coal mining face roof disasters based on edge computing according to claim 2, characterized in that: The clustering algorithm is based on the received time period The roof pressure data within is marked, including: According to the formula: Determine the time period for receiving The first The roof pressure data to a predetermined Cluster centers The distance; among them, For the time period The number of top plate pressure data within; is a positive integer; ; According to The roof pressure data To Cluster centers distance , for The roof pressure data Perform cluster level labeling.

4. The method for monitoring coal mining face roof disasters based on edge computing according to claim 2, characterized in that: The determining the optimal hyperplane according to the top plate pressure mark data includes: The roof pressure marking data is expanded in a three-dimensional space coordinate system with the working face advancement distance, hydraulic support number, and pressure value as characteristic indicators, and the optimal hyperplane is determined based on a preset objective function.

5. The method for monitoring coal mining face roof disasters based on edge computing according to claim 4, characterized in that: The determining of the optimal hyperplane based on a preset objective function includes: Based on the objective function: determining the optimal hyperplane; in, for A direction vector of a hyperplane obtained by expanding the top plate pressure mark data in a three-dimensional space coordinate system; for The range of the hyperplane obtained by expanding the top plate pressure mark data in the three-dimensional space coordinate system; According to the formula: Determine the The top plate pressure marking data arrive The shortest distance between the hyperplanes obtained by expanding the top plate pressure mark data in the three-dimensional space coordinate system; And according to the formula: and: Where, is the intermediate variable, The top plate pressure marking data Assign values ​​so that different top plate pressure mark data are distributed in two sides of a hyperplane obtained by expanding the roof pressure mark data in a three-dimensional space coordinate system; Respectively represent different clustering levels of the roof pressure mark data.

6. The method for monitoring coal mining face roof disasters based on edge computing according to claim 5, characterized in that: In step S103, According to the formula: Determine the time period received by the regional edge gateway The roof pressure data within The vector value to the optimal hyperplane ; According to the top plate pressure data The vector value to the optimal hyperplane The positive and negative of the top plate pressure data to classify.

7. A coal mining face roof disaster monitoring system based on edge computing, characterized in that: The method for monitoring roof disasters in a coal mining face based on edge computing according to any one of claims 1 to 6 is used to determine the roof disaster risk area of ​​the coal mining face during production. The system includes: a data acquisition unit configured to evenly arrange multiple pressure radio frequency sensors on the hydraulic supports of the coal mining face to monitor roof pressure data of the coal mining face and wirelessly transmit the data via a built-in LoRa module; and a regional edge gateway arranged in the air intake lane of the coal mining face to receive the roof pressure data monitored by the pressure radio frequency sensors; The hyperplane unit is configured to receive the time period of the regional edge gateway Marking the roof pressure data within the , and determining the optimal hyperplane according to the obtained roof pressure marked data; The disaster determination unit is configured to receive the time period of the regional edge gateway Roof pressure data Expand the top plate pressure data in the three-dimensional space coordinate system Substitute into the determined optimal hyperplane equation: Where, For time period A direction vector of the optimal hyperplane obtained by expanding the top plate pressure mark data in a three-dimensional space coordinate system; is the range of the optimal hyperplane obtained after the top plate pressure mark data is expanded in the three-dimensional space coordinate system; By calculation: Determine the time period received by the regional edge gateway The roof pressure data within The vector value to the optimal hyperplane After that, according to the top plate pressure data Vector value to the optimal hyperplane The positive and negative of the top plate pressure data to classify; when When the top plate pressure data Classified into hazard categories and time periods The area corresponding to the roof pressure data classified as dangerous category is the roof disaster danger area of ​​the coal mining face during production; wherein, the time period Later than the stated time period .

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