Generative coal mine production risk grading evaluation method

Abstract

The invention discloses a generative coal mine production risk grading evaluation method, and relates to the technical field of coal mine production risk evaluation, and the method comprises the following steps: 1, firstly, collecting various data of a coal mining area, and respectively collecting geological conditions near a mining area and data of the influence of climate change on geology, and the service life and the fault rate of the mining equipment also need to be evaluated. When risk assessment is carried out in the coal mining process, the assessment model is established in advance according to the situation of the mining area, in the assessment process, data of the mining area are collected, the collected data are directly input into the assessment model, and the assessment model carries out assessment directly according to the data; and grade division is directly carried out after evaluation, and a solution is directly given after grade division.

Description

Technical Field

[0001] The present invention relates to the technical field of coal mine production risk assessment, and particularly to a generative coal mine production risk grading and assessment method. Background Technique

[0002] At present, a coal mine refers to a place rich in coal resources, generally divided into underground coal mines and surface coal mines. A coal mine refers to a place rich in coal resources, generally divided into underground coal mines and surface coal mines;

[0003] During the process of coal mine exploitation, especially when carrying out underground mining operations, it is necessary to collect and analyze the influencing factors around the mining area. However, the existing analysis methods all adopt manual and computer-aided analysis. Because there is too much data collected, the processing efficiency will be reduced by the above methods, and the grade division cannot be quickly given during the processing, and a solution cannot be proposed immediately after the division, which will reduce the efficiency during the analysis and processing. Summary of the Invention

[0004] The purpose of the present invention is to provide a generative coal mine production risk grading and assessment method to solve the problems raised in the above background technique.

[0005] To achieve the above purpose, the present invention provides the following technical solution: A generative coal mine production risk grading and assessment method, including the following steps:

[0006] Step 1: First, collect various data of the coal mine mining area, and respectively collect data on the geological conditions near the mining area and the impact of climate change on the geology. It is also necessary to evaluate the service life and failure rate of the mining equipment;

[0007] Step 2: After collecting and gathering each data generated during the mining process of the mining area, use the generative model technology to review and judge the collected data, and set the grade according to the final result after the judgment. Formulate corresponding emergency solutions according to the grade setting, and the emergency solutions are adjusted in a small range every half month and in a large range every month. After evaluating and auditing the data, establish an evaluation model;

[0008] Step 3: When analyzing and evaluating the coal mine, collect data of each area inside the mining area, and after the collection is completed, manually check the collected data. After the collected data is checked, input the data into the evaluation model, and evaluate and grade the risk level of the mining area through the evaluation model.

[0009] Preferably, in step one, data collection on geological changes in the mining area is carried out, including collection of geological data in the mining area, the area of the mining area, the quality of coal mines, and the geological conditions around the mining area. After the collection is completed, the collected data is checked by means of expert review, and the parts with doubts are adjusted and determined. After the data is improved, a geological database is formed.

[0010] Preferably, after the data collection in step one is completed, the layout map of the mining area is modeled by means of three-dimensional simulation technology, and after the modeling is completed, it is simulated in advance in the way of real mining. During the simulation, the geological changes generated during artificial mining are recorded, and the recorded data forms a correction database. Through the comparison and adjustment between the correction database and the geological database, the data in the geological database is optimized;

[0011] And during the simulation, after the goaf is unstable, the receptor exposure degree (D) is characterized by the quasi-Euclidean space distance method. Based on the three factors of the influence range (S) of the goaf instability failure, the influence degree (P) of the goaf instability failure on people, and the influence degree (E) of the goaf instability failure on equipment, a three-dimensional model for evaluating the receptor exposure degree is constructed by the quasi-Euclidean space distance method, and its function expression is as follows:

[0012] D = D(S, P, E)

[0013] It can be seen from the model that the larger the values of S, P, and E, the greater the degree of deviation from the target, that is, the farther the distance. In the three-dimensional risk matrix model, the distance is represented as the space distance, that is:

[0014]

[0015] Simplify the formula to obtain the goaf receptor exposure degree (D)

[0016]

[0017] Preferably, after the three-dimensional modeling in step one is completed, by simulating the external environment during the mining process, and the external environmental changes include rain, earthquake, and debris flow, the changes in the mining area are observed, and the data changes generated by the observation are collected. During the collection process, data is collected in a time-segmented manner. While the external environment changes, the changes in the mining area are collected at intervals of ten minutes, fifteen minutes, and twenty minutes respectively. At the same time, the mining equipment during mining is inspected, and the usage time, failure rate, number of repairs, and number of uses of the mining equipment are analyzed respectively. And the usage levels of the mining equipment are divided respectively, and are divided into Class A, Class B, and Class C respectively, where Class A is serious and Class C is general, and the usage frequency of the mining equipment is adjusted.

[0018] Preferably, after the data generated during the coal mine mining process is centrally collected in Step 2, the problems generated during the mining process of the mining area are graded through expert review, and are divided into three levels: level one, level two, and level three, where level one is the highest, level two is medium, and level three is general. For the problems at levels one, two, and three, three corresponding solutions are formulated respectively: the first solution, the second solution, and the third solution. The first solution corresponds to level one, the second solution corresponds to level two, and the third solution corresponds to level three.

[0019] Preferably, when carrying out mining operations in the mining area in Step 3, when it is necessary to divide the risk level of mining production in advance, the geological information of the mining area is collected, including the area of the mining area, the trend of the mining area, the conditions of the soil layer around the mining area, and the detection data of the coal quality inside the mining area. The collected data is manually checked, and a collection library is formed after the check is completed.

[0020] Preferably, before carrying out mining operations in Step 3, it is also necessary to detect the environment around the mining area, including the thickness of the soil, the density of green plants, the trend of the terrain, the transportation conditions after mining, and the statistics of rainfall. After the data collection is completed, an external database is set up, and the external database is merged into the collection library.

[0021] Preferably, after the data is collected in Step 3, the data is directly input into the evaluation model. The evaluation model analyzes the input data, and after the analysis is completed, it makes a determination and grading. After the level is determined, the corresponding solution is derived, and the level is manually reviewed according to the expert review method, and at the same time, it is compared with the historical data to check whether it exists in the historical data.

[0022] Compared with the prior art, the beneficial effects of the present invention are:

[0023] When the present invention conducts risk assessment during the coal mine mining process, by establishing an evaluation model in advance according to the situation of the mining area, during the evaluation process, the data of the mining area is collected, and the collected data is directly input into the evaluation model. The evaluation model directly evaluates according to the data, and directly conducts level division after the evaluation. After the level division, the solution is directly given, which increases the safety and efficiency in the later mining process, avoids the situation that the solution cannot be immediately formulated after the level is given, delaying the implementation plan of mining, and avoids the need for manual level division after data collection, delaying the mining time and efficiency of the mining area. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a flowchart of the method provided by the embodiment of the present invention. Detailed implementation manners

[0025] Next, in combination with the accompanying drawings in the embodiments of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0026] Please refer to Figure 1 , the present invention provides a technical solution: a generative risk grading and assessment method for coal mine production, including the following steps:

[0027] Step 1: First, collect various data in the coal mine mining area, and separately collect data on the geological conditions near the mining area and the impact of climate change on geology. It is also necessary to evaluate the service life and failure rate of the mining equipment.

[0028] Step 2: After collecting and gathering all the data generated during the mining process in the mining area, use generative model technology to review and judge the collected data. After the judgment is completed, set the level according to the final result, formulate corresponding emergency solutions according to the level setting, and the emergency solutions are adjusted in a small range every half month and in a large range every month. After evaluating and reviewing the data, establish an evaluation model.

[0029] The stability coefficient reflects the ability of the rock mass to stand on its own under certain stress conditions. The shape factor takes into account the size and shape of the exposed surface of a single stope. The calculation formula of the stability coefficient is as follows;

[0030] N, = Q, × A × B × C;

[0031] Wherein, N 、 is the stability coefficient, Q 、 is the modified Q value, A is the rock stress coefficient, B is the joint orientation coefficient, and C is the gravity adjustment coefficient;

[0032] Step 3: When analyzing and evaluating the coal mine, collect data in each area inside the mining area, and after the collection is completed, manually check the collected data. After the collected data is checked, input the data into the evaluation model, and evaluate and grade the risk level of the roasting mining area through the evaluation model.

[0033] In Step 1, data collection is carried out on geological changes in the mining area, including collection of geological data in the mining area, the area of the mining area, the quality of coal mines, and the geological conditions around the mining area. After the collection is completed, the collected data is checked by means of expert review, and the doubtful parts are adjusted and determined. After the data is refined, a geological database is formed.

[0034] In Step 1, after the data collection is completed, the layout map of the mining area is modeled by means of 3D simulation technology. After the modeling is completed, an advance simulation is carried out in the way of simulating real mining. During the simulation, the geological changes generated during artificial mining are recorded, and the recorded data forms a correction database. Through the check and adjustment between the correction database and the geological database, the data in the geological database is optimized.

[0035] And during the simulation, after the goaf becomes unstable, the receptor exposure degree (D) is characterized by the quasi-Euclidean space distance method. Based on the three factors of the influence range (S) of the goaf instability damage, the influence degree (P) of the goaf instability damage on people, and the influence degree (E) of the goaf instability damage on equipment, a three-dimensional model for evaluating the receptor exposure degree is constructed by the quasi-Euclidean space distance method, and its function expression is as follows:

[0036] D = D(S, P, E)

[0037] It can be seen from the model that the larger the values of S, P, and E, the greater the deviation degree from the target, that is, the farther the distance. In the three-dimensional risk matrix model, the distance is represented as the space distance, that is:

[0038]

[0039] Simplify the formula to obtain the goaf receptor exposure degree (D).

[0040]

[0041] In Step 1, after the 3D modeling is completed, by simulating the external environment during the mining process, and the external environmental changes include rain, earthquake, and debris flow, to observe the changes in the mining area, and collect the data changes generated by the observation. During the collection process, data is collected in a time-segmented manner. While the external environment changes, the changes in the mining area are collected at time intervals of ten minutes, fifteen minutes, and twenty minutes respectively. At the same time, the mining equipment during mining is inventoried, and the usage time, failure rate, number of repairs, and number of uses of the mining equipment are analyzed respectively. And the usage levels of the mining equipment are divided respectively, and are divided into Class A, Class B, and Class C respectively, where Class A is serious and Class C is general, and the usage frequency of the mining equipment is adjusted.

[0042] In Step 2, after centrally collecting the data generated during coal mining, the problems generated during the mining process in the mining area are classified through expert review, and are divided into Level 1, Level 2, and Level 3, where Level 1 is the highest, Level 2 is medium, and Level 3 is general. Three solutions are formulated for Level 1, Level 2, and Level 3 problems respectively, namely the First Solution, the Second Solution, and the Third Solution. The First Solution corresponds to Level 1, the Second Solution corresponds to Level 2, and the Third Solution corresponds to Level 3;

[0043] In Step 3, when carrying out mining operations in the mining area and needing to divide the risk level of mining production in advance, the geological information of the mining area is collected, including the area of the mining area, the trend of the mining area, the conditions of the soil layer around the mining area, and the detection data of the coal quality inside the mining area. The collected data is manually checked, and a collection library is formed after the check is completed;

[0044] In Step 3, before carrying out mining operations, it is also necessary to detect the environment around the mining area, including the thickness of the soil, the density of green plants, the trend of the terrain, the transportation conditions after mining, and the statistics of rainfall. After the data collection is completed, an external database is set up, and the external database is merged into the internal of the collection library;

[0045] In Step 3, after the data is collected, the data is directly input into the evaluation model. The evaluation model analyzes the input data, and after the analysis is completed, it makes a judgment and classification. After the level is determined, the corresponding solution is derived, and the level is manually reviewed according to the expert review method, and at the same time, it is compared with the historical data to check whether it exists in the historical data.

[0046] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variation thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or elements inherent to such process, method, article or device.

[0047] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principle and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A generative coal mine production risk grading assessment method, characterized in that The following steps are involved: Step 1: First, collect various data on the coal mining area, and collect data on the geological conditions near the mining area and the impact of climate change on geology. It is also necessary to evaluate the service life of mining equipment and the failure rate. Step 2: After collecting all the data generated during the mining process, the generative model technology is used to review and judge the collected data, and after the judgment is completed, the level is set according to the final result. According to the level setting, the corresponding emergency solution is formulated, and the emergency solution is adjusted in a small range every half a month and in a large range every month. After evaluating and reviewing the data, an evaluation model is established; Step three: When analyzing and evaluating the coal mine, data from each area within the mining area is collected, and after the collection is completed, the collected data is manually checked. After the collection is checked, the data is input into the evaluation model, and the risk level of the roasting mining area is evaluated and graded through the evaluation model.

2. A generative coal mine production risk grading assessment method according to claim 1, characterized in that: In the step one, data collection is performed on geological changes in the mining area, including geological data collection of the mining area, mining area, quality of the coal mine, and geological conditions around the mining area. After the collection is completed, the collected data is checked by expert review, and the questionable parts are adjusted and determined. After the data is perfected and processed, a geological database is formed.

3. A generative coal mine production risk grading assessment method according to claim 1, characterized in that: In the step 1, after the data collection is completed, the layout of the mining area is modeled by three-dimensional simulation using three-dimensional simulation technology, and after the modeling is completed, advance simulation is performed by simulating real mining. During the simulation, the geological changes caused by artificial mining are recorded, and the recorded data are formed into a correction database. By checking and adjusting the correction database with the geological database, the data in the geological database is optimized.

4. A generative coal mine production risk grading assessment method according to claim 1, characterized in that: In the step 1, after the three-dimensional modeling is completed, the external environment is simulated during the mining process, and the external environmental changes include rain, earthquakes and mudslides to observe the changes in the mining area, collect the data changes generated by the observation, and collect data in time periods. When the external environment changes, the changes in the mining area are collected in time periods of ten minutes, fifteen minutes and twenty minutes respectively. At the same time, the mining equipment during mining is checked, and the use time, failure rate, number of repairs and number of uses of the mining equipment are analyzed respectively. The use level of the mining equipment is divided into A, B and C respectively, where A is serious and C is general, and the use frequency of the mining equipment is adjusted.

5. A generative coal mine production risk grading assessment method according to claim 1, characterized in that: In the step 2, after the data generated in the coal mining process are centrally collected, the problems generated in the mining process are graded through expert review, and are divided into level one, level two and level three, where level one is the highest, level two is medium, and level three is general. Three solutions are formulated for the level one, level two and level three problems, namely the first solution, the second solution and the third solution, respectively. The level one corresponds to the first solution, the level two corresponds to the second solution, and the level three corresponds to the third solution.

6. A generative coal mine production risk grading assessment method according to claim 1, characterized in that: When mining operations are carried out in the mining area in the step three, it is necessary to divide the risk level of mining production in advance, and collect geological information of the mining area, including the area of ​​the mining area, the direction of the mining area, the conditions of the soil layers around the mining area, and the detection data of the coal texture inside the mining area. The collected data are manually checked and a collection library is formed after the check is completed.

7. A generative coal mine production risk grading assessment method according to claim 6, characterized in that: In step three, before conducting mining operations, it is also necessary to inspect the environment around the mining area, including soil thickness, density of green plants, direction of topography, conditions for transportation after mining, and statistics of rainfall and precipitation. After data collection is completed, an external database is set up and merged into the collection database.

8. A generative coal mine production risk grading assessment method according to claim 7, characterized in that: In the step three, after the data is adopted, the data is directly input into the evaluation model, the data is analyzed by the evaluation model, and the classification is determined after the analysis is completed. When the level is determined, the corresponding solution is derived, and the level is manually reviewed according to the expert review method, and compared with the historical data at the same time to see whether it exists in the historical data.

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