Coal mine rock burst risk analysis method based on drilling cutting method

Abstract

The invention relates to the technical field of risk analysis, in particular to a coal mine rock burst risk analysis method based on a drilling cutting method, and provides the coal mine rock burst risk analysis method based on the drilling cutting method. The method comprises the steps of data collection, spatial data conversion, analysis unit selection, box diagram generation, time sequence analysis, risk assessment, result feedback and the like, and through the steps, the system accurately assesses mine risks, timely generates an early warning report, guides construction and guarantees mine safety. According to the method, through multi-dimensional analysis, especially time sequence analysis and an anomaly detection technology, the mine rock burst risk is accurately evaluated, timely early warning is provided, and the accuracy and timeliness of risk early warning are greatly improved.

Description

Technical Field

[0001] The invention relates to the technical field of risk analysis, and in particular to a method for analyzing rock burst risks in coal mines based on a drill cuttings method. Background Art

[0002] The rock burst problem in coal mines has always been a major challenge in coal mine safety production. Rock burst refers to the violent destructive phenomenon caused by the sudden release of energy from the rock mass during coal mining. In severe cases, it can cause damage to the mine structure, equipment, and even casualties. Traditional rock burst prediction methods, such as seismic wave method, stress monitoring method, and acoustic emission method, can provide early warning of rock burst to a certain extent.

[0003] However, the inventors have discovered that this technical solution still has at least the following defects:

[0004] First, existing early warning methods are costly and have limited applicability: Traditional rock burst prediction methods, such as seismic wave method, stress monitoring method and acoustic emission method, usually rely on expensive and complex monitoring equipment, resulting in high implementation costs.

[0005] Second, the limitations of data processing and analysis methods: Existing technologies lack effective data standardization, spatial transformation and time series analysis methods when processing drill cuttings data, which makes it difficult to accurately assess rock burst risks. Summary of the invention

[0006] Based on the above purpose, the present invention provides a coal mine rock burst risk analysis method based on the drill cuttings method.

[0007] The coal mine rock burst risk analysis method based on the drill cuttings method includes the following steps:

[0008] S1, data collection and preprocessing: collect drilling cuttings data of the target mine, including drilling location, depth, and cuttings volume, and combine relevant geological information, including working face status, excavation and mining data, to form a complete data set, standardize the collected data, perform data cleaning, remove outliers and missing values, and normalize and format the data;

[0009] S2, spatial data conversion and model building: based on the preprocessed data, spatial data conversion is performed, including converting the location coordinates of the drilling location into distance parameters related to risk analysis, and building a data model;

[0010] S3, selection of analysis unit and data extraction: according to the analysis requirements, a specific analysis unit is selected, including a certain working face or mine area, and all relevant drill cuttings data and geological data in the unit are extracted from the database;

[0011] S4, generation of box-type statistical benchmark map and correlation analysis: the data of the selected analysis unit are grouped according to the predetermined depth range, and a box-type statistical benchmark map is generated to display the statistical characteristics of each group of data. The drilling data to be analyzed is correlated with the benchmark map using the correlation algorithm to identify risk signals;

[0012] S5, time series analysis and map description: Based on the results of correlation analysis, the time series analysis method is used to predict the change trend of the amount of drill cuttings, and generate maps and text descriptions to locate the position of the drill hole in the historical data sequence and analyze its status characteristics;

[0013] S6, risk status assessment and result feedback: Based on the results of time series analysis and map description, the rock burst risk of the borehole is assessed. When the coal powder volume exceeds the maximum value of the box, a high-risk warning is generated, and the anti-rock burst construction personnel are guided to take corresponding measures. The analysis results are output in the form of a standardized report and fed back to relevant personnel;

[0014] S7, system optimization and self-learning: Introduce a self-learning mechanism to continuously optimize algorithms and models and adjust analysis parameters based on actual monitoring results and feedback.

[0015] Optionally, S1 includes:

[0016] S11, Drill cuttings data collection: The coal mine rock burst risk analysis method based on the drill cuttings method collects the drill cuttings data of the target mine, including the drilling location, drilling depth, and drilling cuttings volume, and uses sensors and data recording instruments to monitor and record the drilling cuttings data in real time;

[0017] S12, geological information integration: The coal mine rock burst risk analysis method based on the drill cuttings method collects and integrates geological information related to the drill cuttings data, including the working face status, tunneling speed, and mining progress. The geological information is associated with the drill cuttings data through the data management system to form a unified database;

[0018] S13, data standardization: standardize the collected drill cuttings data and geological information to eliminate the differences caused by different data sources and recording methods;

[0019] S14, data cleaning: perform data cleaning, remove outliers and missing values ​​in the data, and use interpolation to fill in missing data;

[0020] S15, data normalization and formatting: normalize the cleaned data to make data of different dimensions comparable, and format the data.

[0021] Optionally, S2 includes:

[0022] S21, coordinate transformation: converting the pre-processed borehole location data from location coordinates to distance parameters relevant to risk analysis, using geographic information system tools for coordinate transformation;

[0023] S22, data model establishment: based on the converted distance parameters, a data model for rock burst risk analysis is established.

[0024] Optionally, S3 includes:

[0025] S31, analysis unit selection: selecting a specific analysis unit according to specific analysis requirements, wherein the analysis unit includes a certain working face or a mine area;

[0026] S32, data extraction: extracting all relevant drill cuttings data and geological data within the selected analysis unit from the database.

[0027] Optionally, S4 includes:

[0028] S41, data grouping: grouping the drill cuttings data in the selected analysis unit according to a predetermined depth range;

[0029] S42, box-type statistical benchmark graph generation: a box-type statistical benchmark graph is generated based on the grouped data, showing the maximum value, minimum value, median and quartile of each group of data, and intuitively showing the distribution characteristics of the cuttings in different depth intervals;

[0030] S43, correlation analysis: using correlation algorithm, the borehole data to be analyzed is compared and correlated with the generated box-type statistical reference map to identify abnormal changes in the amount of drill cuttings and determine the risk signal of rock burst.

[0031] Optionally, S5 includes:

[0032] S51, time series analysis: Based on the results of correlation analysis, the time series analysis method is used to predict the change trend of drill cuttings volume and identify abnormal changes and risk trends;

[0033] S52, map generation: generating relevant maps based on the results of time series analysis, and visually displaying the change trend of the amount of drill cuttings in different time periods and its position in the historical data series;

[0034] S53, Text description: Provide a text description of the map, analyze the state characteristics of the drill cuttings data, including the extreme value interval and interquartile range in the historical data and whether there are abnormal fluctuations, and explain and illustrate the possible rock burst risks.

[0035] Optionally, the S51 includes:

[0036] S511, data sorting: based on the results of the association analysis, the cuttings data are sorted in chronological order to generate a complete time series data set;

[0037] S512, trend analysis: using time series analysis methods, trend analysis is performed on the sorted cuttings data to identify long-term trends, periodic changes and short-term fluctuations in the data, and to predict future trends in cuttings;

[0038] S513, anomaly detection: Based on trend analysis, anomaly detection algorithms are applied to identify abnormal changes in time series to determine whether there are fluctuations in drill cuttings that are inconsistent with normal trends, indicating rock burst risks.

[0039] Optionally, S6 includes:

[0040] S61, Risk Assessment: Based on the results of time series analysis and map description, assess the rock burst risk of the borehole, focus on comparing the amount of drill cuttings with historical data, and identify whether there is an abnormally high amount of coal dust;

[0041] S62, high-risk warning generation: When the amount of pulverized coal exceeds the maximum value of the box, a high-risk warning is automatically generated, and it is prompted that anti-collision measures need to be taken, guiding anti-collision construction personnel to carry out protective construction in high-risk areas;

[0042] S63, standardized output of results: Generate the analysis and evaluation results in the form of a standardized report, which includes the borehole number, borehole location, coal powder quantity in each meter section, risk level assessment and recommended protective measures;

[0043] S64, Result Feedback: The generated standardized report will be fed back to the relevant mine management personnel and anti-collision construction personnel in a timely manner.

[0044] Beneficial effects of the present invention:

[0045] The present invention uses multi-dimensional analysis, especially time series analysis and anomaly detection technology, to accurately assess the risk of rock burst in mines, provide timely warnings, and greatly improve the accuracy and timeliness of risk warnings. The present invention mainly relies on existing drill cuttings data for analysis, which reduces the implementation cost. The embedded self-learning mechanism enables it to adapt to different mine conditions, improving the practicality and wide applicability of the method. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0047] Figure 1 Schematic diagram of a method for analyzing coal mine rock burst risk based on a drill cuttings method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0048] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0049] It should be noted that the references to "one embodiment", "an embodiment", "an exemplary embodiment", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0050] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0051] like Figure 1 As shown in FIG. 1 , the risk analysis method for coal mine rock burst based on the drill cuttings method includes the following steps:

[0052] S1, data collection and preprocessing: collect drilling cuttings data of the target mine, including drilling location, depth, and cuttings volume, and combine relevant geological information, including working face status, excavation and mining data, to form a complete data set, standardize the collected data, perform data cleaning, remove outliers and missing values, and normalize and format the data;

[0053] S2, spatial data conversion and model building: based on the preprocessed data, spatial data conversion is performed, including converting the location coordinates of the drilling location into distance parameters related to risk analysis, and building a data model;

[0054] S3, selection of analysis unit and data extraction: according to the analysis requirements, a specific analysis unit is selected, including a certain working face or mine area, and all relevant drill cuttings data and geological data in the unit are extracted from the database;

[0055] S4, generation of box-type statistical benchmark map and correlation analysis: the data of the selected analysis unit are grouped according to the predetermined depth range, and a box-type statistical benchmark map is generated to display the statistical characteristics of each group of data. The drilling data to be analyzed is correlated with the benchmark map using the correlation algorithm to identify risk signals;

[0056] S5, time series analysis and map description: Based on the results of correlation analysis, the time series analysis method is used to predict the change trend of the amount of drill cuttings, and generate maps and text descriptions to locate the position of the drill hole in the historical data sequence and analyze its status characteristics;

[0057] S6, risk status assessment and result feedback: Based on the results of time series analysis and map description, the rock burst risk of the borehole is assessed. When the coal powder volume exceeds the maximum value of the box, a high-risk warning is generated, and the anti-rock burst construction personnel are guided to take corresponding measures. The analysis results are output in the form of a standardized report and fed back to relevant personnel;

[0058] S7, system optimization and self-learning: Introduce a self-learning mechanism to continuously optimize algorithms and models and adjust analysis parameters based on actual monitoring results and feedback.

[0059] S1 includes:

[0060] S11, Drill cuttings data collection: The coal mine rock burst risk analysis method based on the drill cuttings method collects the drill cuttings data of the target mine, including the drilling location, drilling depth, and drilling cuttings volume, and uses sensors and data recording instruments to monitor and record the drilling cuttings data in real time;

[0061] S12, geological information integration: The coal mine rock burst risk analysis method based on the drill cuttings method collects and integrates geological information related to the drill cuttings data, including the working face status, tunneling speed, and mining progress. The geological information is associated with the drill cuttings data through the data management system to form a unified database;

[0062] S13, data standardization: The coal mine rock burst risk analysis method based on the drill cuttings method standardizes the collected drill cuttings data and geological information to eliminate the differences caused by different data sources and recording methods;

[0063] S14, data cleaning: Data cleaning is performed based on the coal mine rock burst risk analysis method based on the drill cuttings method, outliers and missing values ​​in the data are removed, and missing data are filled using the interpolation method;

[0064] S15, data normalization and formatting: normalize the cleaned data to make data of different dimensions comparable, and format the data.

[0065] S2 includes:

[0066] S21, coordinate transformation: converting the pre-processed borehole location data from location coordinates to distance parameters relevant to risk analysis, using geographic information system tools for coordinate transformation;

[0067] S22, data model establishment: based on the converted distance parameters, a data model for rock burst risk analysis is established.

[0068] S3 includes:

[0069] S31, analysis unit selection: according to specific analysis requirements, a specific analysis unit is selected, and the analysis unit includes a certain working face or mine area;

[0070] S32, data extraction: extracting all relevant drill cuttings data and geological data within the selected analysis unit from the database.

[0071] S4 includes:

[0072] S41, data grouping: grouping the drill cuttings data in the selected analysis unit according to a predetermined depth range;

[0073] S42, box-type statistical benchmark graph generation: a box-type statistical benchmark graph is generated based on the grouped data, showing the maximum value, minimum value, median and quartile of each group of data, and intuitively showing the distribution characteristics of the cuttings in different depth intervals;

[0074] S43, correlation analysis: using correlation algorithm, the borehole data to be analyzed is compared and correlated with the generated box-type statistical reference map to identify abnormal changes in the amount of drill cuttings and determine the risk signal of rock burst.

[0075] S5 includes:

[0076] S51, time series analysis: Based on the results of correlation analysis, the time series analysis method is used to predict the change trend of drill cuttings volume and identify abnormal changes and risk trends;

[0077] S52, map generation: generating relevant maps based on the results of time series analysis, and visually displaying the change trend of the amount of drill cuttings in different time periods and its position in the historical data series;

[0078] S53, Text description: Provide a text description of the map, analyze the state characteristics of the drill cuttings data, including the extreme value interval and interquartile range in the historical data and whether there are abnormal fluctuations, and explain and illustrate the possible rock burst risks.

[0079] S51 includes:

[0080] S511, data sorting: based on the results of the association analysis, the cuttings data are sorted in chronological order to generate a complete time series data set;

[0081] S512, trend analysis: using time series analysis methods, trend analysis is performed on the sorted cuttings data to identify long-term trends, periodic changes and short-term fluctuations in the data, and to predict future trends in cuttings;

[0082] S513, anomaly detection: Based on trend analysis, anomaly detection algorithms are applied to identify abnormal changes in time series to determine whether there are fluctuations in drill cuttings that are inconsistent with normal trends, indicating rock burst risks.

[0083] S6 includes:

[0084] S61, Risk Assessment: Based on the results of time series analysis and map description, assess the rock burst risk of the borehole, focus on comparing the amount of drill cuttings with historical data, and identify whether there is an abnormally high amount of coal dust;

[0085] S62, high-risk warning generation: When the amount of pulverized coal exceeds the maximum value of the box, a high-risk warning is automatically generated, and it is prompted that anti-collision measures need to be taken, guiding anti-collision construction personnel to carry out protective construction in high-risk areas;

[0086] S63, standardized output of results: Generate the analysis and evaluation results in the form of a standardized report, which includes the borehole number, borehole location, coal powder quantity in each meter section, risk level assessment and recommended protective measures;

[0087] S64, Result Feedback: The generated standardized report will be fed back to the relevant mine management personnel and anti-collision construction personnel in a timely manner.

[0088] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0089] The above are only preferred embodiments of the present invention. It should be pointed out that, for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A coal mine rock burst risk analysis method based on drill cuttings method, characterized in that: The following steps are involved: S1, data collection and preprocessing: collect drilling cuttings data of the target mine, including drilling location, depth, and cuttings volume, and combine relevant geological information, including working face status, excavation and mining data, to form a complete data set, standardize the collected data, perform data cleaning, remove outliers and missing values, and normalize and format the data; S2, spatial data conversion and model building: based on the preprocessed data, spatial data conversion is performed, including converting the location coordinates of the drilling location into distance parameters related to risk analysis, and building a data model; S3, selection of analysis unit and data extraction: according to the analysis requirements, a specific analysis unit is selected, including a certain working face or mine area, and all relevant drill cuttings data and geological data in the unit are extracted from the database; S4, generation of box-type statistical benchmark map and correlation analysis: the data of the selected analysis unit are grouped according to the predetermined depth range, and a box-type statistical benchmark map is generated to display the statistical characteristics of each group of data. The drilling data to be analyzed is correlated with the benchmark map using the correlation algorithm to identify risk signals; S5, time series analysis and map description: Based on the results of correlation analysis, the time series analysis method is used to predict the change trend of the amount of drill cuttings, and generate maps and text descriptions to locate the position of the drill hole in the historical data sequence and analyze its status characteristics; S6, risk status assessment and result feedback: Based on the results of time series analysis and map description, the rock burst risk of the borehole is assessed. When the coal powder volume exceeds the maximum value of the box, a high-risk warning is generated, and the anti-rock burst construction personnel are guided to take corresponding measures. The analysis results are output in the form of a standardized report and fed back to relevant personnel; S7, system optimization and self-learning: Introduce a self-learning mechanism to continuously optimize algorithms and models and adjust analysis parameters based on actual monitoring results and feedback.

2. The method for analyzing coal mine rock burst risk based on the drill cuttings method according to claim 1, characterized in that: The S1 includes: S11, Drill cuttings data collection: The coal mine rock burst risk analysis method based on the drill cuttings method collects the drill cuttings data of the target mine, including the drilling location, drilling depth, and drilling cuttings volume, and uses sensors and data recording instruments to monitor and record the drilling cuttings data in real time; S12, geological information integration: The coal mine rock burst risk analysis method based on the drill cuttings method collects and integrates geological information related to the drill cuttings data, including the working face status, tunneling speed, and mining progress. The geological information is associated with the drill cuttings data through the data management system to form a unified database; S13, data standardization: The coal mine rock burst risk analysis method based on the drill cuttings method standardizes the collected drill cuttings data and geological information to eliminate the differences caused by different data sources and recording methods; S14, data cleaning: Data cleaning is performed based on the coal mine rock burst risk analysis method based on the drill cuttings method, outliers and missing values ​​in the data are removed, and missing data are filled using the interpolation method; S15, data normalization and formatting: normalize the cleaned data to make data of different dimensions comparable, and format the data.

3. The method for analyzing coal mine rock burst risk based on the drill cuttings method according to claim 1, characterized in that: The S2 includes: S21, coordinate transformation: converting the pre-processed borehole location data from location coordinates to distance parameters relevant to risk analysis, using geographic information system tools for coordinate transformation; S22, data model establishment: based on the converted distance parameters, a data model for rock burst risk analysis is established.

4. The method for analyzing coal mine rock burst risk based on the drill cuttings method according to claim 1, characterized in that: The S3 includes: S31, analysis unit selection: selecting a specific analysis unit according to specific analysis requirements, wherein the analysis unit includes a certain working face or a mine area; S32, data extraction: extracting all relevant drill cuttings data and geological data within the selected analysis unit from the database.

5. The method for analyzing coal mine rock burst risk based on the drill cuttings method according to claim 1, characterized in that: The S4 includes: S41, data grouping: grouping the drill cuttings data in the selected analysis unit according to a predetermined depth range; S42, box-type statistical benchmark graph generation: a box-type statistical benchmark graph is generated based on the grouped data, showing the maximum value, minimum value, median and quartile of each group of data, and intuitively showing the distribution characteristics of the cuttings in different depth intervals; S43, correlation analysis: using correlation algorithm, the borehole data to be analyzed is compared and correlated with the generated box-type statistical reference map to identify abnormal changes in the amount of drill cuttings and determine the risk signal of rock burst.

6. The method for analyzing coal mine rock burst risk based on the drill cuttings method according to claim 1, characterized in that: The S5 includes: S51, time series analysis: Based on the results of correlation analysis, the time series analysis method is used to predict the change trend of drill cuttings volume and identify abnormal changes and risk trends; S52, map generation: generating relevant maps based on the results of time series analysis, and visually displaying the change trend of the amount of drill cuttings in different time periods and its position in the historical data series; S53, Text description: Provide a text description of the map, analyze the state characteristics of the drill cuttings data, including the extreme value interval and interquartile range in the historical data and whether there are abnormal fluctuations, and explain and illustrate the possible rock burst risks.

7. The method for analyzing coal mine rock burst risk based on the drill cuttings method according to claim 6, characterized in that: The S51 includes: S511, data sorting: based on the results of the association analysis, the cuttings data are sorted in chronological order to generate a complete time series data set; S512, trend analysis: using time series analysis methods, trend analysis is performed on the sorted cuttings data to identify long-term trends, periodic changes and short-term fluctuations in the data, and to predict future trends in cuttings; S513, anomaly detection: Based on trend analysis, anomaly detection algorithms are applied to identify abnormal changes in time series to determine whether there are fluctuations in drill cuttings that are inconsistent with normal trends, indicating rock burst risks.

8. The method for analyzing coal mine rock burst risk based on the drill cuttings method according to claim 1, characterized in that: The S6 includes: S61, Risk Assessment: Based on the results of time series analysis and map description, assess the rock burst risk of the borehole, focus on comparing the amount of drill cuttings with historical data, and identify whether there is an abnormally high amount of coal dust; S62, high-risk warning generation: When the amount of pulverized coal exceeds the maximum value of the box, a high-risk warning is automatically generated, and it is prompted that anti-collision measures need to be taken, guiding anti-collision construction personnel to carry out protective construction in high-risk areas; S63, standardized output of results: Generate the analysis and evaluation results in the form of a standardized report, which includes the borehole number, borehole location, coal powder quantity in each meter section, risk level assessment and recommended protective measures; S64, Result Feedback: The generated standardized report will be fed back to the relevant mine management personnel and anti-collision construction personnel in a timely manner.

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