Coal mine rock burst risk analysis method based on drill cuttings method

Abstract

The present invention relates to the field of risk analysis technology, and specifically to a method for analyzing coal mine rock burst risk based on the drill cuttings method. The method includes providing a method for analyzing coal mine rock burst risk based on the drill cuttings method, covering steps such as data collection, spatial data conversion, analysis unit selection, box plot generation, time series analysis, risk assessment, and result feedback. Through these steps, the system accurately assesses mine risks and promptly generates early warning reports to guide construction and ensure mine safety. The present invention, through multi-dimensional analysis, particularly time series analysis and anomaly detection technology, accurately assesses mine rock burst risks, provides timely early warnings, and significantly improves the accuracy and timeliness of risk warnings.

Description

Technical Field

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

[0002] Rock burst in coal mines has always been a major challenge to coal mine safety. Rock burst refers to the sudden release of energy from the rock mass during coal mining, causing severe damage. In severe cases, this can lead to structural damage, equipment destruction, and even casualties. Traditional rock burst prediction methods, such as seismic wave methods, stress monitoring, and acoustic emission methods, 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, 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, making it difficult to accurately assess rock burst risks. Summary of the Invention

[0006] Based on the above objectives, 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 drill cuttings data from the target mine, including drill hole location, depth, and cuttings volume, and combine it with relevant geological information, including working face status, tunneling, 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 pre-processed data, spatial data conversion is performed, including converting the 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: Based on the analysis requirements, a specific analysis unit is selected, including a working face or mine area, and all relevant drill cuttings data and geological data within the unit are extracted from the database;

[0011] S4, box-type statistical benchmark map generation and correlation analysis: The data of the selected analysis unit is 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 correlation algorithm is used to correlate the drill hole data to be analyzed with the benchmark map to identify risk signals;

[0012] S5, Time Series Analysis and Graphic Description: Based on the results of the correlation analysis, the time series analysis method is used to predict the trend of the change in the amount of drill cuttings, and generate graphics 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 descriptions, the rock burst risk of the borehole is assessed. When the pulverized coal 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 appropriate 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 drill cuttings data from the target mine, including drill hole location, drill hole depth, and drill cuttings volume. Sensors and data recording instruments are used to monitor and record the drill cuttings data in real time.

[0017] S12, Geological Information Integration: The drill cuttings-based coal mine rock burst risk analysis method collects and integrates geological information related to drill cuttings data, including working face status, tunneling speed, and mining progress. The geological information is then linked to the drill cuttings data through a data management system to form a unified database.

[0018] S13, data standardization: standardize the collected drill cuttings data and geological information to eliminate 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 working face or a mine area;

[0026] S32, data extraction: extract 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: Generate a box-type statistical benchmark graph based on the grouped data, displaying the maximum value, minimum value, median and quartiles of each group of data, and intuitively showing the distribution characteristics of drill cuttings in different depth ranges;

[0030] S43, Correlation Analysis: Using the correlation algorithm, the drilling data to be analyzed is compared with the generated box-type statistical benchmark map and correlation analysis is performed 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 the correlation analysis, the time series analysis method is used to predict the trend of the change of the drill cuttings volume and identify abnormal changes and risk trends;

[0033] S52, map generation: generating relevant maps based on the results of the time series analysis, visually displaying the changing 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 status 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 risk.

[0035] Optionally, the S51 includes:

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

[0037] S512, Trend Analysis: Use time series analysis methods to perform trend analysis on the organized drill cuttings volume data, identify long-term trends, cyclical changes, and short-term fluctuations in the data, and predict future trends in drill cuttings volume;

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

[0039] Optionally, S6 includes:

[0040] S61, Risk Assessment: Based on the results of time series analysis and map descriptions, assess the rock burst risk of the borehole, focusing on comparing drill cuttings with historical data to identify abnormally high coal dust content;

[0041] S62, high-risk warning generation: When the pulverized coal volume exceeds the maximum value of the box, a high-risk warning is automatically generated, prompting the need to take anti-collision measures and guiding anti-collision construction personnel to carry out protective construction in the high-risk area;

[0042] S63, standardized output of results: Generate the analysis and assessment results in the form of a standardized report, which includes the drill hole number, drill hole 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-bumping construction personnel in a timely manner.

[0044] Beneficial effects of the present invention:

[0045] This method uses multi-dimensional analysis, particularly time series analysis and anomaly detection techniques, to accurately assess mine rock burst risk and provide timely warnings, significantly improving the accuracy and timeliness of risk warnings. This method primarily relies on existing drill cuttings data for analysis, reducing implementation costs. An embedded self-learning mechanism enables it to adapt to diverse mine conditions, enhancing its practicality and broad applicability. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

[0048] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments. It is also noted that, to provide a more detailed description, the following embodiments are best and preferred embodiments, and those skilled in the art may employ alternative methods for implementing certain known technologies. Furthermore, the accompanying drawings are intended only to provide a more detailed description of the embodiments and are not intended to limit the present invention.

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

[0050] In general, terms can be understood, at least in part, from their 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, the coal mine rock burst risk analysis method based on the drill cuttings method includes the following steps:

[0052] S1, Data Collection and Preprocessing: Collect drill cuttings data from the target mine, including drill hole location, depth, and cuttings volume, and combine it with relevant geological information, including working face status, tunneling, 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 pre-processed data, spatial data conversion is performed, including converting the 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: Based on the analysis requirements, a specific analysis unit is selected, including a working face or mine area, and all relevant drill cuttings data and geological data within the unit are extracted from the database;

[0055] S4, box-type statistical benchmark map generation and correlation analysis: The data of the selected analysis unit is 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 correlation algorithm is used to correlate the drill hole data to be analyzed with the benchmark map to identify risk signals;

[0056] S5, Time Series Analysis and Graphic Description: Based on the results of the correlation analysis, the time series analysis method is used to predict the trend of the change in the amount of drill cuttings, and generate graphics 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 descriptions, the rock burst risk of the borehole is assessed. When the pulverized coal 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 appropriate 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 drill cuttings data from the target mine, including drill hole location, drill hole depth, and drill cuttings volume. Sensors and data recording instruments are used to monitor and record the drill cuttings data in real time.

[0061] S12, Geological Information Integration: The drill cuttings-based coal mine rock burst risk analysis method collects and integrates geological information related to drill cuttings data, including working face status, tunneling speed, and mining progress. The geological information is then linked to the drill cuttings data through a data management system to form a unified database.

[0062] S13, Data Standardization Processing: 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 of the drill cuttings method to remove outliers and missing values ​​in the data, and the missing data are filled using interpolation;

[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: select a specific analysis unit according to specific analysis requirements, the analysis unit includes a working face or mine area;

[0070] S32, data extraction: extract 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: Generate a box-type statistical benchmark graph based on the grouped data, displaying the maximum value, minimum value, median and quartiles of each group of data, and intuitively showing the distribution characteristics of drill cuttings in different depth ranges;

[0074] S43, Correlation Analysis: Using the correlation algorithm, the drilling data to be analyzed is compared with the generated box-type statistical benchmark map and correlation analysis is performed 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 the correlation analysis, the time series analysis method is used to predict the trend of the change of the drill cuttings volume and identify abnormal changes and risk trends;

[0077] S52, map generation: generating relevant maps based on the results of the time series analysis, visually displaying the changing 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 status 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 risk.

[0079] S51 includes:

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

[0081] S512, Trend Analysis: Use time series analysis methods to perform trend analysis on the organized drill cuttings volume data, identify long-term trends, cyclical changes, and short-term fluctuations in the data, and predict future trends in drill cuttings volume;

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

[0083] S6 includes:

[0084] S61, Risk Assessment: Based on the results of time series analysis and map descriptions, assess the rock burst risk of the borehole, focusing on comparing drill cuttings with historical data to identify abnormally high coal dust content;

[0085] S62, high-risk warning generation: When the pulverized coal volume exceeds the maximum value of the box, a high-risk warning is automatically generated, prompting the need to take anti-collision measures and guiding anti-collision construction personnel to carry out protective construction in the high-risk area;

[0086] S63, standardized output of results: Generate the analysis and assessment results in the form of a standardized report, which includes the drill hole number, drill hole 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-bumping construction personnel in a timely manner.

[0088] The present invention encompasses any alternatives, modifications, equivalents, and solutions that fall within the spirit and scope of the present invention. To provide a thorough understanding of the present invention, specific details are described in detail below in connection with the preferred embodiments of the present invention, but those skilled in the art will be able to fully understand the present invention without these detailed descriptions. Furthermore, to avoid unnecessary confusion regarding the essence of the present invention, well-known methods, processes, procedures, components, and circuits have not been 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 the drill cuttings method is characterized by: The following steps are involved: S1, Data Collection and Preprocessing: Collect drill cuttings data from the target mine, including drill hole location, depth, and cuttings volume, and combine it with relevant geological information, including working face status, tunneling, 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 pre-processed data, spatial data conversion is performed, including converting the 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: Based on the analysis requirements, a specific analysis unit is selected, including a working face or mine area, and all relevant drill cuttings data and geological data within the unit are extracted from the database; S4, box-type statistical benchmark map generation and correlation analysis: The data of the selected analysis unit is 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 correlation algorithm is used to correlate the drill hole data to be analyzed with the benchmark map to identify risk signals; S5, Time Series Analysis and Graphic Description: Based on the results of the correlation analysis, the time series analysis method is used to predict the trend of the change in the amount of drill cuttings, and generate graphics 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 descriptions, the rock burst risk of the borehole is assessed. When the pulverized coal 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 appropriate 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: Said S1 includes: S11, Drill Cuttings Data Collection: The coal mine rock burst risk analysis method based on the drill cuttings method collects drill cuttings data from the target mine, including drill hole location, drill hole depth, and drill cuttings volume. Sensors and data recording instruments are used to monitor and record the drill cuttings data in real time. S12, Geological Information Integration: The drill cuttings-based coal mine rock burst risk analysis method collects and integrates geological information related to drill cuttings data, including working face status, tunneling speed, and mining progress. The geological information is then linked to the drill cuttings data through a data management system to form a unified database. S13, Data Standardization Processing: 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 of the drill cuttings method to remove outliers and missing values ​​in the data, and the missing data are filled using interpolation; 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: Said 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: Said S3 includes: S31, analysis unit selection: selecting a specific analysis unit according to specific analysis requirements, wherein the analysis unit includes a working face or a mine area; S32, data extraction: extract 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: Said 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: Generate a box-type statistical benchmark graph based on the grouped data, displaying the maximum value, minimum value, median and quartiles of each group of data, and intuitively showing the distribution characteristics of drill cuttings in different depth ranges; S43, Correlation Analysis: Using the correlation algorithm, the drilling data to be analyzed is compared with the generated box-type statistical benchmark map and correlation analysis is performed 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 the correlation analysis, the time series analysis method is used to predict the trend of the change of the drill cuttings volume and identify abnormal changes and risk trends; S52, map generation: generating relevant maps based on the results of the time series analysis, visually displaying the changing 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 status 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 risk.

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 collation: based on the results of the correlation analysis, the cuttings data are collated in chronological order to generate a complete time series data set; S512, Trend Analysis: Use time series analysis methods to perform trend analysis on the organized drill cuttings volume data, identify long-term trends, cyclical changes, and short-term fluctuations in the data, and predict future trends in drill cuttings volume; S513, Anomaly Detection: Based on trend analysis, anomaly detection algorithms are applied to identify abnormal changes in the time series to determine whether there are fluctuations in drill cuttings that are inconsistent with the normal trend, indicating the risk of rock burst.

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 descriptions, assess the rock burst risk of the borehole, focusing on comparing drill cuttings with historical data to identify abnormally high coal dust content; S62, high-risk warning generation: When the pulverized coal volume exceeds the maximum value of the box, a high-risk warning is automatically generated, prompting the need to take anti-collision measures and guiding anti-collision construction personnel to carry out protective construction in the high-risk area; S63, standardized output of results: Generate the analysis and assessment results in the form of a standardized report, which includes the drill hole number, drill hole 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-bumping construction personnel in a timely manner.

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