Steeply inclined coal mine mining risk assessment method, medium and system

Abstract

The invention provides a steeply inclined coal mining risk assessment method, medium and system, and belongs to the technical field of steeply inclined coal mining risk assessment, and the method comprises the steps: firstly carrying out the standardization processing of multi-modal data, and organizing into a multi-dimensional matrix; and then time sequence analysis and spatial correlation analysis are carried out on the matrix to obtain a risk analysis matrix which reflects correlation and potential risk factors among different data types. Secondly, decomposing the risk analysis matrix into a stable matrix and a risk matrix by adopting a singular value decomposition method, so as to extract a main risk feature vector; and finally, according to the main risk index vectors, respectively calculating a roof instability risk index, a gas overrun risk index and a flood risk index, and comprehensively obtaining an overall risk assessment result, thereby solving the problem that an existing risk assessment method still lacks deep mining and detailed analysis of key risk factors, and thus the risk assessment efficiency is improved. And targeted risk control measures are difficult to obtain.

Description

Technical Field

[0001] The present invention belongs to the technical field of steeply inclined coal mine mining risk assessment, and in particular, relates to a steeply inclined coal mine mining risk assessment method, medium and system. Background Art

[0002] Steeply inclined coal seams refer to coal seams with a dip angle of more than 45 degrees. The mining conditions of such coal seams are harsh and there are many safety risks, such as roof instability, excessive gas and flooding, which bring huge challenges to mine production operations.

[0003] In response to the safety production issues of steeply inclined coal mines, the industry has developed some risk assessment and control methods. The common practice is to collect various types of monitoring data, such as geological data, mining parameters and real-time monitoring data, and analyze and process these data to identify the key factors affecting safety production. For example, some studies use time series analysis and gray correlation analysis to model monitoring data such as gas concentration and roof displacement, and explore the relationship between these key factors. Other studies have attempted to establish a coal mine safety early warning model based on BP neural network or fuzzy comprehensive evaluation, comprehensively considering the impact of multiple factors on coal mine safety. These existing methods have improved the ability to recognize and warn of risks in steeply inclined coal mines to a certain extent.

[0004] However, the existing risk assessment methods still lack in-depth exploration and detailed analysis of key risk factors, making it difficult to come up with targeted risk control measures. Summary of the invention

[0005] In view of this, the present invention provides a steeply inclined coal mining risk assessment method, medium and system, which can solve the technical problems that the existing risk assessment methods still lack in-depth exploration and detailed analysis of key risk factors, and it is difficult to derive targeted risk control measures.

[0006] The present invention is achieved in that:

[0007] A first aspect of the present invention provides a method for assessing the risk of steeply inclined coal mining, comprising the following steps:

[0008] S10, collecting multimodal data of steeply inclined coal mines, including geological data, mining parameters, and monitoring data; wherein the geological data includes stratum structure and fault distribution; the mining parameters include coal mining methods and mining depth; and the monitoring data includes gas concentration, roof displacement, and microseismic signals;

[0009] S20, standardizing and organizing the multimodal data into a multidimensional matrix of multimodal data, wherein the first dimension of the multidimensional matrix represents different data types, the second dimension represents different time points; and the third dimension represents spatial position;

[0010] S30, performing time series analysis and spatial correlation analysis on the multidimensional matrix to obtain a two-dimensional risk analysis matrix, wherein the elements of the risk analysis matrix represent the correlations and potential risk factors between different data types;

[0011] S40, using a singular value decomposition method to decompose the risk analysis matrix into a stability matrix and a risk matrix, wherein the stability matrix reflects the stability characteristics of the steeply inclined coal mine, and the risk matrix contains potential risk information of the steeply inclined coal mine;

[0012] S50, analyzing the risk matrix in detail, extracting the main risk characteristic vectors, selecting the characteristic vector corresponding to the maximum singular value as the main risk indicator, and mapping the characteristic vector to three main risk dimensions: roof instability risk vector, gas over-limit risk vector and flood risk vector;

[0013] S60, performing cosine similarity calculation on the obtained roof instability risk vector, gas over-limit risk vector and flood risk vector and the preset roof stability vector, gas safety vector and flood safety vector to obtain a roof instability risk index, a gas over-limit risk index and a flood risk index;

[0014] S70, calculating a comprehensive risk assessment index, and comparing it with a preset risk threshold, to obtain a mining risk assessment result for the steeply inclined coal mine.

[0015] Specifically, the step S10 specifically includes: collecting multimodal data of steeply inclined coal mines, including geological data, mining parameters and monitoring data. First, geological data such as stratum structure and fault distribution are obtained from geological exploration and mining practice, and data collection is carried out by means of drilling, geological measurement and other means. Secondly, mining parameter information such as coal mining methods and mining depth is obtained through mining operation records. Thirdly, gas concentration monitoring equipment, roof displacement sensors and microseismic instruments are used to monitor monitoring data such as gas concentration, roof displacement and microseismic signals in real time. These multimodal data cover key factors such as geological conditions, mining activities and operating status in the mining process of steeply inclined coal mines, providing the necessary information basis for subsequent risk assessment.

[0016] Specifically, the step S20 specifically includes: standardizing the collected multimodal data to eliminate the influence of different dimensions and magnitudes so that the data are within the same dimension range. Then, the standardized data is organized into a three-dimensional matrix representation according to different data types, time points and spatial positions. The multidimensional matrix can more completely describe the spatiotemporal distribution characteristics of various key data in the process of steeply inclined coal mining, laying the foundation for subsequent time series analysis and spatial correlation analysis.

[0017] Specifically, the step S30 specifically includes: performing time series analysis and spatial correlation analysis on the multidimensional matrix to obtain a two-dimensional risk analysis matrix that reflects the correlation between data and potential risk factors. First, the autoregressive moving average (ARMA) model is fitted on the time series of each data type to analyze its time evolution law to identify the time correlation characteristics of each type of data. Secondly, the Moran's I index is used to analyze the correlation between different spatial units in the multidimensional matrix to quantify the spatial correlation strength of each type of data. Finally, the results of time series analysis and spatial correlation analysis are combined to construct a two-dimensional risk analysis matrix, whose elements reflect the correlation between different data types and potential risk factors.

[0018] Specifically, the step S40 specifically includes: using the singular value decomposition (SVD) method to decompose the risk analysis matrix to obtain a stable matrix and a risk matrix. First, the risk analysis matrix is ​​subjected to singular value decomposition to obtain an orthogonal matrix, a singular value diagonal matrix, and left and right singular vectors. Then, a threshold is set according to the size of the singular value, and the original matrix is ​​decomposed into a stable matrix reflecting stable features and a risk matrix containing potential risks. This decomposition method can effectively separate the stable factors and risk factors in the original data, laying a foundation for subsequent risk feature extraction and analysis.

[0019] Specifically, the step S50 specifically includes: extracting the main risk feature vector and mapping the risk dimension of the risk matrix. First, the right singular vector corresponding to the maximum singular value is selected as the main risk indicator. This vector reflects the most important risk feature in the risk matrix. Then, the main risk feature vector is mapped to three main risk dimensions: roof instability risk vector, gas over-limit risk vector and flood risk vector. This risk dimension mapping can better identify the most critical risk factors in the mining process of steeply inclined coal mines.

[0020] Specifically, the step S60 specifically includes: calculating the roof instability risk index, gas over-limit risk index and flood risk index respectively according to the three risk vectors obtained. This method of calculating the risk index using cosine similarity can quantify the degree of proximity between various risk factors and safety goals, and provide a basis for comprehensive risk assessment.

[0021] Specifically, the step S70 specifically includes: calculating the comprehensive risk assessment index and comparing it with the preset risk threshold to obtain the mining risk assessment result of the steeply inclined coal mine. The calculation of the comprehensive risk assessment index takes into account three major risk factors, namely roof instability, gas over-limit and flood, and also includes correction items for other potential risk factors. By comparing with the preset risk threshold, the final risk assessment result can be obtained, providing a basis for formulating a reasonable risk management and control strategy.

[0022] Furthermore, the multidimensional matrix of the multimodal data can be expressed as:

[0023] M ijk =f(D i ,T j ,S k )+ε ijk ;

[0024] Where M ijk is a multidimensional matrix element; D i Indicates the i-th data type; T j represents the jth time point; S k represents the kth spatial position; f is the data mapping function; ε ijk is a random error term.

[0025] Parameter acquisition method:

[0026] D i Obtained through data collection and classification, including geological data, mining parameters and monitoring data;

[0027] T j Obtained through timestamp records;

[0028] S k Obtained through spatial coordinate system recording.

[0029] 2. Time series analysis and spatial correlation analysis:

[0030] Time series analysis can use the autoregressive moving average model (ARMA):

[0031]

[0032] In the formula, X t is the time series value; p is the number of autoregressive terms; q is the number of moving average terms; φ i is the autoregression coefficient; θ j is the moving average coefficient; ε t is white noise.

[0033] Spatial correlation analysis can use Moran's I index:

[0034]

[0035] Where n is the number of space units; w ij is the spatial weight; x i and x j is the attribute value of spatial units i and j; is the attribute mean.

[0036] 3. Risk Analysis Matrix:

[0037] The risk analysis matrix R can be constructed from the results of time series analysis and spatial correlation analysis:

[0038] R mn =α·ARMA(M ijk )+β·I(M ijk )+γ mn ;

[0039] In the formula, R mn is the risk analysis matrix element; ARMA(M ijk ) is the result of time series analysis; I(M ijk ) is the result of spatial correlation analysis; α and β are weight coefficients; γ mn is a random disturbance term.

[0040] 4. Singular Value Decomposition:

[0041] Perform singular value decomposition on the risk analysis matrix R:

[0042]

[0043] Where U and V are orthogonal matrices; ∑ is a singular value diagonal matrix; σ i is a singular value; u i and v i are the left and right singular vectors; r is the rank of the matrix.

[0044] The stability matrix S and risk matrix H can be expressed as:

[0045]

[0046] In the formula, It is a preset threshold used to distinguish stable features from risk features.

[0047] 5. Extraction of main risk feature vectors:

[0048] The right singular vector v1 corresponding to the maximum singular value σ1 is selected as the main risk indicator:

[0049] v1=[v 11 ,v 12 ,...,v 1n ] T ;

[0050] 6. Risk Dimension Mapping:

[0051] Map the main risk feature vector v1 to three main risk dimensions:

[0052]

[0053] In the formula, R roof , R gas and R water They are roof instability risk vector, gas over-limit risk vector and flood risk vector respectively; and is the corresponding weight coefficient; n1 and n2 are the dividing points of the risk dimension.

[0054] 7. Risk index calculation:

[0055] Calculate the risk index using cosine similarity:

[0056]

[0057] In the formula, RI roof , RI gas and RI water They are roof instability risk index, gas over-limit risk index and flood risk index; V roof 、V ags and V water They are the preset roof stability vector, gas safety vector and flood safety vector. Indicates modulus.

[0058] 8. Calculation of comprehensive risk assessment index:

[0059] The comprehensive risk assessment index can be calculated by weighted summation:

[0060] RI total =λ1RI roof +λ2RI gas +λ3RI water +δ;

[0061] In the formula, RI total is the comprehensive risk assessment index; λ1, λ2 and λ3 are weight coefficients, and satisfy λ1+λ2+λ3=1; δ is a correction term used to consider other potential risk factors.

[0062] Finally, RI total Compared with the preset risk threshold RIthreshold By comparison, the mining risk assessment results of steeply inclined coal mines are obtained:

[0063] If RI total <RI threshold , then the mining risk is considered to be within an acceptable range;

[0064] If RI total ≥RI threshold , it is considered that the mining risk exceeds the acceptable range and corresponding risk control measures need to be taken.

[0065] A second aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores program instructions, and when the program instructions are run in a computer, they are used to execute the above-mentioned steeply inclined coal mine mining risk assessment method.

[0066] A third aspect of the present invention provides a steeply inclined coal mine mining risk assessment system, which includes the above-mentioned computer-readable storage medium.

[0067] Compared with the prior art, the steeply inclined coal mining risk assessment method, medium and system provided by the present invention have the following beneficial effects:

[0068] 1. Multimodal data fusion is used to fully reflect the geological, mining and operation status of steeply inclined coal mines. It not only collects conventional data such as geological exploration, mining operations and monitoring and supervision, but also integrates real-time monitoring data collected by multiple sensors, such as gas concentration, roof displacement and microseismic signals, which greatly enhances the information content and coverage of the data.

[0069] 2. Use mathematical models such as time series analysis, spatial correlation analysis, and singular value decomposition to deeply explore the inherent correlation patterns and potential risk characteristics in multimodal data. Compared with simple statistical analysis or empirical models, this method can better capture the dynamic interaction between various factors in a complex nonlinear system such as a steeply inclined coal mine.

[0070] 3. The extracted main risk feature vectors are mapped to three major risk dimensions, namely roof instability, gas over-limit and flood, to conduct refined analysis and quantitative evaluation of key risk factors. This analysis framework is closer to the main safety hazards faced in the actual production of steeply inclined coal mines, and provides a basis for the formulation of targeted prevention and control measures.

[0071] 4. A comprehensive risk assessment index was constructed to comprehensively reflect the impact of various risk factors on mining safety, and compared with the preset thresholds to give clear risk assessment results. This assessment method is objective and quantitative, and can provide scientific decision-making support for mine managers.

[0072] In general, the steeply inclined coal mine mining risk assessment method proposed in the present invention makes full use of multi-source heterogeneous data, adopts advanced mathematical analysis technology, deeply explores key risk factors, and realizes accurate identification and comprehensive assessment of risks through the innovation of the analysis framework, which solves the technical problem that the existing risk assessment methods still lack in-depth exploration and detailed analysis of key risk factors, and it is difficult to derive targeted risk control measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 A flow chart of the method provided by the present invention. DETAILED DESCRIPTION

[0074] In order to make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0075] like Figure 1 FIG. 1 is a flow chart of a method for risk assessment of steeply inclined coal mining provided by the present invention. The method comprises the following steps:

[0076] S10. Collect multimodal data of steeply inclined coal mines, including geological data, mining parameters, and monitoring data; wherein the geological data include stratum structure and fault distribution; the mining parameters include coal mining methods and mining depth; the monitoring data include gas concentration, roof displacement, and microseismic signals;

[0077] S20, standardizing and organizing the multimodal data into a multidimensional matrix of multimodal data, wherein the first dimension of the multidimensional matrix represents different data types, the second dimension represents different time points; and the third dimension represents spatial position;

[0078] S30, performing time series analysis and spatial correlation analysis on the multidimensional matrix to obtain a two-dimensional risk analysis matrix, wherein the elements of the risk analysis matrix represent the correlations and potential risk factors between different data types;

[0079] S40, using the singular value decomposition method to decompose the risk analysis matrix into a stability matrix and a risk matrix, wherein the stability matrix reflects the stability characteristics of the steeply inclined coal mine, and the risk matrix contains the potential risk information of the steeply inclined coal mine;

[0080] S50, analyzing the risk matrix in detail, extracting the main risk characteristic vectors, selecting the characteristic vector corresponding to the maximum singular value as the main risk indicator, and mapping the characteristic vector to three main risk dimensions: roof instability risk vector, gas over-limit risk vector and flood risk vector;

[0081] S60, performing cosine similarity calculation on the obtained roof instability risk vector, gas over-limit risk vector and flood risk vector and the preset roof stability vector, gas safety vector and flood safety vector to obtain a roof instability risk index, a gas over-limit risk index and a flood risk index;

[0082] S70, calculating a comprehensive risk assessment index, and comparing it with a preset risk threshold, to obtain a mining risk assessment result for the steeply inclined coal mine.

[0083] The specific implementation methods of the above steps are described in detail below:

[0084] The specific implementation method of step S10 is to collect multimodal data of steeply inclined coal mines, including geological data, mining parameters and monitoring data. First, geological data such as stratum structure and fault distribution are obtained from geological exploration and mining practice. These geological data can be represented as a set D geo ={d geo,1 ,d geo,2 ,...,d geo,m}, where d geo,i represents the i-th geological data indicator. Secondly, mining parameters such as coal mining methods and mining depth can be obtained through mining operation records, and the set D op ={d op,1 ,d op,2 ,...,d op,n} means, where d op,j Represents the jth mining parameter index. Again, using gas concentration monitoring equipment, roof displacement sensors and microseismic instruments, real-time monitoring of gas concentration, roof displacement and microseismic signals and other monitoring data can be used to calculate the jth mining parameter index. mon ={d mon,1 ,d mon,2 ,...,d mon,p} means, where d mon,k Denotes the kth monitoring data indicator. Combining the above three types of data, we can get the multimodal data set D = D of the steeply inclined coal mine. geo ∪D op ∪D mon , where |D|=m+n+p represents the total number of data indicators. These multimodal data cover key factors such as geological conditions, mining activities and operating status in the process of steep coal mining, providing the necessary information basis for subsequent risk assessment.

[0085] The specific implementation of step S20 is to standardize the collected multimodal data and organize them into a multidimensional matrix representation. First, each data indicator in D is normalized to eliminate the influence of different dimensions and magnitudes so that the data is within the same dimension range. The normalized data can be expressed as Where min(D) and max(D) represent the minimum and maximum values ​​of the data in D respectively. Then, the standardized data is sorted according to different data types D. i , time point T j and spatial position S k Organized into a three-dimensional matrix M ijk , where i = 1, 2, ..., |D|, j = 1, 2, ..., J, k = 1, 2, ..., K. This multidimensional matrix can be expressed as:

[0086]

[0087] In the formula, M ijk is a matrix element; represents the i-th normalized data; T j represents the jth time point; S k represents the kth spatial position; f is the data mapping function; ε ijk This multidimensional matrix representation can more completely describe the spatiotemporal distribution characteristics of various key data in the process of steeply inclined coal mining, laying the foundation for subsequent time series analysis and spatial correlation analysis.

[0088] The specific implementation method of step S30 is to perform a multidimensional matrix M ijk Perform time series analysis and spatial correlation analysis to obtain a two-dimensional risk analysis matrix R that reflects the correlation between data and potential risk factors. mn First, for each data indicator In the time series T j The autoregressive moving average (ARMA) model is fitted on the data to analyze its time evolution law. The ARMA model can be expressed as:

[0089]

[0090] In the formula, is the value of the ith data at time t; P and Q are the number of autoregressive terms and the number of moving average terms, respectively; φ p and θ q is the model parameter; ε t is white noise. Through ARMA model analysis, the time correlation characteristics of various types of data can be identified, denoted as ARMA (M ijk ). Secondly, Moran's I index was used to analyze the multidimensional matrix M ijk Different spatial units S k The correlation between the data is used to quantify the spatial correlation strength of various data. The Moran's I index can be expressed as:

[0091]

[0092] Where n is the number of space units; w kl is the spatial weight; and is the value of the i-th data in units k and l; is the average value of the i-th data. This Moran's I index can reflect the degree of correlation between various types of data in spatial distribution, denoted as I(M ijk ). Finally, the results of time series analysis and spatial correlation analysis are combined to construct a two-dimensional risk analysis matrix R mn , whose elements can be expressed as:

[0093] R mn =α·ARMA(M ijk )+β·I(M ijk )+γ mn ;

[0094] In the formula, R mn are the elements of the risk analysis matrix; α and β are weight coefficients; γ mn is a random disturbance term. This risk analysis matrix R mn It reflects the correlation between different data types and potential risk factors, providing a basis for subsequent risk feature extraction and analysis.

[0095] The specific implementation of step S40 is to use the singular value decomposition (SVD) method to transform the risk analysis matrix R mn Decompose into a stable matrix S and a risk matrix H. First, the risk analysis matrix R mn Performing singular value decomposition, it can be expressed as:

[0096]

[0097] Where U and V are orthogonal matrices; ∑ is a singular value diagonal matrix; σ i is a singular value; u i and v i are the left and right singular vectors; r is the rank of the matrix. Then, a threshold is set according to the size of the singular value The matrix R mn Decomposed into a stable matrix S and a risk matrix H, where:

[0098]

[0099] In the formula, is the preset threshold for distinguishing stable features from risk features. This decomposition method can effectively separate the stable factors from the risk factors in the original data. S reflects the stable features of steeply inclined coal mines, and H contains potential risk information, laying the foundation for subsequent risk feature extraction and analysis.

[0100] The specific implementation of step S50 is to extract the main risk feature vector and map the risk dimension of the risk matrix H. First, select the right singular vector v1=[v 11 ,v 12 ,...,v 1n ] T As the main risk indicator, this vector reflects the most important risk characteristics in the risk analysis matrix. Then, this main risk characteristic vector v1 is mapped to three main risk dimensions: roof instability risk vector R roof , Gas over-limit risk vector R gas and flood risk vector R water The calculation formulas for these risk vectors are:

[0101]

[0102] In the formula, and is the corresponding weight coefficient, n1 and n2 are the division points of the risk dimension. This risk dimension mapping can better identify the most critical risk factors in the mining process of steeply inclined coal mines.

[0103] The specific implementation method of step S60 is to calculate the roof instability risk index RI based on the three risk vectors obtained. roof , Gas Exceeding Limit Risk Index RI gas and Flood Risk Index RI water The calculation formulas for these three risk indices are as follows:

[0104]

[0105]

[0106] Among them, V roof 、V gas and V water They are the preset roof stability vector, gas safety vector and flood safety vector. This method of calculating the risk index using cosine similarity can quantify the proximity between various risk factors and safety goals, providing a basis for comprehensive risk assessment.

[0107] The specific implementation of step S70 is to calculate the comprehensive risk assessment index RI total and compared with the preset risk threshold RI threshold By comparison, the mining risk assessment results of steeply inclined coal mines are obtained. The calculation formula of the comprehensive risk assessment index is:

[0108] RI total =λ1RI roof+λ2RI gas +λ3RI water +δ;

[0109] In the formula, λ1, λ2 and λ3 are weight coefficients, and they satisfy λ1+λ2+λ3=1, and δ is a correction term used to consider other potential risk factors. threshold By comparing, we can get the final evaluation result: if RI total <RI threshold , then the mining risk is considered to be within an acceptable range; if RI total ≥RI threshold , it is considered that the mining risk exceeds the acceptable range and corresponding risk control measures need to be taken. This comprehensive assessment method can comprehensively reflect various risk factors in the mining process of steeply inclined coal mines and provide an important basis for formulating reasonable risk control strategies.

[0110] In summary, this steeply inclined coal mining risk assessment method makes full use of multimodal data, identifies key risk features through mathematical models such as time series analysis, spatial correlation analysis, and singular value decomposition, and maps them to three major risk dimensions, including roof instability, gas overlimit, and flooding. Then, various risk indexes are calculated and comprehensively evaluated, providing a scientific and systematic risk assessment method for the safe mining of steeply inclined coal mines.

[0111] A second aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores program instructions, and when the program instructions are run in a computer, they are used to execute the above-mentioned steeply inclined coal mine mining risk assessment method.

[0112] A third aspect of the present invention provides a steeply inclined coal mine mining risk assessment system, which includes the above-mentioned computer-readable storage medium.

[0113] Specifically, the principle of the present invention is to mine the key features reflecting the safety risks of mines through in-depth analysis of multimodal data and establish a quantitative risk assessment model. The reason why this method can effectively solve the problems existing in the prior art is mainly based on the following principles:

[0114] First, the present invention fully considers the characteristics of geological conditions, mining activities and production operations in the process of steeply inclined coal mining, and collects rich multimodal data including geological data, mining parameters and monitoring data. This comprehensive data collection can reflect the actual status of the coal mine in more detail, laying the foundation for subsequent in-depth analysis.

[0115] Secondly, the present invention uses mathematical modeling methods such as time series analysis and spatial correlation analysis to deeply explore the inherent correlation and dynamic change rules in multimodal data. Compared with relying solely on empirical models or simple statistical analysis, this modeling method can better capture the interaction between various elements in complex nonlinear systems and create conditions for accurately identifying key risk factors.

[0116] Thirdly, the present invention decomposes the original risk analysis matrix into two parts: stable features and risk features through the singular value decomposition method. This decomposition can not only effectively identify potential risk factors in the mine, but also retain the stable features of the mine itself, providing a basis for comprehensive and accurate risk assessment.

[0117] Finally, the present invention constructs a comprehensive assessment model based on risk dimensions, maps the extracted key risk factors to three major risk dimensions, namely roof instability, gas exceeding the limit and flooding, and calculates the risk index of each dimension and its comprehensive index.

[0118] In order to better understand and implement the present invention, an embodiment of a specific application scenario of the present invention is provided below: A steeply inclined coal mine is mined in a certain mining area. The coal mine has complex geological conditions, with an inclination of more than 60 degrees, and many safety hazards, such as roof instability, excessive gas, and floods. In order to comprehensively assess the mining risks of the coal mine, the mine owner decided to adopt the risk assessment method based on multimodal data analysis proposed in the present invention.

[0119] 1. Data Collection

[0120] First, the mine organized relevant technical personnel to conduct comprehensive data collection work on the steeply inclined coal mine. Specifically, it included:

[0121] Geological data collection: Through drilling and geological survey, the geological information of the coal mine, such as the stratum structure and fault distribution, was obtained. The main data indicators include: stratum lithology (d geo,1 ), formation dip (d geo,2 ), fault development density (d geo,3 ), fault direction (d geo,4 ), fault dip (d geo,5 )wait.

[0122] Mining parameter collection: Through mining operation records, important parameters such as the coal mining method and mining depth of the coal mine are collected. The main data indicators include: coal mining method (d op,1 ), mining technology (d op,2 ), mining depth (d op,3 ), coal mining intensity (d op,4 )wait.

[0123] Monitoring data collection: Using gas concentration monitoring equipment, roof displacement sensors and microseismic instruments, key operating data such as gas concentration, roof displacement and microseismic signals of the coal mine are collected in real time. The main data indicators include: gas concentration (d mon,1 ), gas outburst rate (d mon,2 ), top plate displacement (d mon,3 ), microseismic signal amplitude (d mon,4 ), microseismic signal frequency (d mon,5 )wait.

[0124] These geological data, mining parameters and monitoring data constitute a multimodal dataset of this steeply inclined coal mine:

[0125] D={d geo,1 ,d geo,2 ,...,d geo,5 ,d op,1 ,d op,2 ,...,d op,4 ,d mon,1 ,d mon,2 ,...,d mon,5}.

[0126] 2. Data Standardization and Matrix Construction

[0127] After collecting the original multimodal data, it is first standardized to eliminate the influence of dimension and magnitude. Specifically, the minimum-maximum standardization method is used to normalize the values ​​of each indicator to the interval [0,1]:

[0128]

[0129] in, is the ith data index value after standardization, min(D) and max(D) are the minimum and maximum values ​​of the data in D respectively.

[0130] The standardized multimodal data are organized into a three-dimensional matrix M according to data type, time point and spatial position. ijk , where i = 1, 2, ..., 14 represents 14 data indicators, j = 1, 2, ..., 30 represents 30 time points, and k = 1, 2, ..., 50 represents 50 spatial positions. The three-dimensional matrix can be expressed as:

[0131]

[0132] In the formula, M ijk is a matrix element; represents the i-th standardized data; T j represents the jth time point; S k represents the kth spatial position; f is the data mapping function; εijk is a random error term.

[0133] The following is the three-dimensional matrix M constructed from the multimodal data of the steeply inclined coal mine: ijk Some sample data:

[0134] serial number index Spatial Position 1 Spatial Position 2 Spatial Position 3 Spatial location 4 Spatial location 5 1 <![CDATA[d geo,1 ]]> 0.28 0.33 0.41 0.39 0.35 2 <![CDATA[d geo,2 ]]> 0.57 0.62 0.68 0.59 0.63 3 <![CDATA[d geo,3 ]]> 0.42 0.38 0.47 0.43 0.4 4 <![CDATA[d geo,4 ]]> 0.51 0.46 0.55 0.49 0.53 5 <![CDATA[d geo,5 ]]> 0.64 0.59 0.71 0.66 0.62 6 <![CDATA[d geo,6 ]]> 0.76 0.72 0.81 0.79 0.74 7 <![CDATA[d geo,7 ]]> 0.53 0.48 0.58 0.55 0.51 8 <![CDATA[d geo,8 ]]> 0.69 0.65 0.73 0.71 0.67 9 <![CDATA[d geo,9 ]]> 0.82 0.78 0.86 0.84 0.8 10 <![CDATA[d geo,10 ]]> 0.35 0.31 0.42 0.38 0.33 11 <![CDATA[d geo,11 ]]> 0.46 0.41 0.52 0.48 0.43 12 <![CDATA[d geo,12 ]]> 0.27 0.22 0.34 0.29 0.24 13 <![CDATA[d geo,13 ]]> 0.58 0.53 0.63 0.61 0.55 14 <![CDATA[d geo,14 ]]> 0.49 0.44 0.54 0.51 0.46

[0135] 3. Time Series Analysis and Spatial Correlation Analysis

[0136] Based on the constructed three-dimensional matrix M ijk ,Next, time series analysis and spatial correlation analysis are conducted to ,explore the inherent correlation rules among the data.

[0137] Time series analysis: Fit the ARMA model to each data indicator in the time series to identify its time evolution characteristics. mon,1 ) as an example, its ARMA model can be expressed as:

[0138]

[0139] Through ARMA model analysis, it is found that the gas concentration in the coal mine shows obvious autocorrelation and moving average characteristics, that is, the gas concentration at the current moment has a significant relationship with the value at the previous moment and the random disturbance term. This reflects the dynamic change law of the gas emission process in the mine.

[0140] Spatial correlation analysis: Moran's I index analysis matrix M is used ijk The correlation between different spatial units in the quantification of the correlation strength of various data in spatial distribution. mon,3 ) as an example, its Moran's I index is calculated as follows:

[0141]

[0142] The results of Moran's I index show that there is a strong positive spatial correlation between the roof displacement data of different mining faces in the coal mine, that is, the displacement change trends of adjacent working faces are similar. This reflects the spatial correlation distribution characteristics of the roof instability risk of the mine.

[0143] The results of time series analysis and spatial correlation analysis are combined to construct a two-dimensional risk analysis matrix R that reflects the correlation and risk factors of various data in steeply inclined coal mines. mn .

[0144] 4. Singular Value Decomposition and Risk Feature Extraction

[0145] Based on the constructed risk analysis matrix R mn,Next, the singular value decomposition method is used to decompose it into the stable ,feature matrix S and the risk feature matrix H.

[0146] First, for R mn Perform singular value decomposition:

[0147]

[0148] Among them, U and V are orthogonal matrices, ∑ is a singular value diagonal matrix, σ i is a singular value, u i and v i are the left and right singular vectors respectively, and r is the rank of the matrix.

[0149] Then, set the threshold according to the size of the singular value R mn Decomposed into stable feature matrix S and risk feature matrix H:

[0150]

[0151] Among them, S reflects the stability characteristics of the coal mine, and H contains potential risk information.

[0152] Next, select the right singular vector v1=[v 11 ,v 12 ,...,v 1n ] T As the main risk indicator, this vector reflects R mn Then, v1 is mapped to three main risk dimensions:

[0153] Roof instability risk vector:

[0154] R roof =0.43v 11 +0.27v 12 +0.30v 13 ;

[0155] Gas over-limit risk vector:

[0156] R gas =0.35v 14 +0.31v 15 +0.34v 16 ;

[0157] Flood risk vector:

[0158] R water =0.41v 17 +0.29v 18 +0.30v 19 ;

[0159] This risk dimension mapping helps to identify the most critical risk factors in the mining process of this steeply inclined coal mine.

[0160] V. Risk Index Calculation and Comprehensive Assessment

[0161] Based on the above three risk dimensions, the roof instability risk index, gas over-limit risk index and flood risk index are calculated respectively:

[0162] Roof instability risk index:

[0163]

[0164] Gas over-limit risk index:

[0165]

[0166] Flood Risk Index:

[0167]

[0168] Among them, V roof 、V gas and V water They are the preset roof stability vector, gas safety vector and flood safety vector respectively.

[0169] Finally, the comprehensive risk assessment index of the steeply inclined coal mine is calculated:

[0170] RI total =0.4·RI roof +0.3 RI gas +0.3 RI water +0.05=0.71;

[0171] By comparing with the preset risk threshold RI threshold =0.65, the results show that the mining risk of the coal mine exceeds the acceptable range and necessary risk control measures need to be taken.

[0172] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A steeply inclined coal mining risk assessment method, characterized in that: The following steps are involved: S10, collecting multimodal data of steeply inclined coal mines, including geological data, mining parameters, and monitoring data; wherein the geological data includes stratum structure and fault distribution; the mining parameters include coal mining methods and mining depth; and the monitoring data includes gas concentration, roof displacement, and microseismic signals; S20, standardizing and organizing the multimodal data into a multidimensional matrix of multimodal data, wherein the first dimension of the multidimensional matrix represents different data types, the second dimension represents different time points; and the third dimension represents spatial position; S30, performing time series analysis and spatial correlation analysis on the multidimensional matrix to obtain a two-dimensional risk analysis matrix, wherein the elements of the risk analysis matrix represent the correlations and potential risk factors between different data types; S40, using a singular value decomposition method to decompose the risk analysis matrix into a stability matrix and a risk matrix, wherein the stability matrix reflects the stability characteristics of the steeply inclined coal mine, and the risk matrix contains potential risk information of the steeply inclined coal mine; S50, analyzing the risk matrix in detail, extracting the main risk characteristic vectors, selecting the characteristic vector corresponding to the maximum singular value as the main risk indicator, and mapping the characteristic vector to three main risk dimensions: roof instability risk vector, gas over-limit risk vector and flood risk vector; S60, performing cosine similarity calculation on the obtained roof instability risk vector, gas over-limit risk vector and flood risk vector and the preset roof stability vector, gas safety vector and flood safety vector to obtain a roof instability risk index, a gas over-limit risk index and a flood risk index; S70, calculating a comprehensive risk assessment index, and comparing it with a preset risk threshold, to obtain a mining risk assessment result for the steeply inclined coal mine.

2. A steeply inclined coal mining risk assessment method according to claim 1, characterized in that: The multidimensional matrix of the multimodal data is expressed as: M ijk =f(D i ,T j ,S k )+ε ijk ; Where M ijk is a multidimensional matrix element; D i Indicates the i-th data type; T j represents the jth time point; S k represents the kth spatial position; f is the data mapping function; ε ijk is a random error term.

3. A steeply inclined coal mining risk assessment method according to claim 2, characterized in that: The time series analysis adopts the autoregressive moving average model, which is specifically expressed as follows: Where, X t is the time series value; p is the number of autoregressive terms; q is the number of moving average terms; φ i is the autoregression coefficient; θ j is the moving average coefficient; ε t is white noise.

4. A steeply inclined coal mining risk assessment method according to claim 3, characterized in that: Spatial correlation analysis uses Moran's I index, which is expressed as follows: Where n is the number of space units; w ij is the spatial weight; x i and x j is the attribute value of spatial units i and j; is the attribute mean.

5. A steeply inclined coal mining risk assessment method according to claim 4, characterized in that: The risk analysis matrix is ​​calculated as follows: R mn =α·ARMA(M ijk )+β·I(M ijk )+c mn ; In the formula, R mn is the risk analysis matrix element; ARMA(M ijk ) is the result of time series analysis; I(M ijk ) is the result of spatial correlation analysis; α and β are weight coefficients; γ mn is a random disturbance term.

6. A steeply inclined coal mining risk assessment method according to claim 5, characterized in that: The risk analysis matrix is ​​decomposed using the singular value decomposition method, which is specifically expressed as follows: Where U and V are orthogonal matrices; ∑ is a singular value diagonal matrix; σ i is a singular value; u i and v i are left and right singular vectors; r is the rank of the matrix; the stability matrix S and the risk matrix H are expressed as: Where N is the preset threshold for distinguishing stable features from risk features.

7. A steeply inclined coal mining risk assessment method according to claim 6, characterized in that: The feature vector is mapped to three main risk dimensions, which are specifically expressed as follows: In the formula, R roof , R gas and R water They are roof instability risk vector, gas over-limit risk vector and flood risk vector respectively; and is the corresponding weight coefficient; n1 and n2 are the dividing points of the risk dimension.

8. A steeply inclined coal mining risk assessment method according to claim 7, characterized in that: The risk index calculation formula is: In the formula, RI roof , RI gas and RI water They are roof instability risk index, gas over-limit risk index and flood risk index; V roof 、V gas and V water They are the preset roof stability vector, gas safety vector and flood safety vector. represents modulus; the comprehensive risk assessment index is calculated by weighted summation: RI total =λ1RI roof +λ2RI gas +λ3RI water +d; In the formula, RI total is the comprehensive risk assessment index; λ1, λ2 and λ3 are weight coefficients, and they satisfy λ1+λ2+λ3=1; δ is a correction term used to consider other potential risk factors; Finally, RI total Compared with the preset risk threshold RI threshold By comparison, the mining risk assessment results of steeply inclined coal mines are obtained: If RI total <RI threshold , then the mining risk is considered to be within an acceptable range; If RI total ≥RI threshold , it is considered that the mining risk exceeds the acceptable range and corresponding risk control measures need to be taken.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program instructions, and when the program instructions are executed in a computer, they are used to execute the steeply inclined coal mine mining risk assessment method according to any one of claims 1 to 8.

10. A steeply inclined coal mining risk assessment system, characterized in that: A computer-readable storage medium comprising the computer-readable storage medium of claim 9.

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