A method and system for processing coal mine disaster data based on principal component analysis

Abstract

The present invention provides a method and system for processing coal mine disaster data based on principal component analysis, including: preprocessing and standardizing working data to obtain valid data, using PCA to perform dimensionality reduction analysis on the valid data to convert it into a number of eigenvectors, constructing a dimensionality reduction space for valid data, respectively calculating the reconstruction error and explained variance ratio corresponding to each eigenvector in the dimensionality reduction space, constructing a dimensionality reduction space adjusted by dimensional information corresponding to each eigenvector, inputting the valid data into the adjusted dimensionality reduction space for processing, generating dimensionality reduction data of the coal mine, screening abnormal dimensionality reduction data segments that do not meet the specified explained variance ratio, and obtaining disaster data corresponding to the coal mine under different detection dimensions. The method can effectively reduce the number of calculation steps and reduce the analysis cost, and the whole process is visualized for analysis, which is convenient for management personnel to understand the ins and outs of the data, and provides a strong reference for the safety of coal mine workers and the environment.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method and system for processing coal mine disaster data based on principal component analysis. Background Art

[0002] Coal mine disasters are a significant factor affecting coal mine safety and efficiency. With the widespread use of sensor technology and data acquisition systems in modern coal mine operations, coal mining companies are able to obtain a large amount of real-time production monitoring data, including geological conditions, equipment status, and environmental monitoring data. By observing and analyzing this data, potential hazards can be promptly identified and preventative measures implemented. However, due to the sheer volume and complexity of this data, effectively utilizing and analyzing it presents a significant challenge.

[0003] Traditional data analysis methods often suffer from limitations and shortcomings when processing coal mine disaster data, such as information loss, weak pattern recognition capabilities, overfitting, high computational complexity, and parameter sensitivity leading to suboptimal solutions. These problems restrict their effective analysis and application to coal mine disaster data. Specific examples are listed below:

[0004] 1. Information loss and weak pattern recognition capabilities: Simple statistical methods, such as mean and standard deviation, cannot capture the multivariate relationships of the data, may ignore important information, and have difficulty extracting meaningful patterns and trends from complex data.

[0005] 2. Multicollinearity and dimensionality issues: Traditional data analysis methods often face problems such as instability, overfitting, high computational complexity, and poor efficiency when processing multivariate data. For example, linear regression can become unstable due to multicollinearity; decision trees and random forests have poor interpretability in high-dimensional data and are prone to overfitting when the data is noisy; support vector machines (SVMs) are very sensitive to parameter settings, and improper parameter selection can significantly affect model performance and result accuracy; and neural networks are prone to overfitting when processing small sample data.

[0006] 3. Assumption Limitations: Traditional data analysis methods often assume linear relationships between data before analysis and are unable to handle nonlinear relationships. For example, linear regression cannot handle nonlinear relationships; while support vector machines (SVMs) can use kernel functions to handle nonlinear data, selecting appropriate kernel functions and parameters is extremely difficult; and time series analysis (ARIMA) struggles with nonlinear data and requires the data to be stationary, making it unsuitable for all data types.

[0007] 4. High computational complexity: Coal mine disaster data involves complex interactions between multiple factors. Traditional data analysis methods often require a large amount of computing resources and time for training when analyzing and calculating them, resulting in high computational complexity and high computational overhead.

[0008] 5. Poor interpretability: Traditional data analysis methods have poor interpretability for high-dimensional data, and it is difficult to understand the clustering features and the relationship between features.

[0009] Therefore, the present invention provides a method and system for processing coal mine disaster data based on principal component analysis. Summary of the Invention

[0010] The present invention provides a method and system for processing coal mine disaster data based on principal component analysis, which can effectively overcome many limitations of traditional data analysis methods. On the one hand, PCA can reduce the complexity of data by reducing dimensionality, thereby improving the efficiency and accuracy of analysis. On the other hand, PCA can extract the main features in the data, remove redundant information and noise, and make pattern recognition more accurate. In addition, by projecting high-dimensional data into a low-dimensional space, PCA can reduce computational complexity while avoiding the problem of overfitting. The fact that PCA does not rely on specific parameter settings also makes the analysis results more stable and reliable.

[0011] The present invention provides a method for processing coal mine disaster data based on principal component analysis, comprising:

[0012] Step 1: Collecting the working data of the coal mine, pre-processing and standardizing the working data to obtain valid data of the coal mine;

[0013] Step 2: Perform dimensionality reduction analysis on the valid data using PCA, convert the valid data into a number of eigenvectors, and use the eigenvectors to construct a dimensionality reduction space of the valid data;

[0014] Step 3: Calculate the reconstruction error and explained variance ratio corresponding to each eigenvector in the dimensionality reduction space, and construct the dimensional information corresponding to each eigenvector;

[0015] Step 4: adjusting the dimensionality reduction space based on the dimensional information, and inputting the valid data into the adjusted dimensionality reduction space for processing to generate dimensionality reduction data of the coal mine;

[0016] Step 5: Screen out abnormal dimensionality reduction data segments that do not meet the specified explained variance ratio, optimize the abnormal dimensionality reduction data segments, and obtain the disaster data corresponding to the coal mine under different detection dimensions.

[0017] In one practicable manner,

[0018] The step 1 comprises:

[0019] Step 11: collecting real-time gas concentration information, real-time temperature information, real-time humidity information, real-time mineralization information, real-time water tank flow information, real-time grouting information, and real-time nitrogen injection information of the coal mine, and generating working data of the coal mine by time sorting;

[0020] Step 12: Obtain the mean of the working data, analyze the standard deviation distance between each data point in the working data and the mean, filter the standard deviation distance, determine a number of abnormal data points contained in the working data, and perform cleaning preprocessing on the abnormal data points;

[0021] Step 13: constructing a box plot of the working data, identifying noise data points in the working data in the box plot, performing noise reduction preprocessing on the noise data points, and treating the working data that has completed the cleaning preprocessing and noise reduction preprocessing as the cleaned data of the coal mine;

[0022] Step 14: Standardize the cleaned data to obtain valid data with a mean of 0 and a variance of 1.

[0023] In one practicable manner,

[0024] Also includes:

[0025] Find the first outlier data point that exceeds the upper quartile + 1.5 times the interquartile range and the second outlier data point that exceeds the lower quartile - 1.5 times the interquartile range in the box plot;

[0026] The first outlier data point and the second outlier data segment are regarded as noise data points in the working data.

[0027] In one practicable manner,

[0028] The step 2 comprises:

[0029] Step 21: Draw the original space of the valid data, search for the first several original coordinate axes with mutually orthogonal characteristics in the original space, find the target coordinate axis with the largest variance with the valid data as the first coordinate axis, locate the first coordinate axis in the original space, and determine the search starting point;

[0030] Step 22: Starting from the search starting point, sequentially search for a second coordinate axis that is orthogonal to the first coordinate axis and has the largest variance with the first coordinate plane, and a third coordinate axis that is orthogonal to the first coordinate axis and the second coordinate axis plane and has the largest variance with the first-second coordinate plane, and obtain N coordinate axes after the search is completed;

[0031] Step 23: establishing a variance change list of the original space based on the variances between adjacent coordinate axes, roughly screening the variance change list to obtain K valid coordinate axes whose variances are not 0, and constructing a corresponding eigenvector based on each of the valid coordinate axes;

[0032] Step 24: Establish a covariance matrix of the eigenvectors, analyze the eigenvalues ​​corresponding to each eigenvector in the covariance matrix using the eigendecomposition method, build a spatial framework based on the eigenvectors, and input the eigenvalues ​​into the spatial framework to generate a dimensionality reduction space for the valid data.

[0033] In one practicable manner,

[0034] The step 3 comprises:

[0035] Step 31: Obtain the explained variance ratio of each eigenvector in the dimensionality reduction space, draw a visual ratio diagram of the valid data, identify the importance corresponding to each eigenvector in the visual ratio diagram, and construct the first dimension sub-information corresponding to the eigenvector;

[0036] Step 32: construct corresponding feature data according to the feature vector, map the feature data to the original space for loss assessment, obtain data loss information corresponding to the feature vector, and construct the reconstruction error of the valid data;

[0037] Step 33: constructing the reconstruction error to determine the spatial structure error and spatial pattern error of the dimensionality reduction space, and generating the second dimensional sub-information corresponding to the eigenvector;

[0038] Step 34: Establish dimensional information corresponding to the feature vector in the dimensionality reduction space according to the first dimensional sub-information and the second dimensional sub-information.

[0039] In one practicable manner,

[0040] The step 4 comprises:

[0041] Step 41: adjusting the spatial structure and spatial pattern of the dimensionality reduction space based on the dimensionality information to obtain a stable dimensionality reduction space;

[0042] Step 42: inputting the valid data into the stable dimensionality reduction space, performing dimensionality reduction training on the valid data in the stable dimensionality reduction space, and generating dimensionality reduction data of the coal mine.

[0043] In one practicable manner,

[0044] The step 5 comprises:

[0045] Step 51: Obtain a standard working range corresponding to each detection dimension in the coal mine, construct a prescribed explained variance ratio range of the corresponding detection dimension based on the standard working range, estimate the dimensional information corresponding to each detection dimension and construct a data partitioning rule;

[0046] Step 52: Divide the dimensionality-reduced data into a plurality of dimensionality-reduced data segments based on the data partitioning rule, and use the corresponding prescribed explained variance rate range to identify abnormal dimensionality data segments that do not meet the requirements;

[0047] Step 53: Mark the abnormal dimension data segment in the valid data, and track the source of each abnormal dimension data segment respectively, construct the disaster data corresponding to the coal mine under different detection dimensions and display it.

[0048] In one practicable manner,

[0049] The process of establishing the covariance matrix of the eigenvector includes:

[0050] Count all eigenvectors and construct the eigenvector set X;

[0051] Calculate the mean value corresponding to each eigenvector according to formula (1);

[0052]

[0053] in, represents the mean of the first j vectors, n represents the total number of the eigenvectors, Represents the i-th coordinate axis feature of the j-th eigenvector in the corresponding coordinate axis;

[0054] According to formula (2), each of the feature vectors is decentralized to generate the target feature vector set ;

[0055]

[0056] Use formula (3) to establish the target feature vector set The covariance matrix of

[0057]

[0058] Where T represents the transpose identifier and C represents the covariance matrix identifier.

[0059] The present invention provides a system for processing coal mine disaster data based on principal component analysis, comprising:

[0060] A data processing module is used to collect the working data of the coal mine, pre-process and standardize the working data to obtain the effective data of the coal mine;

[0061] A dimensionality reduction analysis module is used to perform dimensionality reduction analysis on the valid data using PCA, convert the valid data into a plurality of eigenvectors, and construct a dimensionality reduction space of the valid data using the eigenvectors;

[0062] An information construction module, configured to respectively calculate the reconstruction error and explained variance ratio corresponding to each of the eigenvectors in the dimensionality reduction space, and construct dimensional information corresponding to each of the eigenvectors;

[0063] a dimensionality reduction execution module, configured to adjust the dimensionality reduction space based on the dimensional information, and input the valid data into the adjusted dimensionality reduction space for processing to generate dimensionality reduction data of the coal mine;

[0064] The data processing module is used to screen out abnormal dimensionality reduction data segments that do not meet the specified explained variance ratio, optimize the abnormal dimensionality reduction data segments, and obtain the disaster data corresponding to the coal mine under different detection dimensions.

[0065] In one practicable manner,

[0066] The dimensionality reduction analysis module includes:

[0067] a space construction unit, configured to draw the original space of the valid data, search for a plurality of original coordinate axes having mutually orthogonal characteristics in the original space, search for a target coordinate axis having the largest variance with the valid data as a first coordinate axis, locate the first coordinate axis in the original space, and determine a search starting point;

[0068] a coordinate decomposition unit, configured to sequentially search, starting from the search starting point, for a second coordinate axis that is orthogonal to the first coordinate axis and has the largest variance with the first coordinate plane, and a third coordinate axis that is orthogonal to the first coordinate axis and the second coordinate axis plane and has the largest variance with the first-second coordinate plane, to obtain N coordinate axes upon completion of the search;

[0069] a coordinate screening unit, configured to establish a variance change list of the original space based on the variances between adjacent coordinate axes, perform a rough screening on the variance change list to obtain K valid coordinate axes whose variances are not 0, and construct a corresponding feature vector based on each of the valid coordinate axes;

[0070] A space construction unit is used to establish a covariance matrix of the eigenvector, analyze the eigenvalues ​​corresponding to each eigenvector in the covariance matrix using the eigendecomposition method, build a space framework based on the eigenvector, and input the eigenvalues ​​into the space framework to generate a dimensionality reduction space for the valid data.

[0071] The achievable beneficial effects of the above technical solution are as follows: since a large amount of data is generated during the operation of coal mines, it is necessary to quickly screen the data when analyzing its disasters. Therefore, the working data is first preprocessed and standardized to make its mean 0 and variance 1 to eliminate the influence between different feature dimensions and obtain valid data. Then, the principal component analysis method is used to reduce the dimension of the valid data, determine several feature vectors, and construct a reduced dimension space of the valid data. Further, the adjacent dimensional information of the features is determined based on the reconstruction error and explained variance ratio of the feature vector in the reduced dimension space, and the reduced dimension space is adjusted to achieve the reduced dimension processing of the valid data, and the reduced dimension data of the coal mine is obtained. At this time, the reduced dimension data can be used in the reduced dimension data. By identifying abnormal data segments, the disaster data of the coal mine can be determined. In this way, the collected original data can be processed and converted so that the PCA method can effectively extract key features and perform dimensionality reduction, screen out important features in the working data, reduce the impact of redundant data and redundant features on disaster analysis, and perform error adjustment during dimensionality reduction to avoid data loss. This method can analyze the working data of various types of coal mines and is a comprehensive analysis method. Since this method first screens the features and then performs analysis, it can effectively reduce the calculation steps and reduce the analysis cost. The whole process is visualized, which makes it easier for managers to understand the ins and outs of the data and provides a strong reference for the safety of coal miners and the environment.

[0072] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0073] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0075] Figure 1 Schematic diagram of the workflow of a method for processing coal mine disaster data based on principal component analysis in an embodiment of the present invention;

[0076] Figure 2 The figure is a schematic diagram of the composition of a system for processing coal mine disaster data based on principal component analysis in an embodiment of the present invention. DETAILED DESCRIPTION

[0077] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention and are not used to limit the present invention.

[0078] Example 1

[0079] This embodiment provides a method and system for processing coal mine disaster data based on principal component analysis. Figure 1 Shown, including:

[0080] Step 1: Collecting the working data of the coal mine, pre-processing and standardizing the working data to obtain valid data of the coal mine;

[0081] Step 2: Perform dimensionality reduction analysis on the valid data using PCA, convert the valid data into a number of eigenvectors, and use the eigenvectors to construct a dimensionality reduction space of the valid data;

[0082] Step 3: Calculate the reconstruction error and explained variance ratio corresponding to each eigenvector in the dimensionality reduction space, and construct the dimensional information corresponding to each eigenvector;

[0083] Step 4: adjusting the dimensionality reduction space based on the dimensional information, and inputting the valid data into the adjusted dimensionality reduction space for processing to generate dimensionality reduction data of the coal mine;

[0084] Step 5: Screen out abnormal dimensionality reduction data segments that do not meet the specified explained variance ratio, optimize the abnormal dimensionality reduction data segments, and obtain the disaster data corresponding to the coal mine under different detection dimensions.

[0085] In this instance, preprocessing refers to the process of cleaning the working data;

[0086] In this example, the main idea of ​​PCA is to map n-dimensional features to k-dimensional features. These k-dimensional features are new orthogonal features, also known as principal components. They are k-dimensional features reconstructed based on the original n-dimensional features.

[0087] In this example, the feature vector represents the data vector presented by the valid data in different dimensions;

[0088] In this example, the dimensionality reduction space refers to the space where the reduced-dimensional data is located after the effective data is reduced in dimensionality;

[0089] In this example, the purpose of dimensionality reduction is: PCA projects high-dimensional data into a lower-dimensional space, extracts the main factors of multivariate things through spatial projection and linear transformation, and reveals their essential characteristics. PCA deletes redundant and repeated variables (closely related variables) for all the variables originally proposed, and establishes as few new variables as possible so that these new variables are uncorrelated with each other. These new variables are called principal components, which can well describe the sample information. The core of the PCA algorithm is to reduce the dimension and screen the main features. The purpose of principal component analysis is to reduce the dimension of the data by retaining the most important feature information, thereby simplifying the model training process and improving the model's computational efficiency. Through PCA dimensionality reduction, the redundant information between features can be reduced, and more representative features can be extracted, which helps to improve the model's generalization ability and prediction accuracy;

[0090] In this example, the reconstruction error represents the error generated during the dimensionality reduction process;

[0091] In this example, the explained variance ratio represents an indicator used to measure the proportion of each eigenvector in the dimensionality reduction space;

[0092] In this example, the dimension information represents the information contained in the feature vector under its corresponding dimension;

[0093] In this example, the detection dimensions include: gas concentration dimension, temperature dimension, humidity dimension, mineralization dimension, water tank flow dimension, grouting dimension and nitrogen injection dimension.

[0094] The working principle and beneficial effects of the above technical solution: Since coal mines generate a large amount of data during operation, it is necessary to quickly screen the data when analyzing its disasters. Therefore, the working data is first preprocessed and standardized to make its mean 0 and variance 1 to eliminate the influence between different feature dimensions and obtain valid data. Then, the principal component analysis method is used to reduce the dimension of the valid data, determine several feature vectors, and construct a dimensionality reduction space for the valid data. Further, the adjacent dimensional information of the features is determined based on the reconstruction error and explained variance ratio of the feature vector in the dimensionality reduction space, and the dimensionality reduction space is adjusted to achieve dimensionality reduction of the valid data, and the dimensionality reduction data of the coal mine is obtained. At this time, the dimensionality reduction data can be used in the dimensionality reduction data. By identifying abnormal data segments, the disaster data of the coal mine can be determined. In this way, the collected original data can be processed and converted so that the PCA method can effectively extract key features and perform dimensionality reduction, screen out important features in the working data, reduce the impact of redundant data and redundant features on disaster analysis, and perform error adjustment during dimensionality reduction to avoid data loss. This method can analyze the working data of various types of coal mines and is a comprehensive analysis method. Since this method first screens the features and then performs analysis, it can effectively reduce the calculation steps and reduce the analysis cost. The whole process is visualized, which makes it easier for managers to understand the ins and outs of the data and provides a strong reference for the safety of coal miners and the environment.

[0095] Example 2

[0096] On the basis of Example 1, the method for processing coal mine disaster data based on principal component analysis method, step 1, comprises:

[0097] Step 11: collecting real-time gas concentration information, real-time temperature information, real-time humidity information, real-time mineralization information, real-time water tank flow information, real-time grouting information, and real-time nitrogen injection information of the coal mine, and generating working data of the coal mine by time sorting;

[0098] Step 12: Obtain the mean of the working data, analyze the standard deviation distance between each data point in the working data and the mean, filter the standard deviation distance, determine a number of abnormal data points contained in the working data, and perform cleaning preprocessing on the abnormal data points;

[0099] Step 13: constructing a box plot of the working data, identifying noise data points in the working data in the box plot, performing noise reduction preprocessing on the noise data points, and treating the working data that has completed the cleaning preprocessing and noise reduction preprocessing as the cleaned data of the coal mine;

[0100] Step 14: Standardize the cleaned data to obtain valid data with a mean of 0 and a variance of 1.

[0101] In this example, the data acquisition module is a key link. It is responsible for collecting data on monitoring indicators and environmental factors related to coal mine disasters. Through reasonable data collection strategies and quality control measures, it ensures that the collected data is sufficiently relevant and high-quality, providing reliable input for subsequent data analysis.

[0102] In this example, the standard deviation represents the relative position between the data value of the data point and the mean, and is an indicator used to describe the degree of dispersion between the data point and the mean point;

[0103] In this example, the box plot represents a tool used to demonstrate the distribution, symmetry, outliers, and median of the working data.

[0104] The working principle and beneficial effects of the above technical solution are as follows: In order to improve the efficiency of data analysis and avoid being affected by redundant data, the collected information is first sorted to construct the working data of the coal mine, and then the collected raw data is cleaned according to different situations to remove noise and anomalies, and then standard processing is performed to eliminate the influence between different characteristic dimensions, so that the effective data obtained can not only represent the information of the coal mine, but also ensure the quality of subsequent work.

[0105] Example 3

[0106] Based on Example 2, the method for processing coal mine disaster data based on principal component analysis further includes:

[0107] Find the first outlier data point that exceeds the upper quartile + 1.5 times the interquartile range and the second outlier data point that exceeds the lower quartile - 1.5 times the interquartile range in the box plot;

[0108] The first outlier data point and the second outlier data segment are regarded as noise data points in the working data.

[0109] The working principle and beneficial effects of the above technical solution are: locating noise data points in valid data to facilitate noise elimination.

[0110] Example 4

[0111] On the basis of Example 1, the method for processing coal mine disaster data based on principal component analysis, step 2 includes:

[0112] Step 21: Draw the original space of the valid data, search for the first several original coordinate axes with mutually orthogonal characteristics in the original space, find the target coordinate axis with the largest variance with the valid data as the first coordinate axis, locate the first coordinate axis in the original space, and determine the search starting point;

[0113] Step 22: Starting from the search starting point, sequentially search for a second coordinate axis that is orthogonal to the first coordinate axis and has the largest variance with the first coordinate plane, and a third coordinate axis that is orthogonal to the first coordinate axis and the second coordinate axis plane and has the largest variance with the first-second coordinate plane, and obtain N coordinate axes after the search is completed;

[0114] Step 23: establishing a variance change list of the original space based on the variances between adjacent coordinate axes, roughly screening the variance change list to obtain K valid coordinate axes whose variances are not 0, and constructing a corresponding eigenvector based on each of the valid coordinate axes;

[0115] Step 24: Establish a covariance matrix of the eigenvectors, analyze the eigenvalues ​​corresponding to each eigenvector in the covariance matrix using the eigendecomposition method, build a spatial framework based on the eigenvectors, and input the eigenvalues ​​into the spatial framework to generate a dimensionality reduction space for the valid data.

[0116] In this example, PCA works by sequentially finding a set of mutually orthogonal coordinate axes from the original space. The choice of new coordinate axes is closely related to the data itself. The first new coordinate axis is chosen in the direction with the largest variance in the original data. The second new coordinate axis is chosen in the plane orthogonal to the first coordinate axis with the largest variance. The third new coordinate axis is chosen in the plane orthogonal to the first and second axes with the largest variance. And so on. n such coordinate axes can be obtained. The new coordinate axes obtained in this way;

[0117] In this example, the reason for coarse screening is that most of the variance is contained in the first k coordinate axes, and the variance of the subsequent coordinate axes is almost zero. Therefore, the remaining coordinate axes can be ignored, and only the first k coordinate axes containing most of the variance are retained. In fact, this is equivalent to retaining only the dimensional features containing most of the variance and ignoring the feature dimensions containing almost zero variance, thus achieving dimensionality reduction of data features.

[0118] In this instance, the spatial frame represents the appearance of the dimensionality-reduced space;

[0119] In this example, the mutually orthogonal feature indicates that the inner product of the normal vectors of the plane formed by the two original coordinate axes is 0.

[0120] The working principle and beneficial effects of the above technical solution are as follows: In order to achieve data dimensionality reduction, the original space of valid data is first drawn, and then the original coordinate axes with mutually orthogonal features in the original space are searched according to the rules, and then the meaningful eigenvectors are determined by coarse screening. PCA not only simplifies the structure of coal mine disaster data, but also reduces the computational overhead, while retaining the amount of information of the original data as much as possible, improving the accuracy and interpretability of the analysis results, and further using the matrix method to express the distribution of eigenvectors, thereby building a spatial framework of eigenvectors and generating a dimensionality reduction space. Then, the valid data can complete dimensionality reduction in this space, thereby improving the accuracy of the results.

[0121] Example 5

[0122] On the basis of Example 1, the method for processing coal mine disaster data based on principal component analysis, step 3, includes:

[0123] Step 31: Obtain the explained variance ratio of each eigenvector in the dimensionality reduction space, draw a visual ratio diagram of the valid data, identify the importance corresponding to each eigenvector in the visual ratio diagram, and construct the first dimension sub-information corresponding to the eigenvector;

[0124] Step 32: construct corresponding feature data according to the feature vector, map the feature data to the original space for loss assessment, obtain data loss information corresponding to the feature vector, and construct the reconstruction error of the valid data;

[0125] Step 33: constructing the reconstruction error to determine the spatial structure error and spatial pattern error of the dimensionality reduction space, and generating the second dimensional sub-information corresponding to the eigenvector;

[0126] Step 34: Establish dimensional information corresponding to the feature vector in the dimensionality reduction space according to the first dimensional sub-information and the second dimensional sub-information.

[0127] In this example, the visualization ratio plot shows the result of visualizing the explained variance ratio of the eigenvectors;

[0128] In this example, the first dimension sub-information contains the importance and important content of the feature vector in the dimensionality reduction space;

[0129] In this example, the second dimension sub-information includes the structural influence and pattern influence of the feature vector on the dimensionality reduction space.

[0130] The working principle and beneficial effects of the above technical solution are as follows: the importance of the corresponding feature neighbors and the data loss are analyzed by using the explained variance ratio and reconstruction error of the adjacent features in the dimensionality reduction space, as well as the structure and pattern error of the dimensionality reduction space to establish the dimensional information of the feature vector. This can evaluate whether the selected feature vector can retain sufficient original data variation information and check whether the data maintains the main structure and pattern of the original data in the space after dimensionality reduction to achieve the purpose of deep training.

[0131] Example 6

[0132] On the basis of Example 1, the method for processing coal mine disaster data based on principal component analysis, step 4, includes:

[0133] Step 41: adjusting the spatial structure and spatial pattern of the dimensionality reduction space based on the dimensionality information to obtain a stable dimensionality reduction space;

[0134] Step 42: inputting the valid data into the stable dimensionality reduction space, performing dimensionality reduction training on the valid data in the stable dimensionality reduction space, and generating dimensionality reduction data of the coal mine.

[0135] The working principle and beneficial effects of the above technical solution are as follows: the spatial structure and pattern of the dimensionality reduction space are adjusted according to the reconstruction error, and then valid data is input into it for formal dimensionality reduction training. Since various errors in the stable dimensionality reduction space have been eliminated in the early stage, complete and accurate dimensionality reduction data can be obtained at this time.

[0136] Example 7

[0137] On the basis of Example 1, the method for processing coal mine disaster data based on principal component analysis, step 5 includes:

[0138] Step 51: Obtain a standard working range corresponding to each detection dimension in the coal mine, construct a prescribed explained variance ratio range of the corresponding detection dimension based on the standard working range, estimate the dimensional information corresponding to each detection dimension and construct a data partitioning rule;

[0139] Step 52: Divide the dimensionality-reduced data into a plurality of dimensionality-reduced data segments based on the data partitioning rule, and use the corresponding prescribed explained variance rate range to identify abnormal dimensionality data segments that do not meet the requirements;

[0140] Step 53: Mark the abnormal dimension data segment in the valid data, and track the source of each abnormal dimension data segment respectively, construct the disaster data corresponding to the coal mine under different detection dimensions and display it.

[0141] In this example, the standard operating range indicates the range of normal data under a detection dimension, for example: the normal working stability of a coal mine is not higher than 39°;

[0142] In this example, the data partitioning rule represents the process of dividing the dimensionality reduction data into corresponding detection dimensions.

[0143] The working principle and beneficial effects of the above technical solution are as follows: by dividing the dimensionality reduction data into corresponding dimensionality reduction data segments, then identifying the abnormal dimensionality data segments, and tracing the abnormal data segments to construct the coal mine disaster data, the traceability and positioning of the disaster data are achieved, providing management personnel with powerful data information.

[0144] Example 8

[0145] On the basis of Example 4, the method for processing coal mine disaster data based on principal component analysis method, the process of establishing the covariance matrix of the eigenvector includes:

[0146] Count all eigenvectors and construct the eigenvector set X;

[0147] Calculate the mean value corresponding to each eigenvector according to formula (1);

[0148]

[0149] in, represents the mean of the first j vectors, n represents the total number of the eigenvectors, Represents the i-th coordinate axis feature of the j-th eigenvector in the corresponding coordinate axis;

[0150] According to formula (2), each of the feature vectors is decentralized to generate the target feature vector set ;

[0151]

[0152] Among them, decentralization can be completed by subtracting the mean value of each eigenvector from its vector value;

[0153] Use formula (3) to establish the target feature vector set The covariance matrix of

[0154]

[0155] Where T represents the transpose identifier and C represents the covariance matrix identifier.

[0156] The working principle and beneficial effects of the above technical solution are as follows: by analyzing the mean of the eigenvectors, then using the mean to perform decentralization, and finally constructing a matrix.

[0157] Example 9

[0158] This embodiment provides a system for processing coal mine disaster data based on principal component analysis. Figure 2 Shown, including:

[0159] A data processing module is used to collect the working data of the coal mine, pre-process and standardize the working data to obtain the effective data of the coal mine;

[0160] A dimensionality reduction analysis module is used to perform dimensionality reduction analysis on the valid data using PCA, convert the valid data into a plurality of eigenvectors, and construct a dimensionality reduction space of the valid data using the eigenvectors;

[0161] An information construction module, configured to respectively calculate the reconstruction error and explained variance ratio corresponding to each of the eigenvectors in the dimensionality reduction space, and construct dimensional information corresponding to each of the eigenvectors;

[0162] a dimensionality reduction execution module, configured to adjust the dimensionality reduction space based on the dimensional information, and input the valid data into the adjusted dimensionality reduction space for processing to generate dimensionality reduction data of the coal mine;

[0163] The data processing module is used to screen out abnormal dimensionality reduction data segments that do not meet the specified explained variance ratio, optimize the abnormal dimensionality reduction data segments, and obtain the disaster data corresponding to the coal mine under different detection dimensions.

[0164] In this instance, preprocessing refers to the process of cleaning the working data;

[0165] In this example, the main idea of ​​PCA is to map n-dimensional features to k-dimensional features. These k-dimensional features are new orthogonal features, also known as principal components. They are k-dimensional features reconstructed based on the original n-dimensional features.

[0166] In this example, the feature vector represents the data vector presented by the valid data in different dimensions;

[0167] In this example, the dimensionality reduction space refers to the space where the reduced-dimensional data is located after the effective data is reduced in dimensionality;

[0168] In this example, the purpose of dimensionality reduction is: PCA projects high-dimensional data into a lower-dimensional space, extracts the main factors of multivariate things through spatial projection and linear transformation, and reveals their essential characteristics. PCA deletes redundant and repeated variables (closely related variables) for all the variables originally proposed, and establishes as few new variables as possible so that these new variables are uncorrelated with each other. These new variables are called principal components, which can well describe the sample information. The core of the PCA algorithm is to reduce the dimension and screen the main features. The purpose of principal component analysis is to reduce the dimension of the data by retaining the most important feature information, thereby simplifying the model training process and improving the model's computational efficiency. Through PCA dimensionality reduction, the redundant information between features can be reduced, and more representative features can be extracted, which helps to improve the model's generalization ability and prediction accuracy;

[0169] In this example, the reconstruction error represents the error generated during the dimensionality reduction process;

[0170] In this example, the explained variance ratio represents an indicator used to measure the proportion of each eigenvector in the dimensionality reduction space;

[0171] In this example, the dimension information represents the information contained in the feature vector under its corresponding dimension;

[0172] In this example, the detection dimensions include: gas concentration dimension, temperature dimension, humidity dimension, mineralization dimension, water tank flow dimension, grouting dimension and nitrogen injection dimension.

[0173] The working principle and beneficial effects of the above technical solution: Since coal mines generate a large amount of data during operation, it is necessary to quickly screen the data when analyzing its disasters. Therefore, the working data is first preprocessed and standardized to make its mean 0 and variance 1 to eliminate the influence between different feature dimensions and obtain valid data. Then, the principal component analysis method is used to reduce the dimension of the valid data, determine several feature vectors, and construct a dimensionality reduction space for the valid data. Further, the adjacent dimensional information of the features is determined based on the reconstruction error and explained variance ratio of the feature vector in the dimensionality reduction space, and the dimensionality reduction space is adjusted to achieve dimensionality reduction of the valid data, and the dimensionality reduction data of the coal mine is obtained. At this time, the dimensionality reduction data can be used in the dimensionality reduction data. By identifying abnormal data segments, the disaster data of the coal mine can be determined. In this way, the collected original data can be processed and converted so that the PCA method can effectively extract key features and perform dimensionality reduction, screen out important features in the working data, reduce the impact of redundant data and redundant features on disaster analysis, and perform error adjustment during dimensionality reduction to avoid data loss. This method can analyze the working data of various types of coal mines and is a comprehensive analysis method. Since this method first screens the features and then performs analysis, it can effectively reduce the calculation steps and reduce the analysis cost. The whole process is visualized, which makes it easier for managers to understand the ins and outs of the data and provides a strong reference for the safety of coal miners and the environment.

[0174] Example 10

[0175] Based on Example 9, the system for processing coal mine disaster data based on principal component analysis includes:

[0176] a space construction unit, configured to draw the original space of the valid data, search for a plurality of original coordinate axes having mutually orthogonal characteristics in the original space, search for a target coordinate axis having the largest variance with the valid data as a first coordinate axis, locate the first coordinate axis in the original space, and determine a search starting point;

[0177] a coordinate decomposition unit, configured to sequentially search, starting from the search starting point, for a second coordinate axis that is orthogonal to the first coordinate axis and has the largest variance with the first coordinate plane, and a third coordinate axis that is orthogonal to the first coordinate axis and the second coordinate axis plane and has the largest variance with the first-second coordinate plane, to obtain N coordinate axes upon completion of the search;

[0178] a coordinate screening unit, configured to establish a variance change list of the original space based on the variances between adjacent coordinate axes, perform a rough screening on the variance change list to obtain K valid coordinate axes whose variances are not 0, and construct a corresponding feature vector based on each of the valid coordinate axes;

[0179] A space construction unit is used to establish a covariance matrix of the eigenvector, analyze the eigenvalues ​​corresponding to each eigenvector in the covariance matrix using the eigendecomposition method, build a space framework based on the eigenvector, and input the eigenvalues ​​into the space framework to generate a dimensionality reduction space for the valid data.

[0180] In this example, PCA works by sequentially finding a set of mutually orthogonal coordinate axes from the original space. The choice of new coordinate axes is closely related to the data itself. The first new coordinate axis is chosen in the direction with the largest variance in the original data. The second new coordinate axis is chosen in the plane orthogonal to the first coordinate axis with the largest variance. The third new coordinate axis is chosen in the plane orthogonal to the first and second axes with the largest variance. And so on. n such coordinate axes can be obtained. The new coordinate axes obtained in this way;

[0181] In this example, the reason for coarse screening is that most of the variance is contained in the first k coordinate axes, and the variance of the subsequent coordinate axes is almost zero. Therefore, the remaining coordinate axes can be ignored, and only the first k coordinate axes containing most of the variance are retained. In fact, this is equivalent to retaining only the dimensional features containing most of the variance and ignoring the feature dimensions containing almost zero variance, thus achieving dimensionality reduction of data features.

[0182] In this instance, the spatial frame represents the appearance of the dimensionality-reduced space;

[0183] In this example, the mutually orthogonal feature indicates that the inner product of the normal vectors of the plane formed by the two original coordinate axes is 0.

[0184] The working principle and beneficial effects of the above technical solution are as follows: In order to achieve data dimensionality reduction, the original space of valid data is first drawn, and then the original coordinate axes with mutually orthogonal features in the original space are searched according to the rules, and then the meaningful eigenvectors are determined by coarse screening. PCA not only simplifies the structure of coal mine disaster data, but also reduces the computational overhead, while retaining the amount of information of the original data as much as possible, improving the accuracy and interpretability of the analysis results, and further using the matrix method to express the distribution of eigenvectors, thereby building a spatial framework of eigenvectors and generating a dimensionality reduction space. Then, the valid data can complete dimensionality reduction in this space, thereby improving the accuracy of the results.

[0185] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A method for processing coal mine disaster data based on principal component analysis, characterized in that: include: Step 1: Collecting the working data of the coal mine, pre-processing and standardizing the working data to obtain valid data of the coal mine; Step 2: Perform dimensionality reduction analysis on the valid data using PCA, convert the valid data into a number of eigenvectors, and use the eigenvectors to construct a dimensionality reduction space of the valid data; Step 3: Calculate the reconstruction error and explained variance ratio corresponding to each eigenvector in the dimensionality reduction space, and construct the dimensional information corresponding to each eigenvector; Step 4: adjusting the dimensionality reduction space based on the dimensional information, and inputting the valid data into the adjusted dimensionality reduction space for processing to generate dimensionality reduction data of the coal mine; Step 5: Screening abnormal dimensionality reduction data segments that do not meet the specified explained variance ratio, optimizing the abnormal dimensionality reduction data segments, and obtaining the disaster data corresponding to the coal mine under different detection dimensions; wherein, Step 5 includes: Step 51: Obtain a standard working range corresponding to each detection dimension in the coal mine, construct a prescribed explained variance ratio range of the corresponding detection dimension based on the standard working range, estimate the dimensional information corresponding to each detection dimension and construct a data partitioning rule; Step 52: Divide the dimensionality-reduced data into a plurality of dimensionality-reduced data segments based on the data partitioning rule, and use the corresponding prescribed explained variance rate range to identify abnormal dimensionality data segments that do not meet the requirements; Step 53: Mark the abnormal dimension data segment in the valid data, and track the source of each abnormal dimension data segment respectively, construct the disaster data corresponding to the coal mine under different detection dimensions and display it.

2. The method for processing coal mine disaster data based on principal component analysis according to claim 1, characterized in that: The step 1 comprises: Step 11: collecting real-time gas concentration information, real-time temperature information, real-time humidity information, real-time mineralization information, real-time water tank flow information, real-time grouting information, and real-time nitrogen injection information of the coal mine, and generating working data of the coal mine by time sorting; Step 12: Obtain the mean of the working data, analyze the standard deviation distance between each data point in the working data and the mean, screen the standard deviation distance, determine a number of abnormal data points contained in the working data, and perform cleaning preprocessing on the abnormal data points; Step 13: constructing a box plot of the working data, identifying noise data points in the working data in the box plot, performing noise reduction preprocessing on the noise data points, and treating the working data that has completed the cleaning preprocessing and noise reduction preprocessing as the cleaned data of the coal mine; Step 14: Standardize the cleaned data to obtain valid data with a mean of 0 and a variance of 1.

3. The method for processing coal mine disaster data based on principal component analysis as claimed in claim 2, characterized in that: Also includes: Find the first outlier data point that exceeds the upper quartile + 1.5 times the interquartile range and the second outlier data point that exceeds the lower quartile - 1.5 times the interquartile range in the box plot; The first outlier data point and the second outlier data point are regarded as noise data points in the working data.

4. The method for processing coal mine disaster data based on principal component analysis according to claim 1, characterized in that: The step 2 comprises: Step 21: Draw the original space of the valid data, search for a plurality of original coordinate axes with mutually orthogonal characteristics in the original space, find the target coordinate axis with the largest variance with the valid data as the first coordinate axis, locate the first coordinate axis in the original space, and determine the search starting point; Step 22: Starting from the search starting point, sequentially search for a second coordinate axis that is orthogonal to the first coordinate axis and has the largest variance with the first coordinate plane, and a third coordinate axis that is orthogonal to the first coordinate axis and the second coordinate axis plane and has the largest variance with the first-second coordinate plane, and obtain N coordinate axes after the search is completed; Step 23: establishing a variance change list of the original space based on the variances between adjacent coordinate axes, roughly screening the variance change list to obtain K valid coordinate axes whose variances are not 0, and constructing a corresponding eigenvector based on each of the valid coordinate axes; Step 24: Establish a covariance matrix of the eigenvectors, analyze the eigenvalues ​​corresponding to each eigenvector in the covariance matrix using the eigendecomposition method, build a spatial framework based on the eigenvectors, and input the eigenvalues ​​into the spatial framework to generate a dimensionality reduction space for the valid data.

5. The method for processing coal mine disaster data based on principal component analysis according to claim 1, characterized in that: The step 3 comprises: Step 31: Obtain the explained variance ratio of each eigenvector in the dimensionality reduction space, draw a visual ratio diagram of the valid data, identify the importance corresponding to each eigenvector in the visual ratio diagram, and construct the first dimension sub-information corresponding to the eigenvector; Step 32: construct corresponding feature data according to the feature vector, map the feature data into the original space for loss assessment, obtain data loss information corresponding to the feature vector, and construct the reconstruction error of the valid data; Step 33: constructing the reconstruction error to determine the spatial structure error and spatial pattern error of the dimensionality reduction space, and generating the second dimensional sub-information corresponding to the eigenvector; Step 34: Establish dimensional information corresponding to the feature vector in the dimensionality reduction space according to the first dimensional sub-information and the second dimensional sub-information.

6. The method for processing coal mine disaster data based on principal component analysis according to claim 1, characterized in that: The step 4 comprises: Step 41: adjusting the spatial structure and spatial pattern of the dimensionality reduction space based on the dimensionality information to obtain a stable dimensionality reduction space; Step 42: inputting the valid data into the stable dimensionality reduction space, performing dimensionality reduction training on the valid data in the stable dimensionality reduction space, and generating dimensionality reduction data of the coal mine.

7. The method for processing coal mine disaster data based on principal component analysis as claimed in claim 4, characterized in that: The process of establishing the covariance matrix of the eigenvector includes: Count all eigenvectors and construct the eigenvector set X; Calculate the mean value corresponding to each of the eigenvectors according to formula (1); （1） in, represents the mean of the first j vectors, n represents the total number of the eigenvectors, Represents the i-th coordinate axis feature of the j-th eigenvector in the corresponding coordinate axis; According to formula (2), each of the feature vectors is decentralized to generate the target feature vector set ; （2） Use formula (3) to establish the target feature vector set The covariance matrix of （3） Where T represents the transpose identifier and C represents the covariance matrix identifier.

8. A system for processing coal mine disaster data based on principal component analysis, characterized in that: include: A data processing module is used to collect the working data of the coal mine, pre-process and standardize the working data to obtain the effective data of the coal mine; A dimensionality reduction analysis module is used to perform dimensionality reduction analysis on the valid data using PCA, convert the valid data into a plurality of eigenvectors, and construct a dimensionality reduction space of the valid data using the eigenvectors; An information construction module, configured to respectively calculate the reconstruction error and explained variance ratio corresponding to each of the eigenvectors in the dimensionality reduction space, and construct dimensional information corresponding to each of the eigenvectors; a dimensionality reduction execution module, configured to adjust the dimensionality reduction space based on the dimensional information, and input the valid data into the adjusted dimensionality reduction space for processing to generate dimensionality reduction data of the coal mine; The data processing module is used to screen out abnormal dimensionality reduction data segments that do not meet the specified explained variance ratio, optimize the abnormal dimensionality reduction data segments, and obtain the disaster data corresponding to the coal mine under different detection dimensions; wherein the data processing module is further used to, Obtaining a standard working range corresponding to each detection dimension in the coal mine, constructing a prescribed explained variance ratio range for the corresponding detection dimension based on the standard working range, estimating dimensional information corresponding to each detection dimension, and constructing a data partitioning rule; Dividing the dimensionality-reduced data into a plurality of dimensionality-reduced data segments based on the data partitioning rule, and identifying abnormal dimensionality data segments that do not meet the requirements using the corresponding prescribed explained variance rate range; The abnormal dimension data segments are marked in the valid data, and the source of each abnormal dimension data segment is tracked respectively, and the disaster data corresponding to the coal mine under different detection dimensions are constructed and displayed.

9. The system for processing coal mine disaster data based on principal component analysis as claimed in claim 8, characterized in that: The dimensionality reduction analysis module includes: a space construction unit, configured to draw the original space of the valid data, search for a plurality of original coordinate axes having mutually orthogonal characteristics in the original space, search for a target coordinate axis having the largest variance with the valid data as a first coordinate axis, locate the first coordinate axis in the original space, and determine a search starting point; a coordinate decomposition unit, configured to sequentially search, starting from the search starting point, for a second coordinate axis that has an orthogonal relationship with the first coordinate axis and has a maximum variance with the first coordinate plane, and a third coordinate axis that has an orthogonal relationship with the first coordinate axis and the second coordinate axis plane and has a maximum variance with the first-second coordinate plane, to obtain N coordinate axes after completing the search; a coordinate screening unit, configured to establish a variance change list of the original space based on the variances between adjacent coordinate axes, perform a rough screening on the variance change list to obtain K valid coordinate axes whose variances are not 0, and construct a corresponding feature vector based on each of the valid coordinate axes; A space construction unit is used to establish a covariance matrix of the eigenvector, analyze the eigenvalues ​​corresponding to each eigenvector in the covariance matrix using the eigendecomposition method, build a space framework based on the eigenvector, and input the eigenvalues ​​into the space framework to generate a dimensionality reduction space for the valid data.

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