Mine multi-source data fusion method based on collaborative representation

Abstract

The invention relates to the technical field of smart mines and data processing, and discloses a collaborative representation-based mine multi-source data fusion method, which comprises the following steps of: acquiring sensing data, geological parameters, optical and spectral image data and audio and video data of a mine, and carrying out data preprocessing on the sensing data, the geological parameters, the optical and spectral image data and the audio and video data to form a data set; performing feature extraction on the data in the data set through a mutual information method and a linear discriminant analysis method to obtain feature data; and mapping the feature data to respective representation spaces by using a collaborative representation method, and capturing and fusing the feature data into multi-modal data. According to the method, the problem that a decision-making level data fusion technology is sensitive to sensor data instability and uncertainty can be effectively avoided, and the fault-tolerant capability of data fusion is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of smart mines and data processing, and in particular to a mine multi-source data fusion method based on collaborative representation. Background Art

[0002] Mine operations involve many aspects, including geological exploration, mining, safe production, and environmental protection. Multi-source data fusion technology can effectively integrate data from various sources, such as geological exploration data, production monitoring data, and environmental monitoring data, thereby providing more comprehensive and accurate information. This comprehensive data analysis helps mining companies understand the actual situation of the mine more accurately and provide strong support for decision-making.

[0003] In machine learning, collaborative representation is an important method used to fuse multi-source data and extract key information from them. Collaborative representation mainly focuses on how to map data of different modalities or sources into a unified representation space while maintaining the correlation and complementarity between data, thereby improving the performance and generalization ability of machine learning models; in addition, through collaborative representation, the dimension and complexity of the data can also be reduced, making subsequent analysis and interpretation easier.

[0004] CN116028543A discloses a fusion and collaborative system for multi-information in a mine and a method thereof, the system comprising: a service engine and a data acquisition module for acquiring monitoring data of various monitoring systems; a data bus connected to the data acquisition module, the data bus comprising a data lake and a data subscription and publishing channel, the data lake being used to aggregate various monitoring data, and the data subscription and publishing channel being used for information transmission; a collaborative module connected to the data bus for collaborative processing between multi-information; wherein the data acquisition module, the data bus and the collaborative module are loaded into the service engine in the form of plug-ins for operation; the system can integrate a large amount of multi-information monitoring data to provide a unified data source for smart mine applications and big data analysis, and support message communication and functional collaboration between different businesses; however, the method only performs classification and encapsulation, which is equivalent to packaging the collected data together without further processing and integration of the data.

[0005] CN117351313A discloses a method for intelligently identifying the spatial characteristics of coal mining scene by fusion of multi-source data, which relates to the field of remote sensing technology and application technology. The method comprises the following steps: preprocessing of multi-source data of coal mining scene, collection and labeling of sample data for coal mining scene modeling, division of sample data sets for coal mining scene modeling, training of mine site scene recognition model and mine site boundary recognition model, establishment of multi-scale data mine site recognition model, establishment of multi-source fusion data mine site recognition model, precision inspection and comparison of mine site scene recognition model and application of mine site scene recognition model; the method solves the problem of data screening for mine scene recognition, completes the establishment of multi-scale and multi-source data fusion models, and realizes the functions of automatic, intelligent and batch recognition of mine scene types and boundaries; however, the method processes remote sensing images in an artificial intelligence manner, and the source of processed data is only satellite data, and the optical and other characteristics of satellite remote sensing images are not processed, so the data source is relatively single. Summary of the invention

[0006] The purpose of this section is to summarize some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the abstract of the specification and the title of the invention of this application to avoid blurring the purpose of this section, the abstract of the specification and the title of the invention, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0007] In view of the above existing problems, the present invention is proposed.

[0008] Therefore, the technical problem solved by the present invention is: how to extract, save, unify the format of feature data of multi-source mine data and classify the features so as to integrate them into a multi-source data set with the same data format.

[0009] In order to solve the above technical problems, the present invention provides the following technical solutions: collecting sensor data, geological parameters, optical and spectral image data, audio and video data of the mine, and performing data preprocessing on them to form a data set;

[0010] Extracting features from the data in the data set by mutual information method and linear discriminant analysis method respectively to obtain feature data;

[0011] The feature data are mapped to respective representation spaces using a collaborative representation method, and captured and fused into multimodal data.

[0012] As a preferred solution of the mine multi-source data fusion method based on collaborative representation described in the present invention, the sensor data at least includes temperature, humidity, air pressure, gravity, explosive material concentration, percentage of lower explosion limit, toxic gas concentration, other gas concentration, wind speed, and wind direction;

[0013] The geological parameters at least include lithology, formation thickness, rock density, initial formation porosity, rock heat generation rate, thermal conductivity, and compaction coefficient;

[0014] The optical and spectral image data at least include satellite images taken by Gaofen and Sentinel series satellites;

[0015] The audio and video data at least include monitoring data of key node locations in the mine.

[0016] As a preferred solution of the mine multi-source data fusion method based on collaborative representation described in the present invention, the key nodes in the mine include warehouses, equipment rooms, and main work gathering places for workers.

[0017] As a preferred solution of the mine multi-source data fusion method based on collaborative representation described in the present invention, the data preprocessing at least includes data cleaning, data integration and conversion, and denoising, wherein:

[0018] Clean missing values, outliers and duplicate values ​​by discarding data;

[0019] Use the normalization algorithm in scikit-leam to process the cleaned data to obtain standardized data;

[0020] The standardized data is processed in conjunction with a normal distribution to eliminate noise.

[0021] As a preferred solution of the mine multi-source data fusion method based on collaborative representation described in the present invention, the normal distribution processing can be expressed by the following formula:

[0022]

[0023] Among them, σ can be expressed as the standard deviation of the data set, μ represents the mean of the data set, and x represents the data of the data set.

[0024] As a preferred solution of the mine multi-source data fusion method based on collaborative representation described in the present invention, feature extraction is performed by using the mutual information method, including:

[0025] Calculate the mutual information value between the feature and the target variable;

[0026] According to the mutual information value, features exceeding a certain threshold are selected as a feature set, thereby extracting required features and obtaining feature data.

[0027] As a preferred solution of the mine multi-source data fusion method based on collaborative representation described in the present invention, feature extraction is performed by linear discriminant analysis, including:

[0028] For each category in the data set, calculate the mean of its samples on each feature;

[0029] Constructing an inter-class scatter matrix and an intra-class scatter matrix respectively, and solving the corresponding generalized eigenvalues ​​and eigenvectors by calculating the ratio of the inter-class scatter matrix to the intra-class scatter matrix;

[0030] Select the corresponding eigenvector according to the size of the eigenvalue;

[0031] A projection matrix is ​​constructed using the selected feature vectors, and the original data is projected into the projection matrix, thereby extracting the required features.

[0032] As a preferred solution of the method for fusion of multi-source mine data based on collaborative representation described in the present invention, the characteristic data at least includes the spatial characteristics of ore bodies and strata, the attribute characteristics of ore types and reserves, the temporal characteristics generated by the time relationship of exploration, transportation and processing, and the dynamic characteristics of the mine geological surface.

[0033] As a preferred solution of the mine multi-source data fusion method based on collaborative representation described in the present invention, the collaborative representation method is a d-number fusion formula, which performs weighted averaging on the data of each data source, and the weight is determined by the reliability of each data source. Its mathematical expression formula is as follows:

[0034] d i =w i × i

[0035]

[0036] Among them, d i represents the weighted average of data source i, w i represents the weight of data source i, x i represents the data of data source i, and d is the sum of the weighted averages of all data sources, that is, the final fusion result.

[0037] The beneficial effects of the present invention are as follows: the present invention can avoid the influence of feature data extraction errors caused by the professionalism of interpreters, and effectively improve the efficiency of data extraction and fusion; at the same time, it can also effectively avoid the problem that decision-level data fusion technology is sensitive to the instability and uncertainty of sensor data, and improve the fault tolerance of data fusion. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0039] Figure 1 It is a schematic flow chart of the mine multi-source data fusion method based on collaborative representation shown in the present invention. DETAILED DESCRIPTION

[0040] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, but not all of the embodiments.

[0041] Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without making any creative work should fall within the scope of protection of the present invention.

[0042] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0043] Example 1

[0044] Mining data is usually multi-source data, which is diverse, heterogeneous, complex and uncertain. The data fusion in existing technologies is mostly single-source data, which usually has a relatively consistent data structure and format, and the analysis process is too simple. Therefore, it is impossible to effectively avoid the problem that decision-level data fusion technology is sensitive to the instability and uncertainty of sensor data.

[0045] According to an embodiment of the present invention, Figure 1 The flowchart shown is a method for fusion of multi-source mine data based on collaborative representation, which specifically includes the following steps:

[0046] S1. Collect sensor data, geological parameters, optical and spectral image data, audio and video data of the mine, and perform data preprocessing to form a data set. It should be noted that data preprocessing at least includes data cleaning, data integration and conversion, and denoising, where:

[0047] Clean missing values, outliers and duplicate values ​​by discarding data;

[0048] Use the normalization algorithm in scikit-leam to process the cleaned data to obtain standardized data;

[0049] The normal distribution is combined to standardize the data and thus remove the noise.

[0050] As an example, the normal distribution process can be expressed as follows:

[0051]

[0052] Among them, σ can be expressed as the standard deviation of the data set, μ represents the mean of the data set, and x represents the data of the data set.

[0053] As an example, the sensor data includes at least temperature, humidity, air pressure, gravity, concentration of explosive substances, percentage of lower explosion limit, concentration of toxic gases, concentration of other gases, wind speed, and wind direction.

[0054] As an example, the geological parameters include at least lithology, formation thickness, rock density, initial formation porosity, rock heat generation rate, thermal conductivity, and compaction coefficient.

[0055] As an example, the optical and spectral image data include at least satellite images taken by the Gaofen and Sentinel series satellites.

[0056] As an example, the audio and video data at least include monitoring data of key node locations in the mine.

[0057] As an example, key nodes in a mine include warehouses, equipment rooms, and main work gathering places for workers.

[0058] Preferably, this step ensures the quality and consistency of the data through data preprocessing, provides accurate and reliable input for subsequent feature extraction and data fusion, and reduces the interference of errors and outliers, thereby improving the accuracy and efficiency of data processing.

[0059] S2. Extract features from the data in the data set by mutual information method and linear discriminant analysis method to obtain feature data. The following points need to be explained in this step:

[0060] Feature extraction is performed through the mutual information method, including:

[0061] Calculate the mutual information value between the feature and the target variable;

[0062] According to the mutual information value, features exceeding a certain threshold are selected as the feature set, so as to extract the required features and obtain feature data.

[0063] As an example, the formula for calculating the mutual information value is as follows:

[0064] I(X;Y)=H(Y)-H(YX)

[0065] H(Y) is the entropy of Y, which indicates the uncertainty of the prediction result; H(Y|X) is the entropy of the prediction result given the input features, X is the original data containing the target variable, and Y is the feature data to be extracted;

[0066] The mutual information calculation formula based on entropy is as follows:

[0067] I(X,Y)=H(X)-H(X|Y)=H(Y)-H(Y|X)

[0068] Among them, H(X|Y) and H(Y|X) are conditional entropies, which represent the uncertainty of X (or Y) under the condition of given Y (or X).

[0069] It should also be noted that in this embodiment, the mutual information method achieves the purpose of reducing the dimensionality of the original high-dimensional feature space by establishing an intrinsic connection between the output classification information and the high-dimensional feature extraction vector, wherein the mutual information between each feature and the target variable is first calculated, and the features are sorted according to the calculated mutual information values. The features with higher mutual information values ​​have higher correlation with the target variable, and therefore should be given priority in the dimensionality reduction process; based on the sorted feature list, features with higher mutual information values ​​are selected as the feature subset after dimensionality reduction.

[0070] It is not difficult to understand that the application of the mutual information method in feature extraction is mainly to calculate the mutual information value between each feature and the target variable, so as to evaluate the information contribution of each feature to the target variable. In the present invention, the mutual information method can be used to accurately identify which features have a high correlation and predictive value for predicting the spatial characteristics and ore types of the ore body.

[0071] For example, a high mutual information value between a certain feature (such as the chemical composition of an ore) and the ore type indicates that this feature is very effective in classifying ore types. Selecting features that exceed a certain threshold as a feature set ensures the accuracy and efficiency of data analysis, reduces the noise caused by irrelevant features, and optimizes subsequent data processing and analysis processes.

[0072] Feature extraction is performed through linear discriminant analysis, including:

[0073] For each category in the data set, calculate the mean of its samples on each feature;

[0074] Construct the inter-class scatter matrix and the intra-class scatter matrix respectively, and solve the corresponding generalized eigenvalues ​​and eigenvectors by calculating the ratio of the inter-class scatter matrix to the intra-class scatter matrix;

[0075] Select the corresponding eigenvector according to the size of the eigenvalue;

[0076] A projection matrix is ​​constructed using the selected eigenvectors, and the original data is projected into the projection matrix to extract the desired features.

[0077] As an example, the intra-class scatter matrix S w The formula is as follows:

[0078]

[0079] The inter-class scatter matrix S b The formula is as follows:

[0080] Sb=(μ0-μ1)(μo-μ)

[0081] Among them, μ represents the mean vector, and the subscript represents the mean vector of the i-th class of samples.

[0082] As an example, the characteristic data at least includes the spatial characteristics of the ore body and stratum, the attribute characteristics of the ore type and reserves, the temporal characteristics generated by the time relationship of exploration, transportation and processing, and the dynamic characteristics of the surface of the mine geological body.

[0083] It is not difficult to understand that linear discriminant analysis (LDA) extracts features by maximizing the inter-class divergence and minimizing the intra-class divergence, which effectively distinguishes different categories of ores or strata; further, by constructing the inter-class divergence matrix and the intra-class divergence matrix, it is possible to identify which features are most effective in distinguishing different ore body categories (such as lithology, stratum thickness, etc.).

[0084] Preferably, the solved generalized eigenvalues ​​and eigenvectors further determine the optimal direction of data projection, thereby extracting features that best represent the category differences in the data set, thereby improving the accuracy of decision-making and the efficiency of operation.

[0085] Preferably, the implementation of this step makes data analysis more accurate, because only the most representative and discriminative features are selected to represent the data, which not only reduces the dimension of the data, but also improves the efficiency of subsequent model training and data fusion.

[0086] S3, using collaborative representation methods, map feature data to their respective representation spaces, and capture and fuse them into multimodal data. Among them, this step also needs to be explained:

[0087] The collaborative representation method is the d-number fusion formula, which is a weighted average of the data from each data source, and the weight is determined by the reliability of each data source. The mathematical expression formula is as follows:

[0088] d i =w i × i

[0089]

[0090] Among them, d i represents the weighted average of data source i, w i represents the weight of data source i, x i represents the data of data source i, and d is the sum of the weighted averages of all data sources, that is, the final fusion result.

[0091] This step uses the weighted average method to adjust the contribution of each data source in the fusion result according to its weight (reliability). The weight setting is based on the accuracy of the data source, the update frequency, and the importance factors in practical applications. It can effectively process information from different sensors and data sources, especially when there are differences in the quality and reliability of multiple data sources, to ensure that the data fusion results are more accurate and reliable.

[0092] It should also be noted in this embodiment that, during the mapping process, correlation constraints are introduced to ensure that a certain correlation is maintained between the mapping representations of different modalities, and the original feature data are mapped to their respective representation spaces using the learned mapping function, which are then fused through collaborative representation processing to obtain multimodal data, i.e., multi-source data.

[0093] Preferably, in multimodal data analysis, the effective combination of various data sources can provide more comprehensive information for decision-making. For example, in mine safety monitoring, by integrating geological, climate and real-time monitoring data, potential safety risks can be more effectively predicted and prevented.

[0094] Preferably, the embodiments of the present invention effectively combine information from different sensors and data sources, optimize the comprehensive expressiveness of the data, and enhance the interoperability between data, such as mine environment monitoring, which can achieve better decision support and improve the operational efficiency and safety of mines.

[0095] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A mine multi-source data fusion method based on collaborative representation, characterized in that: include: Collect sensor data, geological parameters, optical and spectral image data, audio and video data of the mine, and perform data preprocessing on them to form a data set; Extracting features from the data in the data set by mutual information method and linear discriminant analysis method respectively to obtain feature data; The feature data are mapped to respective representation spaces using a collaborative representation method, and captured and fused into multimodal data.

2. The method for fusion of mine multi-source data based on collaborative representation according to claim 1 is characterized in that: The sensor data at least includes temperature, humidity, air pressure, gravity, explosive material concentration, percentage of lower explosion limit, toxic gas concentration, other gas concentration, wind speed, and wind direction; The geological parameters at least include lithology, formation thickness, rock density, initial formation porosity, rock heat generation rate, thermal conductivity, and compaction coefficient; The optical and spectral image data at least include satellite images taken by Gaofen and Sentinel series satellites; The audio and video data at least include monitoring data of key node locations in the mine.

3. The method for fusion of mine multi-source data based on collaborative representation according to claim 2 is characterized in that: The key nodes in the mine include warehouses, equipment rooms, and main work gathering places for workers.

4. The method for fusion of mine multi-source data based on collaborative representation according to claim 1 is characterized in that: The data preprocessing at least includes data cleaning, data integration and conversion, and denoising, wherein: Clean missing values, outliers and duplicate values ​​by discarding data; Use the normalization algorithm in scikit-leam to process the cleaned data to obtain standardized data; The standardized data is processed in conjunction with a normal distribution to eliminate noise.

5. The method for fusion of mine multi-source data based on collaborative representation according to claim 4 is characterized in that: The normal distribution process can be expressed by the following formula: Among them, σ can be expressed as the standard deviation of the data set, μ represents the mean of the data set, and x represents the data of the data set.

6. The method for fusion of mine multi-source data based on collaborative representation according to claim 1 is characterized in that: The feature extraction is performed by the mutual information method, including: Calculate the mutual information value between the feature and the target variable; According to the mutual information value, features exceeding a certain threshold are selected as a feature set, thereby extracting required features and obtaining feature data.

7. The method for fusion of mine multi-source data based on collaborative representation according to claim 1 is characterized in that: Feature extraction is performed through linear discriminant analysis, including: For each category in the data set, calculate the mean of its samples on each feature; Constructing an inter-class scatter matrix and an intra-class scatter matrix respectively, and solving the corresponding generalized eigenvalues ​​and eigenvectors by calculating the ratio of the inter-class scatter matrix to the intra-class scatter matrix; Select the corresponding eigenvector according to the size of the eigenvalue; A projection matrix is ​​constructed using the selected feature vectors, and the original data is projected into the projection matrix, thereby extracting the required features.

8. The method for fusion of mine multi-source data based on collaborative representation according to claim 1 is characterized in that: The characteristic data at least include the spatial characteristics of the ore body and stratum, the attribute characteristics of the ore type and reserves, the time series characteristics generated by the time relationship of exploration, transportation and processing, and the dynamic characteristics of the mine geological surface.

9. The method for fusion of mine multi-source data based on collaborative representation according to claim 1, characterized in that: The collaborative representation method is a d-number fusion formula, which is a weighted average of the data from each data source, where the weight is determined by the reliability of each data source. Its mathematical expression formula is as follows: d i =w i ×x i Among them, d i represents the weighted average of data source i, w i represents the weight of data source i, x i represents the data of data source i, and d is the sum of the weighted averages of all data sources, that is, the final fusion result.

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