Intelligent mining area multi-source data fusion and intelligent analysis system

Abstract

The invention discloses an intelligent mining area multi-source data fusion and intelligent analysis system, and the system comprises the steps: constructing a big data platform which integrates data collection, storage and processing, and is used for collecting real-time data, historical data and external data sources in a mine production process, and forming a unified and complete data set; data from different sources and in different formats are cleaned, converted and integrated by using a data fusion technology, data redundancy and conflicts are eliminated, the accuracy and consistency of the data are ensured, and a high-quality data resource pool is formed; the beneficial effects of the invention are that the system can remarkably improve the intelligent level of mining area management, optimizes the resource configuration, improves the production efficiency, reduces the operation cost, and effectively guarantees the production safety; the system has high expandability and flexibility, can adapt to management requirements of different mining areas and different stages, and provides powerful support for sustainable development of mine enterprises.

Description

Technical Field

[0001] The present invention belongs to the technical field of multi-source data fusion and analysis, and specifically relates to a smart mining area multi-source data fusion and intelligent analysis system. Background Art

[0002] Smart mining multi-source data has the following notable features:

[0003] Data comes from a wide range of sources, including geological exploration, remote sensing, geophysics, geochemistry, mining equipment monitoring, personnel management, etc. Data in each field has its own unique value and limitations;

[0004] Since data comes from various sources, the data types and formats are also different, such as text, images, videos, sensor data, etc., and these data have differences in structure and expression;

[0005] With the improvement of mine informatization and automation, the amount of data generated by smart mines has exploded, which has put forward higher requirements on data storage, processing and analysis capabilities;

[0006] Many data in the mining production process are generated in real time, such as equipment operating status, environmental monitoring data, etc. These data need to be collected, processed and analyzed in a timely manner to support decision-making and control;

[0007] In the massive amount of data, truly valuable information may only account for a small part, which needs to be mined and extracted through advanced data analysis technology;

[0008] The fusion analysis of multi-source data in smart mining areas is based on the following considerations:

[0009] A single data source can often only provide limited information, while the fusion of multi-source data can combine the advantages of different data sources to improve the comprehensiveness and accuracy of data; for example, the fusion of geological exploration data and remote sensing data can more accurately understand the geological structure and mineral distribution of the mining area;

[0010] Through the fusion analysis of multi-source data, the inherent connections and potential laws between data can be revealed, providing more scientific and comprehensive support for production decision-making, safety management, resource scheduling, etc. of mining enterprises;

[0011] In smart mining areas, through the fusion analysis of multi-source data, the mining situation of mine resources and the operating status of equipment can be monitored and evaluated in real time, thereby optimizing resource allocation and improving production efficiency and resource utilization;

[0012] In the process of mining production, various emergencies may occur, such as geological disasters, equipment failures, etc. Through the fusion analysis of multi-source data, these events can be discovered and warned in time, providing strong support for emergency response;

[0013] The fusion analysis of multi-source data involves cutting-edge technologies such as big data, cloud computing, and artificial intelligence. Its research and application will promote technological innovation and development in the mining industry.

[0014] In traditional mine management, there is a serious problem of information islands and difficulty in effectively integrating and deeply utilizing data resources. The data between systems are isolated, which makes system integration difficult and makes it impossible to form a comprehensive and accurate data support system. At the same time, mine management relies heavily on experience and judgment, lacks scientific data analysis and intelligent decision-making support, which seriously restricts the development efficiency and safety of mines. Summary of the invention

[0015] The purpose of the present invention is to provide a smart mining area multi-source data fusion and intelligent analysis system, which can realize the comprehensive collection, efficient integration and intelligent analysis of mining area data.

[0016] To achieve the above purpose, the present invention provides the following technical solutions: a smart mining area multi-source data fusion and intelligent analysis system, comprising

[0017] Build a big data platform that integrates data collection, storage, and processing to collect real-time data, historical data, and external data sources in the mining production process to form a unified and complete data set;

[0018] Using data fusion technology, clean, convert and integrate data from different sources and formats, eliminate data redundancy and conflict, ensure data accuracy and consistency, and form a high-quality data resource pool;

[0019] Based on machine learning algorithms, we build a variety of intelligent analysis models, and through deep mining and analysis of data in the big data platform, we achieve automatic classification, prediction and optimization of data;

[0020] The results of the intelligent analysis model are presented to mine managers in a visual manner, providing intuitive and comprehensive decision-making support information.

[0021] As a preferred technical solution of the present invention, a big data platform integrating data collection, storage and processing is constructed to collect real-time data, historical data and external data sources in the mining production process to form a unified and complete data set. The implementation method is as follows:

[0022] Clarify the types of data that the big data platform needs to support, as well as the real-time, accuracy, and completeness requirements of the data;

[0023] Design the overall architecture of the big data platform, including the data collection layer, data storage layer, data processing layer, and data service layer, and determine the interaction mode and data flow between each layer;

[0024] For real-time data in the mining production process, the Internet of Things technology is used to collect data using sensors, RFID, and camera devices, and real-time transmission is carried out through MQTT and Kafka message queues; for existing historical data, data migration is required to export it from the original storage system and import it into the big data platform; for external data sources, data interfaces or APIs need to be established to obtain data from the data source regularly or in real time;

[0025] Choose the appropriate storage technology based on the type, size, and access frequency of the data.

[0026] As a preferred technical solution of the present invention, a suitable storage technology is selected according to the type, size, and access frequency of the data: for large-scale unstructured data, a distributed file system is used for storage; for structured or semi-structured data, a distributed database or data warehouse is used for storage.

[0027] As a preferred technical solution of the present invention, data from different sources and in different formats are cleaned, converted and integrated. The corresponding method is as follows:

[0028] Identify and delete or merge identical records; for similar but not identical records, perform deduplication processing through similarity calculation and other methods;

[0029] For missing values, choose the appropriate processing method based on the business scenario; check the outliers and erroneous values ​​in the data and make necessary corrections; identify and correct erroneous data by setting thresholds, using rule engines or machine learning algorithms;

[0030] Convert data from one format to another, adjust the organizational structure of data, and convert semi-structured or unstructured data to structured data;

[0031] Convert fields in your data from one data type to another;

[0032] Determine appropriate data integration strategies based on business needs and data characteristics;

[0033] Use data integration tools or programming languages ​​to merge, aggregate, and split data;

[0034] Verify the integrated data to ensure its accuracy and completeness.

[0035] As a preferred technical solution of the present invention, for missing values, a suitable processing method is selected according to the business scenario, including filling with default values, filling with statistical values, and interpolating according to data distribution.

[0036] As a preferred technical solution of the present invention, according to business needs and data characteristics, a suitable data integration strategy is determined, including data merging, data aggregation, and data splitting. As a preferred technical solution of the present invention, multiple intelligent analysis models are constructed, including production trend prediction models, equipment failure warning models, and employee performance evaluation models.

[0037] As a preferred technical solution of the present invention, the content corresponding to the deep mining is as follows:

[0038] Select appropriate classification algorithms based on data characteristics and classification requirements; extract features useful for classification from raw data, reduce redundant information, and improve classification efficiency; use the selected algorithms and features to learn training data and build classification models;

[0039] Evaluate model performance through cross-validation, accuracy, and recall metrics, and perform parameter tuning to optimize the model;

[0040] Find interesting associations and correlations between data items, and use Apriori and FP-Growth algorithms to mine association rules;

[0041] Divide the data into different groups or clusters to reveal the inherent structure of the data and use K-means and DBSCAN clustering algorithms for analysis.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] It can significantly improve the intelligent level of mine management, optimize resource allocation, improve production efficiency, reduce operating costs, and effectively ensure production safety;

[0044] It is highly scalable and flexible, and can adapt to the management needs of different mining areas and different stages, providing strong support for the sustainable development of mining enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a system principle diagram of the present invention. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] Example 1

[0048] See also Figure 1, which is the first embodiment of the present invention, and provides a smart mining area multi-source data fusion and intelligent analysis system, including

[0049] Build a big data platform that integrates data collection, storage, and processing to collect real-time data, historical data, and external data sources in the mining production process to form a unified and complete data set;

[0050] Using data fusion technology, clean, convert and integrate data from different sources and formats, eliminate data redundancy and conflict, ensure data accuracy and consistency, and form a high-quality data resource pool;

[0051] Based on machine learning algorithms, we build a variety of intelligent analysis models, including production trend prediction models, equipment failure warning models, employee performance evaluation models, etc., and achieve automatic classification, prediction and optimization of data through in-depth mining and analysis of data in the big data platform;

[0052] The results of the intelligent analysis model are presented to mine managers in a visual manner, providing intuitive and comprehensive decision-making support information.

[0053] In this embodiment, preferably, a big data platform integrating data collection, storage and processing is constructed to collect real-time data, historical data and external data sources in the mining production process to form a unified and complete data set. The implementation method is as follows:

[0054] It is necessary to clarify what types of data the big data platform needs to support (such as equipment status, production volume, energy consumption, safety monitoring, geological data, meteorological data, etc.), as well as the requirements for the real-time, accuracy, and completeness of the data;

[0055] Design the overall architecture of the big data platform, including the data collection layer, data storage layer, data processing layer, data service layer, etc., and determine the interaction mode and data flow between each layer;

[0056] For real-time data in the mining production process, such as equipment operating status and production volume, the Internet of Things (IoT) technology can be used to collect data using sensors, RFID, cameras and other devices, and transmit them in real time through message queues such as MQTT and Kafka;

[0057] For existing historical data, data migration is required to export it from the original storage system and import it into the big data platform;

[0058] For external data sources, such as meteorological data and geological data, it is necessary to establish a data interface or API to obtain data from the data source regularly or in real time;

[0059] Choose the appropriate storage technology based on factors such as data type, size, and access frequency. For large-scale unstructured data, such as videos and pictures, you can use a distributed file system (such as HDFS) for storage; for structured or semi-structured data, you can use a distributed database (such as HBase, Cassandra) or a data warehouse (such as Hive, Greenplum) for storage.

[0060] Example 2

[0061] See also Figure 1 , which is the second embodiment of the present invention, is based on the previous embodiment, except that:

[0062] Clean, transform and integrate data from different sources and formats. The corresponding methods are as follows:

[0063] Identify and delete or merge identical records; for similar but not identical records, duplicate removal can be performed using methods such as similarity calculation;

[0064] For missing values, you can choose the appropriate processing method based on the business scenario, such as filling with default values, using statistical values ​​(such as mean, median), or interpolating according to data distribution.

[0065] Check the data for outliers, errors, etc. and make necessary corrections; you can identify and correct erroneous data by setting thresholds, using rule engines or machine learning algorithms;

[0066] Convert data from one format to another, such as converting a CSV file to JSON format, or exporting a table in a database to an Excel file; adjust the organizational structure of data, such as converting data in a relational database to the storage structure of a non-relational database (NoSQL); convert semi-structured or unstructured data (such as text, images) to structured data for subsequent processing; convert a field in the data from one data type to another, such as converting a string date to a date type;

[0067] Determine appropriate data integration strategies, such as data merging, data aggregation, and data splitting, based on business needs and data characteristics; use data integration tools (such as ETL tools) or programming languages ​​(such as Python and SQL) to implement operations such as data merging, aggregation, and splitting; during the integration process, resolve redundancy and conflict issues between data to ensure consistency and accuracy of the integrated data;

[0068] Verify the integrated data to ensure its accuracy and completeness;

[0069] The quality of the integration results can be evaluated through data quality assessment indicators (such as accuracy, completeness, consistency, etc.);

[0070] The implementation steps of data fusion technology:

[0071] Data source identification and extraction:

[0072] Identify and determine the data sources that need to be integrated and their types, formats and structures;

[0073] Extract data from data sources and convert them into standardized data structures;

[0074] Data cleaning and conversion:

[0075] Clean and transform the extracted data to improve data quality and unify data format and structure;

[0076] Data Integration:

[0077] Merge the cleaned and transformed data into a unified data set according to the integration strategy;

[0078] Data quality assessment:

[0079] Conduct quality assessment on the integrated data to ensure data accuracy and consistency;

[0080] Data storage and management:

[0081] Store the consolidated data in an appropriate data storage system and manage and maintain it effectively.

[0082] In this embodiment, preferably, the content corresponding to the deep mining is as follows:

[0083] Select appropriate classification algorithms based on data characteristics and classification requirements; extract features useful for classification from raw data, reduce redundant information, and improve classification efficiency; use the selected algorithms and features to learn training data and build classification models;

[0084] Evaluate model performance through cross-validation, accuracy, and recall metrics, and perform parameter tuning to optimize the model;

[0085] Find interesting associations and correlations between data items, and use Apriori and FP-Growth algorithms to mine association rules;

[0086] Divide the data into different groups or clusters to reveal the inherent structure of the data and use K-means and DBSCAN clustering algorithms for analysis.

[0087] Although the embodiments of the present invention have been shown and described, as detailed above, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. Smart mining area multi-source data fusion and intelligent analysis system, characterized by: This includes building a big data platform that integrates data collection, storage, and processing to collect real-time data, historical data, and external data sources in the mining production process to form a unified and complete data set; Using data fusion technology, clean, convert and integrate data from different sources and formats, eliminate data redundancy and conflict, ensure data accuracy and consistency, and form a high-quality data resource pool; Based on machine learning algorithms, we build a variety of intelligent analysis models, and through deep mining and analysis of data in the big data platform, we achieve automatic classification, prediction and optimization of data; The results of the intelligent analysis model are presented to mine managers in a visual manner, providing intuitive and comprehensive decision-making support information.

2. The smart mining area multi-source data fusion and intelligent analysis system according to claim 1 is characterized by: Build a big data platform that integrates data collection, storage, and processing to collect real-time data, historical data, and external data sources in the mining production process to form a unified and complete data set. The implementation method is as follows: Clarify the types of data that the big data platform needs to support, as well as the real-time, accuracy, and completeness requirements of the data; Design the overall architecture of the big data platform, including the data collection layer, data storage layer, data processing layer, and data service layer, and determine the interaction mode and data flow between each layer; For real-time data in the mining production process, the Internet of Things technology is used to collect data using sensors, RFID, and camera devices, and real-time transmission is carried out through MQTT and Kafka message queues; for existing historical data, data migration is required to export it from the original storage system and import it into the big data platform; For external data sources, it is necessary to establish a data interface or API to obtain data from the data source regularly or in real time; Choose the appropriate storage technology based on the type, size, and access frequency of the data.

3. The smart mining area multi-source data fusion and intelligent analysis system according to claim 2 is characterized by: Choose the appropriate storage technology based on the data type, size, and access frequency: for large-scale unstructured data, use a distributed file system for storage; for structured or semi-structured data, use a distributed database or data warehouse for storage.

4. The smart mining area multi-source data fusion and intelligent analysis system according to claim 1 is characterized by: Clean, transform and integrate data from different sources and formats. The corresponding methods are as follows: Identify and delete or merge identical records; for similar but not identical records, perform deduplication processing through similarity calculation and other methods; For missing values, choose the appropriate processing method based on the business scenario; check the outliers and erroneous values ​​in the data and make necessary corrections; identify and correct erroneous data by setting thresholds, using rule engines or machine learning algorithms; Convert data from one format to another, adjust the organizational structure of data, and convert semi-structured or unstructured data to structured data; Convert fields in your data from one data type to another; Determine appropriate data integration strategies based on business needs and data characteristics; Use data integration tools or programming languages ​​to merge, aggregate, and split data; Verify the integrated data to ensure its accuracy and completeness.

5. The smart mining area multi-source data fusion and intelligent analysis system according to claim 4 is characterized by: For missing values, choose the appropriate processing method based on the business scenario, including filling with default values, filling with statistical values, and interpolating based on data distribution.

6. The smart mining area multi-source data fusion and intelligent analysis system according to claim 4 is characterized by: Determine appropriate data integration strategies based on business needs and data characteristics, including data merging, data aggregation, and data splitting.

7. The smart mining area multi-source data fusion and intelligent analysis system according to claim 1 is characterized by: Build a variety of intelligent analysis models, including production trend prediction models, equipment failure warning models, and employee performance evaluation models.

8. The smart mining area multi-source data fusion and intelligent analysis system according to claim 1 is characterized by: The corresponding contents of in-depth mining are as follows: Select appropriate classification algorithms based on data characteristics and classification requirements; extract features useful for classification from raw data, reduce redundant information, and improve classification efficiency; use the selected algorithms and features to learn training data and build classification models; Evaluate model performance through cross-validation, accuracy, and recall metrics, and perform parameter tuning to optimize the model; Find interesting associations and correlations between data items, and use Apriori and FP-Growth algorithms to mine association rules; Divide the data into different groups or clusters to reveal the inherent structure of the data and use K-means and DBSCAN clustering algorithms for analysis.

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