Coal mine intelligent mining integrated management system

Abstract

The present invention discloses a comprehensive management system for intelligent mining of coal mines, including several end sides, edge sides and cloud sides; the end side includes a data acquisition module, a data cleaning module and a data forwarding module; the edge side includes a data calculation module, an event alarm module and an equipment posture adjustment module; the cloud side includes a machine learning module, an equipment failure analysis module, a production process and reserve analysis module, an energy consumption analysis module and a visualization interface; the end side, the edge side and the cloud side communicate through wireless and wired networks. The advantages of the present invention are that it opens up the data information links of each mine and each subsystem, uses machine learning to mine data assets, diagnoses and analyzes abnormal information and equipment failures of each mine and each system, and performs multi-user and multi-tenant management, which solves the problem of information islands in intelligent mining of coal mines and makes effective use of data assets. The system is deployed at level 3 to ensure the high availability of the system and the security of data.

Description

Technical Field

[0001] The present invention relates to the technical field of coal mining, and in particular to an integrated management system for intelligent coal mining. Background Art

[0002] my country has abundant coal resources and a huge number of mines. With the gradual improvement of coal mining technology, underground coal mining operations have entered the intelligent stage. At present, mines can use automated network technology to achieve interconnection between the ground and underground, collect information on various equipment underground through the equipment monitoring system, and realize unmanned underground mining operations through the ground terminal control system, which has greatly promoted the development of the coal mining industry.

[0003] However, the data information of various systems in the mine lacks a coordinated, unified and stable standard in the high-speed transmission process, which cannot achieve real comprehensive monitoring. The information between the subsystems cannot be shared, and the information island phenomenon is serious. There are also a lot of manual intervention processes. In addition, the current intelligent systems generally lack effective machine learning and data analysis capabilities, resulting in the loss of a large amount of data assets and the inability to further improve mining efficiency. Summary of the invention

[0004] The present invention aims to provide a comprehensive management system for intelligent mining of coal mines.

[0005] To achieve the above purpose, the present invention can adopt the following technical solutions:

[0006] The invention provides a comprehensive management system for intelligent coal mining, comprising a plurality of end sides, an edge side and a cloud side;

[0007] The terminal side includes a data acquisition module, a data cleaning module, and a data forwarding module;

[0008] The edge side includes a data calculation module, an event alarm module and a device posture adjustment module;

[0009] The cloud side includes a machine learning module, an equipment failure analysis module, a production process and reserve analysis module, an energy consumption analysis module and a visualization interface.

[0010] Furthermore, the data acquisition module collects data from each equipment data center in the mine in real time;

[0011] The data cleaning module organizes the data format according to business rules to facilitate unified data processing;

[0012] The data forwarding module automatically forwards the cleaned data to the data storage modules on the cloud side and the edge side;

[0013] The data calculation module calculates and analyzes the received terminal-side data according to a corresponding data model;

[0014] The event alarm module determines whether to trigger an event alarm based on the calculation and analysis results of the data calculation module, and issues an event alarm in advance;

[0015] The equipment posture adjustment module adjusts the equipment posture according to the calculation and analysis results of the data calculation module and the geological information;

[0016] The machine learning module forms a corresponding data model for the received terminal side data through a machine learning algorithm, and sends it to the edge side to guide the edge side to perform event alarms and device posture adjustment;

[0017] The equipment failure analysis module intelligently analyzes the causes of equipment failures based on the received terminal data, the equipment life, service life and geological information, assigns different weights to the causes of the failures, issues failure warnings and generates analysis results;

[0018] The production process and reserve analysis module builds a simulator to optimize the production process based on the received terminal data;

[0019] The energy consumption analysis module performs equipment energy consumption analysis based on the received terminal data, helping users to better manage equipment assets, issue inventory warning prompts in a timely manner, and analyze increased losses;

[0020] The visualization interface visualizes the cloud-side analysis results and corresponding data;

[0021] Furthermore, the device data center includes a centralized control data center, a controller data center, a device management data center and a device data center;

[0022] The geological data includes the thickness of the coal seam;

[0023] The adjustment of equipment posture includes the lifting and lowering of hydraulic supports, the movement of coal cutters, and the speed of scraper transport;

[0024] Preferably, the edge side provides data reading and permission control for edge side users to ensure data security; the cloud side provides data reading and permission control for cloud side users to ensure data security;

[0025] Preferably, the terminal side, the edge side and the cloud side all have a data quality monitoring module; the data quality monitoring module is used to monitor its own data transmission and data consistency; when it is found that data transmission is interrupted, data value or sequence does not meet the business rules, an alarm is immediately issued and the relevant responsible person is notified; the visual interface can display all data relationships of the system;

[0026] Preferably, the cloud side adopts a server cluster architecture, including at least one master node, one manager node and one node; the node heartbeat is automatically detected through the task management and resource scheduling module, and when the master node heartbeat is lost, the services and data of the master node are automatically migrated to the manager node; when the node heartbeat is lost, the services and data of the lost node are automatically migrated to other node nodes or the master node; at the same time, by monitoring the CPU and memory usage of the server cluster and automatically allocating nodes according to the resource usage of services and data, resource balancing is achieved.

[0027] The advantages of the present invention are that it establishes a comprehensive management system for intelligent coal mining, connects the data information links of various mines and subsystems, uses machine learning to mine data assets, diagnoses and analyzes abnormal information and equipment failures of various mines and systems, and performs multi-user and multi-tenant management, thereby solving the problem of information islands in intelligent coal mining and making effective use of data assets. At the same time, the system is deployed at level 3 to ensure high availability of the system and data security. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a framework diagram of the comprehensive management system for intelligent coal mining described in the present invention.

[0029] Figure 2 It is a block diagram of the end side of the coal mine intelligent mining comprehensive management system described in the present invention.

[0030] Figure 3 It is the edge side frame diagram of the coal mine intelligent mining comprehensive management system described in the present invention.

[0031] Figure 4 It is a cloud side frame diagram of the coal mine intelligent mining integrated management system described in the present invention. DETAILED DESCRIPTION

[0032] The technical solutions in the embodiments of the present invention are described clearly and completely below. 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.

[0033] like Figure 1 As shown, the coal mine intelligent mining integrated management system described in the present invention includes several end sides, edge sides and cloud sides;

[0034] like Figure 2As shown, the end side includes a data acquisition module, a data cleaning module, and a data forwarding module

[0035] Among them, the data collection module collects data from each equipment data center in the mine in real time through restful API & database reading operations; the equipment data center includes the centralized control data center, controller data center, equipment management data center and equipment data center;

[0036] The data cleaning module organizes the data format according to business rules to facilitate unified data processing; for example, the group code + mine code + working face code combination is used as the data label, and abnormal data is deleted, and the null values ​​in the data are supplemented according to the data type;

[0037] The data forwarding module automatically forwards the cleaned data to the data storage modules on the cloud and edge sides through Kafka (a high-throughput distributed publish-subscribe messaging system).

[0038] like Figure 3 As shown, the edge side includes a data calculation module, an event alarm module, and a device posture adjustment module;

[0039] The data calculation module calculates and analyzes the received terminal data according to the corresponding data model;

[0040] The event alarm module determines whether to trigger an event alarm based on the calculation and analysis results of the data calculation module, and issues an event alarm 24 hours in advance. In event applications, the accuracy of event alarms reaches more than 90%;

[0041] The equipment posture adjustment module adjusts the equipment posture, such as the lifting and lowering of the hydraulic support, the movement of the coal cutter, the speed of the scraper transport, etc., based on the calculation and analysis results of the data calculation module and the comprehensive geological data, such as the thickness of each layer;

[0042] At the same time, the edge side provides data reading and permission control for edge side users to ensure data security;

[0043] like Figure 4 As shown, the cloud side includes a machine learning module, an equipment failure analysis module, a production process and reserve analysis module, an energy consumption analysis module, and a visualization interface;

[0044] The machine learning module uses machine learning algorithms to generate corresponding data models for the received end-side data, and sends them to the edge side to guide the edge side to issue event alarms and adjust the device posture.

[0045] The equipment failure analysis module intelligently analyzes the possible causes of equipment failures based on the received terminal data, the equipment life and service life, geological information and other related data, and assigns different weights to the causes of the failures. It can generate analysis results at the same time as the fault warning is issued. The probability weight of the first cause exceeds 60%, and the precise positioning accuracy reaches 80%;

[0046] The production process and reserve analysis module builds a simulator to optimize the production process based on the received end-side data;

[0047] The energy consumption analysis module analyzes the energy consumption of devices based on the received end-side data, helping users better manage device assets, issue inventory warnings in a timely manner, and analyze increased losses.

[0048] Visualization interface, which visualizes the cloud-side analysis results and corresponding data, including a 3D model for production process optimization;

[0049] At the same time, the cloud side provides data reading and permission control for cloud side users to ensure data security;

[0050] In addition, the end side, edge side, and cloud side all have data quality monitoring modules to monitor their own data transmission and data consistency. When data transmission is interrupted or data values ​​or sequences do not meet business rules, an alarm is immediately issued and the relevant responsible persons are notified. The data relationship of the entire system is finally displayed in the cloud-side visualization interface.

[0051] The cloud side adopts a server cluster architecture, which includes at least one master node, one manager node and one node node. The node heartbeat is automatically detected through the task management and resource scheduling module. When the master node heartbeat is lost, the services and data of the master node are automatically migrated to the manager node. When the node heartbeat is lost, the services and data of the lost node are migrated to other node nodes or the master node. At the same time, by monitoring the CPU and memory usage of the entire cluster, as well as the resource usage of services and data, nodes are automatically allocated to achieve resource balancing.

Claims

1. A comprehensive management system for intelligent mining of coal mines, characterized by: Includes several end sides, edge sides, and cloud sides; The terminal side includes a data acquisition module, a data cleaning module, and a data forwarding module; The edge side includes a data calculation module, an event alarm module and a device posture adjustment module; The cloud side includes a machine learning module, an equipment failure analysis module, a production process and reserve analysis module, an energy consumption analysis module and a visualization interface; The end side, edge side, and cloud side communicate through wireless and wired networks; The data acquisition module collects data from each equipment data center in the mine in real time; The data cleaning module organizes the data format according to business rules to facilitate unified data processing; The data forwarding module automatically forwards the cleaned data to the data storage modules on the cloud side and the edge side; The data calculation module calculates and analyzes the received terminal-side data according to a corresponding data model; The event alarm module determines whether to trigger an event alarm based on the calculation and analysis results of the data calculation module, and issues an event alarm in advance; The equipment posture adjustment module adjusts the equipment posture according to the calculation and analysis results of the data calculation module and the geological information; The machine learning module forms a corresponding data model for the received terminal side data through a machine learning algorithm, and sends it to the edge side to guide the edge side to perform event alarms and device posture adjustment; The equipment failure analysis module intelligently analyzes the causes of equipment failures based on the received terminal data, the equipment life, service life and geological information, assigns different weights to the causes of the failures, issues failure warnings and generates analysis results; The production process and reserve analysis module builds a simulator to optimize the production process based on the received terminal data; The energy consumption analysis module performs equipment energy consumption analysis based on the received terminal data, helping users to better manage equipment assets, issue inventory warning prompts in a timely manner, and analyze increased losses; The visualization interface visualizes the cloud-side analysis results and corresponding data; The equipment data center includes a centralized control data center, a controller data center, an equipment management data center and an equipment data center; The geological information includes the thickness of the coal seam; The adjustment of equipment posture includes the lifting and lowering of hydraulic supports, the movement of coal cutting knives, and the speed of scraper transportation.

2. The integrated management system for intelligent mining of coal mines according to claim 1 is characterized by: The edge side provides data reading and permission control for edge side users to ensure data security; the cloud side provides data reading and permission control for cloud side users to ensure data security.

3. The integrated management system for intelligent mining of coal mines according to claim 1 is characterized by: The end side, the edge side and the cloud side all have a data quality monitoring module; the data quality monitoring module is used to monitor its own data transmission status and data consistency; when it is found that data transmission is interrupted, or the data value or sequence does not comply with the business rules, an alarm is immediately issued and the relevant responsible persons are notified; the visualization interface can display all data relationships of the system.

4. The integrated management system for intelligent mining of coal mines according to claim 1 is characterized by: The cloud side adopts a server cluster architecture, including at least one master node, one manager node and one node node; the node heartbeat is automatically detected through the task management and resource scheduling module, and when the master node heartbeat is lost, the services and data of the master node are automatically migrated to the manager node; When the heartbeat of a node is lost, the services and data of the lost node are automatically migrated to other nodes or the master node. At the same time, by monitoring the CPU and memory usage of the server cluster and automatically allocating nodes based on the resource usage of services and data, resource balancing is achieved.

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