Coal mine disaster prediction system based on big data

Abstract

The invention discloses a coal mine disaster prediction system based on big data. The system comprises a main disaster monitoring data standard management unit used for establishing a main disaster monitoring data management standard; the main disaster monitoring data acquisition and storage unit is used for acquiring monitoring data from each disaster early warning system and uniformly storing the monitoring data; the main disaster monitoring information integrated display unit is used for combining the monitored data and displaying and analyzing the data; the main disaster early-warning visual display unit is supported by a GIS platform, integrates monitoring and early-warning data of different positions and types, integrates disaster early-warning index results of all specialties, and carries out visual analysis and display of multiple disaster types, multiple dimensions and multiple indexes. According to the coal mine disaster prediction system based on big data, abnormal early screening, automatic locking, backtracking analysis and trend prediction of various disasters can be realized on the same platform interface, and technical support is provided for safety production.

Description

Technical Field

[0001] The present invention relates to the technical field of coal mine disaster prediction, and in particular to a coal mine disaster prediction system based on big data. Background Art

[0002] Coal mine accident prediction refers to people's judgment and estimation of the possibility and trend of coal mine accidents. In order to take measures to prevent coal mine accidents. The basis of coal mine accident prediction is based on past accident experience. Through analysis and research, the mechanism of coal mine accidents is clarified, the laws of accident occurrence and development are mastered, and the occurrence of coal mine accidents in the future is predicted. It includes the prediction of the possibility of coal mine accidents and the prediction of the trend of coal mine accidents.

[0003] However, the existing coal mine disaster prediction system has a single analysis data and cannot provide sufficient data support, resulting in incomplete information and low integration, which makes the prediction accuracy of safe production insufficient. Summary of the invention

[0004] The present invention discloses a coal mine disaster prediction system based on big data, aiming to solve the technical problem that the existing coal mine disaster prediction system has a single analysis data and cannot provide sufficient data support, resulting in incomplete information and low integration, thus making the prediction accuracy of safe production insufficient.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] The coal mine disaster prediction system based on big data includes a major disaster monitoring data standard management unit, which is used to establish the major disaster monitoring data management standard; a major disaster monitoring data collection and storage unit, which is used to collect monitoring data from various disaster warning systems and store them uniformly; a major disaster monitoring information integration and display unit, which is used to combine the monitored data and display and analyze them; a major disaster warning visualization display unit, which is supported by a GIS platform, integrates monitoring and warning data of different locations and types, integrates the results of various professional disaster warning indicators, and conducts multi-disaster, multi-dimensional, and multi-indicator visualization analysis and display; a major disaster comprehensive graded warning unit is used to make advance judgments on what kind of secondary disasters and their credibility will be caused after the occurrence of the leading disaster, and issue comprehensive warning results; a safety situation dynamic assessment and accident possibility prediction unit, which is based on the safety situation warning indicator system and model to conduct a dynamic assessment of the safety situation; a disaster avoidance route and emergency plan calling unit, which can automatically call emergency rescue plans and disaster avoidance routes according to disaster monitoring and warning and comprehensive assessment results, and realize the emergency rescue auxiliary command function.

[0007] By using the scene construction technology of spatiotemporal GIS, a precise disaster warning system with comprehensive information perception, autonomous integration, dynamic identification, accurate warning, and collaborative control will be built, which will realize early screening of anomalies, automatic locking, retrospective analysis, and trend prediction, providing technical support for safe production. At the same time, through a unified data interface, a centralized disaster alarm control platform will be built to solve the problems of isolation and dispersion of various monitoring systems, integrate various types of monitoring and alarm information, and use the scene construction technology of spatiotemporal GIS to build a precise intelligent disaster warning system with comprehensive information perception, autonomous integration, dynamic identification, accurate warning, and collaborative control. On the same platform interface, early screening of anomalies, automatic locking, retrospective analysis, and trend prediction of multiple disasters will be realized, providing technical support for safe production.

[0008] In a preferred solution, the main disaster monitoring data standard management unit includes: a data collection and storage standard formation module: used to support the coal mine safety basic data meta description method, form a data management model for coal mine safety management, monitoring, analysis, early warning and utilization, and establish metadata collection and storage standards for water, fire, roof, rock burst, dust, gas, etc.; a disaster information metadata model construction module: in accordance with the public data warehouse metamodel specification, construct a main disaster information metadata model; a data transmission module: used to process data import and export operations, as well as version management; a data reference relationship analysis module: used to analyze the reference relationship of each data;

[0009] The main disaster monitoring data acquisition and storage unit includes: a monitoring data database establishment module: establishing a unified disaster monitoring data access interface, collecting data according to a unified data standard, and establishing a unified database for disaster monitoring data, in which storage categories are established according to disaster types and monitoring categories; a data acquisition and storage module: used to uniformly collect measurement point definition information and real-time values ​​of safety monitoring, bundle pipe monitoring, hydrological monitoring, microseismic monitoring, roof monitoring, mine pressure monitoring and dust monitoring through a data interaction interface, obtain measurement point definition information, measurement point real-time value information, measurement point historical value information and related configuration information from each monitoring system, and obtain warning information of different disasters from each disaster warning system; a data classification storage module: used to classify and store various types of monitoring data according to disaster types and monitoring categories after the information is uniformly collected, and the data is mainly divided into six categories: safety monitoring, hydrological monitoring, fire monitoring, mine pressure monitoring, rock burst, and dust monitoring.

[0010] By obtaining warning information of different disasters from various disaster warning systems, basic data support can be provided for comprehensive warning of major disasters. To break through data silos, a unified disaster monitoring data access interface is needed, and data collection is carried out in accordance with unified data standards. At the same time, a unified database for disaster monitoring data is established, and various types of monitoring data are classified and stored according to disaster types and monitoring categories. Comprehensive digital integration of multiple systems and multiple parameters is achieved, which effectively improves the accuracy of prediction and warning information and provides basic data support for comprehensive warning of major disasters.

[0011] In a preferred solution, the integrated display unit for major disaster monitoring information includes: a gas disaster monitoring display module: used to display the monitoring data of gas disasters, and at the same time provide the functions of determination definition, real-time monitoring, key attention, curve, bar chart, and GIS map display; a hydrological monitoring display module: used to collect data such as water inflow, water level, water temperature, and water quality of underground water collection locations, water tanks, and drainage pipes in real time, over-limit alarm, and equipment failure description, and dynamically monitor the damage of the top and bottom plates, hidden structures, and water-rich changes through microseismic and advanced electrical methods, and predict the danger of water inrush, and at the same time have monitoring daily reports, curve charts, bar charts, and voice alarm displays; an external fire monitoring module: used to collect optical fiber temperature measurement data in real time, and monitor fire-prone areas such as belt conveyors, main substations, and cables in real time, comprehensively, and accurately, so as to achieve early identification and positioning of fires at each monitoring point. At the same time, it can use the temperature, smoke, carbon monoxide and other sensor data in the existing safety monitoring system to realize correlation analysis, and has monitoring daily reports, curves, bar charts, and GIS map displays;

[0012] The main disaster monitoring information integrated display unit also includes: goaf fire monitoring module: used to dynamically collect coal mine goaf fire information. 2 、N 2 ,CO,CO 2 , CH 4 , C 2 H 4 , C 2 H 6 , C 2 H 2 , temperature and other real-time values ​​and the status of each monitoring point, and display, alarm and handle according to the information of each pipeline and the set alarm limit, and have monitoring time, curve, column chart, GIS map display; roof monitoring display module: used for real-time online monitoring and alarm of support pressure, roof delamination, surrounding rock deformation, anchor stress and other mining pressure parameters, and have monitoring time, curve, column chart, GIS map display; dust monitoring module: used for real-time collection of concentration data of each dust source point, and synchronous matching over-limit alarm, timing and over-limit automatic watering control, equipment failure analysis, curve report, equipment installation process diagram matching functions;

[0013] The major disaster warning visualization display unit includes: the main visualization content display module: the basic situation of the mine, including mining conditions, gas distribution, geological structure, hydrogeology, underground personnel, safety management, etc., the major disaster warning results, including different levels of warning results and warning times for single disasters such as water, fire, roof, impact rock pressure, gas, dust, etc., with a single disaster historical warning result query function, the major disaster warning basis, including the basis for warning results of single disasters such as water, fire, roof, impact rock pressure, gas, dust, etc., such as various single factor indicator data, composite factor indicator calculation results, critical values, etc., with historical value query and analysis functions for various indicators, disaster fusion real-time warning results, including disaster type, warning level, warning area, etc. The warning level is divided into four levels: blue, orange, yellow and red, based on the comprehensive danger status and trend of safety information of water, fire, roof, rock burst, gas and dust. The warning results are integrated, including the single disaster warning results, single factor indicator data, composite factor indicator calculation results, critical values, etc. of various disasters. It has the functions of querying and analyzing the historical values ​​of various indicators, historical fusion warning results, including statistical reports and curves of historical warning results, and can simultaneously query the values ​​of various indicators based on the warning, and display the content in multiple aspects;

[0014] Visual solution display module: used to display from chart reports. The system provides a series of lists, bar charts, pie charts, curves, relationship diagrams and other forms to display the warning results and composition, so that managers at all levels can intuitively understand the safety risk status of the mine, and can drill down to obtain detailed data that affects safety and conduct in-depth analysis of different dimensions. The system conducts trend analysis on different dimensions. Through the chart, you can directly understand the multi-dimensional drill display. The indicator information can be displayed in the form of labels, segments, hot spots, pie charts, bar charts, etc. It supports multi-dimensional drill and aggregate query based on personnel, equipment, environment, management and other contents; it also supports multi-level drill and aggregate query based on time, region and other conditions, and thematic analysis display. It is aimed at different indicator sets and safety business management needs, and provides business analysis and display for multiple different topics such as environment, personnel, disaster monitoring, disaster warning, risk point management, etc. Thematic analysis display makes full use of graphic chart display, multi-dimensional drill display and other methods to achieve centralized and detailed analysis and display of coal mine thematic information in three aspects;

[0015] The major disaster comprehensive graded warning unit includes: a mine risk assessment index establishment module: used to dynamically collect mine gas, water, fire, roof, rock burst, dust monitoring and other monitoring and detection data and various professional disaster warning index results, and establish a comprehensive mine safety situation risk assessment index system; an operation index system establishment module: used to establish a comprehensive information operation and index system; an early warning specification establishment module: used to establish early warning rules suitable for mines; a multi-factor comprehensive early warning model establishment module: used to develop a multi-factor comprehensive early warning software model; an electronic task work order push module: used to automatically push electronic task work orders according to abnormal alarms and early warning results, and conduct full-process tracking and management of abnormalities; a result visualization display module: used to visualize the early warning information release channels, transmission node delays, audience reception and confirmation time in real time on the early warning emergency corresponding interface after releasing the sub-disaster early warning results or integrating the early warning results.

[0016] By setting up a comprehensive graded warning unit for major disasters, we can realize the multi-factor coupling and fusion analysis warning of major disasters, and provide decision-making references for middle and senior technical managers to comprehensively evaluate the safety situation of mines. At the same time, by automatically pushing electronic task work orders, we can track and manage abnormalities throughout the process, and further provide reference and assistance for middle and senior technical managers in their decision-making operations on mines.

[0017] In a preferred scheme, the dynamic assessment of safety situation and accident possibility prediction unit includes: a safety status early warning indicator system establishment module: based on safety science theory and the main factors causing coal mine safety risks, a safety status early warning indicator system is constructed on the basis of classifying, sorting, analyzing and refining the risk factors affecting coal mine safety, and from the six types of disasters of "water, fire, roof, rock burst, gas, and dust", comprehensive consideration is given to the main disaster early warning results of coal mines, changes in environmental monitoring data and other factors, and coal mine safety situation early warning indicators are established; a safety status early warning indicator model establishment module: on the basis of the established safety status assessment and early warning indicator system, a hierarchical model is established through the system, an evaluation matrix is ​​constructed, a hierarchical single sorting is formed, and a weight set is obtained through a consistency test, a judgment set is obtained through an evaluation method, and a membership function is obtained using fuzzy mathematics methods to establish a coal mine safety status assessment and early warning indicator model.

[0018] Through the setting of dynamic safety situation assessment and accident possibility prediction unit, the accident possibility prediction can first determine the accident possibility prediction rule base according to the degree of over-limit (abnormal) of the warning results, the data (level) rise and fall status, the abnormality duration and other information. Then, the most optimized accident prediction model is selected, and based on the monitoring information and safety situation warning results, the machine learning algorithm is used to correct the accident prediction model to achieve accurate prediction of the possibility of mine accidents.

[0019] As can be seen from the above, the coal mine disaster prediction system based on big data includes a major disaster monitoring data standard management unit, which is used to establish the major disaster monitoring data management standard; a major disaster monitoring data collection and storage unit, which is used to collect monitoring data from various disaster warning systems and store them uniformly; a major disaster monitoring information integration and display unit, which is used to combine the monitored data and display and analyze them; the major disaster warning visualization display unit is supported by the GIS platform, integrates monitoring and warning data of different locations and types, integrates the results of various professional disaster warning indicators, and conducts multi-disaster, multi-dimensional, and multi-indicator visualization analysis and display; the major disaster comprehensive graded warning unit is used to make advance judgments on what kind of secondary disasters and their credibility will be caused after the occurrence of the leading disaster, and publish comprehensive warning results; the safety situation dynamic assessment and accident possibility prediction unit is based on the safety situation warning indicator system and model to conduct dynamic safety situation assessment; the disaster avoidance route and emergency plan calling unit can automatically call emergency rescue plans and disaster avoidance routes according to disaster monitoring and warning and comprehensive assessment results, and realize the emergency rescue auxiliary command function. The coal mine disaster prediction system based on big data provided by the present invention can realize abnormal early screening, automatic locking, retrospective analysis, and trend prediction of various disasters on the same platform interface, providing technical support for safe production. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This is a schematic diagram of the structure of the coal mine disaster prediction system based on big data proposed by the present invention.

[0021] Figure 2 This is a schematic diagram of the structure of the main disaster monitoring data standard management unit of the coal mine disaster prediction system based on big data proposed by the present invention.

[0022] Figure 3 This is a schematic diagram of the structure of the main disaster monitoring data acquisition and storage unit of the coal mine disaster prediction system based on big data proposed by the present invention.

[0023] Figure 4 This is a schematic diagram of the structure of the main disaster monitoring information integration display unit of the coal mine disaster prediction system based on big data proposed by the present invention.

[0024] Figure 5 This is a schematic diagram of the structure of the main disaster warning visualization display unit of the coal mine disaster prediction system based on big data proposed by the present invention.

[0025] Figure 6 This is a schematic diagram of the structure of the main disaster comprehensive hierarchical warning units of the coal mine disaster prediction system based on big data proposed by the present invention.

[0026] Figure 7This is a schematic diagram of the structure of the safety situation dynamic assessment and accident possibility prediction unit of the coal mine disaster prediction system based on big data proposed by the present invention. DETAILED DESCRIPTION

[0027] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0028] The coal mine disaster prediction system based on big data disclosed in the present invention is mainly used in the scenario of coal mine disaster prediction.

[0029] Reference Figure 1 The coal mine disaster prediction system based on big data includes a major disaster monitoring data standard management unit, which is used to establish major disaster monitoring data management standards; a major disaster monitoring data collection and storage unit, which is used to collect monitoring data from various disaster warning systems and store them uniformly; a major disaster monitoring information integration and display unit, which is used to combine the monitored data and display and analyze them; a major disaster warning visualization display unit, which is supported by a GIS platform, integrates monitoring and warning data of different locations and types, integrates the results of various professional disaster warning indicators, and conducts multi-disaster, multi-dimensional, and multi-indicator visualization analysis and display; a major disaster comprehensive graded warning unit is used to make advance judgments on what kind of secondary disasters and their credibility will be caused after the occurrence of the leading disaster, and to issue comprehensive warning results; a safety situation dynamic assessment and accident possibility prediction unit, which is based on the safety situation warning indicator system and model to conduct a dynamic assessment of the safety situation; disaster avoidance The route and emergency plan calling unit can automatically call emergency rescue plans and disaster avoidance routes according to disaster monitoring and early warning and comprehensive assessment results, realize the emergency rescue auxiliary command function, and build a disaster precise early warning system with comprehensive information perception, autonomous fusion, dynamic identification, accurate early warning and collaborative control by using the scene construction technology of spatiotemporal GIS. It can realize early screening of abnormalities, automatic locking, retrospective analysis and trend prediction, and provide technical support for safe production. At the same time, through a unified data interface, a centralized disaster alarm control platform is built to solve the problems of isolation and dispersion of various monitoring systems, integrate various monitoring and alarm information, and use the scene construction technology of spatiotemporal GIS to build a disaster precise intelligent early warning system with comprehensive information perception, autonomous fusion, dynamic identification, accurate early warning and collaborative control. It can realize early screening of abnormalities, automatic locking, retrospective analysis and trend prediction of multiple disasters on the same platform interface, and provide technical support for safe production.

[0030] Reference Figure 2In a preferred embodiment, the main disaster monitoring data standard management unit includes: a data collection and storage standard formation module: used to support the coal mine safety basic data meta-description method, form a data management model for coal mine safety management, monitoring, analysis, and early warning, and establish metadata collection and storage standards such as water, fire, roof, rock burst, dust, and gas; a disaster information metadata model construction module: it constructs a main disaster information metadata model in accordance with the public data warehouse meta-model specification; a data transmission module: it is used to handle data import and export operations, as well as version management; a data reference relationship analysis module: it is used to analyze the reference relationship of each data.

[0031] Reference Figure 3 In a preferred embodiment, the main disaster monitoring data acquisition and storage unit includes: a monitoring data database establishment module: establishing a unified disaster monitoring data access interface, collecting data according to a unified data standard, and establishing a unified disaster monitoring data database, in which storage categories are established according to disaster types and monitoring categories; a data acquisition and storage module: used to uniformly collect measurement point definition information and real-time values ​​of safety monitoring, bundle pipe monitoring, hydrological monitoring, microseismic monitoring, roof monitoring, mine pressure monitoring and dust monitoring through a data interaction interface, obtain measurement point definition information, measurement point real-time value information, measurement point historical value information and related configuration information from each monitoring system, and obtain warning information of different disasters from each disaster warning system; a data classification storage module Block: It is used to classify and store various monitoring data according to disaster types and monitoring categories after unified information collection. It is mainly divided into six types of data: safety monitoring, hydrological monitoring, fire monitoring, mine pressure monitoring, rock burst, and dust monitoring. By obtaining early warning information of different disasters from various disaster early warning systems, it provides basic data support for comprehensive early warning of major disasters and connects data islands. It is necessary to have a unified disaster monitoring data access interface and collect data according to unified data standards. At the same time, a unified database for disaster monitoring data is established. According to disaster types and monitoring categories, various types of monitoring data are classified and stored to achieve comprehensive digital integration of multiple systems and multiple parameters, effectively improve the accuracy of prediction and early warning information, and provide basic data support for comprehensive early warning of major disasters.

[0032] Reference Figure 4In a preferred embodiment, the integrated display unit of major disaster monitoring information includes: a gas disaster monitoring display module: used to display the monitoring data of gas disasters, and at the same time provide the functions of measurement definition, real-time monitoring, key attention, curve, bar chart, and GIS map display; a hydrological monitoring display module: used to collect data such as water inflow, water level, water temperature, and water quality of underground water collection locations, water tanks, and drainage pipes in real time, over-limit alarm, and equipment failure description, and dynamically monitor the damage of the top and bottom plates, hidden structures, and water-rich changes through microseismic and advanced electrical methods, and predict the danger of water inrush, and at the same time have monitoring daily reports, curve charts, bar charts, and voice alarm displays; an external fire monitoring module: used to collect optical fiber temperature measurement data in real time, and monitor fire-prone areas such as belt conveyors, main substations, and cables in real time, comprehensively, and accurately, so as to achieve early identification and positioning of fires at each monitoring point. At the same time, it can use the temperature, smoke, carbon monoxide and other sensor data in the existing safety monitoring system to realize correlation analysis, and has monitoring daily reports, curves, bar charts, and GIS map displays.

[0033] Reference Figure 4 In a preferred embodiment, the main disaster monitoring information integrated display unit further includes: a goaf fire monitoring module: used to dynamically collect information about the coal mine goaf. 2 、N 2 ,CO,CO 2 , CH 4 , C 2 H 4 , C 2 H 6 , C 2 H 2 , temperature and other real-time values ​​and the status of each monitoring point, and display, alarm and handle according to the information of each pipeline and the set alarm limits, and have monitoring times, curves, bar graphs, GIS map display; Roof monitoring and display module: used for real-time online monitoring and alarm of support pressure, roof delamination, surrounding rock deformation, anchor stress and other mining pressure parameters, and have monitoring times, curves, bar graphs, GIS map display; Dust monitoring module: used for real-time collection of concentration data of each dust source point, and simultaneously matching over-limit alarm, timing and over-limit automatic watering control, equipment failure analysis, curve reports, and equipment installation process diagram matching functions.

[0034] Reference Figure 5In a preferred embodiment, the major disaster warning visualization display unit includes: the main visualization content display module: the basic situation of the mine, including mining conditions, gas distribution, geological structure, hydrogeology, underground personnel, safety management, etc., the major disaster warning results, including different levels of warning results and warning times for single disasters such as water, fire, roof, impact rock pressure, gas, and dust, with a single disaster historical warning result query function, the main disaster warning basis, including the basis for warning results of single disasters such as water, fire, roof, impact rock pressure, gas, and dust, such as various single factor indicator data, composite factor indicator calculation results, critical values, etc., with historical value query and analysis functions for various indicators, disaster fusion real-time warning results, including disaster type, warning level, warning area, etc. The warning level is divided into four levels: blue, orange, yellow and red, based on the comprehensive danger status and trend of safety information of water, fire, roof, rock burst, gas and dust. The warning results are integrated, including the single disaster warning results, single factor indicator data, composite factor indicator calculation results, critical values, etc. of various disasters. It has the functions of querying and analyzing the historical values ​​of various indicators, historical fusion warning results, including statistical reports and curves of historical warning results, and can simultaneously query the values ​​of various indicators based on the warning, and display the content in multiple aspects;

[0035] Visualization scheme display module: used to display from chart reports, through the system provides a series of lists, bar charts, pie charts, curves, relationship diagrams and other forms to display the warning results and composition, so that managers at all levels can intuitively understand the safety risk status of the mine, and can drill down to obtain detailed data affecting safety and conduct in-depth analysis of different dimensions. The system conducts trend analysis on different dimensions. Through the chart, you can directly understand the multi-dimensional drilling display. The indicator information can be displayed in the form of labels, segments, hot spots, pie charts, bar charts, etc., supporting multi-dimensional drilling and aggregation queries based on personnel, equipment, environment, management and other contents; at the same time, it supports multi-level drilling and aggregation queries based on time, region and other conditions, and thematic analysis display. It is aimed at different indicator sets and safety business management needs, providing business analysis and display for multiple different topics such as environment, personnel, disaster monitoring, disaster warning, risk point management, etc. Thematic analysis display makes full use of graphic chart display, multi-dimensional drilling display and other methods to achieve centralized and detailed analysis and display of coal mine thematic information in three aspects.

[0036] Reference Figure 6In a preferred embodiment, the comprehensive graded warning unit for major disasters includes: a mine risk assessment index establishment module: used to dynamically collect monitoring and detection data such as mine gas, water, fire, roof, rock burst, dust monitoring, and various professional disaster warning index results, and establish a comprehensive mine safety situation risk assessment index system; an operation index system establishment module: used to establish a comprehensive information operation and index system; an early warning specification establishment module: used to establish early warning rules suitable for mines; a multi-factor comprehensive early warning model establishment module: used to develop a multi-factor comprehensive early warning software model; an electronic task work order push module: used to automatically push electronic task work orders according to abnormal alarms and early warning results, and conduct full-process tracking and management of abnormalities; a result visualization display module: used to visualize the early warning information release channel, transmission node delay, audience reception and confirmation time in real time on the early warning emergency corresponding interface after releasing the sub-disaster early warning results or integrating the early warning results.

[0037] Reference Figure 7 In a preferred embodiment, the dynamic safety situation assessment and accident possibility prediction unit includes: a safety status early warning indicator system establishment module: based on the safety science theory and the main factors causing coal mine safety risks, a safety status early warning indicator system is constructed on the basis of classifying, sorting, analyzing and refining the risk factors affecting coal mine safety, and from the six disasters of "water, fire, roof, rock burst, gas, and dust", the coal mine safety situation early warning indicators are established by comprehensively considering the main disaster early warning results of coal mines, changes in environmental monitoring data and other factors; a safety status early warning indicator model establishment module: on the basis of the established safety status assessment and early warning indicator system, a hierarchical model is established through the system , construct an evaluation matrix, form a hierarchical single sorting, and obtain a weight set through consistency test, obtain a judgment set through the evaluation method, and use fuzzy mathematics method to obtain the membership function, establish a coal mine safety status assessment and early warning index model, and through the dynamic evaluation of the safety situation and the setting of the accident possibility prediction unit, the accident possibility prediction first choice can be based on the over-limit (abnormal) degree of the early warning result, the data (level) rise and fall status, the abnormal duration and other information, determine the accident possibility prediction rule base, and then select the most optimized accident prediction model, and based on the monitoring information and safety situation early warning results, use the machine learning algorithm to correct the accident prediction model to achieve accurate prediction of the possibility of mine accidents.

[0038] Working principle: By using the scene construction technology of spatiotemporal GIS, a precise disaster warning system with comprehensive information perception, autonomous integration, dynamic identification, precise warning, and collaborative control is built, which realizes early screening of abnormalities, automatic locking, retrospective analysis, and trend prediction, providing technical support for safe production. At the same time, through a unified data interface, a centralized disaster alarm control platform is built to solve the problems of isolation and dispersion of various monitoring systems, integrate various types of monitoring and alarm information, and use the scene construction technology of spatiotemporal GIS to build a precise intelligent disaster warning system with comprehensive information perception, autonomous integration, dynamic identification, precise warning, and collaborative control. On the same platform interface, early screening of abnormalities, automatic locking, retrospective analysis, and trend prediction of multiple disasters are realized, providing technical support for safe production.

[0039] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A coal mine disaster prediction system based on big data, characterized in that: include: The major disaster monitoring data standard management unit is used to establish the major disaster monitoring data management standards; The main disaster monitoring data collection and storage unit is used to collect monitoring data from various disaster early warning systems and store them uniformly; The integrated display unit for major disaster monitoring information is used to combine, display and analyze the monitored data; The main disaster warning visualization display unit is supported by the GIS platform, integrating monitoring and warning data of different locations and types, integrating the results of various professional disaster warning indicators, and conducting multi-disaster, multi-dimensional, and multi-indicator visualization analysis and display; The comprehensive graded early warning unit for major disasters is used to make advance judgments on the types of secondary disasters that will be triggered by the leading disaster and their credibility, and to issue comprehensive early warning results; The safety situation dynamic assessment and accident possibility prediction unit is based on the safety situation early warning indicator system and model to conduct dynamic safety situation assessment; The disaster evacuation route and emergency plan calling unit can automatically call emergency rescue plans and disaster evacuation routes according to disaster monitoring and early warning and comprehensive assessment results, thereby realizing the emergency rescue auxiliary command function.

2. The coal mine disaster prediction system based on big data according to claim 1 is characterized in that: The main disaster monitoring data standard management unit includes: Data collection and storage standard formation module: used to support the coal mine safety basic data meta-description method, form a data management model for coal mine safety management, monitoring, analysis, and early warning, and establish metadata collection and storage standards for water, fire, roof, rock burst, dust, gas, etc.; Disaster information metadata model construction module: It builds the main disaster information metadata model according to the public data warehouse metamodel specification; Data transfer module: used to handle data import and export operations, as well as version management; Data reference relationship analysis module: used to analyze the reference relationship of each data.

3. The coal mine disaster prediction system based on big data according to claim 1 is characterized in that: The main disaster monitoring data collection and storage unit includes: Monitoring data database establishment module: establish a unified disaster monitoring data access interface, collect data according to unified data standards, and establish a unified database for disaster monitoring data. In the database, storage categories are set up according to disaster types and monitoring categories; Data acquisition and storage module: used to uniformly collect the measurement point definition information and real-time values ​​of safety monitoring, beam pipe monitoring, hydrological monitoring, microseismic monitoring, roof monitoring, mine pressure monitoring and dust monitoring through the data interaction interface, obtain the measurement point definition information, measurement point real-time value information, measurement point historical value information and related configuration information from each monitoring system, and obtain the early warning information of different disasters from each disaster early warning system; Data classification storage module: used to classify and store various monitoring data according to disaster types and monitoring categories after unified information collection.

4. The coal mine disaster prediction system based on big data according to claim 1 is characterized in that: The main disaster monitoring information integrated display unit includes: Gas disaster monitoring and display module: used to display the monitoring data of gas disasters, and also provides the functions of determination definition, real-time monitoring, key attention, curve, bar chart, and GIS map display; Hydrological monitoring display module: used for real-time data collection of water inflow, water level, water temperature, water quality, etc. of underground water inflow collection location, water tank, and drainage pipe, over-limit alarm, equipment failure description, and dynamic monitoring of top and bottom plate damage, hidden structure, water-rich changes through microseismic and advanced electrical methods, and prediction of water inrush danger. It also has monitoring daily report, curve chart, bar chart, and voice alarm display; External fire monitoring module: used to collect optical fiber temperature measurement data in real time, and conduct real-time, comprehensive and accurate monitoring of fire-prone areas. It also has monitoring time, curve, bar chart and GIS map display.

5. The coal mine disaster prediction system based on big data according to claim 4 is characterized in that: The main disaster monitoring information integrated display unit also includes: Goaf fire monitoring module: used to dynamically collect various types of gases and real-time temperature values ​​in coal mine goafs and the status of each monitoring point, and display, alarm and handle according to the information of each pipeline and the set alarm limits. It also has monitoring time, curve, bar chart and GIS map display; Roof monitoring and display module: used for real-time online monitoring and alarm of support pressure, roof separation, surrounding rock deformation, anchor stress and other mining pressure parameters, and also has monitoring time, curve, column chart, and GIS map display; Dust monitoring module: used to collect concentration data of each dust source point in real time, and simultaneously provide functions such as over-limit alarm, timing and over-limit automatic watering control, equipment failure analysis, curve report, and equipment installation process diagram matching.

6. The coal mine disaster prediction system based on big data according to claim 1 is characterized in that: The main disaster warning visualization display unit includes: Main visualization content display module: content display of the basic situation of the mine, main disaster warning results, main disaster warning basis, disaster fusion real-time warning results, fusion warning result basis, and historical fusion warning results; Visual solution display module: used for display from three aspects: chart report display, multi-dimensional drill display and special analysis display.

7. The coal mine disaster prediction system based on big data according to claim 1, characterized in that: The major disaster comprehensive graded early warning units include: Mine risk assessment index establishment module: used to dynamically collect monitoring and detection data and various professional disaster warning index results, and establish a comprehensive mine safety situation risk assessment index system; Operation indicator system establishment module: used to establish comprehensive information operation and indicator system; Warning specification establishment module: used to establish warning rules suitable for mines; Multi-factor comprehensive early warning model building module: used to develop a multi-factor comprehensive early warning software model; Electronic task work order push module: used to automatically push electronic task work orders according to abnormal alarm and early warning results, and conduct full-process abnormal tracking management; Result visualization display module: After releasing the individual disaster warning results or integrated warning results, it is used to visualize the warning information release channels, transmission node delays, audience reception and confirmation time in real time on the warning emergency response interface.

8. The coal mine disaster prediction system based on big data according to claim 1, characterized in that: The safety situation dynamic assessment and accident possibility prediction unit includes: Safety status early warning indicator system establishment module: It is based on the safety science theory and the main factors causing coal mine safety risks. The safety status early warning indicator system is established on the basis of classifying, sorting, analyzing and refining the risk factors affecting coal mine safety; Safety status early warning indicator model establishment module: On the basis of the established safety status assessment and early warning indicator system, a hierarchical model is established through the system, an evaluation matrix is ​​constructed, a hierarchical single sorting is formed, and a weight set is obtained through a consistency test. The judgment set is obtained through the judgment method, and the membership function is obtained using fuzzy mathematics methods to establish a coal mine safety status assessment and early warning indicator model.

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