CN116090823A - Risk monitoring method and device for coal mine disasters, electronic equipment and storage medium - Google Patents
Risk monitoring method and device for coal mine disasters, electronic equipment and storage medium Download PDFInfo
- Publication number
- CN116090823A CN116090823A CN202310077185.5A CN202310077185A CN116090823A CN 116090823 A CN116090823 A CN 116090823A CN 202310077185 A CN202310077185 A CN 202310077185A CN 116090823 A CN116090823 A CN 116090823A
- Authority
- CN
- China
- Prior art keywords
- disaster
- data
- information
- coal mine
- detected
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 239000003245 coal Substances 0.000 title claims abstract description 141
- 238000000034 method Methods 0.000 title claims abstract description 53
- 238000012544 monitoring process Methods 0.000 title claims abstract description 37
- 238000003860 storage Methods 0.000 title abstract description 16
- 238000012545 processing Methods 0.000 claims abstract description 136
- 239000002243 precursor Substances 0.000 claims abstract description 115
- 238000004519 manufacturing process Methods 0.000 claims abstract description 108
- 230000003068 static effect Effects 0.000 claims abstract description 91
- 238000004458 analytical method Methods 0.000 claims abstract description 33
- 239000013598 vector Substances 0.000 claims description 42
- 238000004140 cleaning Methods 0.000 claims description 26
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 25
- 239000000428 dust Substances 0.000 claims description 23
- 238000007405 data analysis Methods 0.000 claims description 22
- 239000011435 rock Substances 0.000 claims description 21
- 238000000605 extraction Methods 0.000 claims description 20
- 238000012806 monitoring device Methods 0.000 claims description 10
- 230000000007 visual effect Effects 0.000 claims description 4
- 238000004590 computer program Methods 0.000 description 7
- 238000010586 diagram Methods 0.000 description 7
- 230000006870 function Effects 0.000 description 7
- 230000004927 fusion Effects 0.000 description 7
- 230000009467 reduction Effects 0.000 description 6
- 238000005065 mining Methods 0.000 description 5
- 230000003287 optical effect Effects 0.000 description 5
- 230000008569 process Effects 0.000 description 5
- 230000006378 damage Effects 0.000 description 4
- 238000007418 data mining Methods 0.000 description 4
- 238000013500 data storage Methods 0.000 description 4
- 238000005553 drilling Methods 0.000 description 4
- 238000003491 array Methods 0.000 description 3
- 238000007726 management method Methods 0.000 description 3
- 230000002093 peripheral effect Effects 0.000 description 3
- 230000002265 prevention Effects 0.000 description 3
- 238000012800 visualization Methods 0.000 description 3
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 2
- 229910002091 carbon monoxide Inorganic materials 0.000 description 2
- 238000005520 cutting process Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000007689 inspection Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 230000007246 mechanism Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000004445 quantitative analysis Methods 0.000 description 2
- 238000012502 risk assessment Methods 0.000 description 2
- 238000000926 separation method Methods 0.000 description 2
- 238000009825 accumulation Methods 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000006073 displacement reaction Methods 0.000 description 1
- 238000010291 electrical method Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 230000035784 germination Effects 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 230000001960 triggered effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/02—Agriculture; Fishing; Mining
Abstract
The disclosure provides a risk monitoring method, a device, electronic equipment and a storage medium for coal mine disasters, wherein the method comprises the following steps: acquiring initial production data and initial static data of an area to be detected, processing the initial production data and the initial static data to obtain disaster-causing precursor information corresponding to a plurality of disaster types in the area to be detected, determining a coal mine disaster early warning processing model according to mine condition information of the area to be detected, and carrying out joint analysis processing on a plurality of disaster-causing precursor information according to the coal mine disaster early warning processing model to obtain comprehensive disaster risk information of the area to be detected. According to the method and the device for predicting the disaster in the coal mine, prediction data of various disasters in the area to be detected can be analyzed in a combined mode, the possibility and the harmfulness of occurrence of the disasters are evaluated before the disasters are completed according to the obtained hidden danger and precursor information, real-time accurate early warning of the disasters in the coal mine is achieved, and the safety of coal mine production is effectively improved.
Description
Technical Field
The disclosure relates to the technical field of disaster risk assessment, in particular to a risk monitoring method, a risk monitoring device, electronic equipment and a storage medium for coal mine disasters.
Background
Currently, with the continuous increase of the mining depth and intensity of the coal mine, the problems of high gas, high stress, high ground temperature, complex hydrogeological conditions and the like are increasingly prominent, and the coal mine safety work is more challenged.
In the related art, data analysis and processing are generally performed on single-source and single-disaster data to monitor the risk of coal mine disasters.
In this way, along with the increasing diversification and complicacy of data of different types, structures and source data generated by different systems in the coal mine industry, the mode of performing single analysis processing on the data cannot monitor and pre-warn the occurrence of coal mine disasters in real time and accurately, so that the prevention effect of the coal mine disasters is affected.
Disclosure of Invention
The present disclosure aims to solve, at least to some extent, one of the technical problems in the related art.
Therefore, the purpose of the disclosure is to provide a risk monitoring method, device, electronic equipment, storage medium and computer program product for coal mine disasters, which can jointly analyze prediction data of various disasters in a region to be detected, evaluate the possibility and hazard of the disasters according to the obtained hidden danger and precursor information before the disasters occur, realize real-time accurate early warning of the coal mine disasters, and effectively improve the safety of coal mine production.
An embodiment of a first aspect of the present disclosure provides a risk monitoring method for coal mine disasters, including: acquiring initial production data and initial static data of an area to be detected; processing the initial production data and the initial static data to obtain disaster-causing precursor information corresponding to a plurality of disaster types in the area to be detected; determining a coal mine disaster early warning processing model according to mine condition information of a region to be detected; and carrying out joint analysis processing on the disaster-causing precursor information of the plurality of disasters according to the coal mine disaster early warning processing model to obtain comprehensive disaster risk information of the area to be detected.
According to the risk monitoring method for the coal mine disasters, which is provided by the embodiment of the first aspect of the disclosure, the initial production data and the initial static data of the region to be detected are obtained, the initial production data and the initial static data are processed to obtain disaster causing precursor information corresponding to a plurality of disaster types in the region to be detected, a coal mine disaster early warning processing model is determined according to mine condition information of the region to be detected, and the plurality of disaster causing precursor information is subjected to joint analysis processing according to the coal mine disaster early warning processing model to obtain comprehensive disaster risk information of the region to be detected, so that prediction data of a plurality of disasters of the region to be detected can be jointly analyzed, the possibility and the hazard of the disasters are evaluated according to the obtained disaster hidden danger and precursor information before the disasters are formed, real-time accurate early warning of the coal mine disasters is realized, and the safety of coal mine production is effectively improved.
An embodiment of a second aspect of the present disclosure provides a risk monitoring device for coal mine disasters, including: the acquisition module is used for acquiring initial production data and initial static data of the area to be detected; the first processing module is used for processing the initial production data and the initial static data to obtain disaster-causing precursor information corresponding to a plurality of disaster types in the area to be detected; the determining module is used for determining a coal mine disaster early warning processing model according to mine condition information of the area to be detected; and the second processing module is used for carrying out joint analysis processing on the disaster-causing precursor information of the plurality of disasters according to the coal mine disaster early warning processing model to obtain comprehensive disaster risk information of the area to be detected.
According to the risk monitoring device for the coal mine disasters, provided by the embodiment of the second aspect of the disclosure, the initial production data and the initial static data of the to-be-detected area are obtained, the initial production data and the initial static data are processed to obtain disaster-causing precursor information corresponding to a plurality of disaster types in the to-be-detected area, a coal mine disaster early warning processing model is determined according to mine condition information of the to-be-detected area, and the plurality of disaster-causing precursor information is subjected to joint analysis processing according to the coal mine disaster early warning processing model to obtain comprehensive disaster risk information of the to-be-detected area, so that prediction data of a plurality of disasters of the to-be-detected area can be jointly analyzed, the possibility and the hazard of the disasters are evaluated according to the obtained disaster hidden danger and precursor information before the disasters occur, real-time accurate early warning of the coal mine disasters is realized, and the safety of coal mine production is effectively improved.
An embodiment of a third aspect of the present disclosure provides an electronic device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, where the processor executes the program to implement a risk monitoring method for a coal mine disaster as set forth in the embodiment of the first aspect of the present disclosure.
An embodiment of a fourth aspect of the present disclosure proposes a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a risk monitoring method for coal mine disasters as proposed by an embodiment of the first aspect of the present disclosure.
An embodiment of a fifth aspect of the present disclosure proposes a computer program product which, when executed by a processor, performs a method of risk monitoring of coal mine disasters as proposed by the embodiment of the first aspect of the present disclosure.
Additional aspects and advantages of the disclosure will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the disclosure.
Drawings
The foregoing and/or additional aspects and advantages of the present disclosure will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which:
FIG. 1 is a flow chart of a method for risk monitoring of coal mine disasters according to one embodiment of the disclosure;
FIG. 2 is a flow chart of a method for risk monitoring of coal mine disasters according to another embodiment of the disclosure;
fig. 3 is a schematic structural diagram of a coal mine disaster monitoring and early warning system in an embodiment of the disclosure;
FIG. 4 is a schematic structural diagram of a risk monitoring device for coal mine disasters according to an embodiment of the present disclosure;
FIG. 5 is a schematic structural view of a risk monitoring device for coal mine disasters according to another embodiment of the present disclosure;
fig. 6 illustrates a block diagram of an exemplary electronic device suitable for use in implementing embodiments of the present disclosure.
Detailed Description
Embodiments of the present disclosure are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are exemplary only for explaining the present disclosure and are not to be construed as limiting the present disclosure. On the contrary, the embodiments of the disclosure include all alternatives, modifications, and equivalents as may be included within the spirit and scope of the appended claims.
Fig. 1 is a flow chart of a method for risk monitoring of coal mine disasters according to an embodiment of the disclosure.
It should be noted that, the execution main body of the risk monitoring method for coal mine disaster in this embodiment is a risk monitoring device for coal mine disaster, the device may be implemented in a software and/or hardware manner, and the device may be configured in an electronic device, which is not limited thereto.
As shown in fig. 1, the risk monitoring method for coal mine disasters includes:
s101: and acquiring initial production data and initial static data of the area to be detected.
The to-be-detected area is an area to be subjected to coal mine disaster risk monitoring in the coal mine production area, and can be used for monitoring coal mine disaster risks in a plurality of areas in the coal mine production area and performing visual display.
The initial production data refer to production data which are acquired in the mine production process and are not subjected to data cleaning and noise reduction treatment.
The initial static data refers to parameter data which is not subjected to data cleaning and noise reduction treatment in the production process, and the initial production data and the initial static data can participate in coal mine disaster risk monitoring of an area to be detected.
In the embodiment of the disclosure, when initial production data and initial static data of an area to be detected are acquired, a data acquisition and storage module can be established to acquire various monitoring systems, production data and static data existing in a mining party, different acquisition and uploading schemes can be determined according to different data types and data structures of data in the area to be detected, and the data acquisition schemes, such as automatic equipment data acquisition, configuration system data acquisition, mining existing informatization system data acquisition, text file data acquisition, video data acquisition and the like, are used as the initial production data and the initial static data of the area to be detected.
S102: and processing the initial production data and the initial static data to obtain disaster-causing precursor information corresponding to a plurality of disaster types in the area to be detected.
The disaster-causing precursor information is precursor data information of a plurality of disasters possibly occurring in the area to be detected, which is obtained after analysis and processing of the collected initial data, and the plurality of disaster types may include: the disaster type of the coal mine disaster is not limited, and the disaster type of the coal mine disaster can be selected from gas disasters, water disasters, roof disasters, rock burst disasters, dust disasters, fire disasters and the like, or any disaster type which can occur in a coal mine production area can be included.
In the embodiment of the disclosure, when initial production data and initial static data are processed to obtain disaster causing precursor information corresponding to a plurality of disaster types in a region to be detected, data cleaning processing can be performed on the initial production data and the initial static data to perform noise reduction processing on the initial production data and the initial static data, a data cleaning module can be constructed based on an integrity constraint data cleaning algorithm, a rule-based data cleaning algorithm, an abnormal data characteristic data cleaning algorithm and a man-machine combined data cleaning algorithm, the data cleaning module is used for performing data cleaning processing on the initial production data and the initial static data based on the constructed data cleaning module so as to realize noise reduction on the acquired data, improve the data quality of the data for performing disaster risk assessment, utilize a characteristic extraction algorithm to extract characteristic vectors of the data while performing data cleaning, for example, the data characteristic vectors can be maximum, mean, super-limit, ultra-limit frequency and the like, the data are not limited, then the data mining and fusion analysis module can be established, the initial production data and the initial static data of the initial disaster types collected by the analysis of the coal mine data multi-element discrete characteristic extraction method can be analyzed, and the disaster causing precursor information is obtained from the disaster information corresponding to the disaster type information, and the disaster information is analyzed from the disaster type information, and the disaster type is obtained from the disaster type precursor information.
S103: and determining a coal mine disaster early warning processing model according to the mine condition information of the area to be detected.
The mine condition information refers to data information that can be used to describe production conditions of a mine, and the mine condition information may be, for example, production parameter information of machinery in the mine, environmental parameter information of the mine, and the like, which is not limited thereto.
The coal mine disaster early warning processing model is a processing model which can be used for processing various data information of an area to be detected so as to obtain coal mine disaster occurrence risk information of the area to be detected.
In the embodiment of the disclosure, when determining a coal mine disaster early warning processing model according to mine condition information of an area to be detected, mine condition information of a mine in the area to be detected can be firstly obtained, and the coal mine disaster evolution and disaster causing mechanism quantitative analysis method obtained in the process of analyzing and processing initial production data and initial static data is utilized, so that a coal mine disaster classification and classification comprehensive early warning index library and an early warning model library are established, and then an early warning rule and an early warning parameter which are suitable for the coal mine disaster early warning processing model can be autonomously determined according to different mine condition information of each area to be detected, and the coal mine disaster early warning processing model which is suitable for the early warning rule and the early warning parameter is configured according to the early warning rule and the early warning parameter.
S104: and carrying out joint analysis processing on the disaster-causing precursor information of the plurality of disasters according to the coal mine disaster early warning processing model to obtain comprehensive disaster risk information of the area to be detected.
The comprehensive disaster risk information refers to data information which can be used for describing disaster risk grades, disaster occurrence specific orientations and disaster occurrence reasons of various disaster types existing in the to-be-detected area.
According to the embodiment of the disclosure, the initial production data and the initial static data are processed to obtain disaster-causing precursor information corresponding to a plurality of disaster types in the region to be detected, and after a coal mine disaster early-warning processing model is determined according to mine condition information of the region to be detected, the plurality of disaster-causing precursor information can be subjected to joint analysis processing according to the coal mine disaster early-warning processing model to obtain comprehensive disaster risk information of the region to be detected.
In the embodiment of the disclosure, when the comprehensive disaster risk information of the to-be-detected area is obtained by performing joint analysis processing on a plurality of disaster precursor information according to a coal mine disaster early warning processing model, the occurrence risk of gas disasters, water disaster disasters, roof disasters, rock burst disasters, dust disasters and fire disasters which may exist in the to-be-detected area can be analyzed by using the coal mine disaster early warning processing model, the number of times of triggering early warning critical values by access data is used as a judgment standard for judging the risk level of the current to-be-detected area, and the accuracy of an early warning index system and a risk judgment model is verified by using on-site feedback and machine learning technology in accordance with the coal mine disaster early warning processing model, so that the disaster occurrence risk level, disaster occurrence cause, specific place where the disaster may occur and the like of each disaster type are respectively obtained, and the obtained data information is used as the comprehensive disaster risk information of the to-be-detected area.
According to the risk monitoring method for the coal mine disasters, the possibility and the harm of the occurrence of the coal mine disasters can be evaluated according to the obtained hidden danger and precursor information before the disasters are completed, alarm signals are sent to the authorities or personnel to remind the authorities or personnel to take prevention and control countermeasures in time, the occurrence of the disasters is avoided, early identification and source control of the safety risks can be achieved through early warning, an intelligent early warning system for the coal mine disasters is created, wherein the intelligent early warning system is suitable for early, high in efficiency and shared in fusion, the bottleneck of big data and small knowledge is broken through, the coal mine safety early warning is changed into true prevention and early, and the disasters are resolved in the germination stage.
In this embodiment, initial production data and initial static data of an area to be detected are obtained, the initial production data and the initial static data are processed to obtain disaster-causing precursor information corresponding to a plurality of disaster types in the area to be detected, a coal mine disaster early warning processing model is determined according to mine condition information of the area to be detected, and the plurality of disaster-causing precursor information is subjected to joint analysis processing according to the coal mine disaster early warning processing model to obtain comprehensive disaster risk information of the area to be detected, prediction data of a plurality of disasters of the area to be detected can be jointly analyzed, and the possibility and harm of occurrence of the disasters are evaluated before the disasters occur according to the obtained disaster hidden danger and precursor information, so that real-time accurate early warning of the coal mine disasters is realized, and the safety of coal mine production is effectively improved.
Fig. 2 is a flow chart of a method for risk monitoring of coal mine disasters according to another embodiment of the disclosure.
As shown in fig. 2, the risk monitoring method for coal mine disasters includes:
s201: and acquiring initial production data and initial static data of the area to be detected.
Optionally, in some embodiments, when initial production data and initial static data of an area to be detected are acquired, data type information and data structure information of data in the area to be detected can be acquired, and the initial production data and the initial static data of the area to be detected are acquired and stored according to the data type information and the data structure information.
In the embodiment of the disclosure, when initial production data and initial static data of an area to be detected are acquired, data type information and data structure information contained in the initial production data and the initial static data of the area to be detected can be acquired, a data acquisition and storage module is established according to the data type information and the data structure information, various existing monitoring systems, production data and static data of a mining party are acquired, different acquisition uploading schemes are determined according to differences of acquired data types and data structures, data acquisition is performed, for example, automatic equipment data acquisition, configuration system data acquisition, mine existing informatization system data acquisition, text file data acquisition, video data acquisition and the like, and data storage comprises production, safety state data storage, management service data storage and file data storage, and the acquired and stored data is used as the initial production data and the initial static data of the area to be detected.
S202: and performing data cleaning processing and data feature extraction processing on the initial production data and the initial static data to obtain data feature vectors.
The data feature vector refers to a data feature that may be used to describe critical data information in the initial production data and the initial static data, and is not limited to this, for example, a maximum value, a mean value, a median value, an overrun frequency per unit time, and the like.
After the initial production data and the initial static data of the area to be detected are obtained, the embodiment of the disclosure can perform data cleaning processing and data feature extraction processing on the initial production data and the initial static data to obtain data feature vectors.
In the embodiment of the disclosure, when data cleaning processing and data feature extraction processing are performed on initial production data and initial static data to obtain a data feature vector, a data cleaning module can be constructed based on a data cleaning algorithm of integrity constraint, a data cleaning algorithm based on rules, a data cleaning algorithm based on abnormal data features and a data cleaning algorithm combined by a man-machine, noise reduction of collected data is achieved on the initial production data and the initial static data by the data cleaning module, data quality is improved, and critical information in the initial production data and the initial static data, such as data maximum value, data average value, data median value, ultra-limit frequency of unit time and the like in the initial production data and the initial static data, is extracted by the feature extraction algorithm, and the extracted data critical information is used as the data feature vector.
S203: and performing multi-element discrete feature extraction processing on the initial production data, the initial static data and the data feature vector to obtain disaster-causing precursor information.
After the data cleaning process and the data feature extraction process are performed on the initial production data and the initial static data to obtain the data feature vector, the embodiment of the disclosure can perform the multi-element discrete feature extraction process on the initial production data, the initial static data and the data feature vector to obtain disaster precursor information.
In the embodiment of the disclosure, when initial production data, initial static data and data feature vectors are subjected to multi-element discrete feature extraction processing to obtain disaster-causing precursor information, a data mining and fusion analysis module can be established, each disaster data is analyzed by using a coal mine disaster data multi-element discrete feature extraction method, key feature extraction such as gas content dynamic prediction, coal seam gas emission, salient feature analysis, water damage water level, water quality, water quantity, microseism, an electric method and the like is realized, data analysis such as roof disaster support resistance, roof separation, anchor rod and cable load data analysis, rock burst microseism, drilling stress, drilling cuttings quantity and the like is performed, dust concentration analysis, fire carbon monoxide concentration, goaf temperature analysis and the like are performed, coal mine disaster occurrence precursor information is mined from the data based on a data mining technology, and coupling relation analysis among various disaster-causing precursor information is performed from three data level angles of original information, feature information and decision information, so that disaster-causing precursor information of various disasters possibly existing in a region to be detected is obtained.
Optionally, in some embodiments, when performing multi-element discrete feature extraction processing on the initial production data, the initial static data and the data feature vector to obtain disaster causing precursor information, performing gas disaster data analysis processing on the initial production data, the initial static data and the data feature vector to obtain gas disaster causing precursor information, performing flood disaster data analysis processing on the initial production data, the initial static data and the data feature vector to obtain flood disaster causing precursor information, performing roof disaster data analysis processing on the initial production data, the initial static data and the data feature vector to obtain roof disaster causing precursor information, performing rock burst disaster data analysis processing on the initial production data, the initial static data and the data feature vector to obtain rock burst disaster causing precursor information, performing dust disaster data analysis processing on the initial production data, the initial static data and the data feature vector to obtain disaster causing precursor information, and performing analysis processing on the initial production data, the initial static data and the data feature vector to obtain the fire disaster causing precursor information, the flood disaster causing precursor information, the roof rock burst disaster causing precursor information, the disaster causing precursor information and the disaster causing precursor information.
In the implementation of the disclosure, when the initial production data, the initial static data and the data feature vectors are subjected to gas disaster data analysis processing to obtain the gas disaster-induced precursor information, the dynamic prediction of gas content, the gas emission of the coal seam and the salient feature analysis can be performed to obtain the gas disaster-induced precursor information, when the initial production data, the initial static data and the data feature vectors are subjected to water disaster data analysis processing to obtain the water disaster-induced precursor information, the key features such as water level, water quality, water quantity, micro-vibration and electrical method and the like can be extracted to obtain the water disaster-induced precursor information, when the initial production data, the initial static data and the data feature vectors are subjected to roof disaster data analysis processing to obtain the roof disaster-induced precursor information, the roof disaster support resistance, roof separation and anchor rod load can be subjected to data analysis, so as to obtain the roof disaster-causing precursor information, when the initial production data, the initial static data and the data feature vectors are subjected to rock burst disaster data analysis processing to obtain the rock burst disaster-causing precursor information, the rock burst data analysis, the microseism, the drilling stress, the drilling cuttings amount and the like can be subjected to data analysis to obtain the rock burst disaster-causing precursor information, when the initial production data, the initial static data and the data feature vectors are subjected to dust disaster data analysis processing to obtain the dust disaster precursor information, the dust concentration analysis, the dust accumulation thickness, the dust fall effect analysis and the like can be subjected to data analysis to obtain the dust disaster precursor information, when the initial production data, the initial static data and the data feature vectors are subjected to fire disaster data analysis processing to obtain the fire disaster precursor information, the fire disaster information management, the internal cause fire disaster early warning, the external cause fire disaster early warning, the intelligent decision function, the fire disaster simulation and other modes can be used for analyzing and processing to obtain the fire disaster causing precursor information, and the gas disaster causing precursor information, the water disaster causing precursor information, the roof disaster causing precursor information, the rock burst disaster causing precursor information, the dust disaster causing precursor information and the fire disaster causing precursor information obtained through analysis are used as the disaster causing precursor information.
S204: and determining a coal mine disaster early warning processing model according to the mine condition information of the area to be detected.
Optionally, in some embodiments, when determining the coal mine disaster early warning processing model according to mine condition information of the to-be-detected area, a disaster early warning rule and a disaster early warning parameter corresponding to the to-be-detected area may be determined according to the mine condition information, and the coal mine disaster early warning processing model may be configured according to the disaster early warning rule and the disaster early warning parameter.
S205: and inputting the disaster causing precursor information into a coal mine disaster early warning processing model for processing to obtain a plurality of target secondary values of the disaster causing precursor information triggering disaster early warning critical values.
S206: and determining comprehensive disaster risk information according to the target secondary value.
In the embodiment of the disclosure, when the comprehensive disaster risk information is determined, disaster-causing precursor information can be input into a coal mine disaster early warning processing model for processing, a target secondary value of a disaster early warning critical value triggered by a plurality of disaster-causing precursor information is obtained, then a coal mine disaster classification and classification comprehensive early warning index library can be established according to a coal mine disaster evolution and disaster-causing mechanism quantitative analysis method, and the coal mine disaster classification and classification comprehensive early warning index library is compared with the coal mine disaster classification and classification comprehensive early warning index according to the target secondary value so as to determine disaster risk grade values of various disaster types, and the possible occurrence places of the disasters of the various disaster types, disaster occurrence reason information and disaster risk grade values are used as the comprehensive disaster risk information together.
Optionally, in some embodiments, when determining the comprehensive disaster risk information according to the target secondary value, the comprehensive disaster risk information may be determined according to the target secondary value and the comprehensive disaster risk information of the coal mine disaster classification and classification.
S207: and determining disaster reason information and disaster risk grades according to the comprehensive disaster risk information.
S208: and carrying out visual processing on the comprehensive disaster risk information, the disaster reason information and the disaster risk grades of the plurality of areas to be detected.
In the embodiment of the disclosure, after the comprehensive disaster risk information is determined, disaster cause information and disaster risk level can be determined according to the comprehensive disaster risk information, then the comprehensive disaster risk information, the disaster cause information and the disaster risk level of a plurality of areas to be detected are subjected to visual processing, an early warning information integrated visualization and release sharing module can be established, the three-dimensional one-picture design mode is adopted to integrate and display the disaster risk areas, risk levels, risk cause information of gas, water disaster, roof, rock burst, fire disaster, dust and the like, a disaster early warning index system, an early warning model and a data service interface service are provided, the early warning information is timely and automatically released, the risk information is conveniently extracted by different demand personnel and systems, and the coal mine safety production is ensured.
For example, as shown in fig. 3, fig. 3 is a schematic structural diagram of a coal mine disaster monitoring and early warning system in an embodiment of the disclosure, where the system mainly includes five parts of modules, and the modules are respectively as follows: the system comprises a data acquisition module, a data cleaning and feature vector extraction module, a data mining and fusion analysis module, a disaster early warning and risk judging module and an early warning information integrated visualization and release sharing module.
The risk monitoring method for coal mine disasters provided in the embodiment constructs a dynamic database for coal mine gas, water disaster, roof, rock burst, fire disaster and dust disaster early warning, constructs a set of coal mine disaster information management and storage method which comprises various disasters such as gas, water disaster, roof, rock burst, fire and dust, is compatible with different data types and supports semi-structured and unstructured data, constructs a dynamic database for coal mine disaster early warning, constructs a comprehensive early warning index database and an early warning model database based on data driving, comprises an early warning index model database of more than 20 types of data such as micro-earthquake, stress, displacement, gas concentration, water level, water quality, water quantity, dust concentration, carbon monoxide concentration, goaf temperature and the like, realizes fusion analysis among different disaster data, and establishes a unified coal mine disaster information integration and early warning platform, the method has the advantages that the disaster data of the coal mine are efficiently and integrally managed, analyzed and processed, and transparently shared, the utilization efficiency of the data is improved, the real-time dynamic early warning of various disasters such as coal mine gas, water disaster, roof, rock burst, fire, dust and the like is realized, the safe production of the coal mine is ensured, the fusion sharing, the analysis and the mining among different disaster data are realized, the multi-disaster data fusion analysis is realized, the data acquired in real time by the system are comprehensively considered when the data analysis of monitoring systems such as the roof, the gas, hydrology, dust, fire disaster and the like is carried out, the data acquired in real time by the system are utilized in combination, such as micro-vibration, support resistance and a large amount of static data and dynamic data, the dimension reduction processing such as data feature extraction and the like are not carried out, the data cleaning such as data integrity inspection is completed, the flexible increase, the decrease and the configuration of different data analysis methods are realized by adopting the modes of an early warning index library and a model library, and the modes of mutual inspection among different indexes are adopted, and the accuracy of risk prediction is improved.
In this embodiment, initial production data and initial static data of an area to be detected are obtained, the initial production data and the initial static data are processed, disaster-causing precursor information corresponding to a plurality of disaster types in the area to be detected is obtained, a coal mine disaster early warning processing model is determined according to mine condition information of the area to be detected, and the plurality of disaster-causing precursor information is subjected to joint analysis processing according to the coal mine disaster early warning processing model, so that comprehensive disaster risk information of the area to be detected is obtained, prediction data of a plurality of disasters of the area to be detected can be jointly analyzed, the possibility and the hazard of occurrence of the disasters are evaluated according to the obtained disaster hidden danger and precursor information before disaster formation, real-time accurate early warning of coal mine disasters is realized, safety of coal mine production is effectively improved, the initial production data and the initial static data of the area to be detected are acquired and stored according to data type information and data structure information, and the different types of different systems in the area to be detected are increasingly multiplexed and complicated, the acquisition speed of data is increased, the acquisition of the data is gradually increased, the data acquisition speed of the data is increased, the data is more and more convenient, the data is more convenient, the interface is based on the acquisition of the data, the characteristics of the data is more and the characteristics are more convenient, and the data are more convenient to store.
Fig. 4 is a schematic structural diagram of a risk monitoring device for coal mine disaster according to another embodiment of the present disclosure.
As shown in fig. 4, the risk monitoring device for coal mine disasters includes:
an acquisition module 401, configured to acquire initial production data and initial static data of an area to be detected;
the first processing module 402 is configured to process the initial production data and the initial static data to obtain disaster-causing precursor information corresponding to a plurality of disaster types in the area to be detected;
the first determining module 403 is configured to determine a coal mine disaster early warning processing model according to mine condition information of a to-be-detected area; and
and the second processing module 404 is configured to perform joint analysis processing on the disaster-causing precursor information of multiple disasters according to the coal mine disaster early warning processing model, so as to obtain comprehensive disaster risk information of the area to be detected.
In some embodiments of the present disclosure, as shown in fig. 5, fig. 5 is a schematic structural diagram of a risk monitoring device for coal mine disaster according to another embodiment of the present disclosure, where the second processing module 404 includes:
the processing submodule 4041 is used for inputting disaster-causing precursor information into the coal mine disaster early-warning processing model for processing to obtain target secondary values of a plurality of disaster-causing precursor information triggering disaster early-warning critical values;
A determining submodule 4042 is configured to determine comprehensive disaster risk information according to the target secondary value.
In some embodiments of the present disclosure, the determining submodule 4042 is specifically configured to:
acquiring comprehensive early warning index information of classification and grading of coal mine disasters;
and determining comprehensive disaster risk information according to the target secondary value and the coal mine disaster classification and grading comprehensive early warning index information.
In some embodiments of the present disclosure, the first determining module 403 is specifically configured to:
determining disaster early warning rules and disaster early warning parameters corresponding to the area to be detected according to mine condition information;
and configuring a coal mine disaster early warning processing model according to the disaster early warning rules and the disaster early warning parameters.
In some embodiments of the present disclosure, the first processing module 402 is specifically configured to:
performing data cleaning and data feature extraction on the initial production data and the initial static data to obtain data feature vectors;
and performing multi-element discrete feature extraction processing on the initial production data, the initial static data and the data feature vector to obtain disaster-causing precursor information.
In some embodiments of the present disclosure, wherein the first processing module 402 is further configured to:
Analyzing and processing the initial production data, the initial static data and the data feature vector to obtain disaster precursor information of the gas disaster;
analyzing and processing the water disaster data of the initial production data, the initial static data and the data feature vector to obtain disaster precursor information of the water disaster;
analyzing and processing the top plate disaster data of the initial production data, the initial static data and the data feature vector to obtain top plate disaster precursor information;
analyzing and processing rock burst disaster data of the initial production data, the initial static data and the data feature vector to obtain disaster precursor information of the rock burst disaster;
carrying out dust disaster data analysis processing on the initial production data, the initial static data and the data feature vector to obtain dust disaster precursor information;
analyzing and processing fire disaster data of the initial production data, the initial static data and the data feature vector to obtain disaster precursor information of the fire disaster;
the disaster-causing precursor information of the gas disaster, the disaster-causing precursor information of the water disaster, the disaster-causing precursor information of the roof disaster, the disaster-causing precursor information of the rock burst disaster, the disaster-causing precursor information of the dust disaster and the disaster-causing precursor information of the fire disaster are used as disaster-causing precursor information.
In some embodiments of the present disclosure, further comprising:
a second determining module 405, configured to determine disaster cause information and a disaster risk level according to the comprehensive disaster risk information;
and a third processing module 406, configured to perform visualization processing on the comprehensive disaster risk information, the disaster cause information and the disaster risk level of the plurality of areas to be detected.
In some embodiments of the present disclosure, the obtaining module 401 is specifically configured to:
acquiring data type information and data structure information of data in a region to be detected;
and acquiring and storing initial production data and initial static data of the area to be detected according to the data type information and the data structure information.
Corresponding to the method for monitoring the risk of a coal mine disaster provided by the embodiments of fig. 1 to 3, the present disclosure further provides a device for monitoring the risk of a coal mine disaster, and since the device for monitoring the risk of a coal mine disaster provided by the embodiments of the present disclosure corresponds to the method for monitoring the risk of a coal mine disaster provided by the embodiments of fig. 1 to 3, an implementation of the method for monitoring the risk of a coal mine disaster is also applicable to the device for monitoring the risk of a coal mine disaster provided by the embodiments of the present disclosure, which is not described in detail in the embodiments of the present disclosure.
In this embodiment, initial production data and initial static data of an area to be detected are obtained, the initial production data and the initial static data are processed to obtain disaster-causing precursor information corresponding to a plurality of disaster types in the area to be detected, a coal mine disaster early warning processing model is determined according to mine condition information of the area to be detected, and the plurality of disaster-causing precursor information is subjected to joint analysis processing according to the coal mine disaster early warning processing model to obtain comprehensive disaster risk information of the area to be detected, prediction data of a plurality of disasters of the area to be detected can be jointly analyzed, and the possibility and harm of occurrence of the disasters are evaluated before the disasters occur according to the obtained disaster hidden danger and precursor information, so that real-time accurate early warning of the coal mine disasters is realized, and the safety of coal mine production is effectively improved.
In order to implement the above-described embodiments, the present disclosure also proposes a non-transitory computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements a risk monitoring method for coal mine disasters as proposed in the foregoing embodiments of the present disclosure.
To achieve the above embodiments, the present disclosure also proposes a computer program product which, when executed by an instruction processor in the computer program product, performs a risk monitoring method for coal mine disasters as proposed in the foregoing embodiments of the present disclosure.
Fig. 6 illustrates a block diagram of an exemplary electronic device suitable for use in implementing embodiments of the present disclosure.
The computer device 12 shown in fig. 6 is merely an example and should not be construed as limiting the functionality and scope of use of the disclosed embodiments.
As shown in FIG. 6, the computer device 12 is in the form of a general purpose computing device. Components of computer device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, a bus 18 that connects the various system components, including the system memory 28 and the processing units 16.
Computer device 12 typically includes a variety of computer system readable media. Such media can be any available media that is accessible by computer device 12 and includes both volatile and nonvolatile media, removable and non-removable media.
Although not shown in fig. 6, a magnetic disk drive for reading from and writing to a removable non-volatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive for reading from or writing to a removable non-volatile optical disk (e.g., a compact disk read only memory (Compact Disc Read Only Memory; hereinafter CD-ROM), digital versatile read only optical disk (Digital Video Disc Read Only Memory; hereinafter DVD-ROM), or other optical media) may be provided. In such cases, each drive may be coupled to bus 18 through one or more data medium interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to carry out the functions of the various embodiments of the disclosure.
A program/utility 40 having a set (at least one) of program modules 42 may be stored in, for example, memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment. Program modules 42 generally perform the functions and/or methods in the embodiments described in this disclosure.
The computer device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), one or more devices that enable a person to interact with the computer device 12, and/or any devices (e.g., network card, modem, etc.) that enable the computer device 12 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 22. Moreover, the computer device 12 may also communicate with one or more networks such as a local area network (Local Area Network; hereinafter LAN), a wide area network (Wide Area Network; hereinafter WAN) and/or a public network such as the Internet via the network adapter 20. As shown, network adapter 20 communicates with other modules of computer device 12 via bus 18. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with computer device 12, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
The processing unit 16 executes various functional applications and parameter information determination by running programs stored in the system memory 28, for example, implementing the risk monitoring method of coal mine disasters mentioned in the foregoing embodiments.
It should be noted that in the description of the present disclosure, the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. Furthermore, in the description of the present disclosure, unless otherwise indicated, the meaning of "a plurality" is two or more.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and further implementations are included within the scope of the preferred embodiment of the present disclosure in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present disclosure.
It should be understood that portions of the present disclosure may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.
Furthermore, each functional unit in the embodiments of the present disclosure may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules may also be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product.
The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, or the like.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present disclosure. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Although embodiments of the present disclosure have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the present disclosure, and that variations, modifications, alternatives, and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the present disclosure.
Claims (10)
1. A risk monitoring method for coal mine disasters, comprising:
acquiring initial production data and initial static data of an area to be detected;
processing the initial production data and the initial static data to obtain disaster-causing precursor information corresponding to a plurality of disaster types in the area to be detected;
determining a coal mine disaster early warning processing model according to the mine condition information of the region to be detected; and
and carrying out joint analysis processing on a plurality of disaster-causing precursor information according to the coal mine disaster early warning processing model to obtain comprehensive disaster risk information of the region to be detected.
2. The method of claim 1, wherein the performing joint analysis processing on the disaster-causing precursor information according to the coal mine disaster early warning processing model to obtain comprehensive disaster risk information of the to-be-detected area includes:
Inputting the disaster causing precursor information into the coal mine disaster early warning processing model for processing to obtain a plurality of target secondary values of the disaster causing precursor information triggering disaster early warning critical values;
and determining the comprehensive disaster risk information according to the target secondary value.
3. The method of claim 2, wherein said determining said integrated hazard risk information based on said target secondary value comprises:
acquiring comprehensive early warning index information of classification and grading of coal mine disasters;
and determining the comprehensive disaster risk information according to the target secondary value and the coal mine disaster classification and grading comprehensive early warning index information.
4. The method of claim 1, wherein the determining a coal mine disaster pre-warning processing model according to the mine condition information of the area to be detected comprises:
determining disaster early warning rules and disaster early warning parameters corresponding to the to-be-detected area according to the mine condition information;
and configuring the coal mine disaster early warning processing model according to the disaster early warning rules and the disaster early warning parameters.
5. The method of claim 1, wherein the processing the initial production data and the initial static data to obtain disaster-causing precursor information corresponding to a plurality of disaster categories in the area to be detected comprises:
Performing data cleaning and data feature extraction on the initial production data and the initial static data to obtain data feature vectors;
and performing multi-element discrete feature extraction processing on the initial production data, the initial static data and the data feature vector to obtain disaster-causing precursor information.
6. The method of claim 5, wherein performing a multi-component discrete feature extraction process on the initial production data, the initial static data, and the data feature vector to obtain the disaster-causing precursor information comprises:
analyzing and processing the initial production data, the initial static data and the data feature vector to obtain disaster precursor information of the gas disaster;
analyzing and processing the initial production data, the initial static data and the data feature vector to obtain disaster precursor information of the water disaster;
analyzing and processing the initial production data, the initial static data and the data feature vector to obtain roof disaster precursor information;
analyzing and processing rock burst disaster data of the initial production data, the initial static data and the data feature vector to obtain rock burst disaster-induced precursor information;
Carrying out dust disaster data analysis processing on the initial production data, the initial static data and the data feature vector to obtain dust disaster precursor information;
analyzing and processing the initial production data, the initial static data and the data feature vector to obtain disaster precursor information of the fire disaster;
and taking the disaster precursor information caused by the gas disaster, the disaster precursor information caused by the water disaster, the disaster precursor information caused by the roof disaster, the disaster precursor information caused by the rock burst disaster, the disaster precursor information caused by the dust disaster and the disaster precursor information caused by the fire disaster as the disaster precursor information caused by the disaster.
7. The method as recited in claim 1, further comprising:
determining disaster cause information and disaster risk level according to the comprehensive disaster risk information;
and carrying out visual processing on the comprehensive disaster risk information, the disaster reason information and the disaster risk grades of the plurality of areas to be detected.
8. The method of claim 1, wherein the acquiring initial production data and initial static data for the area to be inspected comprises:
Acquiring data type information and data structure information of the data in the area to be detected;
and acquiring and storing the initial production data and the initial static data of the area to be detected according to the data type information and the data structure information.
9. A risk monitoring device for coal mine disasters, comprising:
the acquisition module is used for acquiring initial production data and initial static data of the area to be detected;
the first processing module is used for processing the initial production data and the initial static data to obtain disaster-causing precursor information corresponding to a plurality of disaster types in the area to be detected;
the determining module is used for determining a coal mine disaster early warning processing model according to the mine condition information of the to-be-detected area; and
and the second processing module is used for carrying out joint analysis processing on the disaster-causing precursor information according to the coal mine disaster early warning processing model to obtain comprehensive disaster risk information of the region to be detected.
10. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
The memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310077185.5A CN116090823A (en) | 2023-01-16 | 2023-01-16 | Risk monitoring method and device for coal mine disasters, electronic equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310077185.5A CN116090823A (en) | 2023-01-16 | 2023-01-16 | Risk monitoring method and device for coal mine disasters, electronic equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116090823A true CN116090823A (en) | 2023-05-09 |
Family
ID=86208058
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310077185.5A Pending CN116090823A (en) | 2023-01-16 | 2023-01-16 | Risk monitoring method and device for coal mine disasters, electronic equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116090823A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117114206A (en) * | 2023-10-23 | 2023-11-24 | 北京联创高科信息技术有限公司 | Calculation method for coal mine water damage index data trend |
| CN117349269A (en) * | 2023-08-24 | 2024-01-05 | 长江水上交通监测与应急处置中心 | Full-river-basin data resource management and exchange sharing method and system |
-
2023
- 2023-01-16 CN CN202310077185.5A patent/CN116090823A/en active Pending
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117349269A (en) * | 2023-08-24 | 2024-01-05 | 长江水上交通监测与应急处置中心 | Full-river-basin data resource management and exchange sharing method and system |
| CN117114206A (en) * | 2023-10-23 | 2023-11-24 | 北京联创高科信息技术有限公司 | Calculation method for coal mine water damage index data trend |
| CN117114206B (en) * | 2023-10-23 | 2024-01-26 | 北京联创高科信息技术有限公司 | Calculation method for coal mine water damage index data trend |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN116090823A (en) | Risk monitoring method and device for coal mine disasters, electronic equipment and storage medium | |
| Østerlie et al. | Digital sand: The becoming of digital representations | |
| CN115186917A (en) | Active early warning type risk management and control system and method | |
| CN113688532B (en) | Mine disaster early warning method and device, storage medium and electronic device | |
| Oskouei et al. | Statistical methods for evaluating the effect of operators on energy efficiency of mining machines | |
| US20170306726A1 (en) | Stuck pipe prediction | |
| CN201687808U (en) | Oil pollution monitoring device of hydraulic system | |
| CN116777085B (en) | Coal mine water damage prediction system based on data analysis and machine learning technology | |
| CN116842765B (en) | Method and system for realizing underground safety management of petroleum logging based on Internet of things | |
| CN107506832B (en) | Hidden danger mining method for assisting monitoring tour | |
| CN116579601B (en) | Mine safety production risk monitoring and early warning system and method | |
| CN116579214A (en) | Digital twinning-based three-dimensional visual bridge pier monitoring system and method | |
| CN115759351A (en) | Slurry shield tunneling comprehensive early warning method and system and storage medium | |
| Antipova et al. | Data-driven model for the drilling accidents prediction | |
| Gjertsen et al. | IADC Dull Code Upgrade: Photometric Classification and Quantification of the New Dull Codes | |
| CN113642487A (en) | Artificial intelligence-based method and system applied to safety production | |
| CN112667704A (en) | Coal mine industry internet data middle platform system structure | |
| Jin et al. | Machine learning-based identification of segment joint failure in underground tunnels | |
| US20140067352A1 (en) | Presenting attributes of interest in a physical system using process maps based modeling | |
| Devi et al. | A Hybrid Structural Building Data Mining System Based On the Data Tracking Through Devices | |
| CN116757494A (en) | Coal mine safety evaluation method and system | |
| Bragatto et al. | Online Condition Monitoring: Sensors, Models and Software for a Safe Management of Aged Process Plants | |
| CN113408322B (en) | Method and device for identifying sudden permeable scene in mine | |
| Little | The benefit to open pit rock slope design of geotechnical databases | |
| Belov et al. | Revolutionizing Drilling Operations: An Automated Approach for Drilling Analysis and Optimization Using Real-Time Data |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |