CN115830829A - General forecast early warning system of colliery water damage - Google Patents
General forecast early warning system of colliery water damage Download PDFInfo
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Abstract
The invention discloses a general forecasting and early warning system for water damage of a coal mine, which comprises a data monitoring subsystem, a data processing subsystem and a data processing subsystem, wherein the data monitoring subsystem is used for acquiring mine data; the data processing module comprises a data warehouse and an application layer; the water disaster risk dynamic analysis subsystem is used for constructing an early warning analysis model and analyzing the whole water disaster risk of the coal mine according to the mine data; the evaluation model management subsystem is used for managing the early warning analysis model; and the threshold management library is used for setting a risk threshold according to the overall water disaster risk of the coal mine. The method provided by the invention integrates coal mine basic information data, combs a coal mine water disaster master control risk evaluation index system, constructs a water disaster risk analysis model, performs data mining, association and multidimensional analysis by combining cloud computing, big data and artificial intelligence technologies, effectively displays different levels and different types of risks, dynamically masters the coal mine water disaster risk variation trend, and provides powerful data support for the services of accurate coal mine law enforcement, remote monitoring, accident tracing and back-up and the like.
Description
Technical Field
The invention belongs to the field of coal mine water disaster early warning, and particularly relates to a coal mine water disaster universal forecasting and early warning system.
Background
Since coal mining, the safety production accidents are continuous, particularly the water damage accidents, the injury is large and the influence is wide. The development of coal mine water damage monitoring and early warning is the key to realizing coal mine safety mining and reducing water damage loss. The coal mine water damage early warning and forecasting technology is still in a continuous exploration stage, the key of water inrush prediction is to research the water inrush mechanism of the coal seam top and bottom plate and the damage rule of coal seam mining on the top and bottom plate, and certain progress and achievement are achieved through long-time exploration and experiments, both theory and practice. However, from the current research degree, compared with the existing early warning and forecasting technology, the key technology of the universal forecasting and early warning system suitable for the water damage of the coal mine realizes the advanced prediction and timely early warning of water inrush, and further research and improvement are still needed in future.
The development of the coal mine water damage monitoring and early warning system in China is relatively slow, and the current situation is as follows:
firstly, the coal mine emergency management department cannot be communicated longitudinally and is isolated transversely, and in the longitudinal direction, the coal mine monitoring department, the coal mine monitoring bureau, the mining group, the mining company and the coal mine do not form network connection; in the transverse direction, the information of the group or the mine in the transverse direction cannot be shared, and an information island is formed;
secondly, the system is incomplete, the function is limited, a water damage index system is not established, the water damage early warning function is basically lost, and the functions of service supervision and safety production are limited;
and thirdly, construction standards are lacked, and system monitoring indexes, functional parameters, target effects and the like have no construction standards and are respectively administrative.
Disclosure of Invention
The invention aims to provide a universal forecasting and early warning system for water damage of a coal mine, which aims to solve the problems in the prior art.
In order to achieve the aim, the invention provides a coal mine water disaster universal forecasting and early warning system which comprises a data monitoring subsystem, a data processing module, a water disaster risk dynamic analysis subsystem, an evaluation model management subsystem, a threshold management library and a data processing module;
the data monitoring subsystem is used for acquiring mine data;
the data processing module comprises a data warehouse and an application layer;
the water disaster risk dynamic analysis subsystem is used for constructing an early warning analysis model and analyzing the whole water disaster risk of the coal mine according to the mine data;
the evaluation model management subsystem is used for managing the early warning analysis model;
and the threshold management library is used for setting a risk threshold according to the overall water disaster risk of the coal mine.
Optionally, the mine data includes real-time detection data and mine static data, the real-time detection data is used for reflecting a real-time operation state of the mine water disaster in real time, and the mine static data is used for determining a safety level of the mine.
Optionally, the data warehouse utilizes a big data technology to store, process and calculate historical data and real-time data, and utilizes a big data algorithm to perform real-time calculation and offline calculation;
the application layer adopts a micro-service cluster architecture mode to split and deploy services.
Optionally, the types of the early warning analysis model include a risk evaluation model based on a single dynamic index and a risk evaluation model based on a multiple dynamic index, a static index risk evaluation model based on a safety check sheet scoring method, and a static index risk evaluation model based on an analytic hierarchy process.
Optionally, the evaluation model management subsystem performs model query according to related conditions, displays the predicted duration of the early warning analysis model, and classifies according to the predicted duration, where the related conditions include, but are not limited to, model type, model early warning direction, and model name.
Optionally, the evaluation model management subsystem divides the early warning analysis model into a real-time dynamic prediction model, a medium-short term prediction model and a long-term prediction model;
the real-time dynamic prediction model comprises a gray prediction model and a Fourier fitting model; the medium-short term prediction model comprises a phase space reconstruction support vector machine combined model, a wavelet neural network model, a seasonal model and a wavelet support vector machine combined model, and the long term prediction model comprises an exponential smoothing model and a decision tree multiple regression model.
Optionally, the threshold management library determines a dynamic risk early warning weight according to the early warning analysis model; thresholds are set for different risk levels through water inflow, water level, water temperature and precipitation, and meanwhile hierarchical setting is supported.
Optionally, the threshold setting of the threshold management library includes rainfall early warning level division, long observation hole water level early warning level division, environment temperature early warning level division, and mine water inflow early warning level division.
The invention has the technical effects that:
the invention uses the original coal mine safety standardized information management system, the coal mine safety training examination system and the coal mine safety monitoring business system for reference, fuses coal mine basic information data, combs a coal mine water disaster master control risk evaluation index system and constructs a water disaster risk analysis model, and combines the technologies of cloud computing, big data, artificial intelligence and the like to carry out data mining, association and multidimensional analysis, thereby realizing the purposes of intelligent analysis and prediction and prejudgment. Through early warning, monitoring and studying, the national coal mine water damage risk base number is figured out, meanwhile, risks in different levels and different types are effectively displayed, the coal mine water damage risk change trend is dynamically mastered, and powerful data support is provided for services such as precise coal mine law enforcement, remote monitoring, accident tracing and back-up.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application. In the drawings:
FIG. 1 is a general architecture diagram of a coal mine water disaster general forecasting and early warning system in an embodiment of the invention;
FIG. 2 is a deployment architecture diagram of a coal mine water disaster general forecasting and early warning system in an embodiment of the invention;
FIG. 3 is a network architecture diagram of a coal mine water disaster general forecasting and early warning system in an embodiment of the present invention;
FIG. 4 is a graph of water inflow versus microseismic events for the working face of the south pavilion coal mine 207 in accordance with an embodiment of the present invention;
fig. 5 is a structural diagram of a south pavilion mine water condition monitoring and water damage risk early warning system in the embodiment of the invention.
Detailed Description
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
It should be noted that the steps illustrated in the flow charts of the figures may be performed in a computer system such as a set of computer executable instructions and that, although a logical order is illustrated in the flow charts, in some cases, the steps illustrated or described may be performed in an order different than presented herein.
Example one
As shown in fig. 1 to 3, the present embodiment provides a general forecasting and early warning system for water damage of a coal mine, and the specific implementation manner is as follows:
overall technology architecture design
Principle of design
The system architecture design adopts an advanced and mature technology to meet the requirements of the system, takes other related management requirements into consideration, and embodies the advancement associated with software and hardware while meeting the system application. In the design, the advanced technology is combined with the existing mature technology, standard and equipment, the application requirement and the future development trend are fully considered, the mature and advanced technology is adopted to adapt to the requirements of data acquisition, analysis, service and the like, and the current and future potential technology expansion requirements can be adapted. In addition, the software technology selected by the architecture design supports national standards, and a standard protocol is adopted for interconnection and intercommunication, so that the construction can be seamlessly interconnected with the existing system and other systems, the structure is really open, the standards are unified and standardized, and the data standard is customized, thereby laying a foundation for future development, and therefore the architecture design of the system follows the principle: the method comprises a technical advance principle, a safe reliability principle, a standardization principle, an operable flexible usability principle, an expandable maintainability principle and a cross-platform principle.
System architecture
The construction of a general service management system usually focuses on a single service target, and the main focus is on realizing the service function. However, as the innovation application of big data classes, the more the internal and external interaction, the more the relationship between data becomes complex, and the more the requirements on the data interaction between systems become. Therefore, a data-centric architecture should be built, data integration, relational computation should be performed on the basis of a big data platform, and data-based API services should be implemented. The platform construction should design a system technical architecture by following a data-core architecture, data required in the system can be acquired from an external source through a data acquisition subsystem, all data involved in the system should be subjected to data analysis after being subjected to standardization processing through the platform, and an API is formed on the basis of the data to provide data services for the outside. The overall architecture diagram of the system is shown in fig. 1, and the deployment architecture diagram and the network architecture diagram of the system are shown in fig. 2 and fig. 3, respectively.
Based on the above architecture, the data processing mode of the system is as follows:
data acquisition platform
The data acquisition of the system depends on a data acquisition platform, the platform meets the real-time increment synchronization among various heterogeneous data sources, provides an abstract model, supports high expandability, supports the configuration and integration of various data media, supports the ETL processing of mass data, and really realizes zero-coding data acquisition and exchange. Meanwhile, the platform provides unified infrastructure (high availability, dynamic load, synchronous task management, plug-in management, monitoring alarm, public service components and the like), so that designers can concentrate on synchronous development, and the platform is once put into use and can benefit for a long time.
Big data storage, calculation
A data warehouse of the system adopts Hadoop, massive historical data and real-time data are stored, processed and calculated by using a big data technology, real-time calculation and offline calculation are performed by combining various data models and risk models and using a big data algorithm, and the analyzed and summarized data are sent to upper-layer application, so that the calculation capacity of the upper-layer application is ensured.
Micro-services
The application layer adopts a micro-service cluster architecture mode to split and deploy the services, code coupling among the functional service modules is reduced, each service is not affected, independent deployment and operation are realized, the services are lighter, and later-stage function upgrading and iteration are facilitated.
Security architecture
The security architecture system mainly comprises a strategy system, an organization system, an operation system and a technical system water disaster subject library design
The water damage thematic design library comprises a water damage risk evaluation index system library, a thematic library model management library and a dynamic index system weight and threshold management library.
The water disaster risk evaluation index system library establishes a risk early warning analysis model on the basis of a coal mine water disaster risk evaluation index system, and shows coal mine risk points in a multi-dimensional, multi-view and omnibearing manner through various basic data of a coal mine, hydrologic monitoring system real-time monitoring data, coal mine management data and other contents, and analyzes the whole water disaster risk of the coal mine according to the early warning analysis model. The coal mine water disaster risk state is dynamically known in real time, the coal mine water disaster high risk points are timely disposed, and a suggestion measure is provided for coal mine safety production.
In the monitoring data of the water damage risk evaluation indexes, part of the data is real-time monitoring data, and the other part of the data is mine static basic data. For dynamic data, the real-time running state of mine water damage can be reflected in real time according to main test points
The provincial bureau access data condition mainly comprises the following steps: hydrologic dynamic observation data, monitoring water level, water temperature, water quality, water inflow, rainfall and the like; safety management monitoring data, video monitoring, personnel positioning, personnel quantity management, microseismic data and the like; monitoring dynamic data of underground environment, wind speed, temperature, humidity and the like. A dynamic water disaster risk evaluation index system can form a mine water disaster dynamic risk analysis system consisting of 'state-provincial administration-branch administration-coal mine' according to a user supervision level, the system with the lower level pays more attention to specific services, displayed dynamic index value data are more detailed, and the displayed dynamic index value data are sequentially traced to specific monitoring equipment from top to bottom, so that specific positions and reasons with high safety accident risk are found in time, and better supervision and supervision are achieved. For example, the state level can visually display the real-time state of the water inflow of mines in the country, and the real-time safety level partitions of the water inflow of all monitored mines in each province of the country are displayed; the provincial level can present the real-time safety level subareas of the water inflow quantity of each branch monitoring mine of each province; the branch level can present the real-time safety level subareas of the water inflow of the monitoring mines of each large group; the coal mine can present the safety level of each level of water inflow.
The static data of the mine can also be used for evaluating the running state of the mine by establishing an index system, and determining the safety level of the mine. As the index parameters monitored in the online monitoring data system of each province are not completely refined, the provided static information is limited, and based on the actual situation, the construction of the project index system mainly comprises the following 4 types of data, namely a management system, hydrogeology, water control engineering and water hazard emergency management.
The special subject library model comprises a risk evaluation model based on a single dynamic index and a risk evaluation model based on a plurality of dynamic indexes,
The method comprises a safety inspection table scoring method-based static index risk evaluation model and an analytic hierarchy process-based static index risk evaluation model.
Dynamic analysis of water damage risk
The water disaster risk dynamic analysis comprises key indexes such as coal mine basic information, online monitoring information and the like. Searching a corresponding coal mine according to the administrative region, the hydrogeological type and the water disaster type, displaying coal mine basic information and a related monitoring equipment information list, and meanwhile, exporting a query result; the basic data adopts a MySQL database, and the MySQL database has small volume, high speed, low cost and open source codes; the key indexes of online monitoring are dynamic indexes and static indexes.
4. Water damage risk analysis model
The key technology of the coal mine water disaster universal forecasting and early warning system lies in a water disaster risk analysis model, and the water disaster risk analysis model of the system comprises an evaluation model management library (a dynamic risk early warning model and a static risk early warning model), weight setting and threshold setting.
The evaluation model management library is used for managing the coal mine early warning model, can perform model query according to relevant conditions such as model type, model early warning direction, model name and the like, displays the prediction duration of each model, and divides the early warning analysis model into: the real-time dynamic prediction model comprises a gray prediction model and a Fourier fitting model respectively. The medium-short term prediction model comprises a phase space reconstruction support vector machine combination model, a wavelet neural network model, a seasonal model and a wavelet support vector machine combination model. And the long-term prediction model comprises an exponential smoothing model and a decision tree multiple regression model.
The weight setting is the dynamic risk early warning weight determined according to different prediction models; the risk values, namely the threshold values, of the water inflow amount, the water level, the water temperature, the precipitation amount and the like are flexibly set according to different risk levels, and hierarchical setting is supported.
Setting a threshold value:
rainfall early warning grade division
Rainstorm blue warning signal: the rainfall will be above 50 mm in 24 hours, or already above 50 mm and the rainfall may continue.
Rainstorm yellow warning signal: the rainfall will be above 50 mm in 6 hours, or already above 50 mm and the rainfall may continue.
Rainstorm orange warning signal: the rainfall will be above 50 mm in 3 hours, or already above 50 mm and the rainfall may continue.
Rainstorm red warning signal: the rainfall will be above 100 mm in 3 hours, or already above 100 mm and the rainfall may continue.
(II) division of long observation hole water level early warning grade
And (5) completely compiling the statistics of the monthly amplitude and the annual amplitude of the water level of the long observation hole as characteristic values.
Red early warning: when the absolute value of the difference between the water level monitoring value and the average monthly water level is more than 2 times of the maximum amplitude of the monthly (year); or the water level of the single hydrological long observation hole has the amplitude of variation of more than or equal to 0.5m per hour, and the continuous time exceeds 2 hours or continuously decreases; and starting a red-level early warning (except for sensor calibration).
Orange early warning: when the absolute value of the difference between the water level monitoring value and the average monthly water level is more than 1 time and less than 2 times of the maximum amplitude of the month (year); or the variation of the water level of the single hydrological long observation hole per hour is more than or equal to 0.3 and less than 0.5m, and the duration time exceeds 2 hours or continuously decreases; an orange warning (except for sensor calibration) is initiated.
Yellow early warning: when the absolute value of the difference between the water level monitoring value and the average monthly water level is more than 0.7 time and less than 1 time of the maximum amplitude of the month (year); or the variation of the water level of the single hydrological long observation hole per hour is more than or equal to 0.2 and less than 0.3m, and the duration time exceeds 2 hours or continuously decreases; a yellow warning (except for sensor calibration) is initiated.
Blue early warning: when the absolute value of the difference between the water level monitoring value and the average monthly water level is more than 0.5 time and less than 0.7 time of the maximum amplitude of the month (year); or the variation of the water level of the single hydrological long observation hole per hour is more than or equal to 0.1 and less than 0.2m, and the duration time exceeds 2 hours or continuously decreases; and starting a yellow-blue early warning (except for sensor calibration).
(III) early warning grade division of environmental temperature
And taking the statistics of the monthly amplitude and the annual amplitude of the whole temperature monitoring value as characteristic values.
Red early warning: when the absolute value of the difference between the temperature monitoring value and the average monthly temperature is more than 1 time of the maximum amplitude of the monthly (year); or the temperature of a single monitoring point is suddenly changed by more than 4 degrees, and the duration time exceeds 2 hours, and an orange early warning program (except for sensor calibration) is started.
Orange early warning: when the absolute value of the difference between the temperature monitoring value and the average monthly temperature is 0.75 times and less than 1 time of the maximum variation of the monthly (annual) temperature; or the temperature of a single monitoring point is suddenly changed to be more than 3 degrees and less than 4 degrees, and the duration is more than 2 hours, and an orange early warning program (except for sensor calibration) is started.
Yellow early warning: when the absolute value of the difference between the temperature monitoring value and the average monthly temperature is 0.5 times and less than 0.75 times greater than the maximum variation of the monthly (annual) temperature; or the temperature of a single monitoring point is suddenly changed to be more than 2 degrees and less than 3 degrees, and the duration is more than 2 hours, and a yellow early warning program (except for sensor calibration) is started.
Blue early warning: when the absolute value of the difference between the temperature monitoring value and the average monthly temperature is 0.25 times and less than 0.5 times larger than the maximum variation of the monthly (annual) temperature; or the temperature of a single monitoring point is suddenly changed to be more than 1 degree and less than 2 degrees, and the duration is more than 2 hours, and a blue early warning program (except for sensor calibration) is started.
(IV) mine water inflow early warning grading
And (4) completely compiling a total value of the monthly variation of the water inflow monitoring value of the mine, the mining area, the working face or the water inrush danger site as a characteristic value.
Red early warning: when the absolute value of the difference between the water inflow monitoring value and the average value of the month is more than 3 times of the maximum amplitude of the month (year); or the water inflow suddenly increases by more than or equal to 100 percent (except for centralized drainage), and a red pre-alarm program is started.
Orange early warning: when the absolute value of the difference between the water inflow monitoring value and the average value of the month is more than 2 times and less than 3 times of the maximum amplitude of the month (year); or the sudden increase of the water inflow is more than or equal to 50% and less than 100% (except for centralized drainage), and an orange early warning program is started.
Yellow early warning: when the absolute value of the difference between the water inflow monitoring value and the average value of the month is more than 1 time and less than 2 times of the maximum amplitude of the month (year); or the sudden increase of the water inflow is more than or equal to 30% and less than 50% (except for concentrated drainage), and a yellow early warning program is started.
Blue early warning: when the absolute value of the difference between the water inflow monitoring value and the average value of the month is more than 0.5 time and less than 1 time of the maximum amplitude of the month (year); or the sudden increase of the water inflow is more than or equal to 20% and less than 30% (except for concentrated drainage), and a blue early warning program is started.
Comprehensive data query
The comprehensive data query comprises one-key positioning query, query data interactive display, alarm data situation query, comprehensive report query and hydrologic monitoring data comprehensive query
Big data analysis scheme
The big data analysis scheme comprises a big data platform architecture, a big data analysis environment construction scheme, data extraction and data cleaning, data storage, real-time calculation, offline calculation, data preprocessing, data fusion, text mining, machine learning
Butt-joint nationwide coal mine high-risk disaster risk analysis system
The national coal mine high-risk disaster risk analysis system is developed and deployed by means of a cloud platform built by an emergency department, is in butt joint with an emergency cloud safety management system, and is integrated with a national coal mine safety production risk monitoring and early warning system (first stage) and a national coal mine remote monitoring comprehensive management system.
The system adds the butt joint content aiming at the national coal mine high-risk disaster risk analysis system, and comprises the following steps:
data interworking
By analyzing the reported data and the locally stored data of the national coal mine high-risk disaster risk analysis system, the system designs a targeted data conversion logic, supports the coal mine safety risk monitoring pre-alarm platform to trigger data intercommunication operation in a specified service, and realizes flexible interaction of data.
Flexible access to business processes
The system provides flexible service flow docking for the national coal mine high-risk disaster risk analysis system, and when the coal mine safety risk monitoring and early warning platform and the system generate service intersection, service scripts can be generated rapidly, so that the rapid realization of the service flow is achieved.
Model calculation result output
At the beginning of design, the system fully considers the horizontal and vertical expansion of each service, and reserves data interfaces for interfacing with other systems based on data security, including a data access interface and a data output interface. After integration, a unified external interface service is formed, and the transverse and longitudinal rapid expansion of services is realized. When the model calculation result is output, data needs to be encrypted, including client request encryption and server request decryption. A unified parameter encryption algorithm is set in the external interface service, so that the safety of data in the transmission process is guaranteed.
On the basis of comprehensively researching the conditions of geological structure, mining, hydrogeology and the like of coal mine flood, the invention summarizes the intrinsic mechanism of coal mine flood, provides main control elements causing coal mine flood, converts the main control elements into a monitoring and early warning index system of disaster occurrence, applies flood precursor information in-situ pickup sensing technology, flood factor timely detection technology, flood factor remote monitoring technology and artificial intelligence discrimination technology, establishes a sensing system, a data acquisition and transmission system, a receiving system, a data processing system, a result display system and the like of mine flood disaster early warning, establishes a flood prediction artificial intelligence expert system, and completes the integration and demonstration of a system field bus technology and the system through industrial tests, and the system can operate independently and can operate in a bus or protocol mode and be networked with a mine monitoring and monitoring system.
Example two
As shown in fig. 4-5, in this embodiment, a tenna coal mine in a middenna mining area with complex hydrogeology type is selected as a research object, and a water damage early warning and forecasting system for the tenna coal mine is established to explain a key technology of a coal mine water damage general early warning and forecasting system, which specifically includes the following steps:
basic situation of water damage prevention and control in south pavilion
In the process of mine construction and production, no major water inrush accident occurs, and only large and small water outlet points 12 appear, wherein the water outlet points 9 of more than 30m & lt 3 & gt/h account for 75%. The maximum water outlet occurs in 6, 8 and 6 months in 2017, the maximum water outlet amount of 305 working surfaces is 868.0m3/h, the main water filling source is accumulated water in an aquifer of the Chalk system Luohui and an old goaf of the Chalk system, the main water filling channel is a water guiding crack zone and a sealed poor drilled hole, and the main water damage types are roof water damage and old goaf water damage, so that certain influence is caused on mine production. And 2 times of water inrush of more than 30m3/h occurs in the mine in 2015-2017, and 868.0m3/h is maximum. The water inflow of the working surface of the second panel 207 shows an overall increasing trend with 4 relatively obvious sudden increases, and fig. 4 is a graph of the relation between the water inflow of the working surface 207 and microseismic events.
Water disaster forecasting and early warning system for south pavilion coal mine
The construction of the early warning system is closely matched with the actual mine exploitation, a water condition monitoring index system and a water damage early warning system are designed based on the mine water damage mechanism and the water prevention and control working requirements, on the basis, a hardware monitoring device is matched, and a comprehensive early warning platform based on big data analysis is built. As shown in fig. 5, the main module includes:
hardware system
The method comprises the steps of integrating a ground hydrological dynamic monitoring unit, an underground water regime environment monitoring unit and a mining working face parameter dynamic monitoring unit to form a hardware system with a network structure, transmitting acquired information to a software control system, establishing a water regime information management database, performing simulation processing such as mathematical simulation, setting a measuring point warning range by an expert system by applying an artificial intelligence technology, carrying out critical identification on water damage information, and carrying out pre-warning in different modes such as sound, screen highlighting, mobile phone short messages and the like when monitoring data is out of limit.
Communication network
The ground hydrological monitoring unit is communicated with a hardware system center host by using a GSM/GPRS national public network. The GSM network coverage is applicable. The communication network of the underground water regime environment monitoring unit, the dynamic excavation face parameter monitoring unit and the hardware system center host adopts a digital communication network formed based on the field industrial control bus CAN technology, and the transmission medium of the digital communication network is the same as that of a monitoring cable used for monitoring gas.
Ground hydrology dynamic monitoring unit
The unit comprises a ground drilling water level monitoring part and a river (ditch, canal) water level (flow) monitoring part.
1) Ground drilling water level monitoring
And numbering the ground drill holes, and adopting a ground wireless monitoring mode. The substation, the sensor and the power supply are arranged in the underground safety protection cover and are connected with the drill hole in a sealing mode. The liquid level sensor is arranged below the water level of the drill hole, and the measurement process can be selected according to the change range of the previous year.
2) River (ditch, canal) water level (flow) monitoring
The river (ditch, canal) water level measurement adopts a liquid level sensor or an ultrasonic sensor to convert the river (ditch, canal) water level change into an electric signal, a computer arranged in the substation controls the A/D analog-to-digital conversion and data acquisition, and the data is calculated and processed according to the functional relation between the flow and the river (ditch, canal) water level, so as to display the flow.
(4) Underground water regime environment monitoring unit
The unit comprises three parts of underground drilling water level (water pressure), a drainage system and underground environment condition change monitoring.
1) Drainage system pipeline flow monitoring
An electromagnetic flow sensor is connected with a pipeline to be detected through a flange, a substation is arranged near a measuring point, and power is supplied by AC 127 VAC. The real-time monitoring of the pipeline flow is controlled by a system through a communication network in a downhole wired mode to realize data uploading, storage, display and network publishing.
2) Sump water level monitoring for drainage system
The water level monitoring of temporary water sumps such as underground main pump room water sump, disc area pump room water sump, working surface and the like adopts a piezoresistive liquid level or ultrasonic liquid level sensor and a wired mode to transmit signals. The substation, the sensors and the measuring range are selected, arranged and installed according to the field and field conditions.
3) Downhole open channel flow monitoring
The open channel flow monitoring is that a liquid level sensor or an ultrasonic sensor is adopted to convert the water level change of a measuring tank into an electric signal, a built-in computer of an underground substation is used for controlling A/D (analog/digital) conversion and data acquisition, and the data is calculated and processed according to the functional relation between the flow and the water level of the measuring tank to display the flow. The device is powered by AC 127 VAC.
4) Downhole borehole water level (hydraulic) monitoring
The pressure sensor is adopted, the substation and the sensor are arranged in the underground safety sealing protective cover, the equipment is powered by alternating current 127VAC, and signals are transmitted in a wired mode.
5) Mining environment information integrated monitoring
Mining-influenced space environment information is sometimes very important, and monitoring indexes thereof include: working surface temperature, humidity, air quality information; gas outburst and dynamic phenomenon information; top and bottom plate pressure and mine earthquake information; water inrush symptom information, etc. The information collection and transmission are brought into the system and are analyzed and processed by an expert system, so that a basis is provided for early warning decision.
6) Effective water barrier distance Δ L monitoring
The effective water-resisting layer spacing around the working face of the southwest coal mine is obtained through inversion of a microseismic monitoring system, according to the monitoring principle of the microseismic system, when the microcracks of the surrounding rock water inrush channel are formed, elastic waves can be generated and can be received by sensors arranged in an effective range in real time, three elements of 'space-time intensity', namely time, position and property, of the occurrence of micro-fracture can be obtained through inversion analysis of real-time data of the sensors, so that the real-time position of the development of the water guide channel around the working face is determined, and after three-dimensional coordinate conversion, the effective water-resisting layer spacing delta L real-time value can be calculated.
Prediction and risk early warning system
Theoretically speaking, the water damage risk monitoring and early warning system comprises two levels of scientific problems, one is to predict the future, namely prediction, on the basis of the existing information to realize early warning; and secondly, intelligent monitoring and intelligent early warning are realized, and intelligent related theoretical support is also needed.
(1) Deterministic theoretical prediction
The behavior of the mine water is expressed by using a deterministic theory, the disaster-causing mechanism of the mine water hazard needs to be explored, and the behavior can be expressed by using a mathematical constitutive relation after the mechanism is clear, so that the future behavior is predicted. From the development degree of the current scientific technology, the disaster mechanism can be or can be found out, the disaster mechanism is still the disaster mechanism under specific conditions, and not all mine floods can be given the disaster mechanism. The research method is explained by taking the theoretically mature water bursting mechanism of the soleplate as an example.
In this respect, according to the typical water inrush mode, a constitutive model of the geologic body is established, a large-scale digital discretization solving model is solved, and early warnings of different levels can be sent out according to a given early warning threshold value.
(2) Non-deterministic theoretical prediction
Due to the complexity of geological conditions and irregularities in mining activities, the certainty of mine water inrush represents limited information that can be considered, far from meeting the requirements of accurate prediction. According to the current technological level, not all water inrush accidents can be clarified about the occurrence and development mechanisms, and the water flow state in the rock mass structure can be accurately described in the constitutive relation under all mining conditions, so that a new method can be developed, the constitutive relation is not required, the internal rules of data are found by means of the technology of 'big data, cloud computing and the Internet of things', the future is deduced according to a large amount of sensing information, and prediction and early warning are realized.
Collecting and sensing information according to different index systems, selecting a proper mathematical method, and establishing an evaluation and prediction model; the evaluation model is mostly realized by means of cluster analysis, judgment analysis and the like. The single index water damage analysis model mainly comprises a grey theoretical prediction model, a time series prediction model and the like; the multi-index water damage comprehensive analysis model comprises a fuzzy analytic hierarchy process, an entropy weight method, an extension theory, a combined weighting method, a principal component analysis prediction model, a neural network prediction model, a deep machine learning model and the like.
In the transmission processing process of data flow, 6 functional modules of system design dynamic data transmission, data preprocessing, water disaster scene recognition, water condition monitoring current situation evaluation, water condition development trend evaluation, water disaster risk early warning analysis and achievement display.
Intelligent water damage risk early warning 'one picture'
The 'one picture' is a display platform for the system monitoring index dynamics and the final outcome of risk early warning. The whole design is based on a network of field data monitoring, and data such as ground topography, geological drilling, roadway working face and drainage system are comprehensively utilized to establish a whole-space water disaster risk early warning 'map' above and below the well. The 'one-picture' integrates unmanned aerial vehicle aerial photography data, topographic data, map data, geological drilling data, well lane data and the like, and really realizes multi-source data fusion and spatial linkage analysis.
On a map system platform, mine map data standards are established according to national standards and industrial specifications, map services and cooperative services are established based on a GIS platform, and hydrological monitoring information, alarm information and information of surrounding conditions of monitoring points are displayed. The whole mine and the working face are taken as different dimensions, and the integrated management of water condition monitoring and water disaster risk dynamic early warning in the safety production process is realized.
The above description is only for the preferred embodiment of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present application should be covered within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (8)
1. A coal mine water disaster universal forecasting and early warning system is characterized by comprising a data monitoring subsystem, a data processing module, a water disaster risk dynamic analysis subsystem, an evaluation model management subsystem, a threshold management base and a data processing module;
the data monitoring subsystem is used for acquiring mine data;
the data processing module comprises a data warehouse and an application layer;
the water disaster risk dynamic analysis subsystem is used for constructing an early warning analysis model and analyzing the whole water disaster risk of the coal mine according to the mine data;
the evaluation model management subsystem is used for managing the early warning analysis model;
and the threshold management library is used for setting a risk threshold according to the overall water disaster risk of the coal mine.
2. The universal forecasting and early warning system for water disaster in coal mines as claimed in claim 1, wherein the mine data comprises real-time detection data and mine static data, the real-time detection data is used for reflecting the real-time running state of the water disaster in the mines in real time, and the mine static data is used for determining the safety level of the mines.
3. The universal forecasting and early warning system for coal mine water damage as claimed in claim 1, wherein the data warehouse utilizes big data technology to store, process and calculate historical and real-time data, and utilizes big data algorithm to calculate in real time and off-line;
the application layer adopts a micro-service cluster architecture mode to split and deploy services.
4. The universal forecasting and early warning system for coal mine water damage as claimed in claim 1, wherein the types of the early warning analysis model include a risk evaluation model based on a single dynamic index and a risk evaluation model based on a multiple dynamic index, a static index risk evaluation model based on a safety check list scoring method, and a static index risk evaluation model based on an analytic hierarchy process.
5. The universal forecasting and early warning system for coal mine water disasters according to claim 1, wherein the evaluation model management subsystem carries out model query according to relevant conditions, displays the prediction duration of the early warning analysis model, and classifies according to the prediction duration, wherein the relevant conditions comprise but are not limited to model type, model early warning direction and model name.
6. The universal forecasting and early warning system for coal mine water disasters according to claim 5, wherein the evaluation model management subsystem divides the early warning analysis model into a real-time dynamic prediction model, a medium-short term prediction model and a long term prediction model;
the real-time dynamic prediction model comprises a gray prediction model and a Fourier fitting model; the medium-short term prediction model comprises a phase space reconstruction support vector machine combined model, a wavelet neural network model, a seasonal model and a wavelet support vector machine combined model, and the long term prediction model comprises an exponential smoothing model and a decision tree multiple regression model.
7. The universal forecasting and early warning system for water damage of coal mines as claimed in claim 1, wherein the threshold management library is based on the dynamic risk early warning weight determined by the early warning analysis model; through the amount of gushing water, water level, temperature, precipitation, set up the threshold value to different risk levels, support hierarchical setting simultaneously.
8. The universal forecasting and early warning system for coal mine water damage as claimed in claim 7, wherein the threshold setting of the threshold management library comprises rainfall early warning level division, long observation hole water level early warning level division, environment temperature early warning level division and mine water inflow early warning level division.
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| CN116708510A (en) * | 2023-07-11 | 2023-09-05 | 北京凡米物联科技有限公司 | Processing method and system for collecting data under coal mine multi-disaster coupling environment |
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| CN116757494A (en) * | 2023-05-23 | 2023-09-15 | 天地科技股份有限公司北京技术研究分公司 | Coal mine safety evaluation method and system |
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| CN117809433A (en) * | 2023-08-31 | 2024-04-02 | 应急管理部大数据中心 | Internet of things equipment-closing processing method and system supporting accurate fusion early warning |
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| CN117726181A (en) * | 2024-02-06 | 2024-03-19 | 山东科技大学 | Collaborative fusion and hierarchical prediction method for typical disaster risk heterogeneous information of coal mine |
| CN117726181B (en) * | 2024-02-06 | 2024-04-30 | 山东科技大学 | Collaborative fusion and hierarchical prediction method for typical disaster risk heterogeneous information of coal mine |
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