CN116484186A - Multi-field coupling-based rock burst intelligent early warning method and device - Google Patents
Multi-field coupling-based rock burst intelligent early warning method and device Download PDFInfo
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Abstract
The method combines a stress field, a fracture field, a displacement field and an energy field generated in the coal mining process, adopts instrument real-time monitoring, takes the stress field, the fracture field and the displacement field as input characteristics, takes the energy field as a label to construct a data training set, establishes a multi-layer deep neural network model, inputs data monitored by the stress field, the fracture field and the displacement field into corresponding models to respectively predict the sizes of the energy fields, selects the predicted maximum energy field as a predicted value, performs impact classification according to the sizes of the energy fields and performs early warning, can reduce the prediction inaccuracy caused by human factor interference, provides the accuracy of the impact pre-warning effect, can effectively reduce the damage of impact accidents to personnel and equipment, and has simple required data acquisition and strong universality.
Description
Technical Field
The application relates to the field of coal mining, in particular to an intelligent rock burst early warning method and device based on multi-field coupling.
Background
Coal is taken as a main energy source in China and will take the dominant role for a long time. The rock burst is one of main factors influencing the safe production of the coal mine, the safe and efficient mining of the coal mine is severely restricted, the prevention of the rock burst is mainly divided into rock burst assessment, early warning and danger elimination, and the premise of danger elimination is that the rock burst can be accurately predicted. Meanwhile, the rock burst early warning information is timely released, precious time can be provided for workers to evacuate timely and safely, and the rock burst early warning and forecasting method has extremely important theoretical significance and practical significance.
Based on the field investigation and laboratory study of impact mine pressure, each scholars sequentially put forward a series of important theories such as strength theory, rigidity theory, energy theory, impact tendency theory, three-criterion theory, deformation system instability theory and the like from different angles, but the impact ground pressure early warning effect of the theories is limited.
Disclosure of Invention
Aiming at the problems, the intelligent rock burst early warning method and device based on multi-field coupling are provided.
The first aspect of the application provides an intelligent rock burst early warning method based on multi-field coupling, which comprises the following steps:
Arranging various monitoring instruments in a stope roadway and a working face coal seam, and acquiring stress field characteristic samples, fracture field characteristic samples, displacement field characteristic samples and microseismic energy value samples to construct a data training set;
respectively taking the stress field characteristic sample, the fracture field characteristic sample and the displacement field characteristic sample as input characteristics, and taking microseismic energy value samples corresponding to the characteristic samples as labels for training to generate a first multi-layer deep neural network model, a second multi-layer deep neural network model and a third multi-layer deep neural network model;
the method comprises the steps of obtaining stress field characteristics, fracture field characteristics and displacement field characteristics in the same time period, inputting the stress field characteristics into the first multi-layer depth neural network model to obtain working face predicted top plate energy based on stress distribution, inputting the fracture field characteristics into the second multi-layer depth neural network model to obtain working face predicted top plate energy based on fracture distribution, and inputting the displacement field characteristics into the third multi-layer depth neural network model to obtain working face predicted top plate energy based on displacement distribution;
and taking the maximum value of the predicted roof energy of the working face based on the stress distribution, the predicted roof energy of the working face based on the crack distribution and the predicted roof energy of the working face based on the displacement distribution as a final predicted value, and performing impact classification on the final predicted value based on an energy and impact classification comparison table.
Optionally, obtaining the stress field feature sample includes:
installing a plurality of advanced support pressure stress meters in an advanced preset range of a stope roadway from a working face coal seam, and taking the monitored maximum advanced support pressure as a first characteristic sample of a stress field, wherein the calculation formula of the interval distance L of each advanced support pressure stress meter is as follows:
wherein h is the basic roof layer thickness, R T Q is the load born for tensile strength;
setting a monitoring point at each interval of a working face coal seam from a stoping roadway, taking the monitored maximum lateral supporting pressure as a second characteristic sample of a stress field, wherein the interval of each monitoring point is L, and arranging 3 lateral supporting pressure stress meters in each monitoring point, wherein the distances between the monitoring points and the monitoring points are 3m, 6m and 9m respectively;
installing a plurality of anchor cable stress meters within a preset range of the stoping roadway, taking the monitored maximum anchor cable stress as a third characteristic sample of the stress field, wherein the spacing distance of each anchor cable stress meter is L, and the anchor cable stress meters are preferentially arranged in anchor cables of the roadway on the temporary side;
and acquiring the load of the hydraulic support in the working face coal seam, and taking the monitored maximum hydraulic support working resistance as a fourth characteristic sample of the stress field.
Optionally, obtaining the fracture field feature sample includes:
setting a monitoring point at each interval of a working face coal seam from a stoping roadway, taking the monitored maximum ground sound value as a first characteristic sample of a fracture field, wherein the interval distance of each monitoring point is L, arranging 3 ground sound test probes in each monitoring point, and the distances of the 3 ground sound test probes from the ground sound test probes to the coal wall are as follows:
wherein l d The arrangement width of the coal seam is the working face;
recording the ground sound burial depth position and the ground sound probe hole depth of the ground sound test probe with the maximum ground sound value, and taking the ground sound burial depth position and the ground sound probe hole depth as a second characteristic sample of the crack field and a third characteristic sample of the crack field respectively.
Optionally, acquiring the displacement field feature sample includes:
arranging a plurality of roadway surrounding rock displacement measuring points in a preset range of a stoping roadway, analyzing monitoring results of the various roadway surrounding rock displacement measuring points at a preset time point, and taking the calculated top-bottom plate approaching amount, the calculated top-bottom plate approaching speed, the calculated roadway two-side approaching amount maximum value and the calculated roadway two-side approaching speed maximum value as first characteristic samples of a displacement field, wherein the interval distance of the various roadway surrounding rock displacement measuring points is the daily pushing progress of a working face;
Three measuring lines are arranged above a working face coal bed according to a propelling direction, monitoring results of all monitoring points in the measuring lines are analyzed at a preset time point by adopting a real-time dynamic measurement technology, the calculated sinking speed maximum value, the calculated sinking acceleration maximum value, the calculated sinking amount maximum value, the calculated horizontal movement maximum value and the calculated horizontal deformation maximum value are taken as second characteristic samples of a displacement field, wherein the three measuring lines are respectively positioned at a middle position, a left side position and a right side position right above the working face, and the distance between the monitoring points in each measuring line is changed based on the burial depth of the coal bed.
Optionally, the training is performed by using the stress field feature sample, the fracture field feature sample and the displacement field feature sample as input features and using microseismic energy value samples corresponding to the feature samples as labels, so as to generate a first multi-layer deep neural network model, a second multi-layer deep neural network model and a third multi-layer deep neural network model, which includes:
recording a time period of a microseismic energy event of a working face coal seam, and training by taking the stress field characteristic sample and a public characteristic sample as input characteristics and taking a microseismic energy value sample corresponding to the stress field characteristic sample recorded by a microseismic instrument as a label in the same time period to generate the first multi-layer deep neural network model, wherein the public characteristic sample comprises a working face daily pushing progress, a working face pushing speed and a working face pushing distance;
Recording the time period of a microseismic energy event of a working face coal seam, and training by taking the fracture field characteristic sample and the public characteristic sample as input characteristics and taking a microseismic energy value sample corresponding to the fracture field characteristic sample recorded by a microseismic instrument as a label in the same time period to generate the second multi-layer deep neural network model;
and recording the time period of the microseismic energy event of the working face coal bed, and training by taking the displacement field characteristic sample as an input characteristic and taking a microseismic energy value sample corresponding to the displacement field characteristic sample recorded by a microseismic instrument as a label in the same time period to generate the third multilayer deep neural network model.
Optionally, the impact classification of the final predicted value based on the energy and impact classification comparison table includes;
if the final predicted value is less than the first released energy, classifying the impact as no impact;
if the final predicted value is not less than the first released energy and less than the second released energy, classifying the impact as a weak impact;
if the final predicted value is not less than the second released energy and less than the third released energy, classifying the impact as a mid-impact;
if the final predicted value is not less than the third released energy, the impact is classified as a strong impact.
Optionally, the method further comprises:
if the impact of the final predicted value is classified as weak impact, releasing weak impact early warning information;
if the impact of the final predicted value is classified as a middle impact, issuing middle impact early warning information;
and if the impact of the final predicted value is classified as strong impact, issuing strong impact early warning information.
The second aspect of the application provides an intelligent rock burst early warning device based on multi-field coupling, which comprises:
the acquisition module is used for arranging various monitoring instruments in the stoping roadway and the working face coal seam to acquire stress field characteristic samples, fracture field characteristic samples, displacement field characteristic samples and microseismic energy value samples;
the training module is used for training by taking the stress field characteristic sample, the fracture field characteristic sample and the displacement field characteristic sample as input characteristics and taking the microseismic energy value sample corresponding to each characteristic sample as a label to generate a first multi-layer deep neural network model, a second multi-layer deep neural network model and a third multi-layer deep neural network model;
the application module is used for acquiring stress field characteristics, fracture field characteristics and displacement field characteristics of the same time period, inputting the stress field characteristics into the first multi-layer depth neural network model to obtain working face predicted top plate energy based on stress distribution, inputting the fracture field characteristics into the second multi-layer depth neural network model to obtain working face predicted top plate energy based on fracture distribution, and inputting the displacement field characteristics into the third multi-layer depth neural network model to obtain working face predicted top plate energy based on displacement distribution;
And the classification module is used for taking the maximum value of the working face predicted roof energy based on the stress distribution, the working face predicted roof energy based on the crack distribution and the working face predicted roof energy based on the displacement distribution as a final predicted value, and performing impact classification on the final predicted value based on an energy and impact classification comparison table.
A third aspect of the present application proposes a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method according to any of the first aspects when executing the computer program.
A fourth aspect of the present application proposes a non-transitory computer readable storage medium having stored thereon a computer program, characterized in that the computer program, when executed by a processor, implements the method according to any of the first aspects.
The technical scheme provided by the embodiment of the application at least brings the following beneficial effects:
the method is characterized in that the stress field, the fracture field, the displacement field and the energy field generated in the coal mining process are combined, instrument real-time monitoring is adopted, the stress field, the fracture field and the displacement field are used as input features, the energy field is used as a label to construct a data training set, a multi-layer deep neural network model is built, then data monitored by the stress field, the fracture field and the displacement field are input into corresponding models to respectively predict the sizes of the energy fields, the predicted maximum energy field is selected as a predicted value, impact classification is carried out according to the sizes of the energy fields, early warning is carried out, prediction inaccuracy caused by human factor interference is reduced, accuracy of impact ground pressure early warning effect is provided, damage to personnel and equipment caused by impact accidents can be effectively reduced, and the required data acquisition is simple and has strong universality.
Additional aspects and advantages of the application 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 application.
Drawings
The foregoing and/or additional aspects and advantages of the present application 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 summarized flow chart of an intelligent pre-warning method for rock burst based on multi-field coupling according to an embodiment of the present application;
FIG. 2 is a detailed flow chart of an intelligent pre-warning method for rock burst based on multi-field coupling according to an embodiment of the present application;
FIG. 3 is a graph of distribution of various stations shown in accordance with an embodiment of the present application;
FIG. 4 is a diagram illustrating a distribution of lines arranged in a direction of propulsion according to an embodiment of the present application;
FIG. 5 is a block diagram of an intelligent rock burst early warning apparatus based on multi-field coupling, according to an embodiment of the present application;
fig. 6 is a block diagram of an electronic device.
Detailed Description
Embodiments of the present application 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 and intended for the purpose of explaining the present application and are not to be construed as limiting the present application.
Along with the deep arming of the coal mine, under the action of dynamic and static combined loading, the number of mines and working faces for impact accidents is increased, and many challenges are brought to safe production.
Meanwhile, along with large-scale popularization and application of artificial intelligence, the national energy agency issues a coal mine intelligent mine construction scheme, the prediction precision of the deep multi-layer neural network is higher and higher, large-scale popularization and application of public clouds are facilitated for model training of mass data, a fracture field, a stress field and a displacement field are subjected to data fusion, the fracture field, the stress field and the displacement field are used as input characteristics, the energy field is used as a label, the deep multi-layer neural network is established, model training is performed by utilizing public clouds and private cloud servers, and the size of the energy field is a necessary trend.
As shown in fig. 1 and 2, the method includes:
And 101, arranging various monitoring instruments in a stope roadway and a working face coal seam, and acquiring a stress field characteristic sample, a fracture field characteristic sample, a displacement field characteristic sample and a microseismic energy value sample to construct a data training set.
In this embodiment, the positions of each monitoring instrument and each monitoring point are shown in fig. 3.
In this embodiment, the stress field feature samples include a stress field first feature sample, a stress field second feature sample, a stress field third feature sample, and a stress field fourth feature sample.
The method is flexible in arrangement scheme and definite in data physical meaning, is one of the main means of current static load on-line monitoring, and the main utilization way of rock burst mine stress monitoring is as follows: grasping the change condition of the danger of the concentrated dead load and rock burst of surrounding rock; grasping the mining disturbance range, degree and time characteristics; and (3) verifying the timeliness of the pressure relief effect of the coal-rock coal seam, and reflecting the internal stress change rule of the coal pillar of the working face advance section stope.
The process for acquiring the first characteristic sample of the stress field comprises the following steps:
as shown in fig. 3, a plurality of advance support pressure stress gauges are installed in an advance preset range of a stope from a working face coal seam, and the monitored maximum advance support pressure is used as a first characteristic sample of a stress field, wherein the calculation formula of the interval distance L of each advance support pressure stress gauge is as follows:
wherein h is the basic roof layer thickness, R T For tensile strength, q is the load that is taken.
The stoping roadway is a roadway for forming a coal face and serving the coal face in coal mining.
In one possible embodiment, the preset range of the instrument is required to be completely arranged in the stope at one time or arranged in the range of 0-80m, then the instrument is taken out, and the instrument is circularly arranged along with the advancing of the working face, so that the monitoring instrument can always monitor the advanced supporting pressure in the range of 0-80m of the advancing range of the working face.
The process for acquiring the first characteristic sample of the stress field comprises the following steps:
as shown in fig. 3, a monitoring point is arranged at each interval of the working face coal seam from the stope roadway, the monitored maximum lateral supporting pressure is used as a second characteristic sample of the stress field, the interval of each monitoring point is L, and 3 lateral supporting pressure stress meters are arranged in each monitoring point and are respectively 3m, 6m and 9m away from the deep coal wall.
The distance between each monitoring point is the same as the distance between each advance support pressure stress gauge.
The process for obtaining the third characteristic sample of the stress field comprises the following steps:
as shown in fig. 3, a plurality of anchor cable stress meters are installed in a preset range of the stoping roadway, the monitored maximum anchor cable stress is used as a third characteristic sample of the stress field, and each anchor cable stress meter is spaced by a distance L and is preferentially arranged in an anchor cable of the roadway on the side of the goaf.
In this application embodiment, prestressing anchor pole, anchor rope in time initiatively support, reduce surrounding rock shallow portion partial stress and stress gradient, restrain the discontinuous, uncoordinated dilatation deformation of surrounding rock in the anchor district, reduce the reduction of surrounding rock intensity, the roof takes place to strike the front, roof accumulation energy, causes roof anchor rope atress can change equally, and anchor rope atress monitoring cost is low, and the measurement station is arranged simply, and can arrange in a large number.
In one possible embodiment, the preset range of the instrument is required to be completely laid in the stope at one time or to be laid in the range of 0-80m, then the instrument is taken out, and the instrument is circularly arranged along with the advancing of the working face, so that the monitoring instrument can always monitor the stress of the anchor cable in the range of 0-80m in the advancing range of the working face.
The process for obtaining the fourth characteristic sample of the stress field comprises the following steps:
and acquiring the load of the hydraulic support in the working face coal seam, and taking the monitored maximum hydraulic support working resistance as a fourth characteristic sample of the stress field.
In the embodiment of the application, a large amount of comprehensive, accurate and reliable ore pressure data and a modern big data analysis method provide possibility for realizing ore pressure prediction, the hydraulic support load is large in data quantity and convenient to acquire, and also comprises a large amount of ore pressure information such as roof movement and support movement, so that the hydraulic support load is a reliable basis for analyzing the ore pressure law of a stope and realizing ore pressure prediction, the support load is the direct manifestation of roof stratum structure and movement characteristics on a fully-mechanized mining working face, the support load and stratum movement have necessary internal connection, and the interaction relation between the support load and stratum movement is explored, so that the hydraulic support load and stratum movement are effective ways for realizing ore pressure prediction and forecast.
In a possible embodiment, taking 10-blade coal a day as an example, a 300m long working surface will produce about 300 ten thousand pieces of data for one month, and the whole working surface finishes collecting data volume exceeding 1G.
In the embodiment of the application, the fracture field characteristic samples comprise a first fracture field characteristic sample, a second fracture field characteristic sample and a third fracture field characteristic sample.
The process for obtaining the first characteristic sample of the fracture field is as follows:
as shown in fig. 3, a monitoring point is set at each interval of a working face coal seam from a stoping roadway, the monitored maximum ground sound value is used as a first characteristic sample of a fracture field, the interval distance of each monitoring point is L, 3 ground sound test probes are arranged in each monitoring point, and the distances of the 3 ground sound test probes from the coal wall are as follows:
wherein l d Is the arrangement width of the coal seam of the working face.
In the embodiment of the application, the ground sound is energy released by fracture of the coal rock mass and is transmitted outwards in the form of elastic waves to generate an acoustic effect in the process. In mines, the ground sound is induced by underground mining activities, the vibration energy is generally 0-103j, compared with the micro-vibration phenomenon, the ground sound is high-frequency and low-energy vibration, the ground sound is a precursor of the release of internal stress of coal and rock, and indexes such as the quantity, the size and the like of the ground sound signals reflect the stress condition of the rock
The distance between each monitoring point is the same as the distance between each advance support pressure stress gauge.
In one possible embodiment, the preset range of the instrument is required to be completely arranged in the stope at one time or arranged in the range of 0-80m, and then the instrument is taken out and circularly arranged along with the advancing of the working face, so that the monitoring instrument can always monitor the ground sound value in the range of 0-80m in the advancing range of the working face.
In addition, the ground sound burial depth position and the ground sound probe hole depth of the ground sound test probe with the maximum ground sound value are recorded, and the ground sound burial depth position and the ground sound probe hole depth are respectively used as a second characteristic sample of the crack field and a third characteristic sample of the crack field.
In this embodiment of the present application, the displacement field feature samples include a displacement field first feature sample and a displacement field second feature sample.
The process of acquiring the first characteristic sample of the displacement field is as follows:
as shown in fig. 3, a plurality of roadway surrounding rock displacement measuring points are arranged in a preset range of a stoping roadway, monitoring results of the roadway surrounding rock displacement measuring points are analyzed at preset time points, and the calculated top and bottom plate approaching amount, the calculated top and bottom plate approaching speed, the calculated roadway two-side approaching amount maximum value and the calculated roadway two-side approaching speed maximum value are taken as first characteristic samples of a displacement field, wherein the interval distance of the roadway surrounding rock displacement measuring points is the daily pushing progress of a working face.
At present, accidents with rock burst percentage of more than seventy percent occur in a roadway, the accident occurrence range is within 80m from the front of a working face, and based on the accident occurrence range, roadway surrounding rock displacement monitoring is arranged in the roadway.
In a possible embodiment, the preset range is 80m and the preset time point is every night.
The process of obtaining the second characteristic sample of the displacement field is as follows:
as shown in fig. 4, three measuring lines are arranged above the working surface coal seam according to the advancing direction, the monitoring results of all monitoring points in the measuring lines are analyzed at a preset time point by adopting a real-time dynamic measurement technology, the calculated sinking speed maximum value, the calculated sinking acceleration maximum value, the calculated sinking amount maximum value, the calculated horizontal movement maximum value and the calculated horizontal deformation maximum value are used as displacement field second characteristic samples, wherein the three measuring lines are respectively positioned at the middle position, the left side position and the right side position right above the working surface, and the distance between the monitoring points in each measuring line is changed based on the burial depth of the coal seam.
At present, earth surface movement and rock burst are two environmental geological disasters with stronger destructiveness, the occurrence of earth surface subsidence is caused by the movement and development of a coal seam roof and an overlying strata layer to the earth surface, the damage of facilities such as buildings, farmlands and roads on the earth can be caused, the rock burst is caused by the movement and stress redistribution of the coal seam roof and the overlying strata layer due to mining disturbance, and finally, surrounding rocks are subjected to tensile instability damage, so that coal rock bodies are rapidly thrown out in a very short time to cause damage.
Therefore, the ground subsidence and rock burst are influenced by the movements of the coal seam roof and overlying strata, so that the ground movement can be monitored to predict the rock burst.
And 102, training by taking stress field characteristic samples, fracture field characteristic samples and displacement field characteristic samples as input characteristics and taking microseismic energy value samples corresponding to the characteristic samples as labels, so as to generate a first multi-layer deep neural network model, a second multi-layer deep neural network model and a third multi-layer deep neural network model.
In the embodiment of the application, a time period of a microseismic energy event of a working face coal seam is recorded, a stress field characteristic sample and a public characteristic sample are taken as input characteristics in the same time period, and a microseismic energy value sample corresponding to the stress field characteristic sample recorded by a microseismic instrument is taken as a label to train so as to generate a first multi-layer deep neural network model, wherein the public characteristic sample comprises a working face daily pushing progress, a working face pushing speed and a working face pushing distance;
recording the time period of a microseismic energy event of a working face coal seam, and training by taking a fracture field characteristic sample and a public characteristic sample as input characteristics and taking a microseismic energy value sample corresponding to the fracture field characteristic sample recorded by a microseismic instrument as a label in the same time period to generate a second multi-layer deep neural network model;
recording the time period of the microseismic energy event of the working face coal bed, taking the displacement field characteristic sample as an input characteristic in the same time period, and taking the microseismic energy value sample corresponding to the displacement field characteristic sample recorded by the microseismic instrument as a label for training so as to generate a third multi-layer deep neural network model.
It should be noted that the common feature samples are already determined at the time of exploitation.
And 103, acquiring stress field characteristics, fracture field characteristics and displacement field characteristics of the same time period, inputting the stress field characteristics into a first multi-layer deep neural network model to obtain working face predicted roof energy based on stress distribution, inputting the fracture field characteristics into a second multi-layer deep neural network model to obtain working face predicted roof energy based on fracture distribution, and inputting the displacement field characteristics into a third multi-layer deep neural network model to obtain working face predicted roof energy based on displacement distribution.
In the embodiment of the present application, the process of acquiring the stress field feature, the fracture field feature and the displacement field feature is consistent with the process of acquiring the feature samples in step 101.
And 104, taking the maximum value of the working surface predicted roof energy based on the stress distribution, the working surface predicted roof energy based on the crack distribution and the working surface predicted roof energy based on the displacement distribution as a final predicted value, and performing impact classification on the final predicted value based on an energy and impact classification comparison table.
In the embodiment of the application, if the final predicted value is smaller than the first release energy, the impact is classified as no impact;
If the final predicted value is not less than the first released energy and less than the second released energy, classifying the impact as a weak impact;
if the final predicted value is not less than the second released energy and less than the third released energy, classifying the impact as a medium impact;
if the final predicted value is not less than the third released energy, the impact is classified as a strong impact.
In addition, the early warning information is set according to different impact classifications, wherein,
if the impact of the final predicted value is classified as weak impact, releasing weak impact early warning information;
if the impact of the final predicted value is classified as a middle impact, issuing middle impact early warning information;
if the impact of the final predicted value is classified as strong impact, releasing strong impact early warning information 3002
In a possible embodiment, the first release energy is 10 4 The second release energy is 10 6 The third release energy is 10 7 Wherein the units are joules.
In a possible embodiment, if the value of the final predicted value is 15000, the impact of the final predicted value is classified as a weak impact, and a weak impact early warning signal is issued.
According to the embodiment of the application, the stress field, the fracture field, the displacement field and the energy field generated in the coal mining process are combined, the instrument is used for real-time monitoring, the stress field, the fracture field and the displacement field are used as input features, the energy field is used as a label to construct a data training set, a multi-layer deep neural network model is built, then data monitored by the stress field, the fracture field and the displacement field are input into corresponding models, the size of the energy field is respectively predicted, the predicted maximum energy field is selected as a predicted value, impact classification is carried out according to the size of the energy field, early warning is carried out, prediction inaccuracy caused by human factor interference is reduced, accuracy of impact ground pressure early warning effect is provided, damage to personnel and equipment caused by impact accidents can be effectively reduced, required data acquisition is simple, and universality is strong.
Fig. 5 is a block diagram of a multi-field coupling-based rock burst intelligent pre-warning apparatus 500, as shown in fig. 5, according to an embodiment of the present application, which includes an acquisition module 510, a training module 520, an application module 530, and a classification module 550.
The acquisition module 510 is used for arranging various monitoring instruments in the stope roadway and the working face coal seam to acquire stress field characteristic samples, fracture field characteristic samples, displacement field characteristic samples and microseismic energy value samples;
the training module 520 is configured to train with the stress field feature sample, the fracture field feature sample, and the displacement field feature sample as input features, and with the microseismic energy value sample corresponding to each feature sample as a label, to generate a first multi-layer deep neural network model, a second multi-layer deep neural network model, and a third multi-layer deep neural network model;
the application module 530 is configured to obtain stress field features, fracture field features and displacement field features in the same time period, input the stress field features into the first multi-layer deep neural network model to obtain working face predicted top plate energy based on stress distribution, input the fracture field features into the second multi-layer deep neural network model to obtain working face predicted top plate energy based on fracture distribution, and input the displacement field features into the third multi-layer deep neural network model to obtain working face predicted top plate energy based on displacement distribution;
And the classification module 540 is configured to take the maximum value of the predicted roof energy of the working surface based on stress distribution, the predicted roof energy of the working surface based on fracture distribution and the predicted roof energy of the working surface based on displacement distribution as a final predicted value, and perform impact classification on the final predicted value based on an energy and impact classification comparison table.
The specific manner in which the various modules perform the operations in the apparatus of the above embodiments have been described in detail in connection with the embodiments of the method, and will not be described in detail herein.
Fig. 6 illustrates a schematic block diagram of an example electronic device 600 that may be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 6, the apparatus 600 includes a computing unit 601 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM) 602 or a computer program loaded from a storage unit 603 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the device 600 may also be stored. The computing unit 601, ROM 602, and RAM 603 are connected to each other by a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
Various components in the device 600 are connected to the I/O interface 605, including: an input unit 606 such as a keyboard, mouse, etc.; an output unit 607 such as various types of displays, speakers, and the like; a storage unit 608, such as a magnetic disk, optical disk, or the like; and a communication unit 609 such as a network card, modem, wireless communication transceiver, etc. The communication unit 609 allows the device 600 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The computing unit 601 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 601 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 601 performs the various methods and processes described above, such as a voice instruction response method. For example, in some embodiments, the voice instruction response method may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as storage unit 608. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 600 via the ROM 602 and/or the communication unit 609. When the computer program is loaded into RAM 603 and executed by computing unit 601, one or more steps of the voice instruction response method described above may be performed. Alternatively, in other embodiments, the computing unit 601 may be configured to perform the voice instruction response method in any other suitable manner (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), the internet, and blockchain networks.
The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service ("Virtual Private Server" or simply "VPS") are overcome. The server may also be a server of a distributed system or a server that incorporates a blockchain.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel or sequentially or in a different order, provided that the desired results of the technical solutions of the present disclosure are achieved, and are not limited herein.
The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present disclosure are intended to be included within the scope of the present disclosure.
Claims (10)
1. An intelligent rock burst early warning method based on multi-field coupling is characterized by comprising the following steps:
arranging various monitoring instruments in a stope roadway and a working face coal seam, and acquiring stress field characteristic samples, fracture field characteristic samples, displacement field characteristic samples and microseismic energy value samples to construct a data training set;
respectively taking the stress field characteristic sample, the fracture field characteristic sample and the displacement field characteristic sample as input characteristics, and taking microseismic energy value samples corresponding to the characteristic samples as labels for training to generate a first multi-layer deep neural network model, a second multi-layer deep neural network model and a third multi-layer deep neural network model;
The method comprises the steps of obtaining stress field characteristics, fracture field characteristics and displacement field characteristics in the same time period, inputting the stress field characteristics into the first multi-layer depth neural network model to obtain working face predicted top plate energy based on stress distribution, inputting the fracture field characteristics into the second multi-layer depth neural network model to obtain working face predicted top plate energy based on fracture distribution, and inputting the displacement field characteristics into the third multi-layer depth neural network model to obtain working face predicted top plate energy based on displacement distribution;
and taking the maximum value of the predicted roof energy of the working face based on the stress distribution, the predicted roof energy of the working face based on the crack distribution and the predicted roof energy of the working face based on the displacement distribution as a final predicted value, and performing impact classification on the final predicted value based on an energy and impact classification comparison table.
2. The method of claim 1, wherein obtaining the stress field signature sample comprises:
installing a plurality of advanced support pressure stress meters in an advanced preset range of a stope roadway from a working face coal seam, and taking the monitored maximum advanced support pressure as a first characteristic sample of a stress field, wherein the calculation formula of the interval distance L of each advanced support pressure stress meter is as follows:
Wherein h is the basic roof layer thickness, R T Q is the load born for tensile strength;
setting a monitoring point at each interval of a working face coal seam from a stoping roadway, taking the monitored maximum lateral supporting pressure as a second characteristic sample of a stress field, wherein the interval of each monitoring point is L, and arranging 3 lateral supporting pressure stress meters in each monitoring point, wherein the distances between the monitoring points and the monitoring points are 3m, 6m and 9m respectively;
installing a plurality of anchor cable stress meters within a preset range of the stoping roadway, taking the monitored maximum anchor cable stress as a third characteristic sample of the stress field, wherein the spacing distance of each anchor cable stress meter is L, and the anchor cable stress meters are preferentially arranged in anchor cables of the roadway on the temporary side;
and acquiring the load of the hydraulic support in the working face coal seam, and taking the monitored maximum hydraulic support working resistance as a fourth characteristic sample of the stress field.
3. The method of claim 1, wherein obtaining the fracture field signature sample comprises:
setting a monitoring point at each interval of a working face coal seam from a stoping roadway, taking the monitored maximum ground sound value as a first characteristic sample of a fracture field, wherein the interval distance of each monitoring point is L, arranging 3 ground sound test probes in each monitoring point, and the distances of the 3 ground sound test probes from the ground sound test probes to the coal wall are as follows:
Wherein l d The arrangement width of the coal seam is the working face;
recording the ground sound burial depth position and the ground sound probe hole depth of the ground sound test probe with the maximum ground sound value, and taking the ground sound burial depth position and the ground sound probe hole depth as a second characteristic sample of the crack field and a third characteristic sample of the crack field respectively.
4. The method of claim 1, wherein obtaining the displacement field signature sample comprises:
arranging a plurality of roadway surrounding rock displacement measuring points in a preset range of a stoping roadway, analyzing monitoring results of the various roadway surrounding rock displacement measuring points at a preset time point, and taking the calculated top-bottom plate approaching amount, the calculated top-bottom plate approaching speed, the calculated roadway two-side approaching amount maximum value and the calculated roadway two-side approaching speed maximum value as first characteristic samples of a displacement field, wherein the interval distance of the various roadway surrounding rock displacement measuring points is the daily pushing progress of a working face;
three measuring lines are arranged above a working face coal bed according to a propelling direction, monitoring results of all monitoring points in the measuring lines are analyzed at a preset time point by adopting a real-time dynamic measurement technology, the calculated sinking speed maximum value, the calculated sinking acceleration maximum value, the calculated sinking amount maximum value, the calculated horizontal movement maximum value and the calculated horizontal deformation maximum value are taken as second characteristic samples of a displacement field, wherein the three measuring lines are respectively positioned at a middle position, a left side position and a right side position right above the working face, and the distance between the monitoring points in each measuring line is changed based on the burial depth of the coal bed.
5. The method according to any one of claims 1, 2, 3 or 4, wherein the training with the stress field feature sample, the fracture field feature sample and the displacement field feature sample as input features and the microseismic energy value sample corresponding to each feature sample as a tag to generate a first multi-layer deep neural network model, a second multi-layer deep neural network model and a third multi-layer deep neural network model includes:
recording a time period of a microseismic energy event of a working face coal seam, and training by taking the stress field characteristic sample and a public characteristic sample as input characteristics and taking a microseismic energy value sample corresponding to the stress field characteristic sample recorded by a microseismic instrument as a label in the same time period to generate the first multi-layer deep neural network model, wherein the public characteristic sample comprises a working face daily pushing progress, a working face pushing speed and a working face pushing distance;
recording the time period of a microseismic energy event of a working face coal seam, and training by taking the fracture field characteristic sample and the public characteristic sample as input characteristics and taking a microseismic energy value sample corresponding to the fracture field characteristic sample recorded by a microseismic instrument as a label in the same time period to generate the second multi-layer deep neural network model;
And recording the time period of the microseismic energy event of the working face coal bed, and training by taking the displacement field characteristic sample as an input characteristic and taking a microseismic energy value sample corresponding to the displacement field characteristic sample recorded by a microseismic instrument as a label in the same time period to generate the third multilayer deep neural network model.
6. The method of claim 5, wherein the impact classifying the final predicted value based on an energy versus impact classification look-up table comprises;
if the final predicted value is less than the first released energy, classifying the impact as no impact;
if the final predicted value is not less than the first released energy and less than the second released energy, classifying the impact as a weak impact;
if the final predicted value is not less than the second released energy and less than the third released energy, classifying the impact as a mid-impact;
if the final predicted value is not less than the third released energy, the impact is classified as a strong impact.
7. The method according to claim 6, further comprising:
if the impact of the final predicted value is classified as weak impact, releasing weak impact early warning information;
if the impact of the final predicted value is classified as a middle impact, issuing middle impact early warning information;
And if the impact of the final predicted value is classified as strong impact, issuing strong impact early warning information.
8. Multi-field coupling-based rock burst intelligent early warning device is characterized by comprising:
the acquisition module is used for arranging various monitoring instruments in the stoping roadway and the working face coal seam to acquire stress field characteristic samples, fracture field characteristic samples, displacement field characteristic samples and microseismic energy value samples;
the training module is used for training by taking the stress field characteristic sample, the fracture field characteristic sample and the displacement field characteristic sample as input characteristics and taking the microseismic energy value sample corresponding to each characteristic sample as a label to generate a first multi-layer deep neural network model, a second multi-layer deep neural network model and a third multi-layer deep neural network model;
the application module is used for acquiring stress field characteristics, fracture field characteristics and displacement field characteristics of the same time period, inputting the stress field characteristics into the first multi-layer depth neural network model to obtain working face predicted top plate energy based on stress distribution, inputting the fracture field characteristics into the second multi-layer depth neural network model to obtain working face predicted top plate energy based on fracture distribution, and inputting the displacement field characteristics into the third multi-layer depth neural network model to obtain working face predicted top plate energy based on displacement distribution;
And the classification module is used for taking the maximum value of the working face predicted roof energy based on the stress distribution, the working face predicted roof energy based on the crack distribution and the working face predicted roof energy based on the displacement distribution as a final predicted value, and performing impact classification on the final predicted value based on an energy and impact classification comparison table.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method of any of claims 1-7 when executing the computer program.
10. A non-transitory computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when executed by a processor, implements the method according to any of claims 1-7.
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| CN102587983A (en) * | 2012-01-12 | 2012-07-18 | 山东科技大学 | Comprehensive early-warning observation method for coal mine rock burst |
| WO2019036923A1 (en) * | 2017-08-22 | 2019-02-28 | 深圳企管加企业服务有限公司 | Machine room device fault early-warning method and apparatus based on internet of things, and storage medium |
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